huggingface/text-generation-inference
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Joel Lamy-Poirier 2023-05-03 11:16:35 -04:00
parent cbbc046a79
commit 5677540881
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4 changed files with 189 additions and 449 deletions
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3
Dockerfile
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@ -177,7 +177,8 @@ ENTRYPOINT ["./entrypoint.sh"]
# Final image
FROM base
RUN git clone https://github.com/bigcode-project/bigcode-inference-benchmark.git && \
cd bigcode-inference-benchmark && git checkout text_gen_inference
ENV HUGGINGFACE_HUB_CACHE=/usr/data/.hf_cache/
ENV PYTHONPATH=/usr/src/server/

3
server/text_generation_server/models/__init__.py
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@ -8,6 +8,7 @@ from typing import Optional
from text_generation_server.models.model import Model
from text_generation_server.models.causal_lm import CausalLM
from text_generation_server.models.vectorized_causal_lm import VectorizedCausalLM
from text_generation_server.models.flash_causal_lm import FlashCausalLM
from text_generation_server.models.bloom import BLOOM, BLOOMSharded
from text_generation_server.models.seq2seq_lm import Seq2SeqLM
@ -155,6 +156,8 @@ def get_model(
raise ValueError("sharded is not supported for AutoModel")
if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
if os.environ.get("VECTORIZED_LM") is not None:
return VectorizedCausalLM(model_id, revision, quantize=quantize)
return CausalLM(model_id, revision, quantize=quantize)
if model_type in modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES:
return Seq2SeqLM(model_id, revision, quantize=quantize)

5
server/text_generation_server/models/causal_lm.py
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@ -4,7 +4,6 @@ from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from loguru import logger
from text_generation_server.models import Model
from text_generation_server.models.types import (
@ -54,7 +53,6 @@ class CausalLMBatch(Batch):
keys_head_dim_last: bool = True
def to_pb(self) -> generate_pb2.Batch:
#logger.info(f"to_pb, id={self.batch_id}, requests={self.requests}, size={len(self)}, max_tokens={self.max_tokens}")
return generate_pb2.Batch(
id=self.batch_id,
requests=self.requests,
@ -69,7 +67,6 @@ class CausalLMBatch(Batch):
tokenizer: PreTrainedTokenizerBase,
device: torch.device,
) -> "CausalLMBatch":
#logger.info(f"from_pb, pb={pb}, tokenizer={tokenizer}, device={device}")
inputs = []
next_token_choosers = []
stopping_criterias = []
@ -144,7 +141,6 @@ class CausalLMBatch(Batch):
@tracer.start_as_current_span("filter")
def filter(self, requests: List[generate_pb2.Request]) -> Optional["CausalLMBatch"]:
logger.info(f"filter, requests={requests}")
if len(requests) == 0:
raise ValueError("Batch must have at least one request")
if len(requests) == len(self):
@ -242,7 +238,6 @@ class CausalLMBatch(Batch):
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
logger.info(f"concatenate, batches={batches}")
# Used for padding
total_batch_size = 0
max_input_length = 0

627
server/text_generation_server/models/vectorized_causal_lm.py
View File

@ -20,19 +20,18 @@ tracer = trace.get_tracer(__name__)
@dataclass
class CausalLMBatch(Batch):
class VectorizedCausalLMBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
requests_idx_mapping: Dict[int, int]
# Decoder values
input_ids: torch.Tensor
attention_mask: torch.Tensor
position_ids: torch.Tensor
past_key_values: Optional[List[Tuple]]
# All tokens
all_input_ids: List[torch.Tensor]
input_ids: torch.Tensor
# Lengths of all generations present in the batch
input_lengths: List[int]
@ -45,16 +44,11 @@ class CausalLMBatch(Batch):
# Metadata used for padding
max_input_length: int
padding_right_offset: int
# Maximum number of tokens this batch will grow to
max_tokens: int
# Past metadata
keys_head_dim_last: bool = True
def to_pb(self) -> generate_pb2.Batch:
#logger.info(f"to_pb, id={self.batch_id}, requests={self.requests}, size={len(self)}, max_tokens={self.max_tokens}")
return generate_pb2.Batch(
id=self.batch_id,
requests=self.requests,
@ -68,8 +62,7 @@ class CausalLMBatch(Batch):
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
device: torch.device,
) -> "CausalLMBatch":
#logger.info(f"from_pb, pb={pb}, tokenizer={tokenizer}, device={device}")
) -> "VectorizedCausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
@ -82,11 +75,14 @@ class CausalLMBatch(Batch):
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
next_token_chooser=NextTokenChooser.from_pb(r.parameters, device)
# TODO: Implement
assert len(next_token_chooser.warpers)==0
requests_idx_mapping[r.id] = i
inputs.append(r.inputs)
offsets.append(None)
token_offsets.append(None)
next_token_choosers.append(NextTokenChooser.from_pb(r.parameters, device))
next_token_choosers.append(next_token_chooser)
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
@ -109,17 +105,19 @@ class CausalLMBatch(Batch):
input_lengths = tokenized_inputs["attention_mask"].sum(1)
max_input_length = input_lengths.max()
input_ids = tokenized_inputs["input_ids"]
# Allocate maximum attention_mask
attention_mask = input_ids.new_zeros(
(pb.size, max_input_length + padding_right_offset)
)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length] = tokenized_inputs["attention_mask"]
input_shape=(pb.size, max_input_length + padding_right_offset)
position_ids = tokenized_inputs["attention_mask"].long().cumsum(-1) - 1
position_ids.masked_fill_(tokenized_inputs["attention_mask"] == 0, 1)
all_input_ids = tokenized_inputs["input_ids"].T.split(1, dim=1)
# Allocate maximum attention_mask
attention_mask = torch.empty(input_shape, dtype=torch.bool, device=device)
# Copy tokenizer attention_mask into fully allocated attention_mask
attention_mask[:, :max_input_length].copy_(tokenized_inputs["attention_mask"])
attention_mask[:, max_input_length:].fill_(1)
position_ids = attention_mask.cumsum(-1).sub_(1)
position_ids[:, :max_input_length].relu_()
input_ids = torch.empty(input_shape, dtype=torch.int64, device=device)
input_ids[:, :max_input_length].copy_(tokenized_inputs["input_ids"])
max_tokens = len(inputs) * max_input_length + max_decode_tokens
@ -127,327 +125,148 @@ class CausalLMBatch(Batch):
batch_id=pb.id,
requests=pb.requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=None,
all_input_ids=list(all_input_ids),
input_ids=input_ids,
input_lengths=input_lengths.tolist(),
offsets=offsets,
token_offsets=token_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length.item(),
padding_right_offset=padding_right_offset,
max_tokens=max_tokens,
)
@tracer.start_as_current_span("filter")
def filter(self, requests: List[generate_pb2.Request]) -> Optional["CausalLMBatch"]:
logger.info(f"filter, requests={requests}")
if len(requests) == 0:
raise ValueError("Batch must have at least one request")
if len(requests) == len(self):
return self
keep_indices = []
# New values after filtering
requests_idx_mapping = {}
input_lengths = []
offsets = []
token_offsets = []
all_input_ids = []
max_input_length = 0
next_token_choosers = []
stopping_criterias = []
total_remaining_decode_tokens = 0
new_padding_right_offset = 0
for i, r in enumerate(requests):
idx = self.requests_idx_mapping[r.id]
requests_idx_mapping[r.id] = i
keep_indices.append(idx)
offsets.append(self.offsets[idx])
token_offsets.append(self.token_offsets[idx])
all_input_ids.append(self.all_input_ids[idx])
request_input_length = self.input_lengths[idx]
input_lengths.append(request_input_length)
max_input_length = max(max_input_length, request_input_length)
next_token_choosers.append(self.next_token_choosers[idx])
stopping_criteria = self.stopping_criterias[idx]
stopping_criterias.append(stopping_criteria)
remaining_decode_tokens = (
stopping_criteria.max_new_tokens - stopping_criteria.current_tokens
)
total_remaining_decode_tokens += remaining_decode_tokens
new_padding_right_offset = max(
new_padding_right_offset, remaining_decode_tokens
)
# Apply indices to input_ids, attention mask, past key values and other items that need to be cached
input_ids = self.input_ids[keep_indices]
position_ids = self.position_ids[keep_indices]
self.attention_mask = self.attention_mask[
keep_indices,
-(self.padding_right_offset + max_input_length) : (
self.attention_mask.shape[1] - self.padding_right_offset
)
+ new_padding_right_offset,
]
# Ensure that past_key_values tensors can be updated in-place
if type(self.past_key_values[0]) == tuple:
self.past_key_values = [list(layer) for layer in self.past_key_values]
# Update tensors in-place to allow incremental garbage collection
past_kv_length = max_input_length - 1
for layer in self.past_key_values:
past_keys, past_values = layer
if len(past_keys.shape) == 3:
# Force past to be of dim [self_size, num_heads, ...] for easy indexing
past_keys = past_keys.view(len(self), -1, *past_keys.shape[-2:])
past_values = past_values.view(len(self), -1, *past_values.shape[-2:])
if self.keys_head_dim_last:
layer[0] = past_keys[keep_indices, :, -past_kv_length:, :]
else:
layer[0] = past_keys[keep_indices, :, :, -past_kv_length:]
del past_keys
layer[1] = past_values[keep_indices, :, -past_kv_length:, :]
del past_values
max_tokens = len(requests) * max_input_length + total_remaining_decode_tokens
self.requests = requests
self.requests_idx_mapping = requests_idx_mapping
self.input_ids = input_ids
self.position_ids = position_ids
self.all_input_ids = all_input_ids
self.input_lengths = input_lengths
self.offsets = offsets
self.token_offsets = token_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.max_input_length = max_input_length
self.padding_right_offset = new_padding_right_offset
self.max_tokens = max_tokens
return self
def filter(self, requests: List[generate_pb2.Request]) -> Optional["VectorizedCausalLMBatch"]:
raise NotImplementedError()
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
logger.info(f"concatenate, batches={batches}")
# Used for padding
total_batch_size = 0
max_input_length = 0
padding_right_offset = 0
for batch in batches:
total_batch_size += len(batch)
max_input_length = max(max_input_length, batch.max_input_length)
padding_right_offset = max(padding_right_offset, batch.padding_right_offset)
# Batch attributes
requests = []
requests_idx_mapping = {}
input_lengths = []
offsets = []
token_offsets = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
max_tokens = 0
# Batch tensors
input_ids = None
attention_mask = None
position_ids = None
past_key_values = []
# Used for slicing correctly inside the tensors
# Equivalent to a cumsum on batch sizes
start_index = 0
for i, batch in enumerate(batches):
requests.extend(batch.requests)
input_lengths.extend(batch.input_lengths)
offsets.extend(batch.offsets)
token_offsets.extend(batch.token_offsets)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
if i == 0:
requests_idx_mapping = batch.requests_idx_mapping
else:
# We need to offset the mapping for each batch by the cumulative batch size
for k, v in batch.requests_idx_mapping.items():
requests_idx_mapping[k] = v + start_index
# Slicing end index for this batch
end_index = start_index + len(batch)
# We only concatenate batches that did at least one step
if batch.past_key_values is None:
raise ValueError("only concatenate prefilled batches")
# Create empty tensor
# input_ids is always of shape [batch_size, 1]
# We do not need to pad it
if input_ids is None:
input_ids = batch.input_ids.new_empty((total_batch_size, 1))
# Copy to correct indices
input_ids[start_index:end_index] = batch.input_ids
# Create padded tensor
if attention_mask is None:
attention_mask = batch.attention_mask.new_zeros(
(total_batch_size, max_input_length + padding_right_offset),
)
# We need to slice the attention mask to remove padding from previous steps
# and to remove unused allocated space
left_offset = max_input_length - batch.max_input_length
batch_left_offset = (
batch.attention_mask.shape[1]
- batch.max_input_length
- batch.padding_right_offset
)
attention_mask[
start_index:end_index,
left_offset:-padding_right_offset,
] = batch.attention_mask[
:,
batch_left_offset : -batch.padding_right_offset,
]
# Create empty tensor
# position_ids is always of shape [batch_size, 1]
if position_ids is None:
position_ids = batch.position_ids.new_empty((total_batch_size, 1))
position_ids[start_index:end_index] = batch.position_ids
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM Keys: [batch_size * num_heads, head_dim, seq_length]
# BLOOM Values: [batch_size * num_heads, seq_length, head_dim]
# And ensure that we can update tensors in-place
if type(batch.past_key_values[0]) == tuple:
batch.past_key_values = [
[t.view(len(batch), -1, *t.shape[-2:]) for t in layer]
for layer in batch.past_key_values
]
elif len(batch.past_key_values[0][0].shape) == 3:
for layer in batch.past_key_values:
for k, t in enumerate(layer):
layer[k] = t.view(len(batch), -1, *t.shape[-2:])
# Add eventual padding tokens that were added while concatenating
max_tokens += batch.max_tokens + (
max_input_length - batch.max_input_length
) * len(batch)
start_index = end_index
first_past_kvs = batches[0].past_key_values
_, num_heads, padded_sequence_length, head_dim = first_past_kvs[0][1].shape
padded_past_values_shape = (
total_batch_size,
num_heads,
max_input_length - 1,
head_dim,
)
if batches[0].keys_head_dim_last:
padded_past_keys_shape = padded_past_values_shape
else:
# seq_length is last for BLOOM
padded_past_keys_shape = (
total_batch_size,
num_heads,
head_dim,
max_input_length - 1,
)
# Iterate over attention layers
# Concatenate past key values layer by layer to allow incremental garbage collection
for j in range(len(first_past_kvs)):
padded_past_keys = first_past_kvs[j][0].new_zeros(padded_past_keys_shape)
start_index = 0
for batch in batches:
past_keys = batch.past_key_values[j][0]
# Clear reference to the original tensor
batch.past_key_values[j][0] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the keys to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
if batch.keys_head_dim_last:
padded_past_keys[
start_index:end_index, :, -past_seq_len:, :
] = past_keys[:, :, -past_seq_len:, :]
else:
# BLOOM case
padded_past_keys[
start_index:end_index, :, :, -past_seq_len:
] = past_keys[:, :, :, -past_seq_len:]
del past_keys
start_index = end_index
padded_past_values = first_past_kvs[j][1].new_zeros(
padded_past_values_shape
)
start_index = 0
for batch in batches:
past_values = batch.past_key_values[j][1]
# Clear reference to the original tensor
batch.past_key_values[j][1] = None
# Slicing end index for this batch
end_index = start_index + len(batch)
# We slice the past values to remove the padding from previous batches
past_seq_len = batch.max_input_length - 1
padded_past_values[
start_index:end_index, :, -past_seq_len:, :
] = past_values[:, :, -past_seq_len:, :]
del past_values
# Update values
start_index = end_index
past_key_values.append([padded_past_keys, padded_past_values])
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
all_input_ids=all_input_ids,
input_lengths=input_lengths,
offsets=offsets,
token_offsets=token_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
max_input_length=max_input_length,
padding_right_offset=padding_right_offset,
keys_head_dim_last=batches[0].keys_head_dim_last,
max_tokens=max_tokens,
)
def concatenate(cls, batches: List["VectorizedCausalLMBatch"]) -> "VectorizedCausalLMBatch":
raise NotImplementedError()
def __len__(self):
return len(self.requests)
class CausalLM(Model):
class VectorizedNextTokenChooser:
def __init__(
self,
batch_size:int,
watermark=None,
temperature=None,
repetition_penalty=None,
top_k=None,
top_p=None,
typical_p=None,
do_sample=None,
seed:int=0,
device="cpu",
):
self.batch_size=batch_size
do_sample=self._standardize(do_sample, False)
watermark=self._standardize(watermark, False)
if any(watermark):
raise NotImplementedError("Watermarking not implemented")
repetition_penalty=self._standardize(repetition_penalty, 1.0)
if any([x!=1.0 for x in repetition_penalty]):
self.repetition_penalty=torch.tensor([repetition_penalty], dtype=torch.float32, device=device).unsqueeze(1)
else:
self.repetition_penalty=None
temperature=self._standardize(temperature, 1.0)
if any([x!=1.0 for x in temperature]):
do_sample=[sample or x!=1.0 for x, sample in zip(temperature, do_sample)]
self.temperature=torch.tensor([temperature], dtype=torch.float32, device=device).unsqueeze(1)
else:
self.temperature=None
top_k=self._standardize(top_k, 0)
if any([x!=0 for x in top_k]):
do_sample=[sample or x!=0 for x, sample in zip(top_k, do_sample)]
self.top_k=torch.tensor([top_k], dtype=torch.float32, device=device).unsqueeze(1)
else:
self.top_k=None
top_p=self._standardize(top_p, 1.0)
if any([x<1.0 for x in top_p]):
raise NotImplementedError("Top P not implemented")
typical_p=self._standardize(typical_p, 1.0)
if any([x<1.0 for x in typical_p]):
raise NotImplementedError("Typical P not implemented")
self.do_sample = any(do_sample)
if self.do_sample and not all(do_sample):
raise NotImplementedError("Mixed greedy and probabilistic sampling not supported")
def _standardize(self, values, default):
if isinstance(values, list):
values=values.copy()
else:
values=[values]*self.batch_size
assert len(values)==self.batch_size
for i, v in enumerate(values):
if v is None:
values[i]=default
return values
def __call__(self, input_ids, scores):
# Only process the last token
scores=scores[: -1, :]
if self.repetition_penalty is not None:
score = torch.gather(scores, 1, input_ids)
# if score < 0 then repetition penalty has to be multiplied to reduce the previous token probability
score = torch.where(score < 0, score * self.repetition_penalty, score / self.repetition_penalty)
scores.scatter_(1, input_ids, score)
if self.temperature is not None:
scores.div_(self.temperature)
if self.top_k is not None:
top_k = min(self.top_k, scores.size(-1)) # Safety check
# Remove all tokens with a probability less than the last token of the top-k
indices_to_remove = scores < torch.topk(scores, top_k)[0][..., -1, None]
scores = scores.masked_fill(indices_to_remove, self.filter_value)
# Compute logprobs
logprobs = torch.log_softmax(scores, dim=-1)
if self.do_sample:
raise NotImplementedError()
else:
next_token_ids = torch.argmax(scores, dim=-1)
return next_token_ids, logprobs
@classmethod
def from_pb(
cls,
pb: List[generate_pb2.NextTokenChooserParameters],
device: torch.device,
) -> "VectorizedNextTokenChooser":
# TODO: Seeds are ignored
return VectorizedNextTokenChooser(
watermark=[pb_.watermark for pb_ in pb],
temperature=[pb_.temperature for pb_ in pb],
repetition_penalty=[pb_.repetition_penalty for pb_ in pb],
top_k=[pb_.top_k for pb_ in pb],
top_p=[pb_.top_p for pb_ in pb],
typical_p=[pb_.typical_p for pb_ in pb],
do_sample=[pb_.do_sample for pb_ in pb],
seed=0,
device=device,
)
class VectorizedCausalLM(Model):
def __init__(
self,
model_id: str,
@ -457,6 +276,7 @@ class CausalLM(Model):
):
if torch.cuda.is_available():
device = torch.device("cuda")
# TODO: Choose dtype (fp16?)
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
else:
if quantize:
@ -482,7 +302,7 @@ class CausalLM(Model):
else self.model.config.eos_token_id
)
super(CausalLM, self).__init__(
super().__init__(
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
@ -491,94 +311,58 @@ class CausalLM(Model):
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return CausalLMBatch
def batch_type(self) -> Type[VectorizedCausalLMBatch]:
return VectorizedCausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, cleanup_tokenization_spaces=False
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
) -> Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]:
# Model Forward
outputs = self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
position_ids=position_ids,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, outputs.past_key_values
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: CausalLMBatch
) -> Tuple[List[Generation], Optional[CausalLMBatch]]:
# slice the attention mask to the correct shape
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
self, batch: VectorizedCausalLMBatch
) -> Tuple[List[Generation], Optional[VectorizedCausalLMBatch]]:
key_length=batch.max_input_length
query_length=key_length if batch.past_key_values is None else 1
logits, past = self.forward(
batch.input_ids,
attention_mask,
batch.position_ids,
batch.past_key_values,
outputs = self.model.forward(
input_ids=batch.input_ids[:, key_length-query_length: key_length],
attention_mask=batch.attention_mask[:, : key_length],
position_ids=batch.position_ids[:, key_length-query_length: key_length],
past_key_values=batch.past_key_values,
)
# TODO: Post-processing
next_token_ids = torch.argmax(outputs.logits[:, -1, :], dim=-1)
# Update batch
# TODO: Why do we need all input ids?
batch.input_ids[:, key_length].copy_(next_token_ids)
batch.past_key_values=outputs.past_key_values
batch.input_lengths=[length+1 for length in batch.input_lengths]
batch.max_input_length+=1
# TODO: self.decode_token, offsets?
next_token_ids=next_token_ids.cpu().tolist()
next_token_texts=self.tokenizer.batch_decode(next_token_ids)
# TODO: Vectorize some of this?
# Results
generations: List[Generation] = []
stopped = True
next_batch=None
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.offsets,
batch.token_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
)
# For each member of the batch
for i, (
request,
input_length,
offset,
token_offset,
logits,
next_token_chooser,
stopping_criteria,
all_input_ids,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_input_ids.view(1, -1), logits
)
# Append next token to all tokens
all_input_ids = torch.cat([all_input_ids, next_token_id])
new_input_length = input_length + 1
# Generated token
next_token_logprob = logprobs[-1, next_token_id]
next_token_id_squeezed = next_token_id.squeeze()
next_token_text, offset, token_offset = self.decode_token(
all_input_ids[:, 0], offset, token_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(
next_token_id_squeezed,
for i, (next_token_id, next_token_text) in enumerate(zip(next_token_ids, next_token_texts)):
stopping_criterias=batch.stopping_criterias[i]
next_token_chooser=batch.next_token_choosers[i]
stop, reason = stopping_criterias(
next_token_id,
next_token_text,
)
if stop:
# Decode generated tokens
# TODO: Same as stopping_criteria.current_output?
output_text = self.decode(
all_input_ids[-stopping_criteria.current_tokens :, 0]
batch.input_ids[i, -stopping_criterias.current_tokens :]
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
@ -587,67 +371,24 @@ class CausalLM(Model):
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
output_text, stopping_criterias.current_tokens, reason, seed
)
else:
# Keep request in the batch
generated_text = None
stopped = False
next_batch = batch
# Prefill
if stopping_criteria.current_tokens == 1:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + logprobs.gather(
1, all_input_ids[1:]
).squeeze(1)[-new_input_length:-1].tolist()
prefill_token_ids = all_input_ids[-new_input_length:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = PrefillTokens(
prefill_token_ids, prefill_logprobs, prefill_texts
)
else:
prefill_tokens = None
generation = Generation(
request.id,
prefill_tokens,
next_token_id_squeezed,
next_token_logprob,
batch.requests[i].id,
None,
next_token_id,
0,
next_token_text,
next_token_id_squeezed.item() in self.all_special_ids,
next_token_id in self.all_special_ids,
generated_text,
)
generations.append(generation)
# Update values
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.offsets[i] = offset
batch.token_offsets[i] = token_offset
batch.max_input_length = max(batch.max_input_length, new_input_length)
# We finished all generations in the batch; there is no next batch
if stopped:
return generations, None
# Slice unused values from prefill
batch.input_ids = batch.input_ids[:, :1]
# Update attention_mask as we added a new token to input_ids
batch.attention_mask[:, -batch.padding_right_offset] = 1
# Decrease right offset
batch.padding_right_offset -= 1
# Update position_ids
batch.position_ids = batch.position_ids[:, -1:] + 1
# Update past key values
batch.past_key_values = past
return generations, batch
return generations, next_batch