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FastDeploy/fastdeploy/model_executor/pre_and_post_process.py
T
freeliuzc cf7934a4b2 [Speculative Decoding] Unify Spec and non-spec branch (#6685) 
* optimize spec-inference architecture

* delete debug log

* optimize spec_method usage  && fix unit_test

* add claude unit-test skill

* fix some ugly bug

* enhance robustness and bounds check

* unify method & spec_method to method to avoid bug

* activate CI

* fix unit test

* Unify logprobs computation for naive and speculative decoding, fix CUDA kernel

* fix logprob bug && optimize verify kernel

* fix exist_decode() judge
2026-03-10 23:58:44 -07:00

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"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
import queue
from typing import Dict, List, Optional, Union
import numpy as np
import paddle
from fastdeploy import envs
from fastdeploy.config import SpeculativeConfig
from fastdeploy.platforms import current_platform
from fastdeploy.worker.input_batch import (
InputBatch,
recover_batch_index_for_output,
recover_batch_index_for_sampler_output,
)
if current_platform.is_iluvatar():
from fastdeploy.model_executor.ops.iluvatar import (
get_padding_offset,
limit_thinking_content_length,
save_output,
set_stop_value_multi_ends,
step_paddle,
update_inputs,
update_inputs_v1,
)
elif current_platform.is_gcu():
from fastdeploy.model_executor.ops.gcu import (
get_padding_offset,
save_output,
set_stop_value_multi_ends,
update_inputs,
)
elif current_platform.is_dcu():
from fastdeploy.model_executor.ops.gpu import (
get_padding_offset,
save_output,
set_stop_value_multi_ends,
step_paddle,
update_inputs,
)
elif current_platform.is_maca():
from fastdeploy.model_executor.ops.gpu import (
get_padding_offset,
limit_thinking_content_length,
save_output,
save_output_topk,
set_stop_value_multi_ends,
speculate_limit_thinking_content_length,
speculate_pre_process,
speculate_save_output,
speculate_save_output_topk,
speculate_set_stop_value_multi_seqs,
speculate_step_paddle,
speculate_step_reschedule,
speculate_step_system_cache,
step_paddle,
step_reschedule,
step_system_cache,
unified_update_model_status,
update_inputs,
update_inputs_v1,
)
elif current_platform.is_intel_hpu():
pass
else:
from fastdeploy.model_executor.ops.gpu import (
get_padding_offset,
save_output,
save_output_topk,
set_stop_value_multi_ends,
speculate_pre_process,
speculate_save_output,
speculate_save_output_topk,
speculate_step_paddle,
speculate_step_system_cache,
speculate_set_stop_value_multi_seqs,
unified_update_model_status,
step_paddle,
step_system_cache,
update_inputs,
step_reschedule,
update_inputs_v1,
speculate_step_reschedule,
limit_thinking_content_length,
speculate_limit_thinking_content_length,
custom_numpy_to_tensor,
)
from fastdeploy.model_executor.entropy_utils import (
calculate_logits_entropy,
speculate_calculate_logits_entropy,
)
from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
from fastdeploy.output.pooler import PoolerOutput, PoolingSequenceGroupOutput
from fastdeploy.output.stream_transfer_data import DecoderState, StreamTransferData
from fastdeploy.worker.output import LogprobsTensors, ModelOutputData, SamplerOutput
DISABLE_RECOVER = envs.FD_DISABLED_RECOVER == "1"
if current_platform.is_cuda():
def async_set_value(tgt, src):
if isinstance(src, (int, float, bool)):
src = paddle.full(tgt.shape, fill_value=src, dtype=tgt.dtype)
elif isinstance(src, (list, np.array)):
dtype_str = str(tgt.dtype).split(".")[1]
if isinstance(src, list):
src = np.array(src, dtype=dtype_str if dtype_str != "bfloat16" else "float32")
if str(src.dtype) != dtype_str:
srt_tensor = paddle.empty(tgt.shape, dtype=str(src.dtype))
src = custom_numpy_to_tensor(src, srt_tensor)
else:
return custom_numpy_to_tensor(src, tgt)
elif isinstance(src, paddle.Tensor):
pass
else:
raise ValueError("async_set_value unsupported src type: {}".format(type(src)))
if src.shape != tgt.shape:
src = src.reshape(tgt.shape)
if src.dtype != tgt.dtype:
src = src.cast(tgt.dtype)
if src.place != tgt.place:
src = src.to(tgt.place)
tgt.copy_(src, blocking=False)
else:
def async_set_value(*args, **kwargs):
raise RuntimeError("async_set_value is only available on CUDA")
def pre_process(
token_num_cpu: int,
input_ids: paddle.Tensor,
seq_lens_this_time: paddle.Tensor,
speculative_decoding: bool,
draft_tokens: Optional[paddle.Tensor] = None,
seq_lens_encoder: Optional[paddle.Tensor] = None,
seq_lens_decoder: Optional[paddle.Tensor] = None,
):
"""
Preprocessing before embedding.
Args:
input_ids:
seq_lens_this_time:
speculative_decoding:
draft_tokens:
seq_lens_encoder:
Return:
ids_remove_padding:
cum_offsets:
batch_id_per_token:
cu_seqlens_q:
cu_seqlens_k:
"""
specific_platform = current_platform.is_cuda() or current_platform.is_maca() or current_platform.is_iluvatar()
if specific_platform and not speculative_decoding:
# Note(ZKK): This case's code is very simple!
ids_remove_padding, batch_id_per_token, cu_seqlens_q, cu_seqlens_k = get_padding_offset(
input_ids, seq_lens_this_time, None, None, token_num_cpu
)
return (
ids_remove_padding,
batch_id_per_token,
cu_seqlens_q,
cu_seqlens_k,
None,
None,
None,
)
# Remove padding
if speculative_decoding:
(
ids_remove_padding,
batch_id_per_token,
cu_seqlens_q,
cu_seqlens_k,
cu_seqlens_q_output,
batch_id_per_token_output,
real_output_token_num,
) = speculate_pre_process(
token_num_cpu, input_ids, seq_lens_this_time, draft_tokens, seq_lens_encoder, seq_lens_decoder
)
return (
ids_remove_padding,
batch_id_per_token,
cu_seqlens_q,
cu_seqlens_k,
cu_seqlens_q_output,
batch_id_per_token_output,
real_output_token_num,
)
def _build_stream_transfer_data(
output_tokens: paddle.Tensor,
pooler_outputs: List[PoolingSequenceGroupOutput] = None,
logprobs: Optional[LogprobsTensors] = None,
prompt_logprobs_list: Optional[LogprobsTensors] = None,
):
"""Split output_tokens and output"""
stream_transfer_datas = []
if output_tokens is not None:
output_tokens = output_tokens.numpy().reshape([-1])
output_tokens_lists = np.split(output_tokens, output_tokens.shape[0])
for bid, output_token_per_sample in enumerate(output_tokens_lists):
stream_transfer_data = StreamTransferData(
decoder_state=DecoderState.TEXT, tokens=output_token_per_sample, batch_id=bid
)
if logprobs:
stream_transfer_data.logprobs = logprobs.slice_rows(bid, bid + 1)
if prompt_logprobs_list:
stream_transfer_data.prompt_logprobs = prompt_logprobs_list[bid]
stream_transfer_datas.append(stream_transfer_data)
elif pooler_outputs is not None:
for bid, pooler_output in enumerate(pooler_outputs):
if pooler_output is None:
continue
if pooler_output.dtype == paddle.bfloat16:
pooler_output = pooler_output.astype("float32")
pooler_output = pooler_output.numpy()
stream_transfer_data = StreamTransferData(
decoder_state=DecoderState.TEXT, pooler_output=pooler_output, batch_id=bid
)
stream_transfer_datas.append(stream_transfer_data)
return stream_transfer_datas
def post_process_normal(
sampler_output: SamplerOutput,
model_output: ModelOutputData,
share_inputs: InputBatch,
sampling_metadata: SamplingMetadata,
block_size: int = 64,
think_end_id: int = -1,
splitwise_role_is_decode: bool = False,
enable_entropy: bool = False,
):
"""Post-processing steps after completing a single token generation."""
if think_end_id > 0:
limit_thinking_content_length(
sampler_output.sampled_token_ids,
share_inputs["max_think_lens"],
share_inputs["max_reply_lens"],
share_inputs["step_idx"],
share_inputs["limit_think_status"],
share_inputs["stop_flags"],
share_inputs["eos_token_id"],
share_inputs["inject_token_ids"],
think_end_id,
splitwise_role_is_decode,
)
# 1. Set stop value
paddle.assign(
paddle.where(
model_output.stop_flags,
model_output.step_idx,
model_output.step_idx + 1,
),
model_output.step_idx,
)
length_cond = paddle.greater_equal(model_output.step_idx, model_output.max_dec_len)
paddle.assign(
paddle.logical_or(model_output.stop_flags, length_cond),
model_output.stop_flags,
)
if (
current_platform.is_cuda()
or current_platform.is_iluvatar()
or current_platform.is_dcu()
or current_platform.is_maca()
):
set_stop_value_multi_ends(
sampler_output.sampled_token_ids,
model_output.stop_flags,
model_output.seq_lens_this_time,
model_output.eos_token_id,
model_output.next_tokens,
model_output.token_ids_all,
model_output.prompt_lens,
model_output.step_idx,
model_output.stop_token_ids,
model_output.stop_seqs_len,
model_output.min_tokens,
False,
) # multi ends
else:
set_stop_value_multi_ends(
sampler_output.sampled_token_ids,
model_output.stop_flags,
model_output.seq_lens_this_time,
model_output.eos_token_id,
model_output.next_tokens,
False,
)
if enable_entropy:
calculate_logits_entropy(sampler_output.logits, share_inputs, sampling_metadata.temperature)
# 2. Update the input buffer of the model
with paddle.framework._no_check_dy2st_diff():
if envs.ENABLE_V1_KVCACHE_SCHEDULER:
update_inputs_v1(
model_output.stop_flags,
model_output.not_need_stop_device,
model_output.seq_lens_this_time,
model_output.seq_lens_encoder,
model_output.seq_lens_decoder,
share_inputs["step_seq_lens_decoder"],
share_inputs["prompt_lens"],
sampler_output.sampled_token_ids,
model_output.input_ids,
share_inputs["block_tables"],
model_output.next_tokens,
model_output.is_block_step,
block_size,
)
else:
update_inputs(
model_output.stop_flags,
model_output.not_need_stop_device,
model_output.seq_lens_this_time,
model_output.seq_lens_encoder,
model_output.seq_lens_decoder,
model_output.input_ids,
sampler_output.sampled_token_ids,
model_output.is_block_step,
)
def save_output_normal(
model_output: ModelOutputData,
sampler_output: SamplerOutput,
share_inputs: Dict[str, paddle.Tensor],
async_output_queue: queue.Queue = None,
save_each_rank: bool = False,
):
# Transmit the model's output and stop generation signal via message queue.
# In the future, we will abandon this approach.
if envs.FD_USE_GET_SAVE_OUTPUT_V1:
if save_each_rank or model_output.mp_rank == 0:
recover_share_inputs_map = recover_batch_index_for_output(
share_inputs,
model_output.index_to_batch_id,
model_output.enable_pd_reorder,
["sampled_token_ids"],
)
recover_batch_index_for_sampler_output(
sampler_output, model_output.index_to_batch_id, model_output.enable_pd_reorder
)
output = _build_stream_transfer_data(
recover_share_inputs_map["sampled_token_ids"],
logprobs=sampler_output.logprobs_tensors,
prompt_logprobs_list=model_output.prompt_logprobs_list,
)
async_output_queue.put(output)
else:
if sampler_output.logprobs_tensors is None:
recover_share_inputs_map = recover_batch_index_for_output(
share_inputs,
model_output.index_to_batch_id,
model_output.enable_pd_reorder,
["last_preempted_idx", "sampled_token_ids"],
)
save_output(
recover_share_inputs_map["sampled_token_ids"],
model_output.not_need_stop,
recover_share_inputs_map["last_preempted_idx"],
model_output.mp_rank,
save_each_rank,
)
else:
recover_share_inputs_map = recover_batch_index_for_output(
share_inputs,
model_output.index_to_batch_id,
model_output.enable_pd_reorder,
["last_preempted_idx"],
)
recover_batch_index_for_sampler_output(
sampler_output, model_output.index_to_batch_id, model_output.enable_pd_reorder
)
save_output_topk(
share_inputs["sampled_token_ids"],
sampler_output.logprobs_tensors.logprob_token_ids,
sampler_output.logprobs_tensors.logprobs,
sampler_output.logprobs_tensors.selected_token_ranks,
model_output.not_need_stop,
recover_share_inputs_map["last_preempted_idx"],
model_output.mp_rank,
)
share_inputs["last_preempted_idx"][:] = 0
def post_process_specualate(
sampler_output: SamplerOutput,
model_output: ModelOutputData,
share_inputs: InputBatch,
sampling_metadata: SamplingMetadata,
save_each_rank: bool = False,
skip_save_output: bool = False,
think_end_id: int = -1,
splitwise_role_is_decode: bool = False,
enable_entropy: bool = False,
is_naive_mode: bool = False,
prefill_one_step_stop: bool = False,
):
if think_end_id > 0:
speculate_limit_thinking_content_length(
share_inputs["accept_tokens"],
share_inputs["max_think_lens"],
share_inputs["max_reply_lens"],
share_inputs["step_idx"],
share_inputs["limit_think_status"],
share_inputs["accept_num"],
share_inputs["stop_flags"],
share_inputs["eos_token_id"],
share_inputs["inject_token_ids"],
think_end_id,
splitwise_role_is_decode,
)
speculate_set_stop_value_multi_seqs(
model_output.accept_tokens,
model_output.accept_num,
model_output.token_ids_all,
model_output.prompt_lens,
model_output.step_idx,
model_output.stop_flags,
model_output.seq_lens_this_time,
model_output.stop_token_ids,
model_output.stop_seqs_len,
model_output.eos_token_id,
model_output.min_tokens,
)
if enable_entropy:
speculate_calculate_logits_entropy(sampler_output.logits, share_inputs, sampling_metadata.temperature)
# Unified state update: merges speculate_update + speculate_set_value_by_flags_and_idx
# into a single kernel launch. For MTP/ngram paths, verify_draft_tokens has already
# handled EOS/max_dec_len detection (replacing tokens + updating step_idx), so
# unified_update_model_status acts as a no-op for those checks. For naive mode
# (which skips verify), this kernel handles EOS/max_dec_len detection.
unified_update_model_status(
model_output.seq_lens_encoder, # seq_lens_encoder
model_output.seq_lens_decoder, # seq_lens_decoder
model_output.not_need_stop, # has_running_seqs
model_output.draft_tokens, # step_input_ids
model_output.actual_draft_token_num, # adaptive_step_input_len
model_output.accept_tokens, # step_output_ids (read-write)
model_output.accept_num, # step_output_len (read-write)
model_output.stop_flags, # stop_flags (read-write)
model_output.seq_lens_this_time, # seq_lens_this_time
model_output.is_block_step, # is_paused
model_output.mask_rollback, # mask_rollback
model_output.token_ids_all, # token_ids_all
model_output.prompt_lens, # prompt_lens
model_output.step_idx, # step_idx (read-write)
model_output.eos_token_id, # end_tokens
model_output.max_dec_len, # max_dec_len
is_naive_mode, # is_naive_mode
prefill_one_step_stop, # prefill_one_step_stop
)
if not skip_save_output:
if sampler_output.logprobs_tensors is None:
recover_model_output_map = recover_batch_index_for_output(
model_output,
model_output.index_to_batch_id,
model_output.enable_pd_reorder,
["accept_tokens", "accept_num", "seq_lens_decoder", "prompt_lens"],
)
recover_share_inputs = recover_batch_index_for_output(
share_inputs, model_output.index_to_batch_id, model_output.enable_pd_reorder, ["preempted_idx"]
)
speculate_save_output(
recover_model_output_map["accept_tokens"],
recover_model_output_map["accept_num"],
model_output.not_need_stop,
recover_model_output_map["seq_lens_decoder"],
recover_model_output_map["prompt_lens"],
recover_share_inputs["preempted_idx"],
model_output.mp_rank,
save_each_rank,
bool(envs.ENABLE_V1_KVCACHE_SCHEDULER),
)
else:
recover_batch_index_for_sampler_output(
sampler_output, model_output.index_to_batch_id, model_output.enable_pd_reorder
)
recover_model_output_map = recover_batch_index_for_output(
model_output,
model_output.index_to_batch_id,
model_output.enable_pd_reorder,
["seq_lens_decoder", "prompt_lens"],
)
recover_share_inputs = recover_batch_index_for_output(
share_inputs, model_output.index_to_batch_id, model_output.enable_pd_reorder, ["preempted_idx"]
)
speculate_save_output_topk(
sampler_output.sampled_token_ids,
sampler_output.logprobs_tensors.logprob_token_ids,
sampler_output.logprobs_tensors.logprobs,
sampler_output.logprobs_tensors.selected_token_ranks,
sampler_output.token_num_per_batch,
sampler_output.cu_batch_token_offset,
model_output.not_need_stop,
recover_model_output_map["seq_lens_decoder"],
recover_model_output_map["prompt_lens"],
recover_share_inputs["preempted_idx"],
3, # mtype
model_output.mp_rank,
save_each_rank,
)
def post_process(
sampler_or_pooler_output: Union[SamplerOutput, PoolerOutput],
model_output: ModelOutputData,
share_inputs: InputBatch,
sampling_metadata: SamplingMetadata = None,
block_size: int = 64,
save_each_rank: bool = False,
speculative_decoding: bool = False,
skip_save_output: bool = False,
async_output_queue: queue.Queue = None,
think_end_id: int = -1,
splitwise_role_is_decode: bool = False,
enable_entropy: bool = False,
is_naive_mode: bool = False,
prefill_one_step_stop: bool = False,
) -> None:
"""Post-processing steps after completing a single token generation."""
if isinstance(sampler_or_pooler_output, PoolerOutput):
post_process_pooling(
sampler_or_pooler_output,
model_output,
share_inputs,
block_size,
save_each_rank,
skip_save_output,
async_output_queue,
)
else:
if speculative_decoding:
post_process_specualate(
sampler_or_pooler_output,
model_output,
share_inputs,
sampling_metadata,
save_each_rank,
skip_save_output,
think_end_id,
splitwise_role_is_decode,
enable_entropy,
is_naive_mode,
prefill_one_step_stop,
)
else:
post_process_normal(
sampler_or_pooler_output,
model_output,
share_inputs,
sampling_metadata,
block_size,
think_end_id,
splitwise_role_is_decode,
enable_entropy,
)
share_inputs["last_preempted_idx"].copy_(share_inputs["preempted_idx"])
share_inputs["preempted_idx"][:] = 0
def step_cuda(
share_inputs: InputBatch,
block_size: int,
enc_dec_block_num: int,
speculative_config: SpeculativeConfig,
enable_prefix_caching: bool = False,
) -> None:
"""
TODO(gongshaotian): normalization name
"""
if speculative_config.method is not None:
if DISABLE_RECOVER:
speculate_step_reschedule(
share_inputs["stop_flags"],
share_inputs["seq_lens_this_time"],
share_inputs["step_seq_lens_encoder"],
share_inputs["seq_lens_encoder"],
share_inputs["seq_lens_decoder"],
share_inputs["block_tables"],
share_inputs["encoder_block_lens"],
share_inputs["is_block_step"],
share_inputs["step_block_list"],
share_inputs["step_lens"],
share_inputs["recover_block_list"],
share_inputs["recover_lens"],
share_inputs["need_block_list"],
share_inputs["need_block_len"],
share_inputs["used_list_len"],
share_inputs["free_list"],
share_inputs["free_list_len"],
share_inputs["input_ids"],
share_inputs["pre_ids"],
share_inputs["step_idx"],
share_inputs["next_tokens"],
share_inputs["first_token_ids"],
share_inputs["accept_num"],
block_size,
enc_dec_block_num,
speculative_config.num_speculative_tokens,
)
else:
if enable_prefix_caching:
speculate_step_system_cache(
share_inputs["stop_flags"],
share_inputs["seq_lens_this_time"],
share_inputs["step_seq_lens_encoder"],
share_inputs["step_seq_lens_decoder"],
share_inputs["seq_lens_encoder"],
share_inputs["seq_lens_decoder"],
share_inputs["block_tables"],
share_inputs["encoder_block_lens"],
share_inputs["is_block_step"],
share_inputs["step_block_list"],
share_inputs["step_lens"],
share_inputs["recover_block_list"],
share_inputs["recover_lens"],
share_inputs["need_block_list"],
share_inputs["need_block_len"],
share_inputs["used_list_len"],
share_inputs["free_list"],
share_inputs["free_list_len"],
share_inputs["input_ids"],
share_inputs["pre_ids"],
share_inputs["step_idx"],
share_inputs["next_tokens"],
share_inputs["first_token_ids"],
share_inputs["accept_num"],
block_size,
enc_dec_block_num,
speculative_config.num_speculative_tokens,
)
else:
speculate_step_paddle(
share_inputs["stop_flags"],
share_inputs["seq_lens_this_time"],
share_inputs["step_seq_lens_encoder"],
share_inputs["seq_lens_encoder"],
share_inputs["seq_lens_decoder"],
share_inputs["block_tables"],
share_inputs["encoder_block_lens"],
share_inputs["is_block_step"],
share_inputs["step_block_list"],
share_inputs["step_lens"],
share_inputs["recover_block_list"],
share_inputs["recover_lens"],
share_inputs["need_block_list"],
share_inputs["need_block_len"],
share_inputs["used_list_len"],
share_inputs["free_list"],
share_inputs["free_list_len"],
share_inputs["input_ids"],
share_inputs["pre_ids"],
share_inputs["step_idx"],
share_inputs["next_tokens"],
share_inputs["first_token_ids"],
share_inputs["accept_num"],
block_size,
enc_dec_block_num,
speculative_config.num_speculative_tokens,
)
else:
if DISABLE_RECOVER:
step_reschedule(
share_inputs["stop_flags"],
share_inputs["seq_lens_this_time"],
share_inputs["step_seq_lens_encoder"],
share_inputs["seq_lens_encoder"],
share_inputs["seq_lens_decoder"],
share_inputs["block_tables"],
share_inputs["encoder_block_lens"],
share_inputs["is_block_step"],
share_inputs["step_block_list"],
share_inputs["step_lens"],
share_inputs["recover_block_list"],
share_inputs["recover_lens"],
share_inputs["need_block_list"],
share_inputs["need_block_len"],
share_inputs["used_list_len"],
share_inputs["free_list"],
share_inputs["free_list_len"],
share_inputs["input_ids"],
share_inputs["pre_ids"],
share_inputs["step_idx"],
share_inputs["next_tokens"],
share_inputs["first_token_ids"],
block_size,
enc_dec_block_num,
)
else:
if enable_prefix_caching:
step_system_cache(
share_inputs["stop_flags"],
share_inputs["seq_lens_this_time"],
share_inputs["step_seq_lens_encoder"],
share_inputs["step_seq_lens_decoder"],
share_inputs["seq_lens_encoder"],
share_inputs["seq_lens_decoder"],
share_inputs["block_tables"],
share_inputs["encoder_block_lens"],
share_inputs["is_block_step"],
share_inputs["step_block_list"],
share_inputs["step_lens"],
share_inputs["recover_block_list"],
share_inputs["recover_lens"],
share_inputs["need_block_list"],
share_inputs["need_block_len"],
share_inputs["used_list_len"],
share_inputs["free_list"],
share_inputs["free_list_len"],
share_inputs["input_ids"],
share_inputs["pre_ids"],
share_inputs["step_idx"],
share_inputs["next_tokens"],
share_inputs["first_token_ids"],
block_size,
enc_dec_block_num,
)
else:
step_paddle(
share_inputs["stop_flags"],
share_inputs["seq_lens_this_time"],
share_inputs["step_seq_lens_encoder"],
share_inputs["seq_lens_encoder"],
share_inputs["seq_lens_decoder"],
share_inputs["block_tables"],
share_inputs["encoder_block_lens"],
share_inputs["is_block_step"],
share_inputs["step_block_list"],
share_inputs["step_lens"],
share_inputs["recover_block_list"],
share_inputs["recover_lens"],
share_inputs["need_block_list"],
share_inputs["need_block_len"],
share_inputs["used_list_len"],
share_inputs["free_list"],
share_inputs["free_list_len"],
share_inputs["input_ids"],
share_inputs["pre_ids"],
share_inputs["step_idx"],
share_inputs["next_tokens"],
share_inputs["first_token_ids"],
block_size,
enc_dec_block_num,
)
def rebuild_padding(
tmp_out: paddle.Tensor,
cu_seqlens_q: paddle.Tensor,
seq_len_this_time: paddle.Tensor,
seq_lens_decoder: paddle.Tensor,
seq_lens_encoder: paddle.Tensor,
batch_id_per_token_output: Optional[paddle.Tensor] = None,
cu_seqlens_q_output: Optional[paddle.Tensor] = None,
first_token_out: Optional[paddle.Tensor] = None,
enable_logprob: Optional[bool] = False,
):
"""
Args:
Returns:
"""
if current_platform.is_cuda():
from fastdeploy.model_executor.ops.gpu import rebuild_padding
hidden_states = rebuild_padding(
tmp_out,
cu_seqlens_q,
seq_len_this_time,
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token_output,
cu_seqlens_q_output,
first_token_out,
enable_logprob,
)
elif current_platform.is_dcu():
from fastdeploy.model_executor.ops.gpu import rebuild_padding
hidden_states = rebuild_padding(
tmp_out,
cu_seqlens_q,
seq_len_this_time,
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token_output,
)
elif current_platform.is_iluvatar():
from fastdeploy.model_executor.ops.iluvatar import rebuild_padding
hidden_states = rebuild_padding(
tmp_out,
cu_seqlens_q,
seq_len_this_time,
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token_output,
cu_seqlens_q_output,
first_token_out,
enable_logprob,
)
elif current_platform.is_gcu():
from fastdeploy.model_executor.ops.gcu import rebuild_padding
hidden_states = rebuild_padding(
tmp_out,
cu_seqlens_q,
seq_len_this_time,
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token_output,
)
elif current_platform.is_cpu():
from fastdeploy.model_executor.ops.cpu import rebuild_padding_cpu
hidden_states = rebuild_padding_cpu(
tmp_out,
cu_seqlens_q,
seq_len_this_time,
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token_output,
)
elif current_platform.is_maca():
from fastdeploy.model_executor.ops.gpu import rebuild_padding
hidden_states = rebuild_padding(
tmp_out,
cu_seqlens_q,
seq_len_this_time,
seq_lens_decoder,
seq_lens_encoder,
batch_id_per_token_output,
cu_seqlens_q_output,
first_token_out,
enable_logprob,
)
else:
raise RuntimeError("Not supported platform")
return hidden_states
def post_process_pooling(
pooler_output: PoolerOutput,
model_output: ModelOutputData,
share_inputs: InputBatch,
block_size: int = 64,
save_each_rank: bool = False,
skip_save_output: bool = False,
async_output_queue: queue.Queue = None,
) -> None:
paddle.assign(
paddle.where(
model_output.stop_flags,
model_output.step_idx,
model_output.step_idx + 1,
),
model_output.step_idx,
)
length_cond = paddle.greater_equal(model_output.step_idx, model_output.max_dec_len)
paddle.assign(
paddle.logical_or(model_output.stop_flags, length_cond),
model_output.stop_flags,
)
with paddle.framework._no_check_dy2st_diff():
if envs.ENABLE_V1_KVCACHE_SCHEDULER:
dummy_sampled_tokens = paddle.full_like(model_output.next_tokens, -1, dtype="int64")
paddle.assign(
paddle.ones_like(model_output.stop_flags, dtype="bool"),
model_output.stop_flags,
)
update_inputs_v1(
model_output.stop_flags,
model_output.not_need_stop_device,
model_output.seq_lens_this_time,
model_output.seq_lens_encoder,
model_output.seq_lens_decoder,
share_inputs["step_seq_lens_decoder"],
share_inputs["prompt_lens"],
dummy_sampled_tokens,
model_output.input_ids,
share_inputs["block_tables"],
model_output.next_tokens,
model_output.is_block_step,
block_size,
)
if not skip_save_output:
if save_each_rank or model_output.mp_rank == 0:
output = _build_stream_transfer_data(output_tokens=None, pooler_outputs=pooler_output.outputs)
async_output_queue.put(output)
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