import unittest import numpy import itertools import paddle.v2.framework.core as core from paddle.v2.framework.op import Operator __all__ = ['get_numeric_gradient'] def create_op(op_type): # TODO need to set attrs kwargs = dict() for in_name in Operator.get_op_input_names(op_type): kwargs[in_name] = in_name for out_name in Operator.get_op_output_names(op_type): kwargs[out_name] = out_name return Operator(op_type, **kwargs) def grad_var_name(var_name): return var_name + "@GRAD" def get_numeric_gradient(op, input_values, output_name, input_to_check, delta=0.005, local_scope=None): """ Get Numeric Gradient for an operator's input. :param op: C++ operator instance, could be an network :param input_values: The input variables. Should be an dictionary, key is variable name. Value is numpy array. :param output_name: The final output variable name. :param input_to_check: The input variable need to get gradient. :param delta: The perturbation value for numeric gradient method. The smaller delta is, the more accurate result will get. But if that delta is too small, it could occur numerical stability problem. :param local_scope: The local scope used for get_numeric_gradient. :return: The gradient array in numpy format. """ if local_scope is None: local_scope = core.Scope() # Create all input variable in local_scope for var_name in input_values: var = local_scope.new_var(var_name) tensor = var.get_tensor() tensor.set_dims(input_values[var_name].shape) tensor.alloc_float(core.CPUPlace()) tensor.set(input_values[var_name], core.CPUPlace()) # Create all output variable in local_scope opts = op.outputs() for key in opts: for output in opts[key]: if local_scope.find_var(output) is None: local_scope.new_var(output).get_tensor() op.infer_shape(local_scope) # allocate output memory for key in opts: for output in opts[key]: local_scope.find_var(output).get_tensor().alloc_float(core.CPUPlace( )) cpu_ctx = core.DeviceContext.create(core.CPUPlace()) def get_output(): op.run(local_scope, cpu_ctx) return numpy.array(local_scope.find_var(output_name).get_tensor()).sum() def product(dim): return reduce(lambda a, b: a * b, dim, 1) # get the input tensor that we want to get it's numeric gradient. tensor_to_check = local_scope.find_var(input_to_check).get_tensor() tensor_size = product(tensor_to_check.get_dims()) # prepare a numpy array to store the gradient. gradient_flat = numpy.zeros(shape=(tensor_size, ), dtype='float32') # we only compute gradient of one element each time. # we use a for loop to compute the gradient of every element. for i in xrange(tensor_size): # get one input element throw it's index i. origin = tensor_to_check.get_float_element(i) # add delta to it, run op and then get the sum of the result tensor. x_pos = origin + delta tensor_to_check.set_float_element(i, x_pos) y_pos = get_output() # plus delta to this element, run op and get the sum of the result tensor. x_neg = origin - delta tensor_to_check.set_float_element(i, x_neg) y_neg = get_output() # restore old value tensor_to_check.set_float_element(i, origin) # compute the gradient of this element and store it into a numpy array. gradient_flat[i] = (y_pos - y_neg) / delta / 2 # reshape the gradient result to the shape of the source tensor. return gradient_flat.reshape(tensor_to_check.get_dims()) class GradientChecker(unittest.TestCase): def get_grad(self, forward_op, backward_op, input_vars, grad_names, place): scope = core.Scope() ctx = core.DeviceContext.create(place) inputs = forward_op.inputs() in_names = [item for k in inputs for item in inputs[k]] outputs = forward_op.outputs() out_names = [item for k in outputs for item in outputs[k]] # create input var and set value for name, value in input_vars.iteritems(): if name not in in_names: raise ValueError(name + "does not exist in Op's inputs.") var = scope.new_var(name).get_tensor() var.set_dims(value.shape) var.set(value, place) # run forward op for out_name in out_names: scope.new_var(out_name) forward_op.infer_shape(scope) forward_op.run(scope, ctx) # set output var's shape # set output grad to ones for name in out_names: out_tensor = scope.find_var(name).get_tensor() grad_tensor = scope.new_var(grad_var_name(name)).get_tensor() grad_tensor.set_dims(out_tensor.shape()) data = numpy.ones(out_tensor.shape(), dtype=numpy.float32) grad_tensor.set(data, place) # run backward op for name in backward_op.outputs(): scope.new_var(name) backward_op.infer_shape(scope) backward_op.run(scope, ctx) outs = [ numpy.array(scope.find_var(name).get_tensor()) for name in grad_names ] return outs def compare_grad(self, forward_op, inputs): backward_op = core.Operator.backward(forward_op, set()) # return if not compile with GPU or not implementing GPU kernel if not (core.is_compile_gpu() and backward_op.support_gpu()): return outputs = backward_op.outputs() out_names = [item for k in outputs for item in outputs[k]] cpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names, core.CPUPlace()) gpu_grads = self.get_grad(forward_op, backward_op, inputs, out_names, core.GPUPlace(0)) for c_grad, g_grad, name in itertools.izip(cpu_grads, gpu_grads, out_names): self.assertTrue( numpy.allclose(c_grad, g_grad), "output name: " + name + " has diff") def assert_is_close(self, numeric_grads, analytic_grads, names, max_relative_error, msg_prefix): for a, b, name in itertools.izip(numeric_grads, analytic_grads, names): abs_a = numpy.abs(a) # if abs_a is nearly zero, then use abs error for a, not relative # error. abs_a[abs_a < 1e-3] = 1 diff_mat = numpy.abs(a - b) / abs_a max_diff = numpy.max(diff_mat) def err_msg(): offset = numpy.argmax(diff_mat > max_relative_error) return "%s Variable %s max gradient diff %f over limit %f, the first " \ "error element is %d" % ( msg_prefix, name, max_diff, max_relative_error, offset) self.assertLessEqual(max_diff, max_relative_error, err_msg()) def check_grad(self, forward_op, input_vars, inputs_to_check, output_name, no_grad_set=None, only_cpu=False, max_relative_error=0.005): """ :param forward_op: used to create backward_op :param input_vars: numpy value of input variable. The following computation will use these variables. :param inputs_to_check: inputs var names that should check gradient. :param output_name: output name that used to :param max_relative_error: The relative tolerance parameter. :param no_grad_set: used when create backward ops :param only_cpu: only compute and check gradient on cpu kernel. :return: """ if no_grad_set is None: no_grad_set = set() no_tmp_out = forward_op.no_intermediate_outputs() if len(no_tmp_out) != 1: raise ValueError("non temp out_names should be 1") inputs = forward_op.inputs() in_names = [item for k in inputs for item in inputs[k]] for no_grad in no_grad_set: if no_grad not in in_names: raise ValueError("no_grad should be in in_names") backward_op = core.Operator.backward(forward_op, no_grad_set) places = [core.CPUPlace()] if not only_cpu and core.is_compile_gpu() and backward_op.support_gpu(): places.append(core.GPUPlace(0)) # get numerical gradients numeric_grads = [ get_numeric_gradient(forward_op, input_vars, output_name, name) for name in inputs_to_check ] check_names = [grad_var_name(name) for name in inputs_to_check] for place in places: # get analytical gradients according to different device analytic_grads = self.get_grad(forward_op, backward_op, input_vars, check_names, place) self.assert_is_close(numeric_grads, analytic_grads, check_names, max_relative_error, "Gradient Check On %s" % str(place))