INSTRUCTION
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Gate values corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.float32`
and shapes `[expert_batch_size_i]`
|
def expert_to_gates(self):
"""Gate values corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.float32`
and shapes `[expert_batch_size_i]`
"""
return tf.split(
self._nonzero_gates, self._part_sizes_tensor, 0, num=self._num_experts)
|
Batch indices corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.int64`
and shapes `[expert_batch_size_i]`
|
def expert_to_batch_indices(self):
"""Batch indices corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s with type `tf.int64`
and shapes `[expert_batch_size_i]`
"""
return tf.split(
self._batch_index, self._part_sizes_tensor, 0, num=self._num_experts)
|
Create one input Tensor for each expert.
Args:
inp: a list of length num_datashards `Tensor`s with shapes
`[batch_size[d], <extra_input_dims>]`.
Returns:
a list of `num_experts` `Tensor`s with shapes
`[num_examples[i], <extra_input_dims>]`.
|
def dispatch(self, inp):
"""Create one input Tensor for each expert.
Args:
inp: a list of length num_datashards `Tensor`s with shapes
`[batch_size[d], <extra_input_dims>]`.
Returns:
a list of `num_experts` `Tensor`s with shapes
`[num_examples[i], <extra_input_dims>]`.
"""
dispatched = self._dp(lambda a, b: a.dispatch(b), self._dispatchers, inp)
ret = self._ep(tf.concat, transpose_list_of_lists(dispatched), 0)
if ret[0].dtype == tf.float32:
# see comments on common_layers.convert_gradient_to_tensor
ret = self._ep(common_layers.convert_gradient_to_tensor, ret)
return ret
|
Sum together the expert output, multiplied by the corresponding gates.
Args:
expert_out: a list of `num_experts` `Tensor`s, each with shape
`[expert_batch_size_i, <extra_output_dims>]`.
multiply_by_gates: a boolean.
Returns:
a list of num_datashards `Tensor`s with shapes
`[batch_size[d], <extra_output_dims>]`.
|
def combine(self, expert_out, multiply_by_gates=True):
"""Sum together the expert output, multiplied by the corresponding gates.
Args:
expert_out: a list of `num_experts` `Tensor`s, each with shape
`[expert_batch_size_i, <extra_output_dims>]`.
multiply_by_gates: a boolean.
Returns:
a list of num_datashards `Tensor`s with shapes
`[batch_size[d], <extra_output_dims>]`.
"""
expert_part_sizes = tf.unstack(
tf.stack([d.part_sizes for d in self._dispatchers]),
num=self._ep.n,
axis=1)
# list of lists of shape [num_experts][num_datashards]
expert_output_parts = self._ep(tf.split, expert_out, expert_part_sizes)
expert_output_parts_t = transpose_list_of_lists(expert_output_parts)
def my_combine(dispatcher, parts):
return dispatcher.combine(
common_layers.convert_gradient_to_tensor(tf.concat(parts, 0)),
multiply_by_gates=multiply_by_gates)
return self._dp(my_combine, self._dispatchers, expert_output_parts_t)
|
Gate values corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s of type `tf.float32`.
|
def expert_to_gates(self):
"""Gate values corresponding to the examples in the per-expert `Tensor`s.
Returns:
a list of `num_experts` one-dimensional `Tensor`s of type `tf.float32`.
"""
return self._ep(
tf.concat,
transpose_list_of_lists(
self._dp(lambda d: d.expert_to_gates(), self._dispatchers)), 0)
|
Send the inputs to the experts.
Args:
inp: a `Tensor` of shape "[batch, length, depth]`
Returns:
a tensor with shape [batch, num_experts, expert_capacity, depth]
|
def dispatch(self, inp):
"""Send the inputs to the experts.
Args:
inp: a `Tensor` of shape "[batch, length, depth]`
Returns:
a tensor with shape [batch, num_experts, expert_capacity, depth]
"""
inp = tf.reshape(inp, [self._batch * self._length, -1])
# [batch, num_experts, expert_capacity, depth]
ret = tf.gather(inp, self._flat_indices)
return ret
|
Return the output from the experts.
When one example goes to multiple experts, the outputs are summed.
Args:
x: a Tensor with shape [batch, num_experts, expert_capacity, depth]
Returns:
a `Tensor` with shape `[batch, length, depth]
|
def combine(self, x):
"""Return the output from the experts.
When one example goes to multiple experts, the outputs are summed.
Args:
x: a Tensor with shape [batch, num_experts, expert_capacity, depth]
Returns:
a `Tensor` with shape `[batch, length, depth]
"""
depth = tf.shape(x)[-1]
x *= tf.expand_dims(self._nonpadding, -1)
ret = tf.unsorted_segment_sum(
x, self._flat_indices, num_segments=self._batch * self._length)
ret = tf.reshape(ret, [self._batch, self._length, depth])
return ret
|
Factory function for envs.
|
def make_env(env_type, real_env, sim_env_kwargs):
"""Factory function for envs."""
return {
"real": lambda: real_env.new_like( # pylint: disable=g-long-lambda
batch_size=sim_env_kwargs["batch_size"],
store_rollouts=False,
),
"simulated": lambda: rl_utils.SimulatedBatchGymEnvWithFixedInitialFrames( # pylint: disable=g-long-lambda
**sim_env_kwargs
),
}[env_type]()
|
Factory function for Agents.
|
def make_agent(
agent_type, env, policy_hparams, policy_dir, sampling_temp,
sim_env_kwargs_fn=None, frame_stack_size=None, rollout_agent_type=None,
batch_size=None, inner_batch_size=None, env_type=None, **planner_kwargs
):
"""Factory function for Agents."""
if batch_size is None:
batch_size = env.batch_size
return {
"random": lambda: rl_utils.RandomAgent( # pylint: disable=g-long-lambda
batch_size, env.observation_space, env.action_space
),
"policy": lambda: rl_utils.PolicyAgent( # pylint: disable=g-long-lambda
batch_size, env.observation_space, env.action_space,
policy_hparams, policy_dir, sampling_temp
),
"planner": lambda: rl_utils.PlannerAgent( # pylint: disable=g-long-lambda
batch_size, make_agent(
rollout_agent_type, env, policy_hparams, policy_dir,
sampling_temp, batch_size=inner_batch_size
), make_env(env_type, env.env, sim_env_kwargs_fn()),
lambda env: rl_utils.BatchStackWrapper(env, frame_stack_size),
discount_factor=policy_hparams.gae_gamma, **planner_kwargs
),
}[agent_type]()
|
Collects frames from real env for random starts of simulated env.
|
def collect_frames_for_random_starts(
storage_env, stacked_env, agent, frame_stack_size, random_starts_step_limit,
log_every_steps=None
):
"""Collects frames from real env for random starts of simulated env."""
del frame_stack_size
storage_env.start_new_epoch(0)
tf.logging.info(
"Collecting %d frames for random starts.", random_starts_step_limit
)
rl_utils.run_rollouts(
stacked_env, agent, stacked_env.reset(),
step_limit=random_starts_step_limit,
many_rollouts_from_each_env=True,
log_every_steps=log_every_steps,
)
# Save unfinished rollouts to history.
stacked_env.reset()
|
Creates an Agent from hparams.
|
def make_agent_from_hparams(
agent_type, base_env, stacked_env, loop_hparams, policy_hparams,
planner_hparams, model_dir, policy_dir, sampling_temp, video_writers=()
):
"""Creates an Agent from hparams."""
def sim_env_kwargs_fn():
return rl.make_simulated_env_kwargs(
base_env, loop_hparams, batch_size=planner_hparams.batch_size,
model_dir=model_dir
)
planner_kwargs = planner_hparams.values()
planner_kwargs.pop("batch_size")
planner_kwargs.pop("rollout_agent_type")
planner_kwargs.pop("env_type")
return make_agent(
agent_type, stacked_env, policy_hparams, policy_dir, sampling_temp,
sim_env_kwargs_fn, loop_hparams.frame_stack_size,
planner_hparams.rollout_agent_type,
inner_batch_size=planner_hparams.batch_size,
env_type=planner_hparams.env_type,
video_writers=video_writers, **planner_kwargs
)
|
Returns an out-of-graph eval_fn using the Agent API.
|
def make_eval_fn_with_agent(
agent_type, eval_mode, planner_hparams, model_dir, log_every_steps=None,
video_writers=(), random_starts_step_limit=None
):
"""Returns an out-of-graph eval_fn using the Agent API."""
def eval_fn(env, loop_hparams, policy_hparams, policy_dir, sampling_temp):
"""Eval function."""
base_env = env
env = rl_utils.BatchStackWrapper(env, loop_hparams.frame_stack_size)
agent = make_agent_from_hparams(
agent_type, base_env, env, loop_hparams, policy_hparams,
planner_hparams, model_dir, policy_dir, sampling_temp, video_writers
)
if eval_mode == "agent_simulated":
real_env = base_env.new_like(batch_size=1)
stacked_env = rl_utils.BatchStackWrapper(
real_env, loop_hparams.frame_stack_size
)
collect_frames_for_random_starts(
real_env, stacked_env, agent, loop_hparams.frame_stack_size,
random_starts_step_limit, log_every_steps
)
initial_frame_chooser = rl_utils.make_initial_frame_chooser(
real_env, loop_hparams.frame_stack_size,
simulation_random_starts=True,
simulation_flip_first_random_for_beginning=False,
split=None,
)
env_fn = rl.make_simulated_env_fn_from_hparams(
real_env, loop_hparams, batch_size=loop_hparams.eval_batch_size,
initial_frame_chooser=initial_frame_chooser, model_dir=model_dir
)
sim_env = env_fn(in_graph=False)
env = rl_utils.BatchStackWrapper(sim_env, loop_hparams.frame_stack_size)
kwargs = {}
if not agent.records_own_videos:
kwargs["video_writers"] = video_writers
step_limit = base_env.rl_env_max_episode_steps
if step_limit == -1:
step_limit = None
rl_utils.run_rollouts(
env, agent, env.reset(), log_every_steps=log_every_steps,
step_limit=step_limit, **kwargs
)
if eval_mode == "agent_real":
assert len(base_env.current_epoch_rollouts()) == env.batch_size
return eval_fn
|
Evaluates the world model.
|
def evaluate_world_model(
agent_type, loop_hparams, planner_hparams, model_dir, policy_dir,
random_starts_step_limit, debug_video_path, log_every_steps
):
"""Evaluates the world model."""
if debug_video_path:
debug_video_path = os.path.join(debug_video_path, "0.avi")
storage_env = rl_utils.setup_env(loop_hparams, batch_size=1, max_num_noops=0)
stacked_env = rl_utils.BatchStackWrapper(
storage_env, loop_hparams.frame_stack_size
)
policy_hparams = trainer_lib.create_hparams(loop_hparams.base_algo_params)
agent = make_agent_from_hparams(
agent_type, storage_env, stacked_env, loop_hparams, policy_hparams,
planner_hparams, model_dir, policy_dir,
# TODO(koz4k): Loop over eval_sampling_temps?
sampling_temp=loop_hparams.eval_sampling_temps[0],
)
collect_frames_for_random_starts(
storage_env, stacked_env, agent, loop_hparams.frame_stack_size,
random_starts_step_limit, log_every_steps
)
return rl_utils.evaluate_world_model(
storage_env, loop_hparams, model_dir, debug_video_path, split=None
)
|
Evaluate.
|
def evaluate(
loop_hparams, planner_hparams, policy_dir, model_dir, eval_metrics_dir,
agent_type, eval_mode, eval_with_learner, log_every_steps, debug_video_path,
num_debug_videos=1, random_starts_step_limit=None,
report_fn=None, report_metric=None
):
"""Evaluate."""
if eval_with_learner:
assert agent_type == "policy"
if report_fn:
assert report_metric is not None
eval_metrics_writer = tf.summary.FileWriter(eval_metrics_dir)
video_writers = ()
kwargs = {}
if eval_mode in ["agent_real", "agent_simulated"]:
if not eval_with_learner:
if debug_video_path:
tf.gfile.MakeDirs(debug_video_path)
video_writers = [
common_video.WholeVideoWriter( # pylint: disable=g-complex-comprehension
fps=10,
output_path=os.path.join(debug_video_path, "{}.avi".format(i)),
file_format="avi",
)
for i in range(num_debug_videos)
]
kwargs["eval_fn"] = make_eval_fn_with_agent(
agent_type, eval_mode, planner_hparams, model_dir,
log_every_steps=log_every_steps,
video_writers=video_writers,
random_starts_step_limit=random_starts_step_limit
)
eval_metrics = rl_utils.evaluate_all_configs(
loop_hparams, policy_dir, **kwargs
)
else:
eval_metrics = evaluate_world_model(
agent_type, loop_hparams, planner_hparams, model_dir, policy_dir,
random_starts_step_limit, debug_video_path, log_every_steps
)
rl_utils.summarize_metrics(eval_metrics_writer, eval_metrics, 0)
for video_writer in video_writers:
video_writer.finish_to_disk()
# Report metrics
if report_fn:
if report_metric == "mean_reward":
metric_name = rl_utils.get_metric_name(
sampling_temp=loop_hparams.eval_sampling_temps[0],
max_num_noops=loop_hparams.eval_max_num_noops,
clipped=False
)
report_fn(eval_metrics[metric_name], 0)
else:
report_fn(eval_metrics[report_metric], 0)
return eval_metrics
|
Get game for the given worker (directory) id.
|
def get_game_for_worker(map_name, directory_id):
"""Get game for the given worker (directory) id."""
if map_name == "v100unfriendly":
games = ["chopper_command", "boxing", "asterix", "seaquest"]
worker_per_game = 5
elif map_name == "human_nice":
games = gym_env.ATARI_GAMES_WITH_HUMAN_SCORE_NICE
worker_per_game = 5
else:
raise ValueError("Unknown worker to game map name: %s" % map_name)
games.sort()
game_id = (directory_id - 1) // worker_per_game
tf.logging.info("Getting game %d from %s." % (game_id, games))
return games[game_id]
|
Given a representation of the board, returns a list of open spaces.
|
def get_open_spaces(board):
"""Given a representation of the board, returns a list of open spaces."""
open_spaces = []
for i in range(3):
for j in range(3):
if board[i][j] == 0:
open_spaces.append(encode_pos(i, j))
return open_spaces
|
Given a representation of the board, returns reward and done.
|
def get_reward_and_done(board):
"""Given a representation of the board, returns reward and done."""
# Returns (reward, done) where:
# reward: -1 means lost, +1 means win, 0 means draw or continuing.
# done: True if the game is over, i.e. someone won or it is a draw.
# Sum all rows ...
all_sums = [np.sum(board[i, :]) for i in range(3)]
# ... all columns
all_sums.extend([np.sum(board[:, i]) for i in range(3)])
# and both diagonals.
all_sums.append(np.sum([board[i, i] for i in range(3)]))
all_sums.append(np.sum([board[i, 2 - i] for i in range(3)]))
if -3 in all_sums:
return -1, True
if 3 in all_sums:
return 1, True
done = True
if get_open_spaces(board):
done = False
return 0, done
|
Hyperparameters for decoding.
|
def decode_hparams(overrides=""):
"""Hyperparameters for decoding."""
hp = hparam.HParams(
save_images=False,
log_results=True,
extra_length=100,
min_length_ratio=0.0,
batch_size=0,
beam_size=4,
alpha=0.6,
eos_penalty=0.0,
block_size=0,
guess_and_check_top_k=0,
guess_and_check_epsilon=-1,
insertion_parallel=False,
return_beams=False,
write_beam_scores=False,
max_input_size=-1,
identity_output=False,
num_samples=-1, # Number of examples to decode.
delimiter="\n",
decode_to_file="", # str. Prefix for filename to write decodings to.
decode_reference="", # str. Filename to read references from.
decode_in_memory=False,
# How much decode should wait for the next checkpoint
decode_timeout_mins=240,
summaries_log_dir="decode", # Directory to write hook summaries.
shards=1, # How many shards of data to decode (treating 1 as None).
shard_id=0, # Which shard are we decoding if more than 1 above.
shards_start_offset=0, # Number of the first shard to decode.
shard_google_format=False, # If True use Google shard naming format.
num_decodes=1, # Number of times to go over the dataset.
force_decode_length=False,
display_decoded_images=False,
# Multi-problem decoding task id.
multiproblem_task_id=-1,
# Used for video decoding.
frames_per_second=10,
skip_eos_postprocess=False,
# Creates a blue/red border covering border_percent of the frame.
border_percent=2,
# Maximum number of videos displayed.
# number of videos displayed = max_display_outputs * max_display_decodes
max_display_outputs=10,
max_display_decodes=5,
# Used in computation of VGG feature based video metrics.
# Set this to be the path to a trained VGG ckpt to output
# useful metrics.
vgg_ckpt_path="",
# Used for MLPerf compliance logging.
mlperf_decode_step=0.0,
mlperf_threshold=25.0,
mlperf_success=False)
hp.parse(overrides)
return hp
|
Log inference results.
|
def log_decode_results(inputs,
outputs,
problem_name,
prediction_idx,
inputs_vocab,
targets_vocab,
targets=None,
save_images=False,
output_dir=None,
identity_output=False,
log_results=True,
skip_eos_postprocess=False):
"""Log inference results."""
# TODO(lukaszkaiser) refactor this into feature_encoder
is_video = "video" in problem_name or "gym" in problem_name
if is_video:
def fix_and_save_video(vid, prefix):
save_path_template = os.path.join(
output_dir,
"%s_%s_%05d_{:05d}.png" % (problem_name, prefix, prediction_idx))
# this is only required for predictions
if vid.shape[-1] == 1:
vid = np.squeeze(vid, axis=-1)
save_video(vid, save_path_template)
tf.logging.info("Saving video: {}".format(prediction_idx))
fix_and_save_video(inputs, "inputs")
fix_and_save_video(outputs, "outputs")
fix_and_save_video(targets, "targets")
is_image = "image" in problem_name
is_text2class = isinstance(registry.problem(problem_name),
text_problems.Text2ClassProblem)
skip_eos_postprocess = is_image or is_text2class or skip_eos_postprocess
decoded_inputs = None
if is_image and save_images:
save_path = os.path.join(
output_dir, "%s_prediction_%d.jpg" % (problem_name, prediction_idx))
show_and_save_image(inputs / 255., save_path)
elif inputs is not None and inputs_vocab:
if identity_output:
decoded_inputs = " ".join(map(str, inputs.flatten()))
else:
decoded_inputs = inputs_vocab.decode(_save_until_eos(
inputs, skip_eos_postprocess))
if log_results and not is_video:
tf.logging.info("Inference results INPUT: %s" % decoded_inputs)
decoded_targets = None
decoded_outputs = None
if identity_output:
decoded_outputs = " ".join(map(str, outputs.flatten()))
if targets is not None:
decoded_targets = " ".join(map(str, targets.flatten()))
else:
decoded_outputs = targets_vocab.decode(_save_until_eos(
outputs, skip_eos_postprocess))
if targets is not None and log_results:
decoded_targets = targets_vocab.decode(_save_until_eos(
targets, skip_eos_postprocess))
if log_results and not is_video:
tf.logging.info("Inference results OUTPUT: %s" % decoded_outputs)
if targets is not None and log_results and not is_video:
tf.logging.info("Inference results TARGET: %s" % decoded_targets)
return decoded_inputs, decoded_outputs, decoded_targets
|
Perform decoding from dataset.
|
def decode_from_dataset(estimator,
problem_name,
hparams,
decode_hp,
decode_to_file=None,
dataset_split=None,
checkpoint_path=None):
"""Perform decoding from dataset."""
tf.logging.info("Performing local inference from dataset for %s.",
str(problem_name))
# We assume that worker_id corresponds to shard number.
shard = decode_hp.shard_id if decode_hp.shards > 1 else None
# Setup output directory for any artifacts that may be written out.
output_dir = os.path.join(estimator.model_dir, "decode")
tf.gfile.MakeDirs(output_dir)
# If decode_hp.batch_size is specified, use a fixed batch size
if decode_hp.batch_size:
hparams.batch_size = decode_hp.batch_size
hparams.use_fixed_batch_size = True
dataset_kwargs = {
"shard": shard,
"dataset_split": dataset_split,
"max_records": decode_hp.num_samples
}
# Build the inference input function
problem = hparams.problem
infer_input_fn = problem.make_estimator_input_fn(
tf.estimator.ModeKeys.PREDICT, hparams, dataset_kwargs=dataset_kwargs)
predictions, output_dirs = [], []
for decode_id in range(decode_hp.num_decodes):
tf.logging.info("Decoding {}".format(decode_id))
# Create decode directory if not in-memory decoding.
if not decode_hp.decode_in_memory:
output_dir = os.path.join(estimator.model_dir, "decode_%05d" % decode_id)
tf.gfile.MakeDirs(output_dir)
output_dirs.append(output_dir)
result = decode_once(estimator,
problem_name,
hparams,
infer_input_fn,
decode_hp,
decode_to_file,
output_dir,
log_results=decode_hp.log_results,
checkpoint_path=checkpoint_path)
if decode_hp.decode_in_memory:
output_dirs = [output_dir]
predictions.append(result)
if decode_hp.decode_to_file:
decode_hp.decode_to_file = _decode_filename(
decode_hp.decode_to_file, problem_name, decode_hp)
run_postdecode_hooks(DecodeHookArgs(
estimator=estimator,
problem=problem,
output_dirs=output_dirs,
hparams=hparams,
decode_hparams=decode_hp,
predictions=predictions
), dataset_split)
return predictions
|
Decodes once.
Args:
estimator: tf.estimator.Estimator instance. Used to generate encoded
predictions.
problem_name: str. Name of problem.
hparams: HParams instance. HParams for model training.
infer_input_fn: zero-arg function. Input function for estimator.
decode_hp: HParams instance. See decode_hparams() above.
decode_to_file: str. Prefix for filenames. Used to generated filenames to
which decoded predictions are written.
output_dir: str. Output directory. Only used for writing images.
log_results: bool. If False, return encoded predictions without any
further processing.
checkpoint_path: str. Path to load model checkpoint from. If unspecified,
Estimator's default is used.
Returns:
If decode_hp.decode_in_memory is True:
List of dicts, one per example. Values are either numpy arrays or decoded
strings.
If decode_hp.decode_in_memory is False:
An empty list.
|
def decode_once(estimator,
problem_name,
hparams,
infer_input_fn,
decode_hp,
decode_to_file,
output_dir,
log_results=True,
checkpoint_path=None):
"""Decodes once.
Args:
estimator: tf.estimator.Estimator instance. Used to generate encoded
predictions.
problem_name: str. Name of problem.
hparams: HParams instance. HParams for model training.
infer_input_fn: zero-arg function. Input function for estimator.
decode_hp: HParams instance. See decode_hparams() above.
decode_to_file: str. Prefix for filenames. Used to generated filenames to
which decoded predictions are written.
output_dir: str. Output directory. Only used for writing images.
log_results: bool. If False, return encoded predictions without any
further processing.
checkpoint_path: str. Path to load model checkpoint from. If unspecified,
Estimator's default is used.
Returns:
If decode_hp.decode_in_memory is True:
List of dicts, one per example. Values are either numpy arrays or decoded
strings.
If decode_hp.decode_in_memory is False:
An empty list.
"""
# Get the predictions as an iterable
predictions = estimator.predict(infer_input_fn,
checkpoint_path=checkpoint_path)
if not log_results:
return list(predictions)
# Prepare output file writers if decode_to_file passed
decode_to_file = decode_to_file or decode_hp.decode_to_file
if decode_to_file:
output_filepath = _decode_filename(decode_to_file, problem_name, decode_hp)
parts = output_filepath.split(".")
parts[-1] = "targets"
target_filepath = ".".join(parts)
parts[-1] = "inputs"
input_filepath = ".".join(parts)
output_file = tf.gfile.Open(output_filepath, "w")
target_file = tf.gfile.Open(target_filepath, "w")
input_file = tf.gfile.Open(input_filepath, "w")
problem_hparams = hparams.problem_hparams
# Inputs vocabulary is set to targets if there are no inputs in the problem,
# e.g., for language models where the inputs are just a prefix of targets.
has_input = "inputs" in problem_hparams.vocabulary
inputs_vocab_key = "inputs" if has_input else "targets"
inputs_vocab = problem_hparams.vocabulary[inputs_vocab_key]
targets_vocab = problem_hparams.vocabulary["targets"]
num_eval_samples = 0
# all_outputs[i][j] = (input: str, output: str, target: str). Input,
# decoded output, and target strings for example i, beam rank j.
all_outputs = []
for num_predictions, prediction in enumerate(predictions):
num_eval_samples += 1
num_predictions += 1
inputs = prediction.get("inputs")
targets = prediction.get("targets")
outputs = prediction.get("outputs")
# Log predictions
decoded_outputs = [] # [(str, str, str)]. See all_outputs above.
if decode_hp.decode_in_memory:
all_outputs.append(decoded_outputs)
decoded_scores = []
if decode_hp.return_beams:
output_beams = np.split(outputs, decode_hp.beam_size, axis=0)
scores = None
if "scores" in prediction:
scores = np.split(prediction["scores"], decode_hp.beam_size, axis=0)
for i, beam in enumerate(output_beams):
tf.logging.info("BEAM %d:" % i)
score = scores and scores[i]
decoded = log_decode_results(
inputs,
beam,
problem_name,
num_predictions,
inputs_vocab,
targets_vocab,
save_images=decode_hp.save_images,
output_dir=output_dir,
identity_output=decode_hp.identity_output,
targets=targets,
log_results=log_results)
decoded_outputs.append(decoded)
if decode_hp.write_beam_scores:
decoded_scores.append(score)
else:
decoded = log_decode_results(
inputs,
outputs,
problem_name,
num_predictions,
inputs_vocab,
targets_vocab,
save_images=decode_hp.save_images,
output_dir=output_dir,
identity_output=decode_hp.identity_output,
targets=targets,
log_results=log_results,
skip_eos_postprocess=decode_hp.skip_eos_postprocess)
decoded_outputs.append(decoded)
# Write out predictions if decode_to_file passed
if decode_to_file:
for i, (d_input, d_output, d_target) in enumerate(decoded_outputs):
# Skip if all padding
if d_input and re.match("^({})+$".format(text_encoder.PAD), d_input):
continue
beam_score_str = ""
if decode_hp.write_beam_scores:
beam_score_str = "\t%.2f" % decoded_scores[i]
output_file.write(str(d_output) + beam_score_str + decode_hp.delimiter)
target_file.write(str(d_target) + decode_hp.delimiter)
input_file.write(str(d_input) + decode_hp.delimiter)
if (decode_hp.num_samples >= 0 and
num_predictions >= decode_hp.num_samples):
break
mlperf_log.transformer_print(key=mlperf_log.EVAL_SIZE,
value=num_eval_samples,
hparams=hparams)
if decode_to_file:
output_file.close()
target_file.close()
input_file.close()
return all_outputs
|
Compute predictions on entries in filename and write them out.
|
def decode_from_file(estimator,
filename,
hparams,
decode_hp,
decode_to_file=None,
checkpoint_path=None):
"""Compute predictions on entries in filename and write them out."""
if not decode_hp.batch_size:
decode_hp.batch_size = 32
tf.logging.info(
"decode_hp.batch_size not specified; default=%d" % decode_hp.batch_size)
# Inputs vocabulary is set to targets if there are no inputs in the problem,
# e.g., for language models where the inputs are just a prefix of targets.
p_hp = hparams.problem_hparams
has_input = "inputs" in p_hp.vocabulary
inputs_vocab_key = "inputs" if has_input else "targets"
inputs_vocab = p_hp.vocabulary[inputs_vocab_key]
targets_vocab = p_hp.vocabulary["targets"]
problem_name = FLAGS.problem
filename = _add_shard_to_filename(filename, decode_hp)
tf.logging.info("Performing decoding from file (%s)." % filename)
if has_input:
sorted_inputs, sorted_keys = _get_sorted_inputs(
filename, decode_hp.delimiter)
else:
sorted_inputs = _get_language_modeling_inputs(
filename, decode_hp.delimiter, repeat=decode_hp.num_decodes)
sorted_keys = range(len(sorted_inputs))
num_sentences = len(sorted_inputs)
num_decode_batches = (num_sentences - 1) // decode_hp.batch_size + 1
if estimator.config.use_tpu:
length = getattr(hparams, "length", 0) or hparams.max_length
batch_ids = []
for line in sorted_inputs:
if has_input:
ids = inputs_vocab.encode(line.strip()) + [1]
else:
ids = targets_vocab.encode(line)
if len(ids) < length:
ids.extend([0] * (length - len(ids)))
else:
ids = ids[:length]
batch_ids.append(ids)
np_ids = np.array(batch_ids, dtype=np.int32)
def input_fn(params):
batch_size = params["batch_size"]
dataset = tf.data.Dataset.from_tensor_slices({"inputs": np_ids})
dataset = dataset.map(
lambda ex: {"inputs": tf.reshape(ex["inputs"], (length, 1, 1))})
dataset = dataset.batch(batch_size)
return dataset
else:
def input_fn():
input_gen = _decode_batch_input_fn(
num_decode_batches, sorted_inputs,
inputs_vocab, decode_hp.batch_size,
decode_hp.max_input_size,
task_id=decode_hp.multiproblem_task_id, has_input=has_input)
gen_fn = make_input_fn_from_generator(input_gen)
example = gen_fn()
return _decode_input_tensor_to_features_dict(example, hparams)
decodes = []
result_iter = estimator.predict(input_fn, checkpoint_path=checkpoint_path)
start_time = time.time()
total_time_per_step = 0
total_cnt = 0
def timer(gen):
while True:
try:
start_time = time.time()
item = next(gen)
elapsed_time = time.time() - start_time
yield elapsed_time, item
except StopIteration:
break
for elapsed_time, result in timer(result_iter):
if decode_hp.return_beams:
beam_decodes = []
beam_scores = []
output_beams = np.split(result["outputs"], decode_hp.beam_size, axis=0)
scores = None
if "scores" in result:
if np.isscalar(result["scores"]):
result["scores"] = result["scores"].reshape(1)
scores = np.split(result["scores"], decode_hp.beam_size, axis=0)
for k, beam in enumerate(output_beams):
tf.logging.info("BEAM %d:" % k)
score = scores and scores[k]
_, decoded_outputs, _ = log_decode_results(
result["inputs"],
beam,
problem_name,
None,
inputs_vocab,
targets_vocab,
log_results=decode_hp.log_results,
skip_eos_postprocess=decode_hp.skip_eos_postprocess)
beam_decodes.append(decoded_outputs)
if decode_hp.write_beam_scores:
beam_scores.append(score)
if decode_hp.write_beam_scores:
decodes.append("\t".join([
"\t".join([d, "%.2f" % s])
for d, s in zip(beam_decodes, beam_scores)
]))
else:
decodes.append("\t".join(beam_decodes))
else:
_, decoded_outputs, _ = log_decode_results(
result["inputs"],
result["outputs"],
problem_name,
None,
inputs_vocab,
targets_vocab,
log_results=decode_hp.log_results,
skip_eos_postprocess=decode_hp.skip_eos_postprocess)
decodes.append(decoded_outputs)
total_time_per_step += elapsed_time
total_cnt += result["outputs"].shape[-1]
duration = time.time() - start_time
tf.logging.info("Elapsed Time: %5.5f" % duration)
tf.logging.info("Averaged Single Token Generation Time: %5.7f "
"(time %5.7f count %d)" %
(total_time_per_step / total_cnt,
total_time_per_step, total_cnt))
if decode_hp.batch_size == 1:
tf.logging.info("Inference time %.4f seconds "
"(Latency = %.4f ms/setences)" %
(duration, 1000.0*duration/num_sentences))
else:
tf.logging.info("Inference time %.4f seconds "
"(Throughput = %.4f sentences/second)" %
(duration, num_sentences/duration))
# If decode_to_file was provided use it as the output filename without change
# (except for adding shard_id if using more shards for decoding).
# Otherwise, use the input filename plus model, hp, problem, beam, alpha.
decode_filename = decode_to_file if decode_to_file else filename
if not decode_to_file:
decode_filename = _decode_filename(decode_filename, problem_name, decode_hp)
else:
decode_filename = _add_shard_to_filename(decode_filename, decode_hp)
tf.logging.info("Writing decodes into %s" % decode_filename)
outfile = tf.gfile.Open(decode_filename, "w")
for index in range(len(sorted_inputs)):
outfile.write("%s%s" % (decodes[sorted_keys[index]], decode_hp.delimiter))
outfile.flush()
outfile.close()
output_dir = os.path.join(estimator.model_dir, "decode")
tf.gfile.MakeDirs(output_dir)
run_postdecode_hooks(DecodeHookArgs(
estimator=estimator,
problem=hparams.problem,
output_dirs=[output_dir],
hparams=hparams,
decode_hparams=decode_hp,
predictions=list(result_iter)
), None)
|
Generates decode filename.
Args:
base_filename: A string, base of the decode filename.
problem_name: A string, name of the problem.
decode_hp: HParams for decoding.
Returns:
A string, produced decode filename.
|
def _decode_filename(base_filename, problem_name, decode_hp):
"""Generates decode filename.
Args:
base_filename: A string, base of the decode filename.
problem_name: A string, name of the problem.
decode_hp: HParams for decoding.
Returns:
A string, produced decode filename.
"""
if decode_hp.shards > 1:
base_filename = _add_shard_to_filename(base_filename, decode_hp)
if ("beam{beam}.alpha{alpha}.decodes".format(
beam=str(decode_hp.beam_size), alpha=str(decode_hp.alpha))
in base_filename):
return base_filename
else:
return (
"{base}.{model}.{hp}.{problem}.beam{beam}.alpha{alpha}.decodes".format(
base=base_filename,
model=FLAGS.model,
hp=FLAGS.hparams_set,
problem=problem_name,
beam=str(decode_hp.beam_size),
alpha=str(decode_hp.alpha)))
|
Use py_func to yield elements from the given generator.
|
def make_input_fn_from_generator(gen):
"""Use py_func to yield elements from the given generator."""
first_ex = six.next(gen)
flattened = tf.contrib.framework.nest.flatten(first_ex)
types = [t.dtype for t in flattened]
shapes = [[None] * len(t.shape) for t in flattened]
first_ex_list = [first_ex]
def py_func():
if first_ex_list:
example = first_ex_list.pop()
else:
example = six.next(gen)
return tf.contrib.framework.nest.flatten(example)
def input_fn():
flat_example = tf.py_func(py_func, [], types)
_ = [t.set_shape(shape) for t, shape in zip(flat_example, shapes)]
example = tf.contrib.framework.nest.pack_sequence_as(first_ex, flat_example)
return example
return input_fn
|
Interactive decoding.
|
def decode_interactively(estimator, hparams, decode_hp, checkpoint_path=None):
"""Interactive decoding."""
is_image = "image" in hparams.problem.name
is_text2class = isinstance(hparams.problem,
text_problems.Text2ClassProblem)
skip_eos_postprocess = (
is_image or is_text2class or decode_hp.skip_eos_postprocess)
def input_fn():
gen_fn = make_input_fn_from_generator(
_interactive_input_fn(hparams, decode_hp))
example = gen_fn()
example = _interactive_input_tensor_to_features_dict(example, hparams)
return example
result_iter = estimator.predict(input_fn, checkpoint_path=checkpoint_path)
for result in result_iter:
targets_vocab = hparams.problem_hparams.vocabulary["targets"]
if decode_hp.return_beams:
beams = np.split(result["outputs"], decode_hp.beam_size, axis=0)
scores = None
if "scores" in result:
if np.isscalar(result["scores"]):
result["scores"] = result["scores"].reshape(1)
scores = np.split(result["scores"], decode_hp.beam_size, axis=0)
for k, beam in enumerate(beams):
tf.logging.info("BEAM %d:" % k)
beam_string = targets_vocab.decode(_save_until_eos(
beam, skip_eos_postprocess))
if scores is not None:
tf.logging.info("\"%s\"\tScore:%f" % (beam_string, scores[k]))
else:
tf.logging.info("\"%s\"" % beam_string)
else:
if decode_hp.identity_output:
tf.logging.info(" ".join(map(str, result["outputs"].flatten())))
else:
tf.logging.info(
targets_vocab.decode(_save_until_eos(
result["outputs"], skip_eos_postprocess)))
|
Generator to produce batches of inputs.
|
def _decode_batch_input_fn(num_decode_batches, sorted_inputs, vocabulary,
batch_size, max_input_size,
task_id=-1, has_input=True):
"""Generator to produce batches of inputs."""
tf.logging.info(" batch %d" % num_decode_batches)
for b in range(num_decode_batches):
tf.logging.info("Decoding batch %d" % b)
batch_length = 0
batch_inputs = []
for inputs in sorted_inputs[b * batch_size:(b + 1) * batch_size]:
input_ids = vocabulary.encode(inputs)
if max_input_size > 0:
# Subtract 1 for the EOS_ID.
input_ids = input_ids[:max_input_size - 1]
if has_input or task_id > -1: # Do not append EOS for pure LM tasks.
final_id = text_encoder.EOS_ID if task_id < 0 else task_id
input_ids.append(final_id)
batch_inputs.append(input_ids)
if len(input_ids) > batch_length:
batch_length = len(input_ids)
final_batch_inputs = []
for input_ids in batch_inputs:
assert len(input_ids) <= batch_length
x = input_ids + [0] * (batch_length - len(input_ids))
final_batch_inputs.append(x)
yield {
"inputs": np.array(final_batch_inputs).astype(np.int32),
}
|
Generator that reads from the terminal and yields "interactive inputs".
Due to temporary limitations in tf.learn, if we don't want to reload the
whole graph, then we are stuck encoding all of the input as one fixed-size
numpy array.
We yield int32 arrays with shape [const_array_size]. The format is:
[num_samples, decode_length, len(input ids), <input ids>, <padding>]
Args:
hparams: model hparams
decode_hp: decode hparams
Yields:
numpy arrays
Raises:
Exception: when `input_type` is invalid.
|
def _interactive_input_fn(hparams, decode_hp):
"""Generator that reads from the terminal and yields "interactive inputs".
Due to temporary limitations in tf.learn, if we don't want to reload the
whole graph, then we are stuck encoding all of the input as one fixed-size
numpy array.
We yield int32 arrays with shape [const_array_size]. The format is:
[num_samples, decode_length, len(input ids), <input ids>, <padding>]
Args:
hparams: model hparams
decode_hp: decode hparams
Yields:
numpy arrays
Raises:
Exception: when `input_type` is invalid.
"""
num_samples = decode_hp.num_samples if decode_hp.num_samples > 0 else 1
decode_length = decode_hp.extra_length
input_type = "text"
p_hparams = hparams.problem_hparams
has_input = "inputs" in p_hparams.modality
vocabulary = p_hparams.vocabulary["inputs" if has_input else "targets"]
# This should be longer than the longest input.
const_array_size = 10000
# Import readline if available for command line editing and recall.
try:
import readline # pylint: disable=g-import-not-at-top,unused-variable
except ImportError:
pass
while True:
prompt = ("INTERACTIVE MODE num_samples=%d decode_length=%d \n"
" it=<input_type> ('text' or 'image' or 'label', default: "
"text)\n"
" ns=<num_samples> (changes number of samples, default: 1)\n"
" dl=<decode_length> (changes decode length, default: 100)\n"
" <%s> (decode)\n"
" q (quit)\n"
">" % (num_samples, decode_length,
"source_string" if has_input else "target_prefix"))
input_string = input(prompt)
if input_string == "q":
return
elif input_string[:3] == "ns=":
num_samples = int(input_string[3:])
elif input_string[:3] == "dl=":
decode_length = int(input_string[3:])
elif input_string[:3] == "it=":
input_type = input_string[3:]
else:
if input_type == "text":
input_ids = vocabulary.encode(input_string)
if has_input:
input_ids.append(text_encoder.EOS_ID)
x = [num_samples, decode_length, len(input_ids)] + input_ids
assert len(x) < const_array_size
x += [0] * (const_array_size - len(x))
features = {
"inputs": np.array(x).astype(np.int32),
}
elif input_type == "image":
input_path = input_string
img = vocabulary.encode(input_path)
features = {
"inputs": img.astype(np.int32),
}
elif input_type == "label":
input_ids = [int(input_string)]
x = [num_samples, decode_length, len(input_ids)] + input_ids
features = {
"inputs": np.array(x).astype(np.int32),
}
else:
raise Exception("Unsupported input type.")
for k, v in six.iteritems(
problem_lib.problem_hparams_to_features(p_hparams)):
features[k] = np.array(v).astype(np.int32)
yield features
|
Save frames of the videos into files.
|
def save_video(video, save_path_template):
"""Save frames of the videos into files."""
try:
from PIL import Image # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logging.warning(
"Showing and saving an image requires PIL library to be "
"installed: %s", e)
raise NotImplementedError("Image display and save not implemented.")
for i, frame in enumerate(video):
save_path = save_path_template.format(i)
with tf.gfile.Open(save_path, "wb") as sp:
Image.fromarray(np.uint8(frame)).save(sp)
|
Shows an image using matplotlib and saves it.
|
def show_and_save_image(img, save_path):
"""Shows an image using matplotlib and saves it."""
try:
import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top
except ImportError as e:
tf.logging.warning(
"Showing and saving an image requires matplotlib to be "
"installed: %s", e)
raise NotImplementedError("Image display and save not implemented.")
plt.imshow(img)
with tf.gfile.Open(save_path, "wb") as sp:
plt.savefig(sp)
|
Read a file of partial texts to continue.
The purpose of append_space_to_final_punctionation is that SubwordTokenizer
groups punctuation and the ensuing space in the same token. Adding a space
causes the token to be completed.
Args:
filename: a string
delimiter: a string
repeat: an integer - we repeat the entire file that many times.
append_space_to_final_punctionation: a boolean
Returns:
a list of strings
|
def _get_language_modeling_inputs(filename,
delimiter="\n",
repeat=1,
append_space_to_final_punctionation=True):
"""Read a file of partial texts to continue.
The purpose of append_space_to_final_punctionation is that SubwordTokenizer
groups punctuation and the ensuing space in the same token. Adding a space
causes the token to be completed.
Args:
filename: a string
delimiter: a string
repeat: an integer - we repeat the entire file that many times.
append_space_to_final_punctionation: a boolean
Returns:
a list of strings
"""
with tf.gfile.Open(filename) as f:
text = f.read()
inputs = text.split(delimiter)
if not inputs[-1]:
inputs.pop()
inputs *= repeat
if append_space_to_final_punctionation:
inputs = [
s + " " if s and s[-1] in string.punctuation else s for s in inputs]
return inputs
|
Returning inputs sorted according to decreasing length.
This causes inputs of similar lengths to be processed in the same batch,
facilitating early stopping for short sequences.
Longer sequences are sorted first so that if you're going to get OOMs,
you'll see it in the first batch.
Args:
filename: path to file with inputs, 1 per line.
delimiter: str, delimits records in the file.
Returns:
a sorted list of inputs
|
def _get_sorted_inputs(filename, delimiter="\n"):
"""Returning inputs sorted according to decreasing length.
This causes inputs of similar lengths to be processed in the same batch,
facilitating early stopping for short sequences.
Longer sequences are sorted first so that if you're going to get OOMs,
you'll see it in the first batch.
Args:
filename: path to file with inputs, 1 per line.
delimiter: str, delimits records in the file.
Returns:
a sorted list of inputs
"""
tf.logging.info("Getting sorted inputs")
with tf.gfile.Open(filename) as f:
text = f.read()
records = text.split(delimiter)
inputs = [record.strip() for record in records]
# Strip the last empty line.
if not inputs[-1]:
inputs.pop()
input_lens = [(i, -len(line.split())) for i, line in enumerate(inputs)]
sorted_input_lens = sorted(input_lens, key=operator.itemgetter(1))
# We'll need the keys to rearrange the inputs back into their original order
sorted_keys = {}
sorted_inputs = []
for i, (index, _) in enumerate(sorted_input_lens):
sorted_inputs.append(inputs[index])
sorted_keys[index] = i
return sorted_inputs, sorted_keys
|
Strips everything after the first <EOS> token, which is normally 1.
|
def _save_until_eos(ids, skip=False):
"""Strips everything after the first <EOS> token, which is normally 1."""
ids = ids.flatten()
if skip:
return ids
try:
index = list(ids).index(text_encoder.EOS_ID)
return ids[0:index]
except ValueError:
# No EOS_ID: return the array as-is.
return ids
|
Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: dict with inputs.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
|
def _interactive_input_tensor_to_features_dict(feature_map, hparams):
"""Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: dict with inputs.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
"""
inputs = tf.convert_to_tensor(feature_map["inputs"])
input_is_image = False if len(inputs.get_shape()) < 3 else True
x = inputs
if input_is_image:
x = tf.image.resize_images(x, [299, 299])
x = tf.reshape(x, [1, 299, 299, -1])
x = tf.to_int32(x)
else:
# Remove the batch dimension.
num_samples = x[0]
length = x[2]
x = tf.slice(x, [3], tf.to_int32([length]))
x = tf.reshape(x, [1, -1, 1, 1])
# Transform into a batch of size num_samples to get that many random
# decodes.
x = tf.tile(x, tf.to_int32([num_samples, 1, 1, 1]))
p_hparams = hparams.problem_hparams
input_space_id = tf.constant(p_hparams.input_space_id)
target_space_id = tf.constant(p_hparams.target_space_id)
features = {}
features["input_space_id"] = input_space_id
features["target_space_id"] = target_space_id
features["decode_length"] = (
IMAGE_DECODE_LENGTH if input_is_image else inputs[1])
features["inputs"] = x
return features
|
Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: dict with inputs.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
|
def _decode_input_tensor_to_features_dict(feature_map, hparams):
"""Convert the interactive input format (see above) to a dictionary.
Args:
feature_map: dict with inputs.
hparams: model hyperparameters
Returns:
a features dictionary, as expected by the decoder.
"""
inputs = tf.convert_to_tensor(feature_map["inputs"])
input_is_image = False
x = inputs
p_hparams = hparams.problem_hparams
# Add a third empty dimension
x = tf.expand_dims(x, axis=[2])
x = tf.to_int32(x)
input_space_id = tf.constant(p_hparams.input_space_id)
target_space_id = tf.constant(p_hparams.target_space_id)
features = {}
features["input_space_id"] = input_space_id
features["target_space_id"] = target_space_id
features["decode_length"] = (
IMAGE_DECODE_LENGTH if input_is_image else tf.shape(x)[1] + 50)
features["inputs"] = x
return features
|
Run hooks after decodes have run.
|
def run_postdecode_hooks(decode_hook_args, dataset_split):
"""Run hooks after decodes have run."""
hooks = decode_hook_args.problem.decode_hooks
if not hooks:
return
global_step = latest_checkpoint_step(decode_hook_args.estimator.model_dir)
if global_step is None:
tf.logging.info(
"Skipping decode hooks because no checkpoint yet available.")
return
tf.logging.info("Running decode hooks.")
parent_dir = os.path.join(decode_hook_args.output_dirs[0], os.pardir)
child_dir = decode_hook_args.decode_hparams.summaries_log_dir
if dataset_split is not None:
child_dir += "_{}".format(dataset_split)
final_dir = os.path.join(parent_dir, child_dir)
summary_writer = tf.summary.FileWriter(final_dir)
for hook in hooks:
# Isolate each hook in case it creates TF ops
with tf.Graph().as_default():
summaries = hook(decode_hook_args)
if summaries:
summary = tf.Summary(value=list(summaries))
summary_writer.add_summary(summary, global_step)
summary_writer.close()
tf.logging.info("Decode hooks done.")
|
Splits of data to produce and number of output shards for each.
|
def dataset_splits(self):
"""Splits of data to produce and number of output shards for each."""
return [{
"split": problem.DatasetSplit.TRAIN,
"shards": _TRAIN_SHARDS,
}, {
"split": problem.DatasetSplit.EVAL,
"shards": _DEV_SHARDS,
}]
|
Image Transformer decoder with local1D spatial layers.
|
def local_attention1d_spatial_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local1D spatial layers."""
batch_dim, length_dim, model_dim = x.shape.dims
blocks_w_dim = mtf.Dimension("blocksw", hparams.block_length)
num_w_blocks_dim = mtf.Dimension("num_wblocks",
length_dim.size // blocks_w_dim.size)
x = mtf.reshape(
x, mtf.Shape([batch_dim, num_w_blocks_dim, blocks_w_dim, model_dim]))
# [ self attention - ffn - residual + dropout] x n
for layer in range(hparams.num_decoder_layers):
layer_name = "decoder_layer_%d" % layer
with tf.variable_scope(layer_name):
# Self attention layer
x += layer_prepostprocess_dropout(
mtf.layers.local_self_attention_spatial_blocks(
mtf.layers.layer_norm(x, model_dim, name="layer_norm_att"),
kv_dim,
heads_dim,
memory_w_dim=blocks_w_dim,
mask_right=True,
name="self_att"), hparams)
# ffn layer
x += layer_prepostprocess_dropout(
mtf.layers.dense_relu_dense(
mtf.layers.layer_norm(x, model_dim, name="layer_norm_ffn"),
feedforward_dim,
hparams.dropout,
dropout_broadcast_dims=[length_dim]), hparams)
output = mtf.layers.layer_norm(x, model_dim, name="final_layer_norm")
return output
|
Image Transformer decoder with local2D spatial layers.
|
def local_attention2d_spatial_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local2D spatial layers."""
batch_dim, length_dim, model_dim = x.shape.dims
blocks_h_dim = mtf.Dimension("blocksh", hparams.block_height)
blocks_w_dim = mtf.Dimension("blocksw", hparams.block_width)
num_h_blocks_dim = mtf.Dimension("num_h_blocks",
hparams.img_len // hparams.block_height)
num_w_blocks_dim = mtf.Dimension(
"num_w_blocks",
hparams.img_len * hparams.num_channels // hparams.block_width)
x = mtf.transpose(
mtf.reshape(
x,
mtf.Shape([
batch_dim, num_h_blocks_dim, blocks_h_dim,
num_w_blocks_dim, blocks_w_dim, model_dim
])),
mtf.Shape([
batch_dim, num_h_blocks_dim, num_w_blocks_dim,
blocks_h_dim, blocks_w_dim, model_dim
]))
# Image Transformer Decoder
# [ self attention - ffn - residual + dropout] x n
for layer in range(hparams.num_decoder_layers):
layer_name = "decoder_layer_%d" % layer
with tf.variable_scope(layer_name):
# Self attention layer
x += layer_prepostprocess_dropout(
mtf.layers.local_2d_self_attention_spatial_blocks(
mtf.layers.layer_norm(x, model_dim, name="layer_norm_att"),
kv_dim,
heads_dim,
memory_h_dim=num_h_blocks_dim,
memory_w_dim=num_w_blocks_dim,
name="self_att"), hparams)
# ffn layer
x += layer_prepostprocess_dropout(
mtf.layers.dense_relu_dense(
mtf.layers.layer_norm(x, model_dim, name="layer_norm_ffn"),
feedforward_dim,
hparams.dropout,
dropout_broadcast_dims=[length_dim]), hparams)
output = mtf.layers.layer_norm(x, model_dim, name="final_layer_norm")
return output
|
Image Transformer decoder with local1D masked layers.
|
def local_attention1d_masked_decoder(x, kv_dim, heads_dim,
feedforward_dim, hparams):
"""Image Transformer decoder with local1D masked layers."""
print(x)
_, length_dim, model_dim = x.shape.dims
for layer in range(hparams.num_decoder_layers):
layer_name = "decoder_layer_%d" % layer
with tf.variable_scope(layer_name):
# Self attention layer
length_per_split = mtf.tensor_dim_to_size_per_split(
hparams.layout, hparams.mesh_shape, length_dim)
x += layer_prepostprocess_dropout(
mtf.layers.masked_local_attention_1d(
mtf.layers.layer_norm(x, model_dim, name="layer_norm_att"),
kv_dim,
heads_dim,
window_size=hparams.block_length,
length_per_split=length_per_split,
name="self_att"), hparams)
# ffn layer
x += layer_prepostprocess_dropout(
mtf.layers.dense_relu_dense(
mtf.layers.layer_norm(x, model_dim, name="layer_norm_ffn"),
feedforward_dim,
hparams.dropout,
dropout_broadcast_dims=[length_dim]), hparams)
output = mtf.layers.layer_norm(x, model_dim, name="final_layer_norm")
return output
|
Set of hyperparameters.
|
def mtf_image_transformer_base():
"""Set of hyperparameters."""
hparams = common_hparams.basic_params1()
hparams.no_data_parallelism = True
hparams.use_fixed_batch_size = True
hparams.batch_size = 1
hparams.max_length = 3072
hparams.hidden_size = 256
hparams.label_smoothing = 0.0
# 8-way model-parallelism
hparams.add_hparam("mesh_shape", "batch:8")
hparams.add_hparam("layout", "batch:batch")
hparams.add_hparam("mtf_mode", True)
hparams.add_hparam("num_heads", 8)
hparams.add_hparam("filter_size", 1024)
hparams.add_hparam("num_encoder_layers", 0)
hparams.add_hparam("num_decoder_layers", 6)
hparams.add_hparam("attention_key_size", 256)
hparams.add_hparam("attention_value_size", 256)
# Share weights between input and target embeddings
hparams.shared_embedding = True
# mixture of experts hparams
hparams.add_hparam("ffn_layer", "dense_relu_dense")
hparams.add_hparam("moe_overhead_train", 1.0)
hparams.add_hparam("moe_overhead_eval", 2.0)
hparams.moe_num_experts = 16
hparams.moe_loss_coef = 1e-3
hparams.shared_embedding_and_softmax_weights = True
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_warmup_steps = 10000
hparams.add_hparam("d_kv", 64)
hparams.add_hparam("d_ff", 2048)
# Image related hparams
hparams.add_hparam("img_len", 32)
hparams.add_hparam("num_channels", 3)
hparams.add_hparam("unconditional", True)
# Local Attention related params
hparams.add_hparam("block_length", 128)
hparams.add_hparam("block_height", 16)
hparams.add_hparam("block_width", 16)
hparams.add_hparam("attention_type", "local1d")
return hparams
|
Catch bugs locally...
|
def mtf_image_transformer_tiny():
"""Catch bugs locally..."""
hparams = mtf_image_transformer_base()
hparams.hidden_size = 128
hparams.d_ff = 256
hparams.batch_size = 4
hparams.num_encoder_layers = 1
hparams.num_decoder_layers = 4
hparams.num_heads = 4
hparams.attention_key_size = 128
hparams.attention_value_size = 128
hparams.block_length = 32
# data parallelism and model-parallelism
hparams.mesh_shape = "batch:2"
hparams.layout = "batch:batch"
return hparams
|
Small single parameters.
|
def mtf_image_transformer_single():
"""Small single parameters."""
hparams = mtf_image_transformer_tiny()
hparams.mesh_shape = ""
hparams.layout = ""
hparams.hidden_size = 32
hparams.filter_size = 32
hparams.batch_size = 1
hparams.num_encoder_layers = 1
hparams.num_decoder_layers = 1
hparams.num_heads = 2
hparams.attention_key_size = 32
hparams.attention_value_size = 32
hparams.block_length = 16
return hparams
|
Small single parameters.
|
def mtf_image_transformer_base_single():
"""Small single parameters."""
hparams = mtf_image_transformer_base()
hparams.num_decoder_layers = 6
hparams.filter_size = 256
hparams.block_length = 128
hparams.mesh_shape = ""
hparams.layout = ""
return hparams
|
Small single parameters.
|
def mtf_image_transformer_tiny_spatial1d():
"""Small single parameters."""
hparams = mtf_image_transformer_tiny()
hparams.num_decoder_layers = 6
hparams.filter_size = 128
hparams.block_height = 8
hparams.block_width = 8
hparams.attention_type = "local1d_spatial"
hparams.mesh_shape = ""
hparams.layout = ""
return hparams
|
Data parallel CIFAR parameters.
|
def mtf_image_transformer_base_cifar():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base()
hparams.mesh_shape = "batch:8"
hparams.layout = "batch:batch"
hparams.learning_rate_decay_steps = 13600 # one epoch
hparams.batch_size = 32
hparams.num_heads = 4
hparams.num_decoder_layers = 12
hparams.block_length = 256
hparams.hidden_size = 512
hparams.d_ff = 2048
hparams.learning_rate = 0.5
hparams.layer_preprocess_sequence = "none"
hparams.layer_postprocess_sequence = "dan"
hparams.layer_prepostprocess_dropout = 0.3
hparams.unconditional = True
return hparams
|
Data parallel CIFAR parameters.
|
def mtf_image_transformer_cifar_4x():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "batch:32"
hparams.layout = "batch:batch"
hparams.batch_size = 128
return hparams
|
Data parallel CIFAR parameters.
|
def mtf_image_transformer_cifar_mp_4x():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "model:4;batch:8"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 32
hparams.num_heads = 8
hparams.d_ff = 8192
return hparams
|
Data parallel CIFAR parameters.
|
def mtf_image_transformer_base_imagenet():
"""Data parallel CIFAR parameters."""
hparams = mtf_image_transformer_base_cifar()
hparams.mesh_shape = "batch:32"
hparams.layout = "batch:batch"
hparams.batch_size = 128
hparams.d_ff = 2048
hparams.hidden_size = 512
hparams.num_decoder_layers = 12
hparams.learning_rate = 0.5
hparams.learning_rate_warmup_steps = 31250
hparams.layer_preprocess_sequence = "none"
hparams.layer_postprocess_sequence = "dan"
hparams.layer_prepostprocess_dropout = 0.1
hparams.unconditional = True
return hparams
|
Model parallel ImageNet parameters.
|
def mtf_image_transformer_base_imagenet_mp():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:4;batch:8"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 32
hparams.num_heads = 8
hparams.d_ff = 8192
hparams.learning_rate_warmup_steps = 31250
hparams.unconditional = True
return hparams
|
Model parallel ImageNet parameters.
|
def mtf_image_transformer_base_imagenet_mp128():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 8
hparams.img_len = 128
hparams.block_length = 128
hparams.num_heads = 8
hparams.num_decoder_layers = 4
hparams.d_ff = 4096
hparams.learning_rate_warmup_steps = 31250
hparams.unconditional = True
hparams.max_length = 256*256*3
return hparams
|
Model parallel ImageNet parameters.
|
def mtf_image_transformer_base_imagenet_mp_sp():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet_mp128()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;num_wblocks:model"
hparams.batch_size = 8
hparams.img_len = 128
hparams.block_length = 128
hparams.attention_type = "local1d_spatial"
return hparams
|
Model parallel ImageNet parameters.
|
def mtf_image_transformer_base_imagenet_mp64():
"""Model parallel ImageNet parameters."""
hparams = mtf_image_transformer_base_imagenet()
hparams.mesh_shape = "model:8;batch:4"
hparams.layout = "batch:batch;d_ff:model;heads:model"
hparams.batch_size = 8
hparams.img_len = 64
hparams.num_decoder_layers = 8
return hparams
|
Returns a list of degree vectors, one for each input and hidden layer.
A unit with degree d can only receive input from units with degree < d. Output
units always have the same degree as their associated input unit.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidden units per layer. It does not
include the output layer. Each hidden unit size must be at least the size
of length (otherwise autoregressivity is not possible).
input_order: Order of degrees to the input units: 'random', 'left-to-right',
'right-to-left', or an array of an explicit order. For example,
'left-to-right' builds an autoregressive model
p(x) = p(x1) p(x2 | x1) ... p(xD | x<D).
hidden_order: Order of degrees to the hidden units: 'random',
'left-to-right'. If 'left-to-right', hidden units are allocated equally
(up to a remainder term) to each degree.
|
def create_degrees(input_dim,
hidden_dims,
input_order='left-to-right',
hidden_order='left-to-right'):
"""Returns a list of degree vectors, one for each input and hidden layer.
A unit with degree d can only receive input from units with degree < d. Output
units always have the same degree as their associated input unit.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidden units per layer. It does not
include the output layer. Each hidden unit size must be at least the size
of length (otherwise autoregressivity is not possible).
input_order: Order of degrees to the input units: 'random', 'left-to-right',
'right-to-left', or an array of an explicit order. For example,
'left-to-right' builds an autoregressive model
p(x) = p(x1) p(x2 | x1) ... p(xD | x<D).
hidden_order: Order of degrees to the hidden units: 'random',
'left-to-right'. If 'left-to-right', hidden units are allocated equally
(up to a remainder term) to each degree.
"""
if (isinstance(input_order, str) and
input_order not in ('random', 'left-to-right', 'right-to-left')):
raise ValueError('Input order is not valid.')
if hidden_order not in ('random', 'left-to-right'):
raise ValueError('Hidden order is not valid.')
degrees = []
if isinstance(input_order, str):
input_degrees = np.arange(1, input_dim + 1)
if input_order == 'right-to-left':
input_degrees = np.flip(input_degrees, 0)
elif input_order == 'random':
np.random.shuffle(input_degrees)
else:
input_order = np.array(input_order)
if np.all(np.sort(input_order) != np.arange(1, input_dim + 1)):
raise ValueError('invalid input order')
input_degrees = input_order
degrees.append(input_degrees)
for units in hidden_dims:
if hidden_order == 'random':
min_prev_degree = min(np.min(degrees[-1]), input_dim - 1)
hidden_degrees = np.random.randint(
low=min_prev_degree, high=input_dim, size=units)
elif hidden_order == 'left-to-right':
hidden_degrees = (np.arange(units) % max(1, input_dim - 1) +
min(1, input_dim - 1))
degrees.append(hidden_degrees)
return degrees
|
Returns a list of binary mask matrices respecting autoregressive ordering.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidden units per layer. It does not
include the output layer; those number of units will always be set to
input_dim downstream. Each hidden unit size must be at least the size of
length (otherwise autoregressivity is not possible).
input_order: Order of degrees to the input units: 'random', 'left-to-right',
'right-to-left', or an array of an explicit order. For example,
'left-to-right' builds an autoregressive model
p(x) = p(x1) p(x2 | x1) ... p(xD | x<D).
hidden_order: Order of degrees to the hidden units: 'random',
'left-to-right'. If 'left-to-right', hidden units are allocated equally
(up to a remainder term) to each degree.
|
def create_masks(input_dim,
hidden_dims,
input_order='left-to-right',
hidden_order='left-to-right'):
"""Returns a list of binary mask matrices respecting autoregressive ordering.
Args:
input_dim: Number of inputs.
hidden_dims: list with the number of hidden units per layer. It does not
include the output layer; those number of units will always be set to
input_dim downstream. Each hidden unit size must be at least the size of
length (otherwise autoregressivity is not possible).
input_order: Order of degrees to the input units: 'random', 'left-to-right',
'right-to-left', or an array of an explicit order. For example,
'left-to-right' builds an autoregressive model
p(x) = p(x1) p(x2 | x1) ... p(xD | x<D).
hidden_order: Order of degrees to the hidden units: 'random',
'left-to-right'. If 'left-to-right', hidden units are allocated equally
(up to a remainder term) to each degree.
"""
degrees = create_degrees(input_dim, hidden_dims, input_order, hidden_order)
masks = []
# Create input-to-hidden and hidden-to-hidden masks.
for input_degrees, output_degrees in zip(degrees[:-1], degrees[1:]):
mask = tf.cast(input_degrees[:, np.newaxis] <= output_degrees, tf.float32)
masks.append(mask)
# Create hidden-to-output mask.
mask = tf.cast(degrees[-1][:, np.newaxis] < degrees[0], tf.float32)
masks.append(mask)
return masks
|
Performs incomplete Sinkhorn normalization to inputs.
By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix
with positive entries can be turned into a doubly-stochastic matrix
(i.e. its rows and columns add up to one) via the succesive row and column
normalization.
-To ensure positivity, the effective input to sinkhorn has to be
exp(inputs) (elementwise).
-However, for stability, sinkhorn works in the log-space. It is only at
return time that entries are exponentiated.
Code is adapted from Mena et al. [2].
[1] Richard Sinkhorn and Paul Knopp. Concerning nonnegative matrices and
doubly stochastic matrices. Pacific Journal of Mathematics, 1967.
[2] Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek.
Learning latent permutations with Gumbel-Sinkhorn networks. International
Conference on Learning Representations, 2018.
Args:
inputs: A `Tensor` with shape `[..., vocab_size, vocab_size]`.
n_iters: Number of sinkhorn iterations (in practice, as little as 20
iterations are needed to achieve decent convergence for `vocab_size` ~100)
Returns:
outputs: A `Tensor` of close-to-doubly-stochastic matrices with shape
`[:, vocab_size, vocab_size]`.
|
def sinkhorn(inputs, n_iters=20):
"""Performs incomplete Sinkhorn normalization to inputs.
By a theorem by Sinkhorn and Knopp [1], a sufficiently well-behaved matrix
with positive entries can be turned into a doubly-stochastic matrix
(i.e. its rows and columns add up to one) via the succesive row and column
normalization.
-To ensure positivity, the effective input to sinkhorn has to be
exp(inputs) (elementwise).
-However, for stability, sinkhorn works in the log-space. It is only at
return time that entries are exponentiated.
Code is adapted from Mena et al. [2].
[1] Richard Sinkhorn and Paul Knopp. Concerning nonnegative matrices and
doubly stochastic matrices. Pacific Journal of Mathematics, 1967.
[2] Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek.
Learning latent permutations with Gumbel-Sinkhorn networks. International
Conference on Learning Representations, 2018.
Args:
inputs: A `Tensor` with shape `[..., vocab_size, vocab_size]`.
n_iters: Number of sinkhorn iterations (in practice, as little as 20
iterations are needed to achieve decent convergence for `vocab_size` ~100)
Returns:
outputs: A `Tensor` of close-to-doubly-stochastic matrices with shape
`[:, vocab_size, vocab_size]`.
"""
vocab_size = tf.shape(inputs)[-1]
log_alpha = tf.reshape(inputs, [-1, vocab_size, vocab_size])
for _ in range(n_iters):
log_alpha -= tf.reshape(tf.reduce_logsumexp(log_alpha, axis=2),
[-1, vocab_size, 1])
log_alpha -= tf.reshape(tf.reduce_logsumexp(log_alpha, axis=1),
[-1, 1, vocab_size])
outputs = tf.exp(log_alpha)
return outputs
|
Random variable for f(x), where x ~ p(x) and f is reversible.
|
def TransformedRandomVariable(random_variable, # pylint: disable=invalid-name
reversible_layer,
name=None,
sample_shape=(),
value=None):
"""Random variable for f(x), where x ~ p(x) and f is reversible."""
return ed.RandomVariable(
distribution=TransformedDistribution(random_variable.distribution,
reversible_layer,
name=name),
sample_shape=sample_shape,
value=value)
|
Returns log det | dx / dy | = num_events * sum log | scale |.
|
def log_det_jacobian(self, inputs):
"""Returns log det | dx / dy | = num_events * sum log | scale |."""
del inputs # unused
# Number of events is number of all elements excluding the batch and
# channel dimensions.
num_events = tf.reduce_prod(tf.shape(inputs)[1:-1])
log_det_jacobian = num_events * tf.reduce_sum(self.log_scale)
return log_det_jacobian
|
Slice encoder hidden state into block_dim.
Args:
x: Encoder hidden state of shape [-1, hidden_size].
Returns:
Sliced states of shape [-1, num_blocks, block_dim].
|
def slice_hidden(self, x):
"""Slice encoder hidden state into block_dim.
Args:
x: Encoder hidden state of shape [-1, hidden_size].
Returns:
Sliced states of shape [-1, num_blocks, block_dim].
"""
x_sliced = tf.reshape(
x, shape=[-1, self.hparams.num_blocks, self.hparams.block_dim])
return x_sliced
|
Find the nearest element in means to elements in x.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape [-1, num_blocks, block_dim].
means: Embedding means of shape.
Returns:
Tensor with nearest element in mean encoded in one-hot notation.
|
def nearest_neighbor(self, x, means):
"""Find the nearest element in means to elements in x.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape [-1, num_blocks, block_dim].
means: Embedding means of shape.
Returns:
Tensor with nearest element in mean encoded in one-hot notation.
"""
x_norm_sq = tf.reduce_sum(tf.square(x), axis=-1, keep_dims=True)
means_norm_sq = tf.reduce_sum(tf.square(means), axis=-1, keep_dims=True)
scalar_prod = tf.matmul(
tf.transpose(x, perm=[1, 0, 2]), tf.transpose(means, perm=[0, 2, 1]))
scalar_prod = tf.transpose(scalar_prod, perm=[1, 0, 2])
dist = x_norm_sq + tf.transpose(
means_norm_sq, perm=[2, 0, 1]) - 2 * scalar_prod
if self.hparams.soft_em:
nearest_idx = tf.stack(
[
tf.multinomial(
-dist[:, i, :], num_samples=self.hparams.num_samples)
for i in range(self.hparams.num_blocks)
],
axis=1)
nearest_hot = tf.one_hot(nearest_idx, depth=self.hparams.block_v_size)
nearest_hot = tf.reduce_mean(nearest_hot, axis=-2)
else:
if self.hparams.random_top_k > 1:
_, top_k_idx = tf.nn.top_k(-dist, k=self.hparams.random_top_k)
nearest_idx = tf.gather(
top_k_idx,
tf.random_uniform(
[1],
minval=0,
maxval=self.hparams.random_top_k - 1,
dtype=tf.int32),
axis=-1)
else:
if self.hparams.use_scales:
dist /= tf.reshape(self.hparams.scales,
[1, 1, self.hparams.moe_num_experts])
nearest_idx = tf.argmax(-dist, axis=-1)
nearest_hot = tf.one_hot(nearest_idx, self.hparams.block_v_size)
return nearest_hot
|
Compute nearest neighbors and loss for training the embeddings.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape
[-1, num_blocks, block_dim].
means: Embedding means.
Returns:
The nearest neighbor in one hot form, the nearest neighbor
itself, the
commitment loss, embedding training loss.
|
def embedding_lookup(self, x, means):
"""Compute nearest neighbors and loss for training the embeddings.
Args:
x: Batch of encoder continuous latent states sliced/projected into
shape
[-1, num_blocks, block_dim].
means: Embedding means.
Returns:
The nearest neighbor in one hot form, the nearest neighbor
itself, the
commitment loss, embedding training loss.
"""
x_means_hot = self.nearest_neighbor(x, means)
x_means_hot_flat = tf.reshape(
x_means_hot, [-1, self.hparams.num_blocks, self.hparams.block_v_size])
x_means = tf.matmul(tf.transpose(x_means_hot_flat, perm=[1, 0, 2]), means)
x_means = tf.transpose(x_means, [1, 0, 2])
q_loss = tf.reduce_mean(
tf.squared_difference(tf.stop_gradient(x), x_means))
e_loss = tf.reduce_mean(
tf.squared_difference(x, tf.stop_gradient(x_means)))
return x_means_hot, x_means, q_loss, e_loss
|
Turn x_int representing numbers into a bitwise (lower-endian) tensor.
Args:
x_int: Tensor containing integer to be converted into base
notation.
num_bits: Number of bits in the representation.
base: Base of the representation.
Returns:
Corresponding number expressed in base.
|
def int_to_bit(self, x_int, num_bits, base=2):
"""Turn x_int representing numbers into a bitwise (lower-endian) tensor.
Args:
x_int: Tensor containing integer to be converted into base
notation.
num_bits: Number of bits in the representation.
base: Base of the representation.
Returns:
Corresponding number expressed in base.
"""
x_l = tf.to_int32(tf.expand_dims(x_int, axis=-1))
# pylint: disable=g-complex-comprehension
x_labels = [
tf.floormod(
tf.floordiv(tf.to_int32(x_l),
tf.to_int32(base)**i), tf.to_int32(base))
for i in range(num_bits)]
res = tf.concat(x_labels, axis=-1)
return tf.to_float(res)
|
Embedding function that takes discrete latent and returns embedding.
Args:
x: Input to the discretization bottleneck.
Returns:
Continuous embedding to be passed on to the decoder.
Raises:
ValueError: For unknown or missing arguments.
|
def embed(self, x):
"""Embedding function that takes discrete latent and returns embedding.
Args:
x: Input to the discretization bottleneck.
Returns:
Continuous embedding to be passed on to the decoder.
Raises:
ValueError: For unknown or missing arguments.
"""
shape_x = common_layers.shape_list(x)
x_flat = tf.reshape(x, [-1, 1])
c = self.int_to_bit(x_flat, num_bits=self.hparams.z_size, base=2)
shape = common_layers.shape_list(c)
new_shape = shape
new_shape.append(self.hparams.num_blocks)
new_shape.append(int(self.hparams.z_size / self.hparams.num_blocks))
c = tf.to_int32(tf.reshape(c, shape=new_shape))
h1_shape = shape_x
h1_shape.append(self.hparams.hidden_size)
h1 = tf.zeros(dtype=tf.float32, shape=h1_shape)
c_int = self.bit_to_int(
c, num_bits=int(self.hparams.z_size / self.hparams.num_blocks), base=2)
c_hot = tf.one_hot(c_int, depth=self.hparams.block_v_size, axis=-1)
c_hot_flat = tf.reshape(
c_hot, shape=[-1, self.hparams.num_blocks, self.hparams.block_v_size])
h1 = tf.matmul(tf.transpose(c_hot_flat, perm=[1, 0, 2]), self.means)
h1 = tf.transpose(h1, perm=[1, 0, 2])
h1 = tf.reshape(h1, shape=h1_shape)
h1_shape[0] = self.hparams.batch_size
h2 = tf.layers.dense(tf.nn.relu(h1), self.hparams.filter_size, name="vch2")
res = tf.layers.dense(
tf.nn.relu(h2), self.hparams.hidden_size, name="vcfin")
return res
|
Discretization bottleneck for latent variables.
Args:
x: Input to the discretization bottleneck.
Returns:
Embedding to pass to the decoder, discrete latent, loss, and the
embedding
function.
Raises:
ValueError: If projection_tensors is None for reshape_method
project, or
ema_count or ema_means is None if we are using ema, or unknown
args.
|
def discrete_bottleneck(self, x):
"""Discretization bottleneck for latent variables.
Args:
x: Input to the discretization bottleneck.
Returns:
Embedding to pass to the decoder, discrete latent, loss, and the
embedding
function.
Raises:
ValueError: If projection_tensors is None for reshape_method
project, or
ema_count or ema_means is None if we are using ema, or unknown
args.
"""
x_reshaped = self.slice_hidden(x)
x_means_hot = []
x_means = 0
loss = 0
x_means_hot, x_means, q_loss, e_loss = self.embedding_lookup(
x_reshaped, self.means)
if self.hparams.ema:
tf.logging.info("Using EMA with beta = {}".format(self.hparams.beta))
updated_ema_count = \
moving_averages.assign_moving_average(
self.ema_count,
tf.reduce_sum(
tf.reshape(
x_means_hot,
shape=[-1, self.hparams.num_blocks,
self.hparams.block_v_size]),
axis=0),
self.hparams.decay,
zero_debias=False)
dw = tf.matmul(
tf.transpose(x_means_hot, perm=[1, 2, 0]),
tf.transpose(x_reshaped, perm=[1, 0, 2]))
updated_ema_means = \
moving_averages.assign_moving_average(
self.ema_means, dw, self.hparams.decay,
zero_debias=False)
n = tf.reduce_sum(updated_ema_count, axis=-1, keep_dims=True)
updated_ema_count = ((updated_ema_count + self.hparams.epsilon) / (
n + 2**self.hparams.z_size * self.hparams.epsilon) * n)
updated_ema_means = updated_ema_means / tf.expand_dims(
updated_ema_count, axis=-1)
with tf.control_dependencies([e_loss]):
update_means = tf.assign(self.means, updated_ema_means)
with tf.control_dependencies([update_means]):
loss += self.hparams.beta * e_loss
else:
# Use a gradient based loss for learning the cluster centers
loss += q_loss + self.hparams.beta * e_loss
# Get the discrete latent representation
x_means_idx = tf.argmax(x_means_hot, axis=-1)
# Get the binary representation
num_bits = int(self.hparams.z_size // self.hparams.num_blocks)
x_means_bits = self.int_to_bit(x_means_idx, num_bits=num_bits, base=2)
x_discrete = self.bit_to_int(
tf.to_int32(x_means_bits), num_bits=self.hparams.z_size, base=2)
# Reshape x_discrete
shape_x = common_layers.shape_list(x)
shape_discrete = shape_x[:-1]
x_discrete = tf.reshape(x_discrete, shape_discrete)
x_means = tf.reshape(x_means, shape=shape_x)
h1 = x + tf.stop_gradient(x_means - x)
h2 = tf.layers.dense(tf.nn.relu(h1), self.hparams.filter_size, name="vch2")
res = tf.layers.dense(
tf.nn.relu(h2), self.hparams.hidden_size, name="vcfin")
embed_fn = partial(self.embed)
return {
"dense": res,
"discrete": x_discrete,
"loss": loss,
"embed": embed_fn
}
|
Switch from Adam to Adafactor, approximating the behavior of Adam.
Some minor things may be different, like epsilon and beta1 correction.
Args:
hparams: model hyperparameters where "adam" in hparams.optimizer
|
def mimic_adam_with_adafactor(hparams):
"""Switch from Adam to Adafactor, approximating the behavior of Adam.
Some minor things may be different, like epsilon and beta1 correction.
Args:
hparams: model hyperparameters where "adam" in hparams.optimizer
"""
assert "adam" in hparams.optimizer
hparams.optimizer = "adafactor"
hparams.optimizer_adafactor_beta1 = hparams.optimizer_adam_beta1
hparams.optimizer_adafactor_beta2 = hparams.optimizer_adam_beta2
hparams.optimizer_adafactor_multiply_by_parameter_scale = False
hparams.optimizer_adafactor_factored = False
hparams.optimizer_adafactor_clipping_threshold = None
hparams.optimizer_adafactor_decay_type = "adam"
|
Old version - Adam.
|
def afx_adam():
"""Old version - Adam."""
hparams = transformer.transformer_base_v2()
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.symbol_modality_num_shards = 1
hparams.batch_size = 2048
hparams.optimizer = "adam"
hparams.learning_rate_schedule = (
"constant*rsqrt_decay*linear_warmup*rsqrt_hidden_size")
hparams.learning_rate_constant = 2.0
return hparams
|
Adafactor with recommended learning rate schedule.
|
def afx_adafactor():
"""Adafactor with recommended learning rate schedule."""
hparams = afx_adam()
hparams.optimizer = "Adafactor"
hparams.learning_rate_schedule = "rsqrt_decay"
hparams.learning_rate_warmup_steps = 10000
return hparams
|
Small transformer model with small batch size for fast step times.
|
def afx_small():
"""Small transformer model with small batch size for fast step times."""
hparams = transformer.transformer_tpu()
hparams.filter_size = 1024
hparams.num_heads = 4
hparams.num_hidden_layers = 3
hparams.batch_size = 512
return hparams
|
Emily's model hparams.
|
def next_frame_emily():
"""Emily's model hparams."""
hparams = sv2p_params.next_frame_sv2p()
hparams.video_num_input_frames = 2
hparams.video_num_target_frames = 10
hparams.learning_rate_constant = 1e-4
seq_length = hparams.video_num_input_frames + hparams.video_num_target_frames
# The latent_loss_multiplier is divided by the number of frames because
# the image sequence loss in t2t is averaged instead of added through
# time as they do in the SVG-LP paper
hparams.latent_loss_multiplier = 1e-4 / seq_length
hparams.reward_prediction = False
hparams.num_iterations_1st_stage = -1
hparams.num_iterations_2nd_stage = -1
hparams.optimizer_adam_beta1 = 0.9
hparams.optimizer_adam_beta2 = 0.999
hparams.optimizer_adam_epsilon = 1e-08
hparams.anneal_end = -1
hparams.clip_grad_norm = 5.0
hparams.add_hparam("learned_prior", True)
hparams.add_hparam("z_dim", 64)
hparams.add_hparam("g_dim", 128)
hparams.add_hparam("rnn_size", 256)
hparams.add_hparam("prior_rnn_layers", 1)
hparams.add_hparam("posterior_rnn_layers", 1)
hparams.add_hparam("predictor_rnn_layers", 2)
hparams.add_hparam("has_skips", True)
hparams.add_hparam("has_batchnorm", True)
return hparams
|
Convert a file to examples.
|
def main(_):
"""Convert a file to examples."""
if FLAGS.subword_text_encoder_filename:
encoder = text_encoder.SubwordTextEncoder(
FLAGS.subword_text_encoder_filename)
elif FLAGS.token_text_encoder_filename:
encoder = text_encoder.TokenTextEncoder(FLAGS.token_text_encoder_filename)
elif FLAGS.byte_text_encoder:
encoder = text_encoder.ByteTextEncoder()
else:
encoder = None
reader = tf.python_io.tf_record_iterator(FLAGS.input_filename)
total_sequences = 0
total_input_tokens = 0
total_target_tokens = 0
nonpadding_input_tokens = 0
nonpadding_target_tokens = 0
max_input_length = 0
max_target_length = 0
for record in reader:
x = tf.train.Example()
x.ParseFromString(record)
inputs = [int(i) for i in x.features.feature["inputs"].int64_list.value]
targets = [int(i) for i in x.features.feature["targets"].int64_list.value]
if FLAGS.print_inputs:
print("INPUTS:\n" + encoder.decode(inputs) if encoder else inputs)
if FLAGS.print_targets:
print("TARGETS:\n" + encoder.decode(targets) if encoder else targets)
nonpadding_input_tokens += len(inputs) - inputs.count(0)
nonpadding_target_tokens += len(targets) - targets.count(0)
total_input_tokens += len(inputs)
total_target_tokens += len(targets)
total_sequences += 1
max_input_length = max(max_input_length, len(inputs))
max_target_length = max(max_target_length, len(targets))
if FLAGS.print_all:
for k, v in six.iteritems(x.features.feature):
print("%s: %s" % (k, v.int64_list.value))
print("total_sequences: %d" % total_sequences)
print("total_input_tokens: %d" % total_input_tokens)
print("total_target_tokens: %d" % total_target_tokens)
print("nonpadding_input_tokens: %d" % nonpadding_input_tokens)
print("nonpadding_target_tokens: %d" % nonpadding_target_tokens)
print("max_input_length: %d" % max_input_length)
print("max_target_length: %d" % max_target_length)
|
Return a mix of env and video data fields and decoders.
|
def example_reading_spec(self):
"""Return a mix of env and video data fields and decoders."""
video_fields, video_decoders = (
video_utils.VideoProblem.example_reading_spec(self))
env_fields, env_decoders = env_problem.EnvProblem.example_reading_spec(self)
# Remove raw observations field since we want to capture them as videos.
env_fields.pop(env_problem.OBSERVATION_FIELD)
env_decoders.pop(env_problem.OBSERVATION_FIELD)
# Add frame number spec and decoder.
env_fields[_FRAME_NUMBER_FIELD] = tf.FixedLenFeature((1,), tf.int64)
env_decoders[
_FRAME_NUMBER_FIELD] = tf.contrib.slim.tfexample_decoder.Tensor(
_FRAME_NUMBER_FIELD)
# Add video fields and decoders
env_fields.update(video_fields)
env_decoders.update(video_decoders)
return env_fields, env_decoders
|
Transforms time step observations to frames of a video.
|
def _generate_time_steps(self, trajectory_list):
"""Transforms time step observations to frames of a video."""
for time_step in env_problem.EnvProblem._generate_time_steps(
self, trajectory_list):
# Convert the rendered observations from numpy to png format.
frame_np = np.array(time_step.pop(env_problem.OBSERVATION_FIELD))
frame_np = frame_np.reshape(
[self.frame_height, self.frame_width, self.num_channels])
# TODO(msaffar) Add support for non RGB rendered environments
frame = png.from_array(frame_np, "RGB", info={"bitdepth": 8})
frame_buffer = six.BytesIO()
frame.save(frame_buffer)
# Put the encoded frame back.
time_step[_IMAGE_ENCODED_FIELD] = [frame_buffer.getvalue()]
time_step[_IMAGE_FORMAT_FIELD] = [_FORMAT]
time_step[_IMAGE_HEIGHT_FIELD] = [self.frame_height]
time_step[_IMAGE_WIDTH_FIELD] = [self.frame_width]
# Add the frame number
time_step[_FRAME_NUMBER_FIELD] = time_step[env_problem.TIMESTEP_FIELD]
yield time_step
|
Iterate through lines of file.
|
def txt_line_iterator(txt_path):
"""Iterate through lines of file."""
with tf.gfile.Open(txt_path) as f:
for line in f:
yield line.strip()
|
Yield dicts for Text2TextProblem.generate_samples from lines of files.
|
def text2text_txt_iterator(source_txt_path, target_txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of files."""
for inputs, targets in zip(
txt_line_iterator(source_txt_path), txt_line_iterator(target_txt_path)):
yield {"inputs": inputs, "targets": targets}
|
Yield dicts for Text2TextProblem.generate_samples from lines of files.
|
def text2text_distill_iterator(source_txt_path, target_txt_path,
distill_txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of files."""
for inputs, targets, dist_targets in zip(
txt_line_iterator(source_txt_path), txt_line_iterator(target_txt_path),
txt_line_iterator(distill_txt_path)):
yield {"inputs": inputs, "targets": targets, "dist_targets": dist_targets}
|
Yield dicts for Text2ClassProblem.generate_samples from lines of files.
Args:
source_txt_path: txt file with record per line.
label_txt_path: txt file with label per line, either as int or str. If
string, must provide class_strs.
class_strs: list<str> of class label names. Must be in correct order (i.e.
["a", "b", "c"] means that "a" will get class ID 0, "b" ID 1, etc.).
Yields:
{"inputs": inputs, "label": label}
|
def text2class_txt_iterator(source_txt_path, label_txt_path, class_strs=None):
"""Yield dicts for Text2ClassProblem.generate_samples from lines of files.
Args:
source_txt_path: txt file with record per line.
label_txt_path: txt file with label per line, either as int or str. If
string, must provide class_strs.
class_strs: list<str> of class label names. Must be in correct order (i.e.
["a", "b", "c"] means that "a" will get class ID 0, "b" ID 1, etc.).
Yields:
{"inputs": inputs, "label": label}
"""
if class_strs:
class_strs = dict([(s, i) for i, s in enumerate(class_strs)])
for inputs, label in zip(
txt_line_iterator(source_txt_path), txt_line_iterator(label_txt_path)):
label = label.strip()
if class_strs:
label = class_strs[label]
else:
label = int(label)
yield {"inputs": inputs, "label": label}
|
Yield dicts for Text2TextProblem.generate_samples from lines of txt_path.
Args:
txt_path: path to txt file with a record per line, source and target
are tab-separated.
Yields:
{"inputs": inputs, "targets": targets}
|
def text2text_txt_tab_iterator(txt_path):
"""Yield dicts for Text2TextProblem.generate_samples from lines of txt_path.
Args:
txt_path: path to txt file with a record per line, source and target
are tab-separated.
Yields:
{"inputs": inputs, "targets": targets}
"""
for line in txt_line_iterator(txt_path):
if line and "\t" in line:
parts = line.split("\t", 1)
inputs, targets = parts[:2]
yield {"inputs": inputs.strip(), "targets": targets.strip()}
|
Encode Text2Text samples from the generator with the vocab.
|
def text2text_generate_encoded(sample_generator,
vocab,
targets_vocab=None,
has_inputs=True,
inputs_prefix="",
targets_prefix=""):
"""Encode Text2Text samples from the generator with the vocab."""
targets_vocab = targets_vocab or vocab
for sample in sample_generator:
if has_inputs:
sample["inputs"] = vocab.encode(inputs_prefix + sample["inputs"])
sample["inputs"].append(text_encoder.EOS_ID)
sample["targets"] = targets_vocab.encode(targets_prefix + sample["targets"])
sample["targets"].append(text_encoder.EOS_ID)
yield sample
|
For packed datasets, returns a function to pack examples.
Returns:
None or a function from list of TFRecords to list of TFRecords
|
def _pack_fn(self):
"""For packed datasets, returns a function to pack examples.
Returns:
None or a function from list of TFRecords to list of TFRecords
"""
if not self.packed_length:
return None
def my_fn(records):
"""Function from list of TFRecords to list of TFRecords."""
examples = []
for record in records:
x = tf.train.Example()
x.ParseFromString(record)
example_dict = {}
if self.has_inputs:
example_dict["inputs"] = [
int(i) for i in x.features.feature["inputs"].int64_list.value]
example_dict["targets"] = [
int(i) for i in x.features.feature["targets"].int64_list.value]
examples.append(example_dict)
examples = list(self._maybe_pack_examples(examples))
return [
generator_utils.to_example(x).SerializeToString() for x in examples]
return my_fn
|
Wraps generator with packer if self.packed_length.
|
def _maybe_pack_examples(self, generator):
"""Wraps generator with packer if self.packed_length."""
if not self.packed_length:
return generator
return generator_utils.pack_examples(
generator,
self.has_inputs,
self.packed_length,
spacing=self.packed_spacing,
chop_long_sequences=not self.has_inputs)
|
List of input filepaths for a particular training or dev shard.
Args:
tmp_dir: a string
task_id: an integer less than self.num_shards
Returns:
a list of tuples (filepath, start_pos, num_bytes)
|
def text_filepaths_for_task(self, tmp_dir, task_id):
"""List of input filepaths for a particular training or dev shard.
Args:
tmp_dir: a string
task_id: an integer less than self.num_shards
Returns:
a list of tuples (filepath, start_pos, num_bytes)
"""
assert task_id >= 0
assert task_id < self.num_train_shards + self.num_dev_shards
if task_id < self.num_train_shards:
return [
f for i, f in enumerate(self.train_text_filepaths(tmp_dir))
if i % self.num_train_shards == task_id
]
else:
return [
f for i, f in enumerate(self.dev_text_filepaths(tmp_dir))
if i % self.num_dev_shards == task_id - self.num_train_shards
]
|
Read text out of an input file.
The default just reads the text, converts to unicode and yields one
unicode string.
Subclasses can override this function in order to preprocess, and can
yield any number of strings.
Args:
filepath: a string
Yields:
unicode strings.
|
def filepath_to_unicode_strings(self, filepath):
"""Read text out of an input file.
The default just reads the text, converts to unicode and yields one
unicode string.
Subclasses can override this function in order to preprocess, and can
yield any number of strings.
Args:
filepath: a string
Yields:
unicode strings.
"""
f = tf.gfile.Open(filepath)
b = f.read()
yield text_encoder.to_unicode_ignore_errors(b)
|
Read complete text of input files and yield unicode strings.
By default, one unicode string is produced per file, but this is
not guaranteed, since subclasses can override
filepath_to_unicode_strings().
max_chars_per_file and max_chars_total can also be specified, in which
case some strings may be truncated or dropped to limit the total
amount of output.
Args:
filepaths: a list of strings
max_chars_per_file: an optional integer
max_chars_total: an optional integer
Yields:
unicode strings
|
def file_generator(self,
filepaths,
max_chars_per_file=None,
max_chars_total=None):
"""Read complete text of input files and yield unicode strings.
By default, one unicode string is produced per file, but this is
not guaranteed, since subclasses can override
filepath_to_unicode_strings().
max_chars_per_file and max_chars_total can also be specified, in which
case some strings may be truncated or dropped to limit the total
amount of output.
Args:
filepaths: a list of strings
max_chars_per_file: an optional integer
max_chars_total: an optional integer
Yields:
unicode strings
"""
chars_total = 0
for fname in filepaths:
chars_this_file = 0
tf.logging.info("reading file %s" % fname)
for text in self.filepath_to_unicode_strings(fname):
if (max_chars_per_file and
chars_this_file + len(text) > max_chars_per_file):
text = text[:max_chars_per_file - chars_this_file]
if max_chars_total and chars_total + len(text) > max_chars_total:
text = text[:max_chars_total - chars_total]
chars_total += len(text)
chars_this_file += len(text)
if text:
yield text
if max_chars_total and chars_total >= max_chars_total:
return
if max_chars_per_file and chars_this_file >= max_chars_per_file:
break
|
Generator for examples.
Args:
encoder: a TextEncoder
tmp_dir: a string
task_id: an integer
Yields:
feature dictionaries
|
def example_generator(self, encoder, tmp_dir, task_id):
"""Generator for examples.
Args:
encoder: a TextEncoder
tmp_dir: a string
task_id: an integer
Yields:
feature dictionaries
"""
filepaths = self.text_filepaths_for_task(tmp_dir, task_id)
if task_id >= self.num_train_shards:
# this is dev data - limit the total length.
max_chars_per_file = self.max_dev_chars // (
self.num_dev_shards * len(filepaths))
else:
max_chars_per_file = None
tokens = []
for ftext in self.file_generator(
filepaths, max_chars_per_file=max_chars_per_file):
tokens.extend(encoder.encode(ftext))
pos = 0
while pos + self.sequence_length <= len(tokens):
yield {"targets": tokens[pos:pos + self.sequence_length]}
pos += self.sequence_length
if pos > 0:
tokens = tokens[pos:]
if self.remainder_policy == "pad":
if tokens:
targets = tokens + [0] * (self.sequence_length - len(tokens))
yield {"targets": targets}
else:
assert self.remainder_policy == "drop"
|
Make sure that the data is prepared and the vocab is generated.
|
def prepare_to_generate(self, data_dir, tmp_dir):
"""Make sure that the data is prepared and the vocab is generated."""
self.get_or_create_vocab(data_dir, tmp_dir)
self.train_text_filepaths(tmp_dir)
self.dev_text_filepaths(tmp_dir)
|
Generates training/dev data.
Args:
data_dir: a string
tmp_dir: a string
task_id: an optional integer
Returns:
shard or shards for which data was generated.
|
def generate_data(self, data_dir, tmp_dir, task_id=-1):
"""Generates training/dev data.
Args:
data_dir: a string
tmp_dir: a string
task_id: an optional integer
Returns:
shard or shards for which data was generated.
"""
tf.logging.info("generate_data task_id=%s" % task_id)
encoder = self.get_or_create_vocab(data_dir, tmp_dir)
assert task_id >= 0 and task_id < self.num_generate_tasks
if task_id < self.num_train_shards:
out_file = self.training_filepaths(
data_dir, self.num_train_shards, shuffled=False)[task_id]
else:
out_file = self.dev_filepaths(
data_dir, self.num_dev_shards,
shuffled=False)[task_id - self.num_train_shards]
generator_utils.generate_files(
self.example_generator(encoder, tmp_dir, task_id), [out_file])
generator_utils.shuffle_dataset([out_file])
|
ResNet convolutional striding block.
|
def ConvBlock(kernel_size, filters, strides):
"""ResNet convolutional striding block."""
ks = kernel_size
filters1, filters2, filters3 = filters
main = layers.Serial(
layers.Conv(filters1, (1, 1), strides),
layers.BatchNorm(),
layers.Relu(),
layers.Conv(filters2, (ks, ks), padding='SAME'),
layers.BatchNorm(),
layers.Relu(),
layers.Conv(filters3, (1, 1)),
layers.BatchNorm()
)
shortcut = layers.Serial(
layers.Conv(filters3, (1, 1), strides),
layers.BatchNorm()
)
return layers.Serial(
layers.Branch(),
layers.Parallel(main, shortcut),
layers.SumBranches(),
layers.Relu()
)
|
ResNet identical size block.
|
def IdentityBlock(kernel_size, filters):
"""ResNet identical size block."""
ks = kernel_size
filters1, filters2, filters3 = filters
main = layers.Serial(
layers.Conv(filters1, (1, 1)),
layers.BatchNorm(),
layers.Relu(),
layers.Conv(filters2, (ks, ks), padding='SAME'),
layers.BatchNorm(),
layers.Relu(),
layers.Conv(filters3, (1, 1)),
layers.BatchNorm()
)
return layers.Serial(
layers.Branch(),
layers.Parallel(main, layers.Identity()),
layers.SumBranches(),
layers.Relu()
)
|
ResNet.
Args:
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: how many classes to distinguish.
mode: whether we are training or evaluating or doing inference.
Returns:
The ResNet model with the given layer and output sizes.
|
def Resnet50(hidden_size=64, num_output_classes=1001, mode='train'):
"""ResNet.
Args:
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: how many classes to distinguish.
mode: whether we are training or evaluating or doing inference.
Returns:
The ResNet model with the given layer and output sizes.
"""
del mode
return layers.Serial(
layers.Conv(hidden_size, (7, 7), (2, 2), 'SAME'),
layers.BatchNorm(), layers.Relu(),
layers.MaxPool(pool_size=(3, 3), strides=(2, 2)),
ConvBlock(3, [hidden_size, hidden_size, 4 * hidden_size], (1, 1)),
IdentityBlock(3, [hidden_size, hidden_size, 4 * hidden_size]),
IdentityBlock(3, [hidden_size, hidden_size, 4 * hidden_size]),
ConvBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size], (2, 2)),
IdentityBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size]),
IdentityBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size]),
IdentityBlock(3, [2 * hidden_size, 2 * hidden_size, 8 * hidden_size]),
ConvBlock(3, [4 * hidden_size, 4 * hidden_size, 16*hidden_size], (2, 2)),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
IdentityBlock(3, [4 * hidden_size, 4 * hidden_size, 16 * hidden_size]),
ConvBlock(3, [8 * hidden_size, 8 * hidden_size, 32*hidden_size], (2, 2)),
IdentityBlock(3, [8 * hidden_size, 8 * hidden_size, 32 * hidden_size]),
IdentityBlock(3, [8 * hidden_size, 8 * hidden_size, 32 * hidden_size]),
layers.AvgPool(pool_size=(7, 7)), layers.Flatten(),
layers.Dense(num_output_classes), layers.LogSoftmax())
|
WideResnet convolutational block.
|
def WideResnetBlock(channels, strides=(1, 1), channel_mismatch=False):
"""WideResnet convolutational block."""
main = layers.Serial(layers.BatchNorm(), layers.Relu(),
layers.Conv(channels, (3, 3), strides, padding='SAME'),
layers.BatchNorm(), layers.Relu(),
layers.Conv(channels, (3, 3), padding='SAME'))
shortcut = layers.Identity() if not channel_mismatch else layers.Conv(
channels, (3, 3), strides, padding='SAME')
return layers.Serial(
layers.Branch(), layers.Parallel(main, shortcut), layers.SumBranches())
|
WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, number of classes to distinguish.
mode: is it training or eval.
Returns:
The WideResnet model with given layer and output sizes.
|
def WideResnet(num_blocks=3, hidden_size=64, num_output_classes=10,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, number of classes to distinguish.
mode: is it training or eval.
Returns:
The WideResnet model with given layer and output sizes.
"""
del mode
return layers.Serial(
layers.Conv(hidden_size, (3, 3), padding='SAME'),
WideResnetGroup(num_blocks, hidden_size),
WideResnetGroup(num_blocks, hidden_size * 2, (2, 2)),
WideResnetGroup(num_blocks, hidden_size * 4, (2, 2)), layers.BatchNorm(),
layers.Relu(), layers.AvgPool(pool_size=(8, 8)), layers.Flatten(),
layers.Dense(num_output_classes), layers.LogSoftmax())
|
Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell.
|
def GRUCell(units):
"""Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell.
"""
return GeneralGRUCell(
candidate_transform=lambda: core.Dense(units=units),
memory_transform=combinators.Identity,
gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh)
|
Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms.
|
def ConvGRUCell(units, kernel_size=(3, 3)):
"""Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms.
"""
def BuildConv():
return core.Conv(filters=units, kernel_size=kernel_size, padding='SAME')
return GeneralGRUCell(
candidate_transform=BuildConv,
memory_transform=combinators.Identity,
gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh)
|
r"""Parametrized Gated Recurrent Unit (GRU) cell construction.
GRU update equations:
$$ Update gate: u_t = \sigmoid(U' * s_{t-1} + B') $$
$$ Reset gate: r_t = \sigmoid(U'' * s_{t-1} + B'') $$
$$ Candidate memory: c_t = \tanh(U * (r_t \odot s_{t-1}) + B) $$
$$ New State: s_t = u_t \odot s_{t-1} + (1 - u_t) \odot c_t $$
See combinators.GateBranches for details on the gating function.
Args:
candidate_transform: Transform to apply inside the Candidate branch. Applied
before nonlinearities.
memory_transform: Optional transformation on the memory before gating.
gate_nonlinearity: Function to use as gate activation. Allows trying
alternatives to Sigmoid, such as HardSigmoid.
candidate_nonlinearity: Nonlinearity to apply after candidate branch. Allows
trying alternatives to traditional Tanh, such as HardTanh
dropout_rate_c: Amount of dropout on the transform (c) gate. Dropout works
best in a GRU when applied exclusively to this branch.
sigmoid_bias: Constant to add before sigmoid gates. Generally want to start
off with a positive bias.
Returns:
A model representing a GRU cell with specified transforms.
|
def GeneralGRUCell(candidate_transform,
memory_transform=combinators.Identity,
gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh,
dropout_rate_c=0.1,
sigmoid_bias=0.5):
r"""Parametrized Gated Recurrent Unit (GRU) cell construction.
GRU update equations:
$$ Update gate: u_t = \sigmoid(U' * s_{t-1} + B') $$
$$ Reset gate: r_t = \sigmoid(U'' * s_{t-1} + B'') $$
$$ Candidate memory: c_t = \tanh(U * (r_t \odot s_{t-1}) + B) $$
$$ New State: s_t = u_t \odot s_{t-1} + (1 - u_t) \odot c_t $$
See combinators.GateBranches for details on the gating function.
Args:
candidate_transform: Transform to apply inside the Candidate branch. Applied
before nonlinearities.
memory_transform: Optional transformation on the memory before gating.
gate_nonlinearity: Function to use as gate activation. Allows trying
alternatives to Sigmoid, such as HardSigmoid.
candidate_nonlinearity: Nonlinearity to apply after candidate branch. Allows
trying alternatives to traditional Tanh, such as HardTanh
dropout_rate_c: Amount of dropout on the transform (c) gate. Dropout works
best in a GRU when applied exclusively to this branch.
sigmoid_bias: Constant to add before sigmoid gates. Generally want to start
off with a positive bias.
Returns:
A model representing a GRU cell with specified transforms.
"""
return combinators.Serial(
combinators.Branch(num_branches=3),
combinators.Parallel(
# s_{t-1} branch - optionally transform
# Typically is an identity.
memory_transform(),
# u_t (Update gate) branch
combinators.Serial(
candidate_transform(),
# Want bias to start out positive before sigmoids.
core.AddConstant(constant=sigmoid_bias),
gate_nonlinearity()),
# c_t (Candidate) branch
combinators.Serial(
combinators.Branch(num_branches=2),
combinators.Parallel(
combinators.Identity(),
# r_t (Reset) Branch
combinators.Serial(
candidate_transform(),
# Want bias to start out positive before sigmoids.
core.AddConstant(constant=sigmoid_bias),
gate_nonlinearity())),
## Gate S{t-1} with sigmoid(candidate_transform(S{t-1}))
combinators.MultiplyBranches(),
# Final projection + tanh to get Ct
candidate_transform(),
candidate_nonlinearity()), # Candidate gate
# Only apply dropout on the C gate.
# Paper reports that 0.1 is a good default.
core.Dropout(rate=dropout_rate_c)),
# Gate memory and candidate
combinators.GateBranches())
|
Create an attention mask to hide padding and future words.
|
def MakeTargetMask(target, pad=0):
"""Create an attention mask to hide padding and future words."""
target_mask = (target != pad)[ :, np.newaxis, :]
target_dtype = target_mask.dtype
causal_mask = onp.tril(onp.ones((1, target.shape[-1], target.shape[-1]),
dtype=target_dtype), k=0)
target_mask = target_mask & causal_mask
return np.expand_dims(target_mask, axis=1)
|
Build masks for this batch.
Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
Returns:
Prepared batch of tuple of arrays: source, input-target, shifted-target,
source mask, target mask, source-target "memory" mask, minibatch token count
|
def PreparePairedSequenceBatch(source, target_in, pad=0):
"""Build masks for this batch.
Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
Returns:
Prepared batch of tuple of arrays: source, input-target, shifted-target,
source mask, target mask, source-target "memory" mask, minibatch token count
"""
target = target_in[:, :-1]
target_y = target_in[:, 1:]
source_mask = np.reshape(source != pad,
(source.shape[0], 1, 1, source.shape[-1]))
target_mask = MakeTargetMask(target, pad)
memory_mask = (
np.reshape(np.arange(target.shape[-1]) < source.shape[-1], [-1, 1]))
ntokens = np.sum(target_y != pad)
return (source, target, target_y,
source_mask, target_mask, memory_mask, ntokens)
|
Helper: create layer norm parameters.
|
def _layer_norm_new_params(input_shape, rng, epsilon=1e-6): # pylint: disable=invalid-name
"""Helper: create layer norm parameters."""
del rng, epsilon
features = input_shape[-1]
scale = np.ones(features)
bias = np.zeros(features)
return (scale, bias)
|
Helper: create positional encoding parameters.
|
def _positional_encoding_new_params(input_shape, rng, max_len=2048): # pylint: disable=invalid-name
"""Helper: create positional encoding parameters."""
del rng
# Check if we are operating on chunked inputs by checking if the first
# shape is a list/tuple of shapes (otherwise it's an int or numpy array).
is_chunked = isinstance(input_shape[0], (list, tuple))
feature_depth = input_shape[0][-1] if is_chunked else input_shape[-1]
pe = onp.zeros((max_len, feature_depth), dtype=onp.float32)
position = onp.arange(0, max_len)[:, onp.newaxis]
div_term = onp.exp(
onp.arange(0, feature_depth, 2) * -(onp.log(10000.0) / feature_depth))
pe[:, 0::2] = onp.sin(position * div_term)
pe[:, 1::2] = onp.cos(position * div_term)
pe = pe[onp.newaxis, :, :] # [1, max_len, feature_depth]
return np.array(pe)
|
Implements bare positional encoding.
|
def PositionalEncoding(x, params, **unused_kwargs):
"""Implements bare positional encoding."""
if not isinstance(x, (list, tuple)): # non-chunked inputs
symbol_size = np.shape(x)[1]
return x + params[:, :symbol_size, :]
# Chunked case: apply to all chunks selecting as much as needed.
offset = 0
results = []
for chunk in x:
symbol_size = np.shape(chunk)[1]
results.append(chunk + params[:, offset:offset + symbol_size, :])
offset += symbol_size
return results
|
Core dot product self-attention.
Args:
query: array of representations
key: array of representations
value: array of representations
mask: attention-mask, gates attention
dropout: float: dropout rate
mode: 'eval' or 'train': whether to use dropout
rng: JAX PRNGKey: subkey for disposable use
Returns:
Self attention for q, k, v arrays.
|
def DotProductAttention(query, key, value, mask, dropout, mode, rng):
"""Core dot product self-attention.
Args:
query: array of representations
key: array of representations
value: array of representations
mask: attention-mask, gates attention
dropout: float: dropout rate
mode: 'eval' or 'train': whether to use dropout
rng: JAX PRNGKey: subkey for disposable use
Returns:
Self attention for q, k, v arrays.
"""
depth = np.shape(query)[-1]
dots = np.matmul(query, np.swapaxes(key, -1, -2)) / np.sqrt(depth)
if mask is not None:
dots = np.where(mask, dots, -1e9)
# Softmax.
dots = np.exp(dots - backend.logsumexp(dots, axis=-1, keepdims=True))
if dropout >= 1.0:
raise ValueError('Dropout rates must be lower than 1.')
if dropout is not None and dropout > 0.0 and mode == 'train':
keep = backend.random.bernoulli(rng, 1.0 - dropout, dots.shape)
dots = np.where(keep, dots / (1.0 - dropout), 0)
out = np.matmul(dots, value)
return out
|
Pure single-headed self-attention.
Args:
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Pure single-headed attention layer. (No Dense transforms on input.)
|
def PureDotProductAttention(dropout=0.0, mode='train'):
"""Pure single-headed self-attention.
Args:
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Pure single-headed attention layer. (No Dense transforms on input.)
"""
def init_fun(_, input_shapes): # pylint: disable=invalid-name
q_shape, _, v_shape, _ = input_shapes
output_shape = q_shape[:-1] + (v_shape[-1],)
return output_shape, ()
def apply_fun(params, inputs, **kwargs): # pylint: disable=invalid-name
del params
q, k, v, mask = inputs
rng = kwargs.get('rng', None)
return DotProductAttention(q, k, v, mask,
dropout=dropout, mode=mode, rng=rng)
return init_fun, apply_fun
|
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