from typing import Any, Callable, List, Optional, Union
import torch
import torch.nn as nn
from transformers import BloomForCausalLM, LlamaForCausalLM
from transformers.generation import GenerationConfig
from transformers.generation.stopping_criteria import StoppingCriteriaList
from transformers.tokenization_utils_base import BatchEncoding
from colossalai.shardformer import ShardConfig, ShardFormer
from colossalai.shardformer.policies.auto_policy import get_autopolicy
from .batch_infer_state import BatchInferState
from .kvcache_manager import MemoryManager
# from dynamic_batching.infer_batch import InferBatch
DP_AXIS, PP_AXIS, TP_AXIS = 0, 1, 2
_supported_models = [
"LlamaForCausalLM",
"LlamaModel",
"BloomForCausalLM",
"ChatGLMModel",
"ChatGLMForConditionalGeneration",
"LlamaGPTQForCausalLM",
"BloomGPTQForCausalLM",
]
class TPInferEngine:
"""Engine class for tensor parallel inference.
Args:
model (Module): original model, e.g. huggingface CausalLM
shard_config (ShardConfig): The config for sharding original model
max_batch_size (int): maximum batch size
max_input_len (int): maximum input length of sequence
max_output_len (int): maximum output length of output tokens
dtype (torch.dtype): datatype used to init KV cache space
device (str): device the KV cache of engine to be initialized on
Examples:
>>> # define model and shard config for your inference
>>> model = ...
>>> generate_kwargs = ...
>>> shard_config = ShardConfig(enable_tensor_parallelism=True, extra_kwargs={"inference_only": True})
>>> infer_engine = TPInferEngine(model, shard_config, MAX_BATCH_SIZE, MAX_INPUT_LEN, MAX_OUTPUT_LEN)
>>> outputs = infer_engine.generate(input_ids, **generate_kwargs)
"""
def __init__(
self,
model: nn.Module,
shard_config: ShardConfig,
max_batch_size: int,
max_input_len: int,
max_output_len: int,
dtype: torch.dtype = torch.float16,
device: str = "cuda",
) -> None:
self.max_batch_size = max_batch_size
self.max_input_len = max_input_len
self.max_output_len = max_output_len
self.max_total_token_num = self.max_batch_size * (self.max_input_len + self.max_output_len)
# Constraints relatable with specs of devices and model
# This may change into an optional arg in the future
assert self.max_batch_size <= 64, "Max batch size exceeds the constraint"
assert self.max_input_len + self.max_output_len <= 4096, "Max length exceeds the constraint"
self.dtype = dtype
self.head_dim = model.config.hidden_size // model.config.num_attention_heads
self.head_num = model.config.num_attention_heads
num_hidden_layers = (
model.config.num_hidden_layers if hasattr(model.config, "num_hidden_layers") else model.config.num_layers
)
self.layer_num = num_hidden_layers
self.multi_query_group_num = model.config.num_attention_heads
# default to attention_heads
if hasattr(model.config, "multi_query_attention"):
self.multi_query_attention = getattr(model.config, "multi_query_attention")
if hasattr(model.config, "multi_query_group_num"):
self.multi_query_group_num = getattr(model.config, "multi_query_group_num")
if hasattr(model.config, "num_key_value_heads"):
self.multi_query_group_num = getattr(model.config, "num_key_value_heads")
self.tp_size = -1 # to be set with given shard config in self.prepare_shard_config
self.cache_manager = None
self.max_dq_buffer_size = 1
self.max_inner_outer_dim = 1
self.gptq_temp_state_buffer = None
self.gptq_temp_dq_buffer = None
self.bits = -1
self.use_act_order = False
self.shard_config = shard_config
self.model = None
self.cache = {}
# optimize the original model by sharding with ShardFormer
self._optimize_model(model=model.to(device))
def _init_manager(self) -> None:
assert self.tp_size >= 1, "TP size not initialized without providing a valid ShardConfig"
assert self.head_num % self.tp_size == 0, f"Cannot shard {self.head_num} heads with tp size {self.tp_size}"
self.head_num //= self.tp_size # update sharded number of heads
if hasattr(self, "multi_query_attention"):
# NOTE the logic of MQA tensor parallelism should be specified.
assert (
self.multi_query_group_num % self.tp_size == 0
), f"Cannot shard {self.multi_query_group_num} query groups with tp size {self.tp_size}"
self.cache_manager = MemoryManager(
self.max_total_token_num,
self.dtype,
self.multi_query_group_num // self.tp_size,
self.head_dim,
self.layer_num,
)
else:
self.cache_manager = MemoryManager(
self.max_total_token_num, self.dtype, self.head_num, self.head_dim, self.layer_num
)
def _post_init_gptq_buffer(self, model: nn.Module) -> None:
from colossalai.inference.quant.gptq.cai_gptq import CaiQuantLinear
HAS_GPTQ_CUDA = False
try:
from colossalai.kernel.op_builder.gptq import GPTQBuilder
gptq_cuda = GPTQBuilder().load()
HAS_GPTQ_CUDA = True
except ImportError:
warnings.warn("CUDA gptq is not installed")
HAS_GPTQ_CUDA = False
for name, submodule in model.named_modules():
if isinstance(submodule, CaiQuantLinear):
self.max_dq_buffer_size = max(self.max_dq_buffer_size, submodule.qweight.numel() * 8)
if self.use_act_order:
self.max_inner_outer_dim = max(
self.max_inner_outer_dim, submodule.infeatures, submodule.outfeatures
)
self.bits = submodule.bits
if not (HAS_GPTQ_CUDA and self.bits == 4):
return
max_input_len = 1
if self.use_act_order:
max_input_len = self.max_input_len
# The temp_state buffer is required to reorder X in the act-order case.
# The temp_dq buffer is required to dequantize weights when using cuBLAS, typically for the prefill.
self.gptq_temp_state_buffer = torch.zeros(
(max_input_len, self.max_inner_outer_dim), dtype=torch.float16, device=torch.cuda.current_device()
)
self.gptq_temp_dq_buffer = torch.zeros(
(1, self.max_dq_buffer_size), dtype=torch.float16, device=torch.cuda.current_device()
)
gptq_cuda.prepare_buffers(
torch.device(torch.cuda.current_device()), self.gptq_temp_state_buffer, self.gptq_temp_dq_buffer
)
# Using the default from exllama repo here.
matmul_recons_thd = 8
matmul_fused_remap = False
matmul_no_half2 = False
gptq_cuda.set_tuning_params(matmul_recons_thd, matmul_fused_remap, matmul_no_half2)
torch.cuda.empty_cache()
def _optimize_model(self, model: nn.Module) -> None:
"""
Optimize the original model by sharding with ShardFormer.
In further generation, use the sharded model instead of original model.
"""
# NOTE we will change to use an inference config later with additional attrs we want
assert self.shard_config.extra_kwargs["inference_only"] is True
shardformer = ShardFormer(shard_config=self.shard_config)
self._prepare_with_shard_config(shard_config=self.shard_config)
self._shard_model_by(shardformer, model)
def _prepare_with_shard_config(self, shard_config: Optional[ShardConfig] = None) -> ShardConfig:
"""Prepare the engine with a given ShardConfig.
Args:
shard_config (ShardConfig): shard config given to specify settings of the engine.
If not provided, a default ShardConfig with tp size 1 will be created.
"""
self.tp_size = 1
if shard_config is None:
shard_config = ShardConfig(
tensor_parallel_process_group=None,
pipeline_stage_manager=None,
enable_tensor_parallelism=False,
enable_fused_normalization=False,
enable_all_optimization=False,
enable_flash_attention=False,
enable_jit_fused=False,
extra_kwargs={"inference_only": True},
)
else:
shard_config.extra_kwargs = {"inference_only": True}
shard_config.pipeline_stage_manager = None
if shard_config.enable_tensor_parallelism:
self.tp_size = shard_config.tensor_parallel_size
self._init_manager()
return shard_config
def _shard_model_by(self, shardformer: ShardFormer, model: nn.Module) -> None:
"""Shard original model by the given ShardFormer and store the sharded model."""
assert (
self.tp_size == shardformer.shard_config.tensor_parallel_size
), "Discrepancy between the tp size of TPInferEngine and the tp size of shard config"
model_name = model.__class__.__name__
assert model_name in self.supported_models, f"Unsupported model cls {model_name} for TP inference."
if self.shard_config.extra_kwargs.get("inference_gptq", False):
model = model.model
policy = get_autopolicy(model, shard_config=self.shard_config)
self.model, _ = shardformer.optimize(model, policy)
if self.shard_config.extra_kwargs.get("inference_gptq", False):
self._post_init_gptq_buffer(self.model)
self.model = self.model.cuda()
@property
def supported_models(self) -> List[str]:
return _supported_models
def generate(self, input_tokens: Union[BatchEncoding, dict, list, torch.Tensor], **generate_kwargs) -> torch.Tensor:
"""Generate token sequence.
Args:
input_tokens: could be one of the following types
1. BatchEncoding or dict (e.g. tokenizer batch_encode)
2. list of input token ids (e.g. appended result of tokenizer encode)
3. torch.Tensor (e.g. tokenizer encode with return_tensors='pt')
Returns:
torch.Tensor: The returned sequence is given inputs + generated_tokens.
"""
if isinstance(input_tokens, torch.Tensor):
input_tokens = dict(input_ids=input_tokens, attention_mask=torch.ones_like(input_tokens, dtype=torch.bool))
for t in input_tokens:
if torch.is_tensor(input_tokens[t]):
input_tokens[t] = input_tokens[t].cuda()
if "max_new_tokens" not in generate_kwargs:
generate_kwargs.update(max_new_tokens=self.max_output_len)
return self._generate_by_set_infer_state(input_tokens, **generate_kwargs)
def prepare_batch_state(self, inputs) -> BatchInferState:
"""
Create and prepare BatchInferState used for inference during model forwrad,
by processing each sequence of the given inputs.
Args:
inputs: should be one of the following types
1. BatchEncoding or dict (e.g. tokenizer batch_encode)
2. list of input token ids (e.g. appended result of tokenizer encode)
3. torch.Tensor (e.g. tokenizer encode with return_tensors='pt')
NOTE For torch.Tensor inputs representing a batch of inputs, we are unable to retrieve
the actual length (e.g. number of tokens) of each input without attention mask
Hence, for torch.Tensor with shape [bs, l] where bs > 1, we will assume
all the inputs in the batch has the maximum length l
Returns:
BatchInferState: the states for the current batch during inference
"""
if not isinstance(inputs, (BatchEncoding, dict, list, torch.Tensor)):
raise TypeError(f"inputs type {type(inputs)} is not supported in prepare_batch_state")
input_ids_list = None
attention_mask = None
if isinstance(inputs, (BatchEncoding, dict)):
input_ids_list = inputs["input_ids"]
attention_mask = inputs["attention_mask"]
else:
input_ids_list = inputs
if isinstance(input_ids_list[0], int): # for a single input
input_ids_list = [input_ids_list]
attention_mask = [attention_mask] if attention_mask is not None else attention_mask
batch_size = len(input_ids_list)
seq_start_indexes = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
seq_lengths = torch.zeros(batch_size, dtype=torch.int32, device="cuda")
start_index = 0
max_len_in_batch = -1
if isinstance(inputs, (BatchEncoding, dict)):
for i, attn_mask in enumerate(attention_mask):
curr_seq_len = len(attn_mask)
# if isinstance(attn_mask, torch.Tensor):
# curr_seq_len = int(torch.sum(attn_mask))
# else:
# curr_seq_len = int(sum(attn_mask))
seq_lengths[i] = curr_seq_len
seq_start_indexes[i] = start_index
start_index += curr_seq_len
max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
else:
length = max(len(input_id) for input_id in input_ids_list)
for i, input_ids in enumerate(input_ids_list):
curr_seq_len = length
seq_lengths[i] = curr_seq_len
seq_start_indexes[i] = start_index
start_index += curr_seq_len
max_len_in_batch = curr_seq_len if curr_seq_len > max_len_in_batch else max_len_in_batch
block_loc = torch.empty((batch_size, self.max_input_len + self.max_output_len), dtype=torch.long, device="cuda")
batch_infer_state = BatchInferState(batch_size, max_len_in_batch)
batch_infer_state.seq_len = seq_lengths.to("cuda")
batch_infer_state.start_loc = seq_start_indexes.to("cuda")
batch_infer_state.block_loc = block_loc
batch_infer_state.decode_layer_id = 0
batch_infer_state.past_key_values_len = 0
batch_infer_state.is_context_stage = True
batch_infer_state.set_cache_manager(self.cache_manager)
return batch_infer_state
@torch.no_grad()
def _generate_by_set_infer_state(self, input_tokens, **generate_kwargs) -> torch.Tensor:
"""
Generate output tokens by setting BatchInferState as an attribute to the model and calling model.generate
Args:
inputs: should be one of the following types
1. BatchEncoding or dict (e.g. tokenizer batch_encode)
2. list of input token ids (e.g. appended result of tokenizer encode)
3. torch.Tensor (e.g. tokenizer encode with return_tensors='pt')
"""
# for testing, always use sharded model
assert self.model is not None, "sharded model does not exist"
batch_infer_state = self.prepare_batch_state(input_tokens)
assert batch_infer_state.max_len_in_batch <= self.max_input_len, "max length in batch exceeds limit"
# set BatchInferState for the current batch as attr to model
# NOTE this is not a preferable way to pass BatchInferState during inference
# we might want to rewrite generate function (e.g. _generate_by_pass_infer_state)
# and pass BatchInferState via model forward
model = self.model
if isinstance(model, LlamaForCausalLM):
model = self.model.model
elif isinstance(model, BloomForCausalLM):
model = self.model.transformer
setattr(model, "infer_state", batch_infer_state)
outputs = self.model.generate(**input_tokens, **generate_kwargs, early_stopping=False)
# NOTE In future development, we're going to let the scheduler to handle the cache,
# instead of freeing space explicitly at the end of generation
self.cache_manager.free_all()
return outputs
# TODO might want to implement the func that generates output tokens by passing BatchInferState
# as an arg into model.forward.
# It requires rewriting model generate and replacing model forward.
@torch.no_grad()
def _generate_by_pass_infer_state(
self,
input_tokens,
max_out_length: int,
generation_config: Optional[GenerationConfig] = None,
stopping_criteria: Optional[StoppingCriteriaList] = None,
prepare_inputs_fn: Optional[Callable[[torch.Tensor, Any], dict]] = None,
**model_kwargs,
) -> torch.Tensor:
raise NotImplementedError("generate by passing BatchInferState is not implemented.")
# might want to use in rewritten generate method: use after model.forward
# BatchInferState is created and kept during generation
# after each iter of model forward, we should update BatchInferState
def _update_batch_state(self, infer_state: Optional[BatchInferState]) -> None:
batch_size = infer_state.batch_size
device = infer_state.start_loc.device
infer_state.start_loc = infer_state.start_loc + torch.arange(0, batch_size, dtype=torch.int32, device=device)
infer_state.seq_len += 1
@torch.no_grad()
def forward(self, batch_id, is_prefill):
"""
Forward is used in Dynamic Batching Manager
"""
batch = self.cache.pop(batch_id)
if is_prefill:
input_ = torch.tensor(batch.all_input_ids).cuda()
else:
input_ = batch.input_ids.reshape(len(batch), 1)
batch_args = {
"batch_size": len(batch),
"max_len_in_batch": batch.nopad_max_len_in_batch,
"block_loc": batch.nopad_b_loc,
"start_loc": batch.nopad_b_start_loc,
"seq_len": batch.nopad_b_seq_len,
"cache_manager": batch.cache_manager,
"is_context_stage": is_prefill,
}
infer_state = BatchInferState(**batch_args)
model = self.model
if isinstance(model, LlamaForCausalLM):
model = self.model.model
elif isinstance(model, BloomForCausalLM):
model = self.model.transformer
setattr(model, "infer_state", infer_state)
output = self.model.forward(input_ids=input_)
logits = output.logits
# bsz, seq_len, vocab_size
prob_out = torch.softmax(
logits[
:,
-1,
],
dim=-1,
).squeeze(1)
# prob_out: bsz, vocab_size
predict_ids = torch.argmax(prob_out, dim=-1, keepdim=True)
prob_out = torch.log(prob_out).detach().cpu().numpy()
predict_ids = predict_ids.detach().cpu().numpy()
# [ batch_size, 1 ]
output_dict = {}
new_input_ids = []
for i, (r, all_input_ids, next_token_id, next_token_logprob) in enumerate(
zip(batch.requests, batch.all_input_ids, predict_ids, prob_out)
):
next_token_id = int(next_token_id)
next_token_logprob = next_token_logprob[next_token_id]
# all_input_ids_tensor = torch.tensor(all_input_ids, dtype=torch.long, device="cuda")
all_input_ids.append(next_token_id)
# all_input_ids_tensor = None
new_input_ids.append(next_token_id)
batch.all_input_ids[i] = all_input_ids
batch.input_lengths[i] += 1
batch.out_token_id_counts[i][next_token_id] += 1
metadata = {
"id": int(next_token_id),
"logprob": float(next_token_logprob),
}
output_dict[r["request_id"]] = (int(next_token_id), metadata)
batch.input_ids = torch.tensor(new_input_ids, dtype=torch.long).cuda()
batch.nopad_total_token_num += len(batch)
batch.nopad_max_len_in_batch += 1 # NOTE: we may repalce this
self.cache[batch.batch_id] = batch
return output_dict
@torch.no_grad()
def _prefill_batch(self, batch_id):
return self.forward(batch_id, is_prefill=True)
@torch.no_grad()
def _decode_batch(self, batch_id):
return self.forward(batch_id, is_prefill=False)
# might want to create a sequence pool
# add a single request/sequence/input text at a time and record its length
# In other words, store the actual length of input tokens representing a single input text
# E.g. "Introduce landmarks in Beijing"
# => add request
# => record token length and other necessary information to be used
# => engine hold all these necessary information until `generate` (or other name) is called,
# => put information already recorded in batchinferstate and pass it to model forward
# => clear records in engine
def add_request():
raise NotImplementedError()