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635 lines
26 KiB
Python
635 lines
26 KiB
Python
# 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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import random
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from typing import List
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import numpy as np
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# tests/speculate_verify.py
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import paddle
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from fastdeploy.model_executor.ops.xpu import speculate_verify
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def topp_sampling_kernel(candidate_ids, candidate_scores, curand_value, candidate_len, topp, tid=0):
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"""
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Python 仿真版 Top-p 样本选择函数。
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参数:
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- candidate_ids: [candidate_len] int64 array,候选 token
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- candidate_scores: [candidate_len] float32 array,对应概率
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- curand_value: float,范围在 [0,1)，模拟 GPU 中的 curand_uniform
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- candidate_len: int,候选个数
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- topp: float,TopP 截断阈值
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- tid: 模拟线程 ID,仅用于调试（非必须）
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返回:
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- 采样得到的 token(int64)
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"""
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rand_top_p = curand_value * topp
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sum_scores = 0.0
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for i in range(candidate_len):
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print(
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f"debug sample i:{i} scores:{candidate_scores[i]},ids:{candidate_ids[i]},curand_value{curand_value},topp{topp}, value*topp{rand_top_p}"
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)
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sum_scores += candidate_scores[i]
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sum_scores += candidate_scores[i]
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if rand_top_p <= sum_scores:
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return candidate_ids[i]
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return candidate_ids[0] # fallback（理论上不会走到这）
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# def is_in_end(id: int, end_ids: np.ndarray, length: int) -> bool:
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# """
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# 判断 id 是否存在于 end_ids 前 length 个元素中。
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# """
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# for i in range(length):
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# if id == end_ids[i]:
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# return True
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# return False
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# def is_in(candidates: np.ndarray, draft: int, candidate_len: int) -> bool:
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# """
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# 判断 draft 是否在 candidates 的前 candidate_len 个元素中。
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# """
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# for i in range(candidate_len):
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# if draft == candidates[i]:
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# return True
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# return False
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# ---------------- NumPy 参考实现 ----------------
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def speculate_verify_np(
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accept_tokens,
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accept_num,
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step_idx,
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stop_flags,
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seq_lens_encoder,
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seq_lens_decoder,
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draft_tokens,
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seq_lens_this_time,
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verify_tokens,
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verify_scores,
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max_dec_len,
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end_tokens,
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is_block_step,
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output_cum_offsets,
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actual_candidate_len,
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actual_draft_token_nums,
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topp,
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max_seq_len,
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verify_window,
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enable_topp,
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):
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def is_in_end(token, end_tokens, end_length):
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return token in end_tokens[:end_length]
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def is_in(candidate_list, token, length):
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return token in candidate_list[:length]
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bsz = accept_tokens.shape[0]
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real_bsz = seq_lens_this_time.shape[0]
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max_draft_tokens = draft_tokens.shape[1]
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end_length = end_tokens.shape[0]
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max_candidate_len = verify_tokens.shape[1]
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use_topk = False
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prefill_one_step_stop = False
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# random
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initial_seed = 0
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infer_seed: List[int] = [initial_seed] * bsz
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dev_curand_states: List[float] = []
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# 循环生成随机数
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for i in range(bsz):
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current_seed = infer_seed[i] # 这里 current_seed 总是等于 initial_seed
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# 使用当前的种子创建一个独立的随机数生成器实例
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# 这对应于 C++ 的 std::mt19937_64 engine(infer_seed[i]);
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rng = random.Random(current_seed)
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# 从独立的生成器中获取一个 [0.0, 1.0) 范围内的浮点数
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# 这对应于 C++ 的 dist(engine);
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dev_curand_states.append(rng.random())
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# --- 在函数内部进行扁平化操作 ---
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# 只有那些在 C++ 中通过指针算术访问的多维数组需要扁平化
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accept_tokens_flat = accept_tokens.reshape(-1)
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draft_tokens_flat = draft_tokens.reshape(-1)
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verify_tokens_flat = verify_tokens.reshape(-1)
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verify_scores_flat = verify_scores.reshape(-1)
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print(f"DEBUG: accept_tokens_flat shape: {accept_tokens_flat.shape}")
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print(f"DEBUG: draft_tokens_flat shape: {draft_tokens_flat.shape}")
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print(f"DEBUG: verify_tokens_flat shape: {verify_tokens_flat.shape}")
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print(f"DEBUG: verify_scores_flat shape: {verify_scores_flat.shape}")
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# 其他数组 (如 accept_num, step_idx, stop_flags, end_tokens, dev_curand_states, actual_candidate_len,
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# seq_lens_encoder, seq_lens_decoder, actual_draft_token_nums, topp_values,
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# seq_lens_this_time, max_dec_len, is_block_step, output_cum_offsets)
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# 根据其 C++ 原始定义，如果本身就是一维的，则不需要额外的 reshape。
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# 这里直接使用其原始引用，或者如果其维度不确定，也可以做 flatten()。
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# 为了明确，我们假设这些参数如果不是 (N, K) 形式，就已经是 (N,) 形式。
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print()
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# 遍历批次中的每个样本
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for bid in range(real_bsz):
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# C++: const int start_token_id = bid * max_seq_len - output_cum_offsets[bid];
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start_token_id = bid * max_seq_len - output_cum_offsets[bid]
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accept_num_now = 1
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stop_flag_now_int = 0
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print(
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f"DEBUG: start_token_id: {start_token_id}, max_seq_len: {max_seq_len}, output_cum_offsets[{bid}]: {output_cum_offsets[bid]}"
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)
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# C++: if (!(is_block_step[bid] || bid >= real_bsz))
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if not (
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is_block_step[bid] or bid >= real_bsz
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): # bid >= real_bsz 在 Python for 循环中天然满足，但为保持一致保留
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if stop_flags[bid]:
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stop_flag_now_int = 1
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else:
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# C++: auto *verify_tokens_now = verify_tokens + start_token_id * max_candidate_len;
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# Python: verify_tokens_now 是一个指向当前批次 verify_tokens 起始的扁平视图
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# 模拟了 C++ 中指针偏移后的“基地址”
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verify_tokens_now = verify_tokens_flat[start_token_id * max_candidate_len :] # 从基址到末尾
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# C++: auto *draft_tokens_now = draft_tokens + bid * max_draft_tokens;
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# Python: draft_tokens_now 是当前批次 draft_tokens 起始的扁平视图
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draft_tokens_now = draft_tokens_flat[bid * max_draft_tokens :] # 从基址到末尾
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# C++: auto *actual_candidate_len_now = actual_candidate_len + start_token_id;
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# Python: actual_candidate_len_now 是当前批次 actual_candidate_len 起始的扁平视图
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actual_candidate_len_now = actual_candidate_len[start_token_id:] # actual_candidate_len 已经是 1D
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# C++: int i = 0;
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i = 0
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# C++: for (; i < seq_lens_this_time[bid] - 1; i++)
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for loop_i in range(seq_lens_this_time[bid] - 1): # 使用 loop_i 作为 Python 的循环变量
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i = loop_i # 保持 C++ 的 i 在每次迭代中更新为当前索引
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# C++: if (seq_lens_encoder[bid] != 0)
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if seq_lens_encoder[bid] != 0:
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break
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if use_topk:
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# C++: if (verify_tokens_now[i * max_candidate_len] == draft_tokens_now[i + 1])
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if verify_tokens_now[i * max_candidate_len] == draft_tokens_now[i + 1]:
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step_idx[bid] += 1
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accept_token = draft_tokens_now[i + 1]
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# C++: accept_tokens[bid * max_draft_tokens + i] = accept_token;
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accept_tokens_flat[bid * max_draft_tokens + i] = accept_token
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# C++: if (is_in_end(accept_token, end_tokens, end_length) || step_idx[bid] >= max_dec_len[bid])
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if is_in_end(accept_token, end_tokens, end_length) or step_idx[bid] >= max_dec_len[bid]:
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stop_flags[bid] = True
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stop_flag_now_int = 1
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if step_idx[bid] >= max_dec_len[bid]:
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accept_tokens_flat[bid * max_draft_tokens + i] = end_tokens[0]
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break
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else:
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accept_num_now += 1
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else:
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break
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else: # C++: else (Top P verify)
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# C++: auto actual_candidate_len_value = actual_candidate_len_now[i] > max_candidate_len ? max_candidate_len : actual_candidate_len_now[i];
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actual_candidate_len_value = min(actual_candidate_len_now[i], max_candidate_len)
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# C++: if (is_in(verify_tokens_now + i * max_candidate_len, draft_tokens_now[i + 1], actual_candidate_len_value))
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# 传入当前候选的扁平视图
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verify_tokens_current_candidate_view = verify_tokens_now[
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i * max_candidate_len : (i + 1) * max_candidate_len
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]
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if is_in(
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verify_tokens_current_candidate_view,
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draft_tokens_now[i + 1],
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actual_candidate_len_value,
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):
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step_idx[bid] += 1
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accept_token = draft_tokens_now[i + 1]
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accept_tokens_flat[bid * max_draft_tokens + i] = accept_token
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if is_in_end(accept_token, end_tokens, end_length) or step_idx[bid] >= max_dec_len[bid]:
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stop_flags[bid] = True
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stop_flag_now_int = 1
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if step_idx[bid] >= max_dec_len[bid]:
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accept_tokens_flat[bid * max_draft_tokens + i] = end_tokens[0]
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break
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else:
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accept_num_now += 1
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else:
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# TopK verify
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ii = i # C++ 中 ii 从 i 开始
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# C++: if (max_candidate_len >= 2 && verify_tokens_now[ii * max_candidate_len + 1] == draft_tokens_now[ii + 1])
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if (
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max_candidate_len >= 2
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and verify_tokens_now[ii * max_candidate_len + 1] == draft_tokens_now[ii + 1]
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): # top-2
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j = 0
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ii += 1 # C++ 中 ii 从下一个位置开始检查
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# C++: for (; j < verify_window && ii < seq_lens_this_time[bid] - 1; j++, ii++)
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while j < verify_window and ii < seq_lens_this_time[bid] - 1:
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if verify_tokens_now[ii * max_candidate_len] != draft_tokens_now[ii + 1]:
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break
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j += 1
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ii += 1
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# C++: if (j >= verify_window)
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if j >= verify_window: # accept all
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accept_num_now += verify_window + 1
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step_idx[bid] += verify_window + 1
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# C++: for (; i < ii; i++)
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for k_accepted_idx in range(i, ii): # i 会被更新
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accept_token = draft_tokens_now[k_accepted_idx + 1]
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accept_tokens_flat[bid * max_draft_tokens + k_accepted_idx] = accept_token
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if (
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is_in_end(
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accept_token,
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end_tokens,
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end_length,
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)
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or step_idx[bid] >= max_dec_len[bid]
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):
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stop_flags[bid] = True
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stop_flag_now_int = 1
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if step_idx[bid] >= max_dec_len[bid]:
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accept_tokens_flat[bid * max_draft_tokens + k_accepted_idx] = (
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end_tokens[0]
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)
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accept_num_now -= 1
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step_idx[bid] -= 1
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break # 跳出内层接受循环
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break # 跳出主验证循环 (TopK 逻辑结束，无论成功与否)
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# else 的 break 对应 is_in(Top P 验证失败，也不是 TopK 匹配)
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break # 跳出主验证循环
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# 采样阶段 (Sampling Phase)
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# C++ 中 i 变量在循环结束后会保留其最终值，直接用于采样
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# Python 同样，loop_i 的最终值赋值给了 i
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if not stop_flag_now_int:
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accept_token: int
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# C++: const float *verify_scores_now = verify_scores + start_token_id * max_candidate_len;
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# Python: verify_scores_now 对应 C++ 中从 start_token_id 开始的 verify_scores 视图
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verify_scores_now = verify_scores_flat[start_token_id * max_candidate_len :]
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step_idx[bid] += 1
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if enable_topp:
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# C++: auto actual_candidate_len_value = actual_candidate_len_now[i] > max_candidate_len ? max_candidate_len : actual_candidate_len_now[i];
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actual_candidate_len_value = min(actual_candidate_len_now[i], max_candidate_len)
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# 传入当前候选的扁平视图
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verify_tokens_sampling_view = verify_tokens_now[
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i * max_candidate_len : (i + 1) * max_candidate_len
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]
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verify_scores_sampling_view = verify_scores_now[
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i * max_candidate_len : (i + 1) * max_candidate_len
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]
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# C++: accept_token = topp_sampling_kernel(...)
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accept_token = topp_sampling_kernel(
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verify_tokens_sampling_view,
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verify_scores_sampling_view,
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dev_curand_states[i], # C++: dev_curand_states + i
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actual_candidate_len_value,
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topp[bid], # C++: topp[bid]
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bid, # C++: bid
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)
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else:
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accept_token = int(verify_tokens_now[i * max_candidate_len])
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print(
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"debug python last accept_token",
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accept_token,
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"prefill_one_step_stop",
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prefill_one_step_stop,
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)
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# C++: accept_tokens[bid * max_draft_tokens + i] = accept_token;
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accept_tokens_flat[bid * max_draft_tokens + i] = accept_token
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if prefill_one_step_stop:
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stop_flags[bid] = True
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if is_in_end(accept_token, end_tokens, end_length) or step_idx[bid] >= max_dec_len[bid]:
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stop_flags[bid] = True
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stop_flag_now_int = 1
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if step_idx[bid] >= max_dec_len[bid]:
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accept_tokens_flat[bid * max_draft_tokens + i] = end_tokens[0]
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accept_num[bid] = accept_num_now
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return accept_tokens, accept_num, step_idx, stop_flags
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# ---------------- 生成随机输入 ----------------
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def gen_speculate_verify_inputs(
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real_bsz=123,
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max_draft_tokens=16,
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max_seq_len=256,
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max_candidate_len=8,
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verify_window=2,
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end_length=4,
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enable_topp=True,
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seed=2025,
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):
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rng = np.random.default_rng(seed)
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# 基础输入
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seq_lens_encoder = rng.integers(0, 3, size=real_bsz, dtype=np.int32)
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seq_lens_decoder = rng.integers(1, max_draft_tokens, size=real_bsz, dtype=np.int32)
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draft_tokens = rng.integers(0, 1000, size=(real_bsz, max_draft_tokens), dtype=np.int64)
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actual_draft_token_nums = rng.integers(1, max_draft_tokens + 1, size=real_bsz, dtype=np.int32)
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seq_lens_this_time = rng.integers(1, max_seq_len + 1, size=real_bsz, dtype=np.int32)
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sum_seq_this_time = int(np.sum(seq_lens_this_time))
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# print("debug param set sum_seq_this_time",sum_seq_this_time)
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# print("debug param real_bsz * max_draft_tokens < 2k",real_bsz * max_draft_tokens)
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# print("debug sum_seq_this_time * max_candidate_len < 2k",sum_seq_this_time * max_candidate_len)
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verify_tokens = rng.integers(0, 1000, size=(sum_seq_this_time, max_candidate_len), dtype=np.int64)
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verify_scores = rng.random(size=(sum_seq_this_time, max_candidate_len)).astype(np.float32)
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max_dec_len = rng.integers(16, 64, size=real_bsz, dtype=np.int64)
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end_tokens = rng.integers(1, 1000, size=end_length, dtype=np.int64)
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is_block_step = rng.integers(0, 2, size=real_bsz, dtype=bool)
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# output_cum_offsets = np.zeros_like(seq_lens_this_time)
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# output_cum_offsets[1:] = np.cumsum(seq_lens_this_time[:-1])
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blank_lengths = max_seq_len - seq_lens_this_time
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output_cum_offsets = np.concatenate([[0], np.cumsum(blank_lengths[:-1])])
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output_cum_offsets = output_cum_offsets.astype("int32")
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actual_candidate_len = rng.integers(1, max_candidate_len + 1, size=sum_seq_this_time, dtype=np.int32)
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topp = (
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rng.uniform(0.8, 1.0, size=real_bsz).astype(np.float32)
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if enable_topp
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else np.zeros(real_bsz, dtype=np.float32)
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)
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# 输出（占位）
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accept_tokens = np.zeros((real_bsz, max_draft_tokens), dtype=np.int64)
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accept_num = np.zeros(real_bsz, dtype=np.int32)
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step_idx = np.zeros(real_bsz, dtype=np.int64)
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stop_flags = np.zeros(real_bsz, dtype=bool)
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return {
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"accept_tokens": accept_tokens,
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"accept_num": accept_num,
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"step_idx": step_idx,
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"stop_flags": stop_flags,
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"seq_lens_encoder": seq_lens_encoder,
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"seq_lens_decoder": seq_lens_decoder,
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"draft_tokens": draft_tokens,
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"seq_lens_this_time": seq_lens_this_time,
|
||
"verify_tokens": verify_tokens,
|
||
"verify_scores": verify_scores,
|
||
"max_dec_len": max_dec_len,
|
||
"end_tokens": end_tokens,
|
||
"is_block_step": is_block_step,
|
||
"output_cum_offsets": output_cum_offsets,
|
||
"actual_candidate_len": actual_candidate_len,
|
||
"actual_draft_token_nums": actual_draft_token_nums,
|
||
"topp": topp,
|
||
"max_seq_len": max_seq_len,
|
||
"verify_window": verify_window,
|
||
"enable_topp": enable_topp,
|
||
}
|
||
|
||
|
||
# ------------------- 单测主体 -------------------
|
||
# # ---- Paddle 端 ----
|
||
def run_speculate_verify_test(
|
||
real_bsz,
|
||
max_draft_tokens,
|
||
max_seq_len,
|
||
max_candidate_len,
|
||
verify_window,
|
||
end_length,
|
||
enable_topp,
|
||
seed,
|
||
):
|
||
inputs = gen_speculate_verify_inputs(
|
||
real_bsz=real_bsz,
|
||
max_draft_tokens=max_draft_tokens,
|
||
max_seq_len=max_seq_len,
|
||
max_candidate_len=max_candidate_len,
|
||
verify_window=verify_window,
|
||
end_length=end_length,
|
||
enable_topp=enable_topp,
|
||
seed=seed,
|
||
)
|
||
|
||
paddle_inputs = {}
|
||
|
||
print("========= 1 xpu process==========")
|
||
|
||
for k, v in inputs.items():
|
||
if isinstance(v, (int, bool)):
|
||
paddle_inputs[k] = v
|
||
# print(f"{k:<25} type: {type(v).__name__}, value: {v}")
|
||
else:
|
||
# paddle_inputs[k] = paddle.to_tensor(v, place=paddle.CPUPlace())
|
||
paddle_inputs[k] = paddle.to_tensor(v, place=paddle.XPUPlace(0))
|
||
# print(f"{k:<25} type: Tensor, dtype: {paddle_inputs[k].dtype}, shape: {paddle_inputs[k].shape}")
|
||
|
||
out_pd = speculate_verify(**paddle_inputs)
|
||
(accept_tokens_pd, accept_num_pd, step_idx_pd, stop_flags_pd) = out_pd
|
||
pd_tensors = [accept_tokens_pd, accept_num_pd, step_idx_pd, stop_flags_pd]
|
||
|
||
print("========= 1 end==========")
|
||
print("========= 2 python process==========")
|
||
|
||
# np_inputs = {k: (paddle_inputs[k].numpy().copy() if isinstance(paddle_inputs[k], paddle.Tensor)
|
||
# else paddle_inputs[k])
|
||
# for k in paddle_inputs}
|
||
|
||
# out_np = speculate_verify_np(**np_inputs)
|
||
# (accept_tokens_np, accept_num_np, step_idx_np, stop_flags_np) = out_np
|
||
# np_tensors = [accept_tokens_np, accept_num_np, step_idx_np, stop_flags_np]
|
||
|
||
print("=========2 end =======")
|
||
|
||
print("========= 3 (CPU)==========")
|
||
paddle_inputs_cpu = {}
|
||
|
||
for k, v in inputs.items(): # 重新使用原始的 inputs 字典，确保数据原始状态
|
||
if isinstance(v, (int, bool)):
|
||
paddle_inputs_cpu[k] = v
|
||
# print(f"{k:<25} type: {type(v).__name__}, value: {v}")
|
||
else:
|
||
# 核心修改：使用 paddle.CPUPlace()
|
||
paddle_inputs_cpu[k] = paddle.to_tensor(v, place=paddle.CPUPlace())
|
||
# print(f"{k:<25} type: Tensor, dtype: {paddle_inputs_cpu[k].dtype}, shape: {paddle_inputs_cpu[k].shape}")
|
||
|
||
out_cpu = speculate_verify(**paddle_inputs_cpu)
|
||
(accept_tokens_cpu, accept_num_cpu, step_idx_cpu, stop_flags_cpu) = out_cpu
|
||
|
||
cpu_tensors = [
|
||
accept_tokens_cpu,
|
||
accept_num_cpu,
|
||
step_idx_cpu,
|
||
stop_flags_cpu,
|
||
]
|
||
print("========= 3 (CPU) end==========")
|
||
|
||
# ---------------- 校对 ----------------
|
||
# print("========= python/cpu vs xpu verify ==========")
|
||
|
||
# names = ["accept_tokens", "accept_num", "step_idx", "stop_flags"]
|
||
# for name, pd_val, np_val in zip(names, pd_tensors, np_tensors):
|
||
# pd_arr = pd_val.numpy()
|
||
# ok = np.array_equal(pd_arr, np_val)
|
||
# print(f"{name:20s} equal: {ok}")
|
||
# if not ok:
|
||
# print(f"{name} mismatch!\nPaddle:\n{pd_arr}\n\nNumPy:\n{np_val}")
|
||
|
||
print("========= cpu vs xpu verify ==========")
|
||
|
||
names = ["accept_tokens", "accept_num", "step_idx", "stop_flags"]
|
||
# for name, pd_val, np_val in zip(names, pd_tensors, cpu_tensors):
|
||
# pd_arr = pd_val.numpy()
|
||
# ok = np.array_equal(pd_arr, np_val)
|
||
# print(f"{name:20s} equal: {ok}")
|
||
# if not ok:
|
||
# print(f"{name} mismatch!\nPaddle:\n{pd_arr}\n\nNumPy:\n{np_val}")
|
||
|
||
for name, pd_val, np_val in zip(names, pd_tensors, cpu_tensors):
|
||
pd_arr = pd_val.numpy()
|
||
ok = np.array_equal(pd_arr, np_val)
|
||
print(f"{name:20s} equal: {ok}")
|
||
if not ok:
|
||
print(f"{name} mismatch!")
|
||
|
||
# 输出不同位置的索引和对应值
|
||
print(f"{name} mismatch!\nPaddle:\n{pd_arr}\n\nNumPy:\n{np_val}")
|
||
mismatches = np.where(pd_arr != np_val)
|
||
for idx in zip(*mismatches):
|
||
print(f" idx {idx}: Paddle = {pd_arr[idx]}, NumPy = {np_val[idx]}")
|
||
|
||
# 如果差异太多可限制输出数量
|
||
if len(mismatches[0]) > 20:
|
||
print(" ... (truncated)")
|
||
|
||
|
||
# -------------------------------------
|
||
# 测试用例
|
||
# -------------------------------------
|
||
test_configs = [
|
||
{
|
||
"real_bsz": 4,
|
||
"max_draft_tokens": 3,
|
||
"max_seq_len": 30,
|
||
"max_candidate_len": 4,
|
||
"verify_window": 2,
|
||
"end_length": 2,
|
||
"enable_topp": True,
|
||
"seed": 2025,
|
||
},
|
||
{
|
||
"real_bsz": 77,
|
||
"max_draft_tokens": 10,
|
||
"max_seq_len": 12000,
|
||
"max_candidate_len": 8,
|
||
"verify_window": 2,
|
||
"end_length": 4,
|
||
"enable_topp": True,
|
||
"seed": 2025,
|
||
},
|
||
{
|
||
"real_bsz": 1,
|
||
"max_draft_tokens": 2,
|
||
"max_seq_len": 10,
|
||
"max_candidate_len": 1,
|
||
"verify_window": 1,
|
||
"end_length": 1,
|
||
"enable_topp": True,
|
||
"seed": 42,
|
||
},
|
||
{
|
||
"real_bsz": 128,
|
||
"max_draft_tokens": 7,
|
||
"max_seq_len": 999,
|
||
"max_candidate_len": 5,
|
||
"verify_window": 3,
|
||
"end_length": 3,
|
||
"enable_topp": True,
|
||
"seed": 422,
|
||
},
|
||
{
|
||
"real_bsz": 99,
|
||
"max_draft_tokens": 5,
|
||
"max_seq_len": 10,
|
||
"max_candidate_len": 3,
|
||
"verify_window": 4,
|
||
"end_length": 4,
|
||
"enable_topp": True,
|
||
"seed": 42,
|
||
},
|
||
{
|
||
"real_bsz": 1,
|
||
"max_draft_tokens": 9,
|
||
"max_seq_len": 11,
|
||
"max_candidate_len": 4,
|
||
"verify_window": 2,
|
||
"end_length": 5,
|
||
"enable_topp": False,
|
||
"seed": 42,
|
||
},
|
||
{
|
||
"real_bsz": 33,
|
||
"max_draft_tokens": 5,
|
||
"max_seq_len": 10111,
|
||
"max_candidate_len": 5,
|
||
"verify_window": 2,
|
||
"end_length": 6,
|
||
"enable_topp": False,
|
||
"seed": 42,
|
||
},
|
||
{
|
||
"real_bsz": 6,
|
||
"max_draft_tokens": 4,
|
||
"max_seq_len": 10001,
|
||
"max_candidate_len": 6,
|
||
"verify_window": 2,
|
||
"end_length": 7,
|
||
"enable_topp": False,
|
||
"seed": 42,
|
||
},
|
||
{
|
||
"real_bsz": 7,
|
||
"max_draft_tokens": 3,
|
||
"max_seq_len": 777,
|
||
"max_candidate_len": 7,
|
||
"verify_window": 2,
|
||
"end_length": 5,
|
||
"enable_topp": False,
|
||
"seed": 42,
|
||
},
|
||
{
|
||
"real_bsz": 55,
|
||
"max_draft_tokens": 5,
|
||
"max_seq_len": 31,
|
||
"max_candidate_len": 9,
|
||
"verify_window": 2,
|
||
"end_length": 3,
|
||
"enable_topp": False,
|
||
"seed": 42,
|
||
},
|
||
]
|
||
|
||
for i, cfg in enumerate(test_configs):
|
||
print(f"\n\n======== Running Test Case {i} ========")
|
||
run_speculate_verify_test(**cfg)
|