ColossalAI/colossalai/shardformer/modeling/llama.py

from typing import Callable, List, Optional, Tuple

import torch
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    SequenceClassifierOutputWithPast,
)
from transformers.models.llama.modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel
from transformers.utils import logging

from colossalai.pipeline.stage_manager import PipelineStageManager


class LlamaPipelineForwards:
    '''
    This class serves as a micro library for forward function substitution of Llama models
    under pipeline setting.
    '''

    def llama_model_forward(
        self: LlamaModel,
        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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
    ):
        logger = logging.get_logger(__name__)

        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 stage_manager.is_first_stage():
            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")
            device = input_ids.device if input_ids is not None else inputs_embeds.device
            if inputs_embeds is None:
                inputs_embeds = self.embed_tokens(input_ids)
            hidden_states = inputs_embeds
        else:
            input_shape = hidden_states.shape[:-1]
            batch_size, seq_length = input_shape
            device = hidden_states.device

        seq_length_with_past = seq_length
        past_key_values_length = 0

        # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
        if output_attentions:
            logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
            output_attentions = False
        if output_hidden_states:
            logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
            output_hidden_states = False
        if use_cache:
            logger.warning_once('use_cache=True is not supported for pipeline models at the moment.')
            use_cache = False

        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:
            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()

        # embed positions, for the first stage, hidden_states is the input embeddings,
        # for the other stages, hidden_states is the output of the previous stage
        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_length_with_past),
                                        dtype=torch.bool,
                                        device=hidden_states.device)
        attention_mask = self._prepare_decoder_attention_mask(attention_mask, (batch_size, seq_length), hidden_states,
                                                              past_key_values_length)

        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

        start_idx, end_idx = stage_index[0], stage_index[1]
        for idx, decoder_layer in enumerate(self.layers[start_idx:end_idx], start=start_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],)

        if stage_manager.is_last_stage():
            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 stage_manager.is_last_stage():
            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,
            )
        # always return dict for imediate stage
        return {'hidden_states': hidden_states}

    def llama_for_causal_lm_forward(
        self: LlamaForCausalLM,
        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,
        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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
    ):
        r"""
            Args:
                labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
                    Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
                    config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
                    (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.

            Returns:

            Example:

            ```python
            >>> from transformers import AutoTokenizer, LlamaForCausalLM

            >>> model = LlamaForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
            >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)

            >>> prompt = "Hey, are you consciours? Can you talk to me?"
            >>> inputs = tokenizer(prompt, return_tensors="pt")

            >>> # Generate
            >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
            >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
            "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
            ```"""
        logger = logging.get_logger(__name__)
        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)
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
        if output_attentions:
            logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
            output_attentions = False
        if output_hidden_states:
            logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
            output_hidden_states = False

        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
        outputs = LlamaPipelineForwards.llama_model_forward(
            self.model,
            input_ids=input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
        )
        past_key_values = None
        all_hidden_states = None
        all_self_attentions = None
        all_cross_attentions = None

        if stage_manager.is_last_stage():
            hidden_states = outputs[0]
            logits = self.lm_head(hidden_states)
            loss = None
            if labels is not None:
                # Shift so that tokens < n predict n
                shift_logits = logits[..., :-1, :].contiguous()
                shift_labels = labels[..., 1:].contiguous()
                # Flatten the tokens
                loss_fct = CrossEntropyLoss()
                shift_logits = shift_logits.view(-1, self.config.vocab_size)
                shift_labels = shift_labels.view(-1)
                # Enable model parallelism
                shift_labels = shift_labels.to(shift_logits.device)
                loss = loss_fct(shift_logits, shift_labels)

            if not return_dict:
                output = (logits,) + outputs[1:]
                return (loss,) + output if loss is not None else output

            return CausalLMOutputWithPast(
                loss=loss,
                logits=logits,
                past_key_values=outputs.past_key_values,
                hidden_states=outputs.hidden_states,
                attentions=outputs.attentions,
            )
        else:
            hidden_states = outputs.get('hidden_states')
            return {'hidden_states': hidden_states}

    def llama_for_sequence_classification_forward(
        self: LlamaForSequenceClassification,
        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,
        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,
        stage_manager: Optional[PipelineStageManager] = None,
        hidden_states: Optional[torch.FloatTensor] = None,
        stage_index: Optional[List[int]] = None,
    ):
        r"""
        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
            Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
        """
        logger = logging.get_logger(__name__)

        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
        # TODO(jianghai): left the recording kv-value tensors as () or None type, this feature may be added in the future.
        if output_attentions:
            logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.')
            output_attentions = False
        if output_hidden_states:
            logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.')
            output_hidden_states = False

        transformer_outputs = LlamaPipelineForwards.llama_model_forward(
            self.model,
            input_ids,
            attention_mask=attention_mask,
            position_ids=position_ids,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            output_hidden_states=output_hidden_states,
            return_dict=return_dict,
            stage_manager=stage_manager,
            hidden_states=hidden_states,
            stage_index=stage_index,
        )

        if input_ids is not None:
            batch_size = input_ids.shape[0]
        elif inputs_embeds is not None:
            batch_size = inputs_embeds.shape[0]
        else:
            batch_size = hidden_states.shape[0]

        if stage_manager.is_last_stage():
            hidden_states = transformer_outputs[0]
            logits = self.score(hidden_states)

            if self.config.pad_token_id is None and batch_size != 1:
                raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
            if self.config.pad_token_id is None:
                sequence_lengths = -1
            else:
                if input_ids is not None:
                    sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
                else:
                    sequence_lengths = -1

            pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]

            loss = None
            if labels is not None:
                labels = labels.to(logits.device)
                if self.config.problem_type is None:
                    if self.num_labels == 1:
                        self.config.problem_type = "regression"
                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                        self.config.problem_type = "single_label_classification"
                    else:
                        self.config.problem_type = "multi_label_classification"

                if self.config.problem_type == "regression":
                    loss_fct = MSELoss()
                    if self.num_labels == 1:
                        loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
                    else:
                        loss = loss_fct(pooled_logits, labels)
                elif self.config.problem_type == "single_label_classification":
                    loss_fct = CrossEntropyLoss()
                    loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
                elif self.config.problem_type == "multi_label_classification":
                    loss_fct = BCEWithLogitsLoss()
                    loss = loss_fct(pooled_logits, labels)
            if not return_dict:
                output = (pooled_logits,) + transformer_outputs[1:]
                return ((loss,) + output) if loss is not None else output

            return SequenceClassifierOutputWithPast(
                loss=loss,
                logits=pooled_logits,
                past_key_values=transformer_outputs.past_key_values,
                hidden_states=transformer_outputs.hidden_states,
                attentions=transformer_outputs.attentions,
            )

        else:
            hidden_states = transformer_outputs.get('hidden_states')
            return {'hidden_states': hidden_states}


def get_llama_flash_attention_forward():

    from transformers.models.llama.modeling_llama import LlamaAttention, apply_rotary_pos_emb

    from colossalai.kernel.cuda_native import AttnMaskType, ColoAttention

    def forward(
        self: LlamaAttention,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_value: Optional[Tuple[torch.Tensor]] = None,
        output_attentions: bool = False,
        use_cache: bool = False,
    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
        bsz, q_len, _ = hidden_states.size()
        assert q_len % 4 == 0, "Flash Attention Error: The sequence length should be a multiple of 4."

        query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
        value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)

        kv_seq_len = key_states.shape[-2]
        if past_key_value is not None:
            kv_seq_len += past_key_value[0].shape[-2]

        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)

        if past_key_value is not None:
            # reuse k, v, self_attention
            key_states = torch.cat([past_key_value[0], key_states], dim=2)
            value_states = torch.cat([past_key_value[1], value_states], dim=2)

        past_key_value = (key_states, value_states) if use_cache else None

        me_input_shape = (bsz, q_len, self.num_heads, self.head_dim)
        query_states = query_states.transpose(1, 2).contiguous().view(*me_input_shape)
        key_states = key_states.transpose(1, 2).contiguous().view(*me_input_shape)
        value_states = value_states.transpose(1, 2).contiguous().view(*me_input_shape)

        flash_attention_mask = None
        attn_mask_type = AttnMaskType.causal
        if attention_mask != None:
            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
                raise ValueError(
                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}")
            flash_attention_mask = ~(attention_mask[:, :, -1].squeeze(1).to(torch.bool)).contiguous()
            attn_mask_type = AttnMaskType.paddedcausal

        attention = ColoAttention(embed_dim=self.hidden_size, num_heads=self.num_heads)
        attn_output = attention(query_states,
                                key_states,
                                value_states,
                                attn_mask=flash_attention_mask,
                                attn_mask_type=attn_mask_type)

        attn_output = self.o_proj(attn_output)

        return attn_output, None, past_key_value

    return forward