gpt_neox.py

# SPDX-License-Identifier: Apache-2.0

# Adapted from
# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/gpt_neox/modeling_gpt_neox.py
# Copyright 2023 The vLLM team.
# Copyright 2022 EleutherAI 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.
"""Inference-only GPT-NeoX model compatible with HuggingFace weights."""
from typing import Iterable, List, Optional, Set, Tuple, Union
import os
import re
import torch
from torch import nn
from transformers import GPTNeoXConfig

from vllm.attention import Attention, AttentionMetadata
from vllm.compilation.decorators import support_torch_compile
from vllm.config import CacheConfig, VllmConfig
from vllm.distributed import get_pp_group, get_tensor_model_parallel_world_size
from vllm.model_executor.layers.activation import get_act_fn
from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                               QKVParallelLinear,
                                               RowParallelLinear)
from vllm.model_executor.layers.logits_processor import LogitsProcessor
from vllm.model_executor.layers.quantization import QuantizationConfig
from vllm.model_executor.layers.rotary_embedding import get_rope
from vllm.model_executor.layers.sampler import SamplerOutput, get_sampler
from vllm.model_executor.layers.vocab_parallel_embedding import (
    ParallelLMHead, VocabParallelEmbedding)
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from vllm.model_executor.sampling_metadata import SamplingMetadata
from vllm.sequence import IntermediateTensors

from .interfaces import SupportsPP
from .utils import (is_pp_missing_parameter,
                    make_empty_intermediate_tensors_factory, make_layers,
                    maybe_prefix)
from vllm.utils import is_hip,W8a8GetCacheJSON
from vllm import _custom_ops as ops

class GPTNeoXAttention(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.total_num_heads = config.num_attention_heads
        self.hidden_size = config.hidden_size
        self.head_size = self.hidden_size // self.total_num_heads
        self.bias = getattr(config, "attention_bias", True)

        tensor_model_parallel_world_size = (
            get_tensor_model_parallel_world_size())
        assert self.total_num_heads % tensor_model_parallel_world_size == 0
        self.num_heads = (self.total_num_heads //
                          tensor_model_parallel_world_size)

        self.query_key_value = QKVParallelLinear(
            config.hidden_size,
            self.head_size,
            self.total_num_heads,
            bias=self.bias,
            quant_config=quant_config,
        )
        self.dense = RowParallelLinear(
            config.hidden_size,
            config.hidden_size,
            bias=self.bias,
            quant_config=quant_config,
        )
        scaling = self.head_size**-0.5
        rotary_dim = int(self.head_size * config.rotary_pct)
        assert rotary_dim % 2 == 0
        rope_theta = getattr(config, "rope_theta", 10000)
        max_position_embeddings = getattr(config, "max_position_embeddings",
                                          8192)
        self.rotary_emb = get_rope(
            self.head_size,
            rotary_dim=rotary_dim,
            max_position=max_position_embeddings,
            base=rope_theta,
        )
        self.attn = Attention(self.num_heads,
                              self.head_size,
                              scaling,
                              cache_config=cache_config,
                              quant_config=quant_config,
                              prefix=f"{prefix}.attn")

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        qkv, _ = self.query_key_value(hidden_states)
        q, k, v = qkv.chunk(chunks=3, dim=-1)
        q, k = self.rotary_emb(position_ids, q, k)
        attn_output = self.attn(q, k, v, kv_cache, attn_metadata)
        output, _ = self.dense(attn_output)
        return output


class GPTNeoXMLP(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        quant_config: Optional[QuantizationConfig] = None,
    ):
        super().__init__()
        self.dense_h_to_4h = ColumnParallelLinear(
            config.hidden_size,
            config.intermediate_size,
            quant_config=quant_config,
        )
        self.dense_4h_to_h = RowParallelLinear(
            config.intermediate_size,
            config.hidden_size,
            quant_config=quant_config,
        )
        self.act = get_act_fn(config.hidden_act)

    def forward(self, hidden_states):
        hidden_states, _ = self.dense_h_to_4h(hidden_states)
        hidden_states = self.act(hidden_states)
        hidden_states, _ = self.dense_4h_to_h(hidden_states)
        return hidden_states


class GPTNeoXLayer(nn.Module):

    def __init__(
        self,
        config: GPTNeoXConfig,
        cache_config: Optional[CacheConfig] = None,
        quant_config: Optional[QuantizationConfig] = None,
        prefix: str = "",
    ):
        super().__init__()
        self.use_parallel_residual = config.use_parallel_residual
        self.input_layernorm = nn.LayerNorm(config.hidden_size,
                                            eps=config.layer_norm_eps)
        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size,
                                                     eps=config.layer_norm_eps)
        self.attention = GPTNeoXAttention(config,
                                          cache_config,
                                          quant_config,
                                          prefix=f"{prefix}.attention")
        self.mlp = GPTNeoXMLP(config, quant_config)

    def forward(
        self,
        position_ids: torch.Tensor,
        hidden_states: torch.Tensor,
        kv_cache: torch.Tensor,
        attn_metadata: AttentionMetadata,
    ) -> torch.Tensor:
        attn_input = self.input_layernorm(hidden_states)
        attn_output = self.attention(
            position_ids=position_ids,
            hidden_states=attn_input,
            kv_cache=kv_cache,
            attn_metadata=attn_metadata,
        )

        if self.use_parallel_residual:
            # pseudocode:
            # x = x + attn(ln1(x)) + mlp(ln2(x))
            mlp_input = self.post_attention_layernorm(hidden_states)
            mlp_output = self.mlp(mlp_input)
            hidden_states = mlp_output + attn_output + hidden_states
        else:
            # pseudocode:
            # x = x + attn(ln1(x))
            # x = x + mlp(ln2(x))
            attn_output = attn_output + hidden_states
            mlp_input = self.post_attention_layernorm(attn_output)
            mlp_output = self.mlp(mlp_input)
            hidden_states = mlp_output + attn_output
        return hidden_states


@support_torch_compile
class GPTNeoXModel(nn.Module):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()

        config = vllm_config.model_config.hf_config
        cache_config = vllm_config.cache_config
        quant_config = vllm_config.quant_config

        self.config = config

        self.embed_in = VocabParallelEmbedding(
            config.vocab_size,
            config.hidden_size,
        )
        self.start_layer, self.end_layer, self.layers = make_layers(
            config.num_hidden_layers,
            lambda prefix: GPTNeoXLayer(
                config, cache_config, quant_config, prefix=prefix),
            prefix=f"{prefix}.layers",
        )
        self.final_layer_norm = nn.LayerNorm(config.hidden_size,
                                             eps=config.layer_norm_eps)
        self.make_empty_intermediate_tensors = (
            make_empty_intermediate_tensors_factory(["hidden_states"],
                                                    config.hidden_size))

    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.embed_in(input_ids)

    def forward(
        self,
        input_ids: torch.Tensor,
        position_ids: torch.Tensor,
        kv_caches: List[torch.Tensor],
        attn_metadata: AttentionMetadata,
        intermediate_tensors: Optional[IntermediateTensors],
        inputs_embeds: Optional[torch.Tensor] = None,
    ) -> Union[torch.Tensor, IntermediateTensors]:
        if get_pp_group().is_first_rank:
            if inputs_embeds is not None:
                hidden_states = inputs_embeds
            else:
                hidden_states = self.get_input_embeddings(input_ids)
        else:
            hidden_states = intermediate_tensors["hidden_states"]
        for i in range(self.start_layer, self.end_layer):
            layer = self.layers[i]
            hidden_states = layer(
                position_ids,
                hidden_states,
                kv_caches[i - self.start_layer],
                attn_metadata,
            )
        if not get_pp_group().is_last_rank:
            return IntermediateTensors({"hidden_states": hidden_states})
        hidden_states = self.final_layer_norm(hidden_states)
        return hidden_states


class GPTNeoXForCausalLM(nn.Module, SupportsPP):

    def __init__(self, *, vllm_config: VllmConfig, prefix: str = ""):
        super().__init__()
        config = vllm_config.model_config.hf_config
        quant_config = vllm_config.quant_config
        self.config = config
        self.quant_config = quant_config
        self.gpt_neox = GPTNeoXModel(vllm_config=vllm_config,
                                     prefix=maybe_prefix(prefix, "gpt_neox"))
        self.embed_out = ParallelLMHead(
            config.vocab_size,
            config.hidden_size,
            quant_config=quant_config,
        )
        if self.config.tie_word_embeddings:
            self.embed_out.weight = self.gpt_neox.embed_in.weight
        self.logits_processor = LogitsProcessor(config.vocab_size)
        self.sampler = get_sampler()
        self.make_empty_intermediate_tensors = (
            self.gpt_neox.make_empty_intermediate_tensors)
        
        self.w8a8_strategy=int(os.getenv('W8A8_SUPPORT_METHODS', '1'))
        self.quant_method = None
        if quant_config is not None:
            self.quant_method=quant_config.get_name()
            self.quant_config=quant_config
        self.tritonsingleton= W8a8GetCacheJSON()
        
    def get_input_embeddings(self, input_ids: torch.Tensor) -> torch.Tensor:
        return self.gpt_neox.get_input_embeddings(input_ids)

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

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

    def sample(
        self,
        logits: torch.Tensor,
        sampling_metadata: SamplingMetadata,
    ) -> Optional[SamplerOutput]:
        next_tokens = self.sampler(logits, sampling_metadata)
        return next_tokens

    def load_weights(self, weights: Iterable[Tuple[str,
                                                   torch.Tensor]]) -> Set[str]:
        params_dict = dict(self.named_parameters())
        loaded_params: Set[str] = set()
        for name, loaded_weight in weights:
            if ("attention.bias" in name or "attention.masked_bias" in name
                    or "rotary_emb.inv_freq" in name):
                continue
            if ("rotary_emb.cos_cached" in name
                    or "rotary_emb.sin_cached" in name):
                # Models trained using OpenRLHF may include
                # these tensors in the checkpoint. Skip them.
                continue
            if is_pp_missing_parameter(name, self):
                continue
            param = params_dict[name]

            if "query_key_value" in name:
                # NOTE: GPT-NeoX's fused QKV's output_dim has the shape of
                # (num_heads * 3 * head_size), while the
                # required shape is (3 * num_heads * head_size).
                # Thus, we need weight conversion.
                output_dim = getattr(param, "output_dim", None)
                num_heads = self.config.num_attention_heads
                if output_dim is not None:
                    loaded_weight_shape = loaded_weight.shape
                    loaded_weight = loaded_weight.view(
                        loaded_weight_shape[:output_dim] + (num_heads, 3, -1) +
                        loaded_weight_shape[output_dim + 1:])
                    loaded_weight = loaded_weight.transpose(
                        output_dim, output_dim + 1)
                    loaded_weight = loaded_weight.reshape(loaded_weight_shape)

            weight_loader = getattr(param, "weight_loader",
                                    default_weight_loader)
            weight_loader(param, loaded_weight)
            loaded_params.add(name)
        #当为triton支持推理的时候不能进行处理
        if self.quant_method == "compressed_tensors":
            lay_key_words = [
                "attention.query_key_value.weight",
                "attention.dense.weight",
                "mlp.dense_h_to_4h.weight",
                "mlp.dense_4h_to_h.weight",
            ]
            combined_words = "|".join(lay_key_words)
            weight_shapes=[]
            all_json={}
            
            for layername, weight in params_dict.items():  
                matches = re.findall(combined_words, layername)
                if matches and "scale" not in layername:
                    weight_data =params_dict[layername]
                    n=weight_data.shape[0]
                    k=weight_data.shape[1]
       
                    #rocblas和cutlass目前都需要weight做处理，但是triton不用
                    if self.w8a8_strategy!=1:
                        _weight=weight_data.T.contiguous().reshape(n,-1)
                        weight_data.data.copy_(_weight)  
                    
                    #下面是针对模型记录模型出现k和n值 
                    elif len(weight_shapes)<4: 
                        #k=weight_data.shape[1]
                        #print("n:{},k:{}".format(n,k))
                        weight_shapes.append({n,k})
                
                        json_file=self.tritonsingleton.get_w8a8json_name(n,k)
                        configs_dict=self.tritonsingleton.get_triton_cache(json_file,n,k)
                        if configs_dict:
                            all_json.update(configs_dict)
            #("weight_shapes:",weight_shapes)
                                            
            if self.w8a8_strategy==1:
                self.tritonsingleton.triton_json_dict.append(all_json)
                #print("self.tritonsingleton.triton_json_dict:",self.tritonsingleton.triton_json_dict)
                #找到的所有config都进行一次warmup
                for key, value in all_json.items():
                    m=int(key.split('_')[0])
                    n=int(key.split('_')[1])
                    k=int(key.split('_')[2])
                    ops.triton_int8_gemm_helper(m=m,n=n,k=k,per_token_act_quant=True,per_out_channel_weight_quant=True,use_bias=False,best_config=value)
                   

        return loaded_params