Upload 11 files

config.json

@@ -1,6 +1,6 @@
 {
   "_from_model_config": true,
-  "_name_or_path": "./merged-pt-baichuan-v2-gpu-a1002",
+  "_name_or_path": "Baichuan2-13B-Chat",
   "architectures": [
     "BaichuanForCausalLM"
   ],
@@ -21,8 +21,10 @@
   "pad_token_id": 0,
   "rms_norm_eps": 1e-06,
   "tie_word_embeddings": false,
+  "tokenizer_class": "BaichuanTokenizer",
   "torch_dtype": "float16",
-  "transformers_version": "4.32.1",
+  "transformers_version": "4.33.1",
   "use_cache": true,
-  "vocab_size": 64000
+  "vocab_size": 125696,
+  "z_loss_weight": 0
 }

configuration_baichuan.py

@@ -2,6 +2,7 @@
 
 from transformers.configuration_utils import PretrainedConfig
 
+
 class BaichuanConfig(PretrainedConfig):
     model_type = "baichuan"
     keys_to_ignore_at_inference = ["past_key_values"]
@@ -23,6 +24,7 @@ class BaichuanConfig(PretrainedConfig):
         eos_token_id=2,
         tie_word_embeddings=False,
         gradient_checkpointing=False,
+        z_loss_weight=0,
         **kwargs,
     ):
         self.vocab_size = vocab_size
@@ -35,7 +37,8 @@ class BaichuanConfig(PretrainedConfig):
         self.initializer_range = initializer_range
         self.rms_norm_eps = rms_norm_eps
         self.use_cache = use_cache
-        self.gradient_checkpointing = gradient_checkpointing,
+        self.z_loss_weight = z_loss_weight
+        self.gradient_checkpointing = (gradient_checkpointing,)
         super().__init__(
             pad_token_id=pad_token_id,
             bos_token_id=bos_token_id,
@@ -43,4 +46,3 @@ class BaichuanConfig(PretrainedConfig):
             tie_word_embeddings=tie_word_embeddings,
             **kwargs,
         )
-

generation_config.json

@@ -5,10 +5,10 @@
   "eos_token_id": 2,
   "max_new_tokens": 2048,
   "pad_token_id": 0,
-  "repetition_penalty": 1.1,
+  "repetition_penalty": 1.05,
   "temperature": 0.3,
   "top_k": 5,
   "top_p": 0.85,
-  "transformers_version": "4.32.1",
+  "transformers_version": "4.33.1",
   "user_token_id": 195
 }

generation_utils.py

@@ -80,3 +80,4 @@ class TextIterStreamer:
             raise StopIteration()
         else:
             return value
+

modeling_baichuan.py

@@ -1,63 +1,77 @@
 # Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
 
+from .configuration_baichuan import BaichuanConfig
+from .generation_utils import build_chat_input, TextIterStreamer
+
 import math
 from threading import Thread
 from typing import List, Optional, Tuple, Union
 
 import torch
-import torch.utils.checkpoint
+from torch import nn
 from torch.nn import CrossEntropyLoss
-from transformers import PreTrainedModel
+from torch.nn import functional as F
+from transformers import PreTrainedModel, PretrainedConfig
 from transformers.activations import ACT2FN
-from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
-from transformers.utils import logging
 from transformers.generation.utils import GenerationConfig
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
+from transformers.utils import logging, ContextManagers
 
-from .configuration_baichuan import BaichuanConfig
-from .generation_utils import build_chat_input, TextIterStreamer
+import os
+from contextlib import contextmanager
+from accelerate import init_empty_weights
 
 logger = logging.get_logger(__name__)
 
+try:
+    from xformers import ops as xops
+except ImportError:
+    xops = None
+    logger.warning(
+        "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\npip install xformers."
+    )
+
 
 def _get_interleave(n):
     def _get_interleave_power_of_2(n):
-        start = (2 ** (-2 ** -(math.log2(n) - 3)))
+        start = 2 ** (-(2 ** -(math.log2(n) - 3)))
         ratio = start
-        return [start * ratio ** i for i in range(n)]
+        return [start * ratio**i for i in range(n)]
 
     if math.log2(n).is_integer():
         return _get_interleave_power_of_2(n)
     else:
         closest_power_of_2 = 2 ** math.floor(math.log2(n))
-        return _get_interleave_power_of_2(closest_power_of_2) + \
-               _get_interleave(2 * closest_power_of_2)[0::2][:n - closest_power_of_2]
+        return (
+            _get_interleave_power_of_2(closest_power_of_2)
+            + _get_interleave(2 * closest_power_of_2)[0::2][: n - closest_power_of_2]
+        )
+
 
 def _fill_with_neg_inf(t):
     """FP16-compatible function that fills a tensor with -inf."""
     return t.float().fill_(float("-inf")).type_as(t)
 
-def _gen_alibi_mask(n_head, max_pos):
-    """used in inference only"""
+
+def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
+    _future_mask = torch.triu(_fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1)
+    _future_mask = _future_mask.unsqueeze(0) + alibi
+    new_future_mask = _future_mask.to(tensor)
+    return new_future_mask[: tensor.shape[0] * attn_heads, :maxpos, :maxpos]
+
+
+def _gen_alibi_mask(tensor, n_head, max_pos):
     slopes = torch.Tensor(_get_interleave(n_head))
-    alibi = slopes.unsqueeze(1).unsqueeze(1) * torch.arange(max_pos).unsqueeze(0).unsqueeze(0).expand(
-        n_head, -1, -1)
+    position_point = torch.arange(max_pos) - max_pos + 1
+    position_point = position_point.unsqueeze(0).unsqueeze(0).expand(n_head, -1, -1)
+    diag = torch.diag(position_point[0])
+    position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(-1, -2)
+    alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
     alibi = alibi.view(n_head, 1, max_pos)
-    alibi_mask = torch.triu(
-        _fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1
-    )
+    alibi_mask = torch.triu(_fill_with_neg_inf(torch.zeros([max_pos, max_pos])), 1)
     alibi_mask = alibi_mask.unsqueeze(0) + alibi
     return alibi_mask
 
-def _buffered_future_mask(tensor, maxpos, alibi, attn_heads):
-    """used in training only"""
-    dim = tensor.size(1)
-    _future_mask = torch.triu(
-        _fill_with_neg_inf(torch.zeros([maxpos, maxpos])), 1
-    )   
-    _future_mask = _future_mask.unsqueeze(0) + alibi
-    _future_mask = _future_mask.to(tensor)
-    return _future_mask[:tensor.shape[0] * attn_heads, :maxpos, :maxpos]
-
 
 class RMSNorm(torch.nn.Module):
     def __init__(self, hidden_size, epsilon=1e-6):
@@ -78,10 +92,10 @@ class RMSNorm(torch.nn.Module):
 
 class MLP(torch.nn.Module):
     def __init__(
-            self,
-            hidden_size: int,
-            intermediate_size: int,
-            hidden_act: str,
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
     ):
         super().__init__()
         self.gate_proj = torch.nn.Linear(hidden_size, intermediate_size, bias=False)
@@ -106,28 +120,46 @@ class BaichuanAttention(torch.nn.Module):
             raise ValueError(
                 f"hidden_size {self.hidden_size} is not divisible by num_heads {self.num_heads}"
             )
-        self.W_pack = torch.nn.Linear(self.hidden_size, 3 * self.hidden_size, bias=False)
-        self.o_proj = torch.nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+        self.W_pack = torch.nn.Linear(
+            self.hidden_size, 3 * self.hidden_size, bias=False
+        )
+        self.o_proj = torch.nn.Linear(
+            self.num_heads * self.head_dim, self.hidden_size, bias=False
+        )
 
     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()
+        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,
-            past_key_value: Optional[Tuple[torch.Tensor]] = None,
-            output_attentions: bool = False,
-            use_cache: bool = False,
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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)
-        key_states = proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
-        value_states = proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        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)
+        )
+        key_states = (
+            proj[1].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        )
+        value_states = (
+            proj[2].view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+        )
 
         kv_seq_len = key_states.shape[-2]
         if past_key_value is not None:
@@ -139,23 +171,37 @@ class BaichuanAttention(torch.nn.Module):
             value_states = torch.cat([past_key_value[1], value_states], dim=2)
 
         past_key_value = (key_states, value_states) if use_cache else None
+        if xops is not None and self.training:
+            attn_weights = None
+            # query_states = query_states.transpose(1, 2)
+            # key_states = key_states.transpose(1, 2)
+            # value_states = value_states.transpose(1, 2)
+            # attn_output = xops.memory_efficient_attention(
+            #     query_states, key_states, value_states, attn_bias=attention_mask
+            # )
+            with torch.backends.cuda.sdp_kernel(enable_flash=True, enable_math=True, enable_mem_efficient=True):
+                attn_output = F.scaled_dot_product_attention(query_states, key_states, value_states, attn_mask = attention_mask)
+            attn_output = attn_output.transpose(1, 2)
+        else:
+            attn_weights = torch.matmul(
+                query_states, key_states.transpose(2, 3)
+            ) / math.sqrt(self.head_dim)
+
+            if attention_mask is not None:
+                if q_len == 1:  # inference with cache
+                    if len(attention_mask.size()) == 4:
+                        attention_mask = attention_mask[:, :, -1:, :]
+                    else:
+                        attention_mask = attention_mask[:, -1:, :]
+                attn_weights = attn_weights + attention_mask
+                attn_weights = torch.max(
+                    attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min)
+                )
 
-        attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
-
-        if attention_mask is not None:
-            if q_len == 1: # inference with cache
-                if len(attention_mask.size()) == 4:
-                    attention_mask = attention_mask[:, :, -1:, :]   
-                else:
-                    attention_mask = attention_mask[:, -1:, :]    
-            attn_weights = attn_weights + attention_mask
-            attn_weights = torch.max(attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min))
-
-        attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
-
-        attn_output = torch.matmul(attn_weights, value_states)
+            attn_weights = torch.nn.functional.softmax(attn_weights, dim=-1)
+            attn_output = torch.matmul(attn_weights, value_states)
 
-        attn_output = attn_output.transpose(1, 2)
+            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)
 
@@ -176,17 +222,20 @@ class BaichuanLayer(torch.nn.Module):
             hidden_act=config.hidden_act,
         )
         self.input_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
-        self.post_attention_layernorm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
+        self.post_attention_layernorm = RMSNorm(
+            config.hidden_size, epsilon=config.rms_norm_eps
+        )
 
     def forward(
-            self,
-            hidden_states: torch.Tensor,
-            attention_mask: Optional[torch.Tensor] = 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]]]:
-
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = 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]]
+    ]:
         residual = hidden_states
 
         hidden_states = self.input_layernorm(hidden_states)
@@ -244,8 +293,12 @@ class BaichuanModel(BaichuanPreTrainedModel):
         self.padding_idx = config.pad_token_id
         self.vocab_size = config.vocab_size
         self.n_head = config.num_attention_heads
-        self.embed_tokens = torch.nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
-        self.layers = torch.nn.ModuleList([BaichuanLayer(config) for _ in range(config.num_hidden_layers)])
+        self.embed_tokens = torch.nn.Embedding(
+            config.vocab_size, config.hidden_size, self.padding_idx
+        )
+        self.layers = torch.nn.ModuleList(
+            [BaichuanLayer(config) for _ in range(config.num_hidden_layers)]
+        )
         self.norm = RMSNorm(config.hidden_size, epsilon=config.rms_norm_eps)
 
         self.gradient_checkpointing = config.gradient_checkpointing
@@ -263,35 +316,61 @@ class BaichuanModel(BaichuanPreTrainedModel):
     def get_alibi_mask(self, tensor, seq_length_with_past):
         if self.training:
             slopes = torch.Tensor(_get_interleave(self.n_head))
-            alibi = slopes.unsqueeze(1).unsqueeze(1) * torch.arange(seq_length_with_past).unsqueeze(0).unsqueeze(0).expand(
-                self.n_head,
-                -1, -1) 
-            alibi = alibi.view(self.n_head, 1, seq_length_with_past)
-            mask = _buffered_future_mask(tensor, seq_length_with_past, alibi, self.n_head)
+            position_point = (
+                torch.arange(seq_length_with_past) - seq_length_with_past + 1
+            )
+            position_point = (
+                position_point.unsqueeze(0)
+                .unsqueeze(0)
+                .expand(self.n_head, seq_length_with_past, -1)
+            )
+            diag = torch.diag(position_point[0])
+            position_point = position_point - diag.unsqueeze(0).unsqueeze(0).transpose(
+                -1, -2
+            )
+            alibi = slopes.unsqueeze(1).unsqueeze(1) * position_point
+            mask = _buffered_future_mask(
+                tensor, seq_length_with_past, alibi, self.n_head
+            )
         else:
             if self.first_run:
                 self.first_run = False
-                self.register_buffer("future_mask", _gen_alibi_mask(self.n_head, self.max_cache_pos).to(tensor), persistent=False)
+                self.register_buffer(
+                    "future_mask",
+                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
+                        tensor
+                    ),
+                    persistent=False,
+                )
             if seq_length_with_past > self.max_cache_pos:
                 self.max_cache_pos = seq_length_with_past
-                self.register_buffer("future_mask", _gen_alibi_mask(self.n_head, self.max_cache_pos).to(tensor), persistent=False)
-            mask = self.future_mask[:self.n_head, :seq_length_with_past, :seq_length_with_past] 
+                self.register_buffer(
+                    "future_mask",
+                    _gen_alibi_mask(tensor, self.n_head, self.max_cache_pos).to(
+                        tensor
+                    ),
+                    persistent=False,
+                )
+            mask = self.future_mask[
+                : self.n_head, :seq_length_with_past, :seq_length_with_past
+            ]
         return mask
 
     def forward(
-            self,
-            input_ids: torch.LongTensor = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            past_key_values: Optional[List[torch.FloatTensor]] = None,
-            inputs_embeds: Optional[torch.FloatTensor] = None,
-            use_cache: Optional[bool] = False,
-            output_attentions: Optional[bool] = False,
-            output_hidden_states: Optional[bool] = False,
-            return_dict: Optional[bool] = True,
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = False,
+        output_attentions: Optional[bool] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
     ) -> Union[Tuple, BaseModelOutputWithPast]:
-
         if input_ids is not None and inputs_embeds is not None:
-            raise ValueError("You cannot provide both input_ids and inputs_embeds simultaneously")
+            raise ValueError(
+                "You cannot provide both input_ids and inputs_embeds simultaneously"
+            )
         elif input_ids is not None:
             batch_size, seq_length = input_ids.shape
         elif inputs_embeds is not None:
@@ -299,7 +378,9 @@ class BaichuanModel(BaichuanPreTrainedModel):
         else:
             raise ValueError("You need to provide input_ids or inputs_embeds")
 
-        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
         seq_length_with_past = seq_length
 
@@ -311,8 +392,13 @@ class BaichuanModel(BaichuanPreTrainedModel):
             inputs_embeds = self.embed_tokens(input_ids)
 
         if self.training:
-            if self.alibi_mask is None or self.alibi_mask.shape[-1] != seq_length_with_past:
-                self.alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
+            if (
+                self.alibi_mask is None
+                or self.alibi_mask.shape[-1] != seq_length_with_past
+            ):
+                self.alibi_mask = self.get_alibi_mask(
+                    inputs_embeds, seq_length_with_past
+                )
             alibi_mask = self.alibi_mask
         else:
             alibi_mask = self.get_alibi_mask(inputs_embeds, seq_length_with_past)
@@ -320,15 +406,22 @@ class BaichuanModel(BaichuanPreTrainedModel):
         if attention_mask is not None:
             if len(attention_mask.shape) == 2:
                 expanded_mask = attention_mask.to(alibi_mask.dtype)
-                expanded_mask = torch.tril(torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
-                                ) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
+                expanded_mask = torch.tril(
+                    torch.gt(expanded_mask[:, :, None] * expanded_mask[:, None, :], 0)
+                ) * torch.eq(expanded_mask[:, :, None] - expanded_mask[:, None, :], 0)
             else:
-                expanded_mask = attention_mask 
+                expanded_mask = attention_mask
             bsz = inputs_embeds.size(0)
             src_len, tgt_len = alibi_mask.size()[-2:]
-            expanded_mask = expanded_mask.unsqueeze(1).expand(bsz, 1, src_len, tgt_len).to(alibi_mask.dtype)
+            expanded_mask = (
+                expanded_mask.unsqueeze(1)
+                .expand(bsz, 1, src_len, tgt_len)
+                .to(alibi_mask.dtype)
+            )
             inverted_mask = 1.0 - expanded_mask
-            inverted_mask = inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min)
+            inverted_mask = inverted_mask.masked_fill(
+                inverted_mask.to(torch.bool), torch.finfo(alibi_mask.dtype).min
+            )
             attention_mask = inverted_mask + alibi_mask.unsqueeze(0)
         else:
             attention_mask = alibi_mask
@@ -351,7 +444,9 @@ class BaichuanModel(BaichuanPreTrainedModel):
             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
+            past_key_value = (
+                past_key_values[idx] if past_key_values is not None else None
+            )
 
             if self.gradient_checkpointing and self.training:
 
@@ -393,7 +488,11 @@ class BaichuanModel(BaichuanPreTrainedModel):
 
         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 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,
@@ -402,12 +501,50 @@ class BaichuanModel(BaichuanPreTrainedModel):
         )
 
 
+class NormHead(nn.Module):
+    def __init__(self, hidden_size, vocab_size, bias=False):
+        super().__init__()
+        self.weight = nn.Parameter(torch.empty((vocab_size, hidden_size)))
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        self.first_flag = True
+
+    def forward(self, hidden_states):
+        if self.training:
+            norm_weight = nn.functional.normalize(self.weight)
+            self.first_flag = True
+        elif self.first_flag:
+            self.first_flag = False
+            self.weight.data = nn.functional.normalize(self.weight)
+            norm_weight = self.weight
+        else:
+            norm_weight = self.weight
+        return nn.functional.linear(hidden_states, norm_weight)
+
+_init_weights = True
+@contextmanager
+def no_init_weights(_enable=True):
+    global _init_weights
+    old_init_weights = _init_weights
+    if _enable:
+        _init_weights = False
+    try:
+        yield
+    finally:
+        _init_weights = old_init_weights
+
+
 class BaichuanForCausalLM(BaichuanPreTrainedModel):
-    def __init__(self, config):
-        super().__init__(config)
+    def __init__(self, config, *model_args, **model_kwargs):
+        super().__init__(config, *model_args, **model_kwargs)
         self.model = BaichuanModel(config)
-        self.lm_head = torch.nn.Linear(config.hidden_size, config.vocab_size, bias=False)
-
+        self.lm_head = NormHead(config.hidden_size, config.vocab_size, bias=False)
+        #if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
+        if hasattr(config, "quantization_config") and isinstance(config.quantization_config, dict) and config.quantization_config.get('load_in_4bit', False):
+            try:
+                from .quantizer import quantize_offline, init_model_weight_int4
+            except ImportError:
+                raise ImportError(f"Needs quantize_offline to run quantize.")
+            quantize_offline(self, 4)
         # Initialize weights and apply final processing
         self.post_init()
 
@@ -428,23 +565,130 @@ class BaichuanForCausalLM(BaichuanPreTrainedModel):
 
     def get_decoder(self):
         return self.model
+    
+    @classmethod
+    def from_pretrained(
+        cls,
+        pretrained_model_name_or_path: Optional[Union[str, os.PathLike]],
+        *model_args,
+        config: Optional[Union[PretrainedConfig, str, os.PathLike]] = None,
+        cache_dir: Optional[Union[str, os.PathLike]] = None,
+        ignore_mismatched_sizes: bool = False,
+        force_download: bool = False,
+        local_files_only: bool = False,
+        token: Optional[Union[str, bool]] = None,
+        revision: str = "main",
+        use_safetensors: bool = None,
+        **kwargs,
+    ):
+    
+        # Load config if we don't provide a configuration
+        if not isinstance(config, PretrainedConfig):
+            config_path = config if config is not None else pretrained_model_name_or_path
+            config, model_kwargs = cls.config_class.from_pretrained(
+                config_path,
+                cache_dir=cache_dir,
+                return_unused_kwargs=True,
+                force_download=force_download,
+                resume_download=False,
+                proxies=None,
+                local_files_only=local_files_only,
+                token=token,
+                revision=revision,
+                subfolder="",
+                _from_auto=False,
+                _from_pipeline=None,
+                **kwargs,
+            )
+        else:
+            model_kwargs = kwargs
+        
+        if hasattr(config, "quantization_config") and config.quantization_config['load_in_4bit']:
+            try:
+                from .quantizer import init_model_weight_int4
+                from accelerate import init_empty_weights, dispatch_model, infer_auto_device_map
+                from accelerate.utils import CustomDtype
+                from accelerate.utils import get_balanced_memory
+            except ImportError:
+                raise ImportError(f"Needs import model weight init func to run quantize.") 
+            # Instantiate model.
+            init_contexts = [no_init_weights(_enable=True)]
+            init_contexts.append(init_empty_weights())
+            with ContextManagers(init_contexts):
+                model = cls(config)
+            
+            model_file = os.path.join(pretrained_model_name_or_path, 'pytorch_model.bin')
+            state_dict = torch.load(model_file, map_location="cpu") 
+            model.is_quantized = True
+                        
+            device_map = kwargs.pop("device_map", None)
+            torch_dtype = kwargs.pop("torch_dtype", None)
+            if device_map is not None:
+                kwargs = {"no_split_module_classes": model._no_split_modules}
+                target_dtype = CustomDtype.INT4
+                max_memory = get_balanced_memory(
+                    model,
+                    dtype=target_dtype,
+                    low_zero=(device_map == "balanced_low_0"),
+                    max_memory=None,
+                    **kwargs,
+                )
+                kwargs["max_memory"] = max_memory
+                device_map = infer_auto_device_map(model, dtype=target_dtype, **kwargs)
+            model = init_model_weight_int4(config, model, state_dict)
+            
+            # Set model in evaluation mode to deactivate DropOut modules by default
+            model.eval()
+            # If it is a model with generation capabilities, attempt to load the generation config
+            if model.can_generate():
+                try:
+                    model.generation_config = GenerationConfig.from_pretrained(
+                        pretrained_model_name_or_path,
+                        cache_dir=cache_dir,
+                        force_download=force_download,
+                        resume_download=False,
+                        proxies=None,
+                        local_files_only=local_files_only,
+                        token=token,
+                        revision=revision,
+                        subfolder="",
+                        _from_auto=False,
+                        _from_pipeline=None,
+                        **kwargs,
+                    )
+                except (OSError, TypeError):
+                    logger.info(
+                        "Generation config file not found, using a generation config created from the model config."
+                    )
+                    pass
+            
+            if device_map is not None:
+                dispatch_model(model, device_map=device_map)
+            
+            return model
+
+        return super(BaichuanForCausalLM, cls).from_pretrained(pretrained_model_name_or_path, *model_args, 
+                config=config, cache_dir=cache_dir, ignore_mismatched_sizes=ignore_mismatched_sizes, 
+                force_download=force_download, local_files_only=local_files_only, token=token, revision=revision, 
+                use_safetensors=use_safetensors, **kwargs)
 
     def forward(
-            self,
-            input_ids: torch.LongTensor = None,
-            attention_mask: Optional[torch.Tensor] = 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] = False,
-            output_hidden_states: Optional[bool] = False,
-            return_dict: Optional[bool] = True,
-            **kwargs
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = 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] = False,
+        output_hidden_states: Optional[bool] = False,
+        return_dict: Optional[bool] = True,
+        **kwargs,
     ) -> Union[Tuple, CausalLMOutputWithPast]:
+        return_dict = (
+            return_dict if return_dict is not None else self.config.use_return_dict
+        )
 
-        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,
@@ -459,7 +703,6 @@ class BaichuanForCausalLM(BaichuanPreTrainedModel):
 
         hidden_states = outputs[0]
         logits = self.lm_head(hidden_states)
-
         loss = None
         if labels is not None:
             # Shift so that tokens < n predict n
@@ -469,9 +712,11 @@ class BaichuanForCausalLM(BaichuanPreTrainedModel):
             loss_fct = CrossEntropyLoss()
             shift_logits = shift_logits.view(-1, self.config.vocab_size)
             shift_labels = shift_labels.view(-1)
+            softmax_normalizer = shift_logits.max(-1).values ** 2
+            z_loss = self.config.z_loss_weight * softmax_normalizer.mean()
             # Enable model parallelism
             shift_labels = shift_labels.to(shift_logits.device)
-            loss = loss_fct(shift_logits, shift_labels)
+            loss = loss_fct(shift_logits, shift_labels) + z_loss
 
         if not return_dict:
             output = (logits,) + outputs[1:]
@@ -485,13 +730,20 @@ class BaichuanForCausalLM(BaichuanPreTrainedModel):
             attentions=outputs.attentions,
         )
 
+    def quantize(self, bits: int):
+        try:
+            from .quantizer import quantize_online
+        except ImportError:
+            raise ImportError(f"Needs QLinear to run quantize.")
+        return quantize_online(self, bits)
+        
     def prepare_inputs_for_generation(
-            self,
-            input_ids: torch.LongTensor,
-            past_key_values: Optional[torch.Tensor] = None,
-            attention_mask: Optional[torch.Tensor] = None,
-            inputs_embeds: Optional[torch.Tensor] = None,
-            **kwargs
+        self,
+        input_ids: torch.LongTensor,
+        past_key_values: Optional[torch.Tensor] = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        inputs_embeds: Optional[torch.Tensor] = None,
+        **kwargs,
     ):
         if past_key_values:
             input_ids = input_ids[:, -1:]
@@ -506,7 +758,7 @@ class BaichuanForCausalLM(BaichuanPreTrainedModel):
             {
                 "past_key_values": past_key_values,
                 "use_cache": kwargs.get("use_cache"),
-                "attention_mask": attention_mask
+                "attention_mask": attention_mask,
             }
         )
         return model_inputs
@@ -518,43 +770,46 @@ class BaichuanForCausalLM(BaichuanPreTrainedModel):
             for layer_past in past_key_values
         )
 
-    def quantize(self, bits: int):
-        try:
-            from .quantizer import QLinear
-        except ImportError:
-            raise ImportError(
-                f"Needs QLinear to run quantize."
-            )
-
-        for layer in self.model.layers:
-            layer.self_attn.W_pack = QLinear(
-                bits=bits,
-                weight=layer.self_attn.W_pack.weight,
-                bias = None,
-            )
-            layer.self_attn.o_proj = QLinear(
-                bits=bits,
-                weight=layer.self_attn.o_proj.weight,
-                bias = None,
-            )
-            layer.mlp.gate_proj = QLinear(
-                bits=bits,
-                weight=layer.mlp.gate_proj.weight,
-                bias = None,
-            )
-            layer.mlp.down_proj = QLinear(
-                bits=bits,
-                weight=layer.mlp.down_proj.weight,
-                bias = None,
-            )
-            layer.mlp.up_proj = QLinear(
-                bits=bits,
-                weight=layer.mlp.up_proj.weight,
-                bias = None,
-            )
-        return self
+    def _build_chat_input(
+        self, tokenizer, messages: List[dict], max_new_tokens: int = 0
+    ):
+        max_new_tokens = max_new_tokens or self.generation_config.max_new_tokens
+        max_input_tokens = self.config.model_max_length - max_new_tokens
+        max_input_tokens = max(self.config.model_max_length // 2, max_input_tokens)
+        total_input, round_input = [], []
+        for i, message in enumerate(messages[::-1]):
+            content_tokens = tokenizer.encode(message["content"])
+            if message["role"] == "user":
+                round_input = (
+                    [self.generation_config.user_token_id]
+                    + content_tokens
+                    + round_input
+                )
+                if (
+                    total_input
+                    and len(total_input) + len(round_input) > max_input_tokens
+                ):
+                    break
+                else:
+                    total_input = round_input + total_input
+                    if len(total_input) >= max_input_tokens:
+                        break
+                    else:
+                        round_input = []
+            elif message["role"] == "assistant":
+                round_input = (
+                    [self.generation_config.assistant_token_id]
+                    + content_tokens
+                    + [self.generation_config.eos_token_id]
+                    + round_input
+                )
+            else:
+                raise ValueError(f"message role not supported yet: {message['role']}")
+        total_input = total_input[-max_input_tokens:]  # truncate left
+        total_input.append(self.generation_config.assistant_token_id)
+        total_input = torch.LongTensor([total_input]).to(self.device)
+        return total_input
 
-    @torch.no_grad()
     def chat(self, tokenizer, messages: List[dict], stream=False,
              generation_config: Optional[GenerationConfig]=None):
         generation_config = generation_config or self.generation_config

pytorch_model.bin.index.json

@@ -1,6 +1,6 @@
 {
   "metadata": {
-    "total_size": 26529802240
+    "total_size": 27793336320
   },
   "weight_map": {
     "lm_head.weight": "pytorch_model-00003-of-00003.bin",
@@ -40,20 +40,20 @@
     "model.layers.12.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
     "model.layers.12.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
     "model.layers.12.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
-    "model.layers.13.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.13.input_layernorm.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.13.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
     "model.layers.13.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
-    "model.layers.13.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
-    "model.layers.13.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.13.mlp.up_proj.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.13.post_attention_layernorm.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.13.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
     "model.layers.13.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
     "model.layers.14.input_layernorm.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.14.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
-    "model.layers.14.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.14.mlp.down_proj.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.14.mlp.gate_proj.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.14.mlp.up_proj.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.14.post_attention_layernorm.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.14.self_attn.W_pack.weight": "pytorch_model-00001-of-00003.bin",
-    "model.layers.14.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
+    "model.layers.14.self_attn.W_pack.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.14.self_attn.o_proj.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.15.input_layernorm.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.15.mlp.down_proj.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.15.mlp.gate_proj.weight": "pytorch_model-00002-of-00003.bin",
@@ -159,11 +159,11 @@
     "model.layers.28.post_attention_layernorm.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.28.self_attn.W_pack.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.28.self_attn.o_proj.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.29.input_layernorm.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.29.mlp.down_proj.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.29.input_layernorm.weight": "pytorch_model-00003-of-00003.bin",
+    "model.layers.29.mlp.down_proj.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.29.mlp.gate_proj.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.29.mlp.up_proj.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.29.post_attention_layernorm.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.29.mlp.up_proj.weight": "pytorch_model-00003-of-00003.bin",
+    "model.layers.29.post_attention_layernorm.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.29.self_attn.W_pack.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.29.self_attn.o_proj.weight": "pytorch_model-00002-of-00003.bin",
     "model.layers.3.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
@@ -175,11 +175,11 @@
     "model.layers.3.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
     "model.layers.30.input_layernorm.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.30.mlp.down_proj.weight": "pytorch_model-00003-of-00003.bin",
-    "model.layers.30.mlp.gate_proj.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.30.mlp.gate_proj.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.30.mlp.up_proj.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.30.post_attention_layernorm.weight": "pytorch_model-00003-of-00003.bin",
-    "model.layers.30.self_attn.W_pack.weight": "pytorch_model-00002-of-00003.bin",
-    "model.layers.30.self_attn.o_proj.weight": "pytorch_model-00002-of-00003.bin",
+    "model.layers.30.self_attn.W_pack.weight": "pytorch_model-00003-of-00003.bin",
+    "model.layers.30.self_attn.o_proj.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.31.input_layernorm.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.31.mlp.down_proj.weight": "pytorch_model-00003-of-00003.bin",
     "model.layers.31.mlp.gate_proj.weight": "pytorch_model-00003-of-00003.bin",

quantizer.py

@@ -1,123 +1,211 @@
-# Copyright (c) 2023, Baichuan Intelligent Technology. All rights reserved.
-
-import torch
-from typing import List
-import bz2
-import base64
-import ctypes
-from transformers.utils import logging
-logger = logging.get_logger(__name__)
-
-try:
-    from cpm_kernels.kernels.base import LazyKernelCModule, KernelFunction, round_up
-
-    class Kernel:
-        def __init__(self, code: bytes, function_names: List[str]):
-            self.code = code
-            self._function_names = function_names
-            self._cmodule = LazyKernelCModule(self.code)
-
-            for name in self._function_names:
-                setattr(self, name, KernelFunction(self._cmodule, name))
-    quantization_code = 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"
-    kernels = Kernel(
-        bz2.decompress(base64.b64decode(quantization_code)),
-        [
-            "int4_to_fp16",
-            "fp16_to_int4",
-            "int8_to_fp16",
-            "fp16_to_int8",
-            "int4_to_bf16",
-            "bf16_to_int4",
-            "int8_to_bf16",
-            "bf16_to_int8",
-        ],
-    )
-except Exception as exception:
-    kernels = None
-    logger.warning("Failed to load kernels:" + str(exception))
-
-def quant4(weight: torch.Tensor, scale: torch.Tensor):
-    stream = torch.cuda.current_stream()
-    num_row = weight.size(0)
-    num_chan_fp16 = weight.size(1)
-    # 4bit
-    num_chan_int = num_chan_fp16 // 8
-    qweight = torch.zeros((num_row, num_chan_int), dtype=torch.int32, device=weight.device)
-    intweight = torch.empty(num_row, num_chan_fp16, dtype = torch.int32)
-    intweight = torch.clip(torch.round(weight.to(scale.dtype) / scale[:, None]),-16, 15).to(dtype=torch.int32)
-
-    for j in range(num_chan_int):
-        qweight[:, j] = ((intweight[:, j*8+7] & 0x0f) << 28) \
-            | ((intweight[:, j*8+6] & 0x0f) << 24) \
-            | ((intweight[:, j*8+5] & 0x0f) << 20) \
-            | ((intweight[:, j*8+4] & 0x0f) << 16) \
-            | ((intweight[:, j*8+3] & 0x0f) << 12) \
-            | ((intweight[:, j*8+2] & 0x0f) << 8) \
-            | ((intweight[:, j*8+1] & 0x0f) << 4) \
-            | ((intweight[:, j*8] & 0x0f))
-    return qweight
-
-def dequant4(qweight: torch.Tensor, scale: torch.Tensor, input: torch.Tensor):
-    stream = torch.cuda.current_stream()
-    num_row = qweight.size(0)
-    num_chan_int = qweight.size(1)
-    # 4bit
-    num_chan_fp16 = num_chan_int * 8
-    
-    out = torch.empty((num_row, num_chan_fp16), dtype=input.dtype, device=qweight.device)
-
-    blockDim = (128, 1, 1)
-    gridDim = ((num_chan_int + blockDim[0] - 1) // blockDim[0], num_row, 1)
-    if input.dtype == torch.bfloat16:
-    	kernels.int4_to_bf16(
-            gridDim,
-       	    blockDim,
-            0,
-            stream,
-            [ctypes.c_void_p(out.data_ptr()), ctypes.c_void_p(qweight.data_ptr()), 
-            ctypes.c_void_p(scale.data_ptr()), ctypes.c_int32(num_row), ctypes.c_int32(num_chan_int), ctypes.c_int32(num_chan_fp16)],
+import bitsandbytes as bnb
+from accelerate import init_empty_weights
+from bitsandbytes.nn.modules import Params4bit, Int8Params
+import torch 
+
+def Params4bitCuda(self, device):
+    self.data = self.data.cuda(device)
+    self.quant_state[0] = self.quant_state[0].cuda(device)
+    self.quant_state[4][0] = self.quant_state[4][0].cuda(device)
+    self.quant_state[4][1][0] = self.quant_state[4][1][0].cuda(device)
+    self.quant_state[4][1][1] = self.quant_state[4][1][1].cuda(device)
+
+    self.quant_state[6] = self.quant_state[6].cuda(device)
+    return self
+
+class Linear4bitOnline(torch.nn.Module):
+    def __init__(self, weight, bias, quant_type):
+        super().__init__()
+        self.weight = Params4bit(
+            weight.data, requires_grad=False, compress_statistics=True, quant_type=quant_type
         )
-    elif input.dtype == torch.float16:
-        kernels.int4_to_fp16(
-            gridDim,
-            blockDim,
-            0,
-            stream,
-            [ctypes.c_void_p(out.data_ptr()), ctypes.c_void_p(qweight.data_ptr()),
-            ctypes.c_void_p(scale.data_ptr()), ctypes.c_int32(num_row), ctypes.c_int32(num_chan_int), ctypes.c_int32(num_chan_fp16)],
+        self.compute_dtype = None
+        #self.weight.cuda(weight.device)
+        self.bias = bias
+
+    def forward(self, x: torch.Tensor):
+        # weights are cast automatically as Int8Params, but the bias has to be cast manually
+        if self.bias is not None and self.bias.dtype != x.dtype:
+            self.bias.data = self.bias.data.to(x.dtype)
+
+        if getattr(self.weight, "quant_state", None) is None:
+            print(
+                "FP4 quantization state not initialized. Please call .cuda() or .to(device) on the LinearFP4 layer first."
+            )
+        inp_dtype = x.dtype
+        if self.compute_dtype is not None:
+            x = x.to(self.compute_dtype)
+
+        bias = None if self.bias is None else self.bias.to(self.compute_dtype)
+        out = bnb.matmul_4bit(
+            x, self.weight.t(), bias=bias, quant_state=self.weight.quant_state
         )
-    return out
 
-class QLinear(torch.nn.Module):
-    def __init__(self, bits: int, weight: torch.Tensor, bias=None):
+        out = out.to(inp_dtype)
+
+        return out
+    
+class Linear8bitLtOnline(torch.nn.Module):
+    def __init__(
+        self,
+        weight,
+        bias,
+        has_fp16_weights=True,
+        memory_efficient_backward=False,
+        threshold=0.0,
+        index=None,
+    ):
         super().__init__()
-        self.quant_bits = bits
-        self.scale = weight.abs().max(dim=-1).values / ((2 ** (bits - 1)) - 1)
-        self.scale = self.scale.to(torch.float32)
-        if self.quant_bits == 4:
-            self.weight = quant4(weight, self.scale)
-        elif self.quant_bits == 8:
-            self.weight = torch.round(weight.to(self.scale.dtype) / self.scale[:, None]).to(torch.int8)
-        if self.quant_bits == 8:
-            self.weight = self.weight.T
-        self.bias = None
-
-    def forward(self, input):
-        if self.quant_bits == 4:
-            assert(input.dtype == torch.bfloat16 or input.dtype == torch.float16)            
-
-        if self.weight.device != input.device:
-            self.weight = self.weight.to(input.device)
-            self.scale = self.scale.to(input.device)
+        assert (
+            not memory_efficient_backward
+        ), "memory_efficient_backward is no longer required and the argument is deprecated in 0.37.0 and will be removed in 0.39.0"
+        self.state = bnb.MatmulLtState()
+        self.index = index
+
+        # Necessary for stacked layers
+        self.state.threshold = threshold
+        self.state.has_fp16_weights = has_fp16_weights
+        self.state.memory_efficient_backward = memory_efficient_backward
+        if threshold > 0.0 and not has_fp16_weights:
+            self.state.use_pool = True
+
+        self.weight = Int8Params(
+            weight.data,
+            has_fp16_weights=has_fp16_weights,
+            requires_grad=has_fp16_weights,
+        )
+        self.bias = bias
+
+    def init_8bit_state(self):
+        self.state.CB = self.weight.CB
+        self.state.SCB = self.weight.SCB
+        self.weight.CB = None
+        self.weight.SCB = None
+
+    def forward(self, x: torch.Tensor):
+        self.state.is_training = self.training
+        if self.weight.CB is not None:
+            self.init_8bit_state()
+
+        # weights are cast automatically as Int8Params, but the bias has to be cast manually
+        if self.bias is not None and self.bias.dtype != x.dtype:
+            self.bias.data = self.bias.data.to(x.dtype)
         
-        if self.quant_bits == 4:
-            self.scale = self.scale.to(input.dtype)
-            rweight = dequant4(self.weight, self.scale, input).T
-            output = torch.matmul(input, rweight)
-        elif self.quant_bits == 8:
-            rweight = self.weight.to(input.dtype) * self.scale.to(input.dtype)
-            output = torch.matmul(input, rweight)
-        if self.bias is not None:
-            output = output + self.bias
-        return output
+        out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
+
+        if not self.state.has_fp16_weights:
+            if self.state.CB is not None and self.state.CxB is not None:
+                # we converted 8-bit row major to turing/ampere format in the first inference pass
+                # we no longer need the row-major weight
+                del self.state.CB
+                self.weight.data = self.state.CxB
+        return out
+    
+def quantize_offline(model, bits: int):
+    assert (bits == 4), f'bits: {bits} is not supported'
+    
+    for i, layer in enumerate(model.model.layers):
+        layer.self_attn.W_pack = bnb.nn.Linear4bit(
+                            layer.self_attn.W_pack.weight.shape[1],
+                            layer.self_attn.W_pack.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+        layer.self_attn.o_proj = bnb.nn.Linear4bit(
+                            layer.self_attn.o_proj.weight.shape[1],
+                            layer.self_attn.o_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+
+        layer.mlp.gate_proj = bnb.nn.Linear4bit(
+                            layer.mlp.gate_proj.weight.shape[1],
+                            layer.mlp.gate_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+        layer.mlp.down_proj = bnb.nn.Linear4bit(
+                            layer.mlp.down_proj.weight.shape[1],
+                            layer.mlp.down_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+        layer.mlp.up_proj = bnb.nn.Linear4bit(
+                            layer.mlp.up_proj.weight.shape[1],
+                            layer.mlp.up_proj.weight.shape[0],
+                            False,
+                            torch.float16,
+                            compress_statistics=True,
+                            quant_type="nf4",
+                        )
+    return model
+
+def quantize_online(model, bits: int):
+    def quant(weight, bias=None):
+        if bits == 8:
+            linear = Linear8bitLtOnline(
+                weight,
+                bias,
+                has_fp16_weights=False,
+                threshold=6.0,
+            )
+            if bias is not None:
+                linear.bias = torch.nn.Parameter(bias)
+        elif bits == 4:
+            linear = Linear4bitOnline(
+                weight,
+                bias,
+                quant_type="nf4", #fp4/nf4
+            )
+        else:
+            raise ValueError("quantize only support 4/8 bit")
+        return linear
+
+    for i, layer in enumerate(model.model.layers):
+        layer.self_attn.W_pack = quant(layer.self_attn.W_pack.weight)
+        layer.self_attn.o_proj = quant(layer.self_attn.o_proj.weight)
+        layer.mlp.gate_proj = quant(layer.mlp.gate_proj.weight)
+        layer.mlp.down_proj = quant(layer.mlp.down_proj.weight)
+        layer.mlp.up_proj = quant(layer.mlp.up_proj.weight)
+    return model
+
+def init_model_weight_int4(config, model, state_dict):
+    #replace Params4bit.cuda with Params4bitCuda
+    Params4bit.cuda = Params4bitCuda
+
+    for i in range(config.num_hidden_layers):
+        weight_data = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.self_attn.W_pack.weight.quant_state']
+        model.model.layers[i].self_attn.W_pack.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.self_attn.o_proj.weight.quant_state']
+        model.model.layers[i].self_attn.o_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.mlp.gate_proj.weight.quant_state']
+        model.model.layers[i].mlp.gate_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.mlp.up_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.mlp.up_proj.weight.quant_state']
+        model.model.layers[i].mlp.up_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        weight_data = state_dict[f'model.layers.{i}.mlp.down_proj.weight.data']
+        weight_quant_state = state_dict[f'model.layers.{i}.mlp.down_proj.weight.quant_state']
+        model.model.layers[i].mlp.down_proj.weight = Params4bit(weight_data, requires_grad=False, quant_state=weight_quant_state)
+        
+        model.model.layers[i].input_layernorm.weight = state_dict[f'model.layers.{i}.input_layernorm.weight']
+        model.model.layers[i].post_attention_layernorm.weight = state_dict[f'model.layers.{i}.post_attention_layernorm.weight']
+    
+    model.model.embed_tokens.weight = state_dict['model.embed_tokens.weight']
+    model.model.norm.weight = state_dict['model.norm.weight']
+    model.lm_head.weight = state_dict['lm_head.weight'] 
+    return model

tokenization_baichuan.py

@@ -48,10 +48,26 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         **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
+        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,
@@ -122,7 +138,9 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         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]:
+    def save_vocabulary(
+        self, save_directory, filename_prefix: Optional[str] = None
+    ) -> Tuple[str]:
         """
         Save the vocabulary and special tokens file to a directory.
 
@@ -137,10 +155,14 @@ class BaichuanTokenizer(PreTrainedTokenizer):
             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"]
+            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):
+        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:
@@ -161,7 +183,10 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         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
+        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
@@ -180,7 +205,9 @@ class BaichuanTokenizer(PreTrainedTokenizer):
         """
         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
+                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 []
@@ -229,4 +256,3 @@ class BaichuanTokenizer(PreTrainedTokenizer):
             output += [1] * len(bos_token_id + token_ids_1 + eos_token_id)
 
         return output
-

tokenizer.model

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:f7d1ab69d25c74644af5c5e4dcd1cc6e96d33783dbd257b6bdea55b643c72813
-size 1136765
+oid sha256:79452955be6b419a65984273a9f08af86042e1c2a75ee3ba989cbf620a133cc2
+size 2001107