llama_inference_offload.py

import torch
import torch.nn as nn

from gptq import GPTQ
import argparse
from utils import find_layers, DEV, set_seed, get_wikitext2, get_ptb, get_c4, get_ptb_new, get_c4_new, get_loaders
import quant

import transformers
from transformers import AutoTokenizer
from transformers.models.llama.modeling_llama import LlamaModel, LlamaConfig
from transformers.modeling_outputs import BaseModelOutputWithPast
from typing import List, Optional, Tuple, Union
from accelerate import cpu_offload_with_hook, load_checkpoint_in_model


class Offload_LlamaModel(LlamaModel):

    def __init__(self, config: LlamaConfig):
        super().__init__(config)

    def cpu_offload(self, preload):
        hook = None
        for cpu_offloaded_model in self.layers[preload:]:
            _, hook = cpu_offload_with_hook(cpu_offloaded_model, DEV, prev_module_hook=hook)

    def forward(
        self,
        input_ids: torch.LongTensor = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[List[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
    ) -> Union[Tuple, BaseModelOutputWithPast]:
        r"""
        Args:
            input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
                Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you
                provide it.
                Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
                [`PreTrainedTokenizer.__call__`] for details.
                [What are input IDs?](../glossary#input-ids)
            attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
                Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
                - 1 for tokens that are **not masked**,
                - 0 for tokens that are **masked**.
                [What are attention masks?](../glossary#attention-mask)
            position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                Indices of positions of each input sequence tokens in the position embeddings. Selected in the range
                `[0, config.n_positions - 1]`.
                [What are position IDs?](../glossary#position-ids)
            past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
                Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
                shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of
                Contains pre-computed hidden-states (key and values in the self-attention blocks and in the
                cross-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
                If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those
                that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of
                all `decoder_input_ids` of shape `(batch_size, sequence_length)`.
            use_cache (`bool`, *optional*):
                If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
                (see `past_key_values`).
            inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
                Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation.
                This is useful if you want more control over how to convert `input_ids` indices into associated vectors
                than the model's internal embedding lookup matrix.
            output_attentions (`bool`, *optional*):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more detail.
            output_hidden_states (`bool`, *optional*):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more detail.
            return_dict (`bool`, *optional*):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
        """
        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
        output_hidden_states = (output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states)
        use_cache = use_cache if use_cache is not None else self.config.use_cache

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # retrieve input_ids and inputs_embeds
        if input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
        elif input_ids is not None:
            batch_size, seq_length = input_ids.shape
        elif inputs_embeds is not None:
            batch_size, seq_length, _ = inputs_embeds.shape
        else:
            raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
        seq_length_with_past = seq_length
        past_key_values_length = 0
        if past_key_values is not None:
            past_key_values_length = past_key_values[0][0].shape[2]
            seq_length_with_past = seq_length_with_past + past_key_values_length

        if position_ids is None:
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            position_ids = torch.arange(past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device)
            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
        else:
            position_ids = position_ids.view(-1, seq_length).long()

        if inputs_embeds is None:
            inputs_embeds = self.embed_tokens(input_ids)

        # embed positions
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device)
        attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length)

        hidden_states = inputs_embeds

        if self.gradient_checkpointing and self.training:
            if use_cache:
                logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...")
                use_cache = False

        # decoder layers
        all_hidden_states = () if output_hidden_states else None
        all_self_attns = () if output_attentions else None
        next_decoder_cache = () if use_cache else None

        for idx in range(len(self.layers)):
            decoder_layer = self.layers[idx]

            if output_hidden_states:
                all_hidden_states += (hidden_states, )

            past_key_value = past_key_values[idx] if past_key_values is not None else None

            if self.gradient_checkpointing and self.training:

                def create_custom_forward(module):

                    def custom_forward(*inputs):
                        # None for past_key_value
                        return module(*inputs, output_attentions, None)

                    return custom_forward

                layer_outputs = torch.utils.checkpoint.checkpoint(
                    create_custom_forward(decoder_layer),
                    hidden_states,
                    attention_mask,
                    position_ids,
                    None,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_value,
                    output_attentions=output_attentions,
                    use_cache=use_cache,
                )

            hidden_states = layer_outputs[0]

            if use_cache:
                next_decoder_cache += (layer_outputs[2 if output_attentions else 1], )

            if output_attentions:
                all_self_attns += (layer_outputs[1], )

        hidden_states = self.norm(hidden_states)

        # add hidden states from the last decoder layer
        if output_hidden_states:
            all_hidden_states += (hidden_states, )

        next_cache = next_decoder_cache if use_cache else None
        if not return_dict:
            return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=next_cache,
            hidden_states=all_hidden_states,
            attentions=all_self_attns,
        )


def load_quant(model, checkpoint, wbits, groupsize, pre_layer, fused_mlp=True, warmup_autotune=True):
    transformers.models.llama.modeling_llama.LlamaModel = Offload_LlamaModel
    from transformers import LlamaConfig, LlamaForCausalLM
    config = LlamaConfig.from_pretrained(model)

    def noop(*args, **kwargs):
        pass

    torch.nn.init.kaiming_uniform_ = noop
    torch.nn.init.uniform_ = noop
    torch.nn.init.normal_ = noop

    torch.set_default_dtype(torch.half)
    transformers.modeling_utils._init_weights = False
    torch.set_default_dtype(torch.half)
    model = LlamaForCausalLM(config)
    torch.set_default_dtype(torch.float)
    model = model.eval()
    layers = find_layers(model)
    for name in ['lm_head']:
        if name in layers:
            del layers[name]
    quant.make_quant_linear(model, layers, wbits, groupsize)

    print('Loading model ...')
    load_checkpoint_in_model(model, checkpoint, dtype='float16')
    model.seqlen = 2048

    if eval:
        quant.make_quant_attn(model)
        quant.make_quant_norm(model)
        if fused_mlp:
            quant.make_fused_mlp(model)


    if warmup_autotune:
        quant.autotune_warmup_linear(model)
        if fused_mlp:
            quant.autotune_warmup_fused(model)

    for i in range(pre_layer):
        model.model.layers[i].to(DEV)
    model.model.embed_tokens.to(DEV)
    model.model.norm.to(DEV)
    model.lm_head.to(DEV)
    model.model.cpu_offload(pre_layer)
    print('Done.')
    return model


if __name__ == '__main__':
    parser = argparse.ArgumentParser()

    parser.add_argument('model', type=str, help='llama model to load')
    parser.add_argument('--wbits', type=int, default=4, choices=[2, 3, 4, 8], help='#bits to use for quantization')
    parser.add_argument('--groupsize', type=int, default=-1, help='Groupsize to use for quantization; default uses full row.')
    parser.add_argument('--load', type=str, default='', help='Load quantized model.')
    parser.add_argument('--text', type=str, help='input text')

    parser.add_argument('--min_length', type=int, default=10, help='The minimum length of the sequence to be generated.')

    parser.add_argument('--max_length', type=int, default=50, help='The maximum length of the sequence to be generated.')

    parser.add_argument('--top_p',
                        type=float,
                        default=0.95,
                        help='If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.')

    parser.add_argument('--temperature', type=float, default=0.8, help='The value used to module the next token probabilities.')

    parser.add_argument('--pre_layer', type=int, default=50, help='The number of layers to preload')

    args = parser.parse_args()

    if type(args.load) is not str:
        args.load = args.load.as_posix()

    model = load_quant(args.model, args.load, args.wbits, args.groupsize, args.pre_layer)

    tokenizer = AutoTokenizer.from_pretrained(args.model, use_fast=False)
    input_ids = tokenizer.encode(args.text, return_tensors="pt").to(DEV)

    with torch.no_grad():
        generated_ids = model.generate(
            input_ids,
            do_sample=True,
            min_length=args.min_length,
            max_length=args.max_length,
            top_p=args.top_p,
            temperature=args.temperature,
        )
    print(tokenizer.decode([el.item() for el in generated_ids[0]]))