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Author: pass-lin

bert4keras3/__init__.py

@@ -1,6 +1,6 @@
 #! -*- coding: utf-8 -*-
 
-__version__ = '1.0.2'
+__version__ = '1.1.2'
 
 from bert4keras3 import backend,layers,models,snippets,tokenizers
 from bert4keras3.backend import ops

bert4keras3/__pycache__/__init__.cpython-310.pyc

@@ -8,13 +8,24 @@
 import tensorflow as tf
 from functools import wraps
 is_tf_keras = strtobool(os.environ.get('TF_KERAS', '0'))
+lora_model = strtobool(os.environ.get('ENABLE_LORA', '0'))
+#jax使用flash参考https://github.com/nshepperd/flash_attn_jax/releases这里安装flash
+enable_flashatt = strtobool(os.environ.get('FLASH_ATTN', '0'))
 os.environ["KERAS_BACKEND"]=os.environ.get("KERAS_BACKEND", 'tensorflow')
 backlib=os.environ["KERAS_BACKEND"]
 if backlib=='tfkeras':
     is_tf_keras = True
+    if enable_flashatt:
+        raise('tensorflow not support flash-attention')
 elif backlib=='torch':
     import torch
+    if enable_flashatt:
+        from flash_attn import flash_attn_func
+        def flash_mha(q,k,v,softmax_scale=None, is_causal=False, window_size=(-1,-1)):
+            return flash_attn_func(q, k, v, softmax_scale=softmax_scale, causal=is_causal,window_size=window_size)
 elif backlib=='jax':
+    if enable_flashatt:
+        from flash_attn_jax import flash_mha
     import jax  
 if is_tf_keras:
     sys.modules['keras'] = tf.keras
@@ -534,7 +545,10 @@ def actual_grad_fn(*doutputs):
 else:
     sys.modules['keras.ops']=ops
 
-
+def slices_index(x,index,axis):
+    shape = list(ops.shape(x))
+    shape[axis] = index
+    return ops.slice(x,ops.zeros_like(shape),shape)
 custom_objects = {
     'gelu_erf': gelu_erf,
     'gelu_tanh': ops.gelu,

bert4keras3/__pycache__/__init__.cpython-39.pyc

@@ -3,11 +3,13 @@
 
 import numpy as np
 
-from bert4keras3.backend import keras, ops, is_tf_keras,K,tf
-from bert4keras3.backend import align, sequence_masking
+from bert4keras3.backend import keras, ops, is_tf_keras,K,tf,enable_flashatt
+if enable_flashatt:
+    from bert4keras3.backend import flash_mha
+from bert4keras3.backend import align, sequence_masking,backlib
 from bert4keras3.backend import recompute_grad,int_shape
 from bert4keras3.backend import attention_normalize,divide_no_nan
-from bert4keras3.backend import sinusoidal_embeddings
+from bert4keras3.backend import sinusoidal_embeddings,slices_index
 from bert4keras3.backend import apply_rotary_position_embeddings
 from keras import initializers, activations
 from keras.layers import *
@@ -27,7 +29,10 @@ def get_config(self):
         return dict(list(base_config.items()) + list(config.items()))
     def call(self,inputs, **kwargs):
         index = kwargs.get('index') 
-        return ops.expand_dims(ops.take(inputs,index,self.axis),self.axis)
+        out = ops.expand_dims(ops.take(inputs,index,self.axis),self.axis)
+        if backlib=='torch':
+            return slices_index(out,index+1,1)
+        return out
     def compute_output_shape(self, input_shape):
         input_shape = list(input_shape)
         input_shape[self.axis]=1
@@ -637,6 +642,16 @@ def pay_attention_to(self, inputs, mask=None, **kwargs):
                 vw = value_cache
         else:
             cache = None
+        
+        if enable_flashatt:
+            is_causal = False
+            if a_bias is not None:
+                is_causal = True
+            softmax_scale = 1.
+            if self.attention_scale:
+                softmax_scale = 1 / self.key_size**0.5
+            o = flash_mha(qw,kw,vw,softmax_scale=softmax_scale, is_causal=is_causal)
+            return o,[],[]
         # Attention
         a = ops.einsum('bjhd,bkhd->bhjk', qw, kw)
         # 处理位置编码
@@ -645,6 +660,7 @@ def pay_attention_to(self, inputs, mask=None, **kwargs):
             a = a + ops.einsum('bjhd,jkd->bhjk', qw, position_bias)
         elif p_bias == 't5_relative':
             position_bias = ops.transpose(inputs[n], (2, 0, 1))
+            #print(a.shape,position_bias.shape)
             a = a + ops.expand_dims(position_bias, 0)
         # Attention（续）
         if self.attention_scale:
@@ -823,21 +839,40 @@ def call(self, inputs, mask=None, a_bias=None, p_bias=None):
         if p_bias == 'rotary':
             q, k = apply_rotary_position_embeddings(inputs[n], q, k)
         # Attention
+        if enable_flashatt and ops.shape(k)==ops.shape(v):
+            z = self.pay_flash_attention_to(q,k,v, a_bias)
+        else:
+            z = self.pay_attention_to(q,k,v,mask, a_bias)   
+        # 计算输出
+        if self.self_attention==False and self.factorization:
+            z = self.vW_dense(z)
+        o = self.o_dense(u * z)
+        return o
+    def pay_flash_attention_to(self, q,k,v, a_bias):
+        is_causal = False
+        if a_bias is not None:
+            is_causal = True
+        softmax_scale = 1.
+        if self.attention_scale:
+            softmax_scale = 1 / self.key_size**0.5
+        if ops.ndim(q)==3:
+            k = ops.expand_dims(k,2)
+            q = ops.expand_dims(q,2)
+            v = ops.expand_dims(v,2)
+        o = flash_mha(q,k,v,softmax_scale=softmax_scale, is_causal=is_causal)
+        return ops.squeeze(o,2)
+    def pay_attention_to(self, q,k,v,mask, a_bias):
         a = ops.einsum('bmd,bnd->bmn', q, k)
         if self.attention_scale:
             a = a / self.key_size**0.5
         A = attention_normalize(a, mask, -1, self.normalization, a_bias)
         if self.attention_dropout:
             A = self.dropout(A)
-        # 计算输出
         try:
             z=ops.einsum('bmn,bnd->bmd', A, v)
         except:
             pass
-        if self.self_attention==False and self.factorization:
-            z = self.vW_dense(z)
-        o = self.o_dense(u * z)
-        return o
+        return z
 
     def compute_mask(self, inputs, mask=None):
         if isinstance(mask, list):

bert4keras3/__pycache__/backend.cpython-310.pyc

@@ -2,7 +2,7 @@
 # 主要模型
 
 import numpy as np
-from bert4keras3.backend import tf,keras,backlib
+from bert4keras3.backend import tf,keras,backlib,lora_model
 from bert4keras3.layers import *
 from bert4keras3.snippets import insert_arguments
 from bert4keras3.snippets import delete_arguments
@@ -473,6 +473,16 @@ def cache_call(self,inputs:list,input_lengths:list,end_token,
             caches = self.initial_cache(inputs[:1])
             key = 0
         x = inputs[key]
+        
+        class start_index(keras.Layer):
+            def call(self,x):
+                z = x!=0
+                if index_bias>0:
+                    t = ops.ones([ops.shape(z)[0],index_bias],dtype=z.dtype)
+                    z = ops.slice_update(z,[0,0],t)
+                return ops.max(ops.sum(z,-1))-1
+        
+        
         length = input_lengths[key]
         self.cache_attention_bias=None
         self.cache_position_bias=None
@@ -494,26 +504,23 @@ def cache_call(self,inputs:list,input_lengths:list,end_token,
                                         attention_mask=self.cache_attention_bias,
                                         position_bias=self.cache_position_bias)
             z,cache = out[:-1],out[-1]
+            
             caches[index*j:index*j+j]=cache
 
-        class start_index(keras.Layer):
-            def call(self,x):
-                z = x!=0
-                if index_bias>0:
-                    t = ops.ones([ops.shape(z)[0],index_bias],dtype=z.dtype)
-                    z = ops.slice_update(z,[0,0],t)
-                return ops.max(ops.sum(z,-1))-1
+        
+        
         index = self.apply(
             inputs=x,
             layer=start_index,
             name='start_index'
         )
+        
         def cond(inputs, caches, index , flags):
             cond1 = ops.less(index,length-1)
             cond2 = ops.logical_not(ops.all(ops.equal(inputs[key][:,index],end_token),-1))
             return ops.logical_and(cond1,cond2)
 
-        def body(inputs, caches, index , flags):
+        def body(inputs, caches, index , flags,cache_shape_torch=None):
             if progress_print:
 
                 print('\r',index,end='')
@@ -529,7 +536,10 @@ def body(inputs, caches, index , flags):
             position_bias = self.compute_cache_position_bias(self_cache_update_index = index) 
 
             for i in range(self.num_hidden_layers):
+                
                 layer_caches = caches[i*j:i*j+j]
+                if backlib=='torch':
+                    layer_caches[0]=ops.concatenate([layer_caches[0],ops.zeros(cache_shape_torch,dtype=layer_caches[0].dtype)],axis=2)
                 out=self.apply_main_cache_layers(z+[layer_caches], i,self_cache_update_index=index,
                                             cross_cache_update_index=None,
                                             attention_mask=attention_mask,
@@ -546,13 +556,18 @@ def body(inputs, caches, index , flags):
             search_in = [o,index,inputs[key],flags]
             inputs[key],flags = self.Search(search_in,k=k,mode=search_mode)
             return (inputs, caches, index , flags)
+        num_hidden_layers = self.num_hidden_layers
         class WhileLayer(keras.Layer):
             def call(self, x):
                 inputs, caches, index =  x[:]
                 flags = ops.ones([ops.shape(caches[0])[0],1],dtype='bool')
                 if backlib=='torch':
+                    cache_shape_torch = list(ops.shape(caches[0]))
+                    cache_shape_torch[2] = 1
+                    for i in range(num_hidden_layers):
+                        caches[i*j]=slices_index(caches[i*j],index,2)
                     while cond(inputs, caches, index , flags):
-                        inputs, caches, index , flags = body(inputs, caches, index , flags)
+                        inputs, caches, index , flags = body(inputs, caches, index , flags,cache_shape_torch)
                     return (inputs, caches, index)
                 outs=ops.while_loop(
                     cond,
@@ -3639,6 +3654,7 @@ def build_transformer_model(
     model='bert',
     application='encoder',
     return_keras_model=True,
+    keras_weights_path=None,
     **kwargs
 ):
     """根据配置文件构建模型，可选加载checkpoint权重
@@ -3718,7 +3734,19 @@ def build_transformer_model(
             shape=[1 if t==None else t for t in shape]
             inputs.append(np.zeros(shape,modelin.dtype))
         transformer.model.predict(inputs,verbose=3)
-    
+        if keras_weights_path is not None:
+            transformer.model.load_weights(keras_weights_path, skip_mismatch=True)
+        if lora_model:
+            
+            def enable_lora(t):
+                if isinstance(t,keras.layers.Embedding) or isinstance(t,keras.layers.Dense):
+                    t.enable_lora(True)
+            for layer in transformer.model.layers:
+                layer.trainable=False
+                enable_lora(layer)
+                for kid in dir (layer):
+                    t = getattr(layer,kid)
+                    enable_lora(t)
     if checkpoint_path is not None:
         transformer.load_weights_from_checkpoint(checkpoint_path)