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config.json

@@ -1,21 +1,21 @@
 {
-  "_name_or_path": "biomap-research/xtrimopglm-1b-clm",
+  "_name_or_path": "Bo1015/proteinglm-1b-clm",
   "add_bias_linear": true,
   "add_qkv_bias": true,
   "apply_query_key_layer_scaling": true,
   "apply_residual_connection_post_layernorm": true,
   "architectures": [
-    "xTrimoPGLMModel"
+    "ProteinPGLMModel"
   ],
   "attention_dropout": 0.0,
   "attention_softmax_in_fp32": true,
   "auto_map": {
-    "AutoConfig": "configuration_xtrimopglm.xTrimoPGLMConfig",
-    "AutoModel": "modeling_xtrimopglm.xTrimoPGLMForMaskedLM",
-    "AutoModelForCausalLM": "modeling_xtrimopglm.xTrimoPGLMForCasualLM",
-    "AutoModelForMaskedLM": "modeling_xtrimopglm.xTrimoPGLMForMaskedLM",
-    "AutoModelForSequenceClassification": "modeling_xtrimopglm.xTrimoPGLMForSequenceClassification",
-    "AutoModelForTokenClassification": "modeling_xtrimopglm.xTrimoPGLMForTokenClassification"
+    "AutoConfig": "configuration_proteinglm.ProteinPGLMConfig",
+    "AutoModel": "modeling_proteinglm.ProteinPGLMForMaskedLM",
+    "AutoModelForCausalLM": "modeling_proteinglm.ProteinPGLMForCasualLM",
+    "AutoModelForMaskedLM": "modeling_proteinglm.ProteinPGLMForMaskedLM",
+    "AutoModelForSequenceClassification": "modeling_proteinglm.ProteinPGLMForSequenceClassification",
+    "AutoModelForTokenClassification": "modeling_proteinglm.ProteinPGLMForTokenClassification"
   },
   "bias_dropout_fusion": true,
   "deepnorm": true,
@@ -30,7 +30,7 @@
   "is_causal": true,
   "kv_channels": 64,
   "layernorm_epsilon": 1e-05,
-  "model_type": "xTrimoPGLM",
+  "model_type": "ProteinPGLM",
   "moe": false,
   "multi_query_attention": false,
   "multi_query_group_num": 1,

configuration_proteinglm.py

@@ -0,0 +1,86 @@
+from transformers import PretrainedConfig
+
+
+class ProteinGLMConfig(PretrainedConfig):
+    model_type = "ProteinGLM"
+    def __init__(
+        self,
+        num_layers=24,
+        padded_vocab_size=128,
+        hidden_size=2048,
+        ffn_hidden_size=5461,
+        kv_channels=64,
+        num_attention_heads=32,
+        seq_length=1024,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        layernorm_epsilon=1e-5,
+        glu_activation='geglu',
+        rmsnorm=False,
+        deepnorm=True,
+        apply_residual_connection_post_layernorm=True,
+        post_layer_norm=True,
+        add_bias_linear=True,
+        add_qkv_bias=True,
+        bias_dropout_fusion=True,
+        multi_query_attention=False,
+        multi_query_group_num=1,
+        apply_query_key_layer_scaling=True,
+        attention_softmax_in_fp32=True,
+        fp32_residual_connection=False,
+        quantization_bit=0,
+        rotary_embedding_2d=False,
+        use_pytorch_sdpa=True,
+        is_causal=True,
+        use_cache=True,
+        initializer_range=0.02,
+        moe=False,
+        num_experts=0, 
+        experts_per_token=0,
+        untie_head=False,
+        head_num=1,
+        **kwargs
+    ):
+
+        if not deepnorm and apply_residual_connection_post_layernorm:
+            print(f"Warning: deepnorm is False and apply_residual_connection_post_layernorm is True")
+
+        if deepnorm:
+            apply_residual_connection_post_layernorm = True
+
+        self.num_layers = num_layers
+        self.vocab_size = padded_vocab_size
+        self.padded_vocab_size = padded_vocab_size
+        self.hidden_size = hidden_size
+        self.ffn_hidden_size = ffn_hidden_size
+        self.kv_channels = kv_channels
+        self.num_attention_heads = num_attention_heads
+        self.seq_length = seq_length
+        self.hidden_dropout = hidden_dropout
+        self.attention_dropout = attention_dropout
+        self.layernorm_epsilon = layernorm_epsilon
+        self.glu_activation = glu_activation
+        self.rmsnorm = rmsnorm
+        self.deepnorm = deepnorm
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.post_layer_norm = post_layer_norm
+        self.add_bias_linear = add_bias_linear
+        self.add_qkv_bias = add_qkv_bias
+        self.bias_dropout_fusion = bias_dropout_fusion
+        self.multi_query_attention = multi_query_attention
+        self.multi_query_group_num = multi_query_group_num
+        self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = attention_softmax_in_fp32
+        self.fp32_residual_connection = fp32_residual_connection
+        self.quantization_bit = quantization_bit
+        self.rotary_embedding_2d = rotary_embedding_2d
+        self.is_causal = is_causal
+        self.use_cache = use_cache
+        self.initializer_range = initializer_range
+        self.use_pytorch_sdpa = use_pytorch_sdpa
+        self.moe = moe
+        self.num_experts = num_experts
+        self.experts_per_token = experts_per_token
+        self.untie_head = untie_head
+        self.head_num=head_num
+        super().__init__(**kwargs)

modeling_proteinglm.py

@@ -0,0 +1,1573 @@
+""" PyTorch ProteinGLM model. """
+
+import math
+import copy
+import warnings
+import re
+import sys
+import os
+import pathlib
+import time
+import random
+import numpy as np
+from tqdm.auto import tqdm
+
+import torch, deepspeed
+import torch.utils.checkpoint
+import torch.nn.functional as F
+from torch import nn
+from torch.nn import CrossEntropyLoss, LayerNorm, MSELoss, BCEWithLogitsLoss
+from torch.nn.utils import skip_init
+from typing import Optional, Tuple, Union, List, Callable, Dict, Any
+from copy import deepcopy
+from collections import namedtuple
+
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    MaskedLMOutput,
+    CausalLMOutputWithPast,
+    SequenceClassifierOutput,
+    TokenClassifierOutput
+)
+from transformers import PreTrainedModel
+from transformers.utils import logging
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
+
+from .configuration_proteinglm import ProteinGLMConfig
+from .quantization import quantize
+
+def get_checkpoint_fn():
+    if deepspeed.checkpointing.is_configured():
+        checkpoint = deepspeed.checkpointing.checkpoint
+    else:
+        checkpoint = torch.utils.checkpoint.checkpoint
+    return checkpoint
+
+# flags required to enable jit fusion kernels
+
+if sys.platform != 'darwin':
+    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 = "Bo1015/proteinglm-100b-int4"
+_CONFIG_FOR_DOC = "ProteinGLMConfig"
+DeepNormCoefficients = namedtuple("DeepNormCoefficients", ["alpha", "beta"])
+
+def default_init(cls, *args, **kwargs):
+    return cls(*args, **kwargs)
+
+
+def get_deepnorm_coefficients(config: ProteinGLMConfig):
+    """
+        DeepNorm coefficients from : https://kexue.fm/archives/8978
+    """
+    num_layers = config.num_layers
+    return DeepNormCoefficients(alpha=(2 * num_layers) ** 0.5, beta=(2 * num_layers) ** -0.5)
+
+
+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[..., 5] = 5e4
+        return scores
+
+
+def split_tensor_along_last_dim(
+        tensor: torch.Tensor,
+        num_partitions: int,
+        contiguous_split_chunks: bool = False,
+) -> List[torch.Tensor]:
+    """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.
+
+    Returns:
+        A list of Tensors
+    """
+    # 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
+
+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)).to(precision)
+        self.dim = dim
+        self.base = base
+        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):
+        if f'{prefix}inv_freq' in state_dict:
+            super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+        else:
+            self.inv_freq.copy_(1. / (self.base ** (torch.arange(0, self.dim, 2).float() / self.dim)).to(self.precision))
+
+    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=torch.float32)
+            freqs = torch.einsum('i,j->ij', t, self.inv_freq.to(x.device))
+            # 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 or self.precision == torch.half:
+                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()
+            elif self.precision == torch.half:
+                cos_cached = cos_cached.half()
+                sin_cached = sin_cached.half()
+            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
+
+def assert_dim_check(tensor, ndim=None, shape=None):
+    if ndim is not None:
+        assert tensor.ndim == ndim, f"Exepct tensor.ndim={ndim}. gut got tensor.shape={tensor.shape}"
+    if shape is not None:
+        assert list(tensor.shape) == list(shape), f"Exepct tensor.shape={shape}. gut got tensor.shape={tensor.shape}"
+
+def apply_rotary_pos_emb_index_torch(q, k, cos, sin, position_id):  # jitting fails with bf16
+    # 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
+
+class RMSNorm(torch.nn.Module):
+    def __init__(self, normalized_shape, eps=1e-5, device=None, dtype=None, **kwargs):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.empty(normalized_shape, device=device, dtype=dtype))
+        self.eps = eps
+
+    def forward(self, hidden_states: torch.Tensor):
+        input_dtype = hidden_states.dtype
+        variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+        hidden_states = hidden_states * torch.rsqrt(variance + self.eps)
+
+        return (self.weight * hidden_states).to(input_dtype)
+
+class CoreAttention(torch.nn.Module):
+    def __init__(self, config: ProteinGLMConfig, layer_number):
+        super(CoreAttention, self).__init__()
+
+        self.apply_query_key_layer_scaling = config.apply_query_key_layer_scaling
+        self.attention_softmax_in_fp32 = config.attention_softmax_in_fp32
+        if self.apply_query_key_layer_scaling:
+            self.attention_softmax_in_fp32 = True
+        self.layer_number = max(1, layer_number)
+
+        projection_size = config.kv_channels * config.num_attention_heads
+
+        # Per attention head and per partition values.
+        self.hidden_size_per_partition = projection_size
+        self.hidden_size_per_attention_head = projection_size // config.num_attention_heads
+        self.num_attention_heads_per_partition = config.num_attention_heads
+
+        coeff = None
+        self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
+        if self.apply_query_key_layer_scaling:
+            coeff = self.layer_number
+            self.norm_factor *= coeff
+        self.coeff = coeff
+
+        self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
+
+        self.is_causal = config.is_causal
+        self.use_pytorch_sdpa = config.use_pytorch_sdpa
+    
+    def forward(self, query_layer, key_layer, value_layer, attention_mask):
+        # query_layer, key_layer, value_layer: [seq_len, batch_size, num_heads, head_dim]
+        # import pdb; pdb.set_trace();
+        pytorch_major_version = int(torch.__version__.split('.')[0])
+        # assert pytorch_major_version >= 2, f"Expect PyTorch version > 2.0"
+        if pytorch_major_version >= 2 and self.use_pytorch_sdpa:
+            dropout_p = self.attention_dropout.p if self.training else 0
+            # [seq_len, batch_size, num_heads, head_dim] -> [batch_size, num_heads, seq_len, head_dim]
+            query_layer, key_layer, value_layer = [k.permute(1, 2, 0, 3) for k in [query_layer, key_layer, value_layer]]
+            # import pdb; pdb.set_trace();
+            if attention_mask is None and query_layer.shape[2] == key_layer.shape[2]:
+                # context_layer: [batch_size, num_heads, seq_len, head_dim]
+                context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, is_causal=self.is_causal, dropout_p=dropout_p)
+            else:
+                if (attention_mask is not None) and (attention_mask.dtype == torch.bool):
+                    attention_mask = attention_mask.logical_not() ## DO NOT inplace operation!!!!
+                context_layer = torch.nn.functional.scaled_dot_product_attention(query_layer, key_layer, value_layer, attention_mask, dropout_p=dropout_p)
+            # [batch_size, num_heads, seq_len, head_dim] -> [seq_len, batch_size, num_heads, head_dim]
+            context_layer = context_layer.permute(2, 0, 1, 3)
+            # [seq_len, batch_size, 2560]
+            new_context_layer_shape = context_layer.size()[:-2] + (self.hidden_size_per_partition,)
+            context_layer = context_layer.reshape(*new_context_layer_shape)
+        else:
+            # Raw attention scores
+
+            # [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)
+
+            # preallocting input tensor: [b * np, sq, sk]
+            matmul_input_buffer = torch.empty(
+                output_size[0] * output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype,
+                device=query_layer.device
+            )
+
+            # Raw attention scores. [b * np, sq, sk]
+            matmul_result = torch.baddbmm(
+                matmul_input_buffer,
+                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 / self.norm_factor),
+            )
+
+            # change view to [b, np, sq, sk]
+            attention_scores = matmul_result.view(*output_size)
+
+            # ===========================
+            # Attention probs and dropout
+            # ===========================
+
+            # attention scores and attention mask [b, np, sq, sk]
+            if self.attention_softmax_in_fp32:
+                attention_scores = attention_scores.float()
+            if self.coeff is not None:
+                attention_scores = attention_scores * self.coeff
+            if self.is_causal and attention_mask is None and attention_scores.shape[2] == attention_scores.shape[3]:
+                attention_mask = torch.ones(output_size[0], 1, output_size[2], output_size[3],
+                                            device=attention_scores.device, dtype=torch.bool)
+                attention_mask.tril_()
+                attention_mask = ~attention_mask
+            if attention_mask is not None:
+                attention_scores = attention_scores.masked_fill(attention_mask, float("-inf"))
+            attention_probs = F.softmax(attention_scores, dim=-1)
+            attention_probs = attention_probs.type_as(value_layer)
+
+            # This is actually dropping out entire tokens to attend to, which might
+            # seem a bit unusual, but is taken from the original Transformer paper.
+            attention_probs = self.attention_dropout(attention_probs)
+            # =========================
+            # 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] + (self.hidden_size_per_partition,)
+            context_layer = context_layer.view(*new_context_layer_shape)
+
+        return context_layer
+
+
+class SelfAttention(torch.nn.Module):
+    """Parallel self-attention layer abstract class.
+
+    Self-attention layer takes input with size [s, b, h]
+    and returns output of the same size.
+    """
+
+    def __init__(self, config: ProteinGLMConfig, layer_number, device=None):
+        super(SelfAttention, self).__init__()
+        self.layer_number = max(1, layer_number)
+
+        self.projection_size = config.kv_channels * config.num_attention_heads
+
+        # Per attention head and per partition values.
+        self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
+        self.num_attention_heads_per_partition = config.num_attention_heads
+
+        self.multi_query_attention = config.multi_query_attention
+        self.qkv_hidden_size = 3 * self.projection_size
+        if self.multi_query_attention:
+            self.num_multi_query_groups_per_partition = config.multi_query_group_num
+            self.qkv_hidden_size = (
+                    self.projection_size + 2 * self.hidden_size_per_attention_head * config.multi_query_group_num
+            )
+        self.query_key_value = nn.Linear(config.hidden_size, self.qkv_hidden_size,
+                                         bias=config.add_bias_linear or config.add_qkv_bias,
+                                         device=device, **_config_to_kwargs(config)
+                                         )
+
+        self.core_attention = CoreAttention(config, self.layer_number)
+
+        # Output.
+        self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear, device=device, **_config_to_kwargs(config))
+        
+        self.rotary_embedding_2d = config.rotary_embedding_2d
+        # dim, base=10000, precision=torch.half, learnable=False
+        self.rotary_emb = RotaryEmbedding(self.hidden_size_per_attention_head // 2 if self.rotary_embedding_2d else self.hidden_size_per_attention_head, 
+                                          base=10000, precision=config.torch_dtype, learnable=False)
+
+
+    def forward(
+            self, hidden_states, attention_mask, position_ids, kv_cache=None, use_cache=True
+    ):
+        # hidden_states: [sq, b, h]
+
+        # =================================================
+        # Pre-allocate memory for key-values for inference.
+        # =================================================
+        # =====================
+        # Query, Key, and Value
+        # =====================
+
+        # Attention heads [sq, b, h] --> [sq, b, (np * 3 * hn)]
+        mixed_x_layer = self.query_key_value(hidden_states)
+
+        if self.multi_query_attention:
+            (query_layer, key_layer, value_layer) = mixed_x_layer.split(
+                [
+                    self.num_attention_heads_per_partition * self.hidden_size_per_attention_head,
+                    self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
+                    self.num_multi_query_groups_per_partition * self.hidden_size_per_attention_head,
+                ],
+                dim=-1,
+            )
+            query_layer = query_layer.view(
+                query_layer.size()[:-1] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head)
+            )
+            key_layer = key_layer.view(
+                key_layer.size()[:-1] + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
+            )
+            value_layer = value_layer.view(
+                value_layer.size()[:-1]
+                + (self.num_multi_query_groups_per_partition, self.hidden_size_per_attention_head)
+            )
+        else:
+            new_tensor_shape = mixed_x_layer.size()[:-1] + (self.num_attention_heads_per_partition, 3 * self.hidden_size_per_attention_head)
+            mixed_x_layer = mixed_x_layer.view(*new_tensor_shape)
+            # [sq, b, np, 3 * hn] --> 3 [sq, b, np, hn]
+            (query_layer, key_layer, value_layer) = split_tensor_along_last_dim(mixed_x_layer, 3)
+
+        # apply relative positional encoding (rotary embedding)
+        if position_ids is not None: # [seq_len, 2, batch_size, 32, 2]
+            
+            if self.rotary_embedding_2d:
+                q1, q2 = query_layer.chunk(2, dim=(query_layer.ndim - 1)) # 32
+                k1, k2 = key_layer.chunk(2, dim=(key_layer.ndim - 1))
+                # import pdb; pdb.set_trace();
+                cos, sin = self.rotary_emb(q1, seq_len=position_ids.max() + 1) # 32
+                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_torch(q1, k1, cos, sin, position_ids)
+                q2, k2 = apply_rotary_pos_emb_index_torch(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:
+                # [b, sq] -> [sq, b]
+                position_ids = position_ids.transpose(0, 1)
+                cos, sin = self.rotary_emb(value_layer, seq_len=position_ids.max() + 1)
+                query_layer, key_layer = apply_rotary_pos_emb_index_torch(query_layer, key_layer, cos, sin, position_ids)
+
+        # adjust key and value for inference
+        if kv_cache is not None:
+            cache_k, cache_v = kv_cache
+            key_layer = torch.cat((cache_k, key_layer), dim=0)
+            value_layer = torch.cat((cache_v, value_layer), dim=0)
+        if use_cache:
+            kv_cache = (key_layer, value_layer)
+        else:
+            kv_cache = None
+
+        if self.multi_query_attention:
+            key_layer = key_layer.unsqueeze(-2)
+            key_layer = key_layer.expand(-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1)
+            key_layer = key_layer.contiguous().view(key_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head))
+            value_layer = value_layer.unsqueeze(-2)
+            value_layer = value_layer.expand(-1, -1, -1, self.num_attention_heads_per_partition // self.num_multi_query_groups_per_partition, -1)
+            value_layer = value_layer.contiguous().view(value_layer.size()[:2] + (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head))
+
+        # ==================================
+        # core attention computation
+        # ==================================
+
+        context_layer = self.core_attention(query_layer, key_layer, value_layer, attention_mask) # context_layer: [seq_len, batch_size, num_heads*head_dim]
+        output = self.dense(context_layer)
+        # =================
+        # Output. [sq, b, h]
+        # =================
+
+        # output = context_layer @ self.dense.weight.T + self.dense.bias
+        return output, kv_cache
+
+
+def _config_to_kwargs(args):
+    common_kwargs = {
+        "dtype": args.torch_dtype,
+    }
+    return common_kwargs
+
+
+class MLP(torch.nn.Module):
+    """MLP.
+
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension.
+    """
+
+    def __init__(self, config: ProteinGLMConfig, device=None):
+        super(MLP, self).__init__()
+
+        self.add_bias = config.add_bias_linear
+        self.moe = config.moe
+        self.num_experts = config.num_experts
+        self.experts_per_token = config.experts_per_token # 2
+
+        # Project to 4h. If using swiglu double the output width, see https://arxiv.org/pdf/2002.05202.pdf
+        self.dense_h_to_4h = nn.Linear(
+            config.hidden_size,
+            config.ffn_hidden_size * 2,
+            bias=self.add_bias,
+            device=device,
+            **_config_to_kwargs(config)
+        )
+
+        def swiglu(x):
+           x = torch.chunk(x, 2, dim=-1)
+           return x[0] * F.silu(x[1])
+
+        def geglu(x):
+            x = torch.chunk(x, 2, dim=-1)
+            return x[0] * F.gelu(x[1])
+
+        if config.glu_activation == 'geglu':
+            self.activation_func = geglu
+        elif config.glu_activation == 'swiglu':
+            self.activation_func = swiglu
+        else:
+            assert RuntimeError(f"Unsupported glu_activation: {config.glu_activation}")
+
+        # Project back to h.
+        self.dense_4h_to_h = nn.Linear(
+            config.ffn_hidden_size,
+            config.hidden_size,
+            bias=self.add_bias,
+            device=device,
+            **_config_to_kwargs(config)
+        )
+
+        if self.moe:
+            assert self.num_experts > 1
+            del self.dense_h_to_4h
+            del self.dense_4h_to_h
+            self.router = nn.Linear(
+                config.hidden_size,
+                config.num_experts,
+                bias=False,
+                device=device,
+                dtype=torch.float32
+            )
+            for i in range(0, self.num_experts):
+                self.register_module(f"dense_h_to_4h_{i}", nn.Linear(
+                    config.hidden_size,
+                    config.ffn_hidden_size * 2,
+                    bias=self.add_bias,
+                    device=device,
+                    **_config_to_kwargs(config)
+                ))
+                self.register_module(f"dense_4h_to_h_{i}", nn.Linear(
+                    config.ffn_hidden_size,
+                    config.hidden_size,
+                    bias=self.add_bias,
+                    device=device,
+                    **_config_to_kwargs(config)
+                ))
+
+    def moe_forward(self, hidden_states, expert_idx):
+        intermediate_parallel = getattr(self, f"dense_h_to_4h_{expert_idx}")(hidden_states) 
+        intermediate_parallel = self.activation_func(intermediate_parallel) 
+        output = getattr(self, f"dense_4h_to_h_{expert_idx}")(intermediate_parallel) 
+        return output
+
+    def forward(self, hidden_states):
+        if self.moe:
+            # import pdb; pdb.set_trace();
+            s, b, n = hidden_states.shape
+            dtype = hidden_states.dtype
+            hidden_states = hidden_states.view(-1, hidden_states.size(2)) # [s*b h]
+            route = self.router(hidden_states).to(dtype)
+
+            weights, selected_experts = torch.topk(route, self.experts_per_token)
+            weights = F.softmax(weights, dim=1, dtype=torch.float).to(hidden_states.dtype)
+            output = torch.zeros_like(hidden_states, dtype=hidden_states.dtype, device=hidden_states.device)
+            for expert_idx in range(self.num_experts):
+                batch_idx, nth_expert = torch.where(selected_experts == expert_idx)
+                if nth_expert.shape[0] == 0:
+                    continue
+                cur_out = self.moe_forward(hidden_states[batch_idx], expert_idx)
+                output[batch_idx] += weights[batch_idx, nth_expert, None] * cur_out
+            output = output.reshape(s, b, n)
+        else:
+            # [s, b, 4hp]
+            intermediate_parallel = self.dense_h_to_4h(hidden_states)
+            intermediate_parallel = self.activation_func(intermediate_parallel)
+            # [s, b, h]
+            output = self.dense_4h_to_h(intermediate_parallel)
+        return output
+
+class ProteinGLMBlock(torch.nn.Module):
+    """A single transformer layer.
+
+    Transformer layer takes input with size [s, b, h] and returns an
+    output of the same size.
+    """
+
+    def __init__(self, config: ProteinGLMConfig, layer_number, device=None):
+        super(ProteinGLMBlock, self).__init__()
+        self.layer_number = layer_number
+
+        self.apply_residual_connection_post_layernorm = config.apply_residual_connection_post_layernorm
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+
+        LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
+        # Layernorm on the input data.
+        self.input_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon)
+
+        # Self attention.
+        self.self_attention = SelfAttention(config, layer_number, device=device)
+        self.hidden_dropout = config.hidden_dropout
+
+        # Layernorm on the attention output
+        self.post_attention_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon)
+
+        # MLP
+        self.mlp = MLP(config, device=device)
+
+        self.deepnorm_coeff = get_deepnorm_coefficients(config) if config.deepnorm else None
+
+    def forward(
+            self, hidden_states, attention_mask, position_ids, kv_cache=None, use_cache=True,
+    ):
+        # hidden_states: [s, b, h]
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+        # Self attention.
+        attention_output, kv_cache = self.self_attention(
+            layernorm_output,
+            attention_mask,
+            position_ids, # [batch_size, 2, seq_len, 32, 2]
+            kv_cache=kv_cache,
+            use_cache=use_cache
+        )
+
+        # Residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        layernorm_input = torch.nn.functional.dropout(attention_output, p=self.hidden_dropout, training=self.training)
+        if self.deepnorm_coeff is not None: 
+            layernorm_input = residual*self.deepnorm_coeff.alpha + layernorm_input
+        else:
+            layernorm_input = residual + layernorm_input
+
+        # Layer norm post the self attention.
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+
+        # MLP.
+        mlp_output = self.mlp(layernorm_output)
+
+        # Second residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = layernorm_input
+
+        output = torch.nn.functional.dropout(mlp_output, p=self.hidden_dropout, training=self.training)
+        if self.deepnorm_coeff is not None: 
+            output = residual*self.deepnorm_coeff.alpha + output
+        else:
+            #print(f"2 self.deepnorm_coeff is None")
+            output = residual + output
+
+        return output, kv_cache
+
+
+class ProteinGLMTransformer(torch.nn.Module):
+    """Transformer class."""
+
+    def __init__(self, config: ProteinGLMConfig, device=None):
+        super(ProteinGLMTransformer, self).__init__()
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+        self.post_layer_norm = config.post_layer_norm
+
+        # Number of layers.
+        self.num_layers = config.num_layers
+
+        # Transformer layers.
+        def build_layer(layer_number):
+            return ProteinGLMBlock(config, layer_number, device=device)
+
+        self.layers = torch.nn.ModuleList([build_layer(i + 1) for i in range(self.num_layers)])
+
+        if self.post_layer_norm:
+            LayerNormFunc = RMSNorm if config.rmsnorm else LayerNorm
+            # Final layer norm before output.
+            self.final_layernorm = LayerNormFunc(config.hidden_size, eps=config.layernorm_epsilon)
+
+        self.gradient_checkpointing = False
+
+    def _get_layer(self, layer_number):
+        return self.layers[layer_number]
+
+    def forward(
+            self, hidden_states, attention_mask, position_ids, kv_caches=None,
+            use_cache: Optional[bool] = True,
+            output_hidden_states: Optional[bool] = False,
+    ):
+        if not kv_caches:
+            kv_caches = [None for _ in range(self.num_layers)]
+        presents = () if use_cache else None
+        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
+
+        all_self_attentions = None
+        all_hidden_states = () if output_hidden_states else None
+        for index in range(self.num_layers):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            layer = self._get_layer(index)
+            if self.gradient_checkpointing and self.training and torch.is_grad_enabled():
+                layer_ret = get_checkpoint_fn()(
+                    layer,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    kv_caches[index],
+                    use_cache
+                )
+            else:
+                layer_ret = layer(
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    kv_cache=kv_caches[index],
+                    use_cache=use_cache
+                )
+            hidden_states, kv_cache = layer_ret
+            if use_cache:
+                presents = presents + (kv_cache,)
+
+
+        # Final layer norm.
+        if self.post_layer_norm:
+            hidden_states = self.final_layernorm(hidden_states)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        return hidden_states, presents, all_hidden_states, all_self_attentions
+
+
+class ProteinGLMPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and
+    a simple interface for downloading and loading pretrained models.
+    """
+
+    is_parallelizable = False
+    supports_gradient_checkpointing = True
+    config_class = ProteinGLMConfig
+    base_model_prefix = "transformer"
+    _no_split_modules = ["ProteinGLMBlock"]
+
+    _quantized = False
+
+
+    def get_masks(self, input_ids, past_key_values, padding_mask=None, is_causal=True):
+        batch_size, seq_length = input_ids.shape
+        full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
+        if is_causal:
+            full_attention_mask.tril_()
+        past_length = 0
+        if past_key_values:
+            past_length = past_key_values[0][0].shape[0]
+        if past_length:
+            full_attention_mask = torch.cat((torch.ones(batch_size, seq_length, past_length,
+                                                        device=input_ids.device), full_attention_mask), dim=-1)
+        if padding_mask is not None:
+            full_attention_mask = full_attention_mask * padding_mask.unsqueeze(1)
+        if not past_length and padding_mask is not None:
+            full_attention_mask -= padding_mask.unsqueeze(-1) - 1
+        full_attention_mask = (full_attention_mask < 0.5).bool()
+        full_attention_mask.unsqueeze_(1)
+        return full_attention_mask
+
+    def get_position_ids(self, input_ids, device, context_length=0):
+        batch_size, seq_length = input_ids.shape
+        if self.config.rotary_embedding_2d:
+            if self.config.is_causal: # 100b model
+                position_ids_1 = torch.zeros(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) # [batch_size, seq_len]
+                position_ids_2 = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) # [batch_size, seq_len]
+                position_ids   = torch.stack([position_ids_1, position_ids_2], axis=1) # [batch_size, 2, seq_len]       
+            else:
+                position_ids_1 = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) # [batch_size, seq_len]
+                position_ids_2 = torch.zeros(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) # [batch_size, seq_len]
+                position_ids   = torch.stack([position_ids_1, position_ids_2], axis=1) # [batch_size, 2, seq_len]
+        else:
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1) # [batch_size, 1, seq_len]
+        return position_ids
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, ProteinGLMTransformer):
+            module.gradient_checkpointing = value
+
+    
+    # Copied from transformers.models.bert.modeling_bert.BertPreTrainedModel._init_weights
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+
+    def quantize(self, weight_bit_width: int, empty_init=True, device=None):
+        if self._quantized:
+            print(f"Model has been quantized...")
+            return
+        self.transformer.encoder = quantize(self.transformer.encoder, weight_bit_width, empty_init, device)
+        self._quantized = True
+        return self
+
+class Embedding(torch.nn.Module):
+    """Language model embeddings."""
+
+    def __init__(self, config: ProteinGLMConfig, device=None):
+        super(Embedding, self).__init__()
+
+        self.hidden_size = config.hidden_size
+        # Word embeddings (parallel).
+        self.word_embeddings = nn.Embedding(
+            config.padded_vocab_size,
+            self.hidden_size,
+            dtype=config.torch_dtype,
+            device=device
+        )
+        self.fp32_residual_connection = config.fp32_residual_connection
+
+
+    def forward(self, input_ids):
+        # Embeddings.
+        words_embeddings = self.word_embeddings(input_ids)
+        embeddings = words_embeddings
+        # Data format change to avoid explicit tranposes : [b s h] --> [s b h].
+        embeddings = embeddings.transpose(0, 1).contiguous()
+        # If the input flag for fp32 residual connection is set, convert for float.
+        if self.fp32_residual_connection:
+            embeddings = embeddings.float()
+        return embeddings
+
+class ProteinGLMModel(ProteinGLMPreTrainedModel):
+    def __init__(self, config: ProteinGLMConfig, device=None, empty_init=True):
+        super().__init__(config)
+        if empty_init:
+            init_method = skip_init
+        else:
+            init_method = default_init
+        init_kwargs = {}
+        if device is not None:
+            init_kwargs["device"] = device
+        self.embedding = init_method(Embedding, config, **init_kwargs)
+        self.num_layers = config.num_layers
+        self.multi_query_group_num = config.multi_query_group_num
+        self.kv_channels = config.kv_channels
+
+        # Rotary positional embeddings
+        self.seq_length = config.seq_length
+        rotary_dim = (
+            config.hidden_size // config.num_attention_heads if config.kv_channels is None else config.kv_channels
+        )
+
+        # self.rotary_pos_emb = RotaryEmbedding(rotary_dim // 2, base=10000, precision=config.torch_dtype, learnable=False)
+        self.encoder = init_method(ProteinGLMTransformer, config, **init_kwargs)
+        
+        self.output_layer = init_method(nn.Linear, config.hidden_size, config.padded_vocab_size, bias=False,
+                                        dtype=config.torch_dtype, **init_kwargs)
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embeddings
+
+    def set_input_embeddings(self, value):
+        self.embedding.word_embeddings = value
+
+    def forward(
+            self,
+            input_ids,
+            position_ids: Optional[torch.Tensor] = None, # position_ids: [batch_size, 2, seq_len]
+            attention_mask: Optional[torch.BoolTensor] = None,
+            full_attention_mask: Optional[torch.BoolTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.Tensor, torch.Tensor], ...]] = None,
+            inputs_embeds: Optional[torch.Tensor] = None,
+            use_cache: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ):
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        if self.config.is_causal:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        batch_size, seq_length = input_ids.shape
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embedding(input_ids)
+
+        if full_attention_mask is None:
+            if (attention_mask is not None and not attention_mask.all()) or (past_key_values and seq_length != 1):
+                full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask)
+        # Run encoder.
+        hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
+            inputs_embeds, full_attention_mask, position_ids=position_ids,
+            kv_caches=past_key_values, use_cache=use_cache, output_hidden_states=output_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 ProteinGLMForMaskedLM(ProteinGLMPreTrainedModel):
+    def __init__(self, config: ProteinGLMConfig, empty_init=True, device=None):
+        super().__init__(config)
+
+        self.max_sequence_length = config.max_length
+        self.transformer = ProteinGLMModel(config, empty_init=empty_init, device=device)
+        self.config = config
+        if self.config.quantization_bit:
+            print(f"Begin Quantization to {self.config.quantization_bit} bit")
+            self.quantize(self.config.quantization_bit, empty_init=True, device=device)
+
+    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,
+            return_last_logit: Optional[bool] = None,
+            return_last_hidden_state: Optional[bool] = None
+    ):
+        if self.config.is_causal:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if position_ids is None:
+            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
+
+        full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask, is_causal=self.config.is_causal)
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            position_ids=position_ids, # position_ids: [batch_size, 2, seq_len]
+            full_attention_mask=full_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = transformer_outputs[0]
+        if return_last_logit:
+            hidden_states = hidden_states[-1:]
+        lm_logits = self.transformer.output_layer(hidden_states)
+        lm_logits = lm_logits.transpose(0, 1).contiguous()
+
+        masked_lm_loss = None
+        if labels is not None:
+            lm_logits = lm_logits.to(torch.float32)
+
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss(ignore_index=-100) # -100 for padding token.
+            masked_lm_loss = loss_fct(lm_logits.view(-1, self.config.vocab_size), 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 MaskedLMOutput(
+            loss = masked_lm_loss,
+            logits=lm_logits,
+            hidden_states=transformer_outputs.last_hidden_state if return_last_hidden_state else transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+
+
+class ProteinGLMForSequenceClassification(ProteinGLMPreTrainedModel):
+    def __init__(self, config: ProteinGLMConfig, empty_init=True, device=None):
+        super().__init__(config)
+        self.config = config
+        self.num_labels = config.num_labels
+        
+        self.transformer = ProteinGLMModel(config, empty_init=empty_init, device=device)
+        self.classifier = ProteinGLMClassificationHead(config)
+        if self.config.quantization_bit:
+            print(f"Begin Quantization to {self.config.quantization_bit} bit")
+            self.quantize(self.config.quantization_bit, empty_init=True, device=device)
+
+    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,
+            return_last_logit: Optional[bool] = None,
+            return_last_hidden_state: Optional[bool] = None,
+            **kwargs
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        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).
+        """
+        if self.config.is_causal:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if position_ids is None:
+            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
+
+        full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask, is_causal=self.config.is_causal)
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            position_ids=position_ids, # position_ids: [batch_size, 2, seq_len]
+            full_attention_mask=full_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        if self.config.add_special_tokens:
+            hidden_states = transformer_outputs[0][:-1] # get rid of <eos> token
+        else:
+            hidden_states = transformer_outputs[0]
+        logits = self.classifier(hidden_states, add_pooling=True)
+        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(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 SequenceClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+class ProteinGLMForTokenClassification(ProteinGLMPreTrainedModel):
+    def __init__(self, config: ProteinGLMConfig, empty_init=True, device=None):
+        super().__init__(config)
+        self.config = config
+        self.num_labels = config.num_labels
+        
+        self.transformer = ProteinGLMModel(config, empty_init=empty_init, device=device)
+        if config.task_modality == "token":
+            self.classifier = ProteinGLMClassificationHead(config)
+        elif config.task_modality == 'pair':
+            self.classifier = ProteinGLMContactHead(config)
+
+        self.quantized = False
+
+        if self.config.quantization_bit:
+            print(f"Begin Quantization to {self.config.quantization_bit} bit")
+            self.quantize(self.config.quantization_bit, empty_init=True, device=device)
+
+
+    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,
+            return_last_logit: Optional[bool] = None,
+            return_last_hidden_state: Optional[bool] = None,
+            **kwargs
+    ) -> Union[Tuple, SequenceClassifierOutput]:
+        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).
+        """
+        if self.config.is_causal:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if position_ids is None:
+            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
+
+        full_attention_mask = self.get_masks(input_ids, past_key_values, padding_mask=attention_mask, is_causal = self.config.is_causal)
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            position_ids=position_ids, # position_ids: [batch_size, 2, seq_len]
+            full_attention_mask=full_attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        if self.config.add_special_tokens:
+            hidden_states = transformer_outputs[0][:-1] # get rid of <eos> token
+        else:
+            hidden_states = transformer_outputs[0]
+
+        logits = self.classifier(hidden_states, add_pooling=False)
+        loss = None
+        if labels is not None:
+            labels = labels.to(logits.device)
+            loss_fct = CrossEntropyLoss()
+            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+
+        if not return_dict:
+            output = (logits,) + transformer_outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+
+        return TokenClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=transformer_outputs.hidden_states,
+            attentions=transformer_outputs.attentions,
+        )
+
+
+
+class ProteinGLMClassificationHead(nn.Module):
+    """Head for classification tasks."""
+    def __init__(self, config):
+        super().__init__()
+        self.activation_func = config.activation_func
+        self.layers = torch.nn.ModuleList()
+        last_size = config.hidden_size
+        for sz in config.inter_hidden_size:
+            this_layer = torch.nn.Linear(last_size, sz, bias=config.bias)
+            last_size = sz
+            self.layers.append(this_layer)
+    
+    def forward(self, 
+                input_features,
+                add_pooling: Optional[bool] = True
+                ):
+        # [s, b, h] -> [b, s ,h]
+        input_features = input_features.transpose(0,1).contiguous()
+        if add_pooling:
+            # [b, h]
+            input_features = torch.mean(input_features, dim = 1)
+        for i, layer in enumerate(self.layers):
+            if i > 0:
+                input_features = self.activation_func(input_features)
+            input_features = layer(input_features)
+        return input_features
+
+class ProteinGLMContactHead(nn.Module):
+    """Head for sentence-level classification tasks."""
+    def __init__(self, config):
+        super().__init__()
+        self.activation_func = config.activation_func
+        self.layers = torch.nn.ModuleList()
+        last_size = config.hidden_size * 2
+        for sz in config.inter_hidden_size:
+            this_layer = torch.nn.Linear(last_size, sz, bias=config.bias)
+            last_size = sz
+            self.layers.append(this_layer)
+    
+    def outer_concat(self, x):
+        batch_size, seq_len, features = x.shape
+        
+        # Permute to [batch_size, features, seq_len]
+        x = x.permute(0, 2, 1)
+        
+        # Introduce new dimensions for broadcasting
+        x_1 = x[:, None, :, :, None]  # [batch_size, 1, features, seq_len, 1]
+        x_2 = x[:, None, :, None, :]  # [batch_size, 1, features, 1, seq_len]
+        
+        # Repeat along new dimensions
+        x_1 = x_1.repeat(1, 1, 1, 1, seq_len)  # [batch_size, 1, features, seq_len, seq_len]
+        x_2 = x_2.repeat(1, 1, 1, seq_len, 1)  # [batch_size, 1, features, seq_len, seq_len]
+        
+        # Concatenate along the second dimension
+        x = torch.cat((x_1, x_2), dim=1)  # [batch_size, 2, features, seq_len, seq_len]
+        
+        # Get lower triangular indices
+        I, J = torch.tril_indices(seq_len, seq_len, -1)
+        
+        # Symmetrize
+        x[:, :, :, I, J] = x[:, :, :, J, I]
+        
+        # Permute to desired shape and make contiguous
+        x = x.permute(0, 3, 4, 2, 1).contiguous()  # [batch_size, seq_len, seq_len, features, 2]
+        
+        # Reshape to combine the last two dimensions
+        x = x.view(batch_size, seq_len, seq_len, features * 2)  # [batch_size, seq_len, seq_len, features * 2]
+        
+        return x
+
+    def forward(self, 
+                input_features,
+                add_pooling: Optional[bool] = True
+                ):
+        # [s, b, h] -> [b, s ,h]
+        input_features = input_features.transpose(0,1).contiguous()
+        input_features = self.outer_concat(input_features)
+        for i, layer in enumerate(self.layers):
+            if i > 0:
+                input_features = self.activation_func(input_features)
+            input_features = layer(input_features)
+        return input_features  
+
+
+
+
+
+class ProteinGLMForCasualLM(ProteinGLMPreTrainedModel):
+    def __init__(self, config: ProteinGLMConfig, empty_init=True, device=None):
+        super().__init__(config)
+
+        self.max_sequence_length = config.max_length
+        self.transformer = ProteinGLMModel(config, empty_init=empty_init, device=device)
+        self.config = config
+        if self.config.quantization_bit:
+            print(f"Begin Quantization to {self.config.quantization_bit} bit")
+            self.quantize(self.config.quantization_bit, empty_init=True, device=device)
+
+    def _update_model_kwargs_for_generation(
+            self,
+            outputs: ModelOutput,
+            model_kwargs: Dict[str, Any],
+            is_encoder_decoder: bool = False,
+            standardize_cache_format: bool = False,
+    ) -> Dict[str, Any]:
+        # update past_key_values
+        model_kwargs["past_key_values"] = self._extract_past_from_model_output(
+            outputs, standardize_cache_format=standardize_cache_format
+        )
+
+        # update attention mask
+        if "attention_mask" in model_kwargs:
+            attention_mask = model_kwargs["attention_mask"]
+            model_kwargs["attention_mask"] = torch.cat(
+                [attention_mask, attention_mask.new_ones((attention_mask.shape[0], 1))], dim=-1
+            )
+
+        # update position ids
+        if "position_ids" in model_kwargs:
+            position_ids = model_kwargs["position_ids"]
+            new_position_id = position_ids[..., -1:].clone() # [batch_size, 2, 1]
+            if self.config.rotary_embedding_2d:
+                new_position_id[:, 1] += 1 # Only update the 2nd dimension
+            else:
+                new_position_id[:] += 1
+            model_kwargs["position_ids"] = torch.cat(
+                [position_ids, new_position_id], dim=-1
+            ) # [batch_size, 2, seq_len+1]
+
+        model_kwargs["is_first_forward"] = False
+        return model_kwargs
+
+    def prepare_inputs_for_generation(
+            self,
+            input_ids: torch.LongTensor,
+            past_key_values: Optional[torch.Tensor] = None,
+            attention_mask: Optional[torch.Tensor] = None,
+            position_ids: Optional[torch.Tensor] = None,
+            use_cache: Optional[bool] = None,
+            is_first_forward: bool = True,
+            **kwargs
+    ) -> dict:
+        # only last token for input_ids if past is not None
+        if position_ids is None:
+            position_ids = self.get_position_ids(input_ids, device=input_ids.device) # position_ids: [batch_size, 2, seq_len]
+        if not is_first_forward:
+            if past_key_values is not None:
+                position_ids = position_ids[..., -1:]
+                input_ids = input_ids[:, -1:]
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values,
+            "position_ids": position_ids,
+            "attention_mask": attention_mask,
+            "return_last_logit": True,
+            "use_cache": use_cache
+        }
+
+    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,
+            return_last_logit: Optional[bool] = False
+    ):
+        if self.config.is_causal:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if position_ids is None:
+            position_ids = self.get_position_ids(input_ids, device=input_ids.device)
+
+        transformer_outputs = self.transformer(
+            input_ids=input_ids,
+            position_ids=position_ids, # position_ids: [batch_size, 2, seq_len]
+            attention_mask=attention_mask,
+            past_key_values=past_key_values,
+            inputs_embeds=inputs_embeds,
+            use_cache=use_cache,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict
+        )
+        hidden_states = transformer_outputs[0]
+        if return_last_logit:
+            hidden_states = hidden_states[-1:]
+        lm_logits = self.transformer.output_layer(hidden_states)
+        lm_logits = lm_logits.transpose(0, 1).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(ignore_index=-100)
+            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.inference_mode()
+    def chat(self, tokenizer, query: str,  max_length: int = 256, num_beams=1, do_sample=True, 
+            top_p=1.0, temperature=1.0, logits_processor=None, **kwargs):
+        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}
+        inputs = tokenizer.apply_chat_template(query, add_generation_prompt=True, tokenize=True,
+                                               return_tensors="pt", return_dict=True)
+        position_ids = self.get_position_ids(inputs['input_ids'], device=self.device) # TODO: ADD BATCH
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<eop>")]
+        inputs["position_ids"] = position_ids
+        inputs = inputs.to(self.device)
+        outputs = self.generate(**inputs, **gen_kwargs, eos_token_id=eos_token_id)
+        outputs = outputs.tolist()[0][3:] # 3 for generation prompt "<gmask><sop><eos>"
+        if outputs[-1] in eos_token_id:
+            outputs = outputs[:-1]
+        response = tokenizer.decode(outputs)
+        return response
+
+    # TODO: fix bug in streaming chat 
+    @torch.inference_mode()
+    def stream_chat(self, tokenizer, query: str,  max_length: int = 56, num_beams=1, do_sample=True, 
+                    top_p=0.8, temperature=0.8, logits_processor=None, past_key_values = None, **kwargs):
+        if logits_processor is None:
+            logits_processor = LogitsProcessorList()
+        logits_processor.append(InvalidScoreLogitsProcessor())
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<eop>")]
+        gen_kwargs = {"max_length": max_length, "do_sample": do_sample, "top_p": top_p,
+                      "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+        inputs = tokenizer.apply_chat_template(query, add_generation_prompt=True, tokenize=True,
+                                            return_tensors="pt", return_dict=True)
+        position_ids = self.get_position_ids(inputs['input_ids'], device=self.device) # TODO: ADD BATCH
+        eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<eop>")]
+        inputs["position_ids"] = position_ids
+        inputs = inputs.to(self.device)
+        offset = 3 # 3 for generation prompt
+        for outputs in self.stream_generate(**inputs, past_key_values=past_key_values,
+                                            eos_token_id=eos_token_id, return_past_key_values=False,
+                                            **gen_kwargs):
+            outputs = outputs.tolist()[0][3:]
+            if outputs[-1] in eos_token_id:
+                outputs = outputs[:-1]
+            # offset = 3 + len(outputs)
+            response = tokenizer.decode(outputs)
+            if response:
+                yield response
+
+    @torch.inference_mode()
+    def stream_generate(
+            self,
+            input_ids,
+            generation_config: Optional[GenerationConfig] = None,
+            logits_processor: Optional[LogitsProcessorList] = None,
+            stopping_criteria: Optional[StoppingCriteriaList] = None,
+            prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
+            return_past_key_values=False,
+            **kwargs,
+    ):
+        breakpoint()
+        batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
+
+        if generation_config is None:
+            generation_config = self.generation_config
+        generation_config = copy.deepcopy(generation_config)
+        model_kwargs = generation_config.update(**kwargs)
+        model_kwargs["use_cache"] = generation_config.use_cache
+        bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
+
+        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) if eos_token_id is not None else None
+
+        has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
+        if has_default_max_length and generation_config.max_new_tokens is None:
+            warnings.warn(
+                f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
+                "This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
+                " recommend using `max_new_tokens` to control the maximum length of the generation.",
+                UserWarning,
+            )
+        elif generation_config.max_new_tokens is not None:
+            generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
+            if not has_default_max_length:
+                logger.warn(
+                    f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
+                    f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
+                    "Please refer to the documentation for more information. "
+                    "(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
+                    UserWarning,
+                )
+
+        if input_ids_seq_length >= generation_config.max_length:
+            input_ids_string = "decoder_input_ids" if self.config.is_encoder_decoder else "input_ids"
+            logger.warning(
+                f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
+                f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
+                " increasing `max_new_tokens`."
+            )
+
+        # 2. Set generation parameters if not already defined
+        logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
+        stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
+
+        logits_processor = self._get_logits_processor(
+            generation_config=generation_config,
+            input_ids_seq_length=input_ids_seq_length,
+            encoder_input_ids=input_ids,
+            prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
+            logits_processor=logits_processor,
+        )
+
+        stopping_criteria = self._get_stopping_criteria(
+            generation_config=generation_config, stopping_criteria=stopping_criteria
+        )
+        logits_warper = self._get_logits_warper(generation_config)
+
+        unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
+        scores = None
+        while True:
+            model_inputs = self.prepare_inputs_for_generation(input_ids, **model_kwargs)
+            # forward pass to get next token
+            outputs = self(
+                **model_inputs,
+                return_dict=True,
+                output_attentions=False,
+                output_hidden_states=False,
+            )
+
+            next_token_logits = outputs.logits[:, -1, :]
+
+            # pre-process distribution
+            next_token_scores = logits_processor(input_ids, next_token_logits)
+            next_token_scores = logits_warper(input_ids, next_token_scores)
+
+            # sample
+            probs = nn.functional.softmax(next_token_scores, dim=-1)
+            if generation_config.do_sample:
+                next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
+            else:
+                next_tokens = torch.argmax(probs, dim=-1)
+            # update generated ids, model inputs, and length for next step
+            input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
+            model_kwargs = self._update_model_kwargs_for_generation(
+                outputs, model_kwargs, is_encoder_decoder=self.config.is_encoder_decoder
+            )
+            unfinished_sequences = unfinished_sequences.mul(
+                next_tokens.tile(eos_token_id_tensor.shape[0], 1).ne(eos_token_id_tensor.unsqueeze(1)).prod(dim=0)
+            )
+            if return_past_key_values:
+                yield input_ids, outputs.past_key_values
+            else:
+                yield input_ids
+            # stop when each sentence is finished, or if we exceed the maximum length
+            if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
+                break

tokenization_proteinglm.py

@@ -0,0 +1,140 @@
+"""Tokenization classes for ProteinGLM."""
+
+import os
+from typing import List, Optional, Union, Dict, Any
+from torch import TensorType
+from transformers import PreTrainedTokenizer, PreTrainedTokenizerFast
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+
+def load_vocab_file(vocab_file: str) -> List[str]:
+    with open(vocab_file, "r") as f:
+        lines = f.read().splitlines()
+        return [line.strip() for line in lines]
+
+
+class ProteinGLMTokenizer(PreTrainedTokenizer):
+    """
+    Constructs a ProteinGLM tokenizer.
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+    def __init__(
+        self,
+        vocab_file: str,
+        unk_token: str = "<unk>",
+        pad_token: str = "<pad>",
+        mask_token: str = "<mask>",
+        eos_token: str = "<eos>",
+        model_max_length: int = 2048,
+        additional_special_tokens: Optional[List[str]] = None,
+        **kwargs,
+    ):
+        self.all_tokens = load_vocab_file(vocab_file)
+        self._id_to_token = dict(enumerate(self.all_tokens))
+        self._token_to_id = {tok: ind for ind, tok in enumerate(self.all_tokens)}
+
+        if additional_special_tokens is None:
+            additional_special_tokens = ['<pad>', '<mask>', '<gmask>', '<smask>', '<eod>', '<sop>', '<eop>', '<eos>', '<unk>']
+
+        super().__init__(
+            unk_token=unk_token,
+            pad_token=pad_token,
+            mask_token=mask_token,
+            eos_token=eos_token,
+            model_max_length=model_max_length,
+            additional_special_tokens=additional_special_tokens,
+            **kwargs,
+        )
+
+        self.unique_no_split_tokens = self.all_tokens
+        self._update_trie(self.unique_no_split_tokens)
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self._id_to_token.get(index, self.unk_token)
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
+
+    def _tokenize(self, text: str, **kwargs) -> List[str]:
+        return text.split()
+
+    def get_vocab(self) -> dict:
+        base_vocab = self._token_to_id.copy()
+        base_vocab.update(self.added_tokens_encoder)
+        return base_vocab
+
+    def token_to_id(self, token: str) -> int:
+        return self._token_to_id.get(token, self._token_to_id.get(self.unk_token))
+
+    def id_to_token(self, index: int) -> str:
+        return self._id_to_token.get(index, self.unk_token)
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        sep = [self.eos_token_id]  
+        if token_ids_1 is None:
+            if self.eos_token_id is None:
+                return token_ids_0
+            else:
+                return token_ids_0 + sep
+        elif self.eos_token_id is None:
+            raise ValueError("Cannot tokenize multiple sequences when EOS token is not set!")
+        return token_ids_0 + sep + token_ids_1 + sep  # Multiple inputs always have an EOS token
+
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> tuple:
+        vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + "tokenizer.model")
+        with open(vocab_file, "w") as f:
+            f.write("\n".join(self.all_tokens))
+        return (vocab_file,)
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self.all_tokens)
+
+    def apply_chat_template(
+        self, 
+        query, 
+        add_generation_prompt: bool = True, 
+        tokenize: bool = True, 
+        padding: bool = False,
+        truncation: bool = False,
+        max_length: Optional[int] = None,
+        return_tensors: Optional[Union[str, TensorType]] = None,
+        return_dict: bool = False,
+        tokenizer_kwargs: Optional[Dict[str, Any]] = None,
+        add_special_tokens: bool = True,
+        **kwargs,
+) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
+
+        generation_prompt = "<gmask><sop><eos>"
+        if isinstance(query, str):
+            query = [query]
+        prompt_query = []
+        if add_generation_prompt:
+            for each in query:
+                assert isinstance(each, str)
+                prompt_query.append(generation_prompt+each)
+        else:
+            prompt_query = query
+        if tokenize:
+            output = self.batch_encode_plus(
+                prompt_query,
+                padding=padding,
+                truncation=truncation,
+                max_length=max_length,
+                return_tensors=return_tensors,
+                is_split_into_words=True,
+                add_special_tokens=False
+            )
+            if return_dict:
+                return output
+            else:
+                return output["input_ids"]
+        else:
+            return prompt_query

tokenizer_config.json

@@ -86,7 +86,7 @@
   ],
   "auto_map": {
     "AutoTokenizer": [
-      "tokenization_xtrimopglm.xTrimoPGLMTokenizer",
+      "tokenization_proteinglm.ProteinGLMTokenizer",
       null
     ]
   },
@@ -95,6 +95,6 @@
   "mask_token": "<mask>",
   "model_max_length": 2048,
   "pad_token": "<pad>",
-  "tokenizer_class": "xTrimoPGLMTokenizer",
+  "tokenizer_class": "ProteinGLMTokenizer",
   "unk_token": "<unk>"
 }