# SPDX-FileCopyrightText: Copyright (c) 2022-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import pytest from defs.common import (convert_weights, get_dummy_spec_decoding_heads, venv_check_call) from defs.conftest import skip_post_blackwell, skip_pre_ada from defs.trt_test_alternative import check_call @skip_post_blackwell @pytest.mark.parametrize("use_dynamic_tree", [False, True], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("batch_size", [1, 8], ids=['bs1', 'bs8']) @pytest.mark.parametrize("data_type", ['float16']) @pytest.mark.parametrize("eagle_model_roots", ["EAGLE-Vicuna-7B-v1.3"], indirect=True) def test_llm_eagle_1gpu(batch_size, data_type, use_dynamic_tree, eagle_model_roots, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir): print("Build engines...") model_name = "eagle" model_dir = convert_weights(llm_venv=llm_venv, example_root=eagle_example_root, cmodel_dir=cmodel_dir, model=model_name, model_path=eagle_model_roots, data_type=data_type) build_cmd = [ "trtllm-build", f"--checkpoint_dir={model_dir}", f"--output_dir={engine_dir}", f"--gpt_attention_plugin={data_type}", f"--gemm_plugin={data_type}", f"--max_beam_width=1", "--remove_input_padding=enable", "--context_fmha=enable", "--use_paged_context_fmha=enable", "--max_input_len=1024", "--max_seq_len=1536", f"--max_batch_size={batch_size}", "--paged_kv_cache=enable", '--speculative_decoding_mode=eagle', ] check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) print("Run run...") run_cmd = [ f"{eagle_example_root}/../run.py", "--max_output_len=100", f"--tokenizer_dir={eagle_model_roots[0]}", "--log_level=verbose", f"--engine_dir={engine_dir}", ] if use_dynamic_tree: run_cmd.extend( [f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"]) venv_check_call(llm_venv, run_cmd) print("Run summarize...") summary_cmd = [ f"{eagle_example_root}/../summarize.py", "--test_trt_llm", "--hf_model_dir", f"{eagle_model_roots[0]}", "--tokenizer_dir", f"{eagle_model_roots[0]}", f"--engine_dir={engine_dir}", "--check_accuracy", "--tensorrt_llm_rouge1_threshold=24", "--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]", f"--max_ite=40", f"--batch_size={batch_size}", f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}" ] if use_dynamic_tree: summary_cmd.extend( [f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"]) venv_check_call(llm_venv, summary_cmd) # TODO: remove skip_post_blackwell after Speculative decoding is supported. @skip_post_blackwell @skip_pre_ada @pytest.mark.parametrize("batch_size", [8], ids=['bs8']) @pytest.mark.parametrize("data_type", ['float16']) @pytest.mark.parametrize("eagle_model_roots", ["llama3.1-eagle-8b-hf_v0.5"], indirect=True) def test_llm_eagle_1gpu_modelopt_ckpt(batch_size, data_type, eagle_model_roots, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir): print("Build engines...") model_name = "eagle" # Although the datatype is float16, the actual weights are FP8. # The datatype in the convert stage is used for the input and output of the plugin. model_dir = convert_weights(llm_venv=llm_venv, example_root=eagle_example_root, cmodel_dir=cmodel_dir, model=model_name, model_path=eagle_model_roots, data_type=data_type) build_cmd = [ "trtllm-build", f"--checkpoint_dir={model_dir}", f"--output_dir={engine_dir}", f"--max_beam_width=1", "--use_paged_context_fmha=enable", f"--max_batch_size={batch_size}", "--speculative_decoding_mode=eagle", "--multiple_profiles=enable" # also test multiple_profiles ] check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) print("Run run...") run_cmd = [ f"{eagle_example_root}/../run.py", f"--engine_dir={engine_dir}", f"--tokenizer_dir={eagle_model_roots}", "--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]", "--max_output_len=100" ] venv_check_call(llm_venv, run_cmd) def test_with_dummy_eagle(hf_model_root, use_dynamic_tree, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, batch_size=8, data_type="bfloat16"): print("Build engines...") model_name = "eagle" print("Creating dummy Eagle heads...") get_dummy_spec_decoding_heads(hf_model_dir=hf_model_root, save_dir=llm_venv.get_working_directory(), mode='eagle') eagle_model_root = os.path.join(llm_venv.get_working_directory(), 'fp8') ckpt_dir = convert_weights(llm_venv=llm_venv, example_root=eagle_example_root, cmodel_dir=cmodel_dir, model=model_name, model_path=eagle_model_root, data_type=data_type) build_cmd = [ "trtllm-build", f"--checkpoint_dir={ckpt_dir}", f"--output_dir={engine_dir}", f"--gpt_attention_plugin={data_type}", f"--gemm_plugin={data_type}", f"--max_beam_width=1", "--remove_input_padding=enable", "--context_fmha=enable", "--use_paged_context_fmha=enable", "--max_input_len=1024", "--max_seq_len=1536", f"--max_batch_size={batch_size}", "--paged_kv_cache=enable", '--speculative_decoding_mode=eagle', ] check_call(" ".join(build_cmd), shell=True, env=llm_venv._new_env) print("Run run...") run_cmd = [ f"{eagle_example_root}/../run.py", "--max_output_len=100", f"--tokenizer_dir={hf_model_root}", "--log_level=verbose", f"--engine_dir={engine_dir}", ] if use_dynamic_tree: run_cmd.extend( [f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"]) venv_check_call(llm_venv, run_cmd) print("Run summarize...") summary_cmd = [ f"{eagle_example_root}/../summarize.py", "--test_trt_llm", "--hf_model_dir", f"{hf_model_root}", "--tokenizer_dir", f"{hf_model_root}", f"--engine_dir={engine_dir}", "--eagle_choices=[[0], [0, 0], [1], [0, 1], [2], [0, 0, 0], [1, 0], [0, 2], [3], [0, 3], [4], [0, 4], [2, 0], [0, 5], [0, 0, 1], [5], [0, 6], [6], [0, 7], [0, 1, 0], [1, 1], [7], [0, 8], [0, 0, 2], [3, 0], [0, 9], [8], [9], [1, 0, 0], [0, 2, 0], [1, 2], [0, 0, 3], [4, 0], [2, 1], [0, 0, 4], [0, 0, 5], [0, 0, 0, 0], [0, 1, 1], [0, 0, 6], [0, 3, 0], [5, 0], [1, 3], [0, 0, 7], [0, 0, 8], [0, 0, 9], [6, 0], [0, 4, 0], [1, 4], [7, 0], [0, 1, 2], [2, 0, 0], [3, 1], [2, 2], [8, 0], [0, 5, 0], [1, 5], [1, 0, 1], [0, 2, 1], [9, 0], [0, 6, 0], [0, 0, 0, 1], [1, 6], [0, 7, 0]]", f"--max_ite=40", f"--batch_size={batch_size}", f"--dataset_dir={llm_datasets_root}", f"--rouge_dir={llm_rouge_root}" ] if use_dynamic_tree: summary_cmd.extend( [f"--eagle_dynamic_tree_max_top_k={3}", "--eagle_use_dynamic_tree"]) venv_check_call(llm_venv, summary_cmd) @pytest.mark.parametrize("use_dynamic_tree", [ False, pytest.param(True, marks=pytest.mark.skip(reason="https://nvbugs/5219534")) ], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("llama_model_root", ['llama-v2-7b-hf', 'llama-3.1-8b', 'llama-3.2-1b'], indirect=True) def test_llama_eagle_1gpu(llama_model_root, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, use_dynamic_tree, batch_size=8, data_type='bfloat16'): test_with_dummy_eagle(hf_model_root=llama_model_root, eagle_example_root=eagle_example_root, llm_venv=llm_venv, cmodel_dir=cmodel_dir, engine_dir=engine_dir, batch_size=batch_size, data_type=data_type, use_dynamic_tree=use_dynamic_tree, llm_datasets_root=llm_datasets_root, llm_rouge_root=llm_rouge_root) @pytest.mark.skip(reason="https://nvbugs/5219534") @pytest.mark.parametrize("use_dynamic_tree", [False, True], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("code_llama_model_root", ['CodeLlama-7b-Instruct'], indirect=True) def test_codellama_eagle_1gpu(code_llama_model_root, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, use_dynamic_tree, batch_size=8, data_type='bfloat16'): test_with_dummy_eagle(hf_model_root=code_llama_model_root, eagle_example_root=eagle_example_root, llm_venv=llm_venv, cmodel_dir=cmodel_dir, engine_dir=engine_dir, batch_size=batch_size, data_type=data_type, use_dynamic_tree=use_dynamic_tree, llm_datasets_root=llm_datasets_root, llm_rouge_root=llm_rouge_root) @pytest.mark.parametrize("use_dynamic_tree", [False, True], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("llm_mistral_model_root", ['mistral-7b-v0.1'], indirect=True) def test_mistral_eagle_1gpu(llm_mistral_model_root, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, use_dynamic_tree, batch_size=8, data_type='bfloat16'): test_with_dummy_eagle(hf_model_root=llm_mistral_model_root, eagle_example_root=eagle_example_root, llm_venv=llm_venv, cmodel_dir=cmodel_dir, engine_dir=engine_dir, batch_size=batch_size, data_type=data_type, use_dynamic_tree=use_dynamic_tree, llm_datasets_root=llm_datasets_root, llm_rouge_root=llm_rouge_root) @skip_post_blackwell @skip_pre_ada @pytest.mark.parametrize("use_dynamic_tree", [False, True], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("mistral_nemo_model_root", ['Mistral-Nemo-12b-Base'], indirect=True) def test_mistral_nemo_eagle_1gpu(mistral_nemo_model_root, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, use_dynamic_tree, batch_size=8, data_type='bfloat16'): test_with_dummy_eagle(hf_model_root=mistral_nemo_model_root, eagle_example_root=eagle_example_root, llm_venv=llm_venv, cmodel_dir=cmodel_dir, engine_dir=engine_dir, batch_size=batch_size, data_type=data_type, use_dynamic_tree=use_dynamic_tree, llm_datasets_root=llm_datasets_root, llm_rouge_root=llm_rouge_root) @pytest.mark.parametrize("use_dynamic_tree", [False, True], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("llm_qwen_model_root", [ "qwen_7b_chat", "qwen1.5_7b_chat", "qwen2_7b_instruct", "qwen2_0.5b_instruct", "qwen2.5_1.5b_instruct" ], indirect=True) def test_qwen_eagle_1gpu(llm_qwen_model_root, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, use_dynamic_tree, batch_size=8, data_type='bfloat16'): test_with_dummy_eagle(hf_model_root=llm_qwen_model_root, eagle_example_root=eagle_example_root, llm_venv=llm_venv, cmodel_dir=cmodel_dir, engine_dir=engine_dir, batch_size=batch_size, data_type=data_type, use_dynamic_tree=use_dynamic_tree, llm_datasets_root=llm_datasets_root, llm_rouge_root=llm_rouge_root) @pytest.mark.parametrize("use_dynamic_tree", [False, True], ids=['eagle1', 'eagle2']) @pytest.mark.parametrize("llm_phi_model_root", [ "phi-2", "Phi-3-mini-128k-instruct", "Phi-3-small-128k-instruct", "Phi-3.5-mini-instruct" ], indirect=True) def test_phi_eagle_1gpu(llm_phi_model_root, eagle_example_root, llm_datasets_root, llm_rouge_root, llm_venv, cmodel_dir, engine_dir, use_dynamic_tree, batch_size=8, data_type='bfloat16'): test_with_dummy_eagle(hf_model_root=llm_phi_model_root, eagle_example_root=eagle_example_root, llm_venv=llm_venv, cmodel_dir=cmodel_dir, engine_dir=engine_dir, batch_size=batch_size, data_type=data_type, use_dynamic_tree=use_dynamic_tree, llm_datasets_root=llm_datasets_root, llm_rouge_root=llm_rouge_root)