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[Inference] Fix Inference Generation Config and Sampling (#5710)

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* refactor and add

* config default values

* fix gen config passing

* fix rpc generation config
This commit is contained in:
Yuanheng Zhao
2024-05-19 15:08:42 +08:00
committed by GitHub
parent 8bcfe360fd
commit 283c407a19
6 changed files with 124 additions and 68 deletions
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65
colossalai/inference/sampler.py
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@@ -1,13 +1,12 @@
from typing import List, Optional, Tuple
from typing import List, Optional, Tuple, Union
import torch
from transformers.generation import GenerationConfig
from colossalai.inference.logit_processors import logit_processor
from colossalai.inference.logit_processors import get_logits_processor
def greedy_sample(
generation_config,
logprobs: torch.Tensor,
) -> torch.Tensor:
"""
@@ -18,7 +17,6 @@ def greedy_sample(
def multinomial_sample(
generation_config,
probs: torch.Tensor,
) -> torch.Tensor:
"""
@@ -29,7 +27,7 @@ def multinomial_sample(
def beam_search_sample(
generation_config,
beam_width: int,
logprobs: torch.Tensor,
is_prompt: bool = False,
) -> List[Tuple[List[int], List[int]]]:
@@ -46,7 +44,6 @@ def beam_search_sample(
# NOTE: this beam search sample function is wrong now.
"""
beam_width = generation_config.num_beams
results = []
if is_prompt:
# Prompt phase.
@@ -64,20 +61,8 @@ def beam_search_sample(
return results
def _sample(probs: torch.Tensor, logprobs: torch.Tensor, generation_config: GenerationConfig, is_prompt: bool = False):
if generation_config.num_beams == 1:
if generation_config.do_sample:
sample_tokens = multinomial_sample(generation_config, probs)
else:
sample_tokens = greedy_sample(generation_config, logprobs)
else:
sample_tokens = beam_search_sample(generation_config, logprobs, is_prompt=is_prompt)
return sample_tokens
def search_tokens(
generation_config: GenerationConfig,
generation_config: Union[GenerationConfig, dict],
logits,
is_prompt: bool = False,
batch_token_ids: Optional[List[List[int]]] = None,
@@ -86,23 +71,41 @@ def search_tokens(
Sample tokens for finished requests.
"""
# NOTE: need to decide the granularity to process logits (sequence or batch)
config_dict = generation_config.to_dict()
# process repetition_penalty, no_repeat_ngram_size
for type in ["repetition_penalty", "no_repeat_ngram_size"]:
if type in config_dict and config_dict[type] is not None:
logits = logit_processor(type, logits, config_dict[type], batch_token_ids)
# do logit processor
if generation_config.do_sample:
# process temperature, top_k, top_p
for type in ["temperature", "top_k", "top_p"]:
if type in config_dict and config_dict[type] is not None:
logits = logit_processor(type, logits, config_dict[type])
# convert GenerationConfig to dict
# temporary fix for compatibility with the usage of RPCInferenceEngine
if isinstance(generation_config, GenerationConfig):
generation_config = generation_config.to_dict()
if (repetition_penalty := generation_config.get("repetition_penalty", 1.0)) != 1.0:
logits = get_logits_processor("repetition_penalty", logits, repetition_penalty, batch_token_ids)
if (no_repeat_ngram_size := generation_config.get("no_repeat_ngram_size", 0)) > 0:
logits = get_logits_processor("no_repeat_ngram_size", logits, no_repeat_ngram_size, batch_token_ids)
if (forced_eos_token_id := generation_config.get("forced_eos_token_id", None)) is not None:
sequence_lengths = [len(batch_token_ids[i]) for i in range(len(batch_token_ids))]
max_out_lengths = [generation_config.max_length for _ in range(len(batch_token_ids))]
logits = get_logits_processor(
"forced_eos_token_id", logits, sequence_lengths, max_out_lengths, forced_eos_token_id
)
if generation_config.get("do_sample"):
if (temperature := generation_config.get("temperature", 1.0)) != 1.0:
logits = get_logits_processor("temperature", logits, temperature)
if (top_k := generation_config.get("top_k", 0)) != 0:
logits = get_logits_processor("top_k", logits, top_k)
if (top_p := generation_config.get("top_p", 1.0)) < 1.0:
logits = get_logits_processor("top_p", logits, top_p)
# calculate probs
probs = torch.softmax(logits, dim=-1, dtype=torch.float)
logprobs = torch.log_softmax(logits, dim=-1, dtype=torch.float)
# sample the next tokens
sample_tokens = _sample(probs, logprobs, generation_config, is_prompt)
if generation_config.get("num_beams", 1) != 1:
raise NotImplementedError("Beam search is not supported yet.")
if generation_config.get("do_sample", False):
sample_tokens = multinomial_sample(probs)
else:
sample_tokens = greedy_sample(logprobs)
return sample_tokens