classifier.py

from transformers import MistralPreTrainedModel
import torch
import numpy as np
from typing import List, Optional, Tuple, Union
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from transformers.modeling_outputs import SequenceClassifierOutputWithPast
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers import (
    MistralModel,
    MistralPreTrainedModel,
    MistralForCausalLM,
    MistralConfig,
)
from transformers.modeling_outputs import BaseModelOutputWithPast
from transformers.cache_utils import Cache, DynamicCache
from transformers.models.mistral.modeling_mistral import (
    MistralDecoderLayer,
    MistralRMSNorm,
    MistralAttention,
    MistralFlashAttention2,
    MistralSdpaAttention,
    MistralMLP,
)
from torch import nn
from transformers.utils import logging


def _prepare_4d_causal_attention_mask(
    attention_mask: Optional[torch.Tensor],
    input_shape: Union[torch.Size, Tuple, List],
    inputs_embeds: torch.Tensor,
    past_key_values_length: int,
    sliding_window: Optional[int] = None,
):
    """
    Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape
    `(batch_size, key_value_length)`

    Args:
        attention_mask (`torch.Tensor` or `None`):
            A 2D attention mask of shape `(batch_size, key_value_length)`
        input_shape (`tuple(int)` or `list(int)` or `torch.Size`):
            The input shape should be a tuple that defines `(batch_size, query_length)`.
        inputs_embeds (`torch.Tensor`):
            The embedded inputs as a torch Tensor.
        past_key_values_length (`int`):
            The length of the key value cache.
        sliding_window (`int`, *optional*):
            If the model uses windowed attention, a sliding window should be passed.
    """
    attn_mask_converter = AttentionMaskConverter(
        is_causal=False, sliding_window=sliding_window
    )  # is_causal=True in original implementation

    key_value_length = input_shape[-1] + past_key_values_length

    # 4d mask is passed through the layers
    if attention_mask is not None and len(attention_mask.shape) == 2:
        attention_mask = attn_mask_converter.to_4d(
            attention_mask,
            input_shape[-1],
            key_value_length=key_value_length,
            dtype=inputs_embeds.dtype,
        )
    elif attention_mask is not None and len(attention_mask.shape) == 4:
        expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
        if tuple(attention_mask.shape) != expected_shape:
            raise ValueError(
                f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
            )
        else:
            # if the 4D mask has correct shape - invert it and fill with negative infinity
            inverted_mask = 1.0 - attention_mask
            attention_mask = inverted_mask.masked_fill(
                inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
            )
    else:
        attention_mask = attn_mask_converter.to_causal_4d(
            input_shape[0],
            input_shape[-1],
            key_value_length,
            dtype=inputs_embeds.dtype,
            device=inputs_embeds.device,
        )

    return attention_mask


# Adapted from _prepare_4d_causal_attention_mask
def _prepare_4d_causal_attention_mask_for_sdpa(
    attention_mask: Optional[torch.Tensor],
    input_shape: Union[torch.Size, Tuple, List],
    inputs_embeds: torch.Tensor,
    past_key_values_length: int,
    sliding_window: Optional[int] = None,
):
    """
    Prepares the correct `attn_mask` argument to be used by `torch.nn.functional.scaled_dot_product_attention`.

    In case no token is masked in the `attention_mask` argument, we simply set it to `None` for the cases `query_length == 1` and
    `key_value_length == query_length`, and rely instead on SDPA `is_causal` argument to use causal/non-causal masks,
    allowing to dispatch to the flash attention kernel (that can otherwise not be used if a custom `attn_mask` is passed).
    """
    attn_mask_converter = AttentionMaskConverter(
        is_causal=False, sliding_window=sliding_window
    )  # is_causal=True in original implementation

    key_value_length = input_shape[-1] + past_key_values_length
    batch_size, query_length = input_shape

    # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture the controlflow `is_causal=attention_mask is None and q_len > 1`
    # used as an SDPA argument. We keep compatibility with these tracing tools by always using SDPA's `attn_mask` argument in case we are tracing.
    # TODO: For dynamo, rather use a check on fullgraph=True once this is
    # possible (https://github.com/pytorch/pytorch/pull/120400).
    is_tracing = (
        torch.jit.is_tracing()
        or isinstance(inputs_embeds, torch.fx.Proxy)
        or (hasattr(torch, "_dynamo") and torch._dynamo.is_compiling())
    )

    if attention_mask is not None:
        # 4d mask is passed through
        if len(attention_mask.shape) == 4:
            expected_shape = (input_shape[0], 1, input_shape[1], key_value_length)
            if tuple(attention_mask.shape) != expected_shape:
                raise ValueError(
                    f"Incorrect 4D attention_mask shape: {tuple(attention_mask.shape)}; expected: {expected_shape}."
                )
            else:
                # if the 4D mask has correct shape - invert it and fill with negative infinity
                inverted_mask = 1.0 - attention_mask.to(inputs_embeds.dtype)
                attention_mask = inverted_mask.masked_fill(
                    inverted_mask.to(torch.bool), torch.finfo(inputs_embeds.dtype).min
                )
                return attention_mask

        elif not is_tracing and torch.all(attention_mask == 1):
            if query_length == 1:
                # For query_length == 1, causal attention and bi-directional attention are the same.
                attention_mask = None
            elif key_value_length == query_length:
                attention_mask = None
            else:
                # Unfortunately, for query_length > 1 and key_value_length != query_length, we cannot generally ignore the attention mask, as SDPA causal mask generation
                # may be wrong. We will set `is_causal=False` in SDPA and rely on Transformers attention_mask instead, hence not setting it to None here.
                # Reference: https://github.com/pytorch/pytorch/issues/108108
                pass
    elif query_length > 1 and key_value_length != query_length:
        # See the comment above (https://github.com/pytorch/pytorch/issues/108108).
        # Ugly: we set it to True here to dispatch in the following controlflow to `to_causal_4d`.
        attention_mask = True
    elif is_tracing:
        raise ValueError(
            'Attention using SDPA can not be traced with torch.jit.trace when no attention_mask is provided. To solve this issue, please either load your model with the argument `attn_implementation="eager"` or pass an attention_mask input when tracing the model.'
        )

    if attention_mask is None:
        expanded_4d_mask = None
    elif attention_mask is True:
        expanded_4d_mask = attn_mask_converter.to_causal_4d(
            input_shape[0],
            input_shape[-1],
            key_value_length,
            dtype=inputs_embeds.dtype,
            device=inputs_embeds.device,
        )
    else:
        expanded_4d_mask = attn_mask_converter.to_4d(
            attention_mask,
            input_shape[-1],
            dtype=inputs_embeds.dtype,
            key_value_length=key_value_length,
        )

        # Attend to all tokens in masked rows from the causal_mask, for example the relevant first rows when
        # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path.
        # Details: https://github.com/pytorch/pytorch/issues/110213
        if not is_tracing and expanded_4d_mask.device.type == "cuda":
            expanded_4d_mask = AttentionMaskConverter._unmask_unattended(
                expanded_4d_mask, min_dtype=torch.finfo(inputs_embeds.dtype).min
            )

    return expanded_4d_mask

class ModifiedMistralAttention(MistralAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.is_causal = False


class ModifiedMistralFlashAttention2(MistralFlashAttention2):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.is_causal = False


class ModifiedMistralSdpaAttention(MistralSdpaAttention):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.is_causal = False


MISTRAL_ATTENTION_CLASSES = {
    "eager": ModifiedMistralAttention,
    "flash_attention_2": ModifiedMistralFlashAttention2,
    "sdpa": ModifiedMistralSdpaAttention,
}


class ModifiedMistralDecoderLayer(MistralDecoderLayer):
    def __init__(self, config: MistralConfig, layer_idx: int):
        nn.Module.__init__(self)
        self.hidden_size = config.hidden_size

        self.self_attn = MISTRAL_ATTENTION_CLASSES[config._attn_implementation](
            config, layer_idx
        )

        self.mlp = MistralMLP(config)
        self.input_layernorm = MistralRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )
        self.post_attention_layernorm = MistralRMSNorm(
            config.hidden_size, eps=config.rms_norm_eps
        )


class MistralBiModel(MistralModel):
    def __init__(self, config: MistralConfig):
        MistralPreTrainedModel.__init__(self, config)
        self.padding_idx = config.pad_token_id
        self.vocab_size = config.vocab_size

        self.embed_tokens = nn.Embedding(
            config.vocab_size, config.hidden_size, self.padding_idx
        )
        self.layers = nn.ModuleList(
            [
                ModifiedMistralDecoderLayer(config, layer_idx)
                for layer_idx in range(config.num_hidden_layers)
            ]
        )
        self._attn_implementation = config._attn_implementation
        self.norm = MistralRMSNorm(config.hidden_size, eps=config.rms_norm_eps)

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

    # Copied from forward() in transformers.models.mistral.modeling_mistral.MistralModel
    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]:
        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"
            )

        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

        past_key_values_length = 0

        if use_cache:
            use_legacy_cache = not isinstance(past_key_values, Cache)
            if use_legacy_cache:
                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
            past_key_values_length = past_key_values.get_usable_length(seq_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)

        if (
            attention_mask is not None
            and self._attn_implementation == "flash_attention_2"
            and use_cache
        ):
            is_padding_right = attention_mask[:, -1].sum().item() != batch_size
            if is_padding_right:
                raise ValueError(
                    "You are attempting to perform batched generation with padding_side='right'"
                    " this may lead to unexpected behaviour for Flash Attention version of Mistral. Make sure to "
                    " call `tokenizer.padding_side  = 'left'` before tokenizing the input. ")

        if self._attn_implementation == "flash_attention_2":
            # 2d mask is passed through the layers
            attention_mask = (
                attention_mask
                if (attention_mask is not None and 0 in attention_mask)
                else None
            )
        elif self._attn_implementation == "sdpa" and not output_attentions:
            # The original implementation is by-passed, see attn_mask_utils.py
            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
            )
        else:
            # 4d mask is passed through the layers
            attention_mask = _prepare_4d_causal_attention_mask(
                attention_mask,
                (batch_size, seq_length),
                inputs_embeds,
                past_key_values_length,
                sliding_window=self.config.sliding_window,
            )

        hidden_states = inputs_embeds

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

        for decoder_layer in self.layers:
            if output_hidden_states:
                all_hidden_states += (hidden_states,)

            if self.gradient_checkpointing and self.training:
                layer_outputs = self._gradient_checkpointing_func(
                    decoder_layer.__call__,
                    hidden_states,
                    attention_mask,
                    position_ids,
                    past_key_values,
                    output_attentions,
                    use_cache,
                )
            else:
                layer_outputs = decoder_layer(
                    hidden_states,
                    attention_mask=attention_mask,
                    position_ids=position_ids,
                    past_key_value=past_key_values,
                    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 = None
        if use_cache:
            next_cache = (
                next_decoder_cache.to_legacy_cache()
                if use_legacy_cache
                else next_decoder_cache
            )

        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,
        )


class MistralBiForMNTP(MistralForCausalLM):
    def __init__(self, config):
        MistralPreTrainedModel.__init__(self, config)
        self.model = MistralBiModel(config)
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        # Initialize weights and apply final processing
        self.post_init()
        
class MistralForSequenceClassification(MistralPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)
        self.num_labels = config.num_labels
        self.model = MistralBiModel(config)
        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)

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

    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,
        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,
    ):
        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).
        """
        return_dict = return_dict if return_dict is not None else self.config.use_return_dict

        transformer_outputs = 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,
        )
        pooled_output = transformer_outputs[0][:, 0]
        logits = self.score(pooled_output)

        loss = None
        if labels is not None:
            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(logits.squeeze(), labels.squeeze())
                else:
                    loss = loss_fct(logits, labels)
            elif self.config.problem_type == "single_label_classification":
                loss_fct = CrossEntropyLoss()
                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
            elif self.config.problem_type == "multi_label_classification":
                loss_fct = BCEWithLogitsLoss()
                loss = loss_fct(logits, labels)
        if not return_dict:
            output = (logits,) + transformer_outputs[2:]
            return ((loss,) + output) if loss is not None else output

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