o 2h'@sdZddlZddlmZddlmZddlmZddlmZddlm Z ddlm Z dd lm Z dd lm Z d d Z ed dejfddZeddejfddZeddejfddZddZddZeddejfddZdS)z1Gradients for operators defined in random_ops.py.N) constant_op)dtypes)ops) array_ops)clip_ops) gen_array_ops)gen_random_ops)math_opscCs2tjtj|gtjdt|gdd}t||S)N)dtyperaxis)rconcatonesrint32shapereshape)xnum_dimensions new_shaper\/var/www/html/chatgem/venv/lib/python3.10/site-packages/tensorflow/python/ops/random_grad.pyadd_leading_unit_dimensionss r RandomGammaopcCs|jd}|jd}|jd}t|g(t|d}t||}t||}dt j ||t |dfWdS1s@wYdS)aReturns the gradient of a Gamma sample w.r.t. alpha. The gradient is computed using implicit differentiation (Figurnov et al., 2018). Args: op: A `RandomGamma` operation. We assume that the inputs to the operation are `shape` and `alpha` tensors, and the output is the `sample` tensor. grad: The incoming gradient `dloss / dsample` of the same shape as `op.outputs[0]`. Returns: A `Tensor` with derivatives `dloss / dalpha`. References: Implicit Reparameterization Gradients: [Figurnov et al., 2018] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients) ([pdf] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients.pdf)) rNr ) inputsoutputsrcontrol_dependenciesrrrrrandom_gamma_gradr reduce_sumrange)rgradralphasamplenum_sample_dimensionsalpha_broadcastable partial_arrr_RandomGammaGrad$s    $r'StatelessRandomGammaV2cCsb|jd}|jd}|jd}t|gddt||||fWdS1s*wYdS)aReturns the gradient of a Gamma sample w.r.t. alpha. The gradient is computed using implicit differentiation (Figurnov et al., 2018). Args: op: A `StatelessRandomGamma` operation. We assume that the inputs to the operation are `shape`, `seed` and `alpha` tensors, and the output is the `sample` tensor. grad: The incoming gradient `dloss / dsample` of the same shape as `op.outputs[0]`. Returns: A `Tensor` with derivatives `dloss / dalpha`. References: Implicit Reparameterization Gradients: [Figurnov et al., 2018] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients) ([pdf] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients.pdf)) rNrrrr_StatelessGammaGradAlpharr!rr"r#rrr_StatelessRandomGammaV2GradLs   $r-StatelessRandomGammaV3c Csf|jd}|jd}|jd}t|gddddt||||fWdS1s,wYdS)aReturns the gradient of a Gamma sample w.r.t. alpha. The gradient is computed using implicit differentiation (Figurnov et al., 2018). Args: op: A `StatelessRandomGamma` operation. We assume that the inputs to the operation are `shape`, `key`, `counter`, `alg`, and `alpha` tensors, and the output is the `sample` tensor. grad: The incoming gradient `dloss / dsample` of the same shape as `op.outputs[0]`. Returns: A `Tensor` with derivatives `dloss / dalpha`. References: Implicit Reparameterization Gradients: [Figurnov et al., 2018] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients) ([pdf] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients.pdf)) rNr*r,rrr_StatelessRandomGammaV3Gradls    $r0cCsFt|dt|}t||}t||}tj||t|dS)z/Returns gradients of a gamma sampler wrt alpha.rr ) rrrankrrrr rr )rr"r#r!r$r%r&rrrr+s r+c Csptjdtd|jdd}||}t|}t||kdt |t|dkdt |t |}d|S)zNormal distribution function.?g@ half_sqrt_2)r nameg?g) rconstantnpsqrtr r absrwhereerferfc)rr3wzyrrr_Ndtrs  r?%StatelessParameterizedTruncatedNormalc!Cs|jd}|jd}|jd}|jd}|jd}|jd}t|g|||}|||} |||} t| t|t| t|} t|jjj } t|jjj } t | | d| } t d| d| dt | }t d| d|dt | }t| }| }t|}t|}t|}t|}t||}t||}t||}t t|t|}t j|||d}t j|||d}t j|||d}t j|||d}t||\}}t||\}}t||\}}t||\}} tt j||d d |}tt j||d d |}tt j||d d |}tt j|| d d |}d d ||||fWd S1sGwYd S) a?Returns the gradient of a TruncatedNormal sample w.r.t. parameters. The gradient is computed using implicit differentiation (Figurnov et al., 2018). Args: op: A `StatelessParameterizedTruncatedNormal` operation. We assume that the inputs to the operation are `shape`, `seed`, `mean`, `stddev`, `minval`, and `maxval` tensors, and the output is the `sample` tensor. grad: The incoming gradient `dloss / dsample` of the same shape as `op.outputs[0]`. Returns: A list of `Tensor` with derivates with respect to each parameter. References: Implicit Reparameterization Gradients: [Figurnov et al., 2018] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients) ([pdf] (http://papers.nips.cc/paper/7326-implicit-reparameterization-gradients.pdf)) rr)r/rr2r T)r keepdimsN)rrrrr?r6finfor as_numpy_dtypetinyepsr clip_by_valuer exploglog1pr ones_likerbroadcast_dynamic_shaper sizerrbroadcast_gradient_argsr)!rr!rmeanstddevminvalmaxvalr# minval_std maxval_std sample_std cdf_samplerFrGdmaxvaldminvaldmeandstddev mean_shape stddev_shape minval_shape maxval_shapebroadcast_shape extra_dims grad_mean grad_stddev grad_minval grad_maxval_rmeanrstddevrminvalrmaxvalrrr*_StatelessParameterizedTruncatedNormalGrads               &rk)__doc__numpyr6tensorflow.python.frameworkrrrtensorflow.python.opsrrrrr rRegisterGradient Operationr'r-r0r+r?rkrrrrs*        '