import torch
import torch.distributed
from transformers import AutoTokenizer, PreTrainedTokenizerBase
from typing import Optional
import os
from text_generation_server.models.custom_modeling.mamba_modeling import (
MambaConfig,
)
from loguru import logger
from text_generation_server.pb import generate_pb2
from text_generation_server.utils import (
initialize_torch_distributed,
weight_files,
Weights,
)
from text_generation_server.models.globals import CUDA_GRAPHS, MEM_POOL
import time
from text_generation_server.models.custom_modeling.mamba_modeling import (
MambaModel,
InferenceParams,
)
from text_generation_server.models import Model
from typing import Any, List, Optional, Tuple, Type, Dict
from text_generation_server.models.types import (
Batch,
Tokens,
Generation,
GeneratedText,
)
from text_generation_server.utils.chunks import concat_text_chunks
from text_generation_server.utils.tokens import batch_top_tokens, Sampling
from dataclasses import dataclass
from text_generation_server.utils import NextTokenChooser, StoppingCriteria, Sampling
def new_inference_params(
n_blocks: int,
batch_size: int,
d_inner: int,
d_conv: int,
d_state: int,
seqlen_offset: int,
dtype: torch.dtype,
device: torch.device,
):
max_seqlen = 0
conv_states = torch.zeros(
(
n_blocks,
batch_size,
d_inner,
d_conv,
),
device=device,
dtype=dtype,
)
ssm_states = torch.zeros(
(
n_blocks,
batch_size,
d_inner,
d_state,
),
device=device,
dtype=dtype,
)
inference_params = InferenceParams(
max_seqlen=max_seqlen,
max_batch_size=batch_size,
seqlen_offset=seqlen_offset,
conv_states=conv_states,
ssm_states=ssm_states,
)
return inference_params
@dataclass
class MambaBatch(Batch):
batch_id: int
requests: List[generate_pb2.Request]
requests_idx_mapping: Dict[int, int]
# Decoder values
input_ids: torch.Tensor
# 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
# Inference params
inference_params: Optional[Dict[str, Any]] = None
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,
) -> "MambaBatch":
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"]
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,
# past_input_ids=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,
)
def filter(self, request_ids: List[int]) -> Optional["MambaBatch"]:
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
indices = []
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)
indices.append(idx)
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]
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.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
# TODO
# Kept it simple by just updating the state, maybe updating the other CPU values is necessary.
self.inference_params.conv_states = self.inference_params.conv_states[
:, indices
]
self.inference_params.ssm_states = self.inference_params.ssm_states[:, indices]
return self
@classmethod
def concatenate(cls, batches: List["MambaBatch"]) -> "MambaBatch":
# 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
max_seqlen = 0
seqlen_offset = 0
(n_blocks, _, d_inner, d_conv) = batches[0].inference_params.conv_states.shape
(_, _, _, d_state) = batches[0].inference_params.ssm_states.shape
dtype = batches[0].inference_params.conv_states.dtype
device = batches[0].inference_params.conv_states.device
inference_params = new_inference_params(
n_blocks=n_blocks,
batch_size=total_batch_size,
d_state=d_state,
d_conv=d_conv,
d_inner=d_inner,
seqlen_offset=seqlen_offset,
device=device,
dtype=dtype,
)
# Batch tensors
input_ids = None
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)
# 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
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
# Add eventual padding tokens that were added while concatenating
max_tokens += batch.max_tokens + (
max_input_length - batch.max_input_length
) * len(batch)
inference_params.max_seqlen = max(
inference_params.max_seqlen, batch.inference_params.max_seqlen
)
assert batch.inference_params.seqlen_offset != 0, "Invalid seqlen offset"
inference_params.seqlen_offset = max(
inference_params.seqlen_offset, batch.inference_params.seqlen_offset
)
inference_params.conv_states[:, start_index:end_index] = (
batch.inference_params.conv_states
)
inference_params.ssm_states[:, start_index:end_index] = (
batch.inference_params.ssm_states
)
start_index = end_index
return cls(
batch_id=batches[0].batch_id,
requests=requests,
requests_idx_mapping=requests_idx_mapping,
input_ids=input_ids,
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,
inference_params=inference_params,
)
def __len__(self):
return len(self.requests)
class Mamba(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,
):
self.process_group, _rank, world_size = initialize_torch_distributed()
if world_size > 1:
raise RuntimeError("Mamba does not support Tensor Parallelism (TP)")
self.cuda_graphs = {}
if torch.cuda.is_available():
device = torch.device("cuda")
# Bf16 is important. In f16 accumulations in the matmul are causing
# differences while the server is under load.
# This is detectable by the integration load test
dtype = torch.bfloat16 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(
"EleutherAI/gpt-neox-20b",
revision=revision,
padding_side="left",
truncation_side="left",
trust_remote_code=trust_remote_code,
)
config = MambaConfig.from_pretrained(
model_id, revision=revision, trust_remote_code=trust_remote_code
)
tokenizer.bos_token_id = config.bos_token_id
tokenizer.eos_token_id = config.eos_token_id
tokenizer.pad_token = tokenizer.eos_token
config.quantize = quantize
config.speculator = speculator
torch.distributed.barrier(group=self.process_group)
filenames = weight_files(model_id, revision=revision, extension=".safetensors")
weights = Weights(filenames, device, dtype, process_group=self.process_group)
model = MambaModel(config, weights)
torch.distributed.barrier(group=self.process_group)
super(Mamba, self).__init__(
model_id=model_id,
model=model,
tokenizer=tokenizer,
requires_padding=True,
dtype=dtype,
device=device,
)
@property
def batch_type(self) -> Type[MambaBatch]:
return MambaBatch
def warmup(self, batch) -> Optional[int]:
# TODO: implement warmup for Mamba if needed
if CUDA_GRAPHS:
if self.speculate is None or self.speculate == 0:
try:
logger.info(f"Cuda Graphs are enabled for sizes {CUDA_GRAPHS}")
# Warmup cuda graphs
for bs in CUDA_GRAPHS:
self.cuda_graph_warmup(bs)
except Exception:
logger.exception(f"Decode cuda graph warmup failed")
else:
logger.info(f"Cuda Graphs are disabled (CUDA_GRAPHS={CUDA_GRAPHS}).")
return None
def cuda_graph_warmup(self, batch_size: int):
input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device)
n_blocks = len(self.model.blocks)
d_state = self.model.config.d_state
d_conv = self.model.config.d_conv
# Inner takes the expand multiplication
d_inner = self.model.config.d_inner
# Important seqlen_offset to go through the update mecanism with the state
seqlen_offset = 1
inference_params = new_inference_params(
n_blocks=n_blocks,
batch_size=batch_size,
d_state=d_state,
d_conv=d_conv,
d_inner=d_inner,
seqlen_offset=seqlen_offset,
device=self.device,
dtype=self.dtype,
)
graph = torch.cuda.CUDAGraph()
torch.cuda.synchronize()
# Run once outside to warmup
self.model.forward(input_ids=input_ids, inference_params=inference_params)
torch.cuda.synchronize()
with torch.cuda.graph(graph, pool=MEM_POOL):
logits, speculative_logits = self.model.forward(
input_ids=input_ids, inference_params=inference_params
)
torch.cuda.synchronize()
graph_dict = {
"input_ids": input_ids,
"inference_params": inference_params,
"graph": graph,
"logits": logits,
"speculative_logits": speculative_logits,
}
self.cuda_graphs[batch_size] = graph_dict
def tunableop_warmup(self, seqlen: int):
input_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=self.device)
n_blocks = len(self.model.blocks)
d_state = self.model.config.d_state
d_conv = self.model.config.d_conv
# Inner takes the expand multiplication
d_inner = self.model.config.d_inner
# Important seqlen_offset to go through the update mecanism with the state
seqlen_offset = 1
inference_params = new_inference_params(
n_blocks=n_blocks,
batch_size=seqlen,
d_state=d_state,
d_conv=d_conv,
d_inner=d_inner,
seqlen_offset=seqlen_offset,
device=self.device,
dtype=self.dtype,
)
self.model.forward(input_ids=input_ids, inference_params=inference_params)
def forward(
self, input_ids: torch.Tensor, inference_params: Any
) -> Tuple[torch.Tensor, torch.Tensor]:
bs = input_ids.shape[0]
padded_bs = bs
if bs == 3:
padded_bs = 4
elif 3 < bs <= 8:
padded_bs = 8
elif bs > 8:
padded_bs = (bs + 7) // 8 * 8
# Try to find an associated cuda graph
cuda_graph = self.cuda_graphs.get(padded_bs, None)
is_prefill = inference_params is None or inference_params.seqlen_offset == 0
if is_prefill or cuda_graph is None:
return self.model(
input_ids,
inference_params=inference_params,
)
# Copy inputs to the static inputs of the cuda graph
# Static inputs are potentially padded
cuda_graph["input_ids"][:bs] = input_ids
cuda_graph["inference_params"].conv_states[
:, :bs
] = inference_params.conv_states
cuda_graph["inference_params"].ssm_states[:, :bs] = inference_params.ssm_states
# Replay the graph
cuda_graph["graph"].replay()
inference_params.conv_states.copy_(
cuda_graph["inference_params"].conv_states[:, :bs]
)
inference_params.ssm_states.copy_(
cuda_graph["inference_params"].ssm_states[:, :bs]
)
# Slice output to the correct shape
speculative_logits = (
cuda_graph["speculative_logits"][:bs]
if cuda_graph["speculative_logits"] is not None
else None
)
logits = cuda_graph["logits"][:bs]
return logits, speculative_logits
def generate_token(self, batch) -> Tuple[List[Any], Optional[Any], Tuple[int, int]]:
start = time.time_ns()
input_ids = (
batch.input_ids
) # batch.past_input_ids if batch.past_input_ids is not None else batch.input_ids
batch_size, max_seqlen = input_ids.shape
# Inference params
if batch.inference_params is None:
# 0 is important here
seqlen_offset = 0
n_blocks = len(self.model.blocks)
d_state = self.model.config.d_state
d_conv = self.model.config.d_conv
d_inner = self.model.config.d_inner
inference_params = new_inference_params(
n_blocks=n_blocks,
batch_size=batch_size,
d_state=d_state,
d_conv=d_conv,
d_inner=d_inner,
seqlen_offset=seqlen_offset,
device=self.device,
dtype=self.dtype,
)
batch.inference_params = inference_params
# Forward pass
logits, speculative_logits = self.forward(
input_ids, inference_params=batch.inference_params
)
# batch.inference_params = new_inference_params
# 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
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:
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,
)
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]
forward_ns = start_decode - start
decode_ns = time.time_ns() - start_decode
return generations, batch, (forward_ns, decode_ns)