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FastDeploy/fastdeploy/model_executor/pre_and_post_process.py
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sunxin 28f7727a3d [Feature] Set overlap schedule as default (#6668) 
* overlap default
2026-03-09 22:34:54 +08:00

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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_set_value_by_flags_and_idx,
speculate_step_paddle,
speculate_step_reschedule,
speculate_step_system_cache,
speculate_update,
step_paddle,
step_reschedule,
step_system_cache,
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_set_value_by_flags_and_idx,
speculate_step_paddle,
speculate_step_system_cache,
speculate_update,
speculate_set_stop_value_multi_seqs,
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,
):
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)
speculate_update(
model_output.seq_lens_encoder,
model_output.seq_lens_decoder,
model_output.not_need_stop,
model_output.draft_tokens,
model_output.actual_draft_token_num,
model_output.accept_tokens,
model_output.accept_num,
model_output.stop_flags,
model_output.seq_lens_this_time,
model_output.is_block_step,
model_output.mask_rollback,
)
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,
)
# Update token_ids_all through accept tokens
speculate_set_value_by_flags_and_idx(
model_output.token_ids_all,
model_output.prompt_lens,
model_output.accept_tokens,
model_output.accept_num,
model_output.stop_flags,
model_output.seq_lens_this_time,
model_output.seq_lens_encoder,
model_output.seq_lens_decoder,
model_output.step_idx,
)
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,
) -> 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,
)
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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