upload config, tokenizer and modeling file

README.md

@@ -1,3 +1,3 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+---

config.json

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+{  "auto_map": {
+    "AutoConfig": "configuration_geblm.GEBConfig",
+    "AutoModel": "modeling_geb.GEBForCausalLM"
+  }, "num_layers": 24, "padded_vocab_size": 64896, "hidden_size": 2048, "ffn_hidden_size": 5632, "kv_channels": 128, "num_attention_heads": 16, "torch_dtype": "bfloat16", "seq_length": 4096, "hidden_dropout": 0.0, "attention_dropout": 0.0, "layernorm_epsilon": 1e-05, "max_position_embeddings": 4096, "bias_dropout_fusion": true, "use_cache": true, "apply_residual_connection_post_layernorm": false, "post_layer_norm": true, "add_bias_linear": false, "use_flash_attn": false, "num_key_value_heads": 4, "apply_query_key_layer_scaling": false, "attention_softmax_in_fp32": false, "fp32_residual_connection": false, "pre_seq_len": null, "prefix_projection": false, "tie_word_embeddings": false, "return_dict": true, "output_hidden_states": false, "output_attentions": false, "torchscript": false, "use_bfloat16": true, "tf_legacy_loss": false, "pruned_heads": {}, "is_encoder_decoder": false, "is_decoder": false, "cross_attention_hidden_size": null, "add_cross_attention": false, "tie_encoder_decoder": false, "max_length": 512, "min_length": 0, "do_sample": true, "early_stopping": false, "num_beams": 1, "num_beam_groups": 1, "diversity_penalty": 0.0, "temperature": 0.3, "top_k": 5, "top_p": 0.5, "typical_p": 1.0, "repetition_penalty": 1.15, "length_penalty": 1.0, "no_repeat_ngram_size": 0, "encoder_no_repeat_ngram_size": 0, "bad_words_ids": null, "num_return_sequences": 1, "chunk_size_feed_forward": 0, "output_scores": false, "return_dict_in_generate": false, "forced_bos_token_id": null, "forced_eos_token_id": null, "remove_invalid_values": false, "exponential_decay_length_penalty": null, "suppress_tokens": null, "begin_suppress_tokens": null, "architectures": ["GEBForCausalLM"], "finetuning_task": null, "id2label": {"0": "LABEL_0", "1": "LABEL_1"}, "label2id": {"LABEL_0": 0, "LABEL_1": 1}, "tokenizer_class": null, "prefix": null, "bos_token_id": 1, "eos_token_id": 2, "pad_token_id": 2, "_name_or_path": "", "transformers_version": "4.35.2", "model_type": "geblm"}

configuration_geblm.py

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+from transformers import PretrainedConfig
+
+
+class GEBConfig(PretrainedConfig):
+    model_type = "geblm"
+    def __init__(
+        self,
+        num_layers=24,
+        padded_vocab_size=64896,
+        hidden_size=2048,
+        ffn_hidden_size=5632,
+        kv_channels=128,
+        num_attention_heads=16,
+        torch_dtype='bfloat16',
+        seq_length=4096,
+        hidden_dropout=0.0,
+        attention_dropout=0.0,
+        layernorm_epsilon=1e-5,
+        max_position_embeddings=4096,
+        bias_dropout_fusion=True,
+        use_cache=True,
+        apply_residual_connection_post_layernorm=False,
+        post_layer_norm=True,
+        add_bias_linear=False,
+        use_flash_attn=True,
+        num_key_value_heads=4,
+        apply_query_key_layer_scaling=False,
+        attention_softmax_in_fp32=False,
+        fp32_residual_connection=False,
+        pre_seq_len=None,
+        prefix_projection=False,
+        tie_word_embeddings=False,
+        **kwargs
+    ):
+        self.num_layers=num_layers
+        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.torch_dtype=torch_dtype
+        self.seq_length=seq_length
+        self.hidden_dropout=hidden_dropout,
+        self.attention_dropout=attention_dropout
+        self.layernorm_epsilon=layernorm_epsilon
+        self.max_position_embeddings=max_position_embeddings
+        self.bias_dropout_fusion=bias_dropout_fusion
+        self.use_cache=use_cache
+        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.use_flash_attn=use_flash_attn
+        self.num_key_value_heads=num_key_value_heads
+        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.pre_seq_len=pre_seq_len
+        self.prefix_projection=prefix_projection
+        self.tie_word_embeddings=tie_word_embeddings
+        super().__init__(**kwargs)

modeling_geb.py

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+"""PyTorch GEB model."""
+
+import math
+import copy
+import os
+import warnings
+from dataclasses import dataclass
+from typing import Optional, Tuple, Dict, Any, List
+import importlib.util
+from torch.nn.utils import skip_init
+import torch.nn.functional as F
+import torch
+import torch.utils.checkpoint
+from torch import einsum, nn
+from torch.cuda.amp import autocast
+from torch.nn import BCEWithLogitsLoss, LayerNorm, CrossEntropyLoss, MSELoss
+from copy import deepcopy
+from deepspeed.accelerator import get_accelerator
+try:
+    from einops import rearrange
+except ImportError:
+    rearrange = None
+from transformers.modeling_outputs import (
+    BaseModelOutputWithPast,
+    CausalLMOutputWithPast,
+    QuestionAnsweringModelOutput,
+    SequenceClassifierOutputWithPast,
+    TokenClassifierOutput,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import (
+    ModelOutput,
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.generation.logits_process import LogitsProcessor
+from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig, ModelOutput
+from transformers.utils.model_parallel_utils import assert_device_map, get_device_map
+from .configuration_geblm import GEBConfig
+try:
+    # FlashAttention-2
+    from flash_attn.flash_attn_interface import flash_attn_varlen_func
+except ImportError:
+    flash_attn_varlen_func = None
+FlashAttentionBuilder = get_accelerator().get_op_builder("FlashAttentionBuilder")
+flash_attn_builder = None
+
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "geb"
+_CONFIG_FOR_DOC = "GEBConfig"
+
+def _config_to_kwargs(args):
+    common_kwargs = {
+        "dtype": args.torch_dtype,
+    }
+    return common_kwargs
+
+def default_init(cls, *args, **kwargs):
+    return cls(*args, **kwargs)
+
+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 PrefixEncoder(torch.nn.Module):
+    """
+    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: GEBConfig):
+        super().__init__()
+        self.prefix_projection = config.prefix_projection
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        if self.prefix_projection:
+            # Use a two-layer MLP to encode the prefix
+            kv_size = config.num_layers * config.kv_channels * self.num_key_value_groups * 2
+            self.embedding = torch.nn.Embedding(config.pre_seq_len, kv_size)
+            self.trans = torch.nn.Sequential(
+                torch.nn.Linear(kv_size, config.hidden_size),
+                torch.nn.Tanh(),
+                torch.nn.Linear(config.hidden_size, kv_size)
+            )
+        else:
+            self.embedding = torch.nn.Embedding(config.pre_seq_len,
+                                                config.num_layers * config.kv_channels * self.num_key_value_groups * 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
+    
+# class RotaryEmbedding(nn.Module):
+#     def __init__(self, dim, original_impl=False, device=None, dtype=None):
+#         super().__init__()
+#         inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2, device=device).to(dtype=dtype) / dim))
+#         self.register_buffer("inv_freq", inv_freq)
+#         self.dim = dim
+#         self.original_impl = original_impl
+
+#     def forward_impl(
+#             self, seq_len: int, n_elem: int, dtype: torch.dtype, device: torch.device, base: int = 10000
+#     ):
+#         """Enhanced Transformer with Rotary Position Embedding.
+
+#         Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
+#         transformers/rope/__init__.py. MIT License:
+#         https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
+#         """
+#         # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
+#         theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, dtype=torch.float, device=device) / n_elem))
+
+#         # Create position indexes `[0, 1, ..., seq_len - 1]`
+#         seq_idx = torch.arange(seq_len, dtype=torch.float, device=device)
+
+#         # Calculate the product of position index and $\theta_i$
+#         idx_theta = torch.outer(seq_idx, theta).float()
+
+#         cache = torch.stack([torch.cos(idx_theta), torch.sin(idx_theta)], dim=-1)
+
+#         # this is to mimic the behaviour of complex32, else we will get different results
+#         if dtype in (torch.float16, torch.bfloat16, torch.int8):
+#             cache = cache.bfloat16() if dtype == torch.bfloat16 else cache.half()
+#         return cache
+
+#     def forward(self, max_seq_len, offset=0):
+#         return self.forward_impl(
+#             max_seq_len, self.dim, dtype=self.inv_freq.dtype, device=self.inv_freq.device
+#         )
+
+
+# @torch.jit.script
+# def apply_rotary_pos_emb(x: torch.Tensor, rope_cache: torch.Tensor) -> torch.Tensor:
+#     # x: [sq, b, np, hn]
+#     sq, b, np, hn = x.size(0), x.size(1), x.size(2), x.size(3)
+#     rot_dim = rope_cache.shape[-2] * 2
+#     x, x_pass = x[..., :rot_dim], x[..., rot_dim:]
+#     # truncate to support variable sizes
+#     rope_cache = rope_cache[:sq]
+#     xshaped = x.reshape(sq, -1, np, rot_dim // 2, 2)
+#     rope_cache = rope_cache.view(sq, -1, 1, xshaped.size(3), 2)
+#     x_out2 = torch.stack(
+#         [
+#             xshaped[..., 0] * rope_cache[..., 0] - xshaped[..., 1] * rope_cache[..., 1],
+#             xshaped[..., 1] * rope_cache[..., 0] + xshaped[..., 0] * rope_cache[..., 1],
+#         ],
+#         -1,
+#     )
+#     x_out2 = x_out2.flatten(3)
+#     return torch.cat((x_out2, x_pass), dim=-1)
+
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim):
+        super().__init__()
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+        if importlib.util.find_spec('einops') is None:
+            raise RuntimeError("einops is required for Rotary Embedding")
+
+    def forward(self, max_seq_len, offset=0):
+        seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
+        # Calculate the product of seq and inv_freq
+        freqs = einsum('i , j -> i j', seq.type_as(self.inv_freq), self.inv_freq)
+        # first part even vector components, second part odd vector components,
+        #  2 * dim in dimension size
+        emb = torch.cat((freqs, freqs), dim=-1)
+        # emb [seq_length, .., dim]
+        from einops import rearrange
+        # print('rearrange:', rearrange(emb, 'n d -> n 1 1 d').size())
+        return rearrange(emb, 'n d -> n 1 1 d')
+
+
+def _rotate_half(x):
+    """
+    change sign so the last dimension becomes [-odd, +even]
+    """
+    from einops import rearrange
+    x = rearrange(x, '... (j d) -> ... j d', j=2)
+    x1, x2 = x.unbind(dim=-2)
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(t, freqs):
+    """
+    input tensor t is of shape [seq_length, ..., dim]
+    rotary positional embeding tensor freqs is of shape [seq_length, ..., dim]
+    check https://kexue.fm/archives/8265 for detailed formulas
+    """
+    # print('t:', t.size())
+    # print('freqs:', freqs.size())
+    rot_dim = freqs.shape[-1]
+    # print('rot_dim:', rot_dim)
+    # ideally t_pass is empty so rotary pos embedding is applied to all tensor t
+    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]
+
+    # first part is cosine component
+    # second part is sine component, need to change signs with _rotate_half method
+    # print(t.shape, t_pass.shape, freqs.shape)
+    t = (t * freqs.cos().to(t.dtype)) + (_rotate_half(t) * freqs.sin().to(t.dtype))
+    
+    return torch.cat((t, t_pass), dim=-1)
+
+
+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 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: GEBConfig, device=None):
+        super(MLP, self).__init__()
+
+        self.add_bias = config.add_bias_linear #false
+
+        # 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, # 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 F.silu(x[0]) * x[1]
+
+        self.activation_func = swiglu
+
+        # 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)
+        )
+    
+    def forward(self, hidden_states):
+        # [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 CoreAttention(torch.nn.Module):
+
+    def __init__(self, config: GEBConfig, layer_number):
+        super(CoreAttention, self).__init__()
+        # self.fp16 = config.fp16
+        # self.bf16 = config.bf16
+
+        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)
+        self.num_layers = config.num_layers
+
+        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
+        # Dropout. Note that for a single iteration, this layer will generate
+        # different outputs on different number of parallel partitions but
+        # on average it should not be partition dependent.
+        self.attention_dropout = torch.nn.Dropout(config.attention_dropout)
+
+    def forward(self, query_layer, key_layer,
+                value_layer, attention_mask):
+
+        # ===================================
+        # 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)
+
+        # preallocting input tensor: [b * np, sq, sk]，Tensor to store matrix multiplication of query and key
+        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 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.contiguous().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 FlashSelfAttention(torch.nn.Module):
+    """Implement the scaled dot product attention with softmax.
+    Arguments
+    ---------
+        softmax_scale: The temperature to use for the softmax attention.
+                      (default: 1/sqrt(d_keys) where d_keys is computed at
+                      runtime)
+        attention_dropout: The dropout rate to apply to the attention
+                           (default: 0.0)
+    """
+    def __init__(self, config: GEBConfig, causal=False, softmax_scale=None, attention_dropout=0.0,
+                 device=None, dtype=None):
+        super().__init__()
+        assert flash_attn_varlen_func is not None or flash_attn_builder is not None, \
+            ('Please install FlashAttention first, e.g., with pip install flash-attn or implement your own flash attention')
+        assert rearrange is not None, 'Please install einops first, e.g., with pip install einops'
+        self.config = config
+        self.causal = causal
+        self.softmax_scale = softmax_scale
+        self.dropout_p = attention_dropout
+
+        # Use FlashAttention-2 when args.use_flash_attn_v2 is True
+        self.flash_attn_func = flash_attn_varlen_func if config.use_flash_attn else print('false to Use FlashAttention-2')
+
+    def forward(self, q, k, v):
+        """Implements the multihead softmax attention.
+        Arguments
+        ---------
+            q, k, v: The tensor containing the query, key, and value. (B, S, H, D)
+        """
+        # print(i.dtype() for i in (q,k,v) )
+        # assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q,k,v)))
+        # assert all((get_accelerator().on_accelerator(i) for i in (q, k, v)))
+        # if get_accelerator().device_name() == 'cuda':
+        #     assert all((i.is_cuda for i in (q,k,v)))
+        # else:
+        #     assert all((i.is_xpu for i in (q,k,v)))
+
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        seqlen_k = k.shape[1]
+
+        if get_accelerator().device_name() == 'cuda':
+            # goes for cuda device
+            q, k, v = [rearrange(x, 'b s ... -> (b s) ...') for x in [q, k, v]]
+            cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32,
+                                        device=q.device)
+        else:
+            # goes for other device
+            q, k, v = [rearrange(x, 'b s h d -> b h s d').contiguous() for x in [q, k, v]]
+
+        if self.training:
+            # during training q,k,v always have same seqlen
+            assert seqlen_k == seqlen_q
+
+            is_causal = self.causal
+            cu_seqlens_k = cu_seqlens_q if get_accelerator().device_name() == 'cuda' else None
+            dropout_p = self.dropout_p
+        else:
+            # turn off FA causal mask after first inference autoregressive iteration
+            # only on first autoregressive step q,k,v have same seqlen
+            is_causal = seqlen_q == seqlen_k
+            cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32,
+                        device=q.device) if get_accelerator().device_name() == 'cuda' else None
+            dropout_p = 0
+
+        output = self.flash_attn_func(
+            q, k, v, cu_seqlens_q, cu_seqlens_k, seqlen_q, seqlen_k,
+            dropout_p,
+            softmax_scale=self.softmax_scale, causal=is_causal
+        ) if get_accelerator().device_name() == 'cuda' else flash_attn_builder.flash_attn_func(
+            q, k, v, self.dropout_p, self.softmax_scale, is_causal
+        )
+
+        output = rearrange(output, '(b s) ... -> b s ...', b=batch_size) if get_accelerator().device_name() == 'cuda' else rearrange(
+            output, 'b h s d -> b s h d').contiguous()
+        return output    
+    
+class GEBAttention(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: GEBConfig, layer_number, device=None):
+        super().__init__()
+        self.config = config
+        self.layer_number = max(1, layer_number)
+        self.projection_size = config.kv_channels * config.num_attention_heads
+        self.use_flash_attn = config.use_flash_attn
+        # Per attention head and per partition values.
+        self.hidden_size_per_partition = self.projection_size
+        self.hidden_size_per_attention_head = self.projection_size // config.num_attention_heads
+        self.num_attention_heads_per_partition = config.num_attention_heads
+        self.num_key_value_heads_per_partition = config.num_key_value_heads
+        self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        self.kv_projection_size = config.kv_channels * config.num_key_value_heads
+        assert self.hidden_size_per_attention_head == self.kv_projection_size // config.num_key_value_heads
+        # self.max_position_embeddings = config.model_max_length
+        if self.use_flash_attn:
+            global flash_attn_builder
+            try:
+                flash_attn_builder = FlashAttentionBuilder().load()
+            except TypeError:
+                flash_attn_builder = None
+            assert flash_attn_varlen_func != None, "Cannot import FlashAttention v2 "
+            if rearrange is None:
+                raise ImportError('einops is not installed, please install with pip install einops')
+        
+        self.query = nn.Linear(config.hidden_size, self.projection_size,
+                                         bias=config.add_bias_linear,
+                                         device=device, **_config_to_kwargs(config)
+                                         )  
+
+        self.key_value = nn.Linear(config.hidden_size, 2 * self.kv_projection_size,
+                                         bias=config.add_bias_linear,
+                                         device=device, **_config_to_kwargs(config)
+                                         )
+        
+        if config.use_flash_attn:
+            self.core_attention_flash = FlashSelfAttention(config, causal=True, attention_dropout=config.attention_dropout)
+        else:
+            self.core_attention = CoreAttention(config, self.layer_number)
+            
+        self.dense = nn.Linear(self.projection_size, config.hidden_size, bias=config.add_bias_linear,
+                        device=device, **_config_to_kwargs(config)
+                        )
+        
+    def _allocate_memory(self, inference_max_sequence_len, batch_size, device=None, dtype=None):
+        return torch.empty(
+            inference_max_sequence_len,
+            batch_size,
+            self.num_key_value_groups,
+            self.hidden_size_per_attention_head,
+            dtype=dtype,
+            device=device)
+            
+    def repeat_kv(self, hidden_states, n_rep):
+        slen, batch, num_key_value_heads_per_partition, head_dim = hidden_states.shape
+        if n_rep == 1:
+            return hidden_states
+        hidden_states = hidden_states[:, :, :, None, :].expand(
+            slen, batch, num_key_value_heads_per_partition, n_rep, head_dim)
+        return hidden_states.reshape(slen, batch,
+                                     num_key_value_heads_per_partition * n_rep,
+                                     head_dim)
+
+    def forward(self, hidden_states, attention_mask,
+                rotary_pos_emb=None, kv_cache=None, use_cache=True):
+        # Attention head [sq, b, h]--> [sq, b, hp]
+        query_layer = self.query(hidden_states)
+        # [sq, b, hp] --> [sq, b, np, hn]
+        new_tensor_shape = query_layer.size()[:-1] + \
+            (self.num_attention_heads_per_partition,
+                self.hidden_size_per_attention_head)
+        query_layer = query_layer.view(*new_tensor_shape)
+
+        # Attention heads [sq, b, h] --> [sq, b, (np * 2 * hn)]
+        mixed_kv_layer = self.key_value(hidden_states)
+        # [sq, b, (np * 2 * hn)] --> [sq, b, np, 2 * hn]
+        new_tensor_shape = mixed_kv_layer.size()[:-1] + \
+            (self.num_key_value_heads_per_partition,
+                2 * self.hidden_size_per_attention_head)
+        mixed_kv_layer = mixed_kv_layer.view(*new_tensor_shape)
+        # [sq, b, np, 2 * hn] --> 2 [sq, b, np, hn]
+        (key_layer,
+            value_layer) = split_tensor_along_last_dim(
+                mixed_kv_layer, 2)
+            
+        
+
+        # Repeat kv
+        key_layer = self.repeat_kv(key_layer, self.num_key_value_groups)
+        value_layer = self.repeat_kv(value_layer,
+                                        self.num_key_value_groups)
+        
+        # if rotary_pos_emb is not None:
+        #     query_layer = apply_rotary_pos_emb(query_layer, rotary_pos_emb)
+        #     key_layer = apply_rotary_pos_emb(key_layer, rotary_pos_emb)
+        
+        # duplicate the pos_emb for self attention
+    
+        if rotary_pos_emb is not None:
+            if isinstance(rotary_pos_emb, tuple):
+                rotary_pos_emb = rotary_pos_emb
+            else:
+                rotary_pos_emb = ((rotary_pos_emb,) * 2)
+            q_pos_emb, k_pos_emb = rotary_pos_emb
+            query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb)
+            key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb)
+            
+            
+        # 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.use_flash_attn:
+            query_layer, key_layer, value_layer = [rearrange(x, 's b ... -> b s ...').contiguous()
+                    for x in (query_layer, key_layer, value_layer)]
+            context_layer = self.core_attention_flash(query_layer, key_layer, value_layer)
+            context_layer = rearrange(context_layer, 'b s h d -> s b (h d)').contiguous()
+        else:
+            context_layer = self.core_attention(
+                query_layer, key_layer, value_layer, attention_mask)
+        
+        output= self.dense(context_layer)# output, bias = self.dense(context_layer)
+       
+        return output, kv_cache
+    
+class GEBBlock(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: GEBConfig, layer_number, device=None):
+        super(GEBBlock, self).__init__()
+        self.layer_number = layer_number
+        self.apply_residual_connection_post_layernorm \
+            = config.apply_residual_connection_post_layernorm
+
+        # self.bf16 = config.bf16
+        self.fp32_residual_connection = config.fp32_residual_connection
+        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon, device=device,
+                                             dtype=config.torch_dtype)
+        self.self_attention = GEBAttention(config, layer_number, device=device)
+        self.hidden_dropout = config.hidden_dropout
+        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon, device=device,
+                                             dtype=config.torch_dtype)
+        self.mlp = MLP(config, device=device)
+        
+    def forward(self, hidden_states, attention_mask=None,
+                rotary_pos_emb=None,
+                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,
+                rotary_pos_emb=rotary_pos_emb,
+                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=0.0,
+                                            training=self.training)
+        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
+        out = torch.nn.functional.dropout(mlp_output,
+                                            p=0.0,
+                                            training=self.training)
+        output = residual + out
+        return output, kv_cache
+    
+class GEBTransformer(torch.nn.Module):
+    """Transformer class."""
+
+    def __init__(self, config: GEBConfig, device=None):
+        super(GEBTransformer, self).__init__()
+
+        self.fp32_residual_connection = config.fp32_residual_connection
+        self.post_layer_norm = config.post_layer_norm
+        self.num_layers = config.num_layers
+        def build_layer(layer_number):
+            return GEBBlock(
+                config,
+                layer_number,
+                device=device)
+        # Build the layers
+        self.layers = []
+        for i in range(self.num_layers):
+            layer_num = i + 1
+            self.layers.append(build_layer(layer_num))
+        self.layers = torch.nn.ModuleList(self.layers)
+        if self.post_layer_norm:
+            self.final_layernorm = RMSNorm(config.hidden_size, eps=config.layernorm_epsilon, device=device,
+                                             dtype=config.torch_dtype)
+        self.gradient_checkpointing = False
+        
+    def _get_layer(self, layer_number):
+        return self.layers[layer_number]
+
+    def forward(
+            self, hidden_states, attention_mask, rotary_pos_emb, 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:
+                layer_hidden = torch.utils.checkpoint.checkpoint(
+                    layer,
+                    hidden_states,
+                    attention_mask,
+                    rotary_pos_emb,
+                    kv_caches[index],
+                    use_cache
+                )
+            else:
+                layer_hidden = layer(
+                    hidden_states,
+                    attention_mask,
+                    rotary_pos_emb,
+                    kv_cache=kv_caches[index],
+                    use_cache=use_cache
+                )
+            hidden_states, kv_cache = layer_hidden
+            if use_cache:
+                presents = presents + (kv_cache,)
+
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+        if self.post_layer_norm:
+            hidden_states = self.final_layernorm(hidden_states)
+        return hidden_states, presents, all_hidden_states, all_self_attentions
+    
+
+class GEBPreTrainedModel(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 = GEBConfig
+    base_model_prefix = "transformer"
+    _no_split_modules = ["GEBBlock"]
+
+    def _init_weights(self, module: nn.Module):
+        """Initialize the weights."""
+        return
+
+    def get_masks(self, input_ids, past_key_values, padding_mask=None):
+        batch_size, seq_length = input_ids.shape
+        full_attention_mask = torch.ones(batch_size, seq_length, seq_length, device=input_ids.device)
+        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):
+        batch_size, seq_length = input_ids.shape
+        position_ids = torch.arange(seq_length, dtype=torch.long, device=device).unsqueeze(0).repeat(batch_size, 1)
+        return position_ids
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, GEBTransformer):
+            module.gradient_checkpointing = value
+
+class Embedding(torch.nn.Module):
+    """Language model embeddings."""
+
+    def __init__(self, config: GEBConfig, device=None):
+        super(Embedding, self).__init__()
+
+        self.hidden_size = config.hidden_size
+        # Word embeddings.
+        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 GEBModel(GEBPreTrainedModel):
+    def __init__(self, config: GEBConfig, 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.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
+        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, original_impl= True, device=device,
+        #                                       dtype=config.torch_dtype)
+        
+        self.rotary_pos_emb = RotaryEmbedding(rotary_dim)
+
+
+        self.encoder = init_method(GEBTransformer, 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)
+        self.pre_seq_len = config.pre_seq_len
+        self.prefix_projection = config.prefix_projection
+        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)
+
+    def get_input_embeddings(self):
+        return self.embedding.word_embeddings
+
+    def get_prompt(self, batch_size, device, dtype=torch.half):
+        prefix_tokens = self.prefix_tokens.unsqueeze(0).expand(batch_size, -1).to(device)
+        past_key_values = self.prefix_encoder(prefix_tokens).type(dtype)
+        past_key_values = past_key_values.view(
+            batch_size,
+            self.pre_seq_len,
+            self.num_layers * 2,
+            self.num_key_value_groups,
+            self.kv_channels
+        )
+        # 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)
+        return past_key_values
+    
+    def forward(
+            self,
+            input_ids,
+            position_ids: Optional[torch.Tensor] = None,
+            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
+        )
+        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
+
+        batch_size, seq_length = input_ids.shape
+        if inputs_embeds is None:
+            inputs_embeds = self.embedding(input_ids)
+
+        if self.pre_seq_len is not None:
+            if past_key_values is None:
+                past_key_values = self.get_prompt(batch_size=batch_size, device=input_ids.device,
+                                                  dtype=inputs_embeds.dtype)
+            if attention_mask is not None:
+                attention_mask = torch.cat([attention_mask.new_ones((batch_size, self.pre_seq_len)),
+                                            attention_mask], dim=-1)
+
+        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)
+                
+        # # Rotary positional embeddings
+        # rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+        # if position_ids is not None:
+        #     rotary_pos_emb = rotary_pos_emb[position_ids]
+        # else:
+        #     rotary_pos_emb = rotary_pos_emb[None, :seq_length]
+        # rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
+            # Rotary positional embeddings
+        # print(position_ids[0].tolist())
+        rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+        rotary_pos_emb = rotary_pos_emb[position_ids[0].tolist()]
+        # rotary_pos_emb = self.rotary_pos_emb(position_ids.shape[-1])
+    
+
+        # # Rotary positional embeddings emb [seq_length, .., dim] no not need transpose
+        # rotary_pos_emb = self.rotary_pos_emb(self.seq_length)
+        # rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
+        # print('rotary_pos_emb:', rotary_pos_emb.size())
+        # if position_ids is not None:
+        #     rotary_pos_emb = rotary_pos_emb[position_ids]
+        #     print('rotary_pos_emb:', rotary_pos_emb.size())
+        # else:
+        #     rotary_pos_emb = rotary_pos_emb[None, :seq_length]
+        # # rotary_pos_emb = rotary_pos_emb.transpose(0, 1).contiguous()
+
+        # Run encoder.
+        hidden_states, presents, all_hidden_states, all_self_attentions = self.encoder(
+            inputs_embeds, full_attention_mask, rotary_pos_emb=rotary_pos_emb,
+            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 GEBForCausalLM(GEBPreTrainedModel):
+    def __init__(self, config: GEBConfig, empty_init=True, device=None):
+        super().__init__(config)
+
+        self.max_sequence_length = config.max_length
+        self.transformer = GEBModel(config, empty_init=empty_init, device=device)
+        self.config = config
+        self.quantized = False
+
+        # if self.config.quantization_bit:
+        #     self.quantize(self.config.quantization_bit, empty_init=True)  
+            
+    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()
+            new_position_id += 1
+            model_kwargs["position_ids"] = torch.cat(
+                [position_ids, new_position_id], dim=-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)
+        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,
+    ):
+        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_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
+        )
+
+    def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
+        prompt = tokenizer.build_prompt(query, history=history)
+        tokens = [tokenizer.get_command("<bos>")] + tokenizer.encode(prompt)
+        inputs = tokenizer.batch_encode_plus([tokens], return_tensors="pt", is_split_into_words=True)
+        inputs = inputs.to(self.device)
+        return inputs
+  
+    # def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = None):
+    #     prompt = tokenizer.build_prompt(query, history=history)
+    #     inputs = tokenizer([prompt], return_tensors="pt")
+    #     # print(inputs)
+    #     inputs = inputs.to(self.device)
+    #     return inputs
+    
+    @torch.inference_mode()
+    def chat(self, tokenizer, query: str, history: List[Tuple[str, str]] = None, max_length: int = 512, num_beams=1,
+             do_sample=True, top_p=0.5, temperature=0.3, logits_processor=None, repetition_penalty = 1.15, **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, "repetition_penalty":repetition_penalty, **kwargs}
+        prompt = tokenizer.build_prompt(query, history=[])
+        system = "You are a helpful assistant.\n"
+        system_ids = [
+            tokenizer.get_command("<bos>") 
+            ] + tokenizer.encode(text=system) + [
+            tokenizer.get_command("<eos>")]
+            
+        prompt_ids = [
+            tokenizer.get_command("<bos>")
+            ] + tokenizer.encode(
+                text=prompt,
+                add_special_tokens=False
+            ) + [
+            tokenizer.get_command("<eos>")] + [
+            tokenizer.get_command("<bos>")]
+        tokens = system_ids + prompt_ids
+        inputs = tokenizer.batch_encode_plus([tokens], return_tensors="pt", is_split_into_words=True)
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        inputs = inputs.to(device)
+        outputs = self.generate(**inputs, **gen_kwargs)
+        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):]
+        response = tokenizer.decode(outputs)
+        return response, history
+
+
+            

tokenization_geb.py

@@ -0,0 +1,280 @@
+import json
+import os
+import re
+from typing import List, Optional, Union, Dict
+from sentencepiece import SentencePieceProcessor
+from transformers import PreTrainedTokenizer
+from transformers.utils import logging, PaddingStrategy
+from transformers.tokenization_utils_base import EncodedInput, BatchEncoding
+class SPTokenizer:
+    def __init__(self, model_path: str):
+        # reload tokenizer
+        assert os.path.isfile(model_path), model_path
+        self.sp_model = SentencePieceProcessor(model_file=model_path)
+
+        # BOS / EOS token IDs
+        self.n_words: int = self.sp_model.vocab_size()
+        self.bos_id: int = self.sp_model.bos_id()
+        self.eos_id: int = self.sp_model.eos_id()
+        self.pad_id: int = self.sp_model.unk_id()
+        assert self.sp_model.vocab_size() == self.sp_model.get_piece_size()
+
+        special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "<sop>", "<eop>", "<eod>", "", "", "",
+                            "","<bos>","<eos>"]
+        self.special_tokens = {}
+        self.index_special_tokens = {}
+        for token in special_tokens:
+            if token == "<bos>":
+                self.special_tokens["<bos>"] = self.bos_id
+                self.index_special_tokens[self.bos_id] = "<bos>"
+            elif token == "<eos>":
+                self.special_tokens["<eos>"] = self.eos_id
+                self.index_special_tokens[self.eos_id] = "<eos>"
+            else:
+                self.special_tokens[token] = self.n_words
+                self.index_special_tokens[self.n_words] = token
+                self.n_words += 1
+
+
+    def tokenize(self, s: str):
+        return self.sp_model.EncodeAsPieces(s)
+
+    def encode(self, s: str, bos: bool = False, eos: bool = False
+               ) -> List[int]:
+        assert type(s) is str
+        t = self.sp_model.encode(s)
+        if bos:
+            t = [self.bos_id] + t
+        if eos:
+            t = t + [self.eos_id]
+        return t
+    
+    def decode(self, t: List[int]) -> str:
+        return self.sp_model.decode(t)
+
+    def decode_tokens(self, tokens: List[str]) -> str:
+        text = self.sp_model.DecodePieces(tokens)
+        return text
+
+    def convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        return self.sp_model.PieceToId(token)
+
+    def convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        if index in self.index_special_tokens or index in [self.eos_id, self.bos_id, self.pad_id] or index < 0:
+            return ""
+        return self.sp_model.IdToPiece(index)
+
+class GEBTokenizer(PreTrainedTokenizer):
+    """SentencePieceTokenizer-Megatron wrapper"""
+    vocab_files_names = {"vocab_file": "GEBtokenizer.model"}
+    model_input_names = ["input_ids", "attention_mask", "position_ids"]
+    def __init__(self, vocab_file, padding_side="left", clean_up_tokenization_spaces=False, **kwargs):
+
+
+        self.name = 'GEBTokenizer' 
+        self.vocab_file = vocab_file
+        self.tokenizer = SPTokenizer(vocab_file)
+        self.special_tokens = {
+            "<bos>": self.tokenizer.bos_id,
+            "<eos>": self.tokenizer.eos_id,
+            "<pad>": self.tokenizer.pad_id
+        }
+        super().__init__(padding_side=padding_side, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs)
+
+    def get_command(self, token):
+        if token in self.special_tokens:
+            return self.special_tokens[token]
+        assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}"
+        return self.tokenizer.special_tokens[token]
+    
+    # def tokenize(self, text):
+    #     return self.tokenizer.encode(text, bos=True, eos=False
+    #                                  )
+    
+    # def detokenize(self, ids):
+    #     return self.tokenizer.decode(ids)
+    
+    # def _convert_token_to_id(self, token):
+    #     """ Converts a token (str) in an id using the vocab. """
+    #     return self.tokenizer.convert_token_to_id(token)
+    
+    # def _convert_id_to_token(self, index):
+    #     """Converts an index (integer) in a token (str) using the vocab."""
+    #     return self.tokenizer.convert_id_to_token(index)
+
+    @property
+    def eos_token(self) -> str:
+        return "<eos>"
+
+    @property
+    def bos_token(self) -> str:
+        return "<bos>"
+    
+    @property
+    def eod_token(self) -> str:
+        return "<eod>"
+
+    @property
+    def pad_token_id(self):
+        return self.get_command("<pad>")
+
+    @property
+    def bos_token_id(self) -> str:
+        return self.get_command("<bos>")
+
+    @property
+    def eos_token_id(self):
+        return self.get_command("<eos>")
+    
+    @property
+    def eod_token_id(self):
+        return self.get_command("<eod>")
+
+    @property
+    def vocab_size(self):
+        return self.tokenizer.n_words
+    
+    def get_vocab(self):
+        """ Returns vocab as a dict """
+        vocab = {self._convert_id_to_token(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+    
+    def _tokenize(self, text, **kwargs):
+        return self.tokenizer.tokenize(text)
+    
+    def _convert_token_to_id(self, token):
+        """ Converts a token (str) in an id using the vocab. """
+        return self.tokenizer.convert_token_to_id(token)
+    
+    def _convert_id_to_token(self, index):
+        """Converts an index (integer) in a token (str) using the vocab."""
+        return self.tokenizer.convert_id_to_token(index)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return self.tokenizer.decode_tokens(tokens)
+
+    def save_vocabulary(self, save_directory, filename_prefix=None):
+        """
+        Save the vocabulary and special tokens file to a directory.
+
+        Args:
+            save_directory (`str`):
+                The directory in which to save the vocabulary.
+            filename_prefix (`str`, *optional*):
+                An optional prefix to add to the named of the saved files.
+
+        Returns:
+            `Tuple(str)`: Paths to the files saved.
+        """
+        if os.path.isdir(save_directory):
+            vocab_file = os.path.join(
+                save_directory, self.vocab_files_names["vocab_file"]
+            )
+        else:
+            vocab_file = save_directory
+
+        with open(self.vocab_file, 'rb') as fin:
+            proto_str = fin.read()
+
+        with open(vocab_file, "wb") as writer:
+            writer.write(proto_str)
+
+        return (vocab_file,)
+
+    def get_prefix_tokens(self):
+        prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")]
+        return prefix_tokens
+    
+    def build_finetune_prompt(self, query, history=None):
+        if history is None:
+            history = []
+        prompt = ""
+        prompt += "问题：{}\n\n回答：".format(query)
+        return prompt
+    
+    def build_single_message(self, message):
+        role_tokens = [self.get_command("<eos>")]
+        message_tokens = self.tokenizer.encode(message)
+        tokens = role_tokens + message_tokens
+        return tokens
+
+    def build_chat_input(self, query, history=None):
+        if history is None:
+            history = []
+        input_ids = []
+        input_ids.extend(self.build_single_message(query))
+        return self.batch_encode_plus([input_ids], return_tensors="pt", is_split_into_words=True)
+    
+    def build_prompt(self, query, history=None):
+        if history is None:
+            history = []
+        prompt = query
+        return prompt
+    
+    def _pad(
+            self,
+            encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
+            max_length: Optional[int] = None,
+            padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
+            pad_to_multiple_of: Optional[int] = None,
+            return_attention_mask: Optional[bool] = None,
+    ) -> dict:
+        """
+        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)
+
+        Args:
+            encoded_inputs:
+                Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
+            max_length: maximum length of the returned list and optionally padding length (see below).
+                Will truncate by taking into account the special tokens.
+            padding_strategy: PaddingStrategy to use for padding.
+
+                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
+                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
+                - PaddingStrategy.DO_NOT_PAD: Do not pad
+                The tokenizer padding sides are defined in self.padding_side:
+
+                    - 'left': pads on the left of the sequences
+                    - 'right': pads on the right of the sequences
+            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
+                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
+                `>= 7.5` (Volta).
+            return_attention_mask:
+                (optional) Set to False to avoid returning attention mask (default: set to model specifics)
+        """
+        # Load from model defaults
+        assert self.padding_side == "left"
+
+        required_input = encoded_inputs[self.model_input_names[0]]
+        seq_length = len(required_input)
+
+        if padding_strategy == PaddingStrategy.LONGEST:
+            max_length = len(required_input)
+
+        if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
+            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
+
+        needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(required_input) != max_length
+
+        # Initialize attention mask if not present.
+        if "attention_mask" not in encoded_inputs:
+            encoded_inputs["attention_mask"] = [1] * seq_length
+
+        if "position_ids" not in encoded_inputs:
+            encoded_inputs["position_ids"] = list(range(seq_length))
+
+        if needs_to_be_padded:
+            difference = max_length - len(required_input)
+
+            if "attention_mask" in encoded_inputs:
+                encoded_inputs["attention_mask"] = [0] * difference + encoded_inputs["attention_mask"]
+            if "position_ids" in encoded_inputs:
+                encoded_inputs["position_ids"] = [0] * difference + encoded_inputs["position_ids"]
+            encoded_inputs[self.model_input_names[0]] = [self.pad_token_id] * difference + required_input
+
+        return encoded_inputs

tokenizer_config.json

@@ -0,0 +1,12 @@
+{
+    "name_or_path": "",
+    "remove_space": false,
+    "do_lower_case": false,
+    "tokenizer_class": "GEBTokenizer",
+    "auto_map": {
+      "AutoTokenizer": [
+        "tokenization_geb.GEBTokenizer",
+        null
+        ]
+    }
+  }