393 lines
15 KiB
Python
393 lines
15 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Gradient checker for any ops, graphs.
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The gradient checker verifies numerically that an op/graph properly
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computes the gradients
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"""
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import indexed_slices
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from tensorflow.python.framework import ops
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import gradients
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import tf_logging as logging
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from tensorflow.python.util import deprecation
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from tensorflow.python.util.tf_export import tf_export
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def _product(t):
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if isinstance(t, int):
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return t
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else:
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y = 1
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for x in t:
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y *= x
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return y
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def _extra_feeds(extra_feed_dict, new_feeds):
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if not extra_feed_dict:
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return new_feeds
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r = {}
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r.update(extra_feed_dict)
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r.update(new_feeds)
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return r
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def _compute_theoretical_jacobian(x, x_shape, x_data, dy, dy_shape, dx,
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extra_feed_dict):
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"""Computes the theoretical Jacobian for dy/dx.
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Computes the theoretical Jacobian using the ops generated by
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compute_gradient().
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Args:
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x: the tensor "x".
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x_shape: the dimensions of x as a tuple or an array of ints.
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x_data: a numpy parray as the input data for x
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dy: the tensor "dy".
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dy_shape: the dimensions of dy as a tuple or an array of ints.
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dx: Tensor or IndexedSlices representing dx
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extra_feed_dict: dict that allows fixing specified tensor values
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during the jacobian calculation.
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Returns:
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A 2-d numpy array representing the Jacobian for dy/dx. It has "x_size" rows
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and "dy_size" columns where "x_size" is the number of elements in x and
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"dy_size" is the number of elements in dy.
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Raises:
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ValueError: If `dy` is empty but the gradient is nonzero.
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"""
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# Complex vectors are treated as vectors of twice as many reals.
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if x.dtype.is_complex:
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x_shape = tuple(x_shape) + (2,)
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dy_factor = 2 if dy.dtype.is_complex else 1
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# To compute the jacobian, we treat x and y as one-dimensional vectors.
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x_size = _product(x_shape)
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x_val_size = _product(x_shape[1:]) # This is used for sparse gradients
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dy_size = _product(dy_shape) * dy_factor
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# Allocate 2-D Jacobian, with x dimensions smashed into the first
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# dimension and y dimensions smashed into the second.
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jacobian = np.zeros((x_size, dy_size),
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dtype=x.dtype.real_dtype.as_numpy_dtype)
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# For each of the entry of dy, we set this to be 1 and
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# everything else to be 0 and compute the backprop -- this will give us one
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# one column of the Jacobian matrix.
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dy_data = np.zeros(dy_shape, dtype=dy.dtype.as_numpy_dtype)
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dy_data_flat = dy_data.ravel().view(dy.dtype.real_dtype.as_numpy_dtype)
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sess = ops.get_default_session()
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for col in range(dy_size):
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dy_data_flat[col] = 1
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if isinstance(dx, indexed_slices.IndexedSlices):
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backprop_indices, backprop_values = sess.run(
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[dx.indices, dx.values],
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feed_dict=_extra_feeds(extra_feed_dict, {x: x_data, dy: dy_data}))
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for i, v in zip(backprop_indices, backprop_values):
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r_begin = i * x_val_size
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r_end = r_begin + x_val_size
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jacobian[r_begin:r_end, col] += v.flat
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else:
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assert isinstance(dx, ops.Tensor), "dx = " + str(dx)
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backprop = sess.run(
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dx, feed_dict=_extra_feeds(extra_feed_dict, {x: x_data, dy: dy_data}))
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jacobian[:, col] = backprop.ravel().view(jacobian.dtype)
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dy_data_flat[col] = 0
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# If the output is empty, run the gradients at least once and make sure
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# they produce zeros.
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if not dy_size:
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backprop = sess.run(
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dx, feed_dict=_extra_feeds(extra_feed_dict, {x: x_data, dy: dy_data}))
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if backprop.shape != x_data.shape:
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raise ValueError("Empty gradient has wrong shape: expected %s, got %s" %
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(x_data.shape, backprop.shape))
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if np.any(backprop):
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raise ValueError("Empty tensor with nonzero gradients")
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logging.vlog(1, "Theoretical Jacobian =\n%s", jacobian)
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return jacobian
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def _compute_numeric_jacobian(x, x_shape, x_data, y, y_shape, delta,
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extra_feed_dict):
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"""Computes the numeric Jacobian for dy/dx.
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Computes the numeric Jacobian by slightly perturbing the inputs and
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measuring the differences on the output.
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Args:
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x: the tensor "x".
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x_shape: the dimensions of x as a tuple or an array of ints.
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x_data: a numpy array as the input data for x
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y: the tensor "y".
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y_shape: the dimensions of y as a tuple or an array of ints.
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delta: the amount of perturbation we give to the input
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extra_feed_dict: dict that allows fixing specified tensor values
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during the jacobian calculation.
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Returns:
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A 2-d numpy array representing the Jacobian for dy/dx. It has "x_size" rows
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and "y_size" columns where "x_size" is the number of elements in x and
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"y_size" is the number of elements in y.
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"""
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# bfloat16 doesn't have enough bits to represent high precision numbers such
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# as delta. Convert to float32 here. Since numeric_jacobian is expected to
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# be the groundtruth to compare against, it shouldn't lose any information.
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if x.dtype == dtypes.bfloat16:
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x = math_ops.cast(x, dtypes.float32) # TODO(wangpeng): Now that the new x
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# is an output of the old x, isn't feeding to the new x a mistake?
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if y.dtype == dtypes.bfloat16:
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y = math_ops.cast(y, dtypes.float32)
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if x_data.dtype == dtypes.bfloat16.as_numpy_dtype:
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x_data = x_data.astype(np.float32)
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# To compute the jacobian, we treat x and y as one-dimensional vectors
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x_size = _product(x_shape) * (2 if x.dtype.is_complex else 1)
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y_size = _product(y_shape) * (2 if y.dtype.is_complex else 1)
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x_dtype = x.dtype.real_dtype.as_numpy_dtype
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y_dtype = y.dtype.real_dtype.as_numpy_dtype
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# Make sure we have the right types
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x_data = np.asarray(x_data, dtype=x.dtype.as_numpy_dtype)
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scale = np.asarray(2 * delta, dtype=y_dtype)[()]
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jacobian = np.zeros((x_size, y_size), dtype=x_dtype)
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# For each of the entry of x, we slightly perturbs this by adding and
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# subtracting a delta and then compute difference between the outputs. This
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# will give us one row of the Jacobian matrix.
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for row in range(x_size):
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x_pos = x_data.copy()
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x_neg = x_data.copy()
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x_pos.ravel().view(x_dtype)[row] += delta
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y_pos = y.eval(feed_dict=_extra_feeds(extra_feed_dict, {x: x_pos}))
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x_neg.ravel().view(x_dtype)[row] -= delta
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y_neg = y.eval(feed_dict=_extra_feeds(extra_feed_dict, {x: x_neg}))
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diff = (y_pos - y_neg) / scale
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jacobian[row, :] = diff.ravel().view(y_dtype)
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logging.vlog(1, "Numeric Jacobian =\n%s", jacobian)
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return jacobian
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def _compute_dx_and_dy(x, y, y_shape):
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"""Returns a node to compute gradient of y wrt x."""
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# We make up a dy so that we can compute the gradients. We don't really use
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# the value of dy -- we will always feed it. We need to add an identity node
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# so that we can always feed it properly. Otherwise, for the Add operation,
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# dx is the same as dy and we cannot fetch the tensor that we are feeding.
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with x.graph.as_default():
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dy_orig = constant_op.constant(1.0, shape=y_shape, dtype=y.dtype)
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dy = array_ops.identity(dy_orig)
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# We compute the gradients for y wrt. x
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grads = gradients.gradients(y, x, dy)
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assert len(grads) == 1
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return grads[0], dy_orig
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def _compute_gradient(x,
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x_shape,
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dx,
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y,
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y_shape,
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dy,
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x_init_value=None,
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delta=1e-3,
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extra_feed_dict=None):
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"""Computes the theoretical and numerical jacobian."""
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t = dtypes.as_dtype(x.dtype)
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allowed_types = [dtypes.float16, dtypes.bfloat16, dtypes.float32,
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dtypes.float64, dtypes.complex64, dtypes.complex128]
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assert t.base_dtype in allowed_types, "Don't support type %s for x" % t.name
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t2 = dtypes.as_dtype(y.dtype)
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assert t2.base_dtype in allowed_types, "Don't support type %s for y" % t2.name
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if x_init_value is not None:
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i_shape = list(x_init_value.shape)
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assert(list(x_shape) == i_shape), "x_shape = %s, init_data shape = %s" % (
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x_shape, i_shape)
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x_data = x_init_value
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else:
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x_data = np.random.random_sample(x_shape).astype(t.as_numpy_dtype)
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if t.is_complex:
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x_data.imag = np.random.random_sample(x_shape)
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jacob_t = _compute_theoretical_jacobian(
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x, x_shape, x_data, dy, y_shape, dx, extra_feed_dict=extra_feed_dict)
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jacob_n = _compute_numeric_jacobian(
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x, x_shape, x_data, y, y_shape, delta, extra_feed_dict=extra_feed_dict)
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return jacob_t, jacob_n
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def _compute_gradient_list(x,
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x_shape,
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y,
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y_shape,
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x_init_value=None,
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delta=1e-3,
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init_targets=None,
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extra_feed_dict=None):
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"""Compute gradients for a list of x values."""
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assert isinstance(x, list)
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dx, dy = zip(*[_compute_dx_and_dy(xi, y, y_shape) for xi in x])
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if init_targets is not None:
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assert isinstance(init_targets, (list, tuple))
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for init in init_targets:
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init.run()
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if x_init_value is None:
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x_init_value = [None] * len(x)
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# pylint: disable=g-complex-comprehension
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ret = [_compute_gradient(xi, x_shapei, dxi, y, y_shape, dyi, x_init_valuei,
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delta, extra_feed_dict=extra_feed_dict)
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for xi, x_shapei, dxi, dyi, x_init_valuei in zip(x, x_shape, dx, dy,
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x_init_value)]
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return ret
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@tf_export(v1=["test.compute_gradient"])
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@deprecation.deprecated(
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date=None,
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instructions="Use tf.test.compute_gradient in 2.0, which has better "
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"support for functions. Note that the two versions have different usage, "
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"so code change is needed.")
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def compute_gradient(x,
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x_shape,
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y,
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y_shape,
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x_init_value=None,
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delta=1e-3,
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init_targets=None,
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extra_feed_dict=None):
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"""Computes and returns the theoretical and numerical Jacobian.
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If `x` or `y` is complex, the Jacobian will still be real but the
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corresponding Jacobian dimension(s) will be twice as large. This is required
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even if both input and output is complex since TensorFlow graphs are not
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necessarily holomorphic, and may have gradients not expressible as complex
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numbers. For example, if `x` is complex with shape `[m]` and `y` is complex
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with shape `[n]`, each Jacobian `J` will have shape `[m * 2, n * 2]` with
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J[:m, :n] = d(Re y)/d(Re x)
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J[:m, n:] = d(Im y)/d(Re x)
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J[m:, :n] = d(Re y)/d(Im x)
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J[m:, n:] = d(Im y)/d(Im x)
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Args:
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x: a tensor or list of tensors
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x_shape: the dimensions of x as a tuple or an array of ints. If x is a list,
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then this is the list of shapes.
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y: a tensor
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y_shape: the dimensions of y as a tuple or an array of ints.
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x_init_value: (optional) a numpy array of the same shape as "x"
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representing the initial value of x. If x is a list, this should be a list
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of numpy arrays. If this is none, the function will pick a random tensor
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as the initial value.
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delta: (optional) the amount of perturbation.
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init_targets: list of targets to run to initialize model params.
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extra_feed_dict: dict that allows fixing specified tensor values
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during the Jacobian calculation.
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Returns:
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Two 2-d numpy arrays representing the theoretical and numerical
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Jacobian for dy/dx. Each has "x_size" rows and "y_size" columns
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where "x_size" is the number of elements in x and "y_size" is the
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number of elements in y. If x is a list, returns a list of two numpy arrays.
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"""
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# TODO(mrry): remove argument `init_targets`
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if extra_feed_dict is None:
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extra_feed_dict = {}
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if isinstance(x, list):
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return _compute_gradient_list(x, x_shape, y, y_shape, x_init_value, delta,
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init_targets, extra_feed_dict=extra_feed_dict)
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else:
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if init_targets is not None:
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assert isinstance(init_targets, (list, tuple))
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for init in init_targets:
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init.run()
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dx, dy = _compute_dx_and_dy(x, y, y_shape)
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ret = _compute_gradient(x, x_shape, dx, y, y_shape, dy, x_init_value, delta,
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extra_feed_dict=extra_feed_dict)
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return ret
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def _compute_error(grad):
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if isinstance(grad, tuple):
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grad = [grad]
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error = 0
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for j_t, j_n in grad:
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if j_t.size or j_n.size: # Handle zero size tensors correctly
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error = np.maximum(error, np.fabs(j_t - j_n).max())
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return error
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@tf_export(v1=["test.compute_gradient_error"])
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@deprecation.deprecated(
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date=None,
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instructions="Use tf.test.compute_gradient in 2.0, which has better "
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"support for functions. Note that the two versions have different usage, "
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"so code change is needed.")
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def compute_gradient_error(x,
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x_shape,
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y,
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y_shape,
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x_init_value=None,
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delta=1e-3,
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init_targets=None,
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extra_feed_dict=None):
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"""Computes the gradient error.
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Computes the maximum error for dy/dx between the computed Jacobian and the
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numerically estimated Jacobian.
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This function will modify the tensors passed in as it adds more operations
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and hence changing the consumers of the operations of the input tensors.
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This function adds operations to the current session. To compute the error
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using a particular device, such as a GPU, use the standard methods for
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setting a device (e.g. using with sess.graph.device() or setting a device
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function in the session constructor).
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Args:
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x: a tensor or list of tensors
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x_shape: the dimensions of x as a tuple or an array of ints. If x is a list,
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then this is the list of shapes.
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y: a tensor
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y_shape: the dimensions of y as a tuple or an array of ints.
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x_init_value: (optional) a numpy array of the same shape as "x"
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representing the initial value of x. If x is a list, this should be a list
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of numpy arrays. If this is none, the function will pick a random tensor
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as the initial value.
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delta: (optional) the amount of perturbation.
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init_targets: list of targets to run to initialize model params.
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extra_feed_dict: dict that allows fixing specified tensor values
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during the Jacobian calculation.
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Returns:
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The maximum error in between the two Jacobians.
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"""
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grad = compute_gradient(x, x_shape, y, y_shape, x_init_value, delta,
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init_targets, extra_feed_dict=extra_feed_dict)
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return _compute_error(grad)
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