mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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f1b5392e20
* [Intel HPU] Support intel hpu platform * fix some issues * apply precommit and move AttentionBackend_HPU * fix format issue * correct ops import * fix ci issue * update code in layers * fix code style issue * remove dense tp moe ep mode * fix enc_dec_block_num * fix rebase issue * rename hpu to gaudi in readme * rename ForwardMeta_HPU to HPUForwardMeta
1464 lines
65 KiB
Python
1464 lines
65 KiB
Python
"""
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License"
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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import os
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import time
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from typing import Dict, List, Optional
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import numpy as np
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import paddle
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import paddle.nn as nn
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from paddleformers.utils.log import logger
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from fastdeploy.config import FDConfig
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from fastdeploy.engine.request import Request
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# from fastdeploy.spec_decode import MTPProposer, NgramProposer
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from fastdeploy.model_executor.forward_meta import HPUForwardMeta
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from fastdeploy.model_executor.guided_decoding import get_guided_backend
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from fastdeploy.model_executor.guided_decoding.base_guided_decoding import (
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LogitsProcessorBase,
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)
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from fastdeploy.model_executor.layers.attention import get_attention_backend
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from fastdeploy.model_executor.layers.attention.base_attention_backend import (
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AttentionBackend,
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)
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from fastdeploy.model_executor.layers.rotary_embedding import get_rope
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from fastdeploy.model_executor.layers.sample.meta_data import SamplingMetadata
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from fastdeploy.model_executor.layers.sample.sampler import Sampler, SpeculativeSampler
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from fastdeploy.model_executor.model_loader import get_model_loader
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from fastdeploy.model_executor.ops.intel_hpu import (
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recover_block,
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save_output,
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step_paddle,
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update_inputs_v3,
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)
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from fastdeploy.utils import get_logger
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from fastdeploy.worker.model_runner_base import ModelRunnerBase
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from fastdeploy.worker.output import ModelOutputData, ModelRunnerOutput
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hpu_model_runner_profile_logger = get_logger("hpu_model_runner_profile", "hpu_model_runner_profile.log")
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def post_process_hpu(sampled_token_ids: paddle.Tensor, model_output: ModelOutputData, is_warmuping: bool) -> None:
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"""Post-processing steps after completing a single token generation."""
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start_time = time.time()
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not_need_stop_hpu = model_output.not_need_stop.to(sampled_token_ids.place)
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is_block_step_hpu = model_output.is_block_step.to(sampled_token_ids.place)
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update_inputs_v3(
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model_output.stop_flags,
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model_output.step_idx,
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not_need_stop_hpu,
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model_output.seq_lens_this_time,
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model_output.seq_lens_encoder,
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model_output.seq_lens_decoder,
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model_output.max_dec_len,
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model_output.input_ids,
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model_output.stop_nums,
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sampled_token_ids,
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is_block_step_hpu,
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model_output.eos_token_id,
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model_output.next_tokens,
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)
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model_output.not_need_stop[:] = not_need_stop_hpu.cpu()
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model_output.is_block_step[:] = is_block_step_hpu.cpu()
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end_time = time.time()
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execution_time = (end_time - start_time) * 1000
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hpu_model_runner_profile_logger.info(f"post_process_hpu::update_inputs_v3 execution time(ms): {execution_time}")
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if is_warmuping:
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return
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start_time = time.time()
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save_output(
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sampled_token_ids,
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model_output.not_need_stop,
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model_output.mp_rank,
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)
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end_time = time.time()
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execution_time = (end_time - start_time) * 1000
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hpu_model_runner_profile_logger.info(f"post_process_hpu::save_output execution time(ms): {execution_time}")
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def recover_block_hpu(
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recover_block_list, # cpu
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recover_len, # cpu
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stop_flags, # hpu
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seq_lens_this_time, # hpu
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ori_seq_lens_encoder, # cpu
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seq_lens_encoder, # hpu
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block_tables, # cpu
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free_list, # cpu
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free_list_len, # cpu
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input_ids, # hpu
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pre_ids, # hpu
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step_idx, # hpu
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encoder_block_lens, # cpu
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used_list_len, # cpu
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next_tokens, # hpu
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first_token_ids,
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): # hpu
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for bid in range(recover_len.item()):
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recover_id = recover_block_list[bid].item()
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ori_seq_len_encoder = ori_seq_lens_encoder[recover_id].item()
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step_idx_now = step_idx[recover_id].item()
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seq_len = ori_seq_len_encoder + step_idx_now
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encoder_block_len = encoder_block_lens[recover_id].item()
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decoder_used_len = used_list_len[recover_id].item()
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seq_lens_this_time[recover_id] = seq_len
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seq_lens_encoder[recover_id] = seq_len
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stop_flags[recover_id] = False
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ori_free_list_len = free_list_len[0]
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free_list_len[0] -= decoder_used_len
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for i in range(decoder_used_len):
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block_tables[recover_id, encoder_block_len + i] = free_list[ori_free_list_len - i - 1]
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recover_block(input_ids, first_token_ids, pre_ids, next_tokens, recover_id, ori_seq_len_encoder, step_idx_now)
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def step_intel_hpu(share_inputs: Dict[str, paddle.Tensor], block_size: int, max_model_len: int) -> None:
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"""
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step cuda
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"""
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step_paddle(
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share_inputs["stop_flags"],
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share_inputs["seq_lens_this_time"],
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share_inputs["seq_lens_encoder"],
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share_inputs["seq_lens_decoder"],
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share_inputs["block_tables"],
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share_inputs["encoder_block_lens"],
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share_inputs["is_block_step"],
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share_inputs["step_block_list"],
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share_inputs["step_lens"],
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share_inputs["recover_block_list"],
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share_inputs["recover_lens"],
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share_inputs["need_block_list"],
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share_inputs["need_block_len"],
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share_inputs["used_list_len"],
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share_inputs["free_list"],
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share_inputs["free_list_len"],
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share_inputs["first_token_ids"],
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block_size,
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max_model_len,
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)
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if share_inputs["recover_lens"].item() > 0:
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recover_block_hpu(
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share_inputs["recover_block_list"],
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share_inputs["recover_lens"],
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share_inputs["stop_flags"],
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share_inputs["seq_lens_this_time"],
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share_inputs["ori_seq_lens_encoder"],
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share_inputs["seq_lens_encoder"],
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share_inputs["block_tables"],
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share_inputs["free_list"],
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share_inputs["free_list_len"],
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share_inputs["input_ids"],
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share_inputs["pre_ids"],
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share_inputs["step_idx"],
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share_inputs["encoder_block_lens"],
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share_inputs["used_list_len"],
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share_inputs["next_tokens"],
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share_inputs["first_token_ids"],
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)
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share_inputs["recover_lens"] = paddle.full([1], 0, dtype="int32").cpu()
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# TODO: replace rebuild_padding_v3 in CustomDevice if we adopt this version pp optimization
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def rebuild_padding_v3_1(
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tmp_out,
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batch_ids,
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total_batch,
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seq_lens_encoder,
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is_prompt=None,
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):
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dim_emb = tmp_out.shape[-1]
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output_data = paddle.zeros((total_batch, dim_emb))
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if is_prompt is True: # context
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tmp_out = tmp_out.reshape([total_batch, -1, dim_emb])
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for i in range(batch_ids.shape[0]):
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seq_len = seq_lens_encoder[batch_ids[i]].item()
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output_data[i] = tmp_out[i, seq_len - 1]
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elif is_prompt is False:
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output_data[0 : batch_ids.shape[0], :] = tmp_out[: batch_ids.shape[0], :]
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return output_data
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from fastdeploy.model_executor.layers.linear import QKVParallelLinear, RowParallelLinear
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from fastdeploy.model_executor.ops.intel_hpu import fused_mlp
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def fused_attention_forward(
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self,
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src: paddle.Tensor = None,
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qkv_proj: QKVParallelLinear = None,
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o_proj: RowParallelLinear = None,
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forward_meta: HPUForwardMeta = None,
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):
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"""
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The forward function of attention layer.
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args:
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src: the hidden states tensor
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residual_input: the residual tensor
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forward_meta: the forward meta data
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"""
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return forward_meta.attn_backend.forward(
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src,
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qkv_proj,
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o_proj,
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self,
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forward_meta,
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)
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def fused_self_atten_forward(
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self,
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forward_meta: HPUForwardMeta,
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hidden_states: paddle.Tensor,
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):
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""" """
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atten_out = self.attn(
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src=hidden_states,
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qkv_proj=self.qkv_proj,
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o_proj=self.o_proj,
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forward_meta=forward_meta,
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)
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return atten_out
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def fused_mlp_forward(self, x):
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""" """
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out = fused_mlp(
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x,
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self.up_gate_proj.weight,
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None,
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self.down_proj.weight,
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)
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# all_reduce
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if self.nranks > 1:
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from fastdeploy.distributed.communication import (
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tensor_model_parallel_all_reduce_custom,
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)
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tensor_model_parallel_all_reduce_custom(out)
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return out
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import types
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from fastdeploy.model_executor.layers.attention.attention import Attention
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from fastdeploy.model_executor.models.ernie4_5_moe import (
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Ernie4_5_Attention,
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Ernie4_5_MLP,
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)
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from fastdeploy.model_executor.models.qwen2 import Qwen2Attention, Qwen2MLP
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def convert_model(model):
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""" """
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for name, module in model.named_children():
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if len(list(module.named_children())) > 0:
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# print(f"********** model {model.__class__.__name__} has submodule: name={name}, module={module.__class__.__name__}")
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if isinstance(module, Ernie4_5_Attention):
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module.forward = types.MethodType(fused_self_atten_forward, module)
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if isinstance(module, Qwen2Attention):
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module.forward = types.MethodType(fused_self_atten_forward, module)
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if isinstance(module, Ernie4_5_MLP):
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module.forward = types.MethodType(fused_mlp_forward, module)
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if isinstance(module, Qwen2MLP):
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module.forward = types.MethodType(fused_mlp_forward, module)
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convert_model(module)
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else:
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# print(f"*********[ Leaf node] Loading submodule: name={name} -- module: {module.__class__.__name__}")
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if isinstance(module, Attention):
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module.forward = types.MethodType(fused_attention_forward, module)
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return model
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class HPUModelRunner(ModelRunnerBase):
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""" """
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def __init__(
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self,
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fd_config: FDConfig,
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device: str, # logic device
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device_id: int, # physical device id
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rank: int,
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local_rank: int,
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):
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super().__init__(fd_config=fd_config, device=device)
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self.rank = rank
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self.local_rank = local_rank
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self.device_id = device_id
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self.speculative_method = self.fd_config.speculative_config.method
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self.speculative_decoding = self.speculative_method is not None
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self.guided_backend = None
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if self.fd_config.parallel_config.guided_decoding_backend != "off":
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self.guided_backend = get_guided_backend(fd_config=self.fd_config)
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# Sampler
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if not self.speculative_decoding:
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self.sampler = Sampler()
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else:
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self.sampler = SpeculativeSampler(fd_config)
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# Lazy initialize kv cache after model loading
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# self.kv_caches: list[paddle.Tensor] = []
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# Cuda Graph
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self.use_cudagraph = self.graph_opt_config.use_cudagraph
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self.cudagraph_capture_sizes = list(reversed(self.graph_opt_config.cudagraph_capture_sizes))
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self.cudagraph_num_of_warmups = self.graph_opt_config.cudagraph_num_of_warmups
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self.input_ids = paddle.zeros(self.scheduler_config.max_num_seqs, dtype="int32")
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# Initialize share inputs
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self._init_share_inputs(self.scheduler_config.max_num_seqs)
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self.infer_seed_increment = paddle.full(
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shape=[self.scheduler_config.max_num_seqs, 1], fill_value=4, dtype="int64"
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).cpu()
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self.restore_chunked_prefill_request = dict()
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# Initialize attention Backend
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# Note(gonshaotian): Currently, all attention layers share one attention backend instance.
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# In the future, we will expand it as a list.
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self.attn_backends: list[AttentionBackend] = []
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# self.attn_metadatas: list[AttentionMetadata] = []
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self.initialize_attn_backend()
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# Forward meta store the global meta information of the forward
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self.forward_meta: HPUForwardMeta = None
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self.is_warmuping = False
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self.is_hpu_perf_breakdown_sync_mode = int(os.environ.get("HPU_PERF_BREAKDOWN_SYNC_MODE", 1)) == 1
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# Postprocess Env params
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os.environ["INFERENCE_MSG_QUEUE_ID"] = str(
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self.local_rank + int(self.parallel_config.engine_worker_queue_port)
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)
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if int(os.environ.get("HABANA_PROFILE", 0)) == 1:
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step_start = int(os.environ.get("PROFILE_START", 0))
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step_end = int(os.environ.get("PROFILE_END", 4))
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import paddle.profiler as profiler
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self.prof = profiler.Profiler(
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targets=[profiler.ProfilerTarget.CPU, profiler.ProfilerTarget.CUSTOM_DEVICE],
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scheduler=(step_start, step_end),
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on_trace_ready=profiler.export_chrome_tracing("./profile"),
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)
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self.prof.start()
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def exist_prefill(self):
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"""
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check whether prefill stage finished
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"""
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if int(paddle.max(self.share_inputs["seq_lens_encoder"])) != 0:
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return 1
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else:
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return 0
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def init_speculative_proposer(self):
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"""
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Init speculative proposer
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"""
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# if self.speculative_method == "ngram":
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# self.proposer = NgramProposer(self.fd_config)
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# elif self.speculative_method == "mtp":
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# self.proposer = MTPProposer(self.fd_config, self.get_model(),
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# self.local_rank, self.device_id,
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# self.share_inputs)
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# else:
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# self.proposer = None
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pass
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def _init_logits_processor(self, request):
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"""
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init logits processor for guided decoding
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"""
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assert self.guided_backend is not None, (
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"guided_backend is None, use " "--guided-decoding-backend to specify the backend at server startup."
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)
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if request.guided_json is not None:
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schemata_key = ("json", request.guided_json)
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elif request.guided_regex is not None:
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schemata_key = ("regex", request.guided_regex)
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elif request.guided_grammar is not None:
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schemata_key = ("grammar", request.guided_grammar)
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elif request.structural_tag is not None:
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schemata_key = ("structural_tag", request.structural_tag)
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return self.guided_backend.get_logits_processor(schemata_key=schemata_key), schemata_key
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def insert_prefill_inputs(self, req_dicts: List[Request], num_running_requests: int = None):
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"""
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Process inputs for prefill tasks and insert it to share_inputs buffer
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req_dict: A list of Request dict
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num_running_requests: batch_size
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"""
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# NOTE(luotingdan): Lazy initialize kv cache
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if "caches" not in self.share_inputs:
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self.initialize_kv_cache()
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# NOTE(luotingdan): Set environment variable of prefill node
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if req_dicts[-1].disaggregate_info is not None and req_dicts[-1].disaggregate_info["role"] == "prefill":
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os.environ["PREFILL_NODE_ONE_STEP_STOP"] = "1"
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req_len = len(req_dicts)
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for i in range(req_len):
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request = req_dicts[i]
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idx = request.idx
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length = len(request.prompt_token_ids)
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prefill_tokens = []
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if (
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request.guided_json is not None
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or request.guided_regex is not None
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or request.structural_tag is not None
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or request.guided_grammar is not None
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):
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logits_info, schemata_key = self._init_logits_processor(request)
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request.logits_processor, request.logits_cached = logits_info
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request.schemata_key = schemata_key
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# Is Decode Node
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if req_dicts[i].disaggregate_info is not None and req_dicts[i].disaggregate_info["role"] == "decode":
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prefill_tokens.append(request.prompt_token_ids[0])
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self.share_inputs["pre_ids"][idx : idx + 1] = request.prompt_token_ids[-1]
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self.share_inputs["input_ids"][idx : idx + 1, 0] = request.prompt_token_ids[0]
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self.share_inputs["seq_lens_encoder"][idx : idx + 1] = 0
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self.share_inputs["seq_lens_decoder"][idx : idx + 1] = length
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self.share_inputs["seq_lens_this_time"][idx : idx + 1] = 1
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self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = 0
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self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = length
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self.share_inputs["step_idx"][idx : idx + 1] = 1
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if self.speculative_decoding:
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num_prefill_send_token = self.speculative_config.num_speculative_tokens + 1
|
|
self.share_inputs["draft_tokens"][idx : idx + 1, 0:num_prefill_send_token] = paddle.to_tensor(
|
|
request.draft_token_ids[0:num_prefill_send_token], dtype="int64"
|
|
)
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = num_prefill_send_token
|
|
else:
|
|
self.share_inputs["pre_ids"][idx : idx + 1] = -1
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 0
|
|
self.share_inputs["input_ids"][idx : idx + 1, :length] = np.array(request.prompt_token_ids)
|
|
|
|
# Use chunked prefill
|
|
if self.cache_config.enable_chunked_prefill:
|
|
request.set("chunk_idx", 1)
|
|
logger.info(f"prefill_chunk_info: {request.prefill_chunk_info}")
|
|
token_chunk_size = request.prefill_chunk_info[0]
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["input_ids"][idx, :token_chunk_size] = np.array(
|
|
request.prompt_token_ids[:token_chunk_size]
|
|
)
|
|
self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
|
|
self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
|
|
else:
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
|
|
self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = request.get("seq_lens_decoder", 0)
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = length
|
|
self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = length
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = length
|
|
|
|
if len(request.eos_token_ids) < self.model_config.eos_tokens_lens:
|
|
request.eos_token_ids.append(request.eos_token_ids[0])
|
|
self.share_inputs["eos_token_id"][:] = np.array(request.eos_token_ids, dtype="int64").reshape(-1, 1)
|
|
|
|
self.share_inputs["top_p"][idx : idx + 1] = request.get("top_p", 0.7)
|
|
self.share_inputs["temperature"][idx : idx + 1] = request.get("temperature", 0.95)
|
|
self.share_inputs["penalty_score"][idx : idx + 1] = request.get("repetition_penalty", 1.0)
|
|
self.share_inputs["frequency_score"][idx : idx + 1] = request.get("frequency_penalty", 0.0)
|
|
self.share_inputs["presence_score"][idx : idx + 1] = request.get("presence_penalty", 0.0)
|
|
|
|
self.share_inputs["min_dec_len"][idx : idx + 1] = request.get("min_tokens", 1)
|
|
self.share_inputs["max_dec_len"][idx : idx + 1] = request.get(
|
|
"max_tokens", self.model_config.max_model_len
|
|
)
|
|
self.share_inputs["stop_flags"][idx : idx + 1] = False
|
|
|
|
self.share_inputs["first_token_ids"][idx : idx + 1] = self.share_inputs["input_ids"][idx : idx + 1, :1]
|
|
self.share_inputs["ori_seq_lens_encoder"][idx : idx + 1] = length
|
|
|
|
if request.get("seed") is not None:
|
|
self.share_inputs["infer_seed"][idx : idx + 1] = request.get("seed")
|
|
encoder_block_num = len(request.get("block_tables"))
|
|
self.share_inputs["encoder_block_lens"][idx : idx + 1] = encoder_block_num
|
|
self.share_inputs["block_tables"][idx : idx + 1, :] = -1
|
|
self.share_inputs["block_tables"][idx : idx + 1, :encoder_block_num] = np.array(
|
|
request.block_tables, dtype="int32"
|
|
)
|
|
|
|
if request.get("stop_token_ids") is not None and request.get("stop_seqs_len") is not None:
|
|
stop_seqs_num = len(request.get("stop_seqs_len"))
|
|
for i in range(stop_seqs_num, self.model_config.max_stop_seqs_num):
|
|
request.stop_seqs_len.append(0)
|
|
self.share_inputs["stop_seqs_len"][:] = np.array(request.stop_seqs_len, dtype="int32")
|
|
self.share_inputs["stop_seqs"][:stop_seqs_num, : len(request.get("stop_token_ids")[0])] = np.array(
|
|
request.get("stop_token_ids"), dtype="int64"
|
|
)
|
|
|
|
self.sampler.apply_logits_processor(idx, request.get("logits_processor"), prefill_tokens)
|
|
|
|
self.share_inputs["not_need_stop"][0] = True
|
|
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.insert_prefill_inputs(req_dicts, num_running_requests)
|
|
|
|
def _dummy_prefill_inputs(self, num_tokens: int, batch_size: int, expected_decode_len: int):
|
|
"""Set dummy prefill inputs to share_inputs"""
|
|
# NOTE(gongshaotian): The maximum decoding length is equal to the expected decoded tokens plus the eos token
|
|
max_dec_len = expected_decode_len + 1
|
|
full_length = min(num_tokens // batch_size, self.parallel_config.max_model_len - max_dec_len)
|
|
input_length = int(full_length * self.cache_config.kv_cache_ratio)
|
|
block_num = (
|
|
input_length + self.cache_config.block_size - 1
|
|
) // self.cache_config.block_size + self.cache_config.enc_dec_block_num
|
|
|
|
for i in range(batch_size):
|
|
idx = i
|
|
self.share_inputs["input_ids"][idx : idx + 1, :input_length] = np.array([5] * input_length)
|
|
self.share_inputs["eos_token_id"][:] = np.array([2], dtype="int64").reshape(-1, 1)
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = input_length
|
|
self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = input_length
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = input_length
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = 0
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 0
|
|
self.share_inputs["max_dec_len"][idx : idx + 1] = max_dec_len
|
|
self.share_inputs["stop_flags"][idx : idx + 1] = False
|
|
|
|
self.share_inputs["first_token_ids"][idx : idx + 1] = self.share_inputs["input_ids"][idx : idx + 1, :1]
|
|
self.share_inputs["ori_seq_lens_encoder"][idx : idx + 1] = input_length
|
|
|
|
self.share_inputs["encoder_block_lens"][idx : idx + 1] = block_num
|
|
self.share_inputs["block_tables"][idx : idx + 1, :block_num] = np.arange(
|
|
idx * block_num, (idx + 1) * block_num, 1
|
|
)
|
|
|
|
def _init_share_inputs(self, max_num_seqs: int):
|
|
"""Initialize all share buffers for model inputs.
|
|
Note: In the future, we may abandon share buffers.
|
|
"""
|
|
self.MAX_INFER_SEED = 9223372036854775806
|
|
self.share_inputs = {}
|
|
|
|
self.share_inputs["pre_ids"] = paddle.full(
|
|
[max_num_seqs, self.parallel_config.max_model_len], -1, dtype="int64"
|
|
)
|
|
self.share_inputs["input_ids"] = paddle.full(
|
|
[max_num_seqs, self.parallel_config.max_model_len], self.model_config.pad_token_id, dtype="int64"
|
|
)
|
|
self.share_inputs["eos_token_id"] = paddle.full([self.model_config.eos_tokens_lens, 1], 0, dtype="int64")
|
|
self.share_inputs["top_p"] = paddle.full([max_num_seqs, 1], self.model_config.top_p, dtype="float32")
|
|
self.share_inputs["temperature"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.temperature, dtype="float32"
|
|
)
|
|
self.share_inputs["penalty_score"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.penalty_score, dtype="float32"
|
|
)
|
|
self.share_inputs["frequency_score"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.frequency_score, dtype="float32"
|
|
)
|
|
self.share_inputs["presence_score"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.presence_score, dtype="float32"
|
|
)
|
|
|
|
self.share_inputs["min_dec_len"] = paddle.full([max_num_seqs, 1], self.model_config.min_length, dtype="int64")
|
|
self.share_inputs["max_dec_len"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.max_model_len, dtype="int64"
|
|
)
|
|
self.share_inputs["min_length"] = paddle.full([max_num_seqs, 1], self.model_config.min_length, dtype="int64")
|
|
self.share_inputs["max_length"] = paddle.full(
|
|
[max_num_seqs, 1], self.model_config.max_model_len, dtype="int64"
|
|
)
|
|
self.share_inputs["seq_lens_this_time"] = paddle.full(max_num_seqs, 0, dtype="int32")
|
|
self.share_inputs["seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["seq_lens_decoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["step_seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["step_seq_lens_decoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["step_idx"] = paddle.full([max_num_seqs, 1], 0, dtype="int64")
|
|
self.share_inputs["not_need_stop"] = paddle.full(
|
|
[1], False, dtype="bool"
|
|
).cpu() # TODO(gongshaotian): move to pinnd memory
|
|
self.share_inputs["stop_flags"] = paddle.full([max_num_seqs, 1], True, dtype="bool")
|
|
self.share_inputs["stop_nums"] = paddle.full([1], max_num_seqs, dtype="int64")
|
|
|
|
self.share_inputs["bad_tokens"] = paddle.full([1], -1, dtype="int64")
|
|
self.share_inputs["next_tokens"] = paddle.full([max_num_seqs, 1], -1, dtype="int64")
|
|
self.share_inputs["is_block_step"] = paddle.full([max_num_seqs], False, dtype="bool").cpu()
|
|
self.share_inputs["encoder_block_lens"] = paddle.full([max_num_seqs], 0, dtype="int32").cpu()
|
|
self.share_inputs["step_block_list"] = paddle.full([max_num_seqs], -1, dtype="int32").cpu()
|
|
self.share_inputs["step_lens"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
self.share_inputs["recover_block_list"] = paddle.full([max_num_seqs], -1, dtype="int32").cpu()
|
|
self.share_inputs["recover_lens"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
self.share_inputs["need_block_list"] = paddle.full([max_num_seqs], -1, dtype="int32").cpu()
|
|
self.share_inputs["need_block_len"] = paddle.full([1], 0, dtype="int32").cpu()
|
|
self.share_inputs["used_list_len"] = paddle.full([max_num_seqs], 0, dtype="int32").cpu()
|
|
self.share_inputs["infer_seed"] = paddle.full([max_num_seqs, 1], 0, dtype="int64").cpu()
|
|
self.share_inputs["first_token_ids"] = paddle.full([max_num_seqs, 1], -1, dtype="int64")
|
|
self.share_inputs["ori_seq_lens_encoder"] = paddle.full([max_num_seqs, 1], 0, dtype="int32").cpu()
|
|
self.share_inputs["system_lens"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["system_ids"] = paddle.full([max_num_seqs, 1], -1, dtype="int32")
|
|
|
|
self.share_inputs["ids_remove_padding"] = paddle.full(
|
|
[max_num_seqs * self.parallel_config.max_model_len], 0, dtype="int64"
|
|
)
|
|
self.share_inputs["cum_offsets"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["padding_offset"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["cu_seqlens_q"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["cu_seqlens_k"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
# AttentionBackend buffers
|
|
self.share_inputs["decoder_batch_ids"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
self.share_inputs["decoder_tile_ids_per_batch"] = paddle.full([max_num_seqs, 1], 0, dtype="int32")
|
|
|
|
# Initialize rotary position embedding
|
|
tmp_position_ids = paddle.arange(self.parallel_config.max_model_len).reshape((1, -1))
|
|
# TODO(gongshaotian): move to models
|
|
self.share_inputs["rope_emb"] = get_rope(
|
|
rotary_dim=self.model_config.head_dim,
|
|
position_ids=tmp_position_ids,
|
|
base=self.model_config.rope_theta,
|
|
model_config=self.model_config,
|
|
)
|
|
|
|
# Set block tables
|
|
pre_max_block_num = (
|
|
self.parallel_config.max_model_len + self.cache_config.block_size - 1
|
|
) // self.cache_config.block_size + self.cache_config.enc_dec_block_num
|
|
self.share_inputs["block_tables"] = paddle.full([max_num_seqs, pre_max_block_num], -1, dtype="int32").cpu()
|
|
|
|
# Initialize free list
|
|
free_list = list(
|
|
range(
|
|
self.parallel_config.total_block_num - 2,
|
|
int(self.parallel_config.total_block_num * self.cache_config.kv_cache_ratio) - 1,
|
|
-1,
|
|
)
|
|
)
|
|
self.free_list_len = len(free_list)
|
|
self.share_inputs["free_list"] = paddle.to_tensor(free_list, dtype="int32").cpu()
|
|
self.share_inputs["free_list_len"] = paddle.full([1], self.free_list_len, dtype="int32").cpu()
|
|
|
|
# Initialize stop seqs
|
|
self.share_inputs["stop_seqs_len"] = paddle.full([self.model_config.max_stop_seqs_num], 0, dtype="int32")
|
|
self.share_inputs["stop_seqs"] = paddle.full(
|
|
[self.model_config.max_stop_seqs_num, self.model_config.stop_seqs_max_len], -1, dtype="int32"
|
|
)
|
|
if self.speculative_decoding:
|
|
max_draft_token_num = self.speculative_config.num_speculative_tokens
|
|
self.share_inputs["input_ids_cpu"] = paddle.full(
|
|
shape=[max_num_seqs, self.parallel_config.max_model_len], fill_value=1, dtype="int64"
|
|
).cpu()
|
|
self.share_inputs["accept_tokens"] = paddle.full(
|
|
shape=[max_num_seqs, max_draft_token_num + 1], fill_value=0, dtype="int64"
|
|
)
|
|
self.share_inputs["accept_num"] = paddle.full(shape=[max_num_seqs], fill_value=0, dtype="int32")
|
|
self.share_inputs["draft_tokens"] = paddle.full(
|
|
shape=[max_num_seqs, max_draft_token_num + 1], fill_value=0, dtype="int64"
|
|
)
|
|
|
|
self.share_inputs["actual_draft_token_num"] = paddle.full(
|
|
shape=[max_num_seqs], fill_value=max_draft_token_num, dtype="int32"
|
|
)
|
|
self.share_inputs["output_cum_offsets"] = paddle.full(shape=[max_num_seqs, 1], fill_value=0, dtype="int32")
|
|
self.share_inputs["output_padding_offset"] = paddle.full(
|
|
shape=[max_num_seqs * (max_draft_token_num + 1)], fill_value=0, dtype="int32"
|
|
)
|
|
|
|
def _prepare_inputs(self) -> None:
|
|
"""prepare the model inputs"""
|
|
from fastdeploy.model_executor.ops.intel_hpu import prepare_block_metadata
|
|
|
|
(
|
|
ids_remove_padding,
|
|
rotary_embs,
|
|
block_groups,
|
|
block_list,
|
|
block_indices,
|
|
block_offsets,
|
|
block_mapping,
|
|
attention_mask,
|
|
batch_ids,
|
|
total_batch,
|
|
is_prompt,
|
|
) = prepare_block_metadata(
|
|
self.share_inputs["input_ids"],
|
|
self.share_inputs["rope_emb"],
|
|
self.share_inputs["block_tables"],
|
|
self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["seq_lens_decoder"],
|
|
self.cache_config.block_size,
|
|
self.parallel_config.dtype,
|
|
)
|
|
is_prompt = is_prompt.item() == 1 if is_prompt.item() > 0 else None
|
|
if is_prompt is True:
|
|
attention_mask = None
|
|
# cum_offsets = None
|
|
self.share_inputs["ids_remove_padding"] = ids_remove_padding
|
|
self.share_inputs["rotary_embs"] = rotary_embs
|
|
self.share_inputs["block_groups"] = block_groups
|
|
self.share_inputs["block_list"] = block_list
|
|
self.share_inputs["block_indices"] = block_indices
|
|
self.share_inputs["block_offsets"] = block_offsets
|
|
self.share_inputs["block_mapping"] = block_mapping
|
|
self.share_inputs["block_bias"] = attention_mask
|
|
self.share_inputs["block_size"] = self.cache_config.block_size
|
|
self.share_inputs["batch_ids"] = batch_ids
|
|
self.share_inputs["total_batch"] = total_batch.item()
|
|
self.share_inputs["is_prompt"] = is_prompt
|
|
self.initialize_forward_meta()
|
|
|
|
def _prepare_sampler_inputs(self, sampled_ids) -> None:
|
|
if self.forward_meta.total_batch == self.share_inputs["temperature"].shape[0]:
|
|
self.sampling_metadata = SamplingMetadata(
|
|
temperature=self.share_inputs["temperature"],
|
|
top_p=self.share_inputs["top_p"],
|
|
step_idx=self.share_inputs["step_idx"],
|
|
prompt_ids=self.share_inputs["input_ids"],
|
|
pre_token_ids=self.share_inputs["pre_ids"],
|
|
stop_flags=self.share_inputs["stop_flags"],
|
|
seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
|
|
seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
|
|
frequency_penalties=self.share_inputs["frequency_score"],
|
|
presence_penalties=self.share_inputs["presence_score"],
|
|
repetition_penalties=self.share_inputs["penalty_score"],
|
|
min_dec_lens=self.share_inputs["min_dec_len"],
|
|
bad_words_token_ids=self.share_inputs["bad_tokens"],
|
|
eos_token_ids=self.share_inputs["eos_token_id"],
|
|
)
|
|
else:
|
|
from fastdeploy.model_executor.ops.intel_hpu import fused_index_select
|
|
|
|
(
|
|
temperature,
|
|
top_p,
|
|
step_idx,
|
|
prompt_token_ids,
|
|
pre_token_ids,
|
|
stop_flags,
|
|
seq_lens_encoder,
|
|
seq_lens_decoder,
|
|
frequency_penalties,
|
|
presence_penalties,
|
|
repetition_penalties,
|
|
min_dec_lens,
|
|
) = fused_index_select(
|
|
self.share_inputs["temperature"],
|
|
self.share_inputs["top_p"],
|
|
self.share_inputs["step_idx"],
|
|
self.share_inputs["input_ids"],
|
|
self.share_inputs["pre_ids"],
|
|
self.share_inputs["stop_flags"],
|
|
self.share_inputs["seq_lens_encoder"],
|
|
self.share_inputs["seq_lens_decoder"],
|
|
self.share_inputs["frequency_score"],
|
|
self.share_inputs["presence_score"],
|
|
self.share_inputs["penalty_score"],
|
|
self.share_inputs["min_dec_len"],
|
|
sampled_ids,
|
|
self.forward_meta.total_batch,
|
|
)
|
|
|
|
self.sampling_metadata = SamplingMetadata(
|
|
temperature=temperature,
|
|
top_p=top_p,
|
|
step_idx=step_idx,
|
|
prompt_ids=prompt_token_ids,
|
|
pre_token_ids=pre_token_ids,
|
|
stop_flags=stop_flags,
|
|
seq_lens_encoder=seq_lens_encoder,
|
|
seq_lens_decoder=seq_lens_decoder,
|
|
frequency_penalties=frequency_penalties,
|
|
presence_penalties=presence_penalties,
|
|
repetition_penalties=repetition_penalties,
|
|
min_dec_lens=min_dec_lens,
|
|
bad_words_token_ids=self.share_inputs["bad_tokens"],
|
|
eos_token_ids=self.share_inputs["eos_token_id"],
|
|
)
|
|
|
|
def load_model(self) -> None:
|
|
"""load or download model"""
|
|
logger.info(f"Starting to load model {self.model_config.architectures[0]}")
|
|
time_before_load = time.perf_counter()
|
|
# 1. Load original model
|
|
model_loader = get_model_loader(load_config=self.fd_config.load_config)
|
|
self.model = model_loader.load_model(fd_config=self.fd_config)
|
|
# 1.1 Load RL dynamic model
|
|
if self.fd_config.load_config.dynamic_load_weight:
|
|
from fastdeploy.rl.dynamic_weight_manager import DynamicWeightManager
|
|
|
|
self.dynamic_weight_manager = DynamicWeightManager(self.fd_config, self.model)
|
|
|
|
# 2. Load lora model
|
|
|
|
# 3. Load drafter model(for speculative decoding)
|
|
|
|
# 4. Convert model to HPU format
|
|
self.model = convert_model(self.model)
|
|
|
|
time_after_load = time.perf_counter()
|
|
logger.info(f"Model loading took {time_after_load - time_before_load} seconds")
|
|
|
|
# 4. Init proposer for speculative method
|
|
self.init_speculative_proposer()
|
|
|
|
def get_model(self) -> nn.Layer:
|
|
"""get current model"""
|
|
return self.model
|
|
|
|
def initialize_forward_meta(self):
|
|
"""
|
|
Initialize forward meta and attention meta data
|
|
"""
|
|
# Initialize forward meta
|
|
self.forward_meta = HPUForwardMeta.init_forward_meta(self.share_inputs, self.attn_backends[0])
|
|
|
|
# Initialzie attention meta data
|
|
for attn_backend in self.attn_backends:
|
|
attn_backend.init_attention_metadata(self.forward_meta)
|
|
|
|
def clear_cache(self):
|
|
"""Clear cached data from shared inputs and forward metadata."""
|
|
self.share_inputs.pop("caches", None)
|
|
if self.forward_meta is not None:
|
|
self.forward_meta.clear_caches()
|
|
|
|
def initialize_kv_cache(self) -> None:
|
|
"""
|
|
Initialize kv cache
|
|
"""
|
|
cache_kvs = {}
|
|
max_block_num = self.num_gpu_blocks
|
|
|
|
kv_cache_shape = self.attn_backends[0].get_kv_cache_shape(max_num_blocks=max_block_num)
|
|
|
|
for i in range(self.model_config.num_hidden_layers):
|
|
cache_type = self.parallel_config.dtype
|
|
cache_kvs["key_caches_{}".format(i)] = paddle.full(
|
|
shape=kv_cache_shape,
|
|
fill_value=0,
|
|
dtype=cache_type,
|
|
)
|
|
cache_kvs["value_caches_{}".format(i)] = paddle.full(
|
|
shape=kv_cache_shape,
|
|
fill_value=0,
|
|
dtype=cache_type,
|
|
)
|
|
self.share_inputs["caches"] = list(cache_kvs.values())
|
|
for value in cache_kvs.values():
|
|
del value
|
|
|
|
def initialize_attn_backend(self) -> None:
|
|
"""
|
|
Initialize attention backends and forward metadata
|
|
"""
|
|
assert len(self.attn_backends) == 0
|
|
|
|
# TODO(gongshaotian): Get rank from config
|
|
num_heads = self.model_config.num_attention_heads // self.parallel_config.tensor_parallel_size
|
|
self.model_config.kv_num_heads = (
|
|
int(self.model_config.num_key_value_heads) // self.parallel_config.tensor_parallel_size
|
|
)
|
|
head_dim = self.model_config.head_dim
|
|
|
|
# Get the attention backend
|
|
attn_cls = get_attention_backend()
|
|
attn_backend = attn_cls(
|
|
self.fd_config, kv_num_heads=self.model_config.kv_num_heads, num_heads=num_heads, head_dim=head_dim
|
|
)
|
|
if attn_backend is None:
|
|
raise NotImplementedError(
|
|
"Attention backend which you specified is not supported, please set FD_ATTENTION_BACKEND correctly."
|
|
)
|
|
self.attn_backends.append(attn_backend)
|
|
|
|
def _dummy_run(
|
|
self,
|
|
num_tokens: paddle.Tensor,
|
|
batch_size: paddle.Tensor,
|
|
expected_decode_len: int = 1,
|
|
in_capturing: bool = False,
|
|
) -> paddle.Tensor:
|
|
"""
|
|
Use dummy inputs to run before formal execution.
|
|
Args:
|
|
num_tokens:
|
|
expected_decode_len: Expected number of tokens generated
|
|
"""
|
|
self._dummy_prefill_inputs(
|
|
num_tokens=num_tokens, batch_size=batch_size, expected_decode_len=expected_decode_len
|
|
)
|
|
if self.speculative_method in ["mtp"]:
|
|
raise NotImplementedError("speculative sampling is not supported on Intel HPU.")
|
|
while True:
|
|
|
|
# 1. Compute real num_tokens
|
|
self._prepare_inputs()
|
|
|
|
# 2. Initialize attention backend and forward meta data
|
|
model_output = self.model(self.share_inputs["ids_remove_padding"], self.forward_meta)
|
|
|
|
hiddden_states = rebuild_padding_v3_1(
|
|
model_output,
|
|
self.forward_meta.batch_ids,
|
|
self.forward_meta.total_batch,
|
|
self.forward_meta.seq_lens_encoder,
|
|
self.forward_meta.is_prompt,
|
|
)
|
|
# 5. Execute spec decode
|
|
logits = self.model.compute_logits(hiddden_states)
|
|
|
|
self._prepare_sampler_inputs(self.forward_meta.batch_ids)
|
|
sampled_token_ids = self.sampler(
|
|
logits,
|
|
self.sampling_metadata,
|
|
self.forward_meta.batch_ids,
|
|
self.forward_meta.seq_lens_encoder.shape[0],
|
|
self.rank,
|
|
self.local_rank,
|
|
)
|
|
if self.parallel_config.tensor_parallel_size > 1:
|
|
dtype = sampled_token_ids.dtype
|
|
sampled_token_ids = sampled_token_ids.to("float32")
|
|
paddle.distributed.broadcast(sampled_token_ids, 0)
|
|
sampled_token_ids = sampled_token_ids.to(dtype)
|
|
|
|
# 6. post process
|
|
model_output_data = ModelOutputData(
|
|
next_tokens=self.share_inputs["next_tokens"],
|
|
stop_flags=self.share_inputs["stop_flags"],
|
|
step_idx=self.share_inputs["step_idx"],
|
|
max_dec_len=self.share_inputs["max_dec_len"],
|
|
pre_ids=self.share_inputs["pre_ids"],
|
|
seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
|
|
eos_token_id=self.share_inputs["eos_token_id"],
|
|
not_need_stop=self.share_inputs["not_need_stop"],
|
|
input_ids=self.share_inputs["input_ids"],
|
|
stop_nums=self.share_inputs["stop_nums"],
|
|
seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
|
|
seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
|
|
is_block_step=self.share_inputs["is_block_step"],
|
|
full_hidden_states=model_output,
|
|
msg_queue_id=self.parallel_config.msg_queue_id,
|
|
mp_rank=self.local_rank,
|
|
use_ep=self.parallel_config.use_ep,
|
|
draft_tokens=self.share_inputs["draft_tokens"] if self.speculative_decoding else None,
|
|
actual_draft_token_num=(
|
|
self.share_inputs["actual_draft_token_num"] if self.speculative_decoding else None
|
|
),
|
|
accept_tokens=self.share_inputs["accept_tokens"] if self.speculative_decoding else None,
|
|
accept_num=self.share_inputs["accept_num"] if self.speculative_decoding else None,
|
|
)
|
|
|
|
post_process_hpu(
|
|
sampled_token_ids=sampled_token_ids, model_output=model_output_data, is_warmuping=self.is_warmuping
|
|
)
|
|
|
|
# 7. Updata 'infer_seed' and step_cuda()
|
|
self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
|
|
self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
|
|
step_intel_hpu(self.share_inputs, self.cache_config.block_size, self.parallel_config.max_model_len)
|
|
|
|
if int((self.share_inputs["seq_lens_this_time"] > 0).sum()) == 0:
|
|
break
|
|
|
|
def _update_chunked_prefill(self, tasks):
|
|
"""
|
|
更新chunked prefill相关参数
|
|
"""
|
|
if not self.cache_config.enable_chunked_prefill:
|
|
return
|
|
|
|
for task in tasks:
|
|
if task.get("prefill_chunk_info", None) is None:
|
|
continue
|
|
|
|
if task.chunk_idx > len(task.prefill_chunk_info):
|
|
continue
|
|
self.restore_chunked_prefill_request[task.request_id] = task
|
|
|
|
for id, task in list(self.restore_chunked_prefill_request.items()):
|
|
idx = task.idx
|
|
logger.debug(f"{task.request_id} chunked prefill {task.chunk_idx}/{len(task.prefill_chunk_info)}")
|
|
start_idx = sum(task.prefill_chunk_info[: task.chunk_idx])
|
|
if task.chunk_idx == len(task.prefill_chunk_info):
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = 1
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = 0
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 1
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = start_idx + task.get("seq_lens_decoder", 0)
|
|
del self.restore_chunked_prefill_request[task.request_id]
|
|
else:
|
|
token_chunk_size = task.prefill_chunk_info[task.chunk_idx]
|
|
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["input_ids"][idx, :token_chunk_size] = np.array(
|
|
task.prompt_token_ids[start_idx : start_idx + token_chunk_size]
|
|
)
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = token_chunk_size
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 0
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = start_idx + task.get("seq_lens_decoder", 0)
|
|
if self.speculative_decoding and self.proposer.is_chunk_prefill_enabled():
|
|
self.proposer.update_task_chunk_prefill(task)
|
|
task.chunk_idx += 1
|
|
|
|
def _dummy_sampler_run(self) -> paddle.Tensor:
|
|
""" """
|
|
pass
|
|
|
|
def update_warmup_inputs(self, requests, is_decode=False):
|
|
for i in range(len(requests)):
|
|
request = requests[i]
|
|
idx = request["idx"]
|
|
length = len(request["input_ids"])
|
|
self.share_inputs["input_ids"][idx : idx + 1, :length] = np.array(request["input_ids"])
|
|
if is_decode:
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = 0
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = length
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = 1
|
|
self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = 0
|
|
self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = length
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 1
|
|
else:
|
|
self.share_inputs["seq_lens_encoder"][idx : idx + 1] = length
|
|
self.share_inputs["seq_lens_decoder"][idx : idx + 1] = 0
|
|
self.share_inputs["seq_lens_this_time"][idx : idx + 1] = length
|
|
self.share_inputs["step_seq_lens_encoder"][idx : idx + 1] = length
|
|
self.share_inputs["step_seq_lens_decoder"][idx : idx + 1] = 0
|
|
self.share_inputs["step_idx"][idx : idx + 1] = 0
|
|
|
|
if len(request["eos_token_ids"]) < self.model_config.eos_tokens_lens:
|
|
request["eos_token_ids"].append(request["eos_token_ids"][0])
|
|
self.share_inputs["eos_token_id"][:] = np.array(request["eos_token_ids"], dtype="int64").reshape(-1, 1)
|
|
|
|
self.share_inputs["top_p"][idx : idx + 1] = request.get("top_p", 0.7)
|
|
self.share_inputs["temperature"][idx : idx + 1] = request.get("temperature", 0.95)
|
|
self.share_inputs["penalty_score"][idx : idx + 1] = request.get("repetition_penalty", 1.0)
|
|
self.share_inputs["frequency_score"][idx : idx + 1] = request.get("frequency_penalty", 0.0)
|
|
self.share_inputs["presence_score"][idx : idx + 1] = request.get("presence_penalty", 0.0)
|
|
|
|
self.share_inputs["min_dec_len"][idx : idx + 1] = request.get("min_tokens", 1)
|
|
self.share_inputs["max_dec_len"][idx : idx + 1] = request.get("max_tokens", 1)
|
|
self.share_inputs["stop_flags"][idx : idx + 1] = False
|
|
|
|
self.share_inputs["first_token_ids"][idx : idx + 1] = self.share_inputs["input_ids"][idx : idx + 1, :1]
|
|
self.share_inputs["ori_seq_lens_encoder"][idx : idx + 1] = length
|
|
|
|
if request.get("seed") is not None:
|
|
self.share_inputs["infer_seed"][idx : idx + 1] = request.get("seed")
|
|
encoder_block_num = len(request["block_tables"])
|
|
self.share_inputs["encoder_block_lens"][idx : idx + 1] = encoder_block_num
|
|
self.share_inputs["block_tables"][idx : idx + 1, :] = -1
|
|
self.share_inputs["block_tables"][idx : idx + 1, :encoder_block_num] = np.array(
|
|
request["block_tables"], dtype="int32"
|
|
)
|
|
|
|
self.share_inputs["not_need_stop"][0] = True
|
|
|
|
def warm_up_bucket(self) -> None:
|
|
max_prefill_batch = 3 # Hard-Code in FastDeploy/fastdeploy/engine/config.py
|
|
warmup_max_model_len = min(
|
|
int(os.environ.get("HPU_WARMUP_MODEL_LEN", 4096)), self.parallel_config.max_model_len
|
|
)
|
|
prefill_batchs = []
|
|
prefill_batch_step = int(os.environ.get("BATCH_STEP_PREFILL", 1))
|
|
current_prefill_batch = prefill_batch_step
|
|
while current_prefill_batch <= max_prefill_batch:
|
|
prefill_batchs.append(int(current_prefill_batch))
|
|
current_prefill_batch += prefill_batch_step
|
|
|
|
max_prefill_length = self.cache_config.block_size + warmup_max_model_len
|
|
for prefill_batch in prefill_batchs:
|
|
for prefill_length in range(
|
|
self.cache_config.block_size, max_prefill_length, self.cache_config.block_size
|
|
):
|
|
if prefill_length * prefill_batch > self.scheduler_config.max_num_batched_tokens:
|
|
continue
|
|
logger.info(f"Warmup prefill_batch: {prefill_batch}, prefill_length: {prefill_length} start")
|
|
requests = [
|
|
{
|
|
"idx": i,
|
|
"input_ids": [5] * (prefill_length - 1),
|
|
"block_tables": list(range(prefill_length // self.cache_config.block_size)),
|
|
"eos_token_ids": [2],
|
|
}
|
|
for i in range(prefill_batch)
|
|
]
|
|
self.update_warmup_inputs(requests, is_decode=False)
|
|
self.execute_model()
|
|
logger.info(f"warmup prefill_batch: {prefill_batch}, prefill_length: {prefill_length} done")
|
|
|
|
decode_batchs = []
|
|
decode_batch_step = int(os.environ.get("BATCH_STEP_DECODE", 4))
|
|
current_decode_batch = decode_batch_step
|
|
while current_decode_batch <= self.scheduler_config.max_num_seqs:
|
|
decode_batchs.append(int(current_decode_batch))
|
|
current_decode_batch += decode_batch_step
|
|
|
|
decode_block_nums = []
|
|
decode_block_num_step = int(os.environ.get("BLOCK_STEP_DECODE", 16))
|
|
current_decode_block_num = decode_block_num_step
|
|
pre_max_block_num = (
|
|
warmup_max_model_len + self.cache_config.block_size - 1
|
|
) // self.cache_config.block_size + self.cache_config.enc_dec_block_num
|
|
while current_decode_block_num <= min(
|
|
self.num_gpu_blocks, pre_max_block_num * self.scheduler_config.max_num_seqs
|
|
):
|
|
decode_block_nums.append(int(current_decode_block_num))
|
|
current_decode_block_num += decode_block_num_step
|
|
|
|
logger.info(f"warmup decode_batchs: {decode_batchs}, decode_block_nums: {decode_block_nums} start")
|
|
for decode_batch in decode_batchs:
|
|
for decode_block_num in decode_block_nums:
|
|
if decode_block_num < decode_batch:
|
|
continue
|
|
if decode_block_num // decode_batch * self.cache_config.block_size > warmup_max_model_len:
|
|
continue
|
|
blocks = [decode_block_num // decode_batch for _ in range(decode_batch)]
|
|
remain_block_num = decode_block_num % decode_batch
|
|
b = 0
|
|
while remain_block_num > 0:
|
|
blocks[b] += 1
|
|
remain_block_num -= 1
|
|
b += 1
|
|
if blocks[0] * self.cache_config.block_size > warmup_max_model_len:
|
|
continue
|
|
logger.info(f"warmup decode_batch: {decode_batch}, decode_block_num: {decode_block_num} start")
|
|
requests = [
|
|
{
|
|
"idx": i,
|
|
"input_ids": [5] * (blocks[i] * self.cache_config.block_size - 1),
|
|
"block_tables": list(range(blocks[i])),
|
|
"eos_token_ids": [2],
|
|
}
|
|
for i in range(decode_batch)
|
|
]
|
|
self.update_warmup_inputs(requests, is_decode=True)
|
|
self.execute_model()
|
|
logger.info(f"Warmup decode_batch: {decode_batch}, decode_block_num: {decode_block_num} done")
|
|
self.share_inputs["not_need_stop"][0] = False
|
|
logger.info("Warmup bucket done")
|
|
|
|
def capture_model(self) -> None:
|
|
"""
|
|
Trigger CUDA Graph capture for all shapes in 'CudaGraphConfig.cudagraph_capture_sizes'
|
|
"""
|
|
if not self.use_cudagraph:
|
|
logger.info("Skipping CUDA graph capture. Please check GraphOptimizationConfig")
|
|
return
|
|
time_before_capture = time.perf_counter()
|
|
expected_decode_len = 1
|
|
capture_sizes = self.cudagraph_capture_sizes.copy()
|
|
for batch_size in sorted(capture_sizes, reverse=True):
|
|
self._dummy_run(
|
|
num_tokens=self.parallel_config.max_model_len,
|
|
batch_size=batch_size,
|
|
in_capturing=True,
|
|
expected_decode_len=expected_decode_len,
|
|
)
|
|
logger.info(f"Warm up the model with the batch size:{batch_size}, num tokens:{expected_decode_len}")
|
|
|
|
time_after_capture = time.perf_counter()
|
|
logger.info(f"Cuda Graph capturing took {time_after_capture - time_before_capture} seconds")
|
|
|
|
def _get_skip_idx(self, model_forward_batch):
|
|
"""
|
|
Get the index of the request that needs to be skipped during execution.
|
|
Args:
|
|
model_forward_batch: A list of requests to be executed by this runner.
|
|
Returns:
|
|
A list of indices corresponding to the requests that need to be skipped.
|
|
"""
|
|
skip_idx_list = []
|
|
if not self.parallel_config.enable_chunked_prefill or self.guided_backend is None:
|
|
return skip_idx_list
|
|
|
|
for task in model_forward_batch:
|
|
if task.get("prefill_chunk_info", None) is None or task.chunk_idx >= len(task.prefill_chunk_info):
|
|
continue
|
|
skip_idx_list.append(task.idx)
|
|
|
|
for task in self.restore_chunked_prefill_request.values():
|
|
if task.idx in skip_idx_list or task.chunk_idx >= len(task.prefill_chunk_info):
|
|
continue
|
|
skip_idx_list.append(task.idx)
|
|
|
|
return skip_idx_list
|
|
|
|
def execute_model(
|
|
self,
|
|
model_forward_batch: Optional[List[Request]] = None,
|
|
) -> Optional[ModelRunnerOutput]:
|
|
"""
|
|
The Entrance of model execute.
|
|
Args:
|
|
model_forward_batch: 'Request' contains information related to prompt and is an abstract
|
|
class at the server level, which is too granular for ModelRunner.
|
|
We plan to replace it with 'ModelForwardBatch'.
|
|
intermediate_tensors:
|
|
"""
|
|
# # 1. Prepare inputs of model and decoder.
|
|
start_time = time.time()
|
|
self._prepare_inputs()
|
|
# self.share_inputs["ids_remove_padding"].cpu()
|
|
# # 2. Padding inputs for cuda grph
|
|
end_time = time.time()
|
|
execution_time = (end_time - start_time) * 1000
|
|
real_bs = self.share_inputs["ids_remove_padding"].shape[0]
|
|
hpu_model_runner_profile_logger.info(f"_prepare_inputs time(ms): {execution_time}, BT={real_bs}")
|
|
start_time = time.time()
|
|
# # 3. Execute model
|
|
model_output = self.model(self.share_inputs["ids_remove_padding"], self.forward_meta)
|
|
if self.is_hpu_perf_breakdown_sync_mode:
|
|
model_output.cpu()
|
|
end_time = time.time()
|
|
execution_time = (end_time - start_time) * 1000
|
|
hpu_model_runner_profile_logger.info(
|
|
f"Model execution time(ms): {execution_time}, BT={real_bs}, block_list_shape={self.share_inputs['block_list'].shape}, block_indices_shape={self.share_inputs['block_indices'].shape}"
|
|
)
|
|
|
|
start_time = time.time()
|
|
start_time0 = time.time()
|
|
hiddden_states = rebuild_padding_v3_1(
|
|
model_output,
|
|
self.forward_meta.batch_ids,
|
|
self.forward_meta.total_batch,
|
|
self.forward_meta.seq_lens_encoder,
|
|
self.forward_meta.is_prompt,
|
|
)
|
|
end_time0 = time.time()
|
|
execution_time0 = (end_time0 - start_time0) * 1000
|
|
hpu_model_runner_profile_logger.info(f"RebuildPadding execution time(ms): {execution_time0}, BT={real_bs}")
|
|
# # 4. Compute logits, Sample
|
|
start_time1 = time.time()
|
|
logits = self.model.compute_logits(hiddden_states)
|
|
end_time1 = time.time()
|
|
execution_time1 = (end_time1 - start_time1) * 1000
|
|
hpu_model_runner_profile_logger.info(f"ComputeLogits execution time(ms): {execution_time1}, BT={real_bs}")
|
|
|
|
# data = np.random.rand(self.scheduler_config.max_num_seqs, self.model_config.vocab_size).astype(np.float32)
|
|
# logits = paddle.to_tensor(data, dtype='bfloat16')
|
|
start_time2 = time.time()
|
|
self._prepare_sampler_inputs(self.forward_meta.batch_ids)
|
|
sampled_token_ids = self.sampler(
|
|
logits,
|
|
self.sampling_metadata,
|
|
self.forward_meta.batch_ids,
|
|
self.forward_meta.seq_lens_encoder.shape[0],
|
|
self.rank,
|
|
self.local_rank,
|
|
)
|
|
if self.parallel_config.tensor_parallel_size > 1:
|
|
dtype = sampled_token_ids.dtype
|
|
sampled_token_ids = sampled_token_ids.to("float32")
|
|
paddle.distributed.broadcast(sampled_token_ids, 0)
|
|
sampled_token_ids = sampled_token_ids.to(dtype)
|
|
if self.is_hpu_perf_breakdown_sync_mode:
|
|
sampled_token_ids.cpu()
|
|
end_time2 = time.time()
|
|
execution_time2 = (end_time2 - start_time2) * 1000
|
|
hpu_model_runner_profile_logger.info(f"Sampler execution time(ms): {execution_time2}, BT={real_bs}")
|
|
# 5. Post Process
|
|
start_time3 = time.time()
|
|
model_output_data = ModelOutputData(
|
|
next_tokens=self.share_inputs["next_tokens"],
|
|
stop_flags=self.share_inputs["stop_flags"],
|
|
step_idx=self.share_inputs["step_idx"],
|
|
max_dec_len=self.share_inputs["max_dec_len"],
|
|
pre_ids=self.share_inputs["pre_ids"],
|
|
seq_lens_this_time=self.share_inputs["seq_lens_this_time"],
|
|
eos_token_id=self.share_inputs["eos_token_id"],
|
|
not_need_stop=self.share_inputs["not_need_stop"],
|
|
input_ids=self.share_inputs["input_ids"],
|
|
stop_nums=self.share_inputs["stop_nums"],
|
|
seq_lens_encoder=self.share_inputs["seq_lens_encoder"],
|
|
seq_lens_decoder=self.share_inputs["seq_lens_decoder"],
|
|
is_block_step=self.share_inputs["is_block_step"],
|
|
full_hidden_states=model_output,
|
|
msg_queue_id=self.parallel_config.msg_queue_id,
|
|
mp_rank=self.local_rank,
|
|
use_ep=self.parallel_config.use_ep,
|
|
draft_tokens=self.share_inputs["draft_tokens"] if self.speculative_decoding else None,
|
|
actual_draft_token_num=self.share_inputs["actual_draft_token_num"] if self.speculative_decoding else None,
|
|
accept_tokens=self.share_inputs["accept_tokens"] if self.speculative_decoding else None,
|
|
accept_num=self.share_inputs["accept_num"] if self.speculative_decoding else None,
|
|
)
|
|
|
|
# if self.speculative_config.method in ["mtp"] and self.parallel_config.splitwise_role == "prefill":
|
|
# skip_save_output = True
|
|
# else:
|
|
# skip_save_output = False
|
|
post_process_hpu(
|
|
sampled_token_ids=sampled_token_ids, model_output=model_output_data, is_warmuping=self.is_warmuping
|
|
)
|
|
end_time3 = time.time()
|
|
execution_time3 = (end_time3 - start_time3) * 1000
|
|
hpu_model_runner_profile_logger.info(f"PostProcessHpu execution time(ms): {execution_time3}, BT={real_bs}")
|
|
end_time = time.time()
|
|
execution_time = (end_time - start_time) * 1000
|
|
hpu_model_runner_profile_logger.info(f"PostProcessing execution time(ms): {execution_time}, BT={real_bs}")
|
|
|
|
# 6. Speculative decode
|
|
if self.speculative_decoding:
|
|
if self.speculative_method == "mtp":
|
|
self.proposer.run(full_hidden_states=hiddden_states)
|
|
else:
|
|
self.proposer.run(share_inputs=self.share_inputs)
|
|
|
|
# 7. Updata 'infer_seed' and step_cuda()
|
|
self.share_inputs["infer_seed"].add_(self.infer_seed_increment)
|
|
self.share_inputs["infer_seed"][:] %= self.MAX_INFER_SEED
|
|
start_time = time.time()
|
|
step_intel_hpu(self.share_inputs, self.cache_config.block_size, self.parallel_config.max_model_len)
|
|
end_time = time.time()
|
|
execution_time = (end_time - start_time) * 1000
|
|
hpu_model_runner_profile_logger.info(f"StepPaddle execution time(ms): {execution_time}, BT={real_bs}")
|
|
self._update_chunked_prefill(model_forward_batch)
|
|
self._add_cache(model_forward_batch)
|
|
|
|
if int(os.environ.get("HABANA_PROFILE", 0)) == 1:
|
|
self.prof.step()
|
|
return None
|
|
|
|
def _add_cache(self, model_forward_batch) -> None:
|
|
"""
|
|
Add cache for guided decoding.
|
|
"""
|
|
if self.guided_backend is None:
|
|
return
|
|
|
|
for request in model_forward_batch:
|
|
logits_cached = request.get("logits_cached", None)
|
|
if logits_cached is None or logits_cached:
|
|
continue
|
|
|
|
request.logits_cached = True
|
|
if isinstance(request.logits_processor, LogitsProcessorBase):
|
|
self.guided_backend.add_cache(request.schemata_key, request.logits_processor)
|
|
else:
|
|
self.guided_backend.add_cache(request.schemata_key, request.logits_processor.result())
|
|
|
|
def _execute_empty_input(self) -> None:
|
|
"""
|
|
In certain scenarios, such as during EP,
|
|
the runner needs to execute partial modules of the model without input data.
|
|
This requires the model to implement the `empty_input_forward` method.
|
|
"""
|
|
if hasattr(self.model, "empty_input_forward"):
|
|
self.model.empty_input_forward()
|
|
else:
|
|
raise ValueError(f"{type(self.model)} has no attribute 'empty_input_forward")
|
|
|
|
def profile_run(self) -> None:
|
|
"""Execute a forward pass with dummy inputs to profile the memory usage of the model."""
|
|
|
|
# Initialize kv cache for profile run. After profile run kv cache will be reset.
|
|
# TODO(gongshaotian): Optimize the management logic of kvcache
|
|
self.num_gpu_blocks = self.parallel_config.total_block_num
|
|
self.initialize_kv_cache()
|
|
|
|
# 1. Profile with multimodal encoder & encoder cache
|
|
|
|
# 2. Dummy run
|
|
self._dummy_run(
|
|
num_tokens=self.scheduler_config.max_num_batched_tokens,
|
|
batch_size=min(self.scheduler_config.max_num_seqs, 3),
|
|
)
|
|
|
|
# 3. gc
|
|
self.clear_cache()
|
|
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.clear_dummy_input()
|
|
|
|
def update_share_input_block_num(self, num_gpu_blocks: int) -> None:
|
|
"""
|
|
Set a globally unified block number and update the model's shared input.
|
|
Args:
|
|
num_gpu_blocks:
|
|
"""
|
|
self.num_gpu_blocks = num_gpu_blocks
|
|
|
|
# Reset block table and kv cache with global block num
|
|
self.initialize_kv_cache()
|
|
|
|
# Reset free list
|
|
free_list = list(
|
|
range(self.num_gpu_blocks - 2, int(self.num_gpu_blocks * self.cache_config.kv_cache_ratio) - 1, -1)
|
|
)
|
|
self.free_list_len = len(free_list)
|
|
self.share_inputs.update(
|
|
{
|
|
"free_list": paddle.to_tensor(free_list, dtype="int32").cpu(),
|
|
"free_list_len": paddle.full([1], self.free_list_len, dtype="int32").cpu(),
|
|
}
|
|
)
|
|
|
|
self.parallel_config.do_profile = False
|
|
|
|
if self.speculative_method in ["mtp"]:
|
|
self.proposer.update_block_num(num_gpu_blocks)
|
|
|
|
def cal_theortical_kvcache(self):
|
|
"""
|
|
Calculate the total block memory required at the model level
|
|
TODO(gongshaotian): Move to Attention Backend
|
|
"""
|
|
"""
|
|
Byte of dtype:
|
|
- default(bf16): 2
|
|
- cache_int8: 1
|
|
- cache_int4:
|
|
"""
|
|
cache_quant_dtype = None
|
|
if (
|
|
self.quant_config
|
|
and hasattr(self.quant_config, "kv_cache_quant_type")
|
|
and self.quant_config.kv_cache_quant_type is not None
|
|
):
|
|
cache_quant_dtype = self.quant_config.kv_cache_quant_type
|
|
|
|
if cache_quant_dtype is not None: # int8, int8_zp, fp8, fp8_zp
|
|
byte_of_dtype = 1
|
|
else: # default
|
|
byte_of_dtype = 2
|
|
|
|
hidden_dim = self.model_config.head_dim * self.model_config.kv_num_heads
|
|
# NOTE(liuzichang): Implement multi-layer MTP architecture in the future
|
|
num_layers = (
|
|
self.model_config.num_hidden_layers + self.speculative_config.num_gpu_block_expand_ratio
|
|
if self.speculative_method in ["mtp"]
|
|
else self.model_config.num_hidden_layers
|
|
)
|
|
required_memory = byte_of_dtype * 2 * (self.cache_config.block_size * hidden_dim) * num_layers # k + v
|
|
return required_memory
|
|
|
|
def not_need_stop(self) -> bool:
|
|
""" """
|
|
return self.share_inputs["not_need_stop"][0]
|