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# coding=utf-8
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Generation utilities."""
import torch
import torch.nn.functional as F
from megatron import get_args, get_tokenizer, mpu
from megatron.utils import get_ltor_masks_and_position_ids
from .communication import (
copy_from_last_to_first_pipeline_stage,
broadcast_from_last_pipeline_stage,
broadcast_from_last_to_first_pipeline_stage)
from .forward_step import ForwardStep
from .sampling import sample
from .beam_utils import BeamHypotheses
MAX_TOKENS_TO_OOM = 12000 # (rprenger) Perfect value depends on hardware and network
def score_and_return_on_first_stage(model, tokens, lengths):
"""Function for just scoring.
Arguments:
model: no interleaving is supported.
tokens: prompt tokens extended to be of size [b, max_prompt_length]
lengths: original prompt length, size: [b]
Note: Outside of model, other parameters only need to be available on
rank 0.
Outputs:
output_log_probs: log probability of the selected tokens. size: [b, s]
"""
args = get_args()
batch_size = tokens.size(0)
max_prompt_length = lengths.max().item()
assert max_prompt_length == tokens.size(1)
max_sequence_length = min(max_prompt_length, args.max_position_embeddings)
# forward step.
forward_step = ForwardStep(model, batch_size, max_sequence_length)
# ===================
# Pre-allocate memory
# ===================
# Log probability of the sequence (prompt + generated tokens).
output_log_probs = None
output_log_probs_size = (batch_size, max_sequence_length - 1)
if mpu.is_pipeline_last_stage():
output_log_probs = torch.empty(output_log_probs_size,
dtype=torch.float32,
device=torch.cuda.current_device())
# =============
# Run infernece
# =============
with torch.no_grad():
attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens)
# logits will be meanigful only in the last pipeline stage.
logits = forward_step(tokens, position_ids, attention_mask)
if mpu.is_pipeline_last_stage():
# Always the last stage should have an output.
assert logits is not None
log_probs = F.log_softmax(logits, dim=2)
# Pick the tokens that we need to get the log
# probabilities for. Note that next input token is
# the token which we selected in the current logits,
# so shift by 1.
indices = torch.unsqueeze(tokens[:, 1:], 2)
output_log_probs = torch.gather(log_probs, 2, indices).squeeze(2)
# ======================================
# Broadcast to the first pipeline stage.
# ======================================
output_log_probs = broadcast_from_last_to_first_pipeline_stage(
output_log_probs_size, torch.float32, output_log_probs)
return tokens, lengths, output_log_probs
def generate_tokens_probs_and_return_on_first_stage(
model, tokens, lengths,
return_output_log_probs=False,
top_k=0, top_p=0.0, top_p_decay=0.0, top_p_bound=0.0,
temperature=1.0,
use_eod_token_for_early_termination=True,
stop_on_double_eol=False,
stop_on_eol=False
):
"""Main token generation function.
Arguments:
model: no interleaving is supported.
tokens: prompt tokens extended to be of size [b, max-sequence-length]
lengths: original prompt length, size: [b]
return_output_log_probs: flag to calculate the log probability of
the generated tokens. Note that the log probability is the one
from the original logit.
top_k, top_p: top-k and top-p sampling parameters.
Note that top-k = 1 is gready. Also, these paramters are
exclusive meaning that:
if top-k > 0 then we expect top-p=0.
if top-p > 0 then we check for top-k=0.
temperature: sampling temperature.
use_eod_token_for_early_termination: if True, do early termination if
all the sequences have reached this token.
Note: Outside of model, other parameters only need to be available on
rank 0.
Outputs: Note that is size is adjusted to a lower value than
max-sequence-length if generation is terminated early.
tokens: prompt and generated tokens. size: [b, :]
generated_sequence_lengths: total length (including prompt) of
the generated sequence. size: [b]
output_log_probs: log probability of the selected tokens. size: [b, s]
"""
args = get_args()
tokenizer = get_tokenizer()
batch_size = tokens.size(0)
min_prompt_length = lengths.min().item()
max_sequence_length = tokens.size(1)
if max_sequence_length > args.max_position_embeddings:
raise ValueError("Length of prompt + tokens_to_generate longer than allowed")
if max_sequence_length * batch_size >= MAX_TOKENS_TO_OOM:
raise ValueError("Too many tokens. " + str(max_sequence_length*batch_size)+ " is greater than "+str(MAX_TOKENS_TO_OOM))
# forward step.
forward_step = ForwardStep(model, batch_size, max_sequence_length)
# Added termination_id to support the case that we want to terminate the
# generation once that id is generated.
if hasattr(args, 'eos_id'):
termination_id = args.eos_id
else:
termination_id = tokenizer.eod
# ===================
# Pre-allocate memory
# ===================
# Log probability of the sequence (prompt + generated tokens).
output_log_probs = None
output_log_probs_size = (batch_size, max_sequence_length - 1)
# Lengths of generated seuquence including including prompts.
generated_sequence_lengths = None
if mpu.is_pipeline_last_stage():
if return_output_log_probs:
output_log_probs = torch.empty(output_log_probs_size,
dtype=torch.float32,
device=torch.cuda.current_device())
generated_sequence_lengths = torch.ones(
batch_size, dtype=torch.int64,
device=torch.cuda.current_device()) * max_sequence_length
# Whether we have reached a termination id.
is_generation_done = torch.zeros(batch_size, dtype=torch.uint8,
device=torch.cuda.current_device())
# =============
# Run infernece
# =============
with torch.no_grad():
attention_mask, position_ids = _build_attention_mask_and_position_ids(
tokens)
prev_context_length = 0
for context_length in range(min_prompt_length, max_sequence_length):
# Pick the slice that we need to pass through the network.
tokens2use = tokens[:, prev_context_length:context_length]
positions2use = position_ids[:, prev_context_length:context_length]
attention_mask2use = attention_mask[
..., prev_context_length:context_length, :context_length]
# logits will be meanigful only in the last pipeline stage.
logits = forward_step(tokens2use, positions2use, attention_mask2use)
if mpu.is_pipeline_last_stage():
# Always the last stage should have an output.
assert logits is not None
# Sample.
last_token_logits = logits[:, -1, :]
new_sample = sample(last_token_logits,
top_k=top_k,
top_p=top_p,
temperature=temperature,
vocab_size=tokenizer.vocab_size)
if top_p > 0.0 and top_p_decay > 0.0:
top_p = top_p * top_p_decay
if top_p_bound > 0.0:
top_p = max(top_p, top_p_bound)
# If a prompt length is smaller or equal th current context
# length, it means we have started generating tokens
started = lengths <= context_length
# Update the tokens.
tokens[started, context_length] = new_sample[started]
# Calculate the log probabilities.
if return_output_log_probs:
log_probs = F.log_softmax(logits, dim=2)
if return_output_log_probs:
# Pick the tokens that we need to get the log
# probabilities for. Note that next input token is
# the token which we selected in the current logits,
# so shift by 1.
indices = torch.unsqueeze(
tokens[
:,
(prev_context_length + 1):(context_length + 1)],
2)
output_log_probs[:,
prev_context_length:context_length] = \
torch.gather(log_probs, 2, indices).squeeze(2)
# Update the tokens on the first stage so the next input to
# the network is correct.
copy_from_last_to_first_pipeline_stage(batch_size, torch.int64,
tokens[:, context_length])
# Update the context length for the next token generation.
prev_context_length = context_length
# Check if all the sequences have hit the termination_id.
done = None
if mpu.is_pipeline_last_stage():
# TODO(rprenger) These stopping methods are tokenizer dependent
# instead tokenization should be in the inference loop so stop sequences can be used
if stop_on_double_eol:
hit_double_eol = (new_sample == 628).byte() & started.byte()
hit_two_eols = (new_sample == 198).byte() & (tokens[:, context_length-1] == 198).byte() & started.byte()
done_token = hit_double_eol | hit_two_eols
elif stop_on_eol:
hit_double_eol = (new_sample == 628).byte() & started.byte()
hit_eol = (new_sample == 198).byte() & started.byte()
done_token = hit_double_eol | hit_eol
else:
done_token = (new_sample == termination_id).byte() & \
started.byte()
just_finished = (done_token & ~is_generation_done).bool()
generated_sequence_lengths[just_finished.view(-1)] = \
context_length + 1
is_generation_done = is_generation_done | done_token
done = torch.all(is_generation_done)
done = broadcast_from_last_pipeline_stage(1, torch.uint8,
tensor=done)
if use_eod_token_for_early_termination and done:
break
# ===================================================
# Update the length of based on max generated length.
# ===================================================
tokens = tokens[:, :(context_length + 1)]
if mpu.is_pipeline_last_stage():
if return_output_log_probs:
output_log_probs = output_log_probs[:, :context_length]
# ======================================
# Broadcast to the first pipeline stage.
# ======================================
generated_sequence_lengths = broadcast_from_last_to_first_pipeline_stage(
batch_size, torch.int64, generated_sequence_lengths)
if return_output_log_probs:
output_log_probs_size = (batch_size, context_length)
output_log_probs = broadcast_from_last_to_first_pipeline_stage(
output_log_probs_size, torch.float32, output_log_probs)
return tokens, generated_sequence_lengths, output_log_probs
def beam_search_and_return_on_first_stage(model, tokens, lengths, beam_size, stop_token, num_return_gen, length_penalty):
args = get_args()
tokenizer = get_tokenizer()
batch_size = tokens.size(0)
assert(batch_size == 1)
prompt_length = lengths.item()
final_sequence_length = tokens.size(1)
final_sequence_length = min(final_sequence_length, args.max_position_embeddings)
# If the context is too big, this happens
if prompt_length >= final_sequence_length:
raise ValueError("context length + tokens_to_generate too large")
# forward step.
forward_step = ForwardStep(model, beam_size, final_sequence_length)
beam_hyp = BeamHypotheses(beam_size, length_penalty)
best_batches = None
done = torch.zeros(1, dtype=torch.uint8, device=torch.cuda.current_device())
scores = torch.zeros(beam_size,
dtype=torch.float32,
device=torch.cuda.current_device()).unsqueeze(1)
scores_size_tensor, tokens_size_tensor = None, None
# =============
# Run infernece
# =============
with torch.no_grad():
tokens = tokens.repeat(beam_size, 1)
attention_mask, position_ids = _build_attention_mask_and_position_ids(tokens)
prev_context_length = 0
for context_length in range(prompt_length, final_sequence_length):
# Pick the slice that we need to pass through the network.
tokens2use = tokens[:, prev_context_length:context_length]
positions2use = position_ids[:, prev_context_length:context_length]
attention_mask2use = attention_mask[
..., prev_context_length:context_length, :context_length]
# logits will be meanigful only in the last pipeline stage.
logits = forward_step(tokens2use, positions2use, attention_mask2use)
if mpu.is_pipeline_last_stage():
vocab_size = logits.size(2)
log_probs = F.log_softmax(logits, dim=2)
new_scores = log_probs[:, -1, :] + scores
if context_length == prompt_length: # if this is the first one
sorted_scores, indices = torch.sort(new_scores[0,:], descending=True)
else:
sorted_scores, indices = torch.sort(new_scores.view(-1), descending=True)
best_beam_ids = torch.div(indices[: 2 * beam_size], vocab_size).trunc().long()
best_words = indices[:2 * beam_size] % vocab_size
best_scores = sorted_scores[: 2 * beam_size]
next_beams = []
for beam_token_rank, (token_id, beam_score, beam_id) in enumerate(
zip(best_words, best_scores, best_beam_ids)
):
if token_id.item() == stop_token:
# if beam_token does not belong to top num_beams tokens, it should not be added
is_beam_token_worse_than_top_num_beams = beam_token_rank >= beam_size
if is_beam_token_worse_than_top_num_beams:
continue
beam_hyp.add(
tokens[beam_id].clone(),
beam_score,
context_length + 1 - prompt_length
)
else:
# add next predicted token since it is not eos_token
next_beams.append((token_id, beam_score, beam_id))
if len(next_beams) == beam_size:
break
if beam_hyp.is_done(best_scores.max().item(), context_length + 1 - prompt_length):
done = torch.ones(1, dtype=torch.uint8, device=torch.cuda.current_device())
best_batches = tokens.new([item[2] for item in next_beams])
tokens = tokens[best_batches,:]
tokens[:, context_length] = tokens.new([item[0] for item in next_beams])
scores = scores.new([item[1] for item in next_beams]).unsqueeze(1)
# torch.distributed.barrier()
done = broadcast_from_last_pipeline_stage(1, torch.uint8, done)
if done:
break
# Update the tokens on the first stage so the next input to
# the network is correct.
copy_from_last_to_first_pipeline_stage(tokens.size(), torch.int64,
tokens)
# set inference key values to make it consistent with best beam index
best_batches = broadcast_from_last_pipeline_stage(beam_size, torch.int64, best_batches)
forward_step.inference_params.swap_key_value_dict(best_batches)
# Update the context length for the next token generation.
prev_context_length = context_length
if mpu.is_pipeline_last_stage():
# if cannot find stop token, add open beams to hyps
if not done:
for beam_id in range(beam_size):
beam_hyp.add(tokens[beam_id].clone(), scores[beam_id], context_length + 1 - prompt_length)
# rank based on scores
sorted_hyps = sorted(beam_hyp.beams, key=lambda x: x[0], reverse=True)
num_return_gen = min(num_return_gen, len(sorted_hyps))
scores = [sorted_hyps[i][0] for i in range(num_return_gen)]
tokens = [sorted_hyps[i][1] for i in range(num_return_gen)]
scores = torch.stack(scores, dim=0)
tokens = torch.stack(tokens, dim=0)
scores_size_tensor = torch.tensor(scores.shape, dtype=torch.int64, device=torch.cuda.current_device())
tokens_size_tensor = torch.tensor(tokens.shape, dtype=torch.int64, device=torch.cuda.current_device())
scores_size_tensor = broadcast_from_last_pipeline_stage(1, torch.int64, scores_size_tensor)
tokens_size_tensor = broadcast_from_last_pipeline_stage(2, torch.int64, tokens_size_tensor)
scores = broadcast_from_last_to_first_pipeline_stage(tuple(scores_size_tensor), torch.float32, scores)
tokens = broadcast_from_last_to_first_pipeline_stage(tuple(tokens_size_tensor), torch.int64, tokens)
return tokens, scores
def _build_attention_mask_and_position_ids(tokens):
"""Build the attention mask and postition ids for the input tokens."""
# Since we are not interested in loss-mask and reset attention/position
# is also False, eod_token is not used so it is safe to set it to None.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
data=tokens,
eod_token=None,
reset_position_ids=False,
reset_attention_mask=False,
eod_mask_loss=False)
return attention_mask, position_ids
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