Witllm/prompt_clue/modeling_t5.py

# Copyright (c) Alibaba, Inc. and its affiliates.
# Copyright 2018 Mesh TensorFlow authors, T5 Authors and HuggingFace Inc. team.
#
# 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.
import copy
import warnings
from typing import Optional, Tuple, Union, List, Dict, Any


import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from transformers.utils.model_parallel_utils import assert_device_map, get_device_map

from modelscope.metainfo import Models
from modelscope.models.builder import MODELS
from modelscope.outputs import (
    AttentionBackboneModelOutput,
    Seq2SeqLMOutput,
    TokenGeneratorOutput,
)
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
from backbone import T5PreTrainedModel, T5Stack
from configuration import T5Config

logger = get_logger()

# Warning message for FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
__HEAD_MASK_WARNING_MSG = """
The input argument `head_mask` was split into two arguments `head_mask` and
`decoder_head_mask`. Currently, `decoder_head_mask` is set to copy `head_mask`,
but this feature is deprecated and will be removed in future versions. If you do
not want to use any `decoder_head_mask` now, please set `decoder_head_mask =
torch.ones(num_layers, num_heads)`.
"""


class T5ForConditionalGeneration(T5PreTrainedModel):
    _keys_to_ignore_on_load_missing = [
        r"encoder\.embed_tokens\.weight",
        r"decoder\.embed_tokens\.weight",
        r"lm_head\.weight",
    ]
    _keys_to_ignore_on_load_unexpected = [
        r"decoder\.block\.0\.layer\.1\.EncDecAttention\.relative_attention_bias\.weight",
    ]

    def __init__(self, config: T5Config, device_map=None, **kwargs):
        super().__init__(config)
        self.model_dim = config.d_model

        self.shared = nn.Embedding(config.vocab_size, config.d_model)

        encoder_config = copy.deepcopy(config)
        encoder_config.is_decoder = False
        encoder_config.use_cache = False
        encoder_config.is_encoder_decoder = False
        self.encoder = T5Stack(encoder_config, self.shared)

        decoder_config = copy.deepcopy(config)
        decoder_config.is_decoder = True
        decoder_config.is_encoder_decoder = False
        decoder_config.num_layers = config.num_decoder_layers
        self.decoder = T5Stack(decoder_config, self.shared)

        self.lm_head = nn.Linear(config.d_model, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()

        # Model parallel
        self.model_parallel = False
        if device_map == "auto":
            self.parallelize()

    def parallelize(self, device_map=None):
        self.device_map = (
            get_device_map(len(self.encoder.block), range(torch.cuda.device_count()))
            if device_map is None
            else device_map
        )
        assert_device_map(self.device_map, len(self.encoder.block))
        self.encoder.parallelize(self.device_map)
        self.decoder.parallelize(self.device_map)
        self.lm_head = self.lm_head.to(self.decoder.first_device)
        self.model_parallel = True

    def deparallelize(self):
        self.encoder.deparallelize()
        self.decoder.deparallelize()
        self.encoder = self.encoder.to("cpu")
        self.decoder = self.decoder.to("cpu")
        self.lm_head = self.lm_head.to("cpu")
        self.model_parallel = False
        self.device_map = None
        torch.cuda.empty_cache()

    def get_input_embeddings(self):
        return self.shared

    def set_input_embeddings(self, new_embeddings):
        self.shared = new_embeddings
        self.encoder.set_input_embeddings(new_embeddings)
        self.decoder.set_input_embeddings(new_embeddings)

    def set_output_embeddings(self, new_embeddings):
        self.lm_head = new_embeddings

    def get_output_embeddings(self):
        return self.lm_head

    def get_encoder(self):
        return self.encoder

    def get_decoder(self):
        return self.decoder

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        attention_mask: Optional[torch.FloatTensor] = None,
        decoder_input_ids: Optional[torch.LongTensor] = None,
        decoder_attention_mask: Optional[torch.BoolTensor] = None,
        head_mask: Optional[torch.FloatTensor] = None,
        decoder_head_mask: Optional[torch.FloatTensor] = None,
        cross_attn_head_mask: Optional[torch.Tensor] = None,
        encoder_outputs: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        decoder_inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        output_attentions: Optional[bool] = None,
        output_hidden_states: Optional[bool] = None,
        return_dict: Optional[bool] = None,
        **kwargs,
    ) -> Union[Tuple[torch.FloatTensor], Seq2SeqLMOutput]:
        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
        )

        # FutureWarning: head_mask was separated into two input args - head_mask, decoder_head_mask
        if head_mask is not None and decoder_head_mask is None:
            if self.config.num_layers == self.config.num_decoder_layers:
                warnings.warn(__HEAD_MASK_WARNING_MSG, FutureWarning)
                decoder_head_mask = head_mask

        # Encode if needed (training, first prediction pass)
        if encoder_outputs is None:
            # Convert encoder inputs in embeddings if needed
            encoder_outputs = self.encoder(
                input_ids=input_ids,
                attention_mask=attention_mask,
                inputs_embeds=inputs_embeds,
                head_mask=head_mask,
                output_attentions=output_attentions,
                output_hidden_states=output_hidden_states,
                return_dict=return_dict,
            )
        elif return_dict and not isinstance(
            encoder_outputs, AttentionBackboneModelOutput
        ):
            encoder_outputs = AttentionBackboneModelOutput(
                last_hidden_state=encoder_outputs[0],
                hidden_states=encoder_outputs[1] if len(encoder_outputs) > 1 else None,
                attentions=encoder_outputs[2] if len(encoder_outputs) > 2 else None,
            )

        hidden_states = encoder_outputs[0]

        if self.model_parallel:
            torch.cuda.set_device(self.decoder.first_device)

        if (
            labels is not None
            and decoder_input_ids is None
            and decoder_inputs_embeds is None
        ):
            # get decoder inputs from shifting lm labels to the right
            decoder_input_ids = self._shift_right(labels)

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.decoder.first_device)
            hidden_states = hidden_states.to(self.decoder.first_device)
            if decoder_input_ids is not None:
                decoder_input_ids = decoder_input_ids.to(self.decoder.first_device)
            if attention_mask is not None:
                attention_mask = attention_mask.to(self.decoder.first_device)
            if decoder_attention_mask is not None:
                decoder_attention_mask = decoder_attention_mask.to(
                    self.decoder.first_device
                )

        # Decode
        decoder_outputs = self.decoder(
            input_ids=decoder_input_ids,
            attention_mask=decoder_attention_mask,
            inputs_embeds=decoder_inputs_embeds,
            past_key_values=past_key_values,
            encoder_hidden_states=hidden_states,
            encoder_attention_mask=attention_mask,
            head_mask=decoder_head_mask,
            cross_attn_head_mask=cross_attn_head_mask,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
        )

        sequence_output = decoder_outputs[0]

        # Set device for model parallelism
        if self.model_parallel:
            torch.cuda.set_device(self.encoder.first_device)
            self.lm_head = self.lm_head.to(self.encoder.first_device)
            sequence_output = sequence_output.to(self.lm_head.weight.device)

        if self.config.tie_word_embeddings:
            # Rescale output before projecting on vocab See
            # https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/transformer/transformer.py#L586
            sequence_output = sequence_output * (self.model_dim ** -0.5)

        lm_logits = self.lm_head(sequence_output)

        loss = None
        if labels is not None:
            loss_fct = CrossEntropyLoss(ignore_index=-100)
            loss = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1))
            # TODO(thom): Add z_loss
            # https://github.com/tensorflow/mesh/blob/fa19d69eafc9a482aff0b59ddd96b025c0cb207d/mesh_tensorflow/layers.py#L666

        if not return_dict:
            output = (lm_logits,) + decoder_outputs[1:] + encoder_outputs
            return ((loss,) + output) if loss is not None else output

        return Seq2SeqLMOutput(
            loss=loss,
            logits=lm_logits,
            past_key_values=decoder_outputs.past_key_values,
            decoder_hidden_states=decoder_outputs.hidden_states,
            decoder_attentions=decoder_outputs.attentions,
            cross_attentions=decoder_outputs.cross_attentions,
            encoder_last_hidden_state=encoder_outputs.last_hidden_state,
            encoder_hidden_states=encoder_outputs.hidden_states,
            encoder_attentions=encoder_outputs.attentions,
        )

    def prepare_inputs_for_generation(
        self,
        input_ids,
        past=None,
        attention_mask=None,
        head_mask=None,
        decoder_head_mask=None,
        cross_attn_head_mask=None,
        use_cache=None,
        encoder_outputs=None,
        **kwargs,
    ):

        # cut decoder_input_ids if past is used
        if past is not None:
            input_ids = input_ids[:, -1:]

        return {
            "decoder_input_ids": input_ids,
            "past_key_values": past,
            "encoder_outputs": encoder_outputs,
            "attention_mask": attention_mask,
            "head_mask": head_mask,
            "decoder_head_mask": decoder_head_mask,
            "cross_attn_head_mask": cross_attn_head_mask,
            "use_cache": use_cache,
        }

    def prepare_decoder_input_ids_from_labels(self, labels: torch.Tensor):
        return self._shift_right(labels)

    def generate(
        self,
        *args,
        **kwargs,
    ):
        output = super().generate(*args, **kwargs)
        return TokenGeneratorOutput(
            sequences=output if isinstance(output, torch.Tensor) else output[0]
        )

    def chat(
        self,
        tokenizer,
        query: str,
        history: List[Tuple[str, str]] = None,
        role: str = "user",
    ):
        if history is None:
            history = []
        token = tokenizer(query)
        inputs = torch.as_tensor([token["input_ids"]])
        inputs_tensor = inputs.to(next(self.parameters()).device)

        generation_config = copy.deepcopy(self.generation_config)
        # inputs_tensor = inputs["input_ids"]
        input_ids = inputs_tensor.repeat_interleave(
            generation_config.num_return_sequences, dim=0
        )

        outputs = self.sample(
            input_ids,
            generation_config.pad_token_id,
            generation_config.eos_token_id,
            generation_config.output_hidden_states,
            tokenizer,
        )

        outputs = outputs.tolist()[0][:]
        response = tokenizer.decode(outputs)
        history.append({"role": role, "content": query})
        return response, history

    def sample(
        self,
        input_ids: torch.LongTensor,
        pad_token_id: Optional[int] = None,
        eos_token_id: Optional[Union[int, List[int]]] = None,
        output_hidden_states: Optional[bool] = None,
        tokenizer=None,
    ):
        if isinstance(eos_token_id, int):
            eos_token_id = [eos_token_id]
        eos_token_id_tensor = torch.tensor(eos_token_id).to(input_ids.device)

        isFinished = torch.zeros(
            input_ids.shape[0], dtype=torch.long, device=input_ids.device
        )
        # token_count = 0
        while True:
            input_ids_in = input_ids
            # batch_size, seq_length = input_ids_in.shape
            # position_ids_in = (
            #     torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
            #     .unsqueeze(0)
            #     .repeat(batch_size, 1)
            # )
            # model_inputs = {"input_ids": input_ids_in, "position_ids": position_ids_in}

            # input_ids_in = self.prepare_inputs_for_generation(input_ids)
            probs, next_tokens = self(input_ids)
            #     **model_inputs,
            #     output_hidden_states=output_hidden_states,
            #     tokenizer=tokenizer,
            # )

            # finished sentences should add a padding token to next
            pad_token = pad_token_id * isFinished
            next_tokens = next_tokens * (1 - isFinished) + pad_token

            isFinished = isFinished | next_tokens.eq(eos_token_id_tensor)
            if isFinished.min() == 1:  # all batch is finish
                break

            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
        return input_ids

    def _reorder_cache(self, past, beam_idx):
        # if decoder past is not included in output
        # speedy decoding is disabled and no need to reorder
        if past is None:
            logger.warning(
                "You might want to consider setting `use_cache=True` to speed up decoding"
            )
            return past

        reordered_decoder_past = ()
        for layer_past_states in past:
            # get the correct batch idx from layer past batch dim
            # batch dim of `past` is at 2nd position
            reordered_layer_past_states = ()
            for layer_past_state in layer_past_states:
                # need to set correct `past` for each of the four key / value states
                reordered_layer_past_states = reordered_layer_past_states + (
                    layer_past_state.index_select(
                        0, beam_idx.to(layer_past_state.device)
                    ),
                )

            assert reordered_layer_past_states[0].shape == layer_past_states[0].shape
            assert len(reordered_layer_past_states) == len(layer_past_states)

            reordered_decoder_past = reordered_decoder_past + (
                reordered_layer_past_states,
            )
        return reordered_decoder_past