def Elman(theta, state0, inputs):
h0, w, b, x = state0.h, theta.w, theta.b, inputs.x
xw = py_utils.Matmul(tf.concat([x, h0], axis=1), w) # 1st part
# 2nd part
padding = inputs.get('padding', None)
h1 = _ApplyPadding(padding, v_no_pad=tf.sigmoid(xw + b), v_pad=state0.h)
state1 = py_utils.NestedMap(h=h1)
if padding is not None:
state1.padding = inputs.padding
return (state1, py_utils.NestedMap(h=h1))
def Grad(h0, w, b, x, padding, h1, dh1):
del b
dh1_orig = dh1
dh1 = _ApplyPadding(padding, dh1, tf.zeros_like(dh1, dtype=dh1.dtype))
# We hand-roll the gradient for the 2nd half of the cell as a demo.
# h1 = tf.sigmoid(xw + b)
# 𝛔'(x) = ((1 - 𝛔(x)) * 𝛔(x))
dxwb = (dh1 * (1 - h1) * h1)
dxw, db = dxwb, tf.reduce_sum(dxwb, axis=0)
# Uses tf.gradient for the 1nd half of the cell as a demo.
xw = py_utils.Matmul(tf.concat([x, h0], axis=1), w)
dh0, dx, dw = tf.gradients(ys=[xw], xs=[h0, x, w], grad_ys=[dxw])
dh0 = _ApplyPadding(padding, dh0, dh1_orig)
return dh0, dx, dw, db
def GetProjectLastDim(cls, inputs, weight, input_dim, output_dim,
proj_obj):
"""Linear projection on the last dim of the input tensor along with pruning.
This is a TPU efficient implementation to avoid reshaping inputs to Rank-2
tensor by using Einsum for the compute.
Args:
inputs: An input Tensor, the last dimension of which is input_dim.
weight: A weight matrix with shape [input_dim, output_dim].
input_dim: An integer or a symbolic dim, the last dimension of the inputs.
output_dim: An integer or a symbolic dim, the last dimension of the
outputs.
proj_obj: a ProjectionLayer object.
Returns:
An output Tensor of the same rank as inputs, the last dimension is
output_dim.
"""
theta = proj_obj.theta
p = proj_obj.params
input_dim = int(
symbolic.ToStatic(input_dim) if symbolic.IsExpr(input_dim
) else input_dim)
output_dim = int(
symbolic.ToStatic(output_dim) if symbolic.IsExpr(output_dim
) else output_dim)
if (py_utils.use_tpu() and inputs.shape is not None
and inputs.shape.rank is not None and inputs.shape.rank < 26):
# Avoids reshape if feasible and uses Einsum.
if inputs.shape.rank == 2:
outputs = tf.matmul(inputs, weight)
else:
outputs = cls.GetEinSumResult(inputs, proj_obj)
else:
if p.pruning_hparams_dict[
'compression_option'] == 9 and p.pruning_hparams_dict[
'compress_input']:
blocked_inputs = tf.reshape(
inputs,
py_utils.ToStaticShape(
[-1, p.pruning_hparams_dict['input_block_size']]))
compressed_inputs = tf.reshape(
py_utils.Matmul(blocked_inputs, theta.b_matrix_tfvar),
py_utils.ToStaticShape([
-1, input_dim //
p.pruning_hparams_dict['input_compression_factor']
]))
else:
compressed_inputs = tf.reshape(
inputs, py_utils.ToStaticShape([-1, input_dim]))
if p.pruning_hparams_dict['compression_option'] == 10:
if p.pruning_hparams_dict['block_method'] == 'mask':
intermediate_result = py_utils.Matmul(
compressed_inputs,
tf.multiply(theta.c_matrix_tfvar, theta.c_mask_tfvar))
elif p.pruning_hparams_dict['block_method'] == 'loop':
num_blocks = p.pruning_hparams_dict[
'block_compression_factor']
input_splitted = tf.split(compressed_inputs,
num_blocks,
axis=-1)
output_splitted = []
for i, input_i in enumerate(input_splitted):
output_splitted.append(
py_utils.Matmul(input_i,
theta.c_matrix_tfvar[i, :, :]))
intermediate_result = tf.concat(output_splitted, axis=-1)
else:
intermediate_result = py_utils.Matmul(compressed_inputs,
theta.c_matrix_tfvar)
if p.pruning_hparams_dict[
'compression_option'] == 9 and p.pruning_hparams_dict[
'compress_output']:
blocked_intermediate_result = tf.reshape(
intermediate_result,
py_utils.ToStaticShape([
-1, p.pruning_hparams_dict['output_block_size'] //
p.pruning_hparams_dict['output_compression_factor']
]))
outputs = py_utils.Matmul(blocked_intermediate_result,
theta.d_matrix_tfvar)
else:
outputs = intermediate_result
outputs = tf.reshape(
outputs,
tf.concat([
tf.cast(py_utils.GetShape(inputs)[:-1], tf.int32),
py_utils.ToStaticShape([output_dim])
],
axis=0))
return outputs
def GetProjectLastDim(cls, inputs, weight, input_dim, output_dim,
proj_obj):
"""Linear projection on the last dim of the input tensor along with pruning.
This is a TPU efficient implementation to avoid reshaping inputs to Rank-2
tensor by using Einsum for the compute.
Args:
inputs: An input Tensor, the last dimension of which is input_dim.
weight: A weight matrix with shape [input_dim, output_dim].
input_dim: An integer or a symbolic dim, the last dimension of the inputs.
output_dim: An integer or a symbolic dim, the last dimension of the
outputs.
proj_obj: a ProjectionLayer object.
Returns:
An output Tensor of the same rank as inputs, the last dimension is
output_dim.
"""
theta = proj_obj.theta
p = proj_obj.params
input_dim = int(
symbolic.ToStatic(input_dim) if symbolic.IsExpr(input_dim
) else input_dim)
output_dim = int(
symbolic.ToStatic(output_dim) if symbolic.IsExpr(output_dim
) else output_dim)
if (py_utils.use_tpu() and inputs.shape is not None
and inputs.shape.rank is not None and inputs.shape.rank < 26):
# Avoids reshape if feasible and uses Einsum.
if inputs.shape.rank == 2:
outputs = tf.matmul(inputs, weight)
else:
s = ''.join([chr(x) for x in range(97, 123)]) # abc...xyz
r = inputs.shape.rank
outputs = cls.GetEinSumResult(
inputs, weight, '{0}y,yz->{0}z'.format(s[:r - 1]),
proj_obj)
else:
if p.pruning_hparams_dict['compress_input']:
blocked_inputs = tf.reshape(
inputs,
py_utils.ToStaticShape(
[-1, p.pruning_hparams_dict['input_block_size']]))
compressed_inputs = tf.reshape(
py_utils.Matmul(blocked_inputs, theta.b_matrix_tfvar),
py_utils.ToStaticShape([
-1, input_dim //
p.pruning_hparams_dict['input_compression_factor']
]))
else:
compressed_inputs = tf.reshape(
inputs, py_utils.ToStaticShape([-1, input_dim]))
intermediate_result = py_utils.Matmul(compressed_inputs,
theta.c_matrix_tfvar)
if p.pruning_hparams_dict['compress_output']:
blocked_intermediate_result = tf.reshape(
intermediate_result,
py_utils.ToStaticShape([
-1, p.pruning_hparams_dict['output_block_size'] //
p.pruning_hparams_dict['output_compression_factor']
]))
outputs = py_utils.Matmul(blocked_intermediate_result,
theta.d_matrix_tfvar)
else:
outputs = intermediate_result
outputs = tf.reshape(
outputs,
tf.concat([
tf.cast(py_utils.GetShape(inputs)[:-1], tf.int32),
py_utils.ToStaticShape([output_dim])
],
axis=0))
return outputs
