modeling_chatglm.py

""" PyTorch ChatGLM model. """

import math
import copy
import os
import time

import torch
import torch.utils.checkpoint
import torch.nn.functional as F
from torch import nn
from torch.nn import CrossEntropyLoss, LayerNorm
from torch.nn.utils import skip_init
from typing import Optional, Tuple, Union, List

from transformers.utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
)
from transformers.modeling_outputs import (
    BaseModelOutputWithPast,
    CausalLMOutputWithPast,
    BaseModelOutputWithPastAndCrossAttentions,
)
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging
from transformers.generation.logits_process import LogitsProcessor
from transformers.generation.utils import LogitsProcessorList

from .configuration_chatglm import ChatGLMConfig

# flags required to enable jit fusion kernels
torch._C._jit_set_profiling_mode(False)
torch._C._jit_set_profiling_executor(False)
torch._C._jit_override_can_fuse_on_cpu(True)
torch._C._jit_override_can_fuse_on_gpu(True)

logger = logging.get_logger(__name__)

_CHECKPOINT_FOR_DOC = "THUDM/ChatGLM-6B"
_CONFIG_FOR_DOC = "ChatGLM6BConfig"

CHATGLM_6B_PRETRAINED_MODEL_ARCHIVE_LIST = [
    "THUDM/chatglm-6b",
    # See all ChatGLM-6B models at https://huggingface.co/models?filter=chatglm
]


class InvalidScoreLogitsProcessor(LogitsProcessor):
    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
        if torch.isnan(scores).any() or torch.isinf(scores).any():
            scores.zero_()
            scores[..., 20005] = 5e4
        return scores


def load_tf_weights_in_chatglm_6b(model, config, tf_checkpoint_path):
    """Load tf checkpoints in a pytorch model."""
    try:
        import re

        import numpy as np
        import tensorflow as tf
    except ImportError:
        logger.error(
            "Loading a TensorFlow model in PyTorch, requires TensorFlow to be installed. Please see "
            "https://www.tensorflow.org/install/ for installation instructions."
        )
        raise
    tf_path = os.path.abspath(tf_checkpoint_path)
    logger.info(f"Converting TensorFlow checkpoint from {tf_path}")
    # Load weights from TF model
    init_vars = tf.train.list_variables(tf_path)
    names = []
    arrays = []
    for name, shape in init_vars:
        logger.info(f"Loading TF weight {name} with shape {shape}")
        array = tf.train.load_variable(tf_path, name)
        names.append(name)
        arrays.append(array)

    for name, array in zip(names, arrays):
        name = name.split("/")
        # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
        # which are not required for using pretrained model
        if any(
                n in ["adam_v", "adam_m", "AdamWeightDecayOptimizer", "AdamWeightDecayOptimizer_1", "global_step"]
                for n in name
        ):
            logger.info(f"Skipping {'/'.join(name)}")
            continue
        pointer = model
        for m_name in name:
            if re.fullmatch(r"[A-Za-z]+_\d+", m_name):
                scope_names = re.split(r"_(\d+)", m_name)
            else:
                scope_names = [m_name]
            if scope_names[0] == "kernel" or scope_names[0] == "gamma":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "output_bias" or scope_names[0] == "beta":
                pointer = getattr(pointer, "bias")
            elif scope_names[0] == "output_weights":
                pointer = getattr(pointer, "weight")
            elif scope_names[0] == "squad":
                pointer = getattr(pointer, "classifier")
            else:
                try:
                    pointer = getattr(pointer, scope_names[0])
                except AttributeError:
                    logger.info(f"Skipping {'/'.join(name)}")
                    continue
            if len(scope_names) >= 2:
                num = int(scope_names[1])
                pointer = pointer[num]
        if m_name[-11:] == "_embeddings":
            pointer = getattr(pointer, "weight")
        elif m_name == "kernel":
            array = np.transpose(array)
        try:
            assert (
                    pointer.shape == array.shape
            ), f"Pointer shape {pointer.shape} and array shape {array.shape} mismatched"
        except AssertionError as e:
            e.args += (pointer.shape, array.shape)
            raise
        logger.info(f"Initialize PyTorch weight {name}")
        pointer.data = torch.from_numpy(array)
    return model


class PrefixEncoder(torch.nn.Module):
    r'''
    The torch.nn model to encode the prefix
    Input shape: (batch-size, prefix-length)
    Output shape: (batch-size, prefix-length, 2*layers*hidden)
    '''
    def __init__(self, config):
        super().__init__()
        self.prefix_projection = config.prefix_projection
        if self.prefix_projection:
            # Use a two-layer MLP to encode the prefix
            self.embedding = torch.nn.Embedding(config.pre_seq_len, config.hidden_size)
            self.trans = torch.nn.Sequential(
                torch.nn.Linear(config.hidden_size, config.hidden_size),
                torch.nn.Tanh(),
                torch.nn.Linear(config.hidden_size, config.num_layers  * config.hidden_size * 2)
            )
        else:
            self.embedding = torch.nn.Embedding(config.pre_seq_len, config.num_layers * config.hidden_size * 2)

    def forward(self, prefix: torch.Tensor):
        if self.prefix_projection:
            prefix_tokens = self.embedding(prefix)
            past_key_values = self.trans(prefix_tokens)
        else:
            past_key_values = self.embedding(prefix)
        return past_key_values


@torch.jit.script
def gelu_impl(x):
    """OpenAI's gelu implementation."""
    return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x *
                                       (1.0 + 0.044715 * x * x)))


def gelu(x):
    return gelu_impl(x)


class RotaryEmbedding(torch.nn.Module):
    def __init__(self, dim, base=10000, precision=torch.half, learnable=False):
        super().__init__()
        inv_freq = 1. / (base ** (torch.arange(0, dim, 2).float() / dim))
        inv_freq = inv_freq.half()
        self.learnable = learnable
        if learnable:
            self.inv_freq = torch.nn.Parameter(inv_freq)
            self.max_seq_len_cached = None
        else:
            self.register_buffer('inv_freq', inv_freq)
            self.max_seq_len_cached = None
            self.cos_cached = None
            self.sin_cached = None
        self.precision = precision

    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
                              error_msgs):
        pass

    def forward(self, x, seq_dim=1, seq_len=None):
        if seq_len is None:
            seq_len = x.shape[seq_dim]
        if self.max_seq_len_cached is None or (seq_len > self.max_seq_len_cached):
            self.max_seq_len_cached = None if self.learnable else seq_len
            t = torch.arange(seq_len, device=x.device, dtype=self.inv_freq.dtype)
            freqs = torch.einsum('i,j->ij', t, self.inv_freq)
            # Different from paper, but it uses a different permutation in order to obtain the same calculation
            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
            if self.precision == torch.bfloat16:
                emb = emb.float()

            # [sx, 1 (b * np), hn]
            cos_cached = emb.cos()[:, None, :]
            sin_cached = emb.sin()[:, None, :]
            if self.precision == torch.bfloat16:
                cos_cached = cos_cached.bfloat16()
                sin_cached = sin_cached.bfloat16()
            if self.learnable:
                return cos_cached, sin_cached
            self.cos_cached, self.sin_cached = cos_cached, sin_cached
        return self.cos_cached[:seq_len, ...], self.sin_cached[:seq_len, ...]


def rotate_half(x):
    x1, x2 = x[..., :x.shape[-1] // 2], x[..., x.shape[-1] // 2:]
    return torch.cat((-x2, x1), dim=x1.ndim - 1)  # dim=-1 triggers a bug in earlier torch versions


@torch.jit.script
def apply_rotary_pos_emb_index(q, k, cos, sin, position_id):
    # position_id: [sq, b], q, k: [sq, b, np, hn], cos: [sq, 1, hn] -> [sq, b, 1, hn]
    cos, sin = F.embedding(position_id, cos.squeeze(1)).unsqueeze(2), \
        F.embedding(position_id, sin.squeeze(1)).unsqueeze(2)
    q, k = (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
    return q, k


def attention_fn(
        self,
        query_layer,
        key_layer,
        value_layer,
        attention_mask,
        hidden_size_per_partition,
        layer_id,
        layer_past=None,
        scaling_attention_score=True,
        use_cache=False,
):
    if layer_past is not None:
        past_key, past_value = layer_past
        key_layer = torch.cat((past_key, key_layer), dim=0)
        value_layer = torch.cat((past_value, value_layer), dim=0)

    # seqlen, batch, num_attention_heads, hidden_size_per_attention_head
    seq_len, b, nh, hidden_size = key_layer.shape

    if use_cache:
        present = (key_layer, value_layer)
    else:
        present = None

    query_key_layer_scaling_coeff = float(layer_id + 1)
    if scaling_attention_score:
        query_layer = query_layer / (math.sqrt(hidden_size) * query_key_layer_scaling_coeff)

    # ===================================
    # Raw attention scores. [b, np, s, s]
    # ===================================

    # [b, np, sq, sk]
    output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0))

    # [sq, b, np, hn] -> [sq, b * np, hn]
    query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1)
    # [sk, b, np, hn] -> [sk, b * np, hn]
    key_layer = key_layer.view(output_size[3], output_size[0] * output_size[1], -1)

    matmul_result = torch.empty(
        output_size[0] * output_size[1],
        output_size[2],
        output_size[3],
        dtype=query_layer.dtype,
        device=query_layer.device,
    )

    matmul_result = torch.baddbmm(
        matmul_result,
        query_layer.transpose(0, 1),  # [b * np, sq, hn]
        key_layer.transpose(0, 1).transpose(1, 2),  # [b * np, hn, sk]
        beta=0.0,
        alpha=1.0,
    )

    # change view to [b, np, sq, sk]
    attention_scores = matmul_result.view(*output_size)

    if self.scale_mask_softmax:
        self.scale_mask_softmax.scale = query_key_layer_scaling_coeff
        attention_probs = self.scale_mask_softmax(attention_scores, attention_mask.contiguous())
    else:
        if not (attention_mask == 0).all():
            # if auto-regressive, skip
            attention_scores.masked_fill_(attention_mask, -10000.0)
        dtype = attention_scores.type()
        attention_scores = attention_scores.float()
        attention_scores = attention_scores * query_key_layer_scaling_coeff

        attention_probs = F.softmax(attention_scores, dim=-1)

        attention_probs = attention_probs.type(dtype)

    # =========================
    # Context layer. [sq, b, hp]
    # =========================

    # value_layer -> context layer.
    # [sk, b, np, hn] --> [b, np, sq, hn]

    # context layer shape: [b, np, sq, hn]
    output_size = (value_layer.size(1), value_layer.size(2), query_layer.size(0), value_layer.size(3))

    # change view [sk, b * np, hn]
    value_layer = value_layer.view(value_layer.size(0), output_size[0] * output_size[1], -1)

    # change view [b * np, sq, sk]
    attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1)

    # matmul: [b * np, sq, hn]
    context_layer = torch.bmm(attention_probs, value_layer.transpose(0, 1))

    # change view [b, np, sq, hn]
    context_layer = context_layer.view(*output_size)

    # [b, np, sq, hn] --> [sq, b, np, hn]
    context_layer = context_layer.permute(2, 0, 1, 3).contiguous()

    # [sq, b, np, hn] --> [sq, b, hp]
    new_context_layer_shape = context_layer.size()[:-2] + (hidden_size_per_partition,)
    context_layer = context_layer.view(*new_context_layer_shape)

    outputs = (context_layer, present, attention_probs)

    return outputs


class SelfAttention(torch.nn.Module):
    def __init__(self, hidden_size, num_attention_heads,
                 layer_id, hidden_size_per_attention_head=None, bias=True,
                 params_dtype=torch.float, position_encoding_2d=True):
        super(SelfAttention, self).__init__()

        self.layer_id = layer_id
        self.hidden_size = hidden_size
        self.hidden_size_per_partition = hidden_size
        self.num_attention_heads = num_attention_heads
        self.num_attention_heads_per_partition = num_attention_heads
        self.position_encoding_2d = position_encoding_2d
        self.rotary_emb = RotaryEmbedding(
            self.hidden_size // (self.num_attention_heads * 2)
            if position_encoding_2d
            else self.hidden_size // self.num_attention_heads,
            base=10000,
            precision=torch.half,
            learnable=False,
        )

        self.scale_mask_softmax = None

        if hidden_size_per_attention_head is None:
            self.hidden_size_per_attention_head = hidden_size // num_attention_heads
        else:
            self.hidden_size_per_attention_head = hidden_size_per_attention_head

        self.inner_hidden_size = num_attention_heads * self.hidden_size_per_attention_head

        # Strided linear layer.
        self.query_key_value = skip_init(
            torch.nn.Linear,
            hidden_size,
            3 * self.inner_hidden_size,
            bias=bias,
            dtype=params_dtype,
        )

        self.dense = skip_init(
            torch.nn.Linear,
            self.inner_hidden_size,
            hidden_size,
            bias=bias,
            dtype=params_dtype,
        )

    @staticmethod
    def attention_mask_func(attention_scores, attention_mask):
        attention_scores.masked_fill_(attention_mask, -10000.0)
        return attention_scores

    def split_tensor_along_last_dim(self, tensor, num_partitions,
                                    contiguous_split_chunks=False):
        """Split a tensor along its last dimension.
        Arguments:
            tensor: input tensor.
            num_partitions: number of partitions to split the tensor
            contiguous_split_chunks: If True, make each chunk contiguous
                                    in memory.
        """
        # Get the size and dimension.
        last_dim = tensor.dim() - 1
        last_dim_size = tensor.size()[last_dim] // num_partitions
        # Split.
        tensor_list = torch.split(tensor, last_dim_size, dim=last_dim)
        # Note: torch.split does not create contiguous tensors by default.
        if contiguous_split_chunks:
            return tuple(chunk.contiguous() for chunk in tensor_list)

        return tensor_list

    def forward(
            self,
            hidden_states: torch.Tensor,
            position_ids,
            attention_mask: torch.Tensor,
            layer_id,
            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
            use_cache: bool = False,
            output_attentions: bool = False,
    ):
        """
        hidden_states: [seq_len, batch, hidden_size]
        attention_mask: [(1, 1), seq_len, seq_len]
        """

        # [seq_len, batch, 3 * hidden_size]
        mixed_raw_layer = self.query_key_value(hidden_states)

        # [seq_len, batch, 3 * hidden_size] --> [seq_len, batch, num_attention_heads, 3 * hidden_size_per_attention_head]
        new_tensor_shape = mixed_raw_layer.size()[:-1] + (
            self.num_attention_heads_per_partition,
            3 * self.hidden_size_per_attention_head,
        )
        mixed_raw_layer = mixed_raw_layer.view(*new_tensor_shape)

        # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
        (query_layer, key_layer, value_layer) = self.split_tensor_along_last_dim(mixed_raw_layer, 3)

        if self.position_encoding_2d:
            q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1))
            k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
            cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1)
            position_ids, block_position_ids = position_ids[:, 0, :].transpose(0, 1).contiguous(), \
                position_ids[:, 1, :].transpose(0, 1).contiguous()
            q1, k1 = apply_rotary_pos_emb_index(q1, k1, cos, sin, position_ids)
            q2, k2 = apply_rotary_pos_emb_index(q2, k2, cos, sin, block_position_ids)
            query_layer = torch.concat([q1, q2], dim=(q1.ndim - 1))
            key_layer = torch.concat([k1, k2], dim=(k1.ndim - 1))
        else:
            position_ids = position_ids.transpose(0, 1)
            cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
            # [seq_len, batch, num_attention_heads, hidden_size_per_attention_head]
            query_layer, key_layer = apply_rotary_pos_emb_index(query_layer, key_layer, cos, sin, position_ids)

        # [seq_len, batch, hidden_size]
        context_layer, present, attention_probs = attention_fn(
            self=self,
            query_layer=query_layer,
            key_layer=key_layer,
            value_layer=value_layer,
            attention_mask=attention_mask,
            hidden_size_per_partition=self.hidden_size_per_partition,
            layer_id=layer_id,
            layer_past=layer_past,
            use_cache=use_cache
        )

        output = self.dense(context_layer)

        outputs = (output, present)

        if output_attentions:
            outputs += (attention_probs,)

        return outputs  # output, present, attention_probs


class GEGLU(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.activation_fn = F.gelu

    def forward(self, x):
        # dim=-1 breaks in jit for pt<1.10
        x1, x2 = x.chunk(2, dim=(x.ndim - 1))
        return x1 * self.activation_fn(x2)


class GLU(torch.nn.Module):
    def __init__(self, hidden_size, inner_hidden_size=None,
                 layer_id=None, bias=True, activation_func=gelu, params_dtype=torch.float):
        super(GLU, self).__init__()
        self.layer_id = layer_id
        self.activation_func = activation_func

        # Project to 4h.
        self.hidden_size = hidden_size
        if inner_hidden_size is None:
            inner_hidden_size = 4 * hidden_size
        self.inner_hidden_size = inner_hidden_size
        self.dense_h_to_4h = skip_init(
            torch.nn.Linear,
            self.hidden_size,
            self.inner_hidden_size,
            bias=bias,
            dtype=params_dtype,
        )
        # Project back to h.
        self.dense_4h_to_h = skip_init(
            torch.nn.Linear,
            self.inner_hidden_size,
            self.hidden_size,
            bias=bias,
            dtype=params_dtype,
        )

    def forward(self, hidden_states):
        """
        hidden_states: [seq_len, batch, hidden_size]
        """

        # [seq_len, batch, inner_hidden_size]
        intermediate_parallel = self.dense_h_to_4h(hidden_states)

        intermediate_parallel = self.activation_func(intermediate_parallel)

        output = self.dense_4h_to_h(intermediate_parallel)

        return output


class GLMBlock(torch.nn.Module):
    def __init__(
            self,
            hidden_size,
            num_attention_heads,
            layernorm_epsilon,
            layer_id,
            inner_hidden_size=None,
            hidden_size_per_attention_head=None,
            layernorm=LayerNorm,
            use_bias=True,
            params_dtype=torch.float,
            num_layers=28,
            position_encoding_2d=True
    ):
        super(GLMBlock, self).__init__()
        # Set output layer initialization if not provided.

        self.layer_id = layer_id

        # Layernorm on the input data.
        self.input_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)

        self.position_encoding_2d = position_encoding_2d

        # Self attention.
        self.attention = SelfAttention(
            hidden_size,
            num_attention_heads,
            layer_id,
            hidden_size_per_attention_head=hidden_size_per_attention_head,
            bias=use_bias,
            params_dtype=params_dtype,
            position_encoding_2d=self.position_encoding_2d
        )

        # Layernorm on the input data.
        self.post_attention_layernorm = layernorm(hidden_size, eps=layernorm_epsilon)

        self.num_layers = num_layers

        # GLU
        self.mlp = GLU(
            hidden_size,
            inner_hidden_size=inner_hidden_size,
            bias=use_bias,
            layer_id=layer_id,
            params_dtype=params_dtype,
        )

    def forward(
            self,
            hidden_states: torch.Tensor,
            position_ids,
            attention_mask: torch.Tensor,
            layer_id,
            layer_past: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
            use_cache: bool = False,
            output_attentions: bool = False,
    ):
        """
        hidden_states: [seq_len, batch, hidden_size]
        attention_mask: [(1, 1), seq_len, seq_len]
        """

        # Layer norm at the begining of the transformer layer.
        # [seq_len, batch, hidden_size]
        attention_input = self.input_layernorm(hidden_states)

        # Self attention.
        attention_outputs = self.attention(
            attention_input,
            position_ids,
            attention_mask=attention_mask,
            layer_id=layer_id,
            layer_past=layer_past,
            use_cache=use_cache,
            output_attentions=output_attentions
        )

        attention_output = attention_outputs[0]

        outputs = attention_outputs[1:]

        # Residual connection.
        alpha = (2 * self.num_layers) ** 0.5
        hidden_states = attention_input * alpha + attention_output

        mlp_input = self.post_attention_layernorm(hidden_states)

        # MLP.
        mlp_output = self.mlp(mlp_input)

        # Second residual connection.
        output = mlp_input * alpha + mlp_output

        if use_cache:
            outputs = (output,) + outputs
        else:
            outputs = (output,) + outputs[1:]

        return outputs  # hidden_states, present, attentions


class ChatGLMPreTrainedModel(PreTrainedModel):
    """
    An abstract class to handle weights initialization and
    a simple interface for downloading and loading pretrained models.
    """

    is_parallelizable = False
    supports_gradient_checkpointing = False
    config_class = ChatGLMConfig
    base_model_prefix = "transformer"
    _no_split_modules = ["GLM6BBlock"]

    def __init__(self, *inputs, **kwargs):
        super().__init__(*inputs, **kwargs)

    def _init_weights(self, module: nn.Module):
        """Initialize the weights."""
        return


CHATGLM_6B_START_DOCSTRING = r"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class.
    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general
    usage and behavior.

    Parameters:
        config ([`~ChatGLM6BConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the configuration.
            Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

CHATGLM_6B_INPUTS_DOCSTRING = r"""
    Args:
        input_ids (`torch.LongTensor` of shape `({0})`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`ChatGLM6BTokenizer`].
            See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        attention_mask (`torch.FloatTensor` of shape `({0})`, *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)
        token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`:

            - 0 corresponds to a *sentence A* token,
            - 1 corresponds to a *sentence B* token.

            [What are token type IDs?](../glossary#token-type-ids)
        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
            Indices of positions of each input sequence tokens in the position embeddings.
            Selected in the range `[0, config.max_position_embeddings - 1]`.

            [What are position IDs?](../glossary#position-ids)
        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:

            - 1 indicates the head is **not masked**,
            - 0 indicates the head is **masked**.

        inputs_embeds (`torch.FloatTensor` of shape `({0}, 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.
"""


@add_start_docstrings(
    "The bare ChatGLM-6B Model transformer outputting raw hidden-states without any specific head on top.",
    CHATGLM_6B_START_DOCSTRING,
)
class ChatGLMModel(ChatGLMPreTrainedModel):
    """

    The model can behave as an encoder (with only self-attention) as well
    as a decoder, in which case a layer of cross-attention is added between
    the self-attention layers, following the architecture described in [Attention is
    all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani,
    Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin.

    To behave as an decoder the model needs to be initialized with the
    `is_decoder` argument of the configuration set to `True`.
    To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder`
    argument and `add_cross_attention` set to `True`; an
    `encoder_hidden_states` is then expected as an input to the forward pass.
    """

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

        # recording parameters
        self.max_sequence_length = config.max_sequence_length
        self.hidden_size = config.hidden_size
        self.params_dtype = torch.half
        self.num_attention_heads = config.num_attention_heads
        self.vocab_size = config.vocab_size
        self.num_layers = config.num_layers
        self.layernorm_epsilon = config.layernorm_epsilon
        self.inner_hidden_size = config.inner_hidden_size
        self.hidden_size_per_attention_head = self.hidden_size // self.num_attention_heads
        self.position_encoding_2d = config.position_encoding_2d
        self.pre_seq_len = config.pre_seq_len
        self.prefix_projection = config.prefix_projection

        self.word_embeddings = skip_init(
            torch.nn.Embedding,
            num_embeddings=self.vocab_size, embedding_dim=self.hidden_size,
            dtype=self.params_dtype
        )

        def get_layer(layer_id):
            return GLMBlock(
                self.hidden_size,
                self.num_attention_heads,
                self.layernorm_epsilon,
                layer_id,
                inner_hidden_size=self.inner_hidden_size,
                hidden_size_per_attention_head=self.hidden_size_per_attention_head,
                layernorm=LayerNorm,
                use_bias=True,
                params_dtype=self.params_dtype,
                position_encoding_2d=self.position_encoding_2d,
            )

        self.layers = torch.nn.ModuleList(
            [get_layer(layer_id) for layer_id in range(self.num_layers)]
        )

        # Final layer norm before output.
        self.final_layernorm = LayerNorm(self.hidden_size, eps=self.layernorm_epsilon)

        if self.pre_seq_len is not None:
            for param in self.parameters():
                param.requires_grad = False
            self.prefix_tokens = torch.arange(self.pre_seq_len).long()
            self.prefix_encoder = PrefixEncoder(config)
            self.dropout = torch.nn.Dropout(0.1)

            # total_params = sum(p.numel() for p in self.parameters())
            # trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad)
            # print("Using p-tuning v2: # trainable_params = {} / {}".format(trainable_params, total_params))

    def get_input_embeddings(self):
        return self.word_embeddings

    def set_input_embeddings(self, new_embeddings: torch.Tensor):
        self.word_embeddings = new_embeddings

    def get_prompt(self, batch_size, device):
        prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
        past_key_values = self.prefix_encoder(prefix_tokens).half()
        past_key_values = past_key_values.view(
            batch_size,
            self.pre_seq_len,
            self.num_layers * 2,
            self.num_attention_heads,
            self.hidden_size // self.num_attention_heads
        )
        #seq_len, b, nh, hidden_size
        past_key_values = self.dropout(past_key_values)
        past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(2)
        past_key_values = [(v[0], v[1]) for v in past_key_values]
        # past_key_values = past_key_values.permute([2, 1, 0, 3, 4]).split(self.num_layers)
        # past_key_values = [(v1,v2) for v1, v2 in zip(past_key_values[0], past_key_values[1])]
        return past_key_values

    @staticmethod
    def get_masks(seq, device):
        context_length = seq.index(150004) + 1

        attention_mask = torch.ones((1, len(seq), len(seq)), device=device)
        attention_mask.tril_()
        attention_mask[..., :context_length - 1] = 1
        attention_mask.unsqueeze_(1)
        attention_mask = (attention_mask < 0.5).bool()

        return attention_mask

    def get_position_ids(self, seq, mask_position, device, gmask=False):
        context_length = seq.index(150004) + 1
        if self.position_encoding_2d:
            seq_length = seq.index(150004)
            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
            if not gmask:
                position_ids[seq_length:] = mask_position
            block_position_ids = torch.cat((
                torch.zeros(seq_length, dtype=torch.long, device=device),
                torch.arange(context_length - seq_length, dtype=torch.long, device=device) + 1
            ))
            position_ids = torch.stack((position_ids, block_position_ids), dim=0)
        else:
            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
            if not gmask:
                position_ids[context_length - 1:] = mask_position

        position_ids = position_ids.unsqueeze(0)

        return position_ids

    @add_start_docstrings_to_model_forward(CHATGLM_6B_INPUTS_DOCSTRING.format("batch_size, sequence_length"))
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,
        output_type=BaseModelOutputWithPastAndCrossAttentions,
        config_class=_CONFIG_FOR_DOC,
    )
    def forward(
            self,
            input_ids: Optional[torch.LongTensor] = None,
            position_ids: Optional[torch.LongTensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
            inputs_embeds: 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,
    ) -> Union[Tuple[torch.Tensor, ...], 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

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

        if past_key_values is None:
            if self.pre_seq_len is not None:
                past_key_values = self.get_prompt(batch_size=input_ids.shape[0], device=input_ids.device)
            else:
                past_key_values = tuple([None] * len(self.layers))

            MASK, gMASK = 150000, 150001
            mask_token = MASK if MASK in input_ids else gMASK
            use_gmask = False if MASK in input_ids else gMASK
            seq = input_ids[0].tolist()

            mask_position = seq.index(mask_token)

            if attention_mask is None:
                attention_mask = self.get_masks(
                    seq=seq,
                    device=input_ids.device
                )

            if self.pre_seq_len is not None:
                prefix_attention_mask = torch.ones(1, 1, len(seq), self.pre_seq_len).to(attention_mask.device)
                prefix_attention_mask = (prefix_attention_mask < 0.5).bool()
                attention_mask = torch.cat((prefix_attention_mask, attention_mask), dim=3)

            if position_ids is None:
                position_ids = self.get_position_ids(
                    seq=seq,
                    mask_position=mask_position,
                    device=input_ids.device,
                    gmask=use_gmask
                )

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

        # [seq_len, batch, hidden_size]
        hidden_states = inputs_embeds.transpose(0, 1)

        presents = () if use_cache else None
        all_self_attentions = () if output_attentions else None
        all_hidden_states = () if output_hidden_states else None

        seq_length_with_past = seq_length
        past_key_values_length = 0
        if past_key_values[0] is not None:
            past_key_values_length = past_key_values[0][0].shape[0]
            seq_length_with_past = seq_length_with_past + past_key_values_length
        if attention_mask is None:
            attention_mask = torch.zeros(1, 1, device=input_ids.device).bool()

        else:
            attention_mask = attention_mask.to(input_ids.device)

        for i, layer in enumerate(self.layers):

            if output_hidden_states:
                all_hidden_states = all_hidden_states + (hidden_states,)

            layer_ret = layer(
                hidden_states,
                position_ids=position_ids,
                attention_mask=attention_mask,
                layer_id=torch.tensor(i),
                layer_past=past_key_values[i],
                use_cache=use_cache,
                output_attentions=output_attentions
            )

            hidden_states = layer_ret[0]

            if use_cache:
                presents = presents + (layer_ret[1],)

            if output_attentions:
                all_self_attentions = all_self_attentions + (layer_ret[2 if use_cache else 1],)

        # Final layer norm.
        hidden_states = self.final_layernorm(hidden_states)

        if output_hidden_states:
            all_hidden_states = all_hidden_states + (hidden_states,)

        if not return_dict:
            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=presents,
            hidden_states=all_hidden_states,
            attentions=all_self_attentions,
        )


class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
    def __init__(self, config):
        super().__init__(config)

        # self.hidden_size = config.hidden_size
        # self.params_dtype = torch.half
        # self.vocab_size = config.vocab_size
        self.max_sequence_length = config.max_sequence_length

        self.position_encoding_2d = config.position_encoding_2d

        self.transformer = ChatGLMModel(config)

        self.lm_head = skip_init(
            nn.Linear,
            config.hidden_size,
            config.vocab_size,
            bias=False,
            dtype=torch.half
        )

    def get_output_embeddings(self):
        return self.lm_head

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

    def get_masks_and_position_ids(self, seq, mask_position, context_length, device, gmask=False):
        attention_mask = torch.ones((1, context_length, context_length), device=device)
        attention_mask.tril_()
        attention_mask[..., :context_length - 1] = 1
        attention_mask.unsqueeze_(1)
        attention_mask = (attention_mask < 0.5).bool()

        if self.position_encoding_2d:
            seq_length = seq.index(150004)
            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
            if not gmask:
                position_ids[seq_length:] = mask_position
            block_position_ids = torch.cat((
                torch.zeros(seq_length, dtype=torch.long, device=device),
                torch.arange(context_length - seq_length, dtype=torch.long, device=device) + 1
            ))
            position_ids = torch.stack((position_ids, block_position_ids), dim=0)
        else:
            position_ids = torch.arange(context_length, dtype=torch.long, device=device)
            if not gmask:
                position_ids[context_length - 1:] = mask_position

        position_ids = position_ids.unsqueeze(0)

        return attention_mask, position_ids

    def prepare_inputs_for_generation(
            self,
            input_ids: torch.LongTensor,
            past: Optional[torch.Tensor] = None,
            past_key_values: Optional[torch.Tensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            **kwargs
    ) -> dict:

        MASK, gMASK = 150000, 150001
        mask_token = MASK if MASK in input_ids else gMASK
        use_gmask = False if MASK in input_ids else gMASK
        seq = input_ids[0].tolist()
        mask_position = seq.index(mask_token)

        if mask_token not in seq:
            raise ValueError("You have to add either [MASK] or [gMASK] in your input")

        # only last token for input_ids if past is not None
        if past is not None or past_key_values is not None:
            context_length = seq.index(150004)
            last_token = input_ids[:, -1].unsqueeze(-1)
            if self.position_encoding_2d:
                position_ids = torch.tensor([[[mask_position], [len(seq) - context_length]]], dtype=torch.long,
                                            device=input_ids.device)
            else:
                position_ids = torch.tensor([[mask_position]], dtype=torch.long, device=input_ids.device)

            if past is None:
                past = past_key_values
            return {
                "input_ids": last_token,
                "past_key_values": past,
                "position_ids": position_ids,
            }
        else:
            attention_mask, position_ids = self.get_masks_and_position_ids(
                seq=seq,
                mask_position=mask_position,
                context_length=len(seq),
                device=input_ids.device,
                gmask=use_gmask
            )

            return {
                "input_ids": input_ids,
                "past_key_values": past,
                "position_ids": position_ids,
                "attention_mask": attention_mask
            }

    def forward(
            self,
            input_ids: Optional[torch.Tensor] = None,
            position_ids: Optional[torch.Tensor] = None,
            attention_mask: Optional[torch.Tensor] = None,
            past_key_values: Optional[Tuple[torch.FloatTensor]] = None,
            inputs_embeds: Optional[torch.Tensor] = None,
            labels: Optional[torch.Tensor] = None,
            use_cache: Optional[bool] = None,
            output_attentions: Optional[bool] = None,
            output_hidden_states: Optional[bool] = None,
            return_dict: Optional[bool] = None,
    ):
        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

        transformer_outputs = self.transformer(
            input_ids=input_ids,
            position_ids=position_ids,
            attention_mask=attention_mask,
            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,
        )

        hidden_states = transformer_outputs[0]

        lm_logits = self.lm_head(hidden_states).permute(1, 0, 2).contiguous()

        loss = None
        if labels is not None:
            lm_logits = lm_logits.to(torch.float32)

            # Shift so that tokens < n predict n
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = labels[..., 1:].contiguous()
            # Flatten the tokens
            loss_fct = CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))

            lm_logits = lm_logits.to(hidden_states.dtype)
            loss = loss.to(hidden_states.dtype)

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

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

    @staticmethod
    def _reorder_cache(
            past: Tuple[Tuple[torch.Tensor, torch.Tensor], ...], beam_idx: torch.LongTensor
    ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], ...]:
        """
        This function is used to re-order the `past_key_values` cache if [`~PreTrainedModel.beam_search`] or
        [`~PreTrainedModel.beam_sample`] is called. This is required to match `past_key_values` with the correct
        beam_idx at every generation step.

        Output shares the same memory storage as `past`.
        """
        return tuple(
            (
                layer_past[0].index_select(1, beam_idx.to(layer_past[0].device)),
                layer_past[1].index_select(1, beam_idx.to(layer_past[1].device)),
            )
            for layer_past in past
        )

    @torch.no_grad()
    def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 2048, num_beams=1,
             do_sample=True, top_p=0.7, temperature=0.95, logits_processor=None, **kwargs):
        if history is None:
            history = []
        if logits_processor is None:
            logits_processor = LogitsProcessorList()
        logits_processor.append(InvalidScoreLogitsProcessor())
        gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
        if not history:
            prompt = query
        else:
            prompt = ""
            for i, (old_query, response) in enumerate(history):
                prompt += "[Round {}]\n问：{}\n答：{}\n".format(i, old_query, response)
            prompt += "[Round {}]\n问：{}\n答：".format(len(history), query)
        input_ids = tokenizer([prompt], return_tensors="pt", padding=True)
        input_ids = input_ids.to(self.device)
        outputs = self.generate(**input_ids, **gen_kwargs)
        outputs = outputs.tolist()[0][len(input_ids["input_ids"][0]) - 2:]
        response = tokenizer.decode(outputs)
        response = response.strip()
        response = response.replace("[[训练时间]]", "2023年")
        history = history + [(query, response)]
        return response, history

    @torch.no_grad()
    def generate(
            self,
            **kwargs,
    ):
        MASK, gMASK = 150000, 150001
        bos, eos = 150004, 150005

        if "eos_token_id" not in kwargs:
            kwargs["eos_token_id"] = eos

        truncate = kwargs.pop("truncate") if "truncate" in kwargs else False

        stop = False

        return_seqs = []

        while True:
            output_ids = super().generate(**kwargs)
            return_seqs = []
            max_length = 0

            for i in range(output_ids.shape[0]):
                output_seq = output_ids[i].tolist()
                if truncate:
                    output_seq = output_seq[len(kwargs["input_ids"][i]) - 2:]
                mask_token = MASK if MASK in output_seq else gMASK
                mask_position = output_seq.index(mask_token)
                bos_position = output_seq.index(bos)
                if eos in output_seq:
                    eos_position = output_seq.index(eos)
                else:
                    eos_position = len(output_seq)

                return_seq = output_seq[:mask_position] + output_seq[bos_position + 1:eos_position] + output_seq[
                                                                                                      mask_position + 1:bos_position]
                max_length = max(max_length, len(return_seq))
                return_seqs.append(return_seq)

            for i in range(output_ids.shape[0]):
                return_seqs[i] = [0] * (max_length - len(return_seqs[i])) + return_seqs[i]  # padding
                if mask_token not in return_seqs[i]:
                    stop = True

            if stop:
                break

            for return_seq in return_seqs:
                return_seq += [bos]

            kwargs['input_ids'] = torch.tensor(return_seqs, dtype=torch.long, device=kwargs['input_ids'].device)

        return torch.tensor(return_seqs, dtype=torch.long, device=kwargs['input_ids'].device)

    def quantize(self, bits: int):
        from .quantization import quantize
        self.transformer = quantize(self.transformer, bits)
        return self