stablelm.py 14.6 KB
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# SPDX-License-Identifier: Apache-2.0

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# Copyright 2023 Stability AI, EleutherAI, and The HuggingFace Inc. team.
# All rights reserved.
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#
# 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
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"""Inference-only StabeLM (https://github.com/Stability-AI/StableLM)
model compatible with HuggingFace weights."""
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from collections.abc import Iterable
from typing import Optional, Union
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import torch
from torch import nn
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from transformers import StableLmConfig
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from vllm.attention import Attention
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from vllm.config import CacheConfig, VllmConfig
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from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
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from vllm.model_executor.layers.activation import SiluAndMul
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from vllm.model_executor.layers.linear import (MergedColumnParallelLinear,
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                                               QKVParallelLinear,
                                               RowParallelLinear)
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from vllm.model_executor.layers.logits_processor import LogitsProcessor
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from vllm.model_executor.layers.quantization import QuantizationConfig
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from vllm.model_executor.layers.rotary_embedding import get_rope
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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
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from .interfaces import SupportsPP
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from .utils import (AutoWeightsLoader, is_pp_missing_parameter,
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                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)
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class StablelmMLP(nn.Module):

    def __init__(self,
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                 config: StableLmConfig,
                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = "") -> None:
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        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,
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            quant_config=quant_config,
            prefix=f"{prefix}.gate_up_proj")
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        self.down_proj = RowParallelLinear(config.intermediate_size,
                                           config.hidden_size,
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                                           bias=False,
                                           quant_config=quant_config,
                                           prefix=f"{prefix}.down_proj")
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        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,
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                 config: StableLmConfig,
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                 cache_config: Optional[CacheConfig] = None,
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                 quant_config: Optional[QuantizationConfig] = None,
                 prefix: str = "") -> None:
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        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
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        self.partial_rotary_factor = getattr(
            config, "rope_pct", getattr(config, "partial_rotary_factor", 1))
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        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:
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            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}).")
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        self.qkv_proj = QKVParallelLinear(self.hidden_size,
                                          self.head_dim,
                                          self.total_num_heads,
                                          self.total_num_key_value_heads,
                                          self.qkv_bias,
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                                          quant_config=quant_config,
                                          prefix=f"{prefix}.qkv_proj")
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        self.o_proj = RowParallelLinear(self.total_num_heads * self.head_dim,
                                        self.hidden_size,
                                        bias=False,
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                                        quant_config=quant_config,
                                        prefix=f"{prefix}.o_proj")
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        self.rotary_emb = get_rope(
            self.head_dim,
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            rotary_dim=self.head_dim,
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            max_position=self.config.max_position_embeddings,
            base=self.config.rope_theta,
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            partial_rotary_factor=self.partial_rotary_factor,
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        )
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        self.attn = Attention(self.num_heads,
                              self.head_dim,
                              self.scaling,
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                              num_kv_heads=self.num_key_value_heads,
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                              cache_config=cache_config,
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                              quant_config=quant_config,
                              prefix=f"{prefix}.attn")
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    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
    ) -> 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)
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        attn_output = self.attn(q, k, v)
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        output, _ = self.o_proj(attn_output)
        return output


class StablelmDecoderLayer(nn.Module):
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    def __init__(
        self,
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        config: StableLmConfig,
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        cache_config: Optional[CacheConfig] = None,
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        quant_config: Optional[QuantizationConfig] = None,
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        prefix: str = "",
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    ) -> None:
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        super().__init__()
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        self.self_attn = StablelmAttention(config,
                                           cache_config,
                                           quant_config,
                                           prefix=f"{prefix}.self_attn")
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        self.mlp = StablelmMLP(config, quant_config, prefix=f"{prefix}.mlp")
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        norm_eps = getattr(config, "norm_eps",
                           getattr(config, "layer_norm_eps", 1e-05))
        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
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        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
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                                                     eps=norm_eps)
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    def forward(
        self,
        positions: torch.Tensor,
        hidden_states: torch.Tensor,
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    ) -> tuple[torch.Tensor, torch.Tensor]:
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        # Self Attention
        residual = hidden_states
        hidden_states = self.input_layernorm(hidden_states)
        hidden_states = self.self_attn(
            positions=positions,
            hidden_states=hidden_states,
        )
        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):

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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

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        self.embed_tokens = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
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            quant_config=quant_config,
            prefix=f"{prefix}.embed_tokens",
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        )
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        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
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            lambda prefix: StablelmDecoderLayer(
                config, cache_config, quant_config, prefix=prefix),
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            prefix=f"{prefix}.layers",
        )
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        norm_eps = getattr(config, "norm_eps",
                           getattr(config, "layer_norm_eps", 1e-05))
        self.norm = nn.LayerNorm(config.hidden_size, eps=norm_eps)
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        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(["hidden_states"],
                                                    config.hidden_size))
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    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_tokens(input_ids)

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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        intermediate_tensors: Optional[IntermediateTensors],
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        inputs_embeds: Optional[torch.Tensor] = None,
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    ) -> Union[torch.Tensor, IntermediateTensors]:
        if get_pp_group().is_first_rank:
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            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
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        else:
            assert intermediate_tensors is not None
            hidden_states = intermediate_tensors["hidden_states"]
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        for layer in self.layers[self.start_layer:self.end_layer]:
            hidden_states, residual = layer(positions, hidden_states)
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        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": hidden_states})
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        hidden_states = self.norm(hidden_states)
        return hidden_states

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    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
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        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())
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        loaded_params: set[str] = set()
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        for name, loaded_weight in weights:
            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
                if is_pp_missing_parameter(name, self):
                    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
                if is_pp_missing_parameter(name, self):
                    continue
                param = params_dict[name]
                weight_loader = getattr(param, "weight_loader",
                                        default_weight_loader)
                weight_loader(param, loaded_weight)
            loaded_params.add(name)
        return loaded_params

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class StablelmForCausalLM(nn.Module, SupportsPP):
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    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
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        super().__init__()
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        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
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        self.config = config
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        self.quant_config = quant_config
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        self.model = StableLMEpochModel(vllm_config=vllm_config,
                                        prefix=maybe_prefix(prefix, "model"))
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        self.lm_head = ParallelLMHead(config.vocab_size,
                                      config.hidden_size,
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                                      quant_config=quant_config,
                                      prefix=f"{prefix}.lm_head")
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        if self.config.tie_word_embeddings:
            self.lm_head.weight = self.model.embed_tokens.weight
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        self.logits_processor = LogitsProcessor(config.vocab_size)
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        self.make_empty_intermediate_tensors = (
            self.model.make_empty_intermediate_tensors)
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    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.model.get_input_embeddings(input_ids)

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    def forward(
        self,
        input_ids: torch.Tensor,
        positions: torch.Tensor,
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        intermediate_tensors: Optional[IntermediateTensors] = None,
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        inputs_embeds: Optional[torch.Tensor] = None,
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    ) -> Union[torch.Tensor, IntermediateTensors]:
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        hidden_states = self.model(input_ids, positions, intermediate_tensors,
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                                   inputs_embeds)
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        return hidden_states

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    def compute_logits(
        self,
        hidden_states: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[torch.Tensor]:
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        logits = self.logits_processor(self.lm_head, hidden_states,
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                                       sampling_metadata)
        return logits

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    def load_weights(self, weights: Iterable[tuple[str,
                                                   torch.Tensor]]) -> set[str]:
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        loader = AutoWeightsLoader(self)
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        return loader.load_weights(weights)