update model 39B

config.json

@@ -0,0 +1,26 @@
+{
+  "architectures": [
+    "GeoVForCausalLM"
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_geov.GeoVConfig",
+    "AutoModelForCausalLM": "modeling_geov.GeoVForCausalLM"
+  },
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_size": 5120,
+  "intermediate_size": 20480,
+  "layer_norm_eps": 0.0001,
+  "max_position_embeddings": 2048,
+  "model_type": "geov",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 32,
+  "rotary_emb_base": 10000,
+  "tie_word_embeddings": false,
+  "tokenizer_class": "GeoVTokenizer",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.28.0.dev0",
+  "use_cache": true,
+  "use_extra_biases_ffn": false,
+  "vocab_size": 65536
+}

configuration_geov.py

@@ -0,0 +1,111 @@
+# coding=utf-8
+# Copyright 2023 Better Planet Investments and labml.ai team. ALl rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" GeoV model configuration"""
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+GEOV_PRETRAINED_CONFIG_ARCHIVE_MAP = {
+    "GeoV/GeoV-9b": "https://huggingface.co/GeoV/GeoV-9b/resolve/main/config.json",
+}
+
+
+class GeoVConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`GeoVModel`]. It is used to instantiate a
+    GeoV model according to the specified arguments, defining the model architecture. Instantiating a configuration
+    with the defaults will yield a similar configuration to that of the GeoV
+    [GeoV/GeoV-9b](https://huggingface.co/GeoV/GeoV-9b) architecture.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 65536):
+            Vocabulary size of the GeoV model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`GeoVModel`].
+        hidden_size (`int`, *optional*, defaults to 5120):
+            Dimension of the encoder layers and the pooler layer.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer encoder.
+        num_attention_heads (`int`, *optional*, defaults to 40):
+            Number of attention heads for each attention layer in the Transformer encoder.
+        intermediate_size (`int`, *optional*, defaults to 20480):
+            Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
+        rotary_emb_base (`int`, *optional*, defaults to 10000)
+            base for computing rotary embeddings frequency
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Typically set this to something large
+            just in case (e.g., 512 or 1024 or 2048).
+        layer_norm_eps (`float`, *optional*, defaults to 1e-4):
+            The epsilon used by the layer normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        use_extra_biases_ffn (`bool`, *optional*, defaults to `False`):
+            Whether or not to have extra bias parameters in the final layer of FFN modules.
+        Example:
+
+    ```python
+    >>> from transformers import GeoVConfig, GeoVModel
+
+    >>> # Initializing a GeoV configuration
+    >>> configuration = GeoVConfig()
+
+    >>> # Initializing a model (with random weights) from the configuration
+    >>> model = GeoVModel(configuration)  # doctest: +SKIP
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config  # doctest: +SKIP
+    ```"""
+    model_type = "geov"
+
+    def __init__(
+            self,
+            vocab_size=65_536,
+            hidden_size=5_120,
+            num_hidden_layers=32,
+            num_attention_heads=40,
+            intermediate_size=1024 * 5 * 4,
+            layer_norm_eps=1e-4,
+            rotary_emb_base=10000,
+            max_position_embeddings=2048,
+            use_extra_biases_ffn=False,
+            use_cache=True,
+            bos_token_id=0,
+            eos_token_id=2,
+            tie_word_embeddings=False,
+            tokenizer_class="GeoVTokenizer",
+            **kwargs,
+    ):
+        super().__init__(
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            tokenizer_class=tokenizer_class,
+            **kwargs
+        )
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.intermediate_size = intermediate_size
+        self.rotary_emb_base = rotary_emb_base
+        self.use_cache = use_cache
+        self.layer_norm_eps = layer_norm_eps
+        self.use_extra_biases_ffn = use_extra_biases_ffn

generation_config.json

@@ -0,0 +1,6 @@
+{
+  "_from_model_config": true,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "transformers_version": "4.28.0.dev0"
+}

modeling_geov.py

@@ -0,0 +1,666 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved.
+# Modifications Copyright 2023 Better Planet Investments and labml.ai team. ALl rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch GeoV model."""
+import math
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import CrossEntropyLoss
+
+from transformers.file_utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    replace_return_docstrings,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.utils import logging
+from .configuration_geov import GeoVConfig
+
+logger = logging.get_logger(__name__)
+
+_CHECKPOINT_FOR_DOC = "GeoV/GeoV-9b"
+_REAL_CHECKPOINT_FOR_DOC = "GeoV/GeoV-9b"
+_CONFIG_FOR_DOC = "GeoVConfig"
+
+GEOV_PRETRAINED_MODEL_ARCHIVE_LIST = [
+    "GeoV/GeoV-9b",
+    # See all GeoV models at https://huggingface.co/models?filter=geov
+]
+
+
+class RotaryEmbedding(torch.nn.Module):
+    def __init__(self, dim, base=10000):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+
+        self.max_seq_len_cached = -1
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        # This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
+        if seq_len > self.max_seq_len_cached:
+            self.max_seq_len_cached = seq_len
+            t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
+            freqs = torch.einsum("i,j->ij", t, self.inv_freq)
+            # Different from paper, but it uses a different permutation in order to obtain the same calculation
+            emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
+            self.cos_cached = emb.cos()[None, None, :, :].to(x.dtype)
+            self.sin_cached = emb.sin()[None, None, :, :].to(x.dtype)
+        return self.cos_cached.to(x.device), self.sin_cached.to(x.device)
+
+
+def rotate_half(x):
+    """Rotates half the hidden dims of the input."""
+    x1 = x[..., : x.shape[-1] // 2]
+    x2 = x[..., x.shape[-1] // 2:]
+    return torch.cat((-x2, x1), dim=-1)
+
+
+def apply_rotary_pos_emb(q, cos, sin, position_ids):
+    """Apply positional embeddings"""
+    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    q_embed = (q * cos) + (rotate_half(q) * sin)
+    return q_embed
+
+
+def apply_rotary_pos_emb_reverse(q, cos, sin, position_ids):
+    """Apply positional embeddings in reverse"""
+    gather_indices = position_ids[:, None, :, None]  # [bs, 1, seq_len, 1]
+    gather_indices = gather_indices.repeat(1, cos.shape[1], 1, cos.shape[3])
+    cos = torch.gather(cos.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    sin = torch.gather(sin.repeat(gather_indices.shape[0], 1, 1, 1), 2, gather_indices)
+    q_embed = (q * cos) - (rotate_half(q) * sin)
+    return q_embed
+
+
+class GeoVAttention(nn.Module):
+    """
+    Attention module
+    """
+
+    def __init__(self, config):
+        super().__init__()
+        self.num_attention_heads = config.num_attention_heads
+        self.hidden_size = config.hidden_size
+        self.head_size = self.hidden_size // self.num_attention_heads
+        max_positions = config.max_position_embeddings
+        self.register_buffer("causal_mask", torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)))
+        self.rotary_emb = RotaryEmbedding(self.head_size, base=config.rotary_emb_base)
+        self.qkv = nn.Linear(config.hidden_size, 3 * config.hidden_size)
+        self.dense = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
+
+    def forward(
+        self,
+        hidden_states: torch.FloatTensor,
+        attention_mask: torch.FloatTensor,
+        position_ids: torch.LongTensor,
+        head_mask: Optional[torch.FloatTensor] = None,
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+    ):
+        has_layer_past = layer_past is not None
+
+        # Compute QKV
+        # Attention heads [batch, seq_len, hidden_size]
+        #   --> [batch, seq_len, (np * 3 * head_size)]
+        qkv = self.qkv(hidden_states)
+        query, key, value = torch.tensor_split(qkv, 3, dim=-1)
+
+        # 'b l (h q) -> b h l q'
+        query = self._split_heads(query, self.num_attention_heads)
+        key = self._split_heads(key, self.num_attention_heads)
+        value = self._split_heads(value, self.num_attention_heads)
+
+        # Compute token offset for rotary embeddings (when decoding)
+        seq_len = key.shape[-2]
+        offset = 0
+        if has_layer_past:
+            seq_len += layer_past[0].shape[-2]
+
+        cos, sin = self.rotary_emb(query, seq_len=seq_len)
+        query = apply_rotary_pos_emb(query, cos, sin, position_ids)
+        key = apply_rotary_pos_emb(key, cos, sin, position_ids)
+        value = apply_rotary_pos_emb(value, cos, sin, position_ids)
+
+        # Cache QKV values
+        if has_layer_past:
+            past_key = layer_past[0]
+            past_value = layer_past[1]
+            key = torch.cat((past_key, key), dim=-2)
+            value = torch.cat((past_value, value), dim=-2)
+        present = (key, value) if use_cache else None
+
+        # Compute attention
+        attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask)
+
+        attn_output = apply_rotary_pos_emb_reverse(attn_output, cos, sin, position_ids)
+
+        # Reshape outputs
+        attn_output = self._merge_heads(attn_output)
+        attn_output = self.dense(attn_output)
+
+        outputs = (attn_output, present)
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        return outputs
+
+    @classmethod
+    def _split_heads(cls, tensor, num_attention_heads):
+        """
+        Splits hidden dim into num_attention_heads
+        """
+        # tensor: [bs, seq_len, hidden_size]
+        new_shape = tensor.shape[:-1] + (num_attention_heads, tensor.shape[-1] // num_attention_heads)
+        # -> [bs, seq_len, num_attention_heads, attn_head_size]
+        tensor = tensor.view(new_shape)
+        # -> [bs, num_attention_heads, seq_len, attn_head_size]
+        tensor = tensor.permute(0, 2, 1, 3)
+        return tensor
+
+    @classmethod
+    def _merge_heads(cls, tensor):
+        """
+        Merges heads
+        """
+        # tensor [bs, num_attention_heads, seq_len, attn_head_size]
+        tensor = tensor.permute(0, 2, 1, 3).contiguous()
+        # -> [bs, seq_len, num_attention_heads, attn_head_size]
+        tensor = tensor.view(*tensor.shape[:2], tensor.shape[2] * tensor.shape[3])
+        # -> [bs, seq_len, hidden_size]
+        return tensor
+
+    def _attn(self, query, key, value, attention_mask=None, head_mask=None):
+        # q, k, v: [bs, num_attention_heads, seq_len, attn_head_size]
+        # compute causal mask from causal mask buffer
+        batch_size, num_attention_heads, query_length, attn_head_size = query.shape
+        key_length = key.shape[-2]
+
+        causal_mask = self.causal_mask[None, None, key_length - query_length: key_length, :key_length]
+
+        attn_scores = torch.einsum("bhid,bhjd->bhij", query, key) / math.sqrt(attn_head_size)
+
+        attn_scores.masked_fill_(causal_mask == 0, torch.finfo(attn_scores.dtype).min)
+
+        if attention_mask is not None:
+            # Apply the attention mask
+            attn_scores = attn_scores + attention_mask
+
+        attn_weights = nn.functional.softmax(attn_scores, dim=-1)
+
+        # Mask heads if we want to
+        if head_mask is not None:
+            attn_weights = attn_weights * head_mask
+
+        attn_output = torch.matmul(attn_weights, value)
+        return attn_output, attn_weights
+
+
+class GeoVMLP(nn.Module):
+    """Position wise Feed-forward network"""
+
+    def __init__(self, config: "GeoVConfig"):
+        super().__init__()
+        self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size)
+        self.dense_2h_to_h = nn.Linear(
+            config.intermediate_size // 2, config.hidden_size, bias=config.use_extra_biases_ffn
+        )
+        self.act = nn.GELU()
+
+    def forward(self, hidden_states):
+        hidden_states = self.dense_h_to_4h(hidden_states)
+        # Gated GELU
+        gate, pass_through = torch.tensor_split(hidden_states, 2, dim=-1)
+        gate = self.act(gate)
+        hidden_states = gate * pass_through
+
+        hidden_states = self.dense_2h_to_h(hidden_states)
+        return hidden_states
+
+
+class GeoVLayer(nn.Module):
+    """GeoV transformer layer"""
+
+    def __init__(self, config: "GeoVConfig"):
+        super().__init__()
+        self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+        self.attention = GeoVAttention(config)
+        self.mlp = GeoVMLP(config)
+
+    def forward(
+        self,
+        hidden_states: Optional[torch.FloatTensor],
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = False,
+        layer_past: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+    ):
+        attention_layer_outputs = self.attention(
+            self.input_layernorm(hidden_states),
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            layer_past=layer_past,
+            head_mask=head_mask,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+        )
+        attn_output = attention_layer_outputs[0]  # output_attn: attn_output, present, (attn_weights)
+        outputs = attention_layer_outputs[1:]
+
+        attn_output = attn_output + hidden_states
+        mlp_output = self.mlp(self.post_attention_layernorm(attn_output))
+        hidden_states = mlp_output + attn_output
+
+        if use_cache:
+            outputs = (hidden_states,) + outputs  # hidden_states, present, (attn_weights)
+        else:
+            outputs = (hidden_states,) + outputs[1:]  # hidden_states, (attn_weights)
+
+        return outputs
+
+
+class GeoVPreTrainedModel(PreTrainedModel):
+    """
+    An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
+    models.
+    """
+
+    config_class = GeoVConfig
+    base_model_prefix = "geov"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["GeoVLayer"]
+
+    def _init_weights(self, module):
+        pass
+
+    def _set_gradient_checkpointing(self, module, value=False):
+        if isinstance(module, GeoVModel):
+            module.gradient_checkpointing = value
+
+
+GEOV_START_DOCSTRING = r"""
+    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
+    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
+    behavior.
+
+    Parameters:
+        config ([`~GeoVConfig`]): Model configuration class with all the parameters of the model.
+            Initializing with a config file does not load the weights associated with the model, only the
+            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+GEOV_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, seq_len)`):
+            Indices of input sequence tokens in the vocabulary.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.FloatTensor` of shape `(batch_size, seq_len)`, *optional*):
+            Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+
+            [What are attention masks?](../glossary#attention-mask)
+        position_ids (`torch.LongTensor` of shape `({0})`, *optional*):
+            Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+            config.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*):
+            Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`:
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+
+        past_key_values (`Tuple[Tuple[torch.FloatTensor]]` of length `n_layers`, with each tuple having 2 tensors of shape `(batch_size, n_heads, seq_len - 1, head_size)`, *optional*):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+        use_cache (`bool`, *optional*):
+            If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+            `past_key_values`).
+        inputs_embeds (`torch.FloatTensor` of shape `(batch_size, seq_len, hidden_size)`, *optional*):
+            Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+            is useful if you want more control over how to convert *input_ids* indices into associated vectors than the
+            model's internal embedding lookup matrix.
+        output_attentions (`bool`, *optional*):
+            Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+            tensors for more detail.
+        output_hidden_states (`bool`, *optional*):
+            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+            more detail.
+        return_dict (`bool`, *optional*):
+            Whether or not to return a [`~file_utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+    "The bare GeoV Model transformer outputting raw hidden-states without any specific head on top.",
+    GEOV_START_DOCSTRING,
+)
+class GeoVModel(GeoVPreTrainedModel):
+    def __init__(self, config: "GeoVConfig"):
+        super().__init__(config)
+        self.config = config
+
+        self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([GeoVLayer(config) for _ in range(config.num_hidden_layers)])
+        self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
+
+        self.embed_in.to(torch.bfloat16)
+        self.layers.to(torch.bfloat16)
+
+        self.gradient_checkpointing = False
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_in
+
+    def set_input_embeddings(self, value):
+        self.embed_in = value
+
+    @add_start_docstrings_to_model_forward(GEOV_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        real_checkpoint=_REAL_CHECKPOINT_FOR_DOC,
+        output_type=BaseModelOutputWithPast,
+        config_class=_CONFIG_FOR_DOC,
+    )
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, BaseModelOutputWithPast]:
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        batch_size, seq_length = input_shape
+
+        if past_key_values is None:
+            past_length = 0
+            past_key_values = tuple([None] * self.config.num_hidden_layers)
+        else:
+            past_length = past_key_values[0][0].size(-2)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(past_length, seq_length + past_length, dtype=torch.long, device=device)
+            position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+        else:
+            position_ids = position_ids.view(-1, seq_length).long()
+
+        # Attention mask.
+        if attention_mask is not None:
+            assert batch_size > 0, "batch_size has to be defined and > 0"
+            attention_mask = attention_mask.view(batch_size, -1)
+            # We create a 3D attention mask from a 2D tensor mask.
+            # Sizes are [batch_size, 1, 1, to_seq_length]
+            # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length]
+            # this attention mask is more simple than the triangular masking of causal attention
+            # used in OpenAI GPT, we just need to prepare the broadcast dimension here.
+            attention_mask = attention_mask[:, None, None, :]
+
+            # Since attention_mask is 1.0 for positions we want to attend and 0.0 for
+            # masked positions, this operation will create a tensor which is 0.0 for
+            # positions we want to attend and the dtype's smallest value for masked positions.
+            # Since we are adding it to the raw scores before the softmax, this is
+            # effectively the same as removing these entirely.
+            attention_mask = attention_mask.to(dtype=self.dtype)  # fp16 compatibility
+            attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_in(input_ids)
+
+        hidden_states = inputs_embeds
+
+        if self.gradient_checkpointing and self.training:
+            if use_cache:
+                logger.warning(
+                    "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+                )
+                use_cache = False
+
+        presents = () if use_cache else None
+        all_attentions = () if output_attentions else None
+        all_hidden_states = () if output_hidden_states else None
+        for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)):
+            if output_hidden_states:
+                all_hidden_states = all_hidden_states + (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+
+                def create_custom_forward(module):
+                    def custom_forward(*inputs):
+                        # None for layer_past
+                        return module(*inputs, use_cache, None, output_attentions)
+
+                    return custom_forward
+
+                outputs = torch.utils.checkpoint.checkpoint(
+                    create_custom_forward(layer),
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    head_mask[i],
+                )
+            else:
+                outputs = layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    head_mask=head_mask[i],
+                    layer_past=layer_past,
+                    use_cache=use_cache,
+                    output_attentions=output_attentions,
+                )
+            hidden_states = outputs[0]
+            if use_cache is True:
+                presents = presents + (outputs[1],)
+            if output_attentions:
+                all_attentions = all_attentions + (outputs[2 if use_cache else 1],)
+
+        # Cast the hidden state to final layer norm data type (this is the modification from GPTNeoX)
+        hidden_states = self.final_layer_norm(hidden_states.to(self.final_layer_norm.weight.dtype))
+        # Add last hidden state
+        if output_hidden_states:
+            all_hidden_states = all_hidden_states + (hidden_states,)
+
+        if not return_dict:
+            return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None)
+
+        return BaseModelOutputWithPast(
+            last_hidden_state=hidden_states,
+            past_key_values=presents,
+            hidden_states=all_hidden_states,
+            attentions=all_attentions,
+        )
+
+
+@add_start_docstrings(
+    """GeoV Model with a `language modeling` head on top for CLM fine-tuning.""", GEOV_START_DOCSTRING
+)
+class GeoVForCausalLM(GeoVPreTrainedModel):
+    _keys_to_ignore_on_load_missing = [r"causal_mask", r"inv_freq"]
+
+    def __init__(self, config: "GeoVConfig"):
+        super().__init__(config)
+
+        self.geov = GeoVModel(config)
+        self.embed_out = nn.Linear(config.hidden_size, config.vocab_size)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_output_embeddings(self):
+        return self.embed_out
+
+    def set_output_embeddings(self, new_embeddings):
+        self.embed_out = new_embeddings
+
+    @add_start_docstrings_to_model_forward(GEOV_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+            self,
+            input_ids: Optional[torch.LongTensor] = None,
+            attention_mask: Optional[torch.FloatTensor] = None,
+            position_ids: Optional[torch.LongTensor] = None,
+            inputs_embeds: Optional[torch.FloatTensor] = None,
+            head_mask: Optional[torch.FloatTensor] = None,
+            past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None,
+            labels: Optional[torch.LongTensor] = None,
+            use_cache: Optional[bool] = None,
+            output_attentions: Optional[bool] = None,
+            output_hidden_states: Optional[bool] = None,
+            return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+            Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in
+            `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are
+            ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, GeoVForCausalLM, GeoVConfig
+        >>> import torch
+
+        >>> tokenizer = AutoTokenizer.from_pretrained("GeoV/GeoV-9b")
+        >>> model = GeoVForCausalLM.from_pretrained("GeoV/GeoV-9b")
+
+        >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
+        >>> outputs = model(**inputs)
+
+        >>> prediction_logits = outputs.logits
+        ```"""
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.geov(
+            input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        hidden_states = outputs[0]
+        lm_logits = self.embed_out(hidden_states)
+
+        lm_loss = None
+        if labels is not None:
+            # we are doing next-token prediction; shift prediction scores and input ids by one
+            shift_logits = lm_logits[:, :-1, :].contiguous()
+            labels = labels[:, 1:].contiguous()
+            loss_fct = CrossEntropyLoss()
+            lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1))
+
+        if not return_dict:
+            output = (lm_logits,) + outputs[1:]
+            return ((lm_loss,) + output) if lm_loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=lm_loss,
+            logits=lm_logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, attention_mask=None, **kwargs):
+        input_shape = input_ids.shape
+
+        # cut decoder_input_ids if past is used
+        if past_key_values and past_key_values[0] is not None:
+            input_ids = input_ids[:, -1:]
+
+        position_ids = kwargs.get("position_ids", None)
+        if attention_mask is not None and position_ids is None:
+            # create position_ids on the fly for batch generation
+            position_ids = attention_mask.long().cumsum(-1) - 1
+            position_ids.masked_fill_(attention_mask == 0, 1)
+            if past_key_values:
+                position_ids = position_ids[:, -1].unsqueeze(-1)
+
+        # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
+        if attention_mask is None:
+            attention_mask = input_ids.new_ones(input_shape)
+
+        return {
+            "input_ids": input_ids,
+            "attention_mask": attention_mask,
+            "position_ids": position_ids,
+            "past_key_values": past_key_values,
+        }
+
+    def _reorder_cache(self, past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx) for past_state in layer_past[:2]) + layer_past[2:],
+            )
+        return reordered_past

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+    "geov.layers.9.post_attention_layernorm.weight": "pytorch_model-00003-of-00010.bin"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,5 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "<unk>"
+}

spiece.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:42005475648603fbcb46bf8d08f4ade44c8546c6b37f8048bb5333b7b725ab85
+size 1269630

tokenization_geov.py

@@ -0,0 +1,177 @@
+# coding=utf-8
+# Copyright 2023 Better Planet Investments and labml.ai team. ALl rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes for GeoV."""
+from pathlib import Path
+from typing import List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.tokenization_utils import PreTrainedTokenizer
+from transformers.utils import SPIECE_UNDERLINE, logging
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "spiece.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+    "vocab_file": {
+        "GeoV/GeoV-9b": "https://huggingface.co/GeoV/GeoV-9b/resolve/main/spiece.model",
+    }
+}
+
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
+    "GeoV-9b": 2048,
+}
+
+
+class GeoVTokenizer(PreTrainedTokenizer):
+    """
+    Construct an GeoV tokenizer. Based on [SentencePiece](https://github.com/google/sentencepiece).
+
+    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
+    this superclass for more information regarding those methods.
+
+    Args:
+        vocab_file (`str`):
+            [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .spm extension) that
+            contains the vocabulary necessary to instantiate a tokenizer.
+        bos_token (`str`, *optional*, defaults to `"<s>"`):
+            The beginning of sequence token that was used during pretraining.
+
+        eos_token (`str`, *optional*, defaults to `"</s>"`):
+            The end of sequence token.
+
+        unk_token (`str`, *optional*, defaults to `"<unk>"`):
+            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
+            token instead.
+
+        new_line_token_id (`int`, *optional*, defaults to `65_499`):
+            The token id of new line character.
+
+    Attributes:
+        sp_model (`SentencePieceProcessor`):
+            The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
+    """
+
+    vocab_files_names = VOCAB_FILES_NAMES
+    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
+    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        vocab_file,
+        bos_token="<s>",
+        eos_token="</s>",
+        unk_token="<unk>",
+        new_line_token_id=65_499,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            bos_token=bos_token,
+            eos_token=eos_token,
+            unk_token=unk_token,
+            new_line_token_id=new_line_token_id,
+            **kwargs,
+        )
+        self.vocab_file = vocab_file
+        self.new_line_token_id = new_line_token_id
+
+        self.sp_model = spm.SentencePieceProcessor()
+        self.sp_model.Load(vocab_file)
+
+    @property
+    def vocab_size(self):
+        return len(self.sp_model)
+
+    def get_vocab(self):
+        vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+        vocab.update(self.added_tokens_encoder)
+        return vocab
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        state["sp_model"] = None
+        return state
+
+    def __setstate__(self, d):
+        self.__dict__ = d
+
+        self.sp_model = spm.SentencePieceProcessor()
+        self.sp_model.Load(self.vocab_file)
+
+    def _tokenize(self, text: str) -> List[str]:
+        """Tokenize a string."""
+        ret = []
+        split_text = text.splitlines()
+        for l in split_text:
+            rl = self.sp_model.encode(l, out_type=str)
+            ret.extend(rl)
+            ret.append("\n")
+        ret = ret[:-1]
+        return ret
+
+    def _convert_token_to_id(self, token):
+        """Converts a token (str) in an id using the vocab."""
+        if token == "\n":
+            return self.new_line_token_id
+        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 == self.new_line_token_id:
+            return "\n"
+        return self.sp_model.IdToPiece(index)
+
+    def convert_tokens_to_string(self, tokens):
+        """Converts a sequence of tokens (strings for sub-words) in a single string."""
+        out_string = "".join(tokens).replace(SPIECE_UNDERLINE, " ").strip()
+        return out_string
+
+    def _decode(
+        self,
+        token_ids: List[int],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = True,
+        spaces_between_special_tokens: bool = True,
+        **kwargs,
+    ) -> str:
+        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)
+
+        if skip_special_tokens:
+            filtered_tokens = [t for t in filtered_tokens if t not in self.all_special_ids]
+
+        text = self.convert_tokens_to_string(filtered_tokens)
+
+        if clean_up_tokenization_spaces:
+            clean_text = self.clean_up_tokenization(text)
+            return clean_text
+        else:
+            return text
+
+    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
+        save_directory = Path(save_directory)
+        if not save_directory.is_dir():
+            raise ValueError(f"Vocabulary path ({save_directory}) should be a directory")
+        vocab_fn = VOCAB_FILES_NAMES["vocab_file"]
+        filename_prefix = f"{filename_prefix}-" if filename_prefix else ""
+
+        vocab_file = save_directory / f"{filename_prefix}{vocab_fn}"
+
+        with open(str(vocab_file), "wb") as fi:
+            content_spiece_model = self.sp_model.serialized_model_proto()
+            fi.write(content_spiece_model)
+
+        return (str(vocab_file),)

tokenizer_config.json

@@ -0,0 +1,14 @@
+{
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_geov.GeoVTokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "model_max_length": 1000000000000000019884624838656,
+  "new_line_token_id": 65500,
+  "tokenizer_class": "GeoVTokenizer",
+  "unk_token": "<unk>"
+}