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yzsydlc
/
qwen-primeai-gptq-wiki-4bit

Text Generation
Transformers
Safetensors
qwen
custom_code
4-bit precision
gptq
Model card Files Community
yzsydlc commited on
Commit
494eb1e
•
1 Parent(s): 1204fab

add model file

Browse files
Files changed (4) hide show
  1. config.json +3 -3
  2. configuration_qwen.py +71 -0
  3. model.safetensors.index.json +874 -0
  4. modeling_qwen.py +1367 -0
config.json CHANGED
@@ -1,12 +1,12 @@
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  {
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- "_name_or_path": "Qwen/Qwen-7B",
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  "architectures": [
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  "QWenLMHeadModel"
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  ],
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  "attn_dropout_prob": 0.0,
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  "auto_map": {
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- "AutoConfig": "Qwen/Qwen-7B--configuration_qwen.QWenConfig",
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- "AutoModelForCausalLM": "Qwen/Qwen-7B--modeling_qwen.QWenLMHeadModel"
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  },
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  "bf16": true,
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  "emb_dropout_prob": 0.0,
 
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  {
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+ "_name_or_path": "yzsydlc/qwen-primeai-gptq-wiki-4bit",
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  "architectures": [
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  "QWenLMHeadModel"
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  ],
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  "attn_dropout_prob": 0.0,
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  "auto_map": {
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+ "AutoConfig": "yzsydlc/qwen-primeai-gptq-wiki-4bit--configuration_qwen.QWenConfig",
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+ "AutoModelForCausalLM": "yzsydlc/qwen-primeai-gptq-wiki-4bit--modeling_qwen.QWenLMHeadModel"
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  },
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  "bf16": true,
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  "emb_dropout_prob": 0.0,
configuration_qwen.py ADDED
@@ -0,0 +1,71 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Copyright (c) Alibaba Cloud.
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+ #
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+ # This source code is licensed under the license found in the
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+ # LICENSE file in the root directory of this source tree.
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+
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+ from transformers import PretrainedConfig
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+
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+
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+ class QWenConfig(PretrainedConfig):
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+ model_type = "qwen"
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+
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+ def __init__(
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+ self,
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+ vocab_size=151936,
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+ hidden_size=4096,
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+ num_hidden_layers=32,
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+ num_attention_heads=32,
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+ emb_dropout_prob=0.0,
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+ attn_dropout_prob=0.0,
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+ layer_norm_epsilon=1e-6,
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+ initializer_range=0.02,
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+ max_position_embeddings=8192,
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+ scale_attn_weights=True,
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+ use_cache=True,
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+ bf16=False,
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+ fp16=False,
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+ fp32=False,
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+ kv_channels=128,
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+ rotary_pct=1.0,
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+ rotary_emb_base=10000,
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+ use_dynamic_ntk=True,
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+ use_logn_attn=True,
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+ use_flash_attn="auto",
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+ intermediate_size=22016,
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+ no_bias=True,
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+ tie_word_embeddings=False,
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+ use_cache_quantization=False,
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+ use_cache_kernel=False,
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+ softmax_in_fp32=False,
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+ **kwargs,
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+ ):
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+ self.vocab_size = vocab_size
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+ self.hidden_size = hidden_size
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+ self.intermediate_size = intermediate_size
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+ self.num_hidden_layers = num_hidden_layers
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+ self.num_attention_heads = num_attention_heads
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+ self.emb_dropout_prob = emb_dropout_prob
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+ self.attn_dropout_prob = attn_dropout_prob
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+ self.layer_norm_epsilon = layer_norm_epsilon
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+ self.initializer_range = initializer_range
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+ self.scale_attn_weights = scale_attn_weights
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+ self.use_cache = use_cache
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+ self.max_position_embeddings = max_position_embeddings
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+ self.bf16 = bf16
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+ self.fp16 = fp16
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+ self.fp32 = fp32
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+ self.kv_channels = kv_channels
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+ self.rotary_pct = rotary_pct
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+ self.rotary_emb_base = rotary_emb_base
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+ self.use_dynamic_ntk = use_dynamic_ntk
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+ self.use_logn_attn = use_logn_attn
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+ self.use_flash_attn = use_flash_attn
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+ self.no_bias = no_bias
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+ self.use_cache_quantization = use_cache_quantization
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+ self.use_cache_kernel = use_cache_kernel
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+ self.softmax_in_fp32 = softmax_in_fp32
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+ super().__init__(
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+ tie_word_embeddings=tie_word_embeddings,
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+ **kwargs
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+ )
model.safetensors.index.json ADDED
@@ -0,0 +1,874 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ "total_size": 5860564992
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+ "transformer.ln_f.weight": "gptq_model-4bit-128g.safetensors",
872
+ "transformer.wte.weight": "gptq_model-4bit-128g.safetensors"
873
+ }
874
+ }
modeling_qwen.py ADDED
@@ -0,0 +1,1367 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Copyright (c) Alibaba Cloud.
2
+ #
3
+ # This source code is licensed under the license found in the
4
+ # LICENSE file in the root directory of this source tree.
5
+
6
+ import copy
7
+ import importlib
8
+ import math
9
+ import pathlib
10
+ from typing import TYPE_CHECKING, Optional, Tuple, Union, Callable, List, Any, Generator
11
+
12
+ import torch
13
+ import torch.nn.functional as F
14
+ import torch.utils.checkpoint
15
+ import warnings
16
+
17
+ from torch.nn import CrossEntropyLoss
18
+ from transformers import PreTrainedTokenizer, GenerationConfig, StoppingCriteriaList
19
+ from transformers.generation.logits_process import LogitsProcessorList
20
+
21
+ if TYPE_CHECKING:
22
+ from transformers.generation.streamers import BaseStreamer
23
+ from transformers.generation.utils import GenerateOutput
24
+ from transformers.modeling_outputs import (
25
+ BaseModelOutputWithPast,
26
+ CausalLMOutputWithPast,
27
+ )
28
+ from transformers.modeling_utils import PreTrainedModel
29
+ from transformers.utils import logging
30
+
31
+ try:
32
+ from einops import rearrange
33
+ except ImportError:
34
+ rearrange = None
35
+ from torch import nn
36
+
37
+ SUPPORT_CUDA = torch.cuda.is_available()
38
+ SUPPORT_BF16 = SUPPORT_CUDA and torch.cuda.is_bf16_supported()
39
+ SUPPORT_FP16 = SUPPORT_CUDA and torch.cuda.get_device_capability(0)[0] >= 7
40
+ SUPPORT_TORCH2 = hasattr(torch, '__version__') and int(torch.__version__.split(".")[0]) >= 2
41
+
42
+
43
+ from .configuration_qwen import QWenConfig
44
+ from .qwen_generation_utils import (
45
+ HistoryType,
46
+ make_context,
47
+ decode_tokens,
48
+ get_stop_words_ids,
49
+ StopWordsLogitsProcessor,
50
+ )
51
+
52
+
53
+ logger = logging.get_logger(__name__)
54
+
55
+ _CHECKPOINT_FOR_DOC = "qwen"
56
+ _CONFIG_FOR_DOC = "QWenConfig"
57
+
58
+ QWen_PRETRAINED_MODEL_ARCHIVE_LIST = ["qwen-7b"]
59
+
60
+ _ERROR_BAD_CHAT_FORMAT = """\
61
+ We detect you are probably using the pretrained model (rather than chat model) for chatting, since the chat_format in generation_config is not "chatml".
62
+ If you are directly using the model downloaded from Huggingface, please make sure you are using our "Qwen/Qwen-7B-Chat" Huggingface model (rather than "Qwen/Qwen-7B") when you call model.chat().
63
+ 我们检测到您可能在使用预训练模型（而非chat模型）进行多轮chat，因为您当前在generation_config指定的chat_format，并未设置为我们在对话中所支持的"chatml"格式。
64
+ 如果您在直接使用我们从Huggingface提供的模型，请确保您在调用model.chat()时，使用的是"Qwen/Qwen-7B-Chat"模型（而非"Qwen/Qwen-7B"预训练模型）。
65
+ """
66
+
67
+ _SENTINEL = object()
68
+ _ERROR_STREAM_IN_CHAT = """\
69
+ Pass argument `stream` to model.chat() is buggy, deprecated, and marked for removal. Please use model.chat_stream(...) instead of model.chat(..., stream=True).
70
+ 向model.chat()传入参数stream的用法可能存在Bug，该用法已被废弃，将在未来被移除。请使用model.chat_stream(...)代替model.chat(..., stream=True)。
71
+ """
72
+
73
+ _ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED = """\
74
+ We detect you have activated flash attention support, but running model computation on CPU. Please make sure that your input data has been placed on GPU. If you actually want to run CPU computation, please following the readme and set device_map="cpu" to disable flash attention when loading the model (calling AutoModelForCausalLM.from_pretrained).
75
+ 检测到您的模型已激活了flash attention支持，但正在执行CPU运算任务。如使用flash attention，请您确认模型输入已经传到GPU上。如果您确认要执行CPU运算，请您在载入模型（调用AutoModelForCausalLM.from_pretrained）时，按照readme说法，指定device_map="cpu"以禁用flash attention。
76
+ """
77
+
78
+ apply_rotary_emb_func = None
79
+ rms_norm = None
80
+ flash_attn_unpadded_func = None
81
+ flash_attn_func = None
82
+
83
+ def _import_flash_attn():
84
+ global apply_rotary_emb_func, rms_norm, flash_attn_unpadded_func, flash_attn_func
85
+ try:
86
+ from flash_attn.layers.rotary import apply_rotary_emb_func as __apply_rotary_emb_func
87
+ apply_rotary_emb_func = __apply_rotary_emb_func
88
+ except ImportError:
89
+ logger.warn(
90
+ "Warning: import flash_attn rotary fail, please install FlashAttention rotary to get higher efficiency "
91
+ "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/rotary"
92
+ )
93
+
94
+ try:
95
+ from flash_attn.ops.rms_norm import rms_norm as __rms_norm
96
+ rms_norm = __rms_norm
97
+ except ImportError:
98
+ logger.warn(
99
+ "Warning: import flash_attn rms_norm fail, please install FlashAttention layer_norm to get higher efficiency "
100
+ "https://github.com/Dao-AILab/flash-attention/tree/main/csrc/layer_norm"
101
+ )
102
+
103
+ try:
104
+ import flash_attn
105
+ _flash_attn_func = None
106
+ if not hasattr(flash_attn, '__version__'):
107
+ from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
108
+ else:
109
+ if int(flash_attn.__version__.split(".")[0]) >= 2:
110
+ if int(flash_attn.__version__.split(".")[1]) >= 1:
111
+ from flash_attn.flash_attn_interface import flash_attn_func as _flash_attn_func
112
+ from flash_attn.flash_attn_interface import flash_attn_varlen_func as __flash_attn_unpadded_func
113
+ else:
114
+ from flash_attn.flash_attn_interface import flash_attn_unpadded_func as __flash_attn_unpadded_func
115
+ flash_attn_unpadded_func = __flash_attn_unpadded_func
116
+ flash_attn_func = _flash_attn_func
117
+ except ImportError:
118
+ logger.warn(
119
+ "Warning: import flash_attn fail, please install FlashAttention to get higher efficiency "
120
+ "https://github.com/Dao-AILab/flash-attention"
121
+ )
122
+
123
+ def quantize_cache_v(fdata, bits, qmax, qmin):
124
+ # b, s, head, h-dim->b, head, s, h-dim
125
+ qtype = torch.uint8
126
+ device = fdata.device
127
+ shape = fdata.shape
128
+
129
+ fdata_cal = torch.flatten(fdata, 2)
130
+ fmax = torch.amax(fdata_cal, dim=-1, keepdim=True)
131
+ fmin = torch.amin(fdata_cal, dim=-1, keepdim=True)
132
+ # Compute params
133
+ if qmax.device != fmax.device:
134
+ qmax = qmax.to(device)
135
+ qmin = qmin.to(device)
136
+ scale = (fmax - fmin) / (qmax - qmin)
137
+ zero = qmin - fmin / scale
138
+ scale = scale.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
139
+ zero = zero.unsqueeze(-1).repeat(1,1,shape[2],1).contiguous()
140
+ # Quantize
141
+ res_data = fdata / scale + zero
142
+ qdata = torch.clamp(res_data, qmin, qmax).to(qtype)
143
+ return qdata.contiguous(), scale, zero
144
+
145
+ def dequantize_cache_torch(qdata, scale, zero):
146
+ data = scale * (qdata - zero)
147
+ return data
148
+
149
+ class FlashSelfAttention(torch.nn.Module):
150
+ def __init__(
151
+ self,
152
+ causal=False,
153
+ softmax_scale=None,
154
+ attention_dropout=0.0,
155
+ ):
156
+ super().__init__()
157
+ assert flash_attn_unpadded_func is not None, (
158
+ "Please install FlashAttention first, " "e.g., with pip install flash-attn"
159
+ )
160
+ assert (
161
+ rearrange is not None
162
+ ), "Please install einops first, e.g., with pip install einops"
163
+ self.causal = causal
164
+ self.softmax_scale = softmax_scale
165
+ self.dropout_p = attention_dropout
166
+
167
+ def unpad_input(self, hidden_states, attention_mask):
168
+ valid_mask = attention_mask.squeeze(1).squeeze(1).eq(0)
169
+ seqlens_in_batch = valid_mask.sum(dim=-1, dtype=torch.int32)
170
+ indices = torch.nonzero(valid_mask.flatten(), as_tuple=False).flatten()
171
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
172
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
173
+ hidden_states = hidden_states[indices]
174
+ return hidden_states, indices, cu_seqlens, max_seqlen_in_batch
175
+
176
+ def pad_input(self, hidden_states, indices, batch, seqlen):
177
+ output = torch.zeros(batch * seqlen, *hidden_states.shape[1:], device=hidden_states.device,
178
+ dtype=hidden_states.dtype)
179
+ output[indices] = hidden_states
180
+ return rearrange(output, '(b s) ... -> b s ...', b=batch)
181
+
182
+ def forward(self, q, k, v, attention_mask=None):
183
+ assert all((i.dtype in [torch.float16, torch.bfloat16] for i in (q, k, v)))
184
+ assert all((i.is_cuda for i in (q, k, v)))
185
+ batch_size, seqlen_q = q.shape[0], q.shape[1]
186
+ seqlen_k = k.shape[1]
187
+ seqlen_out = seqlen_q
188
+
189
+ if flash_attn_func is not None and batch_size == 1:
190
+ dropout_p = self.dropout_p if self.training else 0
191
+ output = flash_attn_func(q, k, v, dropout_p, softmax_scale=self.softmax_scale, causal=self.causal)
192
+ return output
193
+
194
+ q, k, v = [rearrange(x, "b s ... -> (b s) ...") for x in [q, k, v]]
195
+ cu_seqlens_q = torch.arange(
196
+ 0,
197
+ (batch_size + 1) * seqlen_q,
198
+ step=seqlen_q,
199
+ dtype=torch.int32,
200
+ device=q.device,
201
+ )
202
+
203
+ if batch_size > 1 and attention_mask is not None:
204
+ k, indices_k, cu_seqlens_k, seqlen_k = self.unpad_input(k, attention_mask)
205
+ if q.size(0) == v.size(0):
206
+ q = q[indices_k]
207
+ cu_seqlens_q = cu_seqlens_k
208
+ seqlen_q = seqlen_k
209
+ v = v[indices_k]
210
+ else:
211
+ cu_seqlens_k = torch.arange(
212
+ 0,
213
+ (batch_size + 1) * seqlen_k,
214
+ step=seqlen_k,
215
+ dtype=torch.int32,
216
+ device=q.device,
217
+ )
218
+
219
+ if self.training:
220
+ assert seqlen_k == seqlen_q
221
+ is_causal = self.causal
222
+ dropout_p = self.dropout_p
223
+ else:
224
+ is_causal = seqlen_q == seqlen_k
225
+ dropout_p = 0
226
+
227
+ output = flash_attn_unpadded_func(
228
+ q,
229
+ k,
230
+ v,
231
+ cu_seqlens_q,
232
+ cu_seqlens_k,
233
+ seqlen_q,
234
+ seqlen_k,
235
+ dropout_p,
236
+ softmax_scale=self.softmax_scale,
237
+ causal=is_causal,
238
+ )
239
+ if batch_size > 1 and attention_mask is not None and seqlen_q == seqlen_k:
240
+ output = self.pad_input(output, indices_k, batch_size, seqlen_out)
241
+ else:
242
+ new_shape = (batch_size, output.shape[0] // batch_size) + output.shape[1:]
243
+ output = output.view(new_shape)
244
+ return output
245
+
246
+
247
+ class QWenAttention(nn.Module):
248
+ def __init__(self, config):
249
+ super().__init__()
250
+
251
+ self.register_buffer("masked_bias", torch.tensor(-1e4), persistent=False)
252
+ self.seq_length = config.seq_length
253
+
254
+ self.hidden_size = config.hidden_size
255
+ self.split_size = config.hidden_size
256
+ self.num_heads = config.num_attention_heads
257
+ self.head_dim = self.hidden_size // self.num_heads
258
+
259
+ self.use_flash_attn = config.use_flash_attn
260
+ self.scale_attn_weights = True
261
+
262
+ self.projection_size = config.kv_channels * config.num_attention_heads
263
+
264
+ assert self.projection_size % config.num_attention_heads == 0
265
+ self.hidden_size_per_attention_head = (
266
+ self.projection_size // config.num_attention_heads
267
+ )
268
+
269
+ self.c_attn = nn.Linear(config.hidden_size, 3 * self.projection_size)
270
+
271
+ self.c_proj = nn.Linear(
272
+ config.hidden_size, self.projection_size, bias=not config.no_bias
273
+ )
274
+
275
+ self.is_fp32 = not (config.bf16 or config.fp16)
276
+ if (
277
+ self.use_flash_attn
278
+ and flash_attn_unpadded_func is not None
279
+ and not self.is_fp32
280
+ ):
281
+ self.core_attention_flash = FlashSelfAttention(
282
+ causal=True, attention_dropout=config.attn_dropout_prob
283
+ )
284
+ self.bf16 = config.bf16
285
+
286
+ self.use_dynamic_ntk = config.use_dynamic_ntk
287
+ self.use_logn_attn = config.use_logn_attn
288
+
289
+ logn_list = [
290
+ math.log(i, self.seq_length) if i > self.seq_length else 1
291
+ for i in range(1, 32768)
292
+ ]
293
+ logn_tensor = torch.tensor(logn_list)[None, :, None, None]
294
+ self.register_buffer("logn_tensor", logn_tensor, persistent=False)
295
+
296
+ self.attn_dropout = nn.Dropout(config.attn_dropout_prob)
297
+ self.softmax_in_fp32 = config.softmax_in_fp32 if hasattr(config, 'softmax_in_fp32') else False
298
+ self.use_cache_quantization = config.use_cache_quantization if hasattr(config, 'use_cache_quantization') else False
299
+ self.use_cache_kernel = config.use_cache_kernel if hasattr(config,'use_cache_kernel') else False
300
+ cache_dtype = torch.float
301
+ if self.bf16:
302
+ cache_dtype=torch.bfloat16
303
+ elif config.fp16:
304
+ cache_dtype = torch.float16
305
+ self.cache_qmax = torch.tensor(torch.iinfo(torch.uint8).max, dtype=cache_dtype)
306
+ self.cache_qmin = torch.tensor(torch.iinfo(torch.uint8).min, dtype=cache_dtype)
307
+
308
+ if config.use_cache_quantization and config.use_cache_kernel:
309
+ # pre check if the support files existing
310
+ module_root = pathlib.Path(__file__).parent
311
+ src_files = ("cache_autogptq_cuda_256.cpp", "cache_autogptq_cuda_kernel_256.cu")
312
+ if any(not (module_root/src).is_file() for src in src_files):
313
+ warnings.warn("KV cache kernel source files (.cpp and .cu) not found.")
314
+ self.cache_kernels = None
315
+ else:
316
+ try:
317
+ from .cpp_kernels import cache_autogptq_cuda_256
318
+ self.cache_kernels = cache_autogptq_cuda_256
319
+ except ImportError:
320
+ warnings.warn("Failed to import KV cache kernels.")
321
+ self.cache_kernels = None
322
+
323
+ def _attn(self, query, key, value, causal_mask=None, attention_mask=None, head_mask=None):
324
+ device = query.device
325
+ if self.use_cache_quantization:
326
+ qk, qk_scale, qk_zero = key
327
+ if self.use_cache_kernel and self.cache_kernels is not None:
328
+ shape = query.shape[:-1] + (qk.shape[-2],)
329
+ attn_weights = torch.zeros(shape, dtype=torch.float16, device=device)
330
+ self.cache_kernels.vecquant8matmul_batched_faster_old(
331
+ query.contiguous() if query.dtype == torch.float16 else query.to(torch.float16).contiguous(),
332
+ qk.transpose(-1, -2).contiguous(),
333
+ attn_weights,
334
+ qk_scale.contiguous() if qk_scale.dtype == torch.float16 else qk_scale.to(torch.float16).contiguous(),
335
+ qk_zero.contiguous()if qk_zero.dtype == torch.float16 else qk_zero.to(torch.float16).contiguous())
336
+ # attn_weights = attn_weights.to(query.dtype).contiguous()
337
+ else:
338
+ key = dequantize_cache_torch(qk, qk_scale, qk_zero)
339
+ attn_weights = torch.matmul(query, key.transpose(-1, -2))
340
+ else:
341
+ attn_weights = torch.matmul(query, key.transpose(-1, -2))
342
+
343
+ if self.scale_attn_weights:
344
+ if self.use_cache_quantization:
345
+ size_temp = value[0].size(-1)
346
+ else:
347
+ size_temp = value.size(-1)
348
+ attn_weights = attn_weights / (size_temp ** 0.5)
349
+
350
+ mask_value = torch.finfo(attn_weights.dtype).min
351
+ if causal_mask is not None:
352
+ attn_weights = torch.where(
353
+ causal_mask, attn_weights.to(attn_weights.dtype), mask_value
354
+ )
355
+
356
+ if attention_mask is not None:
357
+ attn_weights = attn_weights + attention_mask
358
+
359
+ if self.softmax_in_fp32:
360
+ attn_weights = nn.functional.softmax(attn_weights.float(), dim=-1)
361
+ else:
362
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1)
363
+
364
+ attn_weights = attn_weights.type(query.dtype)
365
+ attn_weights = self.attn_dropout(attn_weights)
366
+
367
+ if head_mask is not None:
368
+ attn_weights = attn_weights * head_mask
369
+
370
+ if self.use_cache_quantization:
371
+ qv, qv_scale, qv_zero = value
372
+ if self.use_cache_kernel and self.cache_kernels is not None:
373
+ shape = attn_weights.shape[:-1] + (query.shape[-1],)
374
+ attn_output = torch.zeros(shape, dtype=torch.float16, device=device)
375
+ self.cache_kernels.vecquant8matmul_batched_column_compression_faster_old(
376
+ attn_weights.contiguous() if attn_weights.dtype == torch.float16 else attn_weights.to(torch.float16).contiguous(),
377
+ qv.contiguous(), # dtype: int32
378
+ attn_output,
379
+ qv_scale.contiguous() if qv_scale.dtype == torch.float16 else qv_scale.to(torch.float16).contiguous(),
380
+ qv_zero.contiguous() if qv_zero.dtype == torch.float16 else qv_zero.to(torch.float16).contiguous())
381
+ if attn_output.dtype != query.dtype:
382
+ attn_output = attn_output.to(query.dtype)
383
+ attn_weights = attn_weights.to(query.dtype)
384
+ else:
385
+ value = dequantize_cache_torch(qv, qv_scale, qv_zero)
386
+ attn_output = torch.matmul(attn_weights, value)
387
+ else:
388
+ attn_output = torch.matmul(attn_weights, value)
389
+
390
+ attn_output = attn_output.transpose(1, 2)
391
+
392
+ return attn_output, attn_weights
393
+
394
+ def _split_heads(self, tensor, num_heads, attn_head_size):
395
+ new_shape = tensor.size()[:-1] + (num_heads, attn_head_size)
396
+ tensor = tensor.view(new_shape)
397
+ return tensor
398
+
399
+ def _merge_heads(self, tensor, num_heads, attn_head_size):
400
+ tensor = tensor.contiguous()
401
+ new_shape = tensor.size()[:-2] + (num_heads * attn_head_size,)
402
+ return tensor.view(new_shape)
403
+
404
+ def forward(
405
+ self,
406
+ hidden_states: Optional[Tuple[torch.FloatTensor]],
407
+ rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
408
+ layer_past: Optional[Tuple[torch.Tensor]] = None,
409
+ attention_mask: Optional[torch.FloatTensor] = None,
410
+ head_mask: Optional[torch.FloatTensor] = None,
411
+ encoder_hidden_states: Optional[torch.Tensor] = None,
412
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
413
+ output_attentions: Optional[bool] = False,
414
+ use_cache: Optional[bool] = False,
415
+ ):
416
+ mixed_x_layer = self.c_attn(hidden_states)
417
+
418
+ query, key, value = mixed_x_layer.split(self.split_size, dim=2)
419
+
420
+ query = self._split_heads(query, self.num_heads, self.head_dim)
421
+ key = self._split_heads(key, self.num_heads, self.head_dim)
422
+ value = self._split_heads(value, self.num_heads, self.head_dim)
423
+
424
+ if rotary_pos_emb_list is not None:
425
+ cur_len = query.shape[1]
426
+ if len(rotary_pos_emb_list) == 1:
427
+ rotary_pos_emb = rotary_pos_emb_list[0]
428
+ rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
429
+ rotary_pos_emb = (rotary_pos_emb,) * 2
430
+ q_pos_emb, k_pos_emb = rotary_pos_emb
431
+ # Slice the pos emb for current inference
432
+ query = apply_rotary_pos_emb(query, q_pos_emb)
433
+ key = apply_rotary_pos_emb(key, k_pos_emb)
434
+ else:
435
+ query_list = []
436
+ key_list = []
437
+ for i, rotary_pos_emb in enumerate(rotary_pos_emb_list):
438
+ rotary_pos_emb = [i[:, -cur_len:, :, :] for i in rotary_pos_emb]
439
+ rotary_pos_emb = (rotary_pos_emb,) * 2
440
+ q_pos_emb, k_pos_emb = rotary_pos_emb
441
+ # Slice the pos emb for current inference
442
+ query_list += [apply_rotary_pos_emb(query[i:i+1, :, :], q_pos_emb)]
443
+ key_list += [apply_rotary_pos_emb(key[i:i+1, :, :], k_pos_emb)]
444
+ query = torch.cat(query_list, dim=0)
445
+ key = torch.cat(key_list, dim=0)
446
+
447
+ if self.use_cache_quantization:
448
+ key = quantize_cache_v(key.permute(0, 2, 1, 3),
449
+ bits=8,
450
+ qmin=self.cache_qmin,
451
+ qmax=self.cache_qmax)
452
+ value = quantize_cache_v(value.permute(0, 2, 1, 3),
453
+ bits=8,
454
+ qmin=self.cache_qmin,
455
+ qmax=self.cache_qmax)
456
+
457
+
458
+ if layer_past is not None:
459
+ past_key, past_value = layer_past[0], layer_past[1]
460
+ if self.use_cache_quantization:
461
+ # use_cache_quantization:
462
+ # present=((q_key,key_scale,key_zero_point),
463
+ # (q_value,value_scale,value_zero_point))
464
+ key = (torch.cat((past_key[0], key[0]), dim=2),
465
+ torch.cat((past_key[1], key[1]), dim=2),
466
+ torch.cat((past_key[2], key[2]), dim=2))
467
+ value = (torch.cat((past_value[0], value[0]), dim=2),
468
+ torch.cat((past_value[1], value[1]), dim=2),
469
+ torch.cat((past_value[2], value[2]), dim=2))
470
+ else:
471
+ # not use_cache_quantization:
472
+ # present=(key,value)
473
+ key = torch.cat((past_key, key), dim=1)
474
+ value = torch.cat((past_value, value), dim=1)
475
+
476
+ if use_cache:
477
+ present = (key, value)
478
+ else:
479
+ present = None
480
+
481
+ key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
482
+ if key_size > self.seq_length and self.use_logn_attn and not self.training:
483
+ if self.use_cache_quantization:
484
+ seq_start = key[0].size(2) - query.size(1)
485
+ seq_end = key[0].size(2)
486
+ else:
487
+ seq_start = key.size(1) - query.size(1)
488
+ seq_end = key.size(1)
489
+ logn_tensor = self.logn_tensor[:, seq_start:seq_end, :, :].type_as(query)
490
+ query = query * logn_tensor.expand_as(query)
491
+
492
+ if (
493
+ self.use_flash_attn
494
+ and flash_attn_unpadded_func is not None
495
+ and not self.is_fp32
496
+ and query.is_cuda
497
+ ):
498
+ q, k, v = query, key, value
499
+ attn_output = self.core_attention_flash(q, k, v, attention_mask=attention_mask)
500
+ else:
501
+ key_size = key[0].size(2) if self.use_cache_quantization else key.size(1)
502
+ if query.size(1) == key_size:
503
+ causal_mask = torch.tril(
504
+ torch.ones((key_size, key_size), dtype=torch.bool, device=query.device)
505
+ ).view(1, 1, key_size, key_size)
506
+ else:
507
+ causal_mask = None
508
+ query = query.permute(0, 2, 1, 3)
509
+ if not self.use_cache_quantization:
510
+ key = key.permute(0, 2, 1, 3)
511
+ value = value.permute(0, 2, 1, 3)
512
+ if (
513
+ causal_mask is None
514
+ and self.use_flash_attn
515
+ and flash_attn_unpadded_func is not None
516
+ and not self.is_fp32
517
+ and not query.is_cuda
518
+ ):
519
+ raise Exception(_ERROR_INPUT_CPU_QUERY_WITH_FLASH_ATTN_ACTIVATED)
520
+
521
+ if not self.use_cache_quantization and SUPPORT_TORCH2:
522
+ if attention_mask is not None:
523
+ attention_mask = attention_mask.expand(-1, -1, query.size(2), -1)
524
+ if causal_mask is not None:
525
+ attention_mask = attention_mask.masked_fill(~causal_mask, torch.finfo(query.dtype).min)
526
+ else:
527
+ attention_mask = causal_mask
528
+ attn_output = F.scaled_dot_product_attention(
529
+ query, key, value, attn_mask=attention_mask
530
+ ).transpose(1, 2)
531
+ attn_weight = None
532
+ else:
533
+ attn_output, attn_weight = self._attn(
534
+ query, key, value, causal_mask, attention_mask, head_mask
535
+ )
536
+ context_layer = self._merge_heads(
537
+ attn_output, self.num_heads, self.head_dim
538
+ )
539
+
540
+ attn_output = self.c_proj(context_layer)
541
+
542
+ outputs = (attn_output, present)
543
+ if output_attentions:
544
+ if (
545
+ self.use_flash_attn
546
+ and flash_attn_unpadded_func is not None
547
+ and not self.is_fp32
548
+ ):
549
+ raise ValueError("Cannot output attentions while using flash-attn")
550
+ elif not self.use_cache_quantization and SUPPORT_TORCH2:
551
+ raise ValueError("Cannot output attentions while using scaled_dot_product_attention")
552
+ else:
553
+ outputs += (attn_weight,)
554
+
555
+ return outputs
556
+
557
+
558
+ class QWenMLP(nn.Module):
559
+ def __init__(self, config):
560
+ super().__init__()
561
+ self.w1 = nn.Linear(
562
+ config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
563
+ )
564
+ self.w2 = nn.Linear(
565
+ config.hidden_size, config.intermediate_size // 2, bias=not config.no_bias
566
+ )
567
+ ff_dim_in = config.intermediate_size // 2
568
+ self.c_proj = nn.Linear(ff_dim_in, config.hidden_size, bias=not config.no_bias)
569
+
570
+ def forward(self, hidden_states):
571
+ a1 = self.w1(hidden_states)
572
+ a2 = self.w2(hidden_states)
573
+ intermediate_parallel = a1 * F.silu(a2)
574
+ output = self.c_proj(intermediate_parallel)
575
+ return output
576
+
577
+
578
+ class QWenBlock(nn.Module):
579
+ def __init__(self, config):
580
+ super().__init__()
581
+ hidden_size = config.hidden_size
582
+ self.bf16 = config.bf16
583
+
584
+ self.ln_1 = RMSNorm(
585
+ hidden_size,
586
+ eps=config.layer_norm_epsilon,
587
+ )
588
+ self.attn = QWenAttention(config)
589
+ self.ln_2 = RMSNorm(
590
+ hidden_size,
591
+ eps=config.layer_norm_epsilon,
592
+ )
593
+
594
+ self.mlp = QWenMLP(config)
595
+
596
+ def forward(
597
+ self,
598
+ hidden_states: Optional[Tuple[torch.FloatTensor]],
599
+ rotary_pos_emb_list: Optional[List[List[torch.Tensor]]] = None,
600
+ layer_past: Optional[Tuple[torch.Tensor]] = None,
601
+ attention_mask: Optional[torch.FloatTensor] = None,
602
+ head_mask: Optional[torch.FloatTensor] = None,
603
+ encoder_hidden_states: Optional[torch.Tensor] = None,
604
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
605
+ use_cache: Optional[bool] = False,
606
+ output_attentions: Optional[bool] = False,
607
+ ):
608
+ layernorm_output = self.ln_1(hidden_states)
609
+
610
+ attn_outputs = self.attn(
611
+ layernorm_output,
612
+ rotary_pos_emb_list,
613
+ layer_past=layer_past,
614
+ attention_mask=attention_mask,
615
+ head_mask=head_mask,
616
+ use_cache=use_cache,
617
+ output_attentions=output_attentions,
618
+ )
619
+ attn_output = attn_outputs[0]
620
+
621
+ outputs = attn_outputs[1:]
622
+
623
+ residual = hidden_states
624
+ layernorm_input = attn_output + residual
625
+
626
+ layernorm_output = self.ln_2(layernorm_input)
627
+
628
+ residual = layernorm_input
629
+ mlp_output = self.mlp(layernorm_output)
630
+ hidden_states = residual + mlp_output
631
+
632
+ if use_cache:
633
+ outputs = (hidden_states,) + outputs
634
+ else:
635
+ outputs = (hidden_states,) + outputs[1:]
636
+
637
+ return outputs
638
+
639
+
640
+ class QWenPreTrainedModel(PreTrainedModel):
641
+ config_class = QWenConfig
642
+ base_model_prefix = "transformer"
643
+ is_parallelizable = False
644
+ supports_gradient_checkpointing = True
645
+ _no_split_modules = ["QWenBlock"]
646
+ _skip_keys_device_placement = "past_key_values"
647
+
648
+ def __init__(self, *inputs, **kwargs):
649
+ super().__init__(*inputs, **kwargs)
650
+
651
+ def _init_weights(self, module):
652
+ """Initialize the weights."""
653
+ if isinstance(module, nn.Linear):
654
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
655
+ if module.bias is not None:
656
+ module.bias.data.zero_()
657
+ elif isinstance(module, nn.Embedding):
658
+ module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
659
+ if module.padding_idx is not None:
660
+ module.weight.data[module.padding_idx].zero_()
661
+ elif isinstance(module, RMSNorm):
662
+ module.weight.data.fill_(1.0)
663
+
664
+ for name, p in module.named_parameters():
665
+ if name == "c_proj.weight":
666
+ p.data.normal_(
667
+ mean=0.0,
668
+ std=(
669
+ self.config.initializer_range
670
+ / math.sqrt(2 * self.config.num_hidden_layers)
671
+ ),
672
+ )
673
+
674
+ def _set_gradient_checkpointing(self, module, value=False):
675
+ if isinstance(module, QWenModel):
676
+ module.gradient_checkpointing = value
677
+
678
+
679
+ class QWenModel(QWenPreTrainedModel):
680
+ _keys_to_ignore_on_load_missing = ["attn.masked_bias"]
681
+
682
+ def __init__(self, config):
683
+ super().__init__(config)
684
+ self.vocab_size = config.vocab_size
685
+ self.num_hidden_layers = config.num_hidden_layers
686
+ self.embed_dim = config.hidden_size
687
+ self.use_cache_quantization = self.config.use_cache_quantization if hasattr(self.config, 'use_cache_quantization') else False
688
+
689
+ self.gradient_checkpointing = False
690
+ self.use_dynamic_ntk = config.use_dynamic_ntk
691
+ self.seq_length = config.seq_length
692
+
693
+ self.wte = nn.Embedding(self.vocab_size, self.embed_dim)
694
+
695
+ self.drop = nn.Dropout(config.emb_dropout_prob)
696
+
697
+ if config.rotary_pct == 1.0:
698
+ self.rotary_ndims = None
699
+ else:
700
+ assert config.rotary_pct < 1
701
+ self.rotary_ndims = int(
702
+ config.kv_channels * config.rotary_pct
703
+ )
704
+ dim = (
705
+ self.rotary_ndims
706
+ if self.rotary_ndims is not None
707
+ else config.kv_channels
708
+ )
709
+ self.rotary_emb = RotaryEmbedding(dim, base=config.rotary_emb_base)
710
+
711
+ self.use_flash_attn = config.use_flash_attn
712
+ self.is_fp32 = not (config.bf16 or config.fp16)
713
+
714
+ self.h = nn.ModuleList(
715
+ [
716
+ QWenBlock(
717
+ config
718
+ )
719
+ for i in range(config.num_hidden_layers)
720
+ ]
721
+ )
722
+ self.ln_f = RMSNorm(
723
+ self.embed_dim,
724
+ eps=config.layer_norm_epsilon,
725
+ )
726
+
727
+ self.post_init()
728
+
729
+ def get_input_embeddings(self):
730
+ return self.wte
731
+
732
+ def set_input_embeddings(self, new_embeddings):
733
+ self.wte = new_embeddings
734
+
735
+ def get_ntk_alpha(self, true_seq_len):
736
+ context_value = math.log(true_seq_len / self.seq_length, 2) + 1
737
+ ntk_alpha = 2 ** math.ceil(context_value) - 1
738
+ ntk_alpha = max(ntk_alpha, 1)
739
+ return ntk_alpha
740
+
741
+ def forward(
742
+ self,
743
+ input_ids: Optional[torch.LongTensor] = None,
744
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
745
+ attention_mask: Optional[torch.FloatTensor] = None,
746
+ token_type_ids: Optional[torch.LongTensor] = None,
747
+ position_ids: Optional[torch.LongTensor] = None,
748
+ head_mask: Optional[torch.FloatTensor] = None,
749
+ inputs_embeds: Optional[torch.FloatTensor] = None,
750
+ encoder_hidden_states: Optional[torch.Tensor] = None,
751
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
752
+ use_cache: Optional[bool] = None,
753
+ output_attentions: Optional[bool] = None,
754
+ output_hidden_states: Optional[bool] = None,
755
+ return_dict: Optional[bool] = None,
756
+ ):
757
+ output_attentions = (
758
+ output_attentions
759
+ if output_attentions is not None
760
+ else self.config.output_attentions
761
+ )
762
+ output_hidden_states = (
763
+ output_hidden_states
764
+ if output_hidden_states is not None
765
+ else self.config.output_hidden_states
766
+ )
767
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
768
+ return_dict = (
769
+ return_dict if return_dict is not None else self.config.use_return_dict
770
+ )
771
+
772
+ if input_ids is not None and inputs_embeds is not None:
773
+ raise ValueError(
774
+ "You cannot specify both input_ids and inputs_embeds at the same time"
775
+ )
776
+ elif input_ids is not None:
777
+ input_shape = input_ids.size()
778
+ input_ids = input_ids.view(-1, input_shape[-1])
779
+ batch_size = input_ids.shape[0]
780
+ elif inputs_embeds is not None:
781
+ input_shape = inputs_embeds.size()[:-1]
782
+ batch_size = inputs_embeds.shape[0]
783
+ else:
784
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
785
+
786
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
787
+
788
+ if token_type_ids is not None:
789
+ token_type_ids = token_type_ids.view(-1, input_shape[-1])
790
+ if position_ids is not None:
791
+ position_ids = position_ids.view(-1, input_shape[-1])
792
+
793
+ if past_key_values is None:
794
+ past_length = 0
795
+ past_key_values = tuple([None] * len(self.h))
796
+ else:
797
+ if self.use_cache_quantization:
798
+ past_length = past_key_values[0][0][0].size(2)
799
+ else:
800
+ past_length = past_key_values[0][0].size(-2)
801
+ if position_ids is None:
802
+ position_ids = torch.arange(
803
+ past_length,
804
+ input_shape[-1] + past_length,
805
+ dtype=torch.long,
806
+ device=device,
807
+ )
808
+ position_ids = position_ids.unsqueeze(0).view(-1, input_shape[-1])
809
+
810
+ if attention_mask is not None:
811
+ if batch_size <= 0:
812
+ raise ValueError("batch_size has to be defined and > 0")
813
+ attention_mask = attention_mask.view(batch_size, -1)
814
+ attention_mask = attention_mask[:, None, None, :]
815
+ attention_mask = attention_mask.to(dtype=self.dtype)
816
+ attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min
817
+
818
+ encoder_attention_mask = None
819
+ head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
820
+
821
+ if inputs_embeds is None:
822
+ inputs_embeds = self.wte(input_ids)
823
+ hidden_states = inputs_embeds
824
+
825
+ kv_seq_len = hidden_states.size()[1]
826
+ if past_key_values[0] is not None:
827
+ # past key values[0][0] shape: bs * seq_len * head_num * dim
828
+ if self.use_cache_quantization:
829
+ kv_seq_len += past_key_values[0][0][0].shape[2]
830
+ else:
831
+ kv_seq_len += past_key_values[0][0].shape[1]
832
+
833
+ if self.training or not self.use_dynamic_ntk:
834
+ ntk_alpha_list = [1.0]
835
+ elif kv_seq_len != hidden_states.size()[1]:
836
+ ntk_alpha_list = self.rotary_emb._ntk_alpha_cached_list
837
+ else:
838
+ ntk_alpha_list = []
839
+ if attention_mask is not None and kv_seq_len > self.seq_length:
840
+ true_seq_lens = attention_mask.squeeze(1).squeeze(1).eq(0).sum(dim=-1, dtype=torch.int32)
841
+ for i in range(hidden_states.size()[0]):
842
+ true_seq_len = true_seq_lens[i].item()
843
+ ntk_alpha = self.get_ntk_alpha(true_seq_len)
844
+ ntk_alpha_list.append(ntk_alpha)
845
+ else:
846
+ ntk_alpha = self.get_ntk_alpha(kv_seq_len)
847
+ ntk_alpha_list.append(ntk_alpha)
848
+ self.rotary_emb._ntk_alpha_cached_list = ntk_alpha_list
849
+ rotary_pos_emb_list = [
850
+ self.rotary_emb(kv_seq_len, ntk_alpha=ntk_alpha) for ntk_alpha in ntk_alpha_list
851
+ ]
852
+
853
+ hidden_states = self.drop(hidden_states)
854
+ output_shape = input_shape + (hidden_states.size(-1),)
855
+
856
+ if self.gradient_checkpointing and self.training:
857
+ if use_cache:
858
+ logger.warning_once(
859
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
860
+ )
861
+ use_cache = False
862
+
863
+ presents = () if use_cache else None
864
+ all_self_attentions = () if output_attentions else None
865
+ all_hidden_states = () if output_hidden_states else None
866
+ for i, (block, layer_past) in enumerate(zip(self.h, past_key_values)):
867
+
868
+ if output_hidden_states:
869
+ all_hidden_states = all_hidden_states + (hidden_states,)
870
+
871
+ if self.gradient_checkpointing and self.training:
872
+
873
+ def create_custom_forward(module):
874
+ def custom_forward(*inputs):
875
+ # None for past_key_value
876
+ return module(*inputs, use_cache, output_attentions)
877
+
878
+ return custom_forward
879
+
880
+ outputs = torch.utils.checkpoint.checkpoint(
881
+ create_custom_forward(block),
882
+ hidden_states,
883
+ rotary_pos_emb_list,
884
+ None,
885
+ attention_mask,
886
+ head_mask[i],
887
+ encoder_hidden_states,
888
+ encoder_attention_mask,
889
+ )
890
+ else:
891
+ outputs = block(
892
+ hidden_states,
893
+ layer_past=layer_past,
894
+ rotary_pos_emb_list=rotary_pos_emb_list,
895
+ attention_mask=attention_mask,
896
+ head_mask=head_mask[i],
897
+ encoder_hidden_states=encoder_hidden_states,
898
+ encoder_attention_mask=encoder_attention_mask,
899
+ use_cache=use_cache,
900
+ output_attentions=output_attentions,
901
+ )
902
+
903
+ hidden_states = outputs[0]
904
+ if use_cache is True:
905
+ presents = presents + (outputs[1],)
906
+
907
+ if output_attentions:
908
+ all_self_attentions = all_self_attentions + (outputs[2 if use_cache else 1],)
909
+
910
+ hidden_states = self.ln_f(hidden_states)
911
+ hidden_states = hidden_states.view(output_shape)
912
+ # Add last hidden state
913
+ if output_hidden_states:
914
+ all_hidden_states = all_hidden_states + (hidden_states,)
915
+
916
+ if not return_dict:
917
+ return tuple(
918
+ v for v in [hidden_states, presents, all_hidden_states] if v is not None
919
+ )
920
+
921
+ return BaseModelOutputWithPast(
922
+ last_hidden_state=hidden_states,
923
+ past_key_values=presents,
924
+ hidden_states=all_hidden_states,
925
+ attentions=all_self_attentions,
926
+ )
927
+
928
+
929
+ class QWenLMHeadModel(QWenPreTrainedModel):
930
+ _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.rotary_emb\.inv_freq"]
931
+ _keys_to_ignore_on_load_unexpected = [r"h\.\d+\.attn\.masked_bias"]
932
+
933
+ def __init__(self, config):
934
+ super().__init__(config)
935
+ assert (
936
+ config.bf16 + config.fp16 + config.fp32 <= 1
937
+ ), "Only one of \"bf16\", \"fp16\", \"fp32\" can be true"
938
+
939
+ autoset_precision = config.bf16 + config.fp16 + config.fp32 == 0
940
+
941
+ if autoset_precision:
942
+ if SUPPORT_BF16:
943
+ logger.warn(
944
+ "The model is automatically converting to bf16 for faster inference. "
945
+ "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
946
+ )
947
+ config.bf16 = True
948
+ elif SUPPORT_FP16:
949
+ logger.warn(
950
+ "The model is automatically converting to fp16 for faster inference. "
951
+ "If you want to disable the automatic precision, please manually add bf16/fp16/fp32=True to \"AutoModelForCausalLM.from_pretrained\"."
952
+ )
953
+ config.fp16 = True
954
+ else:
955
+ config.fp32 = True
956
+
957
+ if config.bf16 and SUPPORT_CUDA and not SUPPORT_BF16:
958
+ logger.warn("Your device does NOT seem to support bf16, you can switch to fp16 or fp32 by by passing fp16/fp32=True in \"AutoModelForCausalLM.from_pretrained\".")
959
+ if config.fp16 and SUPPORT_CUDA and not SUPPORT_FP16:
960
+ logger.warn("Your device does NOT support faster inference with fp16, please switch to fp32 which is likely to be faster")
961
+ if config.fp32:
962
+ if SUPPORT_BF16:
963
+ logger.warn("Your device support faster inference by passing bf16=True in \"AutoModelForCausalLM.from_pretrained\".")
964
+ elif SUPPORT_FP16:
965
+ logger.warn("Your device support faster inference by passing fp16=True in \"AutoModelForCausalLM.from_pretrained\".")
966
+
967
+ if config.use_flash_attn == "auto":
968
+ if config.bf16 or config.fp16:
969
+ logger.warn("Try importing flash-attention for faster inference...")
970
+ config.use_flash_attn = True
971
+ else:
972
+ config.use_flash_attn = False
973
+ if config.use_flash_attn and config.fp32:
974
+ logger.warn("Flash attention will be disabled because it does NOT support fp32.")
975
+
976
+ if config.use_flash_attn:
977
+ _import_flash_attn()
978
+
979
+ self.transformer = QWenModel(config)
980
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
981
+
982
+ if config.bf16:
983
+ self.transformer.bfloat16()
984
+ self.lm_head.bfloat16()
985
+ if config.fp16:
986
+ self.transformer.half()
987
+ self.lm_head.half()
988
+ self.post_init()
989
+
990
+ def get_output_embeddings(self):
991
+ return self.lm_head
992
+
993
+ def set_output_embeddings(self, new_embeddings):
994
+ self.lm_head = new_embeddings
995
+
996
+ def prepare_inputs_for_generation(
997
+ self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs
998
+ ):
999
+ if past_key_values:
1000
+ input_ids = input_ids[:, -1].unsqueeze(-1)
1001
+
1002
+ if input_ids.size(0) == 1:
1003
+ attention_mask = None
1004
+ else:
1005
+ attention_mask = kwargs.get("attention_mask", None)
1006
+
1007
+ if inputs_embeds is not None and past_key_values is None:
1008
+ model_inputs = {"inputs_embeds": inputs_embeds}
1009
+ else:
1010
+ model_inputs = {"input_ids": input_ids}
1011
+
1012
+ model_inputs.update(
1013
+ {
1014
+ "past_key_values": past_key_values,
1015
+ "use_cache": kwargs.get("use_cache"),
1016
+ "attention_mask": attention_mask,
1017
+ }
1018
+ )
1019
+ return model_inputs
1020
+
1021
+ def forward(
1022
+ self,
1023
+ input_ids: Optional[torch.LongTensor] = None,
1024
+ past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None,
1025
+ attention_mask: Optional[torch.FloatTensor] = None,
1026
+ token_type_ids: Optional[torch.LongTensor] = None,
1027
+ position_ids: Optional[torch.LongTensor] = None,
1028
+ head_mask: Optional[torch.FloatTensor] = None,
1029
+ inputs_embeds: Optional[torch.FloatTensor] = None,
1030
+ encoder_hidden_states: Optional[torch.Tensor] = None,
1031
+ encoder_attention_mask: Optional[torch.FloatTensor] = None,
1032
+ labels: Optional[torch.LongTensor] = None,
1033
+ use_cache: Optional[bool] = None,
1034
+ output_attentions: Optional[bool] = None,
1035
+ output_hidden_states: Optional[bool] = None,
1036
+ return_dict: Optional[bool] = None,
1037
+ ) -> Union[Tuple, CausalLMOutputWithPast]:
1038
+
1039
+ return_dict = (
1040
+ return_dict if return_dict is not None else self.config.use_return_dict
1041
+ )
1042
+
1043
+ transformer_outputs = self.transformer(
1044
+ input_ids,
1045
+ past_key_values=past_key_values,
1046
+ attention_mask=attention_mask,
1047
+ token_type_ids=token_type_ids,
1048
+ position_ids=position_ids,
1049
+ head_mask=head_mask,
1050
+ inputs_embeds=inputs_embeds,
1051
+ encoder_hidden_states=encoder_hidden_states,
1052
+ encoder_attention_mask=encoder_attention_mask,
1053
+ use_cache=use_cache,
1054
+ output_attentions=output_attentions,
1055
+ output_hidden_states=output_hidden_states,
1056
+ return_dict=return_dict,
1057
+ )
1058
+ hidden_states = transformer_outputs[0]
1059
+
1060
+ hidden_states = hidden_states.to(torch.bfloat16)
1061
+
1062
+ #解决self和mat2数据类型不一致的bug
1063
+
1064
+ lm_logits = self.lm_head(hidden_states)
1065
+
1066
+ loss = None
1067
+ if labels is not None:
1068
+ labels = labels.to(lm_logits.device)
1069
+ shift_logits = lm_logits[..., :-1, :].contiguous()
1070
+ shift_labels = labels[..., 1:].contiguous()
1071
+ loss_fct = CrossEntropyLoss()
1072
+ loss = loss_fct(
1073
+ shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)
1074
+ )
1075
+
1076
+ if not return_dict:
1077
+ output = (lm_logits,) + transformer_outputs[1:]
1078
+ return ((loss,) + output) if loss is not None else output
1079
+
1080
+ return CausalLMOutputWithPast(
1081
+ loss=loss,
1082
+ logits=lm_logits,
1083
+ past_key_values=transformer_outputs.past_key_values,
1084
+ hidden_states=transformer_outputs.hidden_states,
1085
+ attentions=transformer_outputs.attentions,
1086
+ )
1087
+
1088
+ @staticmethod
1089
+ def _reorder_cache(
1090
+ past_key_values: Tuple[Tuple[torch.Tensor]], beam_idx: torch.Tensor
1091
+ ) -> Tuple[Tuple[torch.Tensor]]:
1092
+
1093
+ return tuple(
1094
+ tuple(
1095
+ past_state.index_select(0, beam_idx.to(past_state.device))
1096
+ for past_state in layer_past
1097
+ )
1098
+ for layer_past in past_key_values
1099
+ )
1100
+
1101
+ def chat(
1102
+ self,
1103
+ tokenizer: PreTrainedTokenizer,
1104
+ query: str,
1105
+ history: Optional[HistoryType],
1106
+ system: str = "You are a helpful assistant.",
1107
+ stream: Optional[bool] = _SENTINEL,
1108
+ stop_words_ids: Optional[List[List[int]]] = None,
1109
+ generation_config: Optional[GenerationConfig] = None,
1110
+ **kwargs,
1111
+ ) -> Tuple[str, HistoryType]:
1112
+ generation_config = generation_config if generation_config is not None else self.generation_config
1113
+
1114
+ assert stream is _SENTINEL, _ERROR_STREAM_IN_CHAT
1115
+ assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
1116
+ if history is None:
1117
+ history = []
1118
+ else:
1119
+ # make a copy of the user's input such that is is left untouched
1120
+ history = copy.deepcopy(history)
1121
+
1122
+ if stop_words_ids is None:
1123
+ stop_words_ids = []
1124
+
1125
+ max_window_size = kwargs.get('max_window_size', None)
1126
+ if max_window_size is None:
1127
+ max_window_size = generation_config.max_window_size
1128
+ raw_text, context_tokens = make_context(
1129
+ tokenizer,
1130
+ query,
1131
+ history=history,
1132
+ system=system,
1133
+ max_window_size=max_window_size,
1134
+ chat_format=generation_config.chat_format,
1135
+ )
1136
+
1137
+ stop_words_ids.extend(get_stop_words_ids(
1138
+ generation_config.chat_format, tokenizer
1139
+ ))
1140
+ input_ids = torch.tensor([context_tokens]).to(self.device)
1141
+ outputs = self.generate(
1142
+ input_ids,
1143
+ stop_words_ids=stop_words_ids,
1144
+ return_dict_in_generate=False,
1145
+ generation_config=generation_config,
1146
+ **kwargs,
1147
+ )
1148
+
1149
+ response = decode_tokens(
1150
+ outputs[0],
1151
+ tokenizer,
1152
+ raw_text_len=len(raw_text),
1153
+ context_length=len(context_tokens),
1154
+ chat_format=generation_config.chat_format,
1155
+ verbose=False,
1156
+ errors='replace'
1157
+ )
1158
+
1159
+ # as history is a copy of the user inputs,
1160
+ # we can always return the new turn to the user.
1161
+ # separating input history and output history also enables the user
1162
+ # to implement more complex history management
1163
+ history.append((query, response))
1164
+
1165
+ return response, history
1166
+
1167
+ def chat_stream(
1168
+ self,
1169
+ tokenizer: PreTrainedTokenizer,
1170
+ query: str,
1171
+ history: Optional[HistoryType],
1172
+ system: str = "You are a helpful assistant.",
1173
+ stop_words_ids: Optional[List[List[int]]] = None,
1174
+ logits_processor: Optional[LogitsProcessorList] = None,
1175
+ generation_config: Optional[GenerationConfig] = None,
1176
+ **kwargs,
1177
+ ) -> Generator[str, Any, None]:
1178
+ generation_config = generation_config if generation_config is not None else self.generation_config
1179
+ assert generation_config.chat_format == 'chatml', _ERROR_BAD_CHAT_FORMAT
1180
+ if history is None:
1181
+ history = []
1182
+ if stop_words_ids is None:
1183
+ stop_words_ids = []
1184
+
1185
+ max_window_size = kwargs.get('max_window_size', None)
1186
+ if max_window_size is None:
1187
+ max_window_size = generation_config.max_window_size
1188
+ raw_text, context_tokens = make_context(
1189
+ tokenizer,
1190
+ query,
1191
+ history=history,
1192
+ system=system,
1193
+ max_window_size=max_window_size,
1194
+ chat_format=generation_config.chat_format,
1195
+ )
1196
+
1197
+ stop_words_ids.extend(get_stop_words_ids(
1198
+ generation_config.chat_format, tokenizer
1199
+ ))
1200
+ if stop_words_ids is not None:
1201
+ stop_words_logits_processor = StopWordsLogitsProcessor(
1202
+ stop_words_ids=stop_words_ids,
1203
+ eos_token_id=generation_config.eos_token_id,
1204
+ )
1205
+ if logits_processor is None:
1206
+ logits_processor = LogitsProcessorList([stop_words_logits_processor])
1207
+ else:
1208
+ logits_processor.append(stop_words_logits_processor)
1209
+ input_ids = torch.tensor([context_tokens]).to(self.device)
1210
+
1211
+ from transformers_stream_generator.main import NewGenerationMixin, StreamGenerationConfig
1212
+ self.__class__.generate_stream = NewGenerationMixin.generate
1213
+ self.__class__.sample_stream = NewGenerationMixin.sample_stream
1214
+ stream_config = StreamGenerationConfig(**generation_config.to_dict(), do_stream=True)
1215
+
1216
+ def stream_generator():
1217
+ outputs = []
1218
+ for token in self.generate_stream(
1219
+ input_ids,
1220
+ return_dict_in_generate=False,
1221
+ generation_config=stream_config,
1222
+ logits_processor=logits_processor,
1223
+ seed=-1,
1224
+ **kwargs):
1225
+ outputs.append(token.item())
1226
+ yield tokenizer.decode(outputs, skip_special_tokens=True, errors='ignore')
1227
+
1228
+ return stream_generator()
1229
+
1230
+ def generate(
1231
+ self,
1232
+ inputs: Optional[torch.Tensor] = None,
1233
+ generation_config: Optional[GenerationConfig] = None,
1234
+ logits_processor: Optional[LogitsProcessorList] = None,
1235
+ stopping_criteria: Optional[StoppingCriteriaList] = None,
1236
+ prefix_allowed_tokens_fn: Optional[
1237
+ Callable[[int, torch.Tensor], List[int]]
1238
+ ] = None,
1239
+ synced_gpus: Optional[bool] = None,
1240
+ assistant_model: Optional["PreTrainedModel"] = None,
1241
+ streamer: Optional["BaseStreamer"] = None,
1242
+ **kwargs,
1243
+ ) -> Union[GenerateOutput, torch.LongTensor]:
1244
+ generation_config = generation_config if generation_config is not None else self.generation_config
1245
+
1246
+ # Process stop_words_ids.
1247
+ stop_words_ids = kwargs.pop("stop_words_ids", None)
1248
+ if stop_words_ids is None and generation_config is not None:
1249
+ stop_words_ids = getattr(generation_config, "stop_words_ids", None)
1250
+ if stop_words_ids is None:
1251
+ stop_words_ids = getattr(generation_config, "stop_words_ids", None)
1252
+
1253
+ if stop_words_ids is not None:
1254
+ stop_words_logits_processor = StopWordsLogitsProcessor(
1255
+ stop_words_ids=stop_words_ids,
1256
+ eos_token_id=generation_config.eos_token_id,
1257
+ )
1258
+ if logits_processor is None:
1259
+ logits_processor = LogitsProcessorList([stop_words_logits_processor])
1260
+ else:
1261
+ logits_processor.append(stop_words_logits_processor)
1262
+
1263
+ return super().generate(
1264
+ inputs,
1265
+ generation_config=generation_config,
1266
+ logits_processor=logits_processor,
1267
+ stopping_criteria=stopping_criteria,
1268
+ prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
1269
+ synced_gpus=synced_gpus,
1270
+ assistant_model=assistant_model,
1271
+ streamer=streamer,
1272
+ **kwargs,
1273
+ )
1274
+
1275
+
1276
+ class RotaryEmbedding(torch.nn.Module):
1277
+ def __init__(self, dim, base=10000):
1278
+ super().__init__()
1279
+ self.dim = dim
1280
+ self.base = base
1281
+ inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
1282
+ self.register_buffer("inv_freq", inv_freq, persistent=False)
1283
+ if importlib.util.find_spec("einops") is None:
1284
+ raise RuntimeError("einops is required for Rotary Embedding")
1285
+
1286
+ self._rotary_pos_emb_cache = None
1287
+ self._seq_len_cached = 0
1288
+ self._ntk_alpha_cached = 1.0
1289
+ self._ntk_alpha_cached_list = [1.0]
1290
+
1291
+ def update_rotary_pos_emb_cache(self, seqlen, ntk_alpha=1.0):
1292
+ if seqlen > self._seq_len_cached or ntk_alpha != self._ntk_alpha_cached:
1293
+ base = self.base * ntk_alpha ** (self.dim / (self.dim - 2))
1294
+ self.inv_freq = 1.0 / (
1295
+ base
1296
+ ** (
1297
+ torch.arange(0, self.dim, 2, device=self.inv_freq.device).float()
1298
+ / self.dim
1299
+ )
1300
+ )
1301
+ self._seq_len_cached = max(2 * seqlen, 16)
1302
+ self._ntk_alpha_cached = ntk_alpha
1303
+ seq = torch.arange(self._seq_len_cached, device=self.inv_freq.device)
1304
+ freqs = torch.outer(seq.type_as(self.inv_freq), self.inv_freq)
1305
+
1306
+ emb = torch.cat((freqs, freqs), dim=-1)
1307
+ from einops import rearrange
1308
+
1309
+ emb = rearrange(emb, "n d -> 1 n 1 d")
1310
+
1311
+ cos, sin = emb.cos(), emb.sin()
1312
+ self._rotary_pos_emb_cache = [cos, sin]
1313
+
1314
+ def forward(self, max_seq_len, ntk_alpha=1.0):
1315
+ self.update_rotary_pos_emb_cache(max_seq_len, ntk_alpha)
1316
+ cos, sin = self._rotary_pos_emb_cache
1317
+ return [cos[:, :max_seq_len], sin[:, :max_seq_len]]
1318
+
1319
+
1320
+ def _rotate_half(x):
1321
+ from einops import rearrange
1322
+
1323
+ x = rearrange(x, "... (j d) -> ... j d", j=2)
1324
+ x1, x2 = x.unbind(dim=-2)
1325
+ return torch.cat((-x2, x1), dim=-1)
1326
+
1327
+
1328
+ def apply_rotary_pos_emb(t, freqs):
1329
+ """ Apply rotary embedding to the first rotary_dim of the iput
1330
+
1331
+ Arguments:
1332
+ t (tensor(batch_size, seq_len, n_head, head_dim)):
1333
+ the input embedding/hidden states
1334
+ freqs (list[tensor(1, seq_len, 1, rotary_dim), tensor(1, seq_len, 1, rotary_dim)]):
1335
+ the cached cos/sin position embeddings
1336
+ """
1337
+ rot_dim = freqs[0].shape[-1]
1338
+ cos, sin = freqs
1339
+ t_float = t.float()
1340
+ if apply_rotary_emb_func is not None and t.is_cuda:
1341
+ # apply_rotary_emb in flash_attn requires cos/sin to be of
1342
+ # shape (seqlen, rotary_dim / 2) and apply rotary embedding
1343
+ # to the first rotary_dim of the input
1344
+ cos = cos.squeeze(0).squeeze(1)[:, : rot_dim // 2]
1345
+ sin = sin.squeeze(0).squeeze(1)[:, : rot_dim // 2]
1346
+ return apply_rotary_emb_func(t_float, cos, sin).type_as(t)
1347
+ else:
1348
+ t_rot, t_pass = t_float[..., :rot_dim], t_float[..., rot_dim:]
1349
+ t_rot = (t_rot * cos) + (_rotate_half(t_rot) * sin)
1350
+ return torch.cat((t_rot, t_pass), dim=-1).type_as(t)
1351
+
1352
+
1353
+ class RMSNorm(torch.nn.Module):
1354
+ def __init__(self, dim: int, eps: float = 1e-6):
1355
+ super().__init__()
1356
+ self.eps = eps
1357
+ self.weight = nn.Parameter(torch.ones(dim))
1358
+
1359
+ def _norm(self, x):
1360
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
1361
+
1362
+ def forward(self, x):
1363
+ if rms_norm is not None and x.is_cuda:
1364
+ return rms_norm(x, self.weight, self.eps)
1365
+ else:
1366
+ output = self._norm(x.float()).type_as(x)
1367
+ return output * self.weight