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refactor

This commit is contained in:
Felix Marty 2024-06-27 13:24:58 +00:00
parent cb37c551ab
commit 770975fa81
15 changed files with 102 additions and 1483 deletions
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2
server/tests/models/test_bloom.py
View File

@ -5,7 +5,7 @@ from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models.transformers_causal_lm import CausalLMBatch
from text_generation_server.utils import weight_hub_files, download_weights
from text_generation_server.models.bloom import BloomCausalLMBatch, BLOOMSharded

5
server/tests/models/test_causal_lm.py
View File

@ -5,7 +5,10 @@ from copy import copy
from transformers import AutoTokenizer
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLM, CausalLMBatch
from text_generation_server.models.transformers_causal_lm import (
TransformersCausalLM,
CausalLMBatch,
)
@pytest.fixture(scope="session")

2
server/tests/models/test_santacoder.py
View File

@ -1,7 +1,7 @@
import pytest
from text_generation_server.pb import generate_pb2
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models.transformers_causal_lm import CausalLMBatch
from text_generation_server.models.santacoder import SantaCoder

101
server/text_generation_server/models/__init__.py
View File

@ -8,11 +8,13 @@ from transformers.models.auto import modeling_auto
from huggingface_hub import hf_hub_download, HfApi
from typing import Optional, List
from pathlib import Path
import transformers
from text_generation_server.utils.speculate import get_speculate, set_speculate
from text_generation_server.models.model import Model
from text_generation_server.models.causal_lm import CausalLM
from text_generation_server.models.causal_lm_ragged import CausalLMRagged
from text_generation_server.models.transformers_causal_lm import TransformersCausalLM
from text_generation_server.models.transformers_flash_causal_lm import (
TransformersFlashCausalLM,
)
from text_generation_server.models.flash_causal_lm import FlashCausalLM
from text_generation_server.models.bloom import BLOOMSharded
from text_generation_server.models.mpt import MPTSharded
@ -25,6 +27,8 @@ from text_generation_server.models.t5 import T5Sharded
from text_generation_server.models.gpt_neox import GPTNeoxSharded
from text_generation_server.models.phi import Phi
from text_generation_server.models.globals import USE_CUSTOM_MODELING
from text_generation_server.utils.import_utils import SYSTEM
# The flag below controls whether to allow TF32 on matmul. This flag defaults to False
@ -289,6 +293,31 @@ def get_model(
)
model_type = config_dict.get("model_type", None)
transformers_causal_lm_class = TransformersCausalLM
if (
not USE_CUSTOM_MODELING
and model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
):
logger.info(
"TGI's flash enabled models could either not be loaded or are disabled, using Transformers fallback."
)
transformers_model_class = getattr(
transformers, modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES[model_type]
)
if (
transformers_model_class._supports_flash_attn_2
and transformers_model_class._supports_cache_class
):
logger.info(
f"Transformers' {model_type} implementation supports custom cache and flash/paged attention. Using TransformersFlashCausalLM with ragged tensors (single dimension for batch and sequence length)."
)
transformers_causal_lm_class = TransformersFlashCausalLM
else:
logger.info(
f"Transformers' {model_type} implementation supports custom cache and flash/paged attention. Using TransformersCausalLM with classic tensors with padding (two dimensions for batch size and sequence length)."
)
speculator = None
if "medusa_num_heads" in config_dict:
medusa_model_id = model_id
@ -450,7 +479,7 @@ def get_model(
or model_type == GPT2
and model_id.startswith("bigcode/")
):
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashSantacoderSharded(
model_id,
revision,
@ -492,7 +521,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
elif model_type == GPT2:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
try:
return FlashGPT2(
model_id,
@ -505,7 +534,8 @@ def get_model(
except RuntimeError as e:
# Lots of legacy models with various weight names.
logger.warning(f"Couldn't load flash gpt2 variant: {e}")
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -516,7 +546,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded GPT-2"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -525,7 +555,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
elif model_type == GPT_NEOX:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashNeoXSharded(
model_id,
revision,
@ -544,7 +574,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -554,7 +584,7 @@ def get_model(
)
elif model_type == PHI:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashPhi(
model_id,
revision,
@ -564,7 +594,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -574,7 +604,7 @@ def get_model(
)
elif model_type == "phi-msft":
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
raise NotImplementedError(
"Legacy phi-msft is not supported with Flash Attention"
)
@ -589,7 +619,7 @@ def get_model(
)
elif model_type == LLAMA or model_type == BAICHUAN or model_type == PHI3:
if FLASH_ATTENTION and False:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashLlama(
model_id,
revision,
@ -602,8 +632,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Llama"))
else:
logger.info("LOADING CAUSALLM!!!!!!!!!!!!!!!!!!")
return CausalLMRagged(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -612,7 +641,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == GEMMA:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashGemma(
model_id,
revision,
@ -624,7 +653,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Gemma"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -634,7 +663,7 @@ def get_model(
)
if model_type == COHERE:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashCohere(
model_id,
revision,
@ -646,7 +675,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Cohere"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -656,7 +685,7 @@ def get_model(
)
if model_type == DBRX:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashDbrx(
model_id,
revision,
@ -668,7 +697,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded DBRX"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -679,7 +708,7 @@ def get_model(
if model_type in ["RefinedWeb", "RefinedWebModel", FALCON]:
if sharded:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
if config_dict.get("alibi", False):
raise NotImplementedError("sharded is not supported for this model")
return FlashRWSharded(
@ -712,7 +741,7 @@ def get_model(
)
if model_type == MISTRAL:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashMistral(
model_id,
revision,
@ -724,7 +753,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mistral"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -734,7 +763,7 @@ def get_model(
)
if model_type == MIXTRAL:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashMixtral(
model_id,
revision,
@ -746,7 +775,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Mixtral"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -756,7 +785,7 @@ def get_model(
)
if model_type == STARCODER2:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashStarcoder2(
model_id,
revision,
@ -769,7 +798,7 @@ def get_model(
FLASH_ATT_ERROR_MESSAGE.format("Sharded Starcoder2")
)
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -779,7 +808,7 @@ def get_model(
)
if model_type == QWEN2:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return FlashQwen2(
model_id,
revision,
@ -790,7 +819,7 @@ def get_model(
elif sharded:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Sharded Qwen2"))
else:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -819,7 +848,7 @@ def get_model(
trust_remote_code=trust_remote_code,
)
if model_type == IDEFICS:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return IDEFICSSharded(
model_id,
revision,
@ -831,7 +860,7 @@ def get_model(
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == IDEFICS2:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return Idefics2(
model_id,
revision,
@ -843,7 +872,7 @@ def get_model(
else:
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == "paligemma":
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return PaliGemma(
model_id,
revision,
@ -856,7 +885,7 @@ def get_model(
raise NotImplementedError(FLASH_ATT_ERROR_MESSAGE.format("Idefics"))
if model_type == LLAVA_NEXT:
if FLASH_ATTENTION:
if FLASH_ATTENTION and USE_CUSTOM_MODELING:
return LlavaNext(
model_id,
revision,
@ -883,7 +912,7 @@ def get_model(
elif quantize == "exl2":
raise NotImplementedError("exl2 quantization is not supported for AutoModel")
if model_type in modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,
@ -904,7 +933,7 @@ def get_model(
auto_map = config_dict.get("auto_map", None)
if trust_remote_code and auto_map is not None:
if "AutoModelForCausalLM" in auto_map.keys():
return CausalLM(
return transformers_causal_lm_class(
model_id,
revision,
quantize=quantize,

8
server/text_generation_server/models/bloom.py
View File

@ -12,8 +12,8 @@ from transformers import (
from text_generation_server.models.custom_modeling.bloom_modeling import (
BloomForCausalLM,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models import TransformersCausalLM
from text_generation_server.models.transformers_causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import (
initialize_torch_distributed,
@ -36,7 +36,7 @@ class BloomCausalLMBatch(CausalLMBatch):
return batch
class BLOOMSharded(CausalLM):
class BLOOMSharded(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -89,7 +89,7 @@ class BLOOMSharded(CausalLM):
model = BloomForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

787
server/text_generation_server/models/causal_lm.py
View File

@ -1,787 +0,0 @@
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.models import Model
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.models.types import (
Batch,
Tokens,
Generation,
GeneratedText,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
tracer = trace.get_tracer(__name__)
@dataclass
class CausalLMBatch(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]
# Lengths of all generations present in the batch
input_lengths: List[int]
prefix_offsets: List[int]
read_offsets: List[int]
# Generation helpers
next_token_choosers: List[NextTokenChooser]
stopping_criterias: List[StoppingCriteria]
top_n_tokens: List[int]
top_n_tokens_tensor: torch.Tensor
# 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.CachedBatch:
return generate_pb2.CachedBatch(
id=self.batch_id,
request_ids=[r.id for r in self.requests],
size=len(self),
max_tokens=self.max_tokens,
)
@classmethod
def from_pb(
cls,
pb: generate_pb2.Batch,
tokenizer: PreTrainedTokenizerBase,
dtype: torch.dtype,
device: torch.device,
) -> "CausalLMBatch":
inputs = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
prefix_offsets = []
read_offsets = []
requests_idx_mapping = {}
# Parse batch
max_truncation = 0
padding_right_offset = 0
max_decode_tokens = 0
for i, r in enumerate(pb.requests):
requests_idx_mapping[r.id] = i
inputs.append(concat_text_chunks(r.input_chunks.chunks))
next_token_choosers.append(
NextTokenChooser.from_pb(r.parameters, device, tokenizer)
)
stopping_criteria = StoppingCriteria.from_pb(
r.stopping_parameters, tokenizer
)
stopping_criterias.append(stopping_criteria)
top_n_tokens.append(r.top_n_tokens)
max_truncation = max(max_truncation, r.truncate)
max_decode_tokens += stopping_criteria.max_new_tokens
padding_right_offset = max(
padding_right_offset, stopping_criteria.max_new_tokens
)
tokenized_inputs = tokenizer(
inputs,
return_tensors="pt",
padding=True,
return_token_type_ids=False,
truncation=True,
max_length=max_truncation,
).to(device)
for _ in pb.requests:
input_len = tokenized_inputs["input_ids"].shape[1]
prefix_offsets.append(input_len - 5)
read_offsets.append(input_len)
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"]
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)
top_n_tokens_tensor = torch.tensor(
top_n_tokens, device=device, dtype=torch.int64
)
max_tokens = len(inputs) * (max_input_length + max_decode_tokens)
return cls(
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_lengths=input_lengths.tolist(),
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
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, request_ids: List[int]) -> Optional["CausalLMBatch"]:
if len(request_ids) == 0:
raise ValueError("Batch must have at least one request")
if len(request_ids) == len(self):
return self
keep_indices = []
# New values after filtering
requests_idx_mapping = {}
requests = []
input_lengths = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
max_input_length = 0
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
total_remaining_decode_tokens = 0
new_padding_right_offset = 0
for i, request_id in enumerate(request_ids):
idx = self.requests_idx_mapping[request_id]
requests_idx_mapping[request_id] = i
keep_indices.append(idx)
requests.append(self.requests[idx])
prefix_offsets.append(self.prefix_offsets[idx])
read_offsets.append(self.read_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)
top_n_tokens.append(self.top_n_tokens[idx])
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
top_n_tokens_tensor = self.top_n_tokens_tensor[keep_indices]
max_tokens = len(request_ids) * 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.prefix_offsets = prefix_offsets
self.read_offsets = read_offsets
self.next_token_choosers = next_token_choosers
self.stopping_criterias = stopping_criterias
self.top_n_tokens = top_n_tokens
self.top_n_tokens_tensor = top_n_tokens_tensor
self.max_input_length = max_input_length
self.padding_right_offset = new_padding_right_offset
self.max_tokens = max_tokens
return self
@classmethod
@tracer.start_as_current_span("concatenate")
def concatenate(cls, batches: List["CausalLMBatch"]) -> "CausalLMBatch":
# 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 = []
prefix_offsets = []
read_offsets = []
all_input_ids = []
next_token_choosers = []
stopping_criterias = []
top_n_tokens = []
max_tokens = 0
# Batch tensors
input_ids = None
attention_mask = None
position_ids = None
past_key_values = []
top_n_tokens_tensor = None
# 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)
prefix_offsets.extend(batch.prefix_offsets)
read_offsets.extend(batch.read_offsets)
all_input_ids.extend(batch.all_input_ids)
next_token_choosers.extend(batch.next_token_choosers)
stopping_criterias.extend(batch.stopping_criterias)
top_n_tokens.extend(batch.top_n_tokens)
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),
)
if top_n_tokens_tensor is None:
top_n_tokens_tensor = batches[0].top_n_tokens_tensor.new_zeros(
total_batch_size,
)
top_n_tokens_tensor[start_index:end_index] = batch.top_n_tokens_tensor
# 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,
prefix_offsets=prefix_offsets,
read_offsets=read_offsets,
next_token_choosers=next_token_choosers,
stopping_criterias=stopping_criterias,
top_n_tokens=top_n_tokens,
top_n_tokens_tensor=top_n_tokens_tensor,
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 __len__(self):
return len(self.requests)
class CausalLM(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if speculator:
raise RuntimeError("Speculator decoding is not enabled for AutoModel")
if torch.cuda.is_available():
device = torch.device("cuda")
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map=(
"auto"
if torch.cuda.is_available() and torch.cuda.device_count() > 1
else None
),
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
)
if (
torch.cuda.is_available()
and torch.cuda.device_count() == 1
and quantize != "bitsandbytes"
):
model = model.cuda()
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super(CausalLM, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@property
def batch_type(self) -> Type[CausalLMBatch]:
return CausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
def forward(
self, input_ids, attention_mask, position_ids, past_key_values: Optional = None
) -> Tuple[
torch.Tensor, Optional[torch.Tensor], List[Tuple[torch.Tensor, torch.Tensor]]
]:
# Model Forward
kwargs = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"past_key_values": past_key_values,
"use_cache": True,
"return_dict": True,
}
if self.has_position_ids:
kwargs["position_ids"] = position_ids
outputs = self.model.forward(**kwargs)
if isinstance(outputs, tuple):
outputs, speculative_logits = outputs
else:
speculative_logits = None
return outputs.logits, speculative_logits, outputs.past_key_values
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: CausalLMBatch
) -> Tuple[List[Generation], Optional[CausalLMBatch], Tuple[int, int]]:
start = time.time_ns()
# slice the attention mask to the correct shape
attention_mask = batch.attention_mask[:, : -batch.padding_right_offset]
logits, speculative_logits, past = self.forward(
batch.input_ids,
attention_mask,
batch.position_ids,
batch.past_key_values,
)
# Results
generations: List[Generation] = []
stopped = True
# Speculation is not active for causal
accepted_ids = torch.ones_like(batch.input_ids)[:, 0]
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
batch.top_n_tokens,
batch.top_n_tokens_tensor,
torch.log_softmax(logits[:, -1], -1),
accepted_ids,
)
start_decode = time.time_ns()
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
logits,
batch.next_token_choosers,
batch.stopping_criterias,
batch.all_input_ids,
batch.top_n_tokens,
batch_top_token_ids,
batch_top_token_logprobs,
)
# For each member of the batch
for i, (
request,
input_length,
prefix_offset,
read_offset,
logits,
next_token_chooser,
stopping_criteria,
all_input_ids,
top_n_tokens,
top_token_ids,
top_token_logprobs,
) in enumerate(iterator):
# Select next token
next_token_id, logprobs = next_token_chooser(
all_input_ids.view(1, -1), logits[-1:, :]
)
# 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, prefix_offset, read_offset = self.decode_token(
all_input_ids[:, 0], prefix_offset, read_offset
)
# Evaluate stopping criteria
stop, reason = stopping_criteria(
next_token_id_squeezed,
next_token_text,
)
if not stop:
stopped = False
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text, _, _ = self.decode_token(
all_input_ids[:, 0],
prefix_offset=len(all_input_ids)
- stopping_criteria.current_tokens
- 1,
read_offset=len(all_input_ids)
- stopping_criteria.current_tokens,
skip_special_tokens=True,
)
# Get seed
if isinstance(next_token_chooser.choice, Sampling):
seed = next_token_chooser.choice.seed
else:
seed = None
generated_text = GeneratedText(
output_text, stopping_criteria.current_tokens, reason, seed
)
else:
generated_text = None
# Prefill
if stopping_criteria.current_tokens == 1 and request.prefill_logprobs:
# Remove generated token to only have prefill and add nan for first prompt token
prefill_logprobs = [float("nan")] + torch.log_softmax(
logits, -1
).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 = Tokens(
prefill_token_ids,
prefill_logprobs,
prefill_texts,
is_special=[],
)
else:
prefill_tokens = None
if top_n_tokens > 0:
all_top_tokens = []
for top_token_ids, top_token_logprobs in zip(
top_token_ids, top_token_logprobs
):
toptoken_texts = self.tokenizer.batch_decode(
top_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
special_toptokens = [
token_id in self.all_special_ids
for token_id in top_token_ids
]
top_tokens = Tokens(
top_token_ids,
top_token_logprobs,
toptoken_texts,
special_toptokens,
)
all_top_tokens.append(top_tokens)
top_tokens = all_top_tokens
else:
top_tokens = None
generation = Generation(
request.id,
prefill_tokens,
Tokens(
[next_token_id_squeezed],
[next_token_logprob],
[next_token_text],
[next_token_id_squeezed.item() in self.all_special_ids],
),
generated_text,
top_tokens,
)
generations.append(generation)
# Update values
batch.next_token_choosers[i] = batch.next_token_choosers[i].advance_grammar(
next_token_id_squeezed.item()
)
batch.input_ids[i, 0] = next_token_id
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] = new_input_length
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_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:
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, None, (forward_ns, decode_ns)
# 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
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)

630
server/text_generation_server/models/causal_lm_ragged.py
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@ -1,630 +0,0 @@
import torch
import time
from dataclasses import dataclass
from opentelemetry import trace
from transformers import AutoTokenizer, AutoModelForCausalLM, PreTrainedTokenizerBase
from typing import Optional, Tuple, List, Type, Dict
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.models import Model
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.tokens import batch_top_tokens
from text_generation_server.models.types import (
Batch,
Tokens,
Generation,
GeneratedText,
)
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
from text_generation_server.models.flash_causal_lm import FlashCausalLMBatch
from text_generation_server.utils.import_utils import (
empty_cache,
synchronize,
get_free_memory,
)
from text_generation_server.utils.speculate import get_speculate
from text_generation_server.utils.dist import MEMORY_FRACTION
tracer = trace.get_tracer(__name__)
from transformers.cache_utils import PagedCache
from loguru import logger
# Why define it here?
BLOCK_SIZE: int = 16
class CausalLMRagged(Model):
def __init__(
self,
model_id: str,
revision: Optional[str] = None,
quantize: Optional[str] = None,
speculator: Optional[str] = None,
dtype: Optional[torch.dtype] = None,
trust_remote_code: bool = False,
):
if speculator:
raise RuntimeError("Speculator decoding is not enabled for AutoModel")
if torch.cuda.is_available():
device = torch.device("cuda:0") # TODO felix: fix support for accelerate
dtype = torch.float16 if dtype is None else dtype
else:
if quantize:
raise ValueError("quantization is not available on CPU")
device = torch.device("cpu")
dtype = torch.float32 if dtype is None else dtype
tokenizer = AutoTokenizer.from_pretrained(
model_id,
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
model = AutoModelForCausalLM.from_pretrained(
model_id,
revision=revision,
torch_dtype=dtype,
device_map=None,
load_in_8bit=quantize == "bitsandbytes",
trust_remote_code=trust_remote_code,
attn_implementation="flash_attention_2",
)
if (
torch.cuda.is_available()
and torch.cuda.device_count() == 1
and quantize != "bitsandbytes"
):
model = model.cuda()
self.kv_cache = []
self.num_layers = len(model.model.layers)
self.num_kv_heads = model.config.num_key_value_heads
self.head_size = model.config.hidden_size // model.config.num_attention_heads
if tokenizer.pad_token_id is None:
if model.config.pad_token_id is not None:
tokenizer.pad_token_id = model.config.pad_token_id
elif model.config.eos_token_id is not None:
tokenizer.pad_token_id = model.config.eos_token_id
elif tokenizer.eos_token_id is not None:
tokenizer.pad_token_id = tokenizer.eos_token_id
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=False,
dtype=dtype,
device=device,
)
def warmup(self, batch: FlashCausalLMBatch):
# The warmup batch is the biggest batch we could ever receive
empty_cache()
try:
self.init_kv_cache(
batch.num_blocks,
self.num_layers,
self.num_kv_heads,
self.head_size,
self.dtype,
self.device,
)
max_bt = batch.max_blocks
max_s = max_bt * BLOCK_SIZE
_, batch, _ = self.generate_token(batch)
except torch.cuda.OutOfMemoryError as e:
raise RuntimeError(
f"Not enough memory to handle {len(batch.input_ids)} prefill tokens. "
f"You need to decrease `--max-batch-prefill-tokens`"
) from e
synchronize(self.device)
# Inspired by the original implementation in [vllm](https://github.com/vllm-project/vllm)
# Calculate the number of blocks that can be allocated with the free memory
dtype_size = torch.tensor([], dtype=self.dtype).element_size()
cache_block_size = BLOCK_SIZE * self.num_kv_heads * self.head_size
total_cache_size = self.num_layers * cache_block_size * 2 * dtype_size
free_memory = get_free_memory(self.device, MEMORY_FRACTION)
batch_num_blocks = batch.num_blocks if batch is not None else 0
num_blocks = (
# Leave 5% for some wiggle room
int((free_memory * 0.95) // total_cache_size)
# Add batch.num_blocks as we allocated it above, so it is included in the peak memory.
+ batch_num_blocks
)
del batch
self.init_kv_cache(
num_blocks,
self.num_layers,
self.num_kv_heads,
self.head_size,
self.dtype,
self.device,
)
return int(num_blocks * BLOCK_SIZE)
def init_kv_cache(
self,
num_blocks: int,
num_layers: int,
num_heads: int,
head_size: int,
dtype: torch.dtype,
device: torch.device,
):
self.kv_cache = []
empty_cache()
element_size = torch.tensor([], dtype=dtype).element_size()
if SYSTEM == "ipex" and device.type == "xpu":
raise ValueError("Untested. Please open an issue")
else:
x = BLOCK_SIZE // element_size
if SYSTEM == "ipex" and device == torch.device("cpu"):
raise ValueError("Untested. Please open an issue")
self.kv_cache = [
(
torch.empty(
(num_blocks, num_heads, head_size // x, BLOCK_SIZE, x),
dtype=dtype,
device=device,
),
torch.empty(
(num_blocks, num_heads, head_size, BLOCK_SIZE),
dtype=dtype,
device=device,
),
)
for _ in range(num_layers)
]
@property
def batch_type(self) -> Type[FlashCausalLMBatch]:
return FlashCausalLMBatch
def decode(self, generated_ids: List[int]) -> str:
return self.tokenizer.decode(
generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
def forward(
self, batch: FlashCausalLMBatch
) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
# NOTE: adapter_data: not supported
input_ids = batch.input_ids
position_ids = batch.position_ids
cu_seqlen_prefill = batch.cu_seqlen_prefill
kv_cache = self.kv_cache
block_tables = batch.block_tables_tensor
slots = batch.slots[batch.slot_indices]
input_lengths = batch.input_lengths_tensor
max_s = batch.max_seqlen
lm_head_indices = batch.prefill_head_indices
# TODO felix: support window attention
# if cu_seqlen_prefill is None and self.max_past() is not None:
# # In decode, not prefill, we're actually overwriting the KV-cache
# # in a circular buffer mode.
# # This makes sure the max_s for the decode pass is correct.
# max_s = min(self.max_past(), max_s)
bs = input_ids.shape[0]
logits = self.model.forward(
input_ids=input_ids,
position_ids=position_ids,
past_key_values=PagedCache(),
cu_seqlen_prefill=cu_seqlen_prefill,
kv_cache=kv_cache,
block_tables=block_tables,
slots=slots,
input_lengths=input_lengths,
max_s=max_s,
prefill_cache_indices=batch.prefill_cache_indices,
lm_head_indices=lm_head_indices,
cache_position=False,
return_dict=False,
)[0]
if lm_head_indices is not None:
logits = logits[lm_head_indices]
if batch.prefill_cache_indices is not None:
batch.prefill_cache_indices = None
speculative_logits = None
return logits, speculative_logits
@tracer.start_as_current_span("generate_token")
def generate_token(
self, batch: FlashCausalLMBatch
) -> Tuple[List[Generation], Optional[FlashCausalLMBatch], Tuple[int, int]]:
start = time.time_ns()
prefill = batch.cu_seqlen_prefill is not None
prefill_logprobs = batch.prefill_next_token_indices is not None
# Update adapter indices for speculative tokens (if present)
# adapter_meta = batch.adapter_meta
# if batch.speculative_ids is not None:
# B, speculative_length = batch.speculative_ids.shape
# new_length = speculative_length + 1
# adapter_indices = (
# adapter_meta.adapter_indices.unsqueeze(-1)
# .expand(B, new_length)
# .reshape(-1)
# )
# adapter_segments = adapter_meta.adapter_segments * new_length
# adapter_meta = AdapterBatchMetadata(
# adapter_indices=adapter_indices,
# adapter_set=adapter_meta.adapter_set,
# adapter_segments=adapter_segments,
# segment_indices=adapter_meta.segment_indices,
# )
# Assign pointers to adapter weights
# TODO(travis): don't update this if indices haven't changed
# adapter_data = AdapterBatchData.from_meta(
# adapter_meta,
# self.layer_to_adapter_weights,
# prefill,
# batch.prefill_head_indices,
# )
logger.info(f"batch.input_ids {batch.input_ids}")
out, speculative_logits = self.forward(batch)
logger.info(f"out {out.shape}")
logger.info(f"speculative_logits {speculative_logits}")
if prefill:
next_token_logits = (
out[batch.prefill_next_token_indices] if prefill_logprobs else out
)
if speculative_logits is not None:
speculative_logits = (
speculative_logits[batch.prefill_next_token_indices]
if prefill_logprobs
else speculative_logits
)
# next_adapter_indices = batch.adapter_meta.adapter_indices.new_empty(
# len(batch)
# )
else:
next_token_logits = out
# next_adapter_indices = batch.adapter_meta.adapter_indices
speculate = get_speculate()
(
next_input_ids,
next_token_logprobs,
logprobs,
accepted_ids,
speculative_ids,
) = batch.next_token_chooser(
batch.all_input_ids_tensor[:, : batch.max_seqlen],
next_token_logits,
speculate,
batch.speculative_ids,
speculative_logits,
)
batch_top_token_ids, batch_top_token_logprobs = batch_top_tokens(
batch.top_n_tokens, batch.top_n_tokens_tensor, logprobs, accepted_ids
)
if prefill:
if len(batch) > 1 and prefill_logprobs:
# We create the prefill_tokens_indices tensor that will be used to gather prefill logprobs
# When batch == 1, we will just use the batch.input_ids values directly
prefill_tokens_indices = batch.input_ids.new_zeros(len(out))
next_position_ids = batch.position_ids.new_empty(len(batch))
batch.slot_indices = batch.slot_indices[batch.cu_seqlen_prefill[1:] - 1]
# We do not need cu_seqlen_prefill anymore
batch.cu_seqlen_prefill = None
else:
prefill_logprobs = None
next_position_ids = batch.position_ids
# Cumulative length
cumulative_length = 0
# Results
generations: List[Generation] = []
stopped = True
# Zipped iterator
iterator = zip(batch.input_lengths, batch.all_input_ids, accepted_ids)
# We do two for loops as the first one can run completely asynchronously from the GPU while for the second
# one, we need to first do a GPU <-> CPU sync
# It is faster if we delay this sync for the maximum amount of time
# For each member of the batch
index = 0
for i, (input_length, all_input_ids, n_accepted_ids) in enumerate(iterator):
# Indexing metadata
start_index = cumulative_length
end_index = cumulative_length + input_length
if prefill:
# Indexing metadata
out_start_index = batch.prefill_cu_outlens[i]
out_end_index = batch.prefill_cu_outlens[i + 1]
out_length = out_end_index - out_start_index
# Initialize position_ids
# In decode, we do not need this as we can just increment position ids
next_position_ids[i] = batch.position_ids[end_index - 1]
# Initialize adapter indices
# In decode, we only have one token per row in the batch, so grab last index
# next_adapter_indices[i] = batch.adapter_meta.adapter_indices[
# end_index - 1
# ]
# Used to gather prefill logprobs
# Copy batch.input_ids to prefill_token_indices
if prefill_logprobs:
if len(batch) > 1:
prefill_tokens_indices[out_start_index : out_end_index - 1] = (
batch.input_ids[start_index + 1 : start_index + out_length]
)
else:
# Set prefill_tokens_indices to the correct slice
prefill_tokens_indices = batch.input_ids[
start_index + 1 : start_index + out_length
]
for j in range(n_accepted_ids):
batch.all_input_ids_tensor[i, input_length + j] = next_input_ids[index]
index += 1
cumulative_length += input_length
logger.info(f"batch.input_lengths_tensor {batch.input_lengths_tensor}")
logger.info(f"accepted_ids {accepted_ids}")
logger.info(f"batch.all_input_ids {batch.all_input_ids}")
# Update values
batch.input_ids = next_input_ids[accepted_ids.cumsum(dim=-1) - 1]
batch.speculative_ids = speculative_ids
batch.position_ids = next_position_ids + accepted_ids
batch.input_lengths_tensor += accepted_ids
batch.slot_indices += accepted_ids
# batch.adapter_meta.adapter_indices = None
# if prefill:
# # adjust segment lengths to account for all request lengths being 1 during decoding
# adapter_segments, _ = find_segments(batch.adapter_meta.adapter_indices)
# batch.adapter_meta.adapter_segments = torch.tensor(
# adapter_segments,
# dtype=torch.int32,
# device=batch.adapter_meta.adapter_segments.device,
# )
if prefill and prefill_logprobs:
# Get prefill logprobs
prefill_logprobs_tensor = torch.log_softmax(out, -1)
prefill_logprobs = torch.gather(
prefill_logprobs_tensor, 1, prefill_tokens_indices.view(-1, 1)
)
# GPU <-> CPU sync
prefill_logprobs = prefill_logprobs.view(-1).tolist()
# GPU <-> CPU sync
next_token_logprobs = next_token_logprobs.tolist()
next_token_ids = next_input_ids.tolist()
accepted_ids = accepted_ids.tolist()
start_decode = time.time_ns()
# Zipped iterator
iterator = zip(
batch.requests,
batch.input_lengths,
batch.prefix_offsets,
batch.read_offsets,
batch.stopping_criterias,
batch.all_input_ids,
batch.next_token_chooser.do_sample,
batch.next_token_chooser.seeds,
batch.top_n_tokens,
accepted_ids,
batch_top_token_ids,
batch_top_token_logprobs,
)
# For each member of the batch
index = 0
for i, (
request,
input_length,
prefix_offset,
read_offset,
stopping_criteria,
all_input_ids,
do_sample,
seed,
top_n_tokens,
n_accepted_ids,
top_token_ids,
top_token_logprobs,
) in enumerate(iterator):
# Append next token to all tokens
next_token_texts = []
left = 0
if n_accepted_ids > 1:
if RANK == 0:
logger.debug(f"Speculated ids {n_accepted_ids - 1}")
current_stopped = False
for j in range(index, index + n_accepted_ids):
# Generated token
next_token_id = next_token_ids[j]
all_input_ids.append(next_token_id)
next_token_text, prefix_offset, read_offset = self.decode_token(
all_input_ids,
prefix_offset,
read_offset,
)
next_token_texts.append(next_token_text)
stop, reason = stopping_criteria(
next_token_id,
next_token_text,
)
if stop:
left = index + n_accepted_ids - j - 1
current_stopped = True
break
else:
current_stopped = False
stopped = stopped and current_stopped
_next_token_ids = next_token_ids[index : index + n_accepted_ids - left]
_next_token_logprobs = next_token_logprobs[
index : index + n_accepted_ids - left
]
index += n_accepted_ids
# Shard generations
# All generations will be appended in the rust sharded client
if i % self.world_size == self.rank:
if stop:
# Decode generated tokens
output_text, _, _ = self.decode_token(
all_input_ids,
prefix_offset=len(all_input_ids)
- stopping_criteria.current_tokens
- 1,
read_offset=len(all_input_ids)
- stopping_criteria.current_tokens,
skip_special_tokens=True,
)
generated_text = GeneratedText(
output_text,
stopping_criteria.current_tokens,
reason,
seed if do_sample else None,
)
else:
generated_text = None
# Prefill
if prefill and request.prefill_logprobs:
out_start_index = batch.prefill_cu_outlens[i]
out_end_index = batch.prefill_cu_outlens[i + 1]
# Remove generated token to only have prefill and add nan for first prompt token
request_prefill_logprobs = [float("nan")] + prefill_logprobs[
out_start_index : out_end_index - 1
]
prefill_token_ids = all_input_ids[:-1]
prefill_texts = self.tokenizer.batch_decode(
prefill_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
prefill_tokens = Tokens(
prefill_token_ids,
request_prefill_logprobs,
prefill_texts,
is_special=[],
)
else:
prefill_tokens = None
if top_n_tokens > 0:
all_top_tokens = []
for top_token_ids, top_token_logprobs in zip(
top_token_ids, top_token_logprobs
):
toptoken_texts = self.tokenizer.batch_decode(
top_token_ids,
clean_up_tokenization_spaces=False,
skip_special_tokens=False,
)
special_toptokens = [
token_id in self.all_special_ids
for token_id in top_token_ids
]
top_tokens = Tokens(
top_token_ids,
top_token_logprobs,
toptoken_texts,
special_toptokens,
)
all_top_tokens.append(top_tokens)
top_tokens = all_top_tokens
else:
top_tokens = None
generation = Generation(
request.id,
prefill_tokens,
Tokens(
_next_token_ids,
_next_token_logprobs,
next_token_texts,
[nid in self.all_special_ids for nid in _next_token_ids],
),
generated_text,
top_tokens,
)
generations.append(generation)
# accept each new token for this specific request since we may
# have more than one new token per request with speculative decoding
for next_token_id in _next_token_ids:
batch.next_token_chooser = (
batch.next_token_chooser.advance_grammar_single(i, next_token_id)
)
# Update values
batch.input_lengths[i] = input_length + n_accepted_ids
if batch.input_lengths[i] > batch.max_seqlen:
batch.max_seqlen = batch.input_lengths[i]
batch.prefix_offsets[i] = prefix_offset
batch.read_offsets[i] = read_offset
batch.all_input_ids[i] = all_input_ids
if stopped:
# No need to return a batch if we know that all requests stopped
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, None, (forward_ns, decode_ns)
batch.prefill_cu_outlens = None
batch.prefill_head_indices = None
batch.prefill_next_token_indices = None
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)

8
server/text_generation_server/models/galactica.py
View File

@ -9,8 +9,8 @@ from transformers import (
AutoConfig,
PreTrainedTokenizerBase,
)
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models import TransformersCausalLM
from text_generation_server.models.transformers_causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.utils import (
@ -164,7 +164,7 @@ class GalacticaCausalLMBatch(CausalLMBatch):
)
class GalacticaSharded(CausalLM):
class GalacticaSharded(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -211,7 +211,7 @@ class GalacticaSharded(CausalLM):
model = OPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

4
server/text_generation_server/models/globals.py
View File

@ -44,3 +44,7 @@ ADAPTER_TO_INDEX: Dict[str, int] = None
def set_adapter_to_index(adapter_to_index: Dict[str, int]):
global ADAPTER_TO_INDEX
ADAPTER_TO_INDEX = adapter_to_index
USE_CUSTOM_MODELING = os.getenv("USE_CUSTOM_MODELING", "true")
USE_CUSTOM_MODELING = USE_CUSTOM_MODELING == "true" or USE_CUSTOM_MODELING == "1"

6
server/text_generation_server/models/gpt_neox.py
View File

@ -7,7 +7,7 @@ from transformers import (
AutoTokenizer,
AutoConfig,
)
from text_generation_server.models import CausalLM
from text_generation_server.models import TransformersCausalLM
from text_generation_server.models.custom_modeling.neox_modeling import (
GPTNeoxForCausalLM,
)
@ -18,7 +18,7 @@ from text_generation_server.utils import (
)
class GPTNeoxSharded(CausalLM):
class GPTNeoxSharded(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -64,7 +64,7 @@ class GPTNeoxSharded(CausalLM):
model = GPTNeoxForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

8
server/text_generation_server/models/mpt.py
View File

@ -8,8 +8,8 @@ from transformers import AutoTokenizer, PretrainedConfig, PreTrainedTokenizerBas
from huggingface_hub import hf_hub_download
import json
from text_generation_server.models import CausalLM
from text_generation_server.models.causal_lm import CausalLMBatch
from text_generation_server.models import TransformersCausalLM
from text_generation_server.models.transformers_causal_lm import CausalLMBatch
from text_generation_server.pb import generate_pb2
from text_generation_server.models.custom_modeling.mpt_modeling import (
MPTForCausalLM,
@ -37,7 +37,7 @@ class MPTCausalLMBatch(CausalLMBatch):
return batch
class MPTSharded(CausalLM):
class MPTSharded(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -89,7 +89,7 @@ class MPTSharded(CausalLM):
model = MPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

6
server/text_generation_server/models/opt.py
View File

@ -8,7 +8,7 @@ from transformers import (
AutoConfig,
)
from text_generation_server.models.custom_modeling.opt_modeling import OPTForCausalLM
from text_generation_server.models import CausalLM
from text_generation_server.models import TransformersCausalLM
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
@ -16,7 +16,7 @@ from text_generation_server.utils import (
)
class OPTSharded(CausalLM):
class OPTSharded(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -62,7 +62,7 @@ class OPTSharded(CausalLM):
model = OPTForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

6
server/text_generation_server/models/phi.py
View File

@ -4,7 +4,7 @@ import torch.distributed
from transformers import AutoConfig, AutoTokenizer
from typing import Optional, List, Tuple
from text_generation_server.models import CausalLM
from text_generation_server.models import TransformersCausalLM
from text_generation_server.models.custom_modeling.phi_modeling import (
PhiConfig,
PhiForCausalLM,
@ -16,7 +16,7 @@ from text_generation_server.utils import (
)
class Phi(CausalLM):
class Phi(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -59,7 +59,7 @@ class Phi(CausalLM):
weights = Weights(filenames, device, dtype, process_group=self.process_group)
model = PhiForCausalLM(config, weights)
torch.distributed.barrier(group=self.process_group)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

6
server/text_generation_server/models/rw.py
View File

@ -3,10 +3,10 @@ import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from typing import List, Optional, Tuple
from text_generation_server.models import CausalLM
from text_generation_server.models import TransformersCausalLM
class RW(CausalLM):
class RW(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -61,7 +61,7 @@ class RW(CausalLM):
else:
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,

6
server/text_generation_server/models/santacoder.py
View File

@ -4,7 +4,7 @@ import torch.distributed
from typing import Optional, List
from transformers import AutoTokenizer, AutoModelForCausalLM
from text_generation_server.models import CausalLM
from text_generation_server.models import TransformersCausalLM
FIM_PREFIX = "<fim-prefix>"
FIM_MIDDLE = "<fim-middle>"
@ -13,7 +13,7 @@ FIM_PAD = "<fim-pad>"
EOD = "<|endoftext|>"
class SantaCoder(CausalLM):
class SantaCoder(TransformersCausalLM):
def __init__(
self,
model_id: str,
@ -61,7 +61,7 @@ class SantaCoder(CausalLM):
trust_remote_code=trust_remote_code,
)
super(CausalLM, self).__init__(
super().__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,