commit from

config.json

@@ -0,0 +1,26 @@
+{
+ "architectures": [
+ "BaiChuanForCausalLM"
+ ],
+ "auto_map": {
+ "AutoConfig": "configuration_baichuan.BaiChuanConfig",
+ "AutoModelForCausalLM": "modeling_baichuan.BaiChuanForCausalLM"
+ },
+ "bos_token_id": 1,
+ "eos_token_id": 2,
+ "hidden_act": "silu",
+ "hidden_size": 4096,
+ "initializer_range": 0.02,
+ "intermediate_size": 11008,
+ "max_position_embeddings": 4096,
+ "model_type": "baichuan",
+ "num_attention_heads": 32,
+ "num_hidden_layers": 32,
+ "pad_token_id": 0,
+ "rms_norm_eps": 1e-06,
+ "tie_word_embeddings": false,
+ "torch_dtype": "float32",
+ "transformers_version": "4.29.1",
+ "use_cache": true,
+ "vocab_size": 64000
+}

configuration_baichuan.py

@@ -0,0 +1,66 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class BaiChuanConfig(PretrainedConfig):
+ model_type = "baichuan"
+ keys_to_ignore_at_inference = ["past_key_values"]
+
+ def __init__(
+ self,
+ vocab_size=64000,
+ hidden_size=4096,
+ intermediate_size=11008,
+ num_hidden_layers=32,
+ num_attention_heads=32,
+ hidden_act="silu",
+ max_position_embeddings=4096,
+ initializer_range=0.02,
+ rms_norm_eps=1e-6,
+ use_cache=True,
+ pad_token_id=0,
+ bos_token_id=1,
+ eos_token_id=2,
+ tie_word_embeddings=False,
+ **kwargs,
+ ):
+ self.vocab_size = vocab_size
+ self.max_position_embeddings = max_position_embeddings
+ self.hidden_size = hidden_size
+ self.intermediate_size = intermediate_size
+ self.num_hidden_layers = num_hidden_layers
+ self.num_attention_heads = num_attention_heads
+ self.hidden_act = hidden_act
+ self.initializer_range = initializer_range
+ self.rms_norm_eps = rms_norm_eps
+ self.use_cache = use_cache
+ super().__init__(
+ pad_token_id=pad_token_id,
+ bos_token_id=bos_token_id,
+ eos_token_id=eos_token_id,
+ tie_word_embeddings=tie_word_embeddings,
+ **kwargs,
+ )

generation_config.json

@@ -0,0 +1,7 @@
+{
+ "_from_model_config": true,
+ "bos_token_id": 1,
+ "eos_token_id": 2,
+ "pad_token_id": 0,
+ "transformers_version": "4.31.0"
+}

modeling_baichuan.py

@@ -0,0 +1,664 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+from .configuration_baichuan import BaiChuanConfig
+from transformers import PreTrainedModel, add_start_docstrings
+from transformers.activations import ACT2FN
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, \
+ SequenceClassifierOutputWithPast
+from transformers.utils import logging, add_start_docstrings_to_model_forward, replace_return_docstrings
+
+import math
+from typing import List, Optional, Tuple, Union
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+
+logger = logging.get_logger(__name__)
+
+# Copied from transformers.models.bart.modeling_bart._make_causal_mask
+def _make_causal_mask(
+ input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+ """
+ Make causal mask used for bi-directional self-attention.
+ """
+ bsz, tgt_len = input_ids_shape
+ mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
+ mask_cond = torch.arange(mask.size(-1), device=device)
+ mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
+ mask = mask.to(dtype)
+
+ if past_key_values_length > 0:
+ mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
+ return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
+
+
+# Copied from transformers.models.bart.modeling_bart._expand_mask
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+ """
+ Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
+ """
+ bsz, src_len = mask.size()
+ tgt_len = tgt_len if tgt_len is not None else src_len
+
+ expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
+
+ inverted_mask = 1.0 - expanded_mask
+
+ return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
+
+
+class RMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ RMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+
+ # convert into half-precision if necessary
+ if self.weight.dtype in [torch.float16, torch.bfloat16]:
+ hidden_states = hidden_states.to(self.weight.dtype)
+
+ return self.weight * hidden_states
+
+
+class RotaryEmbedding(torch.nn.Module):
+ def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+ super().__init__()
+ inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
+ self.register_buffer("inv_freq", inv_freq)
+
+ # Build here to make `torch.jit.trace` work.
+ self.max_seq_len_cached = max_position_embeddings
+ t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.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)
+ self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+
+ 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.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
+ self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
+ return (
+ self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+ self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
+ )
+
+
+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, k, cos, sin, position_ids):
+ # The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
+ cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
+ sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
+ cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
+ sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
+ q_embed = (q * cos) + (rotate_half(q) * sin)
+ k_embed = (k * cos) + (rotate_half(k) * sin)
+ return q_embed, k_embed
+
+
+class MLP(nn.Module):
+ def __init__(
+ self,
+ hidden_size: int,
+ intermediate_size: int,
+ hidden_act: str,
+ ):
+ super().__init__()
+ self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+ self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
+ self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
+ self.act_fn = ACT2FN[hidden_act]
+
+ def forward(self, x):
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class Attention(nn.Module):
+ """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+ def __init__(self, config: BaiChuanConfig):
+ super().__init__()
+ self.config = config
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.max_position_embeddings = config.max_position_embeddings
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.W_pack = nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+ self.rotary_emb = RotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ bsz, q_len, _ = hidden_states.size()
+
+ proj = self.W_pack(hidden_states)
+ proj = proj.unflatten(-1, (3, self.hidden_size)).unsqueeze(0).transpose(0, -2).squeeze(-2)
+ query_states = proj[0].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1,
+ 2) # batch_size x source_len x hidden_size
+ key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1,
+ 2) # batch_size x target_len x head_size
+ value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1,
+ 2) # batch_size x source_len x hidden_size
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value[0].shape[-2]
+ cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+ query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+ # [bsz, nh, t, hd]
+
+ if past_key_value is not None:
+ # reuse k, v, self_attention
+ key_states = torch.cat([past_key_value[0], key_states], dim=2)
+ value_states = torch.cat([past_key_value[1], value_states], dim=2)
+
+ past_key_value = (key_states, value_states) if use_cache else None
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+ attn_weights = attn_weights + attention_mask
+ attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2)
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+class DecoderLayer(nn.Module):
+ def __init__(self, config: BaiChuanConfig):
+ super().__init__()
+ self.hidden_size = config.hidden_size
+ self.self_attn = Attention(config=config)
+ self.mlp = MLP(
+ hidden_size=self.hidden_size,
+ intermediate_size=config.intermediate_size,
+ hidden_act=config.hidden_act,
+ )
+ self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ 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`).
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ # Self Attention
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+ hidden_states = residual + hidden_states
+
+ # Fully Connected
+ residual = hidden_states
+ hidden_states = self.post_attention_layernorm(hidden_states)
+ hidden_states = self.mlp(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ return outputs
+
+
+class PreTrainedModel(PreTrainedModel):
+ config_class = BaiChuanConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["DecoderLayer"]
+ _keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, nn.Linear):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ def _set_gradient_checkpointing(self, module, value=False):
+ if isinstance(module, Model):
+ module.gradient_checkpointing = value
+
+
+class Model(PreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`DecoderLayer`]
+ Args:
+ config: BaiChuanConfig
+ """
+
+ def __init__(self, config: BaiChuanConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+ self.layers = nn.ModuleList([DecoderLayer(config) for _ in range(config.num_hidden_layers)])
+ self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ # Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
+ def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
+ # create causal mask
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ combined_attention_mask = None
+ if input_shape[-1] > 1:
+ combined_attention_mask = _make_causal_mask(
+ input_shape,
+ inputs_embeds.dtype,
+ device=inputs_embeds.device,
+ past_key_values_length=past_key_values_length,
+ )
+
+ if attention_mask is not None:
+ # [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
+ expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
+ inputs_embeds.device
+ )
+ combined_attention_mask = (
+ expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
+ )
+
+ return combined_attention_mask
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, BaseModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape
+ elif inputs_embeds is not None:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ else:
+ raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
+
+ seq_length_with_past = seq_length
+ past_key_values_length = 0
+
+ if past_key_values is not None:
+ past_key_values_length = past_key_values[0][0].shape[2]
+ seq_length_with_past = seq_length_with_past + past_key_values_length
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+ else:
+ position_ids = position_ids.view(-1, seq_length).long()
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+ # embed positions
+ if attention_mask is None:
+ attention_mask = torch.ones(
+ (batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
+ )
+ attention_mask = self._prepare_decoder_attention_mask(
+ attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+ )
+
+ hidden_states = inputs_embeds
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ next_decoder_cache = () if use_cache else None
+
+ for idx, decoder_layer in enumerate(self.layers):
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ past_key_value = past_key_values[idx] if past_key_values is not None else None
+
+ if self.gradient_checkpointing and self.training:
+
+ def create_custom_forward(module):
+ def custom_forward(*inputs):
+ # None for past_key_value
+ return module(*inputs, output_attentions, None)
+
+ return custom_forward
+
+ layer_outputs = torch.utils.checkpoint.checkpoint(
+ create_custom_forward(decoder_layer),
+ hidden_states,
+ attention_mask,
+ position_ids,
+ None,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ hidden_states = self.norm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = next_decoder_cache if use_cache else None
+ if not return_dict:
+ return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
+ return BaseModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ )
+
+
+class BaiChuanForCausalLM(PreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.model = Model(config)
+
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+ Returns:
+ Example:
+ ```python
+ >>> from transformers import AutoTokenizer, ModelForCausalLM
+ >>> model = ModelForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+ >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+ >>> prompt = "Hey, are you consciours? Can you talk to me?"
+ >>> inputs = tokenizer(prompt, return_tensors="pt")
+ >>> # Generate
+ >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+ >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+ "Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
+ ```"""
+
+ 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
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ logits = self.lm_head(hidden_states)
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ return (loss,) + output if loss is not None else output
+
+ return CausalLMOutputWithPast(
+ loss=loss,
+ logits=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, inputs_embeds=None, **kwargs
+ ):
+ if past_key_values:
+ 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 `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(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),)
+ return reordered_past

pytorch_model-00001-of-00002.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dce1613e3c3478fffd4fefa947c38243f5dc3c8a0772820f697a341b296a8ae7
+size 9968213759

pytorch_model-00002-of-00002.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cca2248bc75164d00c277025640369d4419d09804957a9f974e5c87ef08e6d49
+size 4033005709

pytorch_model.bin.index.json

@@ -0,0 +1,266 @@
+{
+ "metadata": {
+ "total_size": 14001127424
+ },
+ "weight_map": {
+ "lm_head.weight": "pytorch_model-00002-of-00002.bin",
+ "model.embed_tokens.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.0.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.1.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.10.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.11.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.12.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.13.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.14.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.15.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.16.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.17.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.18.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.19.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.2.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.20.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.21.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.22.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.23.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.23.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.23.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.23.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.23.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.23.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.23.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.23.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.24.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.24.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.25.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.26.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.27.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.28.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.29.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.3.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.3.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.30.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.30.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.input_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.mlp.down_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.mlp.gate_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.mlp.up_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.post_attention_layernorm.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.self_attn.W_pack.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.self_attn.o_proj.weight": "pytorch_model-00002-of-00002.bin",
+ "model.layers.31.self_attn.rotary_emb.inv_freq": "pytorch_model-00002-of-00002.bin",
+ "model.layers.4.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.4.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.5.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.6.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.7.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.8.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.self_attn.W_pack.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+ "model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+ "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+ }
+}

special_tokens_map.json

@@ -0,0 +1,23 @@
+{
+ "bos_token": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "eos_token": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "unk_token": {
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}

tokenization_baichuan.py

@@ -0,0 +1,241 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# 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.
+
+import os
+from shutil import copyfile
+from typing import Any, Dict, List, Optional, Tuple
+
+import sentencepiece as spm
+
+from transformers.tokenization_utils import AddedToken, PreTrainedTokenizer
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+VOCAB_FILES_NAMES = {"vocab_file": "tokenizer.model"}
+
+PRETRAINED_VOCAB_FILES_MAP = {
+ "vocab_file": {},
+ "tokenizer_file": {},
+}
+PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {}
+
+
+class BaiChuanTokenizer(PreTrainedTokenizer):
+ """
+ Construct a BaiChuan tokenizer. Based on byte-level Byte-Pair-Encoding.
+ Args:
+ vocab_file (`str`):
+ Path to the vocabulary file.
+ """
+
+ 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,
+ unk_token="<unk>",
+ bos_token="<s>",
+ eos_token="</s>",
+ pad_token=None,
+ sp_model_kwargs: Optional[Dict[str, Any]] = None,
+ add_bos_token=True,
+ add_eos_token=False,
+ clean_up_tokenization_spaces=False,
+ **kwargs,
+ ):
+ self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
+ bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
+ eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
+ unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
+ pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token
+ super().__init__(
+ bos_token=bos_token,
+ eos_token=eos_token,
+ unk_token=unk_token,
+ pad_token=pad_token,
+ add_bos_token=add_bos_token,
+ add_eos_token=add_eos_token,
+ sp_model_kwargs=self.sp_model_kwargs,
+ clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+ **kwargs,
+ )
+ self.vocab_file = vocab_file
+ self.add_bos_token = add_bos_token
+ self.add_eos_token = add_eos_token
+ self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
+ self.sp_model.Load(vocab_file)
+
+ 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_kwargs)
+ self.sp_model.Load(self.vocab_file)
+
+ @property
+ def vocab_size(self):
+ """Returns vocab size"""
+ return self.sp_model.get_piece_size()
+
+ def get_vocab(self):
+ """Returns vocab as a dict"""
+ vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
+ vocab.update(self.added_tokens_encoder)
+ return vocab
+
+ def _tokenize(self, text):
+ """Returns a tokenized string."""
+ return self.sp_model.encode(text, out_type=str)
+
+ def _convert_token_to_id(self, token):
+ """Converts a token (str) in an id using the vocab."""
+ return self.sp_model.piece_to_id(token)
+
+ def _convert_id_to_token(self, index):
+ """Converts an index (integer) in a token (str) using the vocab."""
+ token = self.sp_model.IdToPiece(index)
+ return token
+
+ def convert_tokens_to_string(self, tokens):
+ """Converts a sequence of tokens (string) in a single string."""
+ current_sub_tokens = []
+ out_string = ""
+ prev_is_special = False
+ for i, token in enumerate(tokens):
+ # make sure that special tokens are not decoded using sentencepiece model
+ if token in self.all_special_tokens:
+ if not prev_is_special and i != 0:
+ out_string += " "
+ out_string += self.sp_model.decode(current_sub_tokens) + token
+ prev_is_special = True
+ current_sub_tokens = []
+ else:
+ current_sub_tokens.append(token)
+ prev_is_special = False
+ out_string += self.sp_model.decode(current_sub_tokens)
+ return out_string
+
+ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
+ """
+ Save the vocabulary and special tokens file to a directory.
+ Args:
+ save_directory (`str`):
+ The directory in which to save the vocabulary.
+ Returns:
+ `Tuple(str)`: Paths to the files saved.
+ """
+ if not os.path.isdir(save_directory):
+ logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+ return
+ out_vocab_file = os.path.join(
+ save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
+ )
+
+ if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
+ copyfile(self.vocab_file, out_vocab_file)
+ elif not os.path.isfile(self.vocab_file):
+ with open(out_vocab_file, "wb") as fi:
+ content_spiece_model = self.sp_model.serialized_model_proto()
+ fi.write(content_spiece_model)
+
+ return (out_vocab_file,)
+
+ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+ output = bos_token_id + token_ids_0 + eos_token_id
+
+ if token_ids_1 is not None:
+ output = output + bos_token_id + token_ids_1 + eos_token_id
+
+ return output
+
+ def get_special_tokens_mask(
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
+ ) -> List[int]:
+ """
+ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
+ special tokens using the tokenizer `prepare_for_model` method.
+ Args:
+ token_ids_0 (`List[int]`):
+ List of IDs.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ already_has_special_tokens (`bool`, *optional*, defaults to `False`):
+ Whether or not the token list is already formatted with special tokens for the model.
+ Returns:
+ `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
+ """
+ if already_has_special_tokens:
+ return super().get_special_tokens_mask(
+ token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
+ )
+
+ bos_token_id = [1] if self.add_bos_token else []
+ eos_token_id = [1] if self.add_eos_token else []
+
+ if token_ids_1 is None:
+ return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id
+ return (
+ bos_token_id
+ + ([0] * len(token_ids_0))
+ + eos_token_id
+ + bos_token_id
+ + ([0] * len(token_ids_1))
+ + eos_token_id
+ )
+
+ def create_token_type_ids_from_sequences(
+ self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+ ) -> List[int]:
+ """
+ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT
+ sequence pair mask has the following format:
+ ```
+ 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
+ | first sequence | second sequence |
+ ```
+ if token_ids_1 is None, only returns the first portion of the mask (0s).
+ Args:
+ token_ids_0 (`List[int]`):
+ List of ids.
+ token_ids_1 (`List[int]`, *optional*):
+ Optional second list of IDs for sequence pairs.
+ Returns:
+ `List[int]`: List of [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
+ """
+ bos_token_id = [self.bos_token_id] if self.add_bos_token else []
+ eos_token_id = [self.eos_token_id] if self.add_eos_token else []
+
+ output = [0] * len(bos_token_id + token_ids_0 + eos_token_id)
+
+ if token_ids_1 is not None:
+ output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
+
+ return output

tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4be54af290d93c113bcbf421115ae9eed9d6340408f564898f1e966dc738ef01
+size 1136699

tokenizer_config.json

@@ -0,0 +1,39 @@
+{
+ "add_bos_token": false,
+ "add_eos_token": false,
+ "auto_map": {
+ "AutoTokenizer": [
+ "tokenization_baichuan.BaiChuanTokenizer",
+ null
+ ]
+ },
+ "bos_token": {
+ "__type": "AddedToken",
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "clean_up_tokenization_spaces": false,
+ "eos_token": {
+ "__type": "AddedToken",
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ },
+ "model_max_length": 1000000000000000019884624838656,
+ "pad_token": null,
+ "sp_model_kwargs": {},
+ "tokenizer_class": "BaiChuanTokenizer",
+ "unk_token": {
+ "__type": "AddedToken",
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": true,
+ "rstrip": false,
+ "single_word": false
+ }
+}