def test_log_softmax(self):
activations = np.array([[2., 1., -10.], [1., 1., -10.]])
log_probabilities = tl.log_softmax(activations)
np.testing.assert_allclose(
log_probabilities,
[[-0.313, -1.313, -12.313], [-0.693, -0.693, -11.693]],
atol=.001)
def beam_search(model,
inputs=None,
batch_size=1,
n_beams=2,
start_id=0,
eos_id=1,
max_length=100,
length_penalty=1.0,
accelerate=True):
"""Returns a batch of n_beams-sequences created by beam search.
This function uses `model` to generate outputs one position at a time, with
access to inputs for the current position and all preceding positions. The
new output becomes the next position's input, and this loop repeats until
either the model outputs the `eos_id` value or the output sequence reaches
`max_length` items -- but keeping n_beams top beams.
Args:
model: A layer object (subclass of `trax.layers.Layer`) created in
`'predict'` mode and initialized from trained weights. The model
must have a structure that allows it to run as autoregressive
one-sample-at-a-time predictor (e.g., `trax.models.TransformerLM`).
inputs: Sequence of symbols the model sees as input the first time it
generates an output. If None, the model must generate the first output
with no input to guide it.
batch_size: Number of sequences to generate in parallel as a batch.
n_beams: How many beams to consider at the same time.
start_id: The start symbol (ID/integer) for the autoregressive process,
or array of shape (`batch_size`, 1) of such integers.
eos_id: The end-of-sequence symbol (ID/integer) for the autoregressive
process.
max_length: Maximum length for generated sequences.
length_penalty: Factor alpha in calculating the length penalty for beams.
accelerate: If True, create an accelerated version of `model` and use it
for generating outputs.
Returns:
Tensor of integers with shape (`batch_size`, n_beams, output_length) with
a batch of output sequences. output_length is the maximum length of the
output sequences, where each sequence can be no longer than `max_length`.
"""
del eos_id, length_penalty # TODO(lukaszkaiser): add length penalty, eos
assert batch_size == 1, 'Batch size > 1 not supported yet'
if inputs is not None and inputs.shape[0] != batch_size:
raise ValueError(
f'Inputs batch size ({inputs.shape[0]}) does not match '
f'batch_size arg ({batch_size}.')
fast_model = tl.Accelerate(model) if accelerate else model
if np.isscalar(start_id):
start_symbol = np.full((batch_size, 1), start_id, dtype=np.int32)
else:
start_symbol = start_id
if model.n_in == 1 and inputs is not None:
current_symbols = np.concatenate([start_symbol, inputs], axis=1)
else:
current_symbols = start_symbol
beams = [current_symbols for _ in range(n_beams)]
results = [([], 0.0) for _ in range(n_beams)]
states = [fast_model.state for _ in range(n_beams)]
top_k = [None] * n_beams
counter = 0
while counter < max_length:
counter += 1
# Run the model on all beams, collect states and top_k for each beam.
for beam_id in range(n_beams if counter > 1 else 1):
fast_model.state = states[beam_id]
if model.n_in > 1 and inputs is not None:
logits = fast_model((inputs, beams[beam_id]))[0]
else:
logits = fast_model(beams[beam_id])
logits = tl.log_softmax(logits[:, -1, :])
states[beam_id] = fast_model.state
top_k[beam_id] = fastmath.top_k(logits, k=n_beams)
# Select new beams.
cur_values = [] # will hold triples (sum-of-logprobs, beam-id, symbol)
for beam_id in range(n_beams if counter > 1 else 1):
for k in range(n_beams):
values, symbols = top_k[beam_id]
value, symbol = values[:, k], symbols[:, k]
cur_values.append(
(results[beam_id][1] + value, beam_id, symbol))
cur_values.sort(key=lambda x: -x[0][0]) # x[0][0] as batch_size=1
# Collect top beams to the new states and results.
new_results, new_states, new_beams = [], [], []
for (value, beam_id, symbol) in cur_values[:n_beams]:
new_results.append((results[beam_id][0] + [symbol], value))
new_states.append(states[beam_id]) # copy?
new_beams.append(symbol[:, None])
results, states, beams = new_results, new_states, new_beams
return [(np.stack(r, axis=-1), v) for (r, v) in results]
def autoregressive_sample_stream(model,
inputs=None,
batch_size=1,
temperature=1.0,
start_id=0,
accelerate=True):
"""Yields samples from `model`, in autoregressive language model fashion.
This function uses `model` to generate outputs one position at a time, with
access to inputs for the current position and all preceding positions. The
new output becomes the next position's input, and further calls to
`autoregressive_sample_stream` repeat the process for successive positions
indefinitely.
Inputs and outputs always come in batches, even if size 1. If `inputs` is
present, it must have shape (`batch_size`, inputs_sequence_length), and each
output in the stream has shape (`batch_size`, 1).
Args:
model: A layer object (subclass of `trax.layers.Layer`) created in
`'predict'` mode and initialized from trained weights. The model
must have a structure that allows it to run as an autoregressive
one-sample-at-a-time predictor (e.g., `trax.models.TransformerLM`).
inputs: Sequence of symbols the model sees as input the first time it
generates an output. If None, the model generates the first output
based on just the start symbol.
batch_size: Number of sequences to generate in parallel as a batch.
temperature: Parameter that controls the sharpness of the softmax that
feeds the sampling process. Values range from 0.0 (all probability mass
goes to one candidate; like an argmax) to positive infinity (all
candidates have equal probability).
start_id: Integer representing the start symbol for the autoregressive
process, or array of shape (`batch_size`, 1) of such integers.
accelerate: If True, create an accelerated version of `model` and use it
for generating outputs.
Yields:
Tensor of integers with shape (`batch_size`, 1), representing the batch of
outputs for the next position in the stream.
"""
if inputs is not None and inputs.shape[0] != batch_size:
raise ValueError(
f'Inputs batch size ({inputs.shape[0]}) does not match '
f'batch_size arg ({batch_size}.')
fast_model = tl.Accelerate(model) if accelerate else model
if np.isscalar(start_id):
start_symbol = np.full((batch_size, 1), start_id, dtype=np.int32)
else:
start_symbol = start_id
if model.n_in == 1 and inputs is not None:
current_symbols = np.concatenate([start_symbol, inputs], axis=1)
else:
current_symbols = start_symbol
while True:
if model.n_in > 1 and inputs is not None:
logits = fast_model((inputs, current_symbols))[0]
else:
logits = fast_model(current_symbols)
logits = tl.log_softmax(logits[:, -1, :])
sample = tl.logsoftmax_sample(logits, temperature=temperature)
yield sample
# NOTE: Because the model is autoregressive and in 'predict' mode, its
# history is cached in the model state and the next input is the single
# symbol just sampled.
current_symbols = sample[:, None]
def autoregressive_sample_stream(model,
inputs=None,
batch_size=1,
temperature=1.0,
start_id=0,
accelerate=True,
eval_mode=False,
eval_min_length=1):
"""Yields samples from `model`, in autoregressive language model fashion.
This function uses `model` to generate outputs one position at a time, with
access to inputs for the current position and all preceding positions. The
new output becomes the next position's input, and further calls to
`autoregressive_sample_stream` repeat the process for successive positions
indefinitely.
Inputs and outputs always come in batches, even if size 1. If `inputs` is
present, it must have shape (`batch_size`, inputs_sequence_length), and each
output in the stream has shape (`batch_size`, 1).
Args:
model: A layer object (subclass of `trax.layers.Layer`) created in
`'predict'` mode and initialized from trained weights. The model
must have a structure that allows it to run as an autoregressive
one-sample-at-a-time predictor (e.g., `trax.models.TransformerLM`),
except if `eval_mode` is set -- any model can be sampled then,
but the sampling process may be much slower.
inputs: Sequence of symbols the model sees as input the first time it
generates an output. If None, the model generates the first output
based on just the start symbol.
batch_size: Number of sequences to generate in parallel as a batch.
temperature: Parameter that controls the sharpness of the softmax that
feeds the sampling process. Values range from 0.0 (all probability mass
goes to one candidate; like an argmax) to positive infinity (all
candidates have equal probability).
start_id: Integer representing the start symbol for the autoregressive
process, or array of shape (`batch_size`, 1) of such integers.
accelerate: If True, create an accelerated version of `model` and use it
for generating outputs.
eval_mode: If True, assume the model is created in `eval` mode and sample
by collecting all previous outputs and passing the whole tensor.
eval_min_length: If set, the minimum length to pad to in eval mode.
Yields:
Tensor of integers with shape (`batch_size`, 1), representing the batch of
outputs for the next position in the stream.
"""
if inputs is not None and inputs.shape[0] != batch_size:
raise ValueError(
f'Inputs batch size ({inputs.shape[0]}) does not match '
f'batch_size arg ({batch_size}.')
fast_model = tl.Accelerate(model) if accelerate else model
if np.isscalar(start_id):
start_symbol = np.full((batch_size, 1), start_id, dtype=np.int32)
else:
start_symbol = start_id
if model.n_in == 1 and inputs is not None:
current_symbols = np.concatenate([start_symbol, inputs], axis=1)
else:
current_symbols = start_symbol
if eval_mode:
# no start symbol needed in eval mode
current_symbols = current_symbols[:, 1:]
while True:
# Pad inputs to power-of-2 length if needed.
if eval_mode:
# one extra symbol as an initial one will be added
l = max(eval_min_length, current_symbols.shape[1] + 1)
pad_len = int(2**np.ceil(np.log2(l))) - current_symbols.shape[1]
unpadded_symbols = current_symbols
current_symbols = np.pad(current_symbols, [[0, 0], [0, pad_len]],
mode='constant')
last_index = -pad_len # no -1 as the starting one will be added
else:
last_index = -1
# Run the model.
if model.n_in > 1 and inputs is not None:
logits = fast_model((inputs, current_symbols))[0]
else:
logits = fast_model(current_symbols)
logits = tl.log_softmax(logits[:, last_index, :])
sample = tl.logsoftmax_sample(logits, temperature=temperature)
yield sample
if eval_mode:
current_symbols = np.concatenate(
[unpadded_symbols, sample[:, None]], axis=1)
else:
# NOTE: Because the model is autoregressive and in 'predict' mode, its
# history is cached in the model state and the next input is the single
# symbol just sampled.
current_symbols = sample[:, None]
