stop_gradient(x) 停止梯度计算。 custom_linear_solve(matvec, b, solve[, …]) 使用隐式定义的梯度执行无矩阵线性求解。 custom_root(f, initial_guess, solve, …[, …]) 可微分求解函数的根。 并行操作符 all_gather(x, axis_name, *[, …]) 在所有副本中收集x的值。 all_to_all(x, axis_na...
将其标记为stop_gradient可以将这一点传达给JAX的自动差异机制。从而提高梯度计算的效率。您可以通过打印...
将LoRA 参数(lora_a 和 lora_b)与主模型参数分开。 使用jax.lax.stop_gradient (kernel) 来防止对主模型权重的更新。 使用lax.dot_general 进行快速、精确控制的矩阵运算。 LoRA 输出在添加到主输出之前会被缩放为 (self.lora_alpha/self.lora_rank)。 LoRADense 层 在此设定一个自定义的 LoRADense 层,该...
dot_general(lhs, rhs, dimension_numbers[, …]) 通用的点积/收缩运算符。...自定义梯度操作符 stop_gradient(x) 停止梯度计算。 custom_linear_solve(matvec, b, solve[, …]) 使用隐式定义的梯度执行无矩阵线性求解。...当与 JAX 外部系统(例如将数组导出为可序列化格式)交互或将 key 传...
import tensorflow as tfa = tf.Variable(1.0) # Like PyTorch, these are values, not placehodlers. b = tf.Variable(2.0)with tf.GradientTape() as tape: c = a * bdcda = tape.gradient(c, a)print(dcda)JAX JAX 不会向用户公开诸如梯度带等方面的低级别细节。简单说来，JAX的思维方式为：...
(layer, int), f"Layer must be an integer, not {type(layer)}" self.activations[layer].extend(np.asarray(activations).ravel().tolist()) jax.lax.stop_gradient(activations[i])) def reset(self, epoch): self.epoch = epoch self.activations = defaultdict(list) activation_logger = Activation...
dcda = tape.gradient(c, a) print(dcda) JAX JAX 不会向用户公开诸如梯度带等方面的低级别细节。简单说来,JAX的思维方式为:将输入和输出都用Python函数来表示。 import jax a = 2.0 b = 3.0 jax.grad(jax.lax.mul)(a, b) # Compute c = a * b w.r.t. a. The result is b=3. ...
Gradient values are quite off in jax==0.5.0 #25981 closed Jan 18, 2025 Performances sparse matrix-vector product #25949 closed Jan 17, 2025 Add streamlined `_ensure_arraylike` utility #14921 closed Jan 17, 2025 jax_debug_key_reuse incorrectly raises error #25953 closed Jan 17...
(epoch) def __call__(self, layer, activations): D = activations.shape[0] for i in range(D): self.activations[(layer, i)].append( jax.lax.stop_gradient(activations[i])) def reset(self, epoch): self.epoch = epoch self.activations = defaultdict(list) activation_logger = Activation...
--> 252 _, _, _, final_val = _stop_gradient_on_unperturbed(init_val_, final_val_, body_fun_) 253 return final_val JaxStackTraceBeforeTransformation: TypeError: Custom JVP rule must produce primal and tangent outputs with corresponding shapes and dtypes, but got: ...