import os

# support Q,KV Gen with TP
USE_PARALLEL_Q_KV_GEN = True

# support Merge Q,KV Gen, Q,QR weights Merge
USE_MERGE_Q_KV_GEN_AND_Q_QR = True

# Support FP8 Weights for WQ,QR
W_Q_W_QR_WUV_WUK_USE_FP8 = True

# fused prefill
USE_FUSED_PREFILL = True

# fused prefill stage1
USE_FUSED_PREFILL_STAGE1 = True

# All Request Seq Lens
DO_SEQ_LENS = 0
def update_seqence_length(seq_num):
    global DO_SEQ_LENS
    DO_SEQ_LENS = seq_num

USE_DS3_SAMPLER = int(os.getenv("USE_DS3_SAMPLER", 1))
USE_DS3_SAMPLER_OP = int(os.getenv("USE_DS3_SAMPLER_OP", 1))

# cut prefill seq len
CUT_PREFILL_SEQ_LEN = int(os.getenv("CUT_PREFILL_SEQ_LEN", -1))

# llm max prefill seq len
LLM_MAX_PREFILL_SEQ_LEN = int(os.getenv("LLM_MAX_PREFILL_SEQ_LEN", 56 * 1024))

# All Fused Decode, default is cpu loop
USE_DECODER_LAYER_FUSE_MODE = int(os.getenv("USE_DECODER_LAYER_FUSE_MODE", 1))

# Fused all layers, use cmcu loop
FUSE_ALL_DECODER_LAYERS = int(os.getenv("FUSE_ALL_DECODER_LAYERS", 1))

# where to use flash attention (default: 1)
USE_FLASH_ATTENTION = int(os.getenv("USE_FLASH_ATTENTION", 1))

# transpose gptq weight KN => NK
TRANSPOSE_GPTQ_WEIGHT = True

# qwen fused attention
USE_FUSED_QWEN_ATTENTION = int(os.getenv("USE_FUSED_QWEN_ATTENTION", 1))

# support MTP eh_proj with TP
USE_PARALLEL_MTP_EH_PROJ = int(os.getenv("USE_PARALLEL_MTP_EH_PROJ", 1))

# kv_cache group size
BLOCK_GROUP_SIZE = int(os.getenv("BLOCK_GROUP_SIZE", 8192))

# bert fused attention
USE_FUSED_BERT_ATTENTION = int(os.getenv("USE_FUSED_BERT_ATTENTION", 1))

# fused mlp vision
USE_FUSED_MLP_VISION = int(os.getenv("USE_FUSED_MLP_VISION", 1))