test.py

#!/usr/bin/env python3
import flashinfer
print("Testing FlashInfer...")
print('FlashInfer version', flashinfer.__version__)
import torch
import flashinfer

kv_len = 2048
num_kv_heads = 32
head_dim = 128

k = torch.randn(kv_len, num_kv_heads, head_dim).half().to(0)
v = torch.randn(kv_len, num_kv_heads, head_dim).half().to(0)

# decode attention

num_qo_heads = 32
q = torch.randn(num_qo_heads, head_dim).half().to(0)

o = flashinfer.single_decode_with_kv_cache(q, k, v) # decode attention without RoPE on-the-fly
o_rope_on_the_fly = flashinfer.single_decode_with_kv_cache(q, k, v, pos_encoding_mode="ROPE_LLAMA") # decode with LLaMA style RoPE on-the-fly

# append attention
append_qo_len = 128
q = torch.randn(append_qo_len, num_qo_heads, head_dim).half().to(0) # append attention, the last 128 tokens in the KV-Cache are the new tokens
o = flashinfer.single_prefill_with_kv_cache(q, k, v, causal=True) # append attention without RoPE on-the-fly, apply causal mask
o_rope_on_the_fly = flashinfer.single_prefill_with_kv_cache(q, k, v, causal=True, pos_encoding_mode="ROPE_LLAMA") # append attention with LLaMA style RoPE on-the-fly, apply causal mask

# prefill attention
qo_len = 2048
q = torch.randn(qo_len, num_qo_heads, head_dim).half().to(0) # prefill attention
o = flashinfer.single_prefill_with_kv_cache(q, k, v, causal=False) # prefill attention without RoPE on-the-fly, do not apply causal mask
print('FlashInfer OK\n')