mirror of
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2410 lines
109 KiB
Python
2410 lines
109 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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 os
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import sys
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import unittest
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import tensorrt as trt
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import torch
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from parameterized import parameterized
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import tensorrt_llm
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import tensorrt_llm.models.eagle
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from tensorrt_llm import Tensor
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from tensorrt_llm.models.eagle.model import TreeParams
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sys.path.append(os.path.join(os.path.dirname(__file__), os.pardir, os.pardir))
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from utils.util import create_session, run_session, unittest_name_func
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def logsoftmax(input_logits):
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m = torch.nn.LogSoftmax(dim=-1)
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return m(input_logits)
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def generate_ref_eagle2(layerIdx, batch_size, input_logits,
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dynamic_tree_max_topK, input_prev_paths,
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input_prev_scores, input_draft_token_ids,
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input_all_layers_scores,
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input_all_layers_draft_token_ids,
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input_all_layers_draft_token_ids_predecessor):
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# input_logits: [batch_size * dynamic_tree_max_topK, vocab_size_padded]
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# input_prev_paths: [batch_size, max_decoding_tokens, max_path_len]
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# input_prev_scores: [batch_size, max_decoding_draft_tokens]
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# input_draft_token_ids: [batch_size, max_decoding_draft_tokens]
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# input_all_layers_scores: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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# input_all_layers_draft_token_ids: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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# input_all_layers_draft_token_ids_predecessor: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
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ref_return_draft_token_ids = []
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ref_return_current_scores = []
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ref_return_output_all_layers_scores = []
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ref_return_output_all_layers_draft_token_ids = []
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ref_return_next_expand_indices = []
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ref_return_output_all_layers_draft_token_ids_predecessor = []
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for bix in range(batch_size):
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logits = input_logits[
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bix * dynamic_tree_max_topK:(bix + 1) *
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dynamic_tree_max_topK] # shape [dynamic_tree_max_topK, vocab_size_padded]
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# Reference the official implementation: https://github.com/SafeAILab/EAGLE/blob/main/eagle/model/cnets.py#L704
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last_p = logsoftmax(logits)
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top = torch.topk(last_p, dynamic_tree_max_topK, dim=-1)
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topk_index, topk_p = top.indices, top.values # both shape [dynamic_tree_max_topK, dynamic_tree_max_topK]
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# print(f"input_prev_scores[bix, :dynamic_tree_max_topK]: {input_prev_scores[bix, :dynamic_tree_max_topK]}")
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prev_scores = input_prev_scores[
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bix][:dynamic_tree_max_topK] # [dynamic_tree_max_topK]
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cu_scores = topk_p + prev_scores[:,
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None] # shape [dynamic_tree_max_topK, dynamic_tree_max_topK]
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topk_cs = torch.topk(cu_scores.view(-1), dynamic_tree_max_topK, dim=-1)
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topk_cs_index, topk_cs_p = topk_cs.indices, topk_cs.values # both shape [dynamic_tree_max_topK]
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# We sort here to match our implement. We need to ensure that the expand tokenIds index are increase from left to right
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topk_cs_index, topk_cs_sort_idx = torch.sort(topk_cs_index,
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descending=False)
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topk_cs_p = topk_cs_p[topk_cs_sort_idx]
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next_scores = topk_cs_p
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output_ids = topk_index.view(-1)[topk_cs_index]
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# Concat with input
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## draft token ids
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# only slice meaningful values
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cur_input_draft_token_ids = input_draft_token_ids[
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bix][:layerIdx * dynamic_tree_max_topK]
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ref_return_draft_token_ids.append(
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torch.cat((cur_input_draft_token_ids, output_ids), dim=0))
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nun_all_layers_scores = (
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layerIdx - 1
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) * dynamic_tree_max_topK * dynamic_tree_max_topK + dynamic_tree_max_topK
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## all layers scores
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prev_input_all_layers_scores = input_all_layers_scores[bix].view(
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-1)[:nun_all_layers_scores]
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ref_return_output_all_layers_scores.append(
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torch.cat((prev_input_all_layers_scores, cu_scores.view(-1)),
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dim=0))
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## all layers draft tokens
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prev_input_all_layers_draft_token_ids = input_all_layers_draft_token_ids[
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bix].view(-1)[:nun_all_layers_scores]
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ref_return_output_all_layers_draft_token_ids.append(
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torch.cat(
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(prev_input_all_layers_draft_token_ids, topk_index.view(-1)),
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dim=0))
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## current scores
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ref_return_current_scores.append(next_scores)
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## next expand indices
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start_offset = (
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layerIdx - 1
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) * dynamic_tree_max_topK * dynamic_tree_max_topK + dynamic_tree_max_topK + 1
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ref_return_next_expand_indices.append(topk_cs_index + start_offset)
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## all layers draft tokenids predecessor
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assert (len(topk_cs_index) == dynamic_tree_max_topK)
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cur_layer_predecessor = (topk_cs_index +
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start_offset) // dynamic_tree_max_topK
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cur_layer_predecessor = cur_layer_predecessor.repeat_interleave(
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dynamic_tree_max_topK)
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prev_input_all_layers_draft_token_ids_predecessor = input_all_layers_draft_token_ids_predecessor[
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bix].view(-1)[:nun_all_layers_scores]
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ref_return_output_all_layers_draft_token_ids_predecessor.append(
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torch.cat((prev_input_all_layers_draft_token_ids_predecessor,
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cur_layer_predecessor),
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dim=0))
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return ref_return_draft_token_ids, ref_return_current_scores, ref_return_next_expand_indices, \
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ref_return_output_all_layers_scores, ref_return_output_all_layers_draft_token_ids, ref_return_output_all_layers_draft_token_ids_predecessor
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class TestEagleDecodeDraftTokensPlugin(unittest.TestCase):
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def setUp(self):
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tensorrt_llm.logger.set_level('warning')
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def load_test_cases():
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test_cases = []
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################# Eagle-1 test cases ##########################
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################# CASE 0 ##########################
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# BS=1, topK sampling
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# 1 input logits, from node "0"
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# layer_id = 0
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# logits_data_type = float32
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batch_size = 1
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layerId = 0
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dynamic_tree_max_topK_t = -1
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num_eagle_layers = 4
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max_decoding_draft_tokens = 7
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logits_data_type = torch.float32
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logits = torch.tensor(
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[
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[-100, -100, 1, 2, -100, -100, 3, -100], # Top3 id = 6, 3, 2
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],
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dtype=logits_data_type,
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device="cuda") # shape: [num_tokens, vocab_size_padded]
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num_last_token_indices = torch.tensor([1],
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dtype=torch.int32,
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device="cuda") # shape: [1]
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rand_sample = torch.tensor([0], dtype=torch.float32,
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device="cuda") # shape: [num_tokens]
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paths = torch.tensor(
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[[[0, 1, 4, 6], [0, 1, 4, 7], [0, 2, -1, -1], [0, 3, 5, -1],
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[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
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[-1, -1, -1, -1]]],
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dtype=torch.int32,
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device="cuda"
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) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [1, 8, 4]
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input_draft_token_ids = torch.tensor(
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[[-1, -1, -1, -1, -1, -1, -1]], dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_draft_lens = torch.tensor([0], dtype=torch.int32,
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device="cuda") # shape: [batch_size]
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top_k_sampling = True
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ref_return_draft_token_ids = torch.tensor(
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[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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ref_return_draft_len = torch.tensor(
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[3], dtype=torch.int32, device="cuda") # shape: [batch_size]
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# Eagle-2 related inputs/outputs, useless for Eagle-1
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use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
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device="cpu") # shape: [1]
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dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
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dtype=torch.int32,
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device="cpu") # shape: [1]
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input_prev_scores = torch.full(
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(batch_size, max_decoding_draft_tokens),
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float('-inf'),
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dtype=torch.float32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_current_expand_indices = torch.full(
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(batch_size, max_decoding_draft_tokens),
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-1,
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_all_layers_scores = torch.full(
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(batch_size, num_eagle_layers,
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max_decoding_draft_tokens * max_decoding_draft_tokens),
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float('-inf'),
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dtype=torch.float32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_all_layers_draft_token_ids = torch.full(
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(batch_size, num_eagle_layers,
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max_decoding_draft_tokens * max_decoding_draft_tokens),
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-1,
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_all_layers_draft_token_ids_predecessor = torch.full(
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(batch_size, num_eagle_layers,
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max_decoding_draft_tokens * max_decoding_draft_tokens),
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-1,
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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ref_return_output_path = None
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ref_return_current_scores = None
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ref_return_next_expand_indices = None
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ref_return_output_all_layers_scores = None
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ref_return_output_all_layers_draft_token_ids = None
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ref_return_output_all_layers_draft_token_ids_predecessor = None
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test_cases += [[
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logits, num_last_token_indices, rand_sample, paths,
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use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
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input_draft_lens, input_prev_scores, input_current_expand_indices,
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input_all_layers_scores, input_all_layers_draft_token_ids,
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input_all_layers_draft_token_ids_predecessor, top_k_sampling,
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num_eagle_layers, layerId, ref_return_draft_token_ids,
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ref_return_draft_len, ref_return_output_path,
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ref_return_current_scores, ref_return_next_expand_indices,
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ref_return_output_all_layers_scores,
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ref_return_output_all_layers_draft_token_ids,
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ref_return_output_all_layers_draft_token_ids_predecessor
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]]
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################# CASE 1 ##########################
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# BS=2, topK sampling
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# 2 input logits, from req0 node "0" and req1 node "0"
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# layer_id = 0
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# logits_data_type = float32
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batch_size = 2
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layerId = 0
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dynamic_tree_max_topK_t = -1
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num_eagle_layers = 4
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max_decoding_draft_tokens = 7
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logits_data_type = torch.float32
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logits = torch.tensor(
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[
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[-100, -100, 1, 2, -100, -100, -100, -100], # Top2 id = 3, 2
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[-100, 3, -100, 2, -100, 1, -100, -100], # Top3 id = 1, 3, 5
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],
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dtype=logits_data_type,
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device="cuda") # shape: [num_tokens, vocab_size_padded]
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num_last_token_indices = torch.tensor([2],
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dtype=torch.int32,
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device="cuda") # shape: [1]
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rand_sample = torch.tensor([0, 0], dtype=torch.float32,
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device="cuda") # shape: [num_tokens]
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paths = torch.tensor(
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[[[0, 1, -1, -1], [0, 2, -1, -1], [-1, -1, -1, -1],
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[-1, -1, -1, -1], [-1, -1, -1, -1]],
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[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, 4, -1], [-1, -1, -1, -1],
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[-1, -1, -1, -1]]],
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dtype=torch.int32,
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device="cuda"
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) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [2, 5, 4]
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input_draft_token_ids = torch.tensor(
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[[-1, -1, -1, -1], [-1, -1, -1, -1]],
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_draft_lens = torch.tensor([0, 0],
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dtype=torch.int32,
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device="cuda") # shape: [batch_size]
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top_k_sampling = True
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ref_return_draft_token_ids = torch.tensor(
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[[3, 2, -1, -1], [1, 3, 5, -1]], dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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ref_return_draft_len = torch.tensor(
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[2, 3], dtype=torch.int32, device="cuda") # shape: [batch_size]
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# Eagle-2 related inputs/outputs, useless for Eagle-1
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use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
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device="cpu") # shape: [1]
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dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
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dtype=torch.int32,
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device="cpu") # shape: [1]
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input_prev_scores = torch.full(
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(batch_size, max_decoding_draft_tokens),
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float('-inf'),
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dtype=torch.float32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_current_expand_indices = torch.full(
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(batch_size, max_decoding_draft_tokens),
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-1,
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_all_layers_scores = torch.full(
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(batch_size, num_eagle_layers,
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max_decoding_draft_tokens * max_decoding_draft_tokens),
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float('-inf'),
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dtype=torch.float32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_all_layers_draft_token_ids = torch.full(
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(batch_size, num_eagle_layers,
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max_decoding_draft_tokens * max_decoding_draft_tokens),
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-1,
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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input_all_layers_draft_token_ids_predecessor = torch.full(
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(batch_size, num_eagle_layers,
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max_decoding_draft_tokens * max_decoding_draft_tokens),
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-1,
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dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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ref_return_output_path = None
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ref_return_current_scores = None
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ref_return_next_expand_indices = None
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ref_return_output_all_layers_scores = None
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ref_return_output_all_layers_draft_token_ids = None
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ref_return_output_all_layers_draft_token_ids_predecessor = None
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test_cases += [[
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logits, num_last_token_indices, rand_sample, paths,
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use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
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input_draft_lens, input_prev_scores, input_current_expand_indices,
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input_all_layers_scores, input_all_layers_draft_token_ids,
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input_all_layers_draft_token_ids_predecessor, top_k_sampling,
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num_eagle_layers, layerId, ref_return_draft_token_ids,
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ref_return_draft_len, ref_return_output_path,
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ref_return_current_scores, ref_return_next_expand_indices,
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ref_return_output_all_layers_scores,
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ref_return_output_all_layers_draft_token_ids,
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ref_return_output_all_layers_draft_token_ids_predecessor
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]]
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################# CASE 2 ##########################
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# BS=1, topK sampling
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# 2 input loigts, from req0 node "1" and "3"
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# layer_id = 1
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# logits_data_type = float32
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batch_size = 1
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layerId = 1
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dynamic_tree_max_topK_t = -1
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num_eagle_layers = 4
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max_decoding_draft_tokens = 7
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logits_data_type = torch.float32
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logits = torch.tensor(
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[
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[-100, -100, -100, 1, -100, -100, -100, -100], # Top1 id = 3
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[-100, 1, -100, -100, -100, -100, -100, -100], # Top1 id = 1
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],
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dtype=logits_data_type,
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device="cuda") # shape: [num_tokens, vocab_size_padded]
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num_last_token_indices = torch.tensor([2],
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dtype=torch.int32,
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device="cuda") # shape: [1]
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rand_sample = torch.tensor([0, 0], dtype=torch.float32,
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device="cuda") # shape: [num_tokens]
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paths = torch.tensor(
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[[[0, 1, 4, 6], [0, 1, 4, 7], [0, 2, -1, -1], [0, 3, 5, -1],
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[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
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[-1, -1, -1, -1]]],
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dtype=torch.int32,
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device="cuda"
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) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [1, 8, 4]
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input_draft_token_ids = torch.tensor(
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[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_draft_lens = torch.tensor([3], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
|
|
top_k_sampling = True
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, 3, 1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[5], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
# Eagle-2 related inputs/outputs, useless for Eagle-1
|
|
use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_current_expand_indices = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = None
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 3 ##########################
|
|
# BS=2, topK sampling
|
|
# 1 input loigts, from req1, node "3"
|
|
# layer_id = 1
|
|
# logits_data_type = float32
|
|
|
|
batch_size = 1
|
|
layerId = 1
|
|
dynamic_tree_max_topK_t = -1
|
|
num_eagle_layers = 4
|
|
max_decoding_draft_tokens = 7
|
|
|
|
logits_data_type = torch.float32
|
|
logits = torch.tensor(
|
|
[
|
|
[-100, -100, -100, -100, -100, 1, -100, -100], # Top1 id = 5
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([1],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
|
|
paths = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1]],
|
|
[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, 4, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [2, 5, 4]
|
|
|
|
input_draft_token_ids = torch.tensor(
|
|
[[2, 1, -1, -1], [1, 2, 3, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_draft_lens = torch.tensor([2, 3],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
|
|
top_k_sampling = True
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[2, 1, -1, -1], [1, 2, 3, 5]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[2, 4], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
# Eagle-2 related inputs/outputs, useless for Eagle-1
|
|
use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_current_expand_indices = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = None
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 4 ##########################
|
|
# BS=2, topK sampling
|
|
# 2 input logits, from req0 node "4" and req1 node "4"
|
|
# layer_id = 2
|
|
# logits_data_type = float32
|
|
|
|
batch_size = 2
|
|
layerId = 2
|
|
dynamic_tree_max_topK_t = -1
|
|
num_eagle_layers = 4
|
|
max_decoding_draft_tokens = 7
|
|
|
|
logits_data_type = torch.float32
|
|
logits = torch.tensor(
|
|
[
|
|
[-100, -100, 1, 2, -100, -100, -100, -100], # Top2 id = 3, 2
|
|
[-100, -100, -100, -100, 1, 2, -100, -100], # Top2 id = 5, 4
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([2],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0, 0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
|
|
paths = torch.tensor(
|
|
[[[0, 1, 4, 6], [0, 1, 4, 7], [0, 2, -1, -1], [0, 3, 5, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]],
|
|
[[0, 1, 4, 6], [0, 1, 4, 7], [0, 2, -1, -1], [0, 3, 5, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [2, 8, 4]
|
|
|
|
input_draft_token_ids = torch.tensor(
|
|
[[1, 2, 3, 4, 5, -1, -1], [1, 2, 3, 4, 5, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_draft_lens = torch.tensor([5, 5],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
|
|
top_k_sampling = True
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[1, 2, 3, 4, 5, 3, 2], [1, 2, 3, 4, 5, 5, 4]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[7, 7], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
# Eagle-2 related inputs/outputs, useless for Eagle-1
|
|
use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_current_expand_indices = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = None
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 5 ##########################
|
|
# BS=1, topK sampling
|
|
# 1 input logits, from req0 node "0"
|
|
# layer_id = 0
|
|
# logits_data_type = float16
|
|
|
|
batch_size = 1
|
|
layerId = 0
|
|
dynamic_tree_max_topK_t = -1
|
|
num_eagle_layers = 4
|
|
max_decoding_draft_tokens = 7
|
|
|
|
logits_data_type = torch.float16
|
|
logits = torch.tensor(
|
|
[
|
|
[-100, -100, 1, 2, -100, -100, 3, -100], # Top3 id = 6, 3, 2
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([1],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
|
|
paths = torch.tensor(
|
|
[[[0, 1, 4, 6], [0, 1, 4, 7], [0, 2, -1, -1], [0, 3, 5, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [1, 8, 4]
|
|
|
|
input_draft_token_ids = torch.tensor(
|
|
[[-1, -1, -1, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_draft_lens = torch.tensor([0], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
|
|
top_k_sampling = True
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[3], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
# Eagle-2 related inputs/outputs, useless for Eagle-1
|
|
use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_current_expand_indices = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = None
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 6 ##########################
|
|
# BS=1, topK sampling
|
|
# 5 input logits, only the 1st is valid, from req0 node "0"
|
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# layer_id = 0
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# logits_data_type = float16
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|
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batch_size = 1
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layerId = 0
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dynamic_tree_max_topK_t = -1
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num_eagle_layers = 4
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max_decoding_draft_tokens = 7
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|
|
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logits_data_type = torch.float16
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|
logits = torch.tensor(
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[
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|
[-100, -100, 1, 2, -100, -100, 3, -100], # Top3 id = 6, 3, 2
|
|
[1, 2, -100, -100, -100, -100, -100, 3], # Top3 id = 7, 1, 0
|
|
[1, 2, -100, -100, -100, -100, -100, 3], # Top3 id = 7, 1, 0
|
|
[1, 2, -100, -100, -100, -100, -100, 3], # Top3 id = 7, 1, 0
|
|
[1, 2, -100, -100, -100, -100, -100, 3], # Top3 id = 7, 1, 0
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|
],
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|
dtype=logits_data_type,
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device="cuda") # shape: [num_tokens, vocab_size_padded]
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|
|
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num_last_token_indices = torch.tensor([1],
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dtype=torch.int32,
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device="cuda") # shape: [1]
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|
|
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rand_sample = torch.tensor([0], dtype=torch.float32,
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device="cuda") # shape: [num_tokens]
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|
|
|
paths = torch.tensor(
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[[[0, 1, 4, 6], [0, 1, 4, 7], [0, 2, -1, -1], [0, 3, 5, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
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|
dtype=torch.int32,
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|
device="cuda"
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|
) # shape: [batch_size, max_decoding_tokens, max_path_len] -> [1, 8, 4]
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|
|
|
input_draft_token_ids = torch.tensor(
|
|
[[-1, -1, -1, -1, -1, -1, -1]], dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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|
|
|
input_draft_lens = torch.tensor([0], dtype=torch.int32,
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device="cuda") # shape: [batch_size]
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|
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top_k_sampling = True
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ref_return_draft_token_ids = torch.tensor(
|
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[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
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device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
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|
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ref_return_draft_len = torch.tensor(
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[3], dtype=torch.int32, device="cuda") # shape: [batch_size]
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|
|
|
# Eagle-2 related inputs/outputs, useless for Eagle-1
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|
use_dynamic_tree = torch.tensor(False, dtype=torch.bool,
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|
device="cpu") # shape: [1]
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|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
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|
dtype=torch.int32,
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|
device="cpu") # shape: [1]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
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|
float('-inf'),
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|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_current_expand_indices = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = None
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
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|
logits, num_last_token_indices, rand_sample, paths,
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|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
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|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
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|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
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|
num_eagle_layers, layerId, ref_return_draft_token_ids,
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|
ref_return_draft_len, ref_return_output_path,
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|
ref_return_current_scores, ref_return_next_expand_indices,
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|
ref_return_output_all_layers_scores,
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|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
# ################# Eagle-2 test cases ##########################
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|
# ################# CASE 0: test the first layer ##########################
|
|
# BS=1, topK sampling
|
|
# 1 input logits, from node "0"
|
|
# layerId = 0
|
|
# logits_data_type = float32
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 1
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 0
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-100, -100, 1, 2, -100, -100, 3, -100], # Top3 id = 6, 3, 2
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([1],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
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|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.full(
|
|
(batch_size, max_decoding_tokens, max_path_len),
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|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[-1, -1, -1, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([0], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[0, -1, -1, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[3], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
# For the layerIdx = 0
|
|
log_softmax = torch.nn.LogSoftmax(dim=-1)
|
|
pp = log_softmax(logits)
|
|
top_k_result = torch.topk(input=pp, k=dynamic_tree_max_topK_t, dim=-1)
|
|
|
|
ref_return_current_scores = top_k_result.values
|
|
ref_return_next_expand_indices = torch.tensor(
|
|
[[1, 2, 3, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_all_layers_scores = top_k_result.values
|
|
ref_return_output_all_layers_draft_token_ids = top_k_result.indices
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[[0, 0, 0]], dtype=torch.int32, device="cuda"
|
|
) # Actual shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
# Since we will save this value continuously
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 1: test the first layer ##########################
|
|
# BS=2, topK sampling
|
|
|
|
# In this test, in the second sampling, each node will has 1 leaf
|
|
# The input path is:
|
|
# [
|
|
# [0, 1, -1, -1],
|
|
# [0, 2, -1, -1],
|
|
# [0, 3, -1, -1]
|
|
# [-1, -1, -1, -1],
|
|
# ...
|
|
# ]
|
|
# The output path is:
|
|
# [
|
|
# [0, 1, 4, -1],
|
|
# [0, 2, 5, -1],
|
|
# [0, 3, 6, -1],
|
|
# [-1, -1, -1, -1],
|
|
# ...
|
|
# ]
|
|
|
|
# 2 input logits, from node "0"
|
|
# layerId = 0
|
|
# logits_data_type = float32
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 2
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 0
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-100, -100, 1, 2, -100, -100, 3, -100], # Top3 id = 6, 3, 2
|
|
[-100, 10, 1, -100, -100, 20, -100, -100], # Top3 id = 5, 1, 2
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([2],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0, 0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.full(
|
|
(batch_size, max_decoding_tokens, max_path_len),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[-1, -1, -1, -1, -1, -1, -1], [-1, -1, -1, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([0, 0],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[0, -1, -1, -1, -1, -1, -1], [0, -1, -1, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, -1, -1, -1, -1], [5, 1, 2, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[3, 3], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]],
|
|
[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
# For the layerIdx = 0
|
|
log_softmax = torch.nn.LogSoftmax(dim=-1)
|
|
pp = log_softmax(logits)
|
|
top_k_result = torch.topk(input=pp, k=dynamic_tree_max_topK_t, dim=-1)
|
|
|
|
ref_return_current_scores = top_k_result.values
|
|
ref_return_next_expand_indices = torch.tensor(
|
|
[[1, 2, 3, -1, -1, -1, -1], [1, 2, 3, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_output_all_layers_scores = top_k_result.values
|
|
ref_return_output_all_layers_draft_token_ids = top_k_result.indices
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[[0, 0, 0], [0, 0, 0]], dtype=torch.int32, device="cuda"
|
|
) # Actual shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
# Since we will save this value continuously
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 2: test the internal layer ##########################
|
|
# BS=1, topK sampling
|
|
# In this case, new selected draft tokens comes from node_2, node_2, and node_3
|
|
# 3 input logits, from node node_1, node_2 and node_3, respectively
|
|
# layerId = 1
|
|
# logits_data_type = float32
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 1
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 1
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-10, 14, 13, -10, -10, -10, -10, -10, 15, -10, -10, -10
|
|
], # Top3 id = 8, 1, 2
|
|
[-10, -10, 10, 11, -10, -10, 12, -10, -10, -10, -10, -10
|
|
], # Top3 id = 6, 3, 2
|
|
[-10, 16, -10, -10, 17, -10, -10, 18, -10, -10, -10, -10
|
|
], # Top3 id = 7, 4, 1
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda"
|
|
) # shape: [batch_size * dynamic_tree_max_topK, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([3],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([3], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.tensor(
|
|
[[1.1, 5.2, 3.3, -1, -1, -1, -1]],
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[1, 2, 3, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[1.1, 5.2, 3.3] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[6, 3, 2] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[0, 0, 0] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, 4, -1], [0, 2, 5, -1], [0, 3, 6, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda")
|
|
|
|
ref_return_draft_token_ids, ref_return_current_scores, ref_return_next_expand_indices, \
|
|
ref_return_output_all_layers_scores, ref_return_output_all_layers_draft_token_ids, ref_return_output_all_layers_draft_token_ids_predecessor \
|
|
= generate_ref_eagle2(
|
|
layerIdx = layerId,
|
|
batch_size = batch_size,
|
|
input_logits = logits,
|
|
dynamic_tree_max_topK = dynamic_tree_max_topK_t,
|
|
input_prev_paths = paths,
|
|
input_prev_scores = input_prev_scores,
|
|
input_draft_token_ids = input_draft_token_ids,
|
|
input_all_layers_scores = input_all_layers_scores,
|
|
input_all_layers_draft_token_ids = input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor = input_all_layers_draft_token_ids_predecessor
|
|
)
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[6], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 3: test the internal layer ##########################
|
|
# BS=2, topK sampling
|
|
# For bs=0, the new expand nodes are from node_1, node_2, and node_3, respectively
|
|
# For bs=1, the new expand nodes are all from node_1
|
|
# 6 input logits, 3 from bs0, and 3 from bs1. And for each request, these 3 logits are from node_1, node_2 and node_3, respectively
|
|
# layerId = 1
|
|
# logits_data_type = float32
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 2
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 1
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-10, 14, 13, -10, -10, -10, -10, -10, 15, -10, -10, -10
|
|
], # Top3 id = 8, 1, 2
|
|
[-10, -10, 10, 11, -10, -10, 12, -10, -10, -10, -10, -10
|
|
], # Top3 id = 6, 3, 2
|
|
[-10, 16, -10, -10, 17, -10, -10, 18, -10, -10, -10, -10
|
|
], # Top3 id = 7, 4, 1
|
|
[-10, 26, -10, 27, 28, -10, -10, -10, -10, -10, -10, -10
|
|
], # Top3 id = 4, 3, 1
|
|
[-10, 24, 23, -10, -10, 25, -10, -10, -10, -10, -10, -10
|
|
], # Top3 id = 5, 1, 2
|
|
[-10, -10, 20, 21, -10, -10, -10, -10, -10, -10, 22, -10
|
|
], # Top3 id = 10, 3, 2
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda"
|
|
) # shape: [batch_size * dynamic_tree_max_topK, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([6],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0, 0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]],
|
|
[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, -1, -1, -1, -1], [5, 1, 2, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([3, 3],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.tensor(
|
|
[[1.0, 1.0, 1.0, -1, -1, -1, -1], [14.4, 5.5, 6.6, -1, -1, -1, -1]],
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[1, 2, 3, -1, -1, -1, -1], [1, 2, 3, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[1.0, 1.0, 1.0] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
],
|
|
# batchIdx = 1
|
|
[
|
|
[14.4, 5.5, 6.6] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[6, 3, 2] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
],
|
|
|
|
# batchIdx = 1
|
|
[
|
|
[5, 1, 2] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[0, 0, 0] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
],
|
|
|
|
# batchIdx = 1
|
|
[
|
|
[0, 0, 0] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[
|
|
[
|
|
[0, 1, 4, -1], [0, 2, 5, -1], [0, 3, 6, -1],
|
|
[-1, -1, -1, -1
|
|
], [-1, -1, -1, -1
|
|
], [-1, -1, -1, -1
|
|
], [-1, -1, -1, -1
|
|
], [-1, -1, -1, -1]
|
|
], # the new expand nodes are from node_1, node_2, and node_3, respectively
|
|
[[0, 1, 4, -1], [0, 1, 5, -1], [0, 1, 6, -1], [0, 2, -1, -1],
|
|
[0, 3, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]
|
|
], # the new expand nodes are all from node_1
|
|
dtype=torch.int32,
|
|
device="cuda")
|
|
|
|
ref_return_draft_token_ids, ref_return_current_scores, ref_return_next_expand_indices, \
|
|
ref_return_output_all_layers_scores, ref_return_output_all_layers_draft_token_ids, ref_return_output_all_layers_draft_token_ids_predecessor \
|
|
= generate_ref_eagle2(
|
|
layerIdx = layerId,
|
|
batch_size = batch_size,
|
|
input_logits = logits,
|
|
dynamic_tree_max_topK = dynamic_tree_max_topK_t,
|
|
input_prev_paths = paths,
|
|
input_prev_scores = input_prev_scores,
|
|
input_draft_token_ids = input_draft_token_ids,
|
|
input_all_layers_scores = input_all_layers_scores,
|
|
input_all_layers_draft_token_ids = input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor = input_all_layers_draft_token_ids_predecessor
|
|
)
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[6, 6], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 4: test the internal layer ##########################
|
|
# In this test, in the second sampling, node 1 will has 2 leaves, and node 3 will has 1 leaf
|
|
# The input path is:
|
|
# [
|
|
# [0, 1, -1, -1],
|
|
# [0, 2, -1, -1],
|
|
# [0, 3, -1, -1]
|
|
# [-1, -1, -1, -1],
|
|
# ...
|
|
# ]
|
|
# The output path is:
|
|
# [
|
|
# [0, 1, 4, -1],
|
|
# [0, 1, 5, -1],
|
|
# [0, 2, -1, -1],
|
|
# [0, 3, 6, -1],
|
|
# [-1, -1, -1, -1],
|
|
# ...
|
|
# ]
|
|
|
|
# BS=1, topK sampling
|
|
# 3 input logits, from node_1, node_2, and node_3, respectively.
|
|
# layerId = 1
|
|
# logits_data_type = float32
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 1
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 1
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-1, 11.9, 7, -1, -1, -1, -1, -1, 12, -1, -1, -1
|
|
], # Top3 id = 8, 1, 2
|
|
[-1, -1, 19.4, 19.5, -1, -1, 20, -1, -1, -1, -1, -1
|
|
], # Top3 id = 6, 3, 2
|
|
[-1, 3, -1, -1, 4, -1, -1, 5, -1, -1, -1, -1
|
|
], # Top3 id = 7, 4, 1
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda"
|
|
) # shape: [batch_size * dynamic_tree_max_topK, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([3],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[6, 3, 2, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([3], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.tensor(
|
|
[[1.0, 1.0, 1.0, -1, -1, -1, -1]],
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[1, 2, 3, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[1.0, 1.0, 1.0] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[6, 3, 2] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[
|
|
# batchIdx = 0
|
|
[
|
|
[0, 0, 0] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t), # layerIdx = 0
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 1
|
|
[-1] * (max_decoding_draft_tokens *
|
|
max_decoding_draft_tokens), # layerIdx = 2
|
|
]
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[[[0, 1, 4, -1], [0, 1, 5, -1], [0, 2, -1, -1], [0, 3, 6, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda")
|
|
|
|
ref_return_draft_token_ids, ref_return_current_scores, ref_return_next_expand_indices, \
|
|
ref_return_output_all_layers_scores, ref_return_output_all_layers_draft_token_ids, ref_return_output_all_layers_draft_token_ids_predecessor \
|
|
= generate_ref_eagle2(
|
|
layerIdx = layerId,
|
|
batch_size = batch_size,
|
|
input_logits = logits,
|
|
dynamic_tree_max_topK = dynamic_tree_max_topK_t,
|
|
input_prev_paths = paths,
|
|
input_prev_scores = input_prev_scores,
|
|
input_draft_token_ids = input_draft_token_ids,
|
|
input_all_layers_scores = input_all_layers_scores,
|
|
input_all_layers_draft_token_ids = input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor = input_all_layers_draft_token_ids_predecessor
|
|
)
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[6], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 5: test the last layer ##########################
|
|
# BS=1, topK sampling
|
|
# 3 input logits
|
|
# layerId = 2, which is the last layer
|
|
# logits_data_type = float32
|
|
|
|
# The input paths
|
|
# [
|
|
# [0, 1, 4, -1],
|
|
# [0, 1, 5, -1],
|
|
# [0, 1, 6, -1],
|
|
# [0, 2, -1, -1],
|
|
# [0, 3, -1, -1],
|
|
# [-1, -1, -1, -1]
|
|
# ]
|
|
# Three input logits are from node_4, node_5, and node_6
|
|
# We set the node_1 to node_6 have large scores, so they will be selected in the final tree
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 1
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 2
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-1, 11.9, 7, -1, -1, -1, -1, -1, 12, -1, -1, -1
|
|
], # Top3 id = 8, 1, 2
|
|
[-1, -1, 19.4, 19.5, -1, -1, 20, -1, -1, -1, -1, -1
|
|
], # Top3 id = 6, 3, 2
|
|
[-1, 3, -1, -1, 4, -1, -1, 5, -1, -1, -1, -1
|
|
], # Top3 id = 7, 4, 1
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([3],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.tensor(
|
|
[[
|
|
[0, 1, 4, -1],
|
|
[0, 1, 5, -1],
|
|
[0, 1, 6, -1],
|
|
[0, 2, -1, -1],
|
|
[0, 3, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[1, 2, 3, 4, 5, 6, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([6], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.tensor(
|
|
[[10, 10, 10, -1, -1, -1, -1]], dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[4, 5, 6, -1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.tensor(
|
|
[[
|
|
[10, 10, 10, 10, 10, 10, -10, -10, -10, -10, -10, -10] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
]],
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.tensor(
|
|
[[
|
|
[1, 2, 3, 4, 5, 6, 11, 11, 11, 11, 11, 11] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[[
|
|
[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[1, 2, 3, 4, 5, 6, 7]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[7], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[[[0, 1, 4, -1], [0, 1, 5, -1], [0, 1, 6, 7], [0, 2, -1, -1],
|
|
[0, 3, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
# For the last layer, we do not need to check these outputs
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 6: test the last layer ##########################
|
|
# batch_size = 2
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 7
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 4
|
|
num_eagle_layers = 3
|
|
batch_size = 2
|
|
dynamic_tree_max_topK_t = 3
|
|
top_k_sampling = True
|
|
layerId = 2
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-1, 11.9, 7, -1, -1, -1, -1, -1, 12, -1, -1, -1
|
|
], # Top3 id = 8, 1, 2
|
|
[-1, -1, 19.4, 19.5, -1, -1, 20, -1, -1, -1, -1, -1
|
|
], # Top3 id = 6, 3, 2
|
|
[-1, 3, -1, -1, 4, -1, -1, 5, -1, -1, -1, -1
|
|
], # Top3 id = 7, 4, 1
|
|
[-1, 11.9, -1, 7, 12, -1, -1, -1, -1, -1, -1, -1
|
|
], # Top3 id = 4, 3, 1
|
|
[-1, 19.5, 19.4, -1, -1, 20, -1, -1, -1, -1, -1, -1
|
|
], # Top3 id = 5, 1, 2
|
|
[-1, -1, 3, 4, -1, -1, -1, -1, -1, -1, 5, -1
|
|
], # Top3 id = 10, 3, 2
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([6],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0, 0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.tensor(
|
|
[[
|
|
[0, 1, 4, -1],
|
|
[0, 1, 5, -1],
|
|
[0, 1, 6, -1],
|
|
[0, 2, -1, -1],
|
|
[0, 3, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
],
|
|
[
|
|
[0, 1, 4, -1],
|
|
[0, 1, 5, -1],
|
|
[0, 2, 6, -1],
|
|
[0, 3, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[1, 2, 3, 4, 5, 6, -1], [6, 5, 4, 3, 2, 1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([6, 6],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.tensor(
|
|
[[10, 10, 10, -1, -1, -1, -1], [1, 1, 1, -1, -1, -1, -1]],
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
# The index will take all the draft tokens into consideration, even they are not selected.
|
|
# But they will be sampled at the last layer.
|
|
# As to the bi=0, the index '8' is actually correspond to the '6' in the input paths.
|
|
input_current_expand_indices = torch.tensor(
|
|
[[4, 5, 6, -1, -1, -1, -1], [4, 5, 8, -1, -1, -1, -1]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.tensor(
|
|
[[
|
|
[10, 10, 10, 10, 10, 10, -10, -10, -10, -10, -10, -10] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
],
|
|
[
|
|
[16, 15, 14, 13, 12, -10, -10, 11, -10, -10, -10, -10] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
]],
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.tensor(
|
|
[[
|
|
[1, 2, 3, 4, 5, 6, 11, 11, 11, 11, 11, 11] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
],
|
|
[
|
|
[6, 5, 4, 3, 2, 11, 11, 1, 11, 11, 11, 11] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.tensor(
|
|
[[
|
|
[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
],
|
|
[
|
|
[0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3] + [-1] *
|
|
(max_decoding_draft_tokens * max_decoding_draft_tokens -
|
|
dynamic_tree_max_topK_t -
|
|
dynamic_tree_max_topK_t * dynamic_tree_max_topK_t),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
[-1] * (max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[1, 2, 3, 4, 5, 6, 7], [6, 5, 4, 3, 2, 1, 10]],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[7, 7], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[
|
|
[[0, 1, 4, -1], [0, 1, 5, -1], [0, 1, 6, 7], [0, 2, -1, -1],
|
|
[0, 3, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]],
|
|
[[0, 1, 4, -1], [0, 1, 5, -1], [0, 2, 6, 7], [0, 3, -1, -1],
|
|
[-1, -1, -1, -1], [-1, -1, -1, -1], [-1, -1, -1, -1],
|
|
[-1, -1, -1, -1]],
|
|
],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
# For the last layer, we do not need to check these outputs
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
|
|
################# CASE 7: test the fist, but also the last layer ##########################
|
|
# BS=1, topK sampling
|
|
# 1 input logits
|
|
# layerId = 0, which is the first layer, but also the last layer
|
|
# logits_data_type = float32
|
|
|
|
logits_data_type = torch.float32
|
|
max_decoding_draft_tokens = 4
|
|
max_decoding_tokens = max_decoding_draft_tokens + 1
|
|
max_path_len = 2
|
|
num_eagle_layers = 1
|
|
batch_size = 1
|
|
dynamic_tree_max_topK_t = 4
|
|
top_k_sampling = True
|
|
layerId = 0
|
|
|
|
logits = torch.tensor(
|
|
[
|
|
[-1, -1, 2, -1, -1, 5, -1, 4, 3, -1, -1, -1
|
|
], # Top4 id = 5, 7, 8, 2
|
|
],
|
|
dtype=logits_data_type,
|
|
device="cuda") # shape: [num_tokens, vocab_size_padded]
|
|
|
|
num_last_token_indices = torch.tensor([1],
|
|
dtype=torch.int32,
|
|
device="cuda") # shape: [1]
|
|
|
|
rand_sample = torch.tensor([0], dtype=torch.float32,
|
|
device="cuda") # shape: [num_tokens]
|
|
paths = torch.tensor(
|
|
[[
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
[-1, -1, -1, -1],
|
|
]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
use_dynamic_tree = torch.tensor(True, dtype=torch.bool,
|
|
device="cpu") # shape: [1]
|
|
dynamic_tree_max_topK = torch.tensor(dynamic_tree_max_topK_t,
|
|
dtype=torch.int32,
|
|
device="cpu") # shape: [1]
|
|
input_draft_token_ids = torch.tensor(
|
|
[[-1, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
input_draft_lens = torch.tensor([0], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size]
|
|
input_prev_scores = torch.full(
|
|
(batch_size, max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_current_expand_indices = torch.tensor(
|
|
[[0, -1, -1, -1]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
input_all_layers_scores = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
float('-inf'),
|
|
dtype=torch.float32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
input_all_layers_draft_token_ids_predecessor = torch.full(
|
|
(batch_size, num_eagle_layers,
|
|
max_decoding_draft_tokens * max_decoding_draft_tokens),
|
|
-1,
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, num_eagle_layers, max_decoding_draft_tokens x max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_token_ids = torch.tensor(
|
|
[[5, 7, 8, 2]], dtype=torch.int32,
|
|
device="cuda") # shape: [batch_size, max_decoding_draft_tokens]
|
|
|
|
ref_return_draft_len = torch.tensor(
|
|
[4], dtype=torch.int32, device="cuda") # shape: [batch_size]
|
|
|
|
ref_return_output_path = torch.tensor(
|
|
[[[0, 1, -1, -1], [0, 2, -1, -1], [0, 3, -1, -1], [0, 4, -1, -1],
|
|
[-1, -1, -1, -1]]],
|
|
dtype=torch.int32,
|
|
device="cuda"
|
|
) # shape: [batch_size, max_decoding_tokens, max_path_len]
|
|
|
|
# For the last layer, we do not need to check these outputs
|
|
ref_return_current_scores = None
|
|
ref_return_next_expand_indices = None
|
|
ref_return_output_all_layers_scores = None
|
|
ref_return_output_all_layers_draft_token_ids = None
|
|
ref_return_output_all_layers_draft_token_ids_predecessor = None
|
|
|
|
test_cases += [[
|
|
logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor
|
|
]]
|
|
return test_cases
|
|
|
|
@parameterized.expand(load_test_cases, name_func=unittest_name_func)
|
|
def test_sample_draft_tokens_plugin(
|
|
self, logits, num_last_token_indices, rand_sample, paths,
|
|
use_dynamic_tree, dynamic_tree_max_topK, input_draft_token_ids,
|
|
input_draft_lens, input_prev_scores, input_current_expand_indices,
|
|
input_all_layers_scores, input_all_layers_draft_token_ids,
|
|
input_all_layers_draft_token_ids_predecessor, top_k_sampling,
|
|
num_eagle_layers, layerId, ref_return_draft_token_ids,
|
|
ref_return_draft_len, ref_return_output_path,
|
|
ref_return_current_scores, ref_return_next_expand_indices,
|
|
ref_return_output_all_layers_scores,
|
|
ref_return_output_all_layers_draft_token_ids,
|
|
ref_return_output_all_layers_draft_token_ids_predecessor):
|
|
|
|
# test data
|
|
torch.get_default_device()
|
|
torch.set_default_device("cuda")
|
|
|
|
# construct trt network
|
|
builder = tensorrt_llm.Builder()
|
|
network = builder.create_network()
|
|
with tensorrt_llm.net_guard(network):
|
|
logits_t = Tensor(name='logits',
|
|
dtype=tensorrt_llm.torch_dtype_to_trt(
|
|
logits.dtype),
|
|
shape=logits.shape)
|
|
num_last_token_indices_t = Tensor(
|
|
name='num_last_token_indices',
|
|
dtype=tensorrt_llm.torch_dtype_to_trt(
|
|
num_last_token_indices.dtype),
|
|
shape=num_last_token_indices.shape)
|
|
rand_sample_t = Tensor(name='rand_sample',
|
|
dtype=trt.float32,
|
|
shape=rand_sample.shape)
|
|
paths_t = Tensor(name='paths', dtype=trt.int32, shape=paths.shape)
|
|
use_dynamic_tree_t = Tensor(name='use_dynamic_tree',
|
|
dtype=trt.bool,
|
|
shape=use_dynamic_tree.shape)
|
|
dynamic_tree_max_topK_t = Tensor(name='dynamic_tree_max_topK',
|
|
dtype=trt.int32,
|
|
shape=dynamic_tree_max_topK.shape)
|
|
input_draft_token_ids_t = Tensor(name='input_draft_token_ids',
|
|
dtype=trt.int32,
|
|
shape=input_draft_token_ids.shape)
|
|
input_draft_lens_t = Tensor(name='input_draft_lens',
|
|
dtype=trt.int32,
|
|
shape=input_draft_lens.shape)
|
|
input_prev_scores_t = Tensor(name='input_prev_scores',
|
|
dtype=trt.float32,
|
|
shape=input_prev_scores.shape)
|
|
input_current_expand_indices_t = Tensor(
|
|
name='input_current_expand_indices',
|
|
dtype=trt.int32,
|
|
shape=input_current_expand_indices.shape)
|
|
input_all_layers_scores_t = Tensor(
|
|
name='input_all_layers_scores',
|
|
dtype=tensorrt_llm.torch_dtype_to_trt(
|
|
input_all_layers_scores.dtype),
|
|
shape=input_all_layers_scores.shape)
|
|
input_all_layers_draft_token_ids_t = Tensor(
|
|
name='input_all_layers_draft_token_ids',
|
|
dtype=trt.int32,
|
|
shape=input_all_layers_draft_token_ids.shape)
|
|
input_all_layers_draft_token_ids_predecessor_t = Tensor(
|
|
name='input_all_layers_draft_token_ids_predecessor',
|
|
dtype=trt.int32,
|
|
shape=input_all_layers_draft_token_ids_predecessor.shape)
|
|
|
|
output = tensorrt_llm.models.eagle.model.eagle_draft_decoder_plugin(
|
|
layer_idx=layerId,
|
|
num_eagle_layers=num_eagle_layers,
|
|
top_k_sampling=top_k_sampling,
|
|
logits=logits_t,
|
|
num_last_token_indices=num_last_token_indices_t,
|
|
rand_sample=rand_sample_t,
|
|
tree_params=TreeParams(
|
|
paths=paths_t,
|
|
use_dynamic_tree=use_dynamic_tree_t,
|
|
dynamic_tree_max_topK=dynamic_tree_max_topK_t),
|
|
input_draft_token_ids=input_draft_token_ids_t,
|
|
input_draft_lens=input_draft_lens_t,
|
|
input_prev_scores=input_prev_scores_t,
|
|
input_current_expand_indices=input_current_expand_indices_t,
|
|
input_all_layers_scores=input_all_layers_scores_t,
|
|
input_all_layers_draft_token_ids=
|
|
input_all_layers_draft_token_ids_t,
|
|
input_all_layers_draft_token_ids_predecessor=
|
|
input_all_layers_draft_token_ids_predecessor_t)
|
|
|
|
output_draft_token_ids, output_draft_lens, output_paths, output_current_scores, output_next_expand_indices, \
|
|
output_all_layers_scores, output_all_layers_draft_token_ids, output_all_layers_draft_token_ids_predecessor = output
|
|
|
|
output_draft_token_ids.mark_output('output_draft_token_ids')
|
|
output_draft_lens.mark_output('output_draft_lens')
|
|
output_paths.mark_output('output_paths')
|
|
output_current_scores.mark_output('output_current_scores')
|
|
output_next_expand_indices.mark_output('output_next_expand_indices')
|
|
output_all_layers_scores.mark_output('output_all_layers_scores')
|
|
output_all_layers_draft_token_ids.mark_output(
|
|
'output_all_layers_draft_token_ids')
|
|
output_all_layers_draft_token_ids_predecessor.mark_output(
|
|
'output_all_layers_draft_token_ids_predecessor')
|
|
|
|
# trt run
|
|
session = create_session(builder, network, precision='float32')
|
|
inputs = {
|
|
"logits":
|
|
logits,
|
|
"num_last_token_indices":
|
|
num_last_token_indices,
|
|
"rand_sample":
|
|
rand_sample,
|
|
"paths":
|
|
paths,
|
|
"use_dynamic_tree":
|
|
use_dynamic_tree,
|
|
"dynamic_tree_max_topK":
|
|
dynamic_tree_max_topK,
|
|
"input_draft_token_ids":
|
|
input_draft_token_ids,
|
|
"input_draft_lens":
|
|
input_draft_lens,
|
|
"input_prev_scores":
|
|
input_prev_scores,
|
|
"input_current_expand_indices":
|
|
input_current_expand_indices,
|
|
"input_all_layers_scores":
|
|
input_all_layers_scores,
|
|
"input_all_layers_draft_token_ids":
|
|
input_all_layers_draft_token_ids,
|
|
"input_all_layers_draft_token_ids_predecessor":
|
|
input_all_layers_draft_token_ids_predecessor
|
|
}
|
|
outputs = run_session(session, inputs)
|
|
|
|
output_draft_token_ids = outputs['output_draft_token_ids']
|
|
output_draft_lens = outputs['output_draft_lens']
|
|
output_paths = outputs['output_paths']
|
|
output_current_scores = outputs['output_current_scores']
|
|
output_next_expand_indices = outputs['output_next_expand_indices']
|
|
output_all_layers_scores = outputs['output_all_layers_scores']
|
|
output_all_layers_draft_token_ids = outputs[
|
|
'output_all_layers_draft_token_ids']
|
|
output_all_layers_draft_token_ids_predecessor = outputs[
|
|
'output_all_layers_draft_token_ids_predecessor']
|
|
|
|
# Check output
|
|
batch_size = paths.shape[0]
|
|
for bix in range(batch_size):
|
|
# 1) Check output length
|
|
self.assertEqual(ref_return_draft_len[bix], output_draft_lens[bix])
|
|
|
|
# 2) Check output token
|
|
for jj in range(output_draft_lens[bix]):
|
|
self.assertEqual(ref_return_draft_token_ids[bix][jj],
|
|
output_draft_token_ids[bix][jj])
|
|
|
|
# For eagle-2
|
|
if use_dynamic_tree:
|
|
# 3) Check output path
|
|
# max_decoding_tokens = ref_return_output_path.shape[1]
|
|
max_decoding_tokens = output_paths.shape[1]
|
|
max_decoding_tokens - 1
|
|
# max_path_len = ref_return_output_path.shape[2]
|
|
max_path_len = output_paths.shape[2]
|
|
|
|
num_all_layers_draft_tokens = (
|
|
layerId - 1
|
|
) * dynamic_tree_max_topK * dynamic_tree_max_topK + dynamic_tree_max_topK
|
|
|
|
for jj in range(max_decoding_tokens):
|
|
for kk in range(max_path_len):
|
|
self.assertEqual(ref_return_output_path[bix][jj][kk],
|
|
output_paths[bix][jj][kk])
|
|
|
|
if layerId != num_eagle_layers - 1:
|
|
# Only check these output for internal layers
|
|
# 4) Check output current scores, check shape: [batch_size, dynamic_tree_max_topK]
|
|
for jj in range(dynamic_tree_max_topK):
|
|
self.assertAlmostEqual(
|
|
ref_return_current_scores[bix][jj],
|
|
output_current_scores[bix][jj],
|
|
delta=0.1)
|
|
|
|
# 5) Check output next expand indices, check shape: [batch_size, dynamic_tree_max_topK]
|
|
for jj in range(dynamic_tree_max_topK):
|
|
self.assertEqual(
|
|
ref_return_next_expand_indices[bix][jj],
|
|
output_next_expand_indices[bix][jj])
|
|
|
|
# 6) Check output all layers scores
|
|
cur_output_all_layers_scores = output_all_layers_scores[
|
|
bix].view(-1)
|
|
for jj in range(num_all_layers_draft_tokens):
|
|
self.assertAlmostEqual(
|
|
ref_return_output_all_layers_scores[bix][jj],
|
|
cur_output_all_layers_scores[jj],
|
|
delta=0.1)
|
|
|
|
# 7) Check output all layers draft token ids
|
|
cur_output_all_layers_draft_token_ids = output_all_layers_draft_token_ids[
|
|
bix].view(-1)
|
|
for jj in range(num_all_layers_draft_tokens):
|
|
self.assertEqual(
|
|
ref_return_output_all_layers_draft_token_ids[bix]
|
|
[jj], cur_output_all_layers_draft_token_ids[jj])
|
|
|
|
# 8) Check output all layers draft token ids predecessor
|
|
cur_output_all_layers_draft_token_ids_predecessor = output_all_layers_draft_token_ids_predecessor[
|
|
bix].view(-1)
|
|
for jj in range(num_all_layers_draft_tokens):
|
|
self.assertEqual(
|
|
ref_return_output_all_layers_draft_token_ids_predecessor[
|
|
bix][jj],
|
|
cur_output_all_layers_draft_token_ids_predecessor[
|
|
jj])
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|