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feat(server): Support generic AutoModelForCausalLM

This commit is contained in:
OlivierDehaene 2022-11-04 14:22:47 +01:00
parent 755fc0e403
commit c5665f5c8b
11 changed files with 373 additions and 333 deletions
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1
README.md
View File

@ -18,6 +18,7 @@ A Rust and gRPC server for text generation inference.
## Supported models
- BLOOM
- BLOOMZ
- BLOOM-560m
## Load Tests for BLOOM

2
proto/generate.proto
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@ -63,6 +63,8 @@ message GeneratedText {
Request request = 1;
/// Output
string output = 2;
/// Number of generated tokens
uint32 tokens = 3;
}
message GenerateRequest {

2
router/src/batcher.rs
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@ -190,6 +190,7 @@ fn send_generated(finished: Vec<GeneratedText>, db: &Db) {
.expect("ID not found in db. This is a bug.");
let response = InferResponse {
output: output.output,
tokens: output.tokens,
queued: entry.time,
start: entry.batch_time.unwrap(), // unwrap is always valid
end: Instant::now(),
@ -202,6 +203,7 @@ fn send_generated(finished: Vec<GeneratedText>, db: &Db) {
#[derive(Debug)]
pub(crate) struct InferResponse {
pub(crate) output: String,
pub(crate) tokens: u32,
pub(crate) queued: Instant,
pub(crate) start: Instant,
pub(crate) end: Instant,

2
router/src/server.rs
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@ -116,7 +116,7 @@ async fn generate(
let validation_time = response.queued - start_time;
let queue_time = response.start - response.queued;
let inference_time = response.end - response.start;
let time_per_token = inference_time / req.parameters.max_new_tokens;
let time_per_token = inference_time / response.tokens;
// Headers
let mut headers = HeaderMap::new();

13
server/text_generation/models/__init__.py
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@ -1,7 +1,8 @@
from text_generation.models.model import Model
from text_generation.models.bloom import BLOOM, BLOOMSharded
from text_generation.models.bloom import BLOOMSharded
from text_generation.models.causal_lm import CausalLM
__all__ = ["Model", "BLOOM", "BLOOMSharded"]
__all__ = ["Model", "BLOOMSharded", "CausalLM"]
def get_model(model_name: str, sharded: bool, quantize: bool) -> Model:
@ -11,6 +12,10 @@ def get_model(model_name: str, sharded: bool, quantize: bool) -> Model:
else:
if quantize:
raise ValueError("quantization is not supported for non-sharded BLOOM")
return BLOOM(model_name)
return CausalLM(model_name)
else:
raise ValueError(f"model {model_name} is not supported yet")
if sharded:
raise ValueError("sharded is not supported for AutoModel")
if quantize:
raise ValueError("quantize is not supported for AutoModel")
return CausalLM(model_name)

315
server/text_generation/models/bloom.py
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@ -1,7 +1,7 @@
import torch
import torch.distributed
from typing import List, Optional, Tuple, Type
from typing import List, Optional
from accelerate import init_empty_weights
from safetensors import safe_open
@ -11,10 +11,8 @@ from transformers.models.bloom.parallel_layers import (
TensorParallelEmbedding,
TensorParallelRowLinear,
)
from transformers.modeling_outputs import CausalLMOutputWithPast
from text_generation.models import Model
from text_generation.models.types import Batch, GeneratedText
from text_generation.utils import (
initialize_torch_distributed,
weight_files,
@ -31,322 +29,26 @@ except Exception as e:
torch.manual_seed(0)
class BloomBatch(Batch):
@classmethod
def concatenate(cls, batches: List["Batch"]) -> "BloomBatch":
# Used for padding
total_batch_size = sum(batch.size for batch in batches)
max_sequence_length = max(batch.max_sequence_length for batch in batches)
# Batch attributes
input_ids = {"input_ids": None, "attention_mask": None, "past_key_values": []}
requests = []
all_input_lengths = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
# 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)
all_input_lengths.extend(batch.all_input_lengths)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
# Slicing end index for this batch
end_index = start_index + batch.size
# We only concatenate batches that did at least one step
if batch.input_ids["input_ids"].shape[1] > 1:
raise ValueError("Batch input_ids should be of shape (batch_size, 1)")
# Initialize tensors
if i == 0:
input_ids["input_ids"] = torch.empty(
(total_batch_size, 1),
dtype=batch.input_ids["input_ids"].dtype,
device=batch.input_ids["input_ids"].device,
)
input_ids["attention_mask"] = torch.zeros(
(total_batch_size, max_sequence_length),
dtype=batch.input_ids["attention_mask"].dtype,
device=batch.input_ids["attention_mask"].device,
)
# input_ids["input_ids"] is always of shape [batch_size, 1]
# We do not need to pad it
input_ids["input_ids"][start_index:end_index] = batch.input_ids["input_ids"]
# We need to slice the attention mask to remove padding from previous steps
input_ids["attention_mask"][
start_index:end_index, -batch.max_sequence_length:
] = batch.input_ids["attention_mask"][:, -batch.max_sequence_length:]
for j, past in enumerate(batch.input_ids["past_key_values"]):
past_keys = past[0]
past_values = past[1]
_, head_dim, padded_sequence_length = past_keys.shape
# Reshape the tensors to make slicing easier
past_keys = past_keys.view(
batch.size, -1, head_dim, padded_sequence_length
)
past_values = past_values.view(
batch.size, -1, padded_sequence_length, head_dim
)
num_heads = past_keys.shape[1]
# Initialize tensors
# This will run only once per layer
if j == len(input_ids["past_key_values"]):
padded_past_keys = torch.zeros(
(
total_batch_size,
num_heads,
head_dim,
max_sequence_length - 1,
),
dtype=past_keys.dtype,
device=past_keys.device,
)
padded_past_values = torch.zeros(
(
total_batch_size,
num_heads,
max_sequence_length - 1,
head_dim,
),
dtype=past_values.dtype,
device=past_values.device,
)
input_ids["past_key_values"].append(
[padded_past_keys, padded_past_values]
)
# We slice the past keys and values to remove the padding from previous batches
input_ids["past_key_values"][j][0][
start_index:end_index, :, :, -(batch.max_sequence_length - 1):
] = past_keys[:, :, :, -(batch.max_sequence_length - 1):]
input_ids["past_key_values"][j][1][
start_index:end_index, :, -(batch.max_sequence_length - 1):, :
] = past_values[:, :, -(batch.max_sequence_length - 1):, :]
# If we are on the last batch, we need to reshape the tensors
if (i + 1) == len(batches):
input_ids["past_key_values"][j][0] = input_ids["past_key_values"][
j
][0].view(total_batch_size * num_heads, head_dim, -1)
input_ids["past_key_values"][j][1] = input_ids["past_key_values"][
j
][1].view(total_batch_size * num_heads, -1, head_dim)
start_index += batch.size
return cls(
batch_id=batches[0].batch_id,
requests=requests,
all_input_lengths=all_input_lengths,
input_ids=input_ids,
all_input_ids=all_input_ids,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
size=total_batch_size,
max_sequence_length=max_sequence_length,
)
class BLOOM(Model):
def __init__(self, model_name: str):
if not model_name.startswith("bigscience/bloom"):
raise ValueError(f"Model {model_name} is not supported")
if torch.cuda.is_available():
self.device = torch.device("cuda")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float16
else:
self.device = torch.device("cpu")
dtype = torch.float32
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
self.tokenizer.add_special_tokens({"pad_token": "[PAD]"})
self.model = AutoModelForCausalLM.from_pretrained(
model_name, torch_dtype=dtype, device_map="auto" if torch.cuda.is_available() else None
).eval()
self.num_heads = self.model.config.num_attention_heads
@property
def batch_type(self) -> Type[BloomBatch]:
return BloomBatch
def forward(
self, input_ids, attention_mask, past_key_values: Optional = None
) -> CausalLMOutputWithPast:
# Model Forward
return self.model.forward(
input_ids=input_ids,
attention_mask=attention_mask,
past_key_values=past_key_values,
use_cache=True,
)
def generate_token(
self, batch: BloomBatch
) -> Tuple[List[GeneratedText], Optional[BloomBatch]]:
# For some reason, inference_mode does not work well with GLOO which we use on CPU
context_manager = (
torch.no_grad if self.device.type == "cpu" else torch.inference_mode
)
with context_manager():
outputs = self.forward(**batch.input_ids)
# List of indices to cache
next_batch_keep_indices = []
next_batch_past_keep_indices = []
# New input_ids for next forward
next_batch_input_ids = []
next_batch_all_input_ids = []
next_all_input_lengths = []
next_batch_size = 0
next_batch_max_sequence_length = 0
# Finished requests
generated_texts: List[GeneratedText] = []
# Zipped iterator
iterator = zip(
batch.requests,
batch.all_input_lengths,
outputs.logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
)
# For each member of the batch
for i, (
request,
input_length,
logits,
next_token_chooser,
stopping_criteria,
all_tokens,
) in enumerate(iterator):
# Select next token
next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])
# Append next token to all tokens
all_tokens = torch.cat([all_tokens, next_token])
# Evaluate stopping criteria
if stopping_criteria(all_tokens):
# Decode all tokens
output = self.tokenizer.decode(
all_tokens.squeeze(-1), skip_special_tokens=True
)
# Add to the list of finished generations with the original request
generated_texts.append(GeneratedText(request, output))
# add to the next batch
else:
next_batch_keep_indices.append(i)
# past_key_values is of shape [batch_size * num_heads, ...]
# so we need to take into account the `num_heads` stride here
next_batch_past_keep_indices.extend(
[j for j in range(i * self.num_heads, (i + 1) * self.num_heads)]
)
next_batch_input_ids.append(next_token)
next_batch_all_input_ids.append(all_tokens)
next_batch_size += 1
new_input_length = input_length + 1
next_all_input_lengths.append(new_input_length)
next_batch_max_sequence_length = max(
next_batch_max_sequence_length, new_input_length
)
# We finished all generations in the batch; there is no next batch
if not next_batch_keep_indices:
return generated_texts, None
# If we finished at least one generation
next_batch_input_ids = {"input_ids": torch.cat(next_batch_input_ids, dim=0)}
if generated_texts:
# Apply indices to attention mask, past key values and other items that need to be cached
next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"][
next_batch_keep_indices
]
next_batch_input_ids["past_key_values"] = [
(
keys[next_batch_past_keep_indices],
values[next_batch_past_keep_indices],
)
for keys, values in outputs["past_key_values"]
]
next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
next_batch_next_token_choosers = [
batch.next_token_choosers[i] for i in next_batch_keep_indices
]
next_batch_stopping_criterias = [
batch.stopping_criterias[i] for i in next_batch_keep_indices
]
else:
next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"]
next_batch_input_ids["past_key_values"] = outputs["past_key_values"]
next_batch_requests = batch.requests
next_batch_next_token_choosers = batch.next_token_choosers
next_batch_stopping_criterias = batch.stopping_criterias
# Update attention_mask with padding as we added a new token to input_ids
next_batch_input_ids["attention_mask"] = torch.cat(
[
next_batch_input_ids["attention_mask"],
torch.ones((next_batch_size, 1)).to(self.device),
],
dim=1,
)
next_batch = BloomBatch(
batch_id=batch.batch_id,
requests=next_batch_requests,
all_input_lengths=next_all_input_lengths,
input_ids=next_batch_input_ids,
all_input_ids=next_batch_all_input_ids,
next_token_choosers=next_batch_next_token_choosers,
stopping_criterias=next_batch_stopping_criterias,
size=next_batch_size,
max_sequence_length=next_batch_max_sequence_length,
)
return generated_texts, next_batch
class BLOOMSharded(BLOOM):
class BLOOMSharded(Model):
def __init__(self, model_name: str, quantize: bool = False):
super(Model, self).__init__()
if not model_name.startswith("bigscience/bloom"):
raise ValueError(f"Model {model_name} is not supported")
self.process_group, self.rank, self.world_size = initialize_torch_distributed()
self.master = self.rank == 0
if torch.cuda.is_available():
self.device = torch.device(f"cuda:{self.rank}")
device = torch.device(f"cuda:{self.rank}")
dtype = torch.float16
else:
self.device = torch.device("cpu")
device = torch.device("cpu")
dtype = torch.float32
self.tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
config = AutoConfig.from_pretrained(
model_name, slow_but_exact=False, tp_parallel=True
)
config.pad_token_id = 3
self.num_heads = config.n_head // self.process_group.size()
# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
# in PyTorch 1.12 and later.
@ -370,12 +72,14 @@ class BLOOMSharded(BLOOM):
model,
filenames,
quantize=quantize,
device=self.device,
device=device,
rank=self.rank,
world_size=self.world_size,
)
self.model = model.eval().to(dtype)
torch.distributed.barrier(group=self.process_group)
super(BLOOMSharded, self).__init__(tokenizer=tokenizer, num_heads=config.n_head // self.process_group.size(),
device=device)
@staticmethod
def load_weights(
@ -526,5 +230,4 @@ class BLOOMSharded(BLOOM):
torch.distributed.all_gather(logits, logits_shard, group=self.process_group)
logits = torch.cat(logits, dim=1).view(batch_size, 1, vocab_size)
outputs.logits = logits
return outputs
return logits, outputs.past_key_values

38
server/text_generation/models/causal_lm.py Normal file
View File

@ -0,0 +1,38 @@
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import Optional, Tuple, List
from text_generation.models import Model
class CausalLM(Model):
def __init__(self, model_name: str):
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.bfloat16 if torch.cuda.is_bf16_supported() else torch.float32
else:
device = torch.device("cpu")
dtype = torch.float32
tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=dtype,
device_map="auto" if torch.cuda.is_available() else None,
).eval()
super(CausalLM, self).__init__(tokenizer=tokenizer, num_heads=self.model.config.num_attention_heads, device=device)
def forward(
self, input_ids, attention_mask, 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,
past_key_values=past_key_values,
use_cache=True,
)
return outputs.logits, outputs.past_key_values

140
server/text_generation/models/model.py
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@ -1,19 +1,139 @@
import torch
from abc import ABC, abstractmethod
from typing import List, Tuple, Optional, TypeVar, Type
from typing import List, Tuple, Optional
from tokenizers import Tokenizer
from text_generation.models.types import Batch, GeneratedText
B = TypeVar("B", bound=Batch)
class Model(ABC):
@property
@abstractmethod
def batch_type(self) -> Type[B]:
raise NotImplementedError
def __init__(self, tokenizer: Tokenizer, num_heads: int, device: torch.device):
self.tokenizer = tokenizer
self.num_heads = num_heads
self.device = device
@abstractmethod
def generate_token(
self, batch: B
) -> Tuple[List[GeneratedText], Optional[B]]:
def forward(self, input_ids, attention_mask, past_key_values: Optional = None) -> Tuple[torch.Tensor, List[Tuple]]:
raise NotImplementedError
def generate_token(
self, batch: Batch
) -> Tuple[List[GeneratedText], Optional[Batch]]:
# For some reason, inference_mode does not work well with GLOO which we use on CPU
context_manager = (
torch.no_grad if self.device.type == "cpu" else torch.inference_mode
)
with context_manager():
logits, past = self.forward(**batch.input_ids)
# List of indices to cache
next_batch_keep_indices = []
# New input_ids for next forward
next_batch_input_ids = []
next_batch_all_input_ids = []
next_all_input_lengths = []
next_batch_size = 0
next_batch_max_sequence_length = 0
# Finished requests
generated_texts: List[GeneratedText] = []
# Zipped iterator
iterator = zip(
batch.requests,
batch.all_input_lengths,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
)
# For each member of the batch
for i, (
request,
input_length,
logits,
next_token_chooser,
stopping_criteria,
all_tokens,
) in enumerate(iterator):
# Select next token
next_token = next_token_chooser(all_tokens, logits.unsqueeze(0)[:, -1])
# Append next token to all tokens
all_tokens = torch.cat([all_tokens, next_token])
# Evaluate stopping criteria
if stopping_criteria(all_tokens):
# Decode all tokens
output = self.tokenizer.decode(
all_tokens.squeeze(-1), skip_special_tokens=True
)
# Add to the list of finished generations with the original request
generated_texts.append(GeneratedText(request, output, stopping_criteria.current_tokens))
# add to the next batch
else:
next_batch_keep_indices.append(i)
next_batch_input_ids.append(next_token)
next_batch_all_input_ids.append(all_tokens)
next_batch_size += 1
new_input_length = input_length + 1
next_all_input_lengths.append(new_input_length)
next_batch_max_sequence_length = max(
next_batch_max_sequence_length, new_input_length
)
# We finished all generations in the batch; there is no next batch
if not next_batch_keep_indices:
return generated_texts, None
# If we finished at least one generation
next_batch_input_ids = {"input_ids": torch.cat(next_batch_input_ids, dim=0)}
if generated_texts:
# Apply indices to attention mask, past key values and other items that need to be cached
next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"][
next_batch_keep_indices
]
# Force past to be of dim [batch_size, num_heads, ...] for easy indexing
next_batch_input_ids["past_key_values"] = [
[t.view(-1, self.num_heads, *t.shape[-2:])[next_batch_keep_indices] for t in layer]
for layer in past
]
next_batch_requests = [batch.requests[i] for i in next_batch_keep_indices]
next_batch_next_token_choosers = [
batch.next_token_choosers[i] for i in next_batch_keep_indices
]
next_batch_stopping_criterias = [
batch.stopping_criterias[i] for i in next_batch_keep_indices
]
else:
next_batch_input_ids["attention_mask"] = batch.input_ids["attention_mask"]
next_batch_input_ids["past_key_values"] = past
next_batch_requests = batch.requests
next_batch_next_token_choosers = batch.next_token_choosers
next_batch_stopping_criterias = batch.stopping_criterias
# Update attention_mask with padding as we added a new token to input_ids
next_batch_input_ids["attention_mask"] = torch.cat(
[
next_batch_input_ids["attention_mask"],
torch.ones((next_batch_size, 1)).to(self.device),
],
dim=1,
)
next_batch = Batch(
batch_id=batch.batch_id,
requests=next_batch_requests,
all_input_lengths=next_all_input_lengths,
input_ids=next_batch_input_ids,
all_input_ids=next_batch_all_input_ids,
next_token_choosers=next_batch_next_token_choosers,
stopping_criterias=next_batch_stopping_criterias,
size=next_batch_size,
max_sequence_length=next_batch_max_sequence_length,
)
return generated_texts, next_batch

169
server/text_generation/models/types.py
View File

@ -1,6 +1,5 @@
import torch
from abc import abstractmethod
from dataclasses import dataclass
from typing import List, Dict
@ -51,7 +50,11 @@ class Batch:
do_sample=r.parameters.do_sample,
)
)
stopping_criterias.append(StoppingCriteria(max_new_tokens=r.max_new_tokens))
stopping_criterias.append(
StoppingCriteria(
eos_token_id=tokenizer.eos_token_id, max_new_tokens=r.max_new_tokens
)
)
input_ids = tokenizer(
inputs, return_tensors="pt", padding=True, pad_to_multiple_of=8
@ -71,15 +74,171 @@ class Batch:
)
@classmethod
@abstractmethod
def concatenate(cls, batches: List["Batch"]) -> "Batch":
raise NotImplementedError
# Used for padding
total_batch_size = sum(batch.size for batch in batches)
max_sequence_length = max(batch.max_sequence_length for batch in batches)
# Only needed for Seq2SeqLM
max_encoded_sequence_length = None
# Batch attributes
input_ids = {"input_ids": None, "attention_mask": None, "past_key_values": []}
requests = []
all_input_lengths = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
# 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)
all_input_lengths.extend(batch.all_input_lengths)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
# Slicing end index for this batch
end_index = start_index + batch.size
# We only concatenate batches that did at least one step
if batch.input_ids["input_ids"].shape[1] > 1:
raise ValueError("Batch input_ids should be of shape (batch_size, 1)")
# Initialize tensors
if i == 0:
input_ids["input_ids"] = torch.empty(
(total_batch_size, 1),
dtype=batch.input_ids["input_ids"].dtype,
device=batch.input_ids["input_ids"].device,
)
input_ids["attention_mask"] = torch.zeros(
(total_batch_size, max_sequence_length),
dtype=batch.input_ids["attention_mask"].dtype,
device=batch.input_ids["attention_mask"].device,
)
# input_ids["input_ids"] is always of shape [batch_size, 1]
# We do not need to pad it
input_ids["input_ids"][start_index:end_index] = batch.input_ids["input_ids"]
# We need to slice the attention mask to remove padding from previous steps
input_ids["attention_mask"][
start_index:end_index, -batch.max_sequence_length:
] = batch.input_ids["attention_mask"][:, -batch.max_sequence_length:]
for j, past in enumerate(batch.input_ids["past_key_values"]):
# Shenanigans to get dimensions because BLOOM outputs a past with a different shape
# BLOOM: [batch_size * num_heads, ...] vs [batch_size, num_heads, ...]
head_dim, padded_sequence_length = past[0].shape[-2:]
num_heads = (
past[0]
.view(batch.size, -1, head_dim, padded_sequence_length)
.shape[1]
)
# This will run only once per layer
if j == len(input_ids["past_key_values"]):
input_ids["past_key_values"].append([])
# Decoder past
for k, t in enumerate(past[:2]):
# Needed because BLOOM past shapes are not the same for keys and values
# Keys: [batch_size * num_heads, head_dim, seq_length]
# Values: [batch_size * num_heads, seq_length, head_dim]
head_dim_last = False
if t.shape[-2] == head_dim:
t = t.view(
batch.size, num_heads, head_dim, padded_sequence_length
)
padded_t_shape = (
total_batch_size,
num_heads,
head_dim,
max_sequence_length - 1,
)
elif t.shape[-1] == head_dim:
head_dim_last = True
t = t.view(
batch.size, num_heads, padded_sequence_length, head_dim
)
padded_t_shape = (
total_batch_size,
num_heads,
max_sequence_length - 1,
head_dim,
)
else:
raise ValueError(f"shape {t.shape} is not valid")
# Initialize tensors
# This will run only once per layer and per past tensor
if k == len(input_ids["past_key_values"][j]):
input_ids["past_key_values"][j].append(
torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
)
# We slice the past keys and values to remove the padding from previous batches
if not head_dim_last:
input_ids["past_key_values"][j][k][
start_index:end_index,
:,
:,
-(batch.max_sequence_length - 1):,
] = t[:, :, :, -(batch.max_sequence_length - 1):]
else:
input_ids["past_key_values"][j][k][
start_index:end_index,
:,
-(batch.max_sequence_length - 1):,
:,
] = t[:, :, -(batch.max_sequence_length - 1):, :]
# Seq2SeqLM specific past (encoder past)
for k, t in enumerate(past[2:]):
if max_encoded_sequence_length is None:
max_encoded_sequence_length = max(max(batch.all_input_lengths) for batch in batches)
batch_max_encoded_sequence_length = max(batch.all_input_lengths)
padded_t_shape = (total_batch_size, num_heads, max_encoded_sequence_length, head_dim)
idx = k + 2
# Initialize tensors
# This will run only once per layer and per past tensor
if idx == len(input_ids["past_key_values"][j]):
input_ids["past_key_values"][j].append(
torch.zeros(padded_t_shape, dtype=t.dtype, device=t.device)
)
input_ids["past_key_values"][j][idx][
start_index:end_index,
:,
-batch_max_encoded_sequence_length:,
:
] = t[:, :, -batch_max_encoded_sequence_length:, :]
start_index += batch.size
return cls(
batch_id=batches[0].batch_id,
requests=requests,
all_input_lengths=all_input_lengths,
input_ids=input_ids,
all_input_ids=all_input_ids,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
size=total_batch_size,
max_sequence_length=max_sequence_length,
)
@dataclass
class GeneratedText:
request: generate_pb2.Request
output: str
tokens: int
def to_pb(self) -> generate_pb2.GeneratedText:
return generate_pb2.GeneratedText(request=self.request, output=self.output)
return generate_pb2.GeneratedText(request=self.request, output=self.output, tokens=self.tokens)

4
server/text_generation/server.py
View File

@ -27,7 +27,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
return generate_pb2.ClearCacheResponse()
async def Generate(self, request, context):
batch = self.model.batch_type.from_pb(request.batch, self.model.tokenizer, self.model.device)
batch = Batch.from_pb(request.batch, self.model.tokenizer, self.model.device)
generated_texts, next_batch = self.model.generate_token(batch)
self.cache.set(next_batch)
@ -51,7 +51,7 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
batches.append(batch)
if len(batches) > 1:
batch = self.model.batch_type.concatenate(batches)
batch = Batch.concatenate(batches)
else:
batch = batches[0]

18
server/text_generation/utils.py
View File

@ -58,7 +58,8 @@ class NextTokenChooser:
class StoppingCriteria:
def __init__(self, max_new_tokens=20):
def __init__(self, eos_token_id, max_new_tokens=20):
self.eos_token_id = eos_token_id
self.max_new_tokens = max_new_tokens
self.current_tokens = 0
@ -66,6 +67,8 @@ class StoppingCriteria:
self.current_tokens += 1
if self.current_tokens >= self.max_new_tokens:
return True
if self.eos_token_id is not None and all_ids[-1] == self.eos_token_id:
return True
return False
@ -124,11 +127,18 @@ def download_weights(model_name, extension=".safetensors"):
filenames = weight_hub_files(model_name, extension)
download_function = partial(
hf_hub_download, repo_id=model_name, local_files_only=False
hf_hub_download,
repo_id=model_name,
local_files_only=False,
)
executor = ThreadPoolExecutor(max_workers=5)
futures = [executor.submit(download_function, filename=filename) for filename in filenames]
files = [file for file in tqdm(concurrent.futures.as_completed(futures), total=len(futures))]
futures = [
executor.submit(download_function, filename=filename) for filename in filenames
]
files = [
file
for file in tqdm(concurrent.futures.as_completed(futures), total=len(futures))
]
return files