From 29b7da296700b0497284fa420f5ce2cf92e302dd Mon Sep 17 00:00:00 2001 From: Wanli Jiang <35160485+Wanli-Jiang@users.noreply.github.com> Date: Wed, 22 Jul 2026 03:23:48 +0000 Subject: [PATCH] [TRTLLM-14473][chore] Remove legacy TensorRT-backend tests, examples, and CI plumbing Trailing test/example/CI cleanup after the legacy TensorRT backend removal (TRTLLM-14026, PR #16369). CI schedules / jenkins: - Move backend-neutral tests off the retired -TensorRT- stages and delete all dead backend=tensorrt test-db buckets; flip remaining neutral buckets to pytorch so parked coverage runs again. - NEW stages A100X-PyTorch-Post-Merge-1 and L40S-PyTorch-Post-Merge-1 schedule the restored a100/l40s post-merge coverage (CI-owner review). - Retire the -TensorRT- plumbing in L0_Test.groovy (commented stage rows, backend=tensorrt mako branch, TEST_BACKEND changeMap entry, excludedBackends) and the -TensorRT- pattern in jenkins/scripts/cbts/blocks.py. - waives.txt: drop 42 waives for deleted/unscheduled tests (the validator requires waived tests to be in an active list). Legacy engine-flow example tests: - Delete the defs/examples wrappers whose every test drove the removed convert->trtllm-build->run flow: test_{bert,chatglm,commandr, draft_target_model,eagle,enc_dec,gptj,granite,internlm,llama,mamba, medusa,mistral,mixtral,multimodal,nemotron,nemotron_nas,ngram, openai,qwen,qwen2audio,qwenvl,redrafter,whisper,bindings,flux}.py, the whisper validators, and run_llm_fp8_quant_llama_70b.py. - test_gpt.py rewritten keeping only the live test_gpt_oss_20b_lora_torch; test_phi.py drops its quantization engine test; test_e2e.py drops test_gpt3_175b_1layers_build_only. - Remove the dead helper cascade in defs/common.py (convert_weights, quantize_data, prune_checkpoint, refit_model, find_tensorrt), defs/perf/build.py and test_perf.py's trtllm-build branches, and 19 orphaned example_root fixtures across defs/conftest.py and triton_server/conftest.py (verified: no dynamic getfixturevalue resolution exists for any of them). Dormant example scripts (imported deleted ModelRunner*/builder surfaces or drove engine flows; unreachable from CI): - examples/{run,summarize,utils,mmlu,eval_long_context}.py, examples/openai_triton/**, examples/python_plugin/**, examples/models/contrib/sdxl/**, examples/llm-eval/lm-eval-harness, the engine-flow multimodal/qwenvl/qwen2audio scripts, and examples/ngram/run_dtm_ngram.py (READMEs rewritten for the PyTorch flow where a live section remains). examples/ reference sweep (checked against tests, published docs, jenkins, and code): - contrib: delete arctic, blip2, chatglm-6b/2-6b/3-6b-32k, internlm, jais, skywork, smaug (README/requirements-only legacy-flow dirs; hyperclovax stays - live PyTorch README linked from release notes). - core: delete granite and mixtral; multimodal keeps the live Qwen-Image-Bench stub + eval script but drops utils.py, __init__.py and the per-model requirements-*.txt; nemotron drops the Nemo-flow README_nemotron-3.md + requirements.txt (PyTorch READMEs stay). - top level: delete generate_checkpoint_config.py, generate_xgrammar_tokenizer_info.py, hf_lora_convert.py, sample_weight_stripping/, cpp_library/, dora/, language_adapter/. - Kept deliberately: infinitebench (dependency of the retained CLI-flow suite) and longbench/opentelemetry/sparse_attention (unreferenced but document current features). Accuracy suite: - accuracy/test_cli_flow.py and CliFlowAccuracyTestHarness are KEPT (currently unscheduled; retained for possible future reuse). - Port TestQwen2_7BInstruct::test_tp2 to the PyTorch accuracy suite (skipped: TP2 hangs in the AUTO custom allreduce on PCIe-only nodes; the LMHead AllReduce ignores allreduce_strategy, so the strategy knob cannot work around it). Misc: - automodel.py isMedusa/isEagle branch and _utils.py::supports_inflight_batching (mapped to deleted TRT model classes); test-db README example refreshed to a real test id; docs fixes in ci-overview.md and the AutoDeploy testing strategy. Rider (squashed in): [https://nvbugs/6482297][fix] Parse boolean stage-config fields in test_to_stage_mapping (from PR #16671, same author). The stage regex only accepted numeric trailing fields, so the 47 runWithSbatch stage entries (all DGX_B200-PyTorch-*) were silently dropped and commented-out stage rows were matched; test_backend_filtering_consistency now tolerates tests legitimately listed under multiple backends. Carried here because this PR retires stages and reshuffles test-db buckets - the exact surface those consistency tests validate. legacy-files.txt/ruff baseline entries for the deleted files are left to a mechanical 'legacy_utils.py prune-files' pass after this and the python-cleanup PR merge, to avoid cross-PR conflicts. Verified: defs collection passes (4206 tests), pre-commit hooks incl. the test-list AST validator pass, no broken example links. Signed-off-by: Wanli Jiang <35160485+Wanli-Jiang@users.noreply.github.com> --- docs/source/developer-guide/ci-overview.md | 4 +- .../auto_deploy/advanced/testing_strategy.md | 4 +- examples/cpp_library/CMakeLists.txt | 55 - examples/cpp_library/build.sh | 25 - examples/cpp_library/main.cpp | 79 - examples/cpp_library/tensorrt_llm_libutils.h | 62 - examples/dora/README.md | 65 - examples/dora/normalize_weights.py | 289 ---- examples/eval_long_context.py | 331 ---- examples/generate_checkpoint_config.py | 149 -- examples/generate_xgrammar_tokenizer_info.py | 51 - examples/hf_lora_convert.py | 266 --- examples/language_adapter/README.md | 99 -- examples/llm-eval/lm-eval-harness/README.md | 67 - .../lm-eval-harness/lm_eval_tensorrt_llm.py | 317 ---- .../llm-eval/lm-eval-harness/requirements.txt | 1 - examples/mmlu.py | 479 ------ examples/models/contrib/arctic/README.md | 94 - examples/models/contrib/blip2/README.md | 4 - examples/models/contrib/chatglm-6b/README.md | 112 -- .../contrib/chatglm-6b/requirements.txt | 8 - .../chatglm-6b/tokenization_chatglm.py | 467 ----- examples/models/contrib/chatglm2-6b/README.md | 108 -- .../contrib/chatglm2-6b/requirements.txt | 8 - .../chatglm2-6b/tokenization_chatglm.py | 282 --- .../models/contrib/chatglm3-6b-32k/README.md | 112 -- .../contrib/chatglm3-6b-32k/requirements.txt | 8 - .../chatglm3-6b-32k/tokenization_chatglm.py | 313 ---- examples/models/contrib/internlm/.gitignore | 2 - examples/models/contrib/internlm/README.md | 320 ---- .../models/contrib/internlm/requirements.txt | 6 - examples/models/contrib/jais/README.md | 125 -- examples/models/contrib/jais/requirements.txt | 6 - examples/models/contrib/sdxl/README.md | 42 - .../models/contrib/sdxl/build_sdxl_unet.py | 148 -- .../sdxl/pipeline_stable_diffusion_xl.py | 1365 --------------- examples/models/contrib/sdxl/run_sdxl.py | 96 -- examples/models/contrib/skywork/README.md | 113 -- .../models/contrib/skywork/requirements.txt | 6 - examples/models/contrib/smaug/README.md | 62 - .../models/contrib/smaug/requirements.txt | 6 - examples/models/core/granite/README.md | 89 - examples/models/core/mixtral/README.md | 219 --- examples/models/core/mixtral/requirements.txt | 4 - examples/models/core/multimodal/README.md | 1320 +------------- examples/models/core/multimodal/__init__.py | 0 .../multimodal/build_multimodal_engine.py | 12 - examples/models/core/multimodal/eval.py | 317 ---- .../core/multimodal/requirements-eclair.txt | 1 - .../requirements-internlm-xcomposer2.txt | 1 - .../requirements-llava_onevision.txt | 4 - .../core/multimodal/requirements-qwen2vl.txt | 2 - .../core/multimodal/requirements-vila.txt | 2 - examples/models/core/multimodal/run.py | 128 -- examples/models/core/multimodal/utils.py | 163 -- .../models/core/nemotron/README_nemotron-3.md | 214 --- .../models/core/nemotron/requirements.txt | 7 - examples/models/core/qwen2audio/README.md | 80 - .../audio/glass-breaking-151256.mp3 | Bin 129024 -> 0 bytes .../models/core/qwen2audio/requirements.txt | 10 - examples/models/core/qwen2audio/run.py | 640 ------- examples/models/core/qwen2audio/run_chat.py | 81 - examples/models/core/qwen2audio/utils.py | 130 -- examples/models/core/qwenvl/README.md | 114 -- examples/models/core/qwenvl/pics/1.png | Bin 2514941 -> 0 bytes examples/models/core/qwenvl/pics/demo.jpeg | Bin 496395 -> 0 bytes examples/models/core/qwenvl/requirements.txt | 11 - examples/models/core/qwenvl/run.py | 549 ------ examples/models/core/qwenvl/run_chat.py | 128 -- examples/models/core/qwenvl/show_pic.py | 34 - examples/models/core/qwenvl/vit_onnx_trt.py | 196 --- examples/ngram/README.md | 109 +- examples/ngram/run_dtm_ngram.py | 382 ----- examples/openai_triton/README.md | 7 - .../manual_plugin/CMakeLists.txt | 113 -- .../openai_triton/manual_plugin/README.md | 177 -- .../TritonFlashAttentionPlugin.cpp | 386 ----- .../TritonFlashAttentionPlugin.h | 113 -- examples/openai_triton/manual_plugin/build.py | 137 -- .../manual_plugin/fmha_triton.py | 135 -- .../openai_triton/manual_plugin/plugin.py | 133 -- examples/openai_triton/manual_plugin/run.py | 170 -- .../manual_plugin/tritonPlugins.cpp | 133 -- .../openai_triton/plugin_autogen/README.md | 97 -- .../plugin_autogen/build_engine.py | 206 --- .../plugin_autogen/kernel_config.py | 55 - .../plugin_autogen/run_engine.py | 170 -- examples/python_plugin/README.md | 120 -- examples/python_plugin/build_lookup.py | 61 - examples/python_plugin/plugin_lib/__init__.py | 3 - .../python_plugin/plugin_lib/lookup_kernel.py | 17 - .../python_plugin/plugin_lib/lookup_plugin.py | 60 - examples/python_plugin/run_lookup.py | 65 - examples/run.py | 710 -------- examples/sample_weight_stripping/README.md | 275 --- examples/summarize.py | 944 ---------- examples/utils.py | 658 ------- jenkins/L0_Test.groovy | 63 +- jenkins/scripts/cbts/blocks.py | 1 - scripts/test_to_stage_mapping.py | 10 +- tensorrt_llm/_utils.py | 10 +- tensorrt_llm/models/automodel.py | 15 +- .../defs/accuracy/test_llm_api_pytorch.py | 13 + tests/integration/defs/common.py | 495 +----- tests/integration/defs/conftest.py | 220 --- .../examples/run_llm_fp8_quant_llama_70b.py | 41 - tests/integration/defs/examples/test_bert.py | 127 -- .../defs/examples/test_bindings.py | 34 - .../integration/defs/examples/test_chatglm.py | 83 - .../defs/examples/test_commandr.py | 83 - .../defs/examples/test_draft_target_model.py | 152 -- tests/integration/defs/examples/test_eagle.py | 96 -- .../integration/defs/examples/test_enc_dec.py | 340 ---- tests/integration/defs/examples/test_flux.py | 55 - tests/integration/defs/examples/test_gpt.py | 659 +------ tests/integration/defs/examples/test_gptj.py | 27 - .../integration/defs/examples/test_granite.py | 160 -- .../defs/examples/test_internlm.py | 104 -- tests/integration/defs/examples/test_llama.py | 533 ------ tests/integration/defs/examples/test_mamba.py | 123 -- .../integration/defs/examples/test_medusa.py | 156 -- .../integration/defs/examples/test_mistral.py | 219 --- .../integration/defs/examples/test_mixtral.py | 119 -- .../defs/examples/test_multimodal.py | 726 -------- .../defs/examples/test_nemotron.py | 72 - .../defs/examples/test_nemotron_nas.py | 124 -- tests/integration/defs/examples/test_ngram.py | 163 -- .../integration/defs/examples/test_openai.py | 196 --- tests/integration/defs/examples/test_phi.py | 74 +- tests/integration/defs/examples/test_qwen.py | 242 --- .../defs/examples/test_qwen2audio.py | 108 -- .../integration/defs/examples/test_qwenvl.py | 39 - .../defs/examples/test_redrafter.py | 99 -- .../integration/defs/examples/test_whisper.py | 298 ---- .../examples/validate_whisper_beam_logits.py | 205 --- .../validate_whisper_log_probs_determinism.py | 218 --- tests/integration/defs/perf/build.py | 1524 ----------------- tests/integration/defs/perf/test_perf.py | 73 +- tests/integration/defs/test_e2e.py | 29 - .../defs/triton_server/conftest.py | 15 - tests/integration/test_lists/qa/README.md | 2 +- .../test_lists/qa/llm_function_core.txt | 1 + .../integration/test_lists/test-db/README.md | 4 +- .../integration/test_lists/test-db/l0_a10.yml | 34 +- .../test_lists/test-db/l0_a100.yml | 2 +- .../integration/test_lists/test-db/l0_a30.yml | 33 - .../test_lists/test-db/l0_b200.yml | 3 +- .../test_lists/test-db/l0_dgx_h200.yml | 21 - .../test_lists/test-db/l0_gb203.yml | 5 +- .../test_lists/test-db/l0_gh200.yml | 2 +- .../test_lists/test-db/l0_h100.yml | 53 +- .../test_lists/test-db/l0_l40s.yml | 20 +- tests/integration/test_lists/waives.txt | 42 - .../tools/test_test_to_stage_mapping.py | 11 +- 154 files changed, 126 insertions(+), 24298 deletions(-) delete mode 100644 examples/cpp_library/CMakeLists.txt delete mode 100755 examples/cpp_library/build.sh delete mode 100644 examples/cpp_library/main.cpp delete mode 100644 examples/cpp_library/tensorrt_llm_libutils.h delete mode 100644 examples/dora/README.md delete mode 100755 examples/dora/normalize_weights.py delete mode 100644 examples/eval_long_context.py delete mode 100644 examples/generate_checkpoint_config.py delete mode 100644 examples/generate_xgrammar_tokenizer_info.py delete mode 100755 examples/hf_lora_convert.py delete mode 100755 examples/language_adapter/README.md delete mode 100644 examples/llm-eval/lm-eval-harness/README.md delete mode 100644 examples/llm-eval/lm-eval-harness/lm_eval_tensorrt_llm.py delete mode 100644 examples/llm-eval/lm-eval-harness/requirements.txt delete mode 100644 examples/mmlu.py delete mode 100644 examples/models/contrib/arctic/README.md delete mode 100644 examples/models/contrib/blip2/README.md delete mode 100644 examples/models/contrib/chatglm-6b/README.md delete mode 100644 examples/models/contrib/chatglm-6b/requirements.txt delete mode 100755 examples/models/contrib/chatglm-6b/tokenization_chatglm.py delete mode 100644 examples/models/contrib/chatglm2-6b/README.md delete mode 100644 examples/models/contrib/chatglm2-6b/requirements.txt delete mode 100644 examples/models/contrib/chatglm2-6b/tokenization_chatglm.py delete mode 100644 examples/models/contrib/chatglm3-6b-32k/README.md delete mode 100644 examples/models/contrib/chatglm3-6b-32k/requirements.txt delete mode 100644 examples/models/contrib/chatglm3-6b-32k/tokenization_chatglm.py delete mode 100644 examples/models/contrib/internlm/.gitignore delete mode 100644 examples/models/contrib/internlm/README.md delete mode 100644 examples/models/contrib/internlm/requirements.txt delete mode 100644 examples/models/contrib/jais/README.md delete mode 100644 examples/models/contrib/jais/requirements.txt delete mode 100644 examples/models/contrib/sdxl/README.md delete mode 100755 examples/models/contrib/sdxl/build_sdxl_unet.py delete mode 100755 examples/models/contrib/sdxl/pipeline_stable_diffusion_xl.py delete mode 100755 examples/models/contrib/sdxl/run_sdxl.py delete mode 100644 examples/models/contrib/skywork/README.md delete mode 100644 examples/models/contrib/skywork/requirements.txt delete mode 100644 examples/models/contrib/smaug/README.md delete mode 100644 examples/models/contrib/smaug/requirements.txt delete mode 100644 examples/models/core/granite/README.md delete mode 100644 examples/models/core/mixtral/README.md delete mode 100644 examples/models/core/mixtral/requirements.txt delete mode 100644 examples/models/core/multimodal/__init__.py delete mode 100644 examples/models/core/multimodal/build_multimodal_engine.py delete mode 100644 examples/models/core/multimodal/eval.py delete mode 100644 examples/models/core/multimodal/requirements-eclair.txt delete mode 100644 examples/models/core/multimodal/requirements-internlm-xcomposer2.txt delete mode 100644 examples/models/core/multimodal/requirements-llava_onevision.txt delete mode 100644 examples/models/core/multimodal/requirements-qwen2vl.txt delete mode 100644 examples/models/core/multimodal/requirements-vila.txt delete mode 100644 examples/models/core/multimodal/run.py delete mode 100644 examples/models/core/multimodal/utils.py delete mode 100644 examples/models/core/nemotron/README_nemotron-3.md delete mode 100644 examples/models/core/nemotron/requirements.txt delete mode 100644 examples/models/core/qwen2audio/README.md delete mode 100644 examples/models/core/qwen2audio/audio/glass-breaking-151256.mp3 delete mode 100644 examples/models/core/qwen2audio/requirements.txt delete mode 100644 examples/models/core/qwen2audio/run.py delete mode 100644 examples/models/core/qwen2audio/run_chat.py delete mode 100644 examples/models/core/qwen2audio/utils.py delete mode 100644 examples/models/core/qwenvl/README.md delete mode 100644 examples/models/core/qwenvl/pics/1.png delete mode 100644 examples/models/core/qwenvl/pics/demo.jpeg delete mode 100644 examples/models/core/qwenvl/requirements.txt delete mode 100644 examples/models/core/qwenvl/run.py delete mode 100644 examples/models/core/qwenvl/run_chat.py delete mode 100644 examples/models/core/qwenvl/show_pic.py delete mode 100644 examples/models/core/qwenvl/vit_onnx_trt.py delete mode 100644 examples/ngram/run_dtm_ngram.py delete mode 100644 examples/openai_triton/README.md delete mode 100644 examples/openai_triton/manual_plugin/CMakeLists.txt delete mode 100644 examples/openai_triton/manual_plugin/README.md delete mode 100644 examples/openai_triton/manual_plugin/TritonFlashAttentionPlugin.cpp delete mode 100644 examples/openai_triton/manual_plugin/TritonFlashAttentionPlugin.h delete mode 100644 examples/openai_triton/manual_plugin/build.py delete mode 100644 examples/openai_triton/manual_plugin/fmha_triton.py delete mode 100644 examples/openai_triton/manual_plugin/plugin.py delete mode 100644 examples/openai_triton/manual_plugin/run.py delete mode 100644 examples/openai_triton/manual_plugin/tritonPlugins.cpp delete mode 100644 examples/openai_triton/plugin_autogen/README.md delete mode 100644 examples/openai_triton/plugin_autogen/build_engine.py delete mode 100644 examples/openai_triton/plugin_autogen/kernel_config.py delete mode 100644 examples/openai_triton/plugin_autogen/run_engine.py delete mode 100644 examples/python_plugin/README.md delete mode 100644 examples/python_plugin/build_lookup.py delete mode 100644 examples/python_plugin/plugin_lib/__init__.py delete mode 100644 examples/python_plugin/plugin_lib/lookup_kernel.py delete mode 100644 examples/python_plugin/plugin_lib/lookup_plugin.py delete mode 100644 examples/python_plugin/run_lookup.py delete mode 100755 examples/run.py delete mode 100644 examples/sample_weight_stripping/README.md delete mode 100644 examples/summarize.py delete mode 100644 examples/utils.py delete mode 100644 tests/integration/defs/examples/run_llm_fp8_quant_llama_70b.py delete mode 100644 tests/integration/defs/examples/test_bert.py delete mode 100644 tests/integration/defs/examples/test_bindings.py delete mode 100644 tests/integration/defs/examples/test_chatglm.py delete mode 100644 tests/integration/defs/examples/test_commandr.py delete mode 100644 tests/integration/defs/examples/test_draft_target_model.py delete mode 100644 tests/integration/defs/examples/test_eagle.py delete mode 100644 tests/integration/defs/examples/test_enc_dec.py delete mode 100644 tests/integration/defs/examples/test_flux.py delete mode 100644 tests/integration/defs/examples/test_gptj.py delete mode 100644 tests/integration/defs/examples/test_granite.py delete mode 100644 tests/integration/defs/examples/test_internlm.py delete mode 100644 tests/integration/defs/examples/test_llama.py delete mode 100644 tests/integration/defs/examples/test_mamba.py delete mode 100644 tests/integration/defs/examples/test_medusa.py delete mode 100644 tests/integration/defs/examples/test_mistral.py delete mode 100644 tests/integration/defs/examples/test_mixtral.py delete mode 100644 tests/integration/defs/examples/test_multimodal.py delete mode 100644 tests/integration/defs/examples/test_nemotron.py delete mode 100644 tests/integration/defs/examples/test_nemotron_nas.py delete mode 100644 tests/integration/defs/examples/test_ngram.py delete mode 100644 tests/integration/defs/examples/test_openai.py delete mode 100644 tests/integration/defs/examples/test_qwen.py delete mode 100644 tests/integration/defs/examples/test_qwen2audio.py delete mode 100644 tests/integration/defs/examples/test_qwenvl.py delete mode 100644 tests/integration/defs/examples/test_redrafter.py delete mode 100644 tests/integration/defs/examples/test_whisper.py delete mode 100644 tests/integration/defs/examples/validate_whisper_beam_logits.py delete mode 100644 tests/integration/defs/examples/validate_whisper_log_probs_determinism.py delete mode 100644 tests/integration/defs/perf/build.py diff --git a/docs/source/developer-guide/ci-overview.md b/docs/source/developer-guide/ci-overview.md index 8df1a6a00160..e708dce631a6 100644 --- a/docs/source/developer-guide/ci-overview.md +++ b/docs/source/developer-guide/ci-overview.md @@ -98,8 +98,8 @@ Each line contains the fully qualified test name followed by an optional specific hardware family. Example: ```text -examples/test_openai.py::test_llm_openai_triton_1gpu SKIP (https://nvbugspro.nvidia.com/bug/4963654) -full:GH200/examples/test_qwen2audio.py::test_llm_qwen2audio_single_gpu[qwen2_audio_7b_instruct] SKIP (arm is not supported) +accuracy/test_disaggregated_serving.py::TestDeepSeekV32Exp::test_auto_dtype[False] SKIP (https://nvbugs/6120535) +full:A100/accuracy/test_llm_api_pytorch_multimodal.py::TestExaone4_5_33B::test_auto_dtype[full_budget] SKIP (https://nvbugs/6422318) ``` Changes to `waives.txt` should include a bug link or brief explanation so other diff --git a/docs/source/features/auto_deploy/advanced/testing_strategy.md b/docs/source/features/auto_deploy/advanced/testing_strategy.md index d65841862ee6..b60eada7e722 100644 --- a/docs/source/features/auto_deploy/advanced/testing_strategy.md +++ b/docs/source/features/auto_deploy/advanced/testing_strategy.md @@ -117,8 +117,8 @@ Format: `path/to/test_file.py::test_function_name[param_id]` Example from `l0_a30.yml`: ```yaml -- accuracy/test_cli_flow.py::TestLlama3_1_8BInstruct::test_medusa_fp8_prequantized -- examples/test_multimodal.py::test_llm_multimodal_general[Qwen2-VL-7B-Instruct-pp:1-tp:1-float16-bs:1-cpp_e2e:False-nb:4] +- accuracy/test_llm_api_pytorch.py::TestLlama3_1_8B::test_auto_dtype +- unittest/_torch/modeling -k "modeling_llama" ``` ### Example: Adding an Accuracy Test diff --git a/examples/cpp_library/CMakeLists.txt b/examples/cpp_library/CMakeLists.txt deleted file mode 100644 index c60ff48d4cde..000000000000 --- a/examples/cpp_library/CMakeLists.txt +++ /dev/null @@ -1,55 +0,0 @@ -# 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. cmake needs this line -cmake_minimum_required(VERSION 3.1) -# cmake_minimum_required(VERSION 2.8) - -# Enable C++11 -set(CMAKE_CXX_STANDARD 14) -set(CMAKE_CXX_STANDARD_REQUIRED TRUE) - -# Define project name -set(TARGET_NAME trt_llm_plugins_cpp_load_example) -project(${TARGET_NAME}) - -set(CMAKE_VERBOSE_MAKEFILE 1) - -# Compile options -set(CMAKE_C_FLAGS "-Wall -pthread ") -set(CMAKE_C_FLAGS_DEBUG "-g -O0") -set(CMAKE_C_FLAGS_RELEASE "-O2") -set(CMAKE_CXX_FLAGS "${CMAKE_C_FLAGS} -lstdc++") -set(CMAKE_CXX_FLAGS_DEBUG ${CMAKE_C_FLAGS_DEBUG}) -set(CMAKE_CXX_FLAGS_RELEASE ${CMAKE_C_FLAGS_RELEASE}) - -set(CMAKE_BUILD_TYPE release) -# set(CMAKE_BUILD_TYPE debug) - -find_package(CUDA REQUIRED) -message(STATUS "CUDA library status:") -message(STATUS " config: ${CUDA_DIR}") -message(STATUS " version: ${CUDA_VERSION}") -message(STATUS " libraries: ${CUDA_LIBRARIES}") -message(STATUS " include path: ${CUDA_INCLUDE_DIRS}") - -# Declare the executable target built from your sources -add_executable(${TARGET_NAME} main.cpp) - -# Link your application with CUDA libraries -target_link_libraries(${TARGET_NAME} LINK_PRIVATE ${CUDA_LIBRARIES}) -target_link_libraries(${TARGET_NAME} LINK_PRIVATE cudnn) -target_link_libraries(${TARGET_NAME} LINK_PRIVATE nvinfer) -target_link_libraries(${TARGET_NAME} LINK_PRIVATE nvinfer_plugin_tensorrt_llm) - -target_include_directories(${TARGET_NAME} PUBLIC /usr/local/cuda/include) diff --git a/examples/cpp_library/build.sh b/examples/cpp_library/build.sh deleted file mode 100755 index a384e5cad4a0..000000000000 --- a/examples/cpp_library/build.sh +++ /dev/null @@ -1,25 +0,0 @@ -#!/usr/bin/env bash - -BUILD_DIR="build" -SCRIPT_DIR=$( cd -- "$( dirname -- "${BASH_SOURCE[0]}" )" &> /dev/null && pwd ) - -rm -rf ${BUILD_DIR} && mkdir -p ${BUILD_DIR} - -pushd ${BUILD_DIR} - -cmake \ - -DCMAKE_BUILD_TYPE=Release \ - .. - -make -j"$(grep -c ^processor /proc/cpuinfo)" - -export LD_LIBRARY_PATH="${SCRIPT_DIR}:${LD_LIBRARY_PATH}" - -# Test Lib -echo -echo "--------------------------------------------------------------------" -./trt_llm_plugins_cpp_load_example -echo "--------------------------------------------------------------------" -echo - -popd diff --git a/examples/cpp_library/main.cpp b/examples/cpp_library/main.cpp deleted file mode 100644 index 7613a75a140c..000000000000 --- a/examples/cpp_library/main.cpp +++ /dev/null @@ -1,79 +0,0 @@ -/* - * 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. - */ -#include -#include -#include - -#include "tensorrt_llm_libutils.h" - -int main(int argc, char* argv[]) -{ - class TRTLogger : public nvinfer1::ILogger - { - public: - void log(nvinfer1::ILogger::Severity severity, char const* msg) noexcept override - { - if (severity <= nvinfer1::ILogger::Severity::kERROR) - std::cerr << "[TensorRT LLM ERR]: " << msg << std::endl; - else if (severity == nvinfer1::ILogger::Severity::kWARNING) - std::cerr << "[TensorRT LLM WARNING]: " << msg << std::endl; - else - std::cout << "[TensorRT LLM LOG]: " << msg << std::endl; - } - }; - - TRTLogger* trtLogger = new TRTLogger(); - - std::string libname = "libtensorrt_llm_plugin.so"; - - /* =============== initLibNvInferPlugins =============== */ - - typedef bool (*initLibNvInferPlugins_sig)(void*, void const*); - - auto initLibNvInferPlugins = getTrtLLMFunction( - /*libFileSoName=*/libname, - /*symbol=*/"initLibNvInferPlugins"); - - std::cout << std::endl; - - std::string libNamespace = "tensorrt_llm"; - char const* libNamespace_cstr = libNamespace.data(); - - bool status1 = initLibNvInferPlugins(trtLogger, libNamespace_cstr); - std::cout << "Success Status: " << status1 << std::endl << std::endl; - - bool status2 = initLibNvInferPlugins(trtLogger, libNamespace_cstr); - std::cout << "Success Status: " << status2 << std::endl; - - /* =============== getInferLibVersion =============== */ - - std::cout << std::endl; - std::cout << "--------------------------------------------------------------------" << std::endl; - - typedef int32_t (*getInferLibVersion_sig)(); - - auto getInferLibVersion = getTrtLLMFunction( - /*libFileSoName=*/libname, - /*symbol=*/"getInferLibVersion"); - - std::cout << std::endl; - - int32_t version = getInferLibVersion(); - std::cout << "Version: " << version << std::endl; - - return 0; -} diff --git a/examples/cpp_library/tensorrt_llm_libutils.h b/examples/cpp_library/tensorrt_llm_libutils.h deleted file mode 100644 index aa60444eefa3..000000000000 --- a/examples/cpp_library/tensorrt_llm_libutils.h +++ /dev/null @@ -1,62 +0,0 @@ -/* - * 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. - */ -#if !defined(_WIN32) -#include -#endif // !defined(_WIN32) -#include -#include -#include - -#include "NvInfer.h" - -template -tSymbolSignature getTrtLLMFunction(std::string libFileSoName, std::string symbol) -{ -#if !defined(_WIN32) - std::cout << "Trying to load " << libFileSoName << " ..." << std::endl; - - // 1. Defining a handle to the library - void* handle = dlopen(libFileSoName.c_str(), RTLD_LAZY | RTLD_GLOBAL); - - // 2. Check for errors - char const* dl_error1 = dlerror(); - if (!handle) - { - throw std::runtime_error("Cannot open library: " + std::string(dl_error1)); - } - - // 3. Load actual queried `symbol` - std::cout << "Loading symbol `" << symbol << "` ..." << std::endl; - - tSymbolSignature symbolFctn = nullptr; - *(void**) (&symbolFctn) = dlsym(handle, symbol.c_str()); - - // 4. Check for errors - char const* dl_error2 = dlerror(); - if (dl_error2) - { - dlclose(handle); - throw std::runtime_error("Cannot load symbol '" + symbol + "': " + std::string(dl_error2)); - } - - return symbolFctn; -#else // on windows - throw std::runtime_error( - "`tSymbolSignature getTrtLLMFunction(std::string, std::string)` is not implemented on Windows."); - return nullptr; -#endif // !defined(_WIN32) -} diff --git a/examples/dora/README.md b/examples/dora/README.md deleted file mode 100644 index 21f70768a257..000000000000 --- a/examples/dora/README.md +++ /dev/null @@ -1,65 +0,0 @@ -# DoRA - -This document shows how to run a model using DoRA adapters. -DoRA is a PEFT strategy extending LoRA. It is fully supported in the Huggingface `peft` library. For a more detailed description please refer to the DoRA [paper](https://arxiv.org/abs/2402.09353) or official [repo](https://github.com/NVlabs/DoRA). - -## Support Matrix - * FP16/BF16 (over arbitrary precision of the base model). - * Supports adapters from Huggingface `peft` or from the official NVlabs [checkpoints](https://huggingface.co/sliuau/DoRA-weights). - * Multiple adapters (+ mixed LoRA/DoRA setups). - * inflight loading of new adapters to a preloaded base model. - * C++ and python runtime. - * Tensor parallelism and Pipeline parallelism. - -## Usage -Using DoRA is almost exactly the same as using LoRA in TRTLLM, with an additional preprocessing step. -While the official DoRA paper describes the magnitude normalization as part of the execution flow, it can be performed once beforehand to boost inference performance. - -Start by obtaining a local copy of your desired DoRA adapter **and** your base model. We'll use the official NVlabs checkpoint for LLaMA3-8B as an example: - -``` bash -git clone https://huggingface.co/sliuau/DoRA-weights -git clone https://huggingface.co/meta-llama/Meta-Llama-3-8B -``` - -Next, use the [normalize_weights.py](./normalize_weights.py) script to normalize the DoRA magnitude vectors in the adapter checkpoint. -The script requires access to both the local adapter weights and the local base model weights: - -``` bash -export NORMALIZED_DORA_ADAPTER=path/to/normalized/adapter/ckpt - -python ./normalize_weights.py -i DoRA-weights/llama_dora_commonsense_checkpoints/LLama3-8B/dora_r32 -b Meta-Llama-3-8B -o $NORMALIZED_DORA_ADAPTER -``` - -The script will create a new adapter checkpoint, with normalized DoRA vectors, in the provided path. - -Now we may convert our Llama checkpoint and build our TRT engine as described in the Llama [examples](../models/core/llama/README.md). When doing so, ensure you pass `--dora_plugin=enable` to the `trtllm-build` command, as well as enabling the lora plugin: - -``` bash -export CHECKPOINT_DIR=path/to/trtllm/ckpt -export ENGIRE_DIR=path/to/trtllm/engine - -python ../models/core/llama/convert_checkpoint.py --model_dir Meta-Llama-3-8B \ - --output_dir $CHECKPOINT_DIR \ - --dtype float16 - -trtllm-build --checkpoint_dir $CHECKPOINT_DIR \ - --output_dir $ENGINE_DIR \ - --gemm_plugin=auto \ - --lora_plugin=auto \ - --dora_plugin=enable \ - --lora_dir $NORMALIZED_DORA_ADAPTER -``` - -If you wish, you may provide additional LoRA / DoRA adapters to `trtllm-build`. - -**NOTE**: if you omit `--dora_plugin=enable`, you will not receive any warning even if you provide a DoRA adapter to `--lora_dir`. In such a case the DoRA magnitudes will simply be ignored during inference and you may receive wrong output. - -Proceed to execute the engine as you would a normal LoRA engine: - -``` bash -python ../run.py --engine_dir $ENGINE_DIR --tokenizer_dir Meta-Llama-3-8B --lora_task_uids 0 --max_output_len 32 --input_text ... -``` - -## Usage with Triton Server -Using DoRA over Triton is the same as using LoRA, but before using [hf_lora_convert.py](../hf_lora_convert.py), make sure you call [normalize_weights.py](./normalize_weights.py) and use the resulting normalized adapter. diff --git a/examples/dora/normalize_weights.py b/examples/dora/normalize_weights.py deleted file mode 100755 index 40d5ae172431..000000000000 --- a/examples/dora/normalize_weights.py +++ /dev/null @@ -1,289 +0,0 @@ -#! /usr/bin/env python3 -# 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. -""" -This script applies preprocessing to the DoRA magnitude vector to speed up inference. -DoRA applies columnwise normalization and scaling to the LoRA output. -By applying the normalization to the scaling vector, we can skip calculating the normalization vector at inference time. -""" -import abc -import enum -import json -from pathlib import Path - -import numpy as np -import safetensors.torch as st -import torch - -StateDict = dict[str, torch.Tensor] - -PEFT_MODULE_PREFIX = "base_model.model." -PEFT_MODULE_SUFFIXES = [".lora_A.weight", ".lora_B.weight"] -DORA_VECTOR_SUFFIXES = [ - ".lora_magnitude_vector", # HF peft - ".weight_m_wdecomp.weight" # NVLabs -] - - -class HFWeightsReader(abc.ABC): - - @abc.abstractmethod - def __init__(self, model_dir: str) -> None: - ... - - @abc.abstractmethod - def get_weight(self, weight_name: str) -> torch.Tensor: - ... - - @abc.abstractmethod - def get_all(self) -> StateDict: - ... - - -class HFSafeTensorsReader(HFWeightsReader): - - def __init__(self, model_dir: str) -> None: - self.model_dir = Path(model_dir) - - self._fds = [ - st.safe_open(f, framework="torch") - for f in self.model_dir.glob("*.safetensors") - ] - - self._weight_to_fd = {} - for f in self._fds: - for k in f.keys(): - self._weight_to_fd[k] = f - - def get_weight(self, weight_name: str) -> torch.Tensor: - return self._weight_to_fd[weight_name].get_tensor(weight_name) - - def get_all(self) -> StateDict: - return {k: self.get_weight(k) for k in self._weight_to_fd.keys()} - - -class HFBinWeightsReader(HFWeightsReader): - - def __init__(self, model_dir: str) -> None: - self.model_dir = Path(model_dir) - - self._weights = {} - - for f in self.model_dir.glob("*.bin"): - self._weights.update( - torch.load(f, weights_only=True, mmap=True, map_location="cpu")) - - def get_weight(self, weight_name: str) -> torch.Tensor: - weight_name = f"{weight_name}.weight" - return self._weights[weight_name] - - def get_all(self) -> StateDict: - return self._weights - - -class WeightsFormat(enum.Enum): - BINARY = enum.auto() - SAFETENSORS = enum.auto() - UNKNOWN = enum.auto() - - def __str__(self) -> str: - if self == self.BINARY: - return "bin" - elif self == self.SAFETENSORS: - return "safetensors" - return "unknown" - - -def deduce_weights_format(model_dir: str) -> WeightsFormat: - model_dir_p = Path(model_dir) - - if any(model_dir_p.glob("*.safetensors")): - return WeightsFormat.SAFETENSORS - elif any(model_dir_p.glob("*.bin")): - return WeightsFormat.BINARY - return WeightsFormat.UNKNOWN - - -def get_weights_reader(model_dir: str) -> HFWeightsReader: - model_dir_p = Path(model_dir) - - if not model_dir_p.is_dir(): - raise ValueError( - f"{model_dir} is not a valid model directory: not found") - - weights_format = deduce_weights_format(model_dir) - - if weights_format == WeightsFormat.SAFETENSORS: - return HFSafeTensorsReader(model_dir) - elif weights_format == WeightsFormat.BINARY: - return HFBinWeightsReader(model_dir) - else: - raise ValueError( - f"{model_dir} does not contain .safetensors or .bin weights") - - -def normalize_hf_peft_module_name(module_name: str) -> str: - """ - Remove parts of the module name in the peft adapter to derive the module name of the base model. - """ - - if module_name.startswith(PEFT_MODULE_PREFIX): - module_name = module_name[len(PEFT_MODULE_PREFIX):] - - for suffix in PEFT_MODULE_SUFFIXES + DORA_VECTOR_SUFFIXES: - if module_name.endswith(suffix): - module_name = module_name[:-len(suffix)] - - return module_name - - -def get_peft_module_names(base_module_name: str) -> tuple[str, ...]: - """ - Convert the name of a base module to the names of its LoRA A and LoRA B weights. - """ - return tuple([ - f"{PEFT_MODULE_PREFIX}{base_module_name}{suffix}" - for suffix in PEFT_MODULE_SUFFIXES - ]) - - -def get_dora_magnitude_names(base_module_name: str) -> tuple[str, ...]: - """ - Convert the name of a base module to the potential names of its DoRA magnitude vectors. - """ - return tuple([ - f"{PEFT_MODULE_PREFIX}{base_module_name}{suffix}" - for suffix in DORA_VECTOR_SUFFIXES - ]) - - -def normalize_dora_vector(W: torch.Tensor, A: torch.Tensor, B: torch.Tensor, - mag: torch.Tensor, scale: float) -> torch.Tensor: - return mag / torch.linalg.norm(W + scale * B @ A, dim=1).to(W.dtype) - - -def normalize_dora_scales(lora_sd: StateDict, - weights_reader: HFWeightsReader, - alpha: float, - use_rslora: bool, - strip: bool = False) -> StateDict: - out_sd = {} - - while lora_sd: - # take some lora weight name - module_name = next(iter(lora_sd.keys())) - base_module_name = normalize_hf_peft_module_name(module_name) - A_name, B_name = get_peft_module_names(base_module_name) - magnitude_names = get_dora_magnitude_names(base_module_name) - - if module_name not in [A_name, B_name] + list(magnitude_names): - raise ValueError(f"Encountered unknown weight: {module_name}") - - # get lora weights - A = lora_sd.pop(A_name) - B = lora_sd.pop(B_name) - for name in magnitude_names: - if name in lora_sd: - mag_name = name - mag = lora_sd.pop(mag_name).view(-1) - break - else: - mag_name = "" - mag = None - - out_sd[A_name] = A.contiguous() - out_sd[B_name] = B.contiguous() - - if mag is not None and not strip: - # get base weight and normalize - W = weights_reader.get_weight(base_module_name + ".weight") - - adapter_size = A.size(0) - - if use_rslora: - scale = alpha / np.sqrt(adapter_size) - else: - scale = alpha / adapter_size - - mag = normalize_dora_vector(W, A, B, mag, scale) - out_sd[mag_name] = mag.contiguous() - - return out_sd - - -def save_state_dict(out_file: str, sd: StateDict) -> None: - out_path = Path(out_file) - - if out_path.suffix == ".safetensors": - st.save_file(sd, out_path) - elif out_path.suffix == ".bin": - torch.save(sd, out_path) - else: - raise ValueError(f"Unregornized weights format: {out_path.suffix}") - - -def normalize_peft_ckpt(model_dir: str, - base_model_dir: str, - out_dir: str, - strip: bool = False) -> None: - out_path = Path(out_dir) - out_path.mkdir(parents=True, exist_ok=True) - - weights_reader = get_weights_reader(base_model_dir) - lora_sd = get_weights_reader(model_dir).get_all() - - adapter_config_path = Path(f"{model_dir}/adapter_config.json") - with adapter_config_path.open() as f: - adapter_config = json.load(f) - - alpha = adapter_config["lora_alpha"] - use_rslora = adapter_config.get("use_rslora", False) - - new_sd = normalize_dora_scales(lora_sd, weights_reader, alpha, use_rslora, - strip) - - with (out_path / "adapter_config.json").open("w") as f: - json.dump(adapter_config, f) - - weights_format = deduce_weights_format(model_dir) - save_state_dict( - (out_path / f"adapter_model.{str(weights_format)}").as_posix(), new_sd) - - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser() - parser.add_argument( - '--out-dir', - '-o', - type=Path, - help='path to output adapter weights with normalized DoRA vectors', - required=True) - parser.add_argument('--in-dir', - '-i', - type=Path, - help='path to input lora checkpoint file', - required=True) - parser.add_argument("--base-model", - "-b", - help="Path to base model", - required=True) - parser.add_argument("--strip", - action="store_true", - help="remove DoRA vectors entirely") - - args = parser.parse_args() - - normalize_peft_ckpt(args.in_dir, args.base_model, args.out_dir, args.strip) diff --git a/examples/eval_long_context.py b/examples/eval_long_context.py deleted file mode 100644 index 90b7ef2dd270..000000000000 --- a/examples/eval_long_context.py +++ /dev/null @@ -1,331 +0,0 @@ -# MIT License - -# Copyright (c) 2023 OpenBMB - -# Permission is hereby granted, free of charge, to any person obtaining a copy -# of this software and associated documentation files (the "Software"), to deal -# in the Software without restriction, including without limitation the rights -# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -# copies of the Software, and to permit persons to whom the Software is -# furnished to do so, subject to the following conditions: - -# The above copyright notice and this permission notice shall be included in all -# copies or substantial portions of the Software. - -# 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. - -# reference: https://github.com/OpenBMB/InfiniteBench/blob/main/src/eval_yarn_mistral.py - -import argparse -import ast -import json -from pathlib import Path - -import torch -from infinitebench.compute_scores import compute_scores -from infinitebench.eval_utils import (DATA_NAME_TO_MAX_NEW_TOKENS, - create_prompt, dump_jsonl, get_answer, - load_data) -from utils import (DEFAULT_HF_MODEL_DIRS, DEFAULT_PROMPT_TEMPLATES, - add_common_args, load_tokenizer, read_model_name) - -import tensorrt_llm -import tensorrt_llm.profiler as profiler -from tensorrt_llm.logger import logger -from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelRunner - -if PYTHON_BINDINGS: - from tensorrt_llm.runtime import ModelRunnerCpp - -MAX_POSITION_ID = 128 * 1024 # Determined by the model -TRUNCATE_LEN = 128 * 1024 - - -def parse_arguments(args=None): - parser = argparse.ArgumentParser() - parser.add_argument('--batch_size', type=int, default=1) - parser.add_argument('--max_input_length', type=int, default=923) - parser.add_argument('--output_log_probs_npy', - type=str, - help='Numpy file where the log_probs are stored', - default=None) - - parser.add_argument('--output_cum_log_probs_npy', - type=str, - help='Numpy file where the cum_log_probs are stored', - default=None) - - parser.add_argument( - "--task", - type=str, - choices=['passkey', 'kv_retrieval'], - required=True, - help= - "Which task to use. Note that \"all\" can only be used in `compute_scores.py`.", # noqa - ) - parser.add_argument('--data_dir', - type=str, - default='./', - help="The directory of data.") - parser.add_argument("--output_dir", - type=str, - default=None, - help="Where to dump the prediction results.") # noqa - parser.add_argument( - "--start_idx", - type=int, - default=0, - help= - "The index of the first example to infer on. This is used if you want to evaluate on a (contiguous) subset of the data." - ) # noqa - parser.add_argument( - "--stop_idx", - type=int, - help= - "The index of the last example to infer on. This is used if you want to evaluate on a (contiguous) subset of the data. Defaults to the length of dataset." - ) # noqa - parser.add_argument('--tensorrt_llm_accuracy_threshold', - type=float, - default=99) - parser = add_common_args(parser) - - return parser.parse_args(args=args) - - -def parse_input(tokenizer, - input_text=None, - prompt_template=None, - add_special_tokens=True, - max_input_length=923, - pad_id=None, - num_prepend_vtokens=[], - model_name=None, - model_version=None): - if pad_id is None: - pad_id = tokenizer.pad_token_id - - batch_input_ids = [] - for curr_text in input_text: - if prompt_template is not None: - curr_text = prompt_template.format(input_text=curr_text) - input_ids = tokenizer.encode(curr_text, - add_special_tokens=add_special_tokens, - truncation=True, - max_length=max_input_length) - batch_input_ids.append(input_ids) - - if num_prepend_vtokens: - assert len(num_prepend_vtokens) == len(batch_input_ids) - base_vocab_size = tokenizer.vocab_size - len( - tokenizer.special_tokens_map.get('additional_special_tokens', [])) - for i, length in enumerate(num_prepend_vtokens): - batch_input_ids[i] = list( - range(base_vocab_size, - base_vocab_size + length)) + batch_input_ids[i] - - if 'GLM' in model_name and model_version == 'glm': - for ids in batch_input_ids: - ids.append(tokenizer.sop_token_id) - - batch_input_ids = [ - torch.tensor(x, dtype=torch.int32) for x in batch_input_ids - ] - return batch_input_ids - - -def main(args): - # model_name = "yarn-mistral" - runtime_rank = tensorrt_llm.mpi_rank() - logger.set_level(args.log_level) - - print(json.dumps(vars(args), indent=4)) - data_name = args.task - - # Model - max_tokens = DATA_NAME_TO_MAX_NEW_TOKENS[data_name] - - model_name, model_version = read_model_name(args.engine_dir) - if args.tokenizer_dir is None: - logger.warning( - "tokenizer_dir is not specified. Try to infer from model_name, but this may be incorrect." - ) - args.tokenizer_dir = DEFAULT_HF_MODEL_DIRS[model_name] - - tokenizer, pad_id, end_id = load_tokenizer( - tokenizer_dir=args.tokenizer_dir, - vocab_file=args.vocab_file, - model_name=model_name, - model_version=model_version, - tokenizer_type=args.tokenizer_type, - ) - - if not PYTHON_BINDINGS and not args.use_py_session: - logger.warning( - "Python bindings of C++ session is unavailable, fallback to Python session." - ) - args.use_py_session = True - if args.debug_mode and not args.use_py_session: - logger.warning( - "Debug mode is not supported in C++ session for now, fallback to Python session." - ) - args.use_py_session = True - runner_cls = ModelRunner if args.use_py_session else ModelRunnerCpp - runner_kwargs = dict( - engine_dir=args.engine_dir, - lora_dir=args.lora_dir, - rank=runtime_rank, - debug_mode=args.debug_mode, - lora_ckpt_source=args.lora_ckpt_source, - gpu_weights_percent=args.gpu_weights_percent, - ) - if args.medusa_choices is not None: - args.medusa_choices = ast.literal_eval(args.medusa_choices) - assert args.temperature == 1.0, "Medusa should use temperature == 1.0" - assert args.num_beams == 1, "Medusa should use num_beams == 1" - runner_kwargs.update(medusa_choices=args.medusa_choices) - if not args.use_py_session: - runner_kwargs.update( - max_batch_size=args.batch_size, - max_input_len=args.max_input_length, - max_output_len=max_tokens, - max_beam_width=args.num_beams, - max_attention_window_size=args.max_attention_window_size, - sink_token_length=args.sink_token_length, - max_tokens_in_paged_kv_cache=args.max_tokens_in_paged_kv_cache, - kv_cache_enable_block_reuse=args.kv_cache_enable_block_reuse, - kv_cache_free_gpu_memory_fraction=args. - kv_cache_free_gpu_memory_fraction, - enable_chunked_context=args.enable_chunked_context, - ) - runner = runner_cls.from_dir(**runner_kwargs) - - # Data - examples = load_data(data_name, data_dir=args.data_dir) - if args.stop_idx is None: - args.stop_idx = len(examples) - - output_path = None - if runtime_rank == 0: - if args.output_dir is not None: - result_dir = Path(args.output_dir, model_name) - result_dir.mkdir(exist_ok=True, parents=True) - - if args.stop_idx is None: - output_path = (result_dir / f"preds_{data_name}.jsonl") - else: - output_path = ( - result_dir / - f"preds_{data_name}_{args.start_idx}-{args.stop_idx}.jsonl" # noqa - ) - - prompt_template = None - if args.use_prompt_template and model_name in DEFAULT_PROMPT_TEMPLATES: - prompt_template = DEFAULT_PROMPT_TEMPLATES[model_name] - - if runtime_rank == 0: - preds = [] - logger.info("==== Evaluation ====") - logger.info(f"# examples: {len(examples)}") - logger.info(f"Start index: {args.start_idx}") - logger.info(f"Stop index: {args.stop_idx}") - logger.info(f"Max tokens: {max_tokens}") - assert args.batch_size == 1 - profiler.start('Evaluation') - for i in range(args.start_idx, args.stop_idx): - eg = examples[i] - input_text = [create_prompt(eg, data_name, args.data_dir)] - batch_input_ids = parse_input( - tokenizer=tokenizer, - input_text=input_text, - prompt_template=prompt_template, - add_special_tokens=args.add_special_tokens, - max_input_length=args.max_input_length, - pad_id=pad_id, - num_prepend_vtokens=args.num_prepend_vtokens, - model_name=model_name, - model_version=model_version) - input_lengths = [x.size(0) for x in batch_input_ids] - - if runtime_rank == 0: - logger.debug(f"====== Example {i} ======") - logger.debug(f"input_lengths: {input_lengths}") - logger.debug(f"input_text: {input_text}") - logger.debug(f"answer: {get_answer(eg, data_name)}") - outputs = runner.generate( - batch_input_ids, - max_new_tokens=max_tokens, - max_attention_window_size=args.max_attention_window_size, - sink_token_length=args.sink_token_length, - end_id=end_id, - pad_id=pad_id, - temperature=args.temperature, - top_k=args.top_k, - top_p=args.top_p, - num_beams=args.num_beams, - length_penalty=args.length_penalty, - early_stopping=args.early_stopping, - beam_width_array=args.beam_width_array, - repetition_penalty=args.repetition_penalty, - presence_penalty=args.presence_penalty, - frequency_penalty=args.frequency_penalty, - prompt_ignore_length=args.prompt_ignore_length, - # stop_words_list=stop_words_list, - # bad_words_list=bad_words_list, - output_cum_log_probs=(args.output_cum_log_probs_npy != None), - output_log_probs=(args.output_log_probs_npy != None), - lora_uids=args.lora_task_uids, - prompt_table=args.prompt_table_path, - prompt_tasks=args.prompt_tasks, - streaming=args.streaming, - output_sequence_lengths=True, - return_dict=True, - medusa_choices=args.medusa_choices) - torch.cuda.synchronize() - if runtime_rank == 0: - output_ids = outputs['output_ids'] - output_beams_list = [ - tokenizer.batch_decode(output_ids[batch_idx, :, - input_lengths[batch_idx]:], - skip_special_tokens=True) - for batch_idx in range(args.batch_size) - ] - - logger.debug(f"preds: {output_beams_list[0]}") - preds.append({ - "id": i, - "prediction": output_beams_list[0][0], - "ground_truth": get_answer(eg, data_name), - "input_lengths": input_lengths, - }) - if output_path is not None: - dump_jsonl(preds, output_path) - profiler.stop('Evaluation') - - if runtime_rank == 0: - logger.info( - f'Evaluation takes: {profiler.elapsed_time_in_sec("Evaluation")} sec.' - ) - logger.info("Compute the score") - acc = compute_scores(preds, args.task) * 100 - logger.info(f"{args.task} accuracy: {acc:.2f} ({len(preds)})") - - if args.tensorrt_llm_accuracy_threshold is not None: - assert acc >= args.tensorrt_llm_accuracy_threshold, f"acc ({acc}) < tensorrt_llm_accuracy_threshold ({args.tensorrt_llm_accuracy_threshold})" - - -if __name__ == "__main__": - args = parse_arguments() - main(args) diff --git a/examples/generate_checkpoint_config.py b/examples/generate_checkpoint_config.py deleted file mode 100644 index a11104eeba68..000000000000 --- a/examples/generate_checkpoint_config.py +++ /dev/null @@ -1,149 +0,0 @@ -import argparse -import json -import os - -from tensorrt_llm.quantization import KV_CACHE_QUANT_ALGO_LIST, QUANT_ALGO_LIST - - -def parse_arguments(): - parser = argparse.ArgumentParser() - - parser.add_argument( - '--output_path', - type=str, - default='config.json', - help='The path to save the TensorRT LLM checkpoint config.json file') - parser.add_argument('--architecture', type=str, default='GPTForCausalLM') - parser.add_argument('--dtype', - type=str, - default='float16', - choices=['float32', 'bfloat16', 'float16']) - parser.add_argument('--vocab_size', type=int, default=32000) - parser.add_argument('--max_position_embeddings', type=int, default=1024) - parser.add_argument('--hidden_size', type=int, default=768) - parser.add_argument('--intermediate_size', type=int, default=None) - parser.add_argument('--num_hidden_layers', type=int, default=12) - parser.add_argument('--num_attention_heads', type=int, default=12) - parser.add_argument('--num_key_value_heads', type=int, default=None) - parser.add_argument('--hidden_act', type=str, default='gelu') - parser.add_argument('--norm_epsilon', type=float, default=1e-5) - parser.add_argument('--position_embedding_type', - type=str, - default='learned_absolute') - parser.add_argument( - '--use_parallel_embedding', - action='store_true', - default=False, - help= - 'By default embedding parallelism is disabled. By setting this flag, embedding parallelism is enabled' - ) - parser.add_argument( - '--embedding_sharding_dim', - type=int, - default=0, - choices=[0, 1], - help= - 'By default the embedding lookup table is sharded along vocab dimension (embedding_sharding_dim=0). ' - 'To shard it along hidden dimension, set embedding_sharding_dim=1' - 'Note: embedding sharing is only enabled when embedding_sharding_dim = 0' - ) - - parser.add_argument('--tp_size', - type=int, - default=1, - help='N-way tensor parallelism size') - parser.add_argument('--pp_size', - type=int, - default=1, - help='N-way pipeline parallelism size') - - parser.add_argument('--quant_algo', - type=str, - default=None, - choices=[None] + QUANT_ALGO_LIST) - parser.add_argument('--kv_cache_quant_algo', - type=str, - default=None, - choices=[None] + KV_CACHE_QUANT_ALGO_LIST) - parser.add_argument('--group_size', type=int, default=64) - parser.add_argument('--smoothquant_val', type=float, default=None) - parser.add_argument('--has_zero_point', default=False, action='store_true') - parser.add_argument('--pre_quant_scale', default=False, action='store_true') - parser.add_argument('--exclude_modules', nargs='+', default=None) - - parser.add_argument('--bias', default=False, action='store_true') - parser.add_argument('--apply_query_key_layer_scaling', - default=False, - action='store_true') - parser.add_argument('--rotary_pct', type=float, default=1.0) - parser.add_argument('--rotary_base', type=float, default=10000.0) - parser.add_argument('--rotary_scaling', nargs=2, type=str, default=None) - - args = parser.parse_args() - return args - - -if __name__ == '__main__': - args = parse_arguments() - world_size = args.tp_size * args.pp_size - - assert args.output_path.endswith('.json') - output_dir = os.path.dirname(args.output_path) - if output_dir and not os.path.exists(output_dir): - os.makedirs(output_dir) - - config = { - 'architecture': args.architecture, - 'dtype': args.dtype, - 'vocab_size': args.vocab_size, - 'max_position_embeddings': args.max_position_embeddings, - 'hidden_size': args.hidden_size, - 'intermediate_size': args.intermediate_size, - 'num_hidden_layers': args.num_hidden_layers, - 'num_attention_heads': args.num_attention_heads, - 'num_key_value_heads': args.num_key_value_heads, - 'hidden_act': args.hidden_act, - 'norm_epsilon': args.norm_epsilon, - 'position_embedding_type': args.position_embedding_type, - 'use_parallel_embedding': args.use_parallel_embedding, - 'embedding_sharding_dim': args.embedding_sharding_dim, - 'quantization': { - 'quant_algo': args.quant_algo, - 'kv_cache_quant_algo': args.kv_cache_quant_algo, - 'exclude_modules': args.exclude_modules, - }, - 'mapping': { - 'world_size': world_size, - 'tp_size': args.tp_size, - 'pp_size': args.pp_size, - }, - 'bias': args.bias, - 'apply_query_key_layer_scaling': args.apply_query_key_layer_scaling, - 'rotary_pct': args.rotary_pct, - 'rotary_base': args.rotary_base, - 'rotary_scaling': args.rotary_scaling, - } - - if args.intermediate_size is None: - config['intermediate_size'] = args.hidden_size * 4 - if args.num_key_value_heads is None: - config['num_key_value_heads'] = args.num_attention_heads - - if args.quant_algo is not None: - if 'AWQ' in args.quant_algo or 'GPTQ' in args.quant_algo: - config['quantization'].update({ - 'group_size': - args.group_size, - 'has_zero_point': - args.has_zero_point, - 'pre_quant_scale': - args.pre_quant_scale, - }) - if 'SQ' in args.quant_algo: - config['quantization'].update({ - 'smoothquant_val': - args.smoothquant_val, - }) - - with open(args.output_path, 'w') as f: - json.dump(config, f, indent=4) diff --git a/examples/generate_xgrammar_tokenizer_info.py b/examples/generate_xgrammar_tokenizer_info.py deleted file mode 100644 index 67e05eb9c968..000000000000 --- a/examples/generate_xgrammar_tokenizer_info.py +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env python3 -# SPDX-FileCopyrightText: Copyright (c) 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 argparse -import json -import os -from pathlib import Path - -from transformers import AutoTokenizer - -from tensorrt_llm.llmapi.tokenizer import _xgrammar_tokenizer_info - - -def generate_xgrammar_tokenizer_info(args): - - tokenizer = AutoTokenizer.from_pretrained(str(args.model_dir)) - tokenizer_info = _xgrammar_tokenizer_info(tokenizer) - - os.makedirs(args.output_dir, exist_ok=True) - with open(str(args.output_dir / "xgrammar_tokenizer_info.json"), 'w') as f: - json.dump(tokenizer_info, f) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument('--model_dir', - type=Path, - default=None, - required=True, - help="HF model directory") - parser.add_argument( - '--output_dir', - type=Path, - default=None, - required=True, - help="File path to save xgrammar's info. in json format") - args = parser.parse_args() - generate_xgrammar_tokenizer_info(args) diff --git a/examples/hf_lora_convert.py b/examples/hf_lora_convert.py deleted file mode 100755 index 019d78a48563..000000000000 --- a/examples/hf_lora_convert.py +++ /dev/null @@ -1,266 +0,0 @@ -#! /usr/bin/env python3 -# 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 argparse -import datetime -import json -import logging -import re -from collections import defaultdict -from pathlib import Path - -import numpy as np -import torch - -from tensorrt_llm._utils import str_dtype_to_torch, torch_to_numpy -from tensorrt_llm.lora_manager import LoraManager -from tensorrt_llm.models.convert_utils import get_model_path, load_state_dict - -log_format = "%(asctime)s %(name)s [%(levelname)s] %(message)s" -logging.basicConfig(format=log_format) -LOGGER = logging.getLogger(__name__) - - -def save_val(val, dir, key, tp_num=None, write_npy=False): - ext = "npy" if write_npy else "bin" - suffix = ext if tp_num is None else f"{tp_num}.{ext}" - if write_npy: - np.save(dir / f"model.{key}.{suffix}", val) - else: - val.tofile(dir / f"model.{key}.{suffix}") - - -def get_all_lora_weights(lora_weights): - all_weights = defaultdict(lambda: defaultdict(dict)) - pattern = re.compile( - r'(.*\.layers\.([0-9]+)\.(self_attn|mlp)\.([a-z_]+))\.(?:lora_(?:(A|B)\.weight|(magnitude)_vector)|weight_(m_wdecomp).weight).*' - ) - moe_pattern = re.compile( - r'(.*\.layers\.([0-9]+)\.(block_sparse_moe)\.((experts)\.([0-9]+)\.|)([a-zA-Z0-9_]+))\.(?:lora_(?:(A|B)\.weight|(magnitude)_vector)|weight_(m_wdecomp).weight).*' - ) - for key, weights in lora_weights.items(): - m = pattern.match(key) - m_moe = moe_pattern.match(key) - if m: - layer_idx = int(m.group(2)) - hf_module = m.group(4) - inout = m.group(5) - dora_magnitude = m.group(6) or m.group(7) - - if inout: - inout = "in" if inout == "A" else "out" - all_weights[layer_idx][hf_module][inout] = weights - elif dora_magnitude: - LOGGER.warning( - "Detected DoRA magnitude vector, make sure it was preprocessed and normalized using the proper base model weights" - ) - all_weights[layer_idx][hf_module]["magnitude"] = weights.view( - -1) - - elif m_moe: - layer_idx = int(m_moe.group(2)) - hf_module = m_moe.group(7) - inout = m_moe.group(8) - dora_magnitude = m_moe.group(9) or m.group(10) - - if inout: - inout = "in" if inout == "A" else "out" - all_weights[layer_idx][hf_module][inout] = weights - elif dora_magnitude: - LOGGER.warning( - "Detected DoRA magnitude vector, make sure it was preprocessed and normalized using the proper base model weights" - ) - all_weights[layer_idx][hf_module]["magnitude"] = weights.view( - -1) - else: - print(f"no match {key}") - continue - return all_weights - - -def preprocess_lora_weights(lora_model): - # Swap weights of gate_up_proj - for key, value in lora_model.items(): - if "gate_up_proj.lora_B.weight" in key: - print("Swap {}".format(key)) - original_weights = value.contiguous().clone() - half_split = original_weights.shape[0] // 2 - first_half = original_weights[:half_split, :] - second_half = original_weights[half_split:, :] - value = torch.cat((second_half, first_half), dim=0) - lora_model[key] = value - return lora_model - - -hf_modules_to_trtllm_modules = { - "q_proj": "attn_q", - "v_proj": "attn_v", - "k_proj": "attn_k", - "qkv_proj": "attn_qkv", - "query_key_value": "attn_qkv", - "o_proj": "attn_dense", - "dense": "attn_dense", - "gate_proj": "mlp_h_to_4h", - "down_proj": "mlp_4h_to_h", - "up_proj": "mlp_gate", - "gate_up_proj": "mlp_h_to_4h", - "c_fc": "mlp_h_to_4h", - "c_proj": "mlp_4h_to_h", - "w1": "moe_h_to_4h", - "w2": "moe_4h_to_h", - "w3": "moe_gate", - "gate": "moe_router", -} # lora modules on llama -hf_modules_to_module_id = { - k: LoraManager.LORA_MODULE_IDS[v] - for k, v in hf_modules_to_trtllm_modules.items() -} - - -def convert_hf_model(model_dir, dtype, out_dir): - saved_dir = Path(out_dir) - saved_dir.mkdir(parents=True, exist_ok=True) - with open(f"{model_dir}/adapter_config.json", "r") as f: - config = json.load(f) - - alpha = config.get("lora_alpha") - use_rslora = config.get("use_rslora", False) - - lora_model = load_state_dict(get_model_path(model_dir, "adapter_model")) - lora_model = preprocess_lora_weights(lora_model) - all_weights = get_all_lora_weights(lora_model) - converted_weights = [] - converted_config = [] - - def derive_adapter_size(inout_weight: torch.Tensor) -> int: - assert len(inout_weight.shape) == 2 - dim0, dim1 = inout_weight.shape - # assume the hidden dim is the larger of the 2 - adapter_size = min(dim0, dim1) - return adapter_size - - def derive_weights_scale(adapter_size: int, alpha: float, - use_rslora: bool) -> float: - if use_rslora: - return alpha / np.sqrt(adapter_size) - return alpha / adapter_size - - for layer_idx, layer_weights in all_weights.items(): - for hf_module, module_weights in layer_weights.items(): - in_weights = module_weights['in'] - out_weights = module_weights['out'] - magnitude = module_weights.get("magnitude", None) - is_dora = magnitude is not None - - processed_weights = [] - - assert len(in_weights.shape) == 2 - assert len(out_weights.shape) == 2 - assert not is_dora or len(magnitude.shape) == 1 - - adapter_size = derive_adapter_size(in_weights) - assert adapter_size == derive_adapter_size( - out_weights), "adapter size of A mismatches adapter size of B" - scale = derive_weights_scale(adapter_size, alpha, use_rslora) - - for w, inout in ((in_weights, "in"), (out_weights, "out")): - dim0 = w.shape[0] - dim1 = w.shape[1] - # in_weights should have shape [adaper_size, hidden] - if dim1 < dim0 and inout == "in": - w = w.transpose(1, 0) - # out_weights should have shape [hidden, adapter_size] - elif dim0 < dim1 and inout == "out": - w = w.transpose(1, 0) - if inout == "out": - w = w * scale - w = w.contiguous().flatten().to(dtype=str_dtype_to_torch(dtype)) - processed_weights.append(w) - - if is_dora: - processed_weights.append(magnitude.contiguous().flatten().to( - dtype=str_dtype_to_torch(dtype))) - - processed_weights = torch.concatenate(processed_weights).flatten() - converted_weights.append(processed_weights) - converted_config.append([ - hf_modules_to_module_id[hf_module], layer_idx, adapter_size, - 1 if is_dora else 0 - ]) - max_row_size = 0 - for t in converted_weights: - max_row_size = max(max_row_size, t.shape[0]) - for i in range(len(converted_weights)): - converted_weights[i] = torch.nn.functional.pad( - converted_weights[i], - (0, max_row_size - converted_weights[i].shape[0])).unsqueeze(0) - converted_weights = torch_to_numpy( - torch.concatenate( - converted_weights, - dim=0).unsqueeze(0).to(dtype=str_dtype_to_torch(dtype)).cpu()) - converted_config = torch.tensor(converted_config, - dtype=torch.int32, - device='cpu').unsqueeze(0).numpy() - - save_val(converted_weights, - saved_dir, - "lora_weights", - tp_num=None, - write_npy=True) - save_val(converted_config, - saved_dir, - "lora_config", - tp_num=None, - write_npy=True) - - -def main(args): - start_time = datetime.datetime.now() - convert_hf_model(args.in_file, args.storage_type, args.out_dir) - - LOGGER.info("Spent %s (h:m:s) to convert the prompt model", - datetime.datetime.now() - start_time) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument( - '--out-dir', - '-o', - type=Path, - help='path to output embedding table file in the .npy format', - required=True) - parser.add_argument('--in-file', - '-i', - type=Path, - help='path to input lora checkpoint file', - required=True) - parser.add_argument("--verbose", - action="store_true", - help="Provide verbose messages") - parser.add_argument("--storage-type", - type=str, - default="float16", - choices=["float32", "float16", "bfloat16"]) - args = parser.parse_args() - - LOGGER.setLevel(logging.DEBUG if args.verbose else logging.INFO) - - print("\n=============== Argument ===============") - for key in vars(args): - print(f"{key}: {vars(args)[key]}") - print("========================================") - - main(args) diff --git a/examples/language_adapter/README.md b/examples/language_adapter/README.md deleted file mode 100755 index 8487c8ab42a0..000000000000 --- a/examples/language_adapter/README.md +++ /dev/null @@ -1,99 +0,0 @@ -# Language-Adapter - -This document shows how to build and run a model with Language-Adapter plugin in TensorRT LLM on NVIDIA GPUs. - -## Overview -The concept of Language Adapter during inference time was introduced in [MAD-X: An Adapter-Based Framework for Multi-Task Cross-Lingual Transfer -](https://arxiv.org/pdf/2005.00052): -> we can simply replace a language-specific adapter trained for English with a language-specific adapter trained for Quechua at inference time. - -The implementation is done with MOE plugin with static expert selection passed during runtime as a parameter in request. - -For instance, encoder-decoder model may leverage language adapter for language-specific translation tasks when each of the language-adapter is trained for a specific language, this language adapter plugin achieves the language switching within one session only by passing in the `language_task_uid` to the plugin. - -The model checkpoint here is not publicly available. Please leverage `layers/language_adapter.py` in your own model. - -### Engine Preparation (convert and build) -``` -MODEL_DIR="dummy_model" # model not publicly available -INFERENCE_PRECISION="float16" -TP_SIZE=1 -PP_SIZE=1 -WORLD_SIZE=1 -MODEL_TYPE=language_adapter -MODEL_NAME=$MODEL_TYPE -CKPT_DIR=/scratch/tmp/trt_models/${MODEL_NAME}/${WORLD_SIZE}-gpu/${INFERENCE_PRECISION} -ENGINE_DIR=/scratch/tmp/trt_engines/${MODEL_NAME}/${WORLD_SIZE}-gpu/${INFERENCE_PRECISION} - -max_beam=5 -max_batch=32 -max_input_len=1024 -max_output_len=1024 - -python ../enc_dec/convert_checkpoint.py --model_type ${MODEL_TYPE} \ - --model_dir ${MODEL_DIR} \ - --output_dir $CKPT_DIR \ - --tp_size ${TP_SIZE} \ - --pp_size ${PP_SIZE} \ - --dtype ${INFERENCE_PRECISION} \ - --workers 1 - -trtllm-build --checkpoint_dir $CKPT_DIR/encoder \ - --output_dir $ENGINE_DIR/encoder \ - --paged_kv_cache disable \ - --moe_plugin auto \ - --bert_attention_plugin ${INFERENCE_PRECISION} \ - --gpt_attention_plugin ${INFERENCE_PRECISION} \ - --gemm_plugin ${INFERENCE_PRECISION} \ - --remove_input_padding enable \ - --max_input_len ${max_input_len} \ - --max_beam_width ${max_beam} \ - --max_batch_size ${max_batch} - -trtllm-build --checkpoint_dir $CKPT_DIR/decoder \ - --output_dir $ENGINE_DIR/decoder \ - --paged_kv_cache enable \ - --moe_plugin auto \ - --bert_attention_plugin ${INFERENCE_PRECISION} \ - --gpt_attention_plugin ${INFERENCE_PRECISION} \ - --gemm_plugin ${INFERENCE_PRECISION} \ - --remove_input_padding enable \ - --max_input_len 1 \ - --max_beam_width ${max_beam} \ - --max_batch_size ${max_batch} \ - --max_seq_len ${max_output_len} -``` - -### CPP runtime -A list `language_task_uids` that includes the language_task_uid for each input prompt is required: -``` -# translate 2 sentence, 1 to France (language_task_uid=3) 1 to Spanish (language_task_uid=2). -# language_task_uids = [3, 2] - -TEXT="Where is the nearest restaurant? Wikipedia is a free online encyclopedia written and maintained by a community of volunteers (called Wikis) through open collaboration and the use of MediaWiki, a wiki-based editing system." - -python3 ../run.py --engine_dir $ENGINE_DIR --tokenizer_type "language_adapter" --max_input_length 512 --max_output_len 512 --num_beams 1 --input_file input_ids.npy --tokenizer_dir $MODEL_DIR --language_task_uids 3 2 - -# Input [Text 0]: "" -# Output [Text 0 Beam 0]: "Où se trouve le restaurant le plus proche ? Wikipédia est une encyclopédie en ligne gratuite écrite et maintenue par une communauté de bénévoles (appelés Wikis) grâce à une collaboration ouverte et à l'utilisation de MediaWiki, un système d'édition basé sur wiki." -# Input [Text 1]: "" -# Output [Text 1 Beam 0]: "¿Dónde está el restaurante más cercano? Wikipedia es una enciclopedia en línea gratuita escrita y mantenida por una comunidad de voluntarios (llamada Wikis) a través de la colaboración abierta y el uso de MediaWiki, un sistema de edición basado en wiki." - -``` - -### Python runtime -Currently Python runtime does not support beam_width > 1. - -For Python runtime, full routing information of length [num_tokens, 1] is required for both encoder and decoder, which stacks routing information for each token in a batch of requests. -``` -# language_adapter_routing = get_language_adapter_routings(language_task_uid, input_ids) - -TEXT="Where is the nearest restaurant? Wikipedia is a free online encyclopedia written and maintained by a community of volunteers (called Wikis) through open collaboration and the use of MediaWiki, a wiki-based editing system." - -python3 ../enc_dec/run.py --engine_dir $ENGINE_DIR --engine_name ${MODEL_NAME} --model_name $MODEL_DIR --max_new_token=64 --num_beams=1 - -# in the run.py, 2 input prompts and 2 language task uids are provided. The two task uid represent the language of the input prompts to be translated to. - -# TRT-LLM output text: ['¿Dónde está el restaurante más cercano? Wikipedia es una enciclopedia en línea gratuita escrita y mantenida por una comunidad de voluntarios (llamada Wikis) a través de la colaboración abierta y el uso de MediaWiki, un sistema de edición basado en wiki.', "Où se trouve le restaurant le plus proche ? Wikipédia est une encyclopédie en ligne gratuite é -crite et maintenue par une communauté de bénévoles (appelés Wikis) grâce à une collaboration ouverte et à l'utilisation de MediaWiki, un système d'édition basé sur wiki."] -``` diff --git a/examples/llm-eval/lm-eval-harness/README.md b/examples/llm-eval/lm-eval-harness/README.md deleted file mode 100644 index c3854654dd0d..000000000000 --- a/examples/llm-eval/lm-eval-harness/README.md +++ /dev/null @@ -1,67 +0,0 @@ -# Evaluation scripts for LLM tasks - -This folder includes code to use the [LM-Eval-Harness](https://github.com/EleutherAI/lm-evaluation-harness), a unified framework to test generative language models on a large number of different evaluation tasks. The supported eval tasks are [here](https://github.com/EleutherAI/lm-evaluation-harness/tree/main/lm_eval/tasks). - -The following instructions show how to evaluate TRT-LLM engines with the benchmark. - -## Instructions - -### TRT-LLM API - -Build the TRT-LLM engine using `trtllm-build`. - -Install the `lm_eval` package in the `requirements.txt` file in this folder. - -Run the evaluation script with the following command: - -```sh -python lm_eval_tensorrt_llm.py --model trt-llm \ - --model_args tokenizer=,model=,chunk_size= \ - --tasks -``` - -In the LM-Eval-Harness, model args are submitted as a comma-separated list of the form `arg=value`. The `trt-llm` model supports the following `model_args`: - -| Name | Description | Default Value | -|--------------------------|-------------------------------------------------------------------|----------------| -| tokenizer | directory containing the HF tokenizer. | | -| model | directory containing the TRTLLM engine or torch model. | | -| max_gen_toks | max number of tokens to generate (if not specified in gen_kwargs) | 256 | -| chunk_size | number of async requests to send at once to the engine | 200 | -| max_tokens_kv_cache | max tokens in paged KV cache | None | -| free_gpu_memory_fraction | KV cache free GPU memory fraction | 0.9 | -| trust_remote_code | trust remote code; use if necessary to set up the tokenizer | False | -| tp | tensor parallel size (for torch backend) | no. of workers | -| use_cuda_graph | enable CUDA graph | True | -| max_context_length | maximum context length for evaluation | None | -| moe_expert_parallel_size | expert parallel size for MoE models | None | -| moe_backend | backend for MoE models (e.g., "TRTLLM") | "TRTLLM" | - -### Torch backend - -Install the `lm_eval` package in the `requirements.txt` file in this folder. - -Run the evaluation script with the same command as above, but include `backend=torch` in the `model_args`. For example: - -```sh -python lm_eval_tensorrt_llm.py --model trt-llm \ - --model_args model=,backend=torch,chunk_size= \ - --tasks -``` - -### trtllm-serve - -Build the TRT-LLM engine using `trtllm-build` and deploy with `trtllm-serve`. - -Install the `lm_eval` package in the `requirements.txt` file in this folder. - -Run the evaluation script with the following command: - -```sh -python lm_eval_tensorrt_llm.py --model local-completions \ - --model_args base_url=http://${HOST_NAME}:8001/v1/completions,model=,tokenizer= \ - --tasks \ - --batch_size <#> -``` - -Because `trtllm-serve` is OpenAI API compatible, we can use the `local-completions` model built in to `lm_eval`, which supports [these model_args](https://github.com/EleutherAI/lm-evaluation-harness/blob/v0.4.7/lm_eval/models/openai_completions.py#L12). diff --git a/examples/llm-eval/lm-eval-harness/lm_eval_tensorrt_llm.py b/examples/llm-eval/lm-eval-harness/lm_eval_tensorrt_llm.py deleted file mode 100644 index 1738242267d9..000000000000 --- a/examples/llm-eval/lm-eval-harness/lm_eval_tensorrt_llm.py +++ /dev/null @@ -1,317 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 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 gc -import json -import logging -import os -import signal -import threading -import time -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -import torch -import torch.nn.functional as F -import transformers -from lm_eval.__main__ import cli_evaluate -from lm_eval.api.model import TemplateLM -from lm_eval.api.registry import register_model -from packaging.version import parse -from tqdm import tqdm - -import tensorrt_llm -from tensorrt_llm import LLM as TORCH_LLM -from tensorrt_llm._tensorrt_engine import LLM as TRT_LLM -from tensorrt_llm.bindings.executor import DecodingConfig -from tensorrt_llm.llmapi import KvCacheConfig as TRT_KvCacheConfig -from tensorrt_llm.llmapi import RequestOutput, SamplingParams -from tensorrt_llm.llmapi.llm_args import MoeConfig - -logger = logging.getLogger(__name__) - - -@register_model("trt-llm") -class TRTLLMEvalBase(TemplateLM): - - def __init__( - self, - model: str, - tokenizer: Optional[str] = None, - tp: int = 0, # tensor_parallel_size - max_gen_toks: int = 256, - chunk_size: int = 200, - max_tokens_kv_cache: Optional[int] = None, - free_gpu_memory_fraction: float = 0.9, - trust_remote_code: bool = False, - use_cuda_graph: bool = True, - backend: str = 'trt', - max_context_length: Optional[int] = None, - moe_expert_parallel_size: Optional[int] = None, - moe_backend: Optional[str] = "TRTLLM", - enable_chunked_prefill: bool = False, - max_num_tokens: Optional[int] = None, - **kwargs, - ): - # initialize TemplateLM, copied from TemplateAPI - super().__init__() - assert isinstance(model, str) - assert parse(tensorrt_llm.__version__) >= parse("0.15.0") - - self.max_gen_toks = max_gen_toks - self.chunk_size = chunk_size - self.backend = backend - self.max_context_length = max_context_length - self.moe_expert_parallel_size = moe_expert_parallel_size - self.moe_backend = moe_backend - trt_kv_cache_config = TRT_KvCacheConfig(enable_block_reuse=False) - trt_kv_cache_config.free_gpu_memory_fraction = free_gpu_memory_fraction - if max_tokens_kv_cache is not None: - trt_kv_cache_config.max_tokens = max_tokens_kv_cache - - if tokenizer is None: - # Assume the tokenizer is stored in the model_dir if not specified. - tokenizer = model - logger.info(f"Tokenizer: {tokenizer}") - self.tokenizer = transformers.AutoTokenizer.from_pretrained( - tokenizer, trust_remote_code=trust_remote_code) - - if self.tokenizer.pad_token_id is None: - self.tokenizer.pad_token_id = self.tokenizer.eos_token_id - - if self.backend == 'torch': - kwargs.pop('batch_size') - if tp < 1: - tp = torch.cuda.device_count() - - pytorch_config_params = { - 'cuda_graph_config': {} if use_cuda_graph else None, - "print_iter_log": False, - 'moe_config': MoeConfig(backend=self.moe_backend) - } - - # stop words not currently supported by torch backend - self.use_stop_words = False - - self.llm = TORCH_LLM( - model=model, - tensor_parallel_size=tp, - trust_remote_code=trust_remote_code, - enable_chunked_prefill=enable_chunked_prefill, - max_num_tokens=max_num_tokens, - **pytorch_config_params, - tokenizer=self.tokenizer, - kv_cache_config=trt_kv_cache_config, - moe_expert_parallel_size=self.moe_expert_parallel_size, - **kwargs) - logger.info("Loaded TRT-LLM Torch engine") - else: - with open(Path(model) / "config.json", "r") as engine_config_file: - engine_config = json.load(engine_config_file) - build_config = engine_config["build_config"] - world_size = (engine_config.get("pretrained_config", {}).get( - "mapping", {}).get("world_size", 1)) - if max_tokens_kv_cache is None: - max_tokens_kv_cache = build_config[ - "max_seq_len"] * build_config["max_batch_size"] - self.gather_context_logits = build_config.get( - "gather_context_logits", False) - - medusa_choices = kwargs[ - 'medusa_choices'] if 'medusa_choices' in kwargs else None - kwargs = {} - if medusa_choices is not None: - decoding_config = DecodingConfig() - decoding_config.medusa_choices = medusa_choices - kwargs["decoding_config"] = decoding_config - assert world_size == 1, "decoding_config does not support multi TP in HLAPI." - - self.llm = TRT_LLM(model=model, - tokenizer=self.tokenizer, - kv_cache_config=trt_kv_cache_config, - **kwargs) - self.max_length = build_config['max_seq_len'] - 1 - logger.info("Loaded TRT-LLM engine") - - @property - def eot_token_id(self) -> int: - return self.llm.tokenizer.eos_token_id - - def tok_encode(self, string, add_special_tokens=False, **kwargs): - return self.llm.tokenizer.encode(string, - add_special_tokens=add_special_tokens, - **kwargs) - - def _loglikelihood_tokens( - self, - requests: List[Any], - disable_tqdm: bool = False) -> List[Tuple[float, bool]]: - """Compute the log likelihood of the continuation given the context.""" - if self.backend == 'torch': - raise NotImplementedError( - 'Torch backend does not return context logits yet') - - num_r = len(requests) - desc = "Processing loglikelihood requests" - sampling_params = SamplingParams(max_tokens=1, - return_context_logits=True) - - # process requests - futures: Dict[int, RequestOutput] = {} - results = [] - for i, request in tqdm(enumerate(requests), - desc=desc, - total=num_r, - disable=disable_tqdm): - # asynchronously submit a chunk of requests ahead of time... - if i % self.chunk_size == 0: - for j in range(i, min(i + self.chunk_size, num_r)): - prompt_ids = requests[j][1] + requests[j][2] - futures[j] = self.llm.generate_async( - prompt_ids, sampling_params) - - # process the output of the request i - r_out: RequestOutput = futures.pop(i).result() - - # check continuation portion of the prompt - # NOTE: context_logits are offset by 1 since they predict future token - ctxlen = len(request[1]) - token_ids_cont = request[2] - logits_cont = r_out.context_logits[ctxlen - 1:-1] # [sl, vocab] - logprobs_cont = F.log_softmax(logits_cont, dim=-1) # [sl, vocab] - top_tokens_cont = logprobs_cont.argmax(dim=-1).tolist() # [sl] - - # compute logprob and check for greedy - logprob_sum = sum(logprobs_cont[list(range(len(logprobs_cont))), - token_ids_cont]).item() - is_greedy = top_tokens_cont == token_ids_cont - - results.append((logprob_sum, is_greedy)) - - # clear response - del r_out - - return results - - def loglikelihood_rolling(self, requests, disable_tqdm: bool = False): - raise NotImplementedError - - def generate_until(self, - requests: List[Any], - disable_tqdm: bool = False) -> List[str]: - # some book-keeping and parameters... - num_r = len(requests) - desc = "Processing generate requests" - - if self.max_context_length is not None: - """ - Create updated_requests to contain qualified requests with the context length <= max_context_length. - Unqualified requests cannot simply be dropped as lm-eval library requires the number of requests to be the same. - - Note: The final score will drop if disqualified requests exist. - """ - request_idx_to_replace = [] - qualified_requests = [] - updated_requests = [] - for i, request in enumerate(requests): - context, gen_kwargs = request.args - if len(self.tok_encode(context)) > self.max_context_length: - request_idx_to_replace.append(i) - else: - qualified_requests.append(request) - - assert len( - qualified_requests - ) > 1, "No requests with context length <= max_context_length. Cannot run the evaluation." - if len(request_idx_to_replace) > 0: - print( - f"Warning: {len(request_idx_to_replace)} requests with context length > max_context_length will be replaced. The final score will drop." - ) - - for i, request in enumerate(requests): - if i in request_idx_to_replace: - # Replace the requests with context length > max_context_length with the qualified requests - updated_requests.append( - qualified_requests[i % len(qualified_requests)]) - else: - updated_requests.append(request) - assert len( - updated_requests - ) == num_r, "Number of updated requests does not match the number of requests." - requests = updated_requests - - def _get_sp(gen_kwargs): - k_mapping = { - "temperature": "temperature", - "top_p": "top_p", - "max_gen_toks": "max_tokens", - "until": "stop", - } - kwargs_mapped = { - k_sp: gen_kwargs[k_gen] - for k_gen, k_sp in k_mapping.items() if k_gen in gen_kwargs - } - if "max_tokens" not in kwargs_mapped: - kwargs_mapped["max_tokens"] = self.max_gen_toks - return SamplingParams(**kwargs_mapped) - - # process requests - futures: Dict[int, RequestOutput] = {} - future_stop_words: Dict[int, RequestOutput] = {} - results = [] - for i, _ in tqdm(enumerate(requests), - desc=desc, - total=num_r, - disable=disable_tqdm): - # asynchronously submit a chunk of requests ahead of time... - if i % self.chunk_size == 0: - for j in range(i, min(i + self.chunk_size, num_r)): - context, gen_kwargs = requests[j].args - prompt_ids = self.tok_encode(context) - if self.max_context_length is not None: - assert len( - prompt_ids - ) <= self.max_context_length, f"Prompt length > {self.max_context_length}, {len(prompt_ids)}, should be filtered out." - kwargs_mapped = _get_sp(gen_kwargs) - futures[j] = self.llm.generate_async( - prompt_ids, kwargs_mapped) - del kwargs_mapped - future_stop_words[j] = gen_kwargs["until"] - - # process the output of the request i - r_out: RequestOutput = futures.pop(i).result() - stop_words = future_stop_words.pop(i) - txt = r_out.outputs[0].text - if stop_words: - for word in stop_words: - word_index = txt.find(word) - if word_index >= 0: - txt = txt[:word_index] - results.append(txt) - - return results - - -if __name__ == "__main__": - cli_evaluate() - # Force clean up the LLM instance and void hanging. - gc.collect() - - # Force terminate in case gc.collect() is not enough. - def _terminate(): - time.sleep(10) - os.kill(os.getpid(), signal.SIGTERM) - - termination_thread = threading.Thread(target=_terminate, daemon=True) - termination_thread.start() diff --git a/examples/llm-eval/lm-eval-harness/requirements.txt b/examples/llm-eval/lm-eval-harness/requirements.txt deleted file mode 100644 index 44a3383f612d..000000000000 --- a/examples/llm-eval/lm-eval-harness/requirements.txt +++ /dev/null @@ -1 +0,0 @@ -lm_eval[api]==0.4.7 diff --git a/examples/mmlu.py b/examples/mmlu.py deleted file mode 100644 index 82bb9a7ec9ad..000000000000 --- a/examples/mmlu.py +++ /dev/null @@ -1,479 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2020 Dan Hendrycks -# SPDX-FileCopyrightText: Copyright (c) 2023 Deep Cognition and Language Research (DeCLaRe) Lab -# SPDX-FileCopyrightText: Copyright (c) 2023-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. -# SPDX-License-Identifier: Apache-2.0 and MIT -# -# 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. -"""Adapted from https://github.com/declare-lab/instruct-eval -Helper script to compare TRTLLM and HF models on the MMLU dataset. -Example usage: - mkdir data; wget https://people.eecs.berkeley.edu/~hendrycks/data.tar -O data/mmlu.tar - tar -xf data/mmlu.tar -C data && mv data/data data/mmlu - - python mmlu.py --hf_model_dir --engine_dir --test_trt_llm - python mmlu.py --hf_model_dir --engine_dir --test_hf -""" - -import argparse -import os -import random - -import numpy as np -import pandas as pd -import torch -import torch.nn as nn -from tqdm import tqdm -from transformers import (AutoConfig, AutoModel, AutoModelForCausalLM, - AutoModelForSeq2SeqLM, AutoTokenizer, - GenerationConfig) -from utils import (add_common_args, load_tokenizer, prepare_enc_dec_inputs, - read_is_enc_dec, read_model_name) - -import tensorrt_llm -from tensorrt_llm.runtime import PYTHON_BINDINGS, ModelRunner - -if PYTHON_BINDINGS: - from tensorrt_llm.runtime import ModelRunnerCpp - -os.environ["TOKENIZERS_PARALLELISM"] = "false" - -DTYPE_STR_MAPPING = { - "fp32": torch.float32, - "fp16": torch.float16, - "bf16": torch.bfloat16, - "float32": torch.float32, - "float16": torch.float16, - "bfloat16": torch.bfloat16, -} -RAND_SEED = 1234 - - -def get_choices(): - return ["A", "B", "C", "D"] - - -def get_subcategories(): - return { - "abstract_algebra": ["math"], - "anatomy": ["health"], - "astronomy": ["physics"], - "business_ethics": ["business"], - "clinical_knowledge": ["health"], - "college_biology": ["biology"], - "college_chemistry": ["chemistry"], - "college_computer_science": ["computer science"], - "college_mathematics": ["math"], - "college_medicine": ["health"], - "college_physics": ["physics"], - "computer_security": ["computer science"], - "conceptual_physics": ["physics"], - "econometrics": ["economics"], - "electrical_engineering": ["engineering"], - "elementary_mathematics": ["math"], - "formal_logic": ["philosophy"], - "global_facts": ["other"], - "high_school_biology": ["biology"], - "high_school_chemistry": ["chemistry"], - "high_school_computer_science": ["computer science"], - "high_school_european_history": ["history"], - "high_school_geography": ["geography"], - "high_school_government_and_politics": ["politics"], - "high_school_macroeconomics": ["economics"], - "high_school_mathematics": ["math"], - "high_school_microeconomics": ["economics"], - "high_school_physics": ["physics"], - "high_school_psychology": ["psychology"], - "high_school_statistics": ["math"], - "high_school_us_history": ["history"], - "high_school_world_history": ["history"], - "human_aging": ["health"], - "human_sexuality": ["culture"], - "international_law": ["law"], - "jurisprudence": ["law"], - "logical_fallacies": ["philosophy"], - "machine_learning": ["computer science"], - "management": ["business"], - "marketing": ["business"], - "medical_genetics": ["health"], - "miscellaneous": ["other"], - "moral_disputes": ["philosophy"], - "moral_scenarios": ["philosophy"], - "nutrition": ["health"], - "philosophy": ["philosophy"], - "prehistory": ["history"], - "professional_accounting": ["other"], - "professional_law": ["law"], - "professional_medicine": ["health"], - "professional_psychology": ["psychology"], - "public_relations": ["politics"], - "security_studies": ["politics"], - "sociology": ["culture"], - "us_foreign_policy": ["politics"], - "virology": ["health"], - "world_religions": ["philosophy"], - } - - -def get_categories(): - return { - "STEM": [ - "physics", - "chemistry", - "biology", - "computer science", - "math", - "engineering", - ], - "humanities": ["history", "philosophy", "law"], - "social sciences": [ - "politics", - "culture", - "economics", - "geography", - "psychology", - ], - "other (business, health, misc.)": ["other", "business", "health"], - } - - -def format_subject(subject): - line = subject.split("_") - s = "" - for entry in line: - s += " " + entry - return s - - -def format_example(df, idx, include_answer=True): - prompt = df.iloc[idx, 0] - k = df.shape[1] - 2 - for j in range(k): - prompt += "\n{}. {}".format(get_choices()[j], df.iloc[idx, j + 1]) - prompt += "\nAnswer:" - if include_answer: - prompt += " {}\n\n".format(df.iloc[idx, k + 1]) - return prompt - - -def gen_prompt(train_df, subject, k=-1): - prompt = "The following are multiple choice questions (with answers) about {}.\n\n".format( - format_subject(subject)) - if k == -1: - k = train_df.shape[0] - for i in range(k): - prompt += format_example(train_df, i) - return prompt - - -def evaluate(args, subject, pipeline, dev_df, test_df): - rank = tensorrt_llm.mpi_rank() - cors = [] - all_probs = [] - for i in range(test_df.shape[0]): - if i >= args.max_ite: - break - # get prompt and make sure it fits - k = args.ntrain - prompt_end = format_example(test_df, i, include_answer=False) - train_prompt = gen_prompt(dev_df, subject, k) - prompt = train_prompt + prompt_end - - while not pipeline.check_valid_length(prompt) and k > 0: - k -= 1 - train_prompt = gen_prompt(dev_df, subject, k) - prompt = train_prompt + prompt_end - - label = test_df.iloc[i, test_df.shape[1] - 1] - pred = pipeline(prompt) - - if rank == 0: - probs = [0 for _ in get_choices()] - cor = pred.strip().startswith(label) - cors.append(cor) - all_probs.append(probs) - - if rank == 0: - acc = np.mean(cors) - cors = np.array(cors) - - all_probs = np.array(all_probs) - print("Average accuracy {:.3f} - {}".format(acc, subject)) - - return cors, acc, all_probs - else: - return None, 0, None - - -def get_tokenizer(ckpt_path, max_seq_len): - print(f"Initializing tokenizer from {ckpt_path}") - tokenizer = AutoTokenizer.from_pretrained( - ckpt_path, - model_max_length=max_seq_len, - padding_side="left", - trust_remote_code=True, - ) - tokenizer.pad_token = tokenizer.eos_token - - return tokenizer - - -class Pipeline: - - def __init__(self, tokenizer, model, model_name, pad_id, end_id, - max_attention_window_size, is_enc_dec, hf_model_dir, - engine_dir): - self.tokenizer = tokenizer - self.model = model - self.model_name = model_name - self.pad_id = pad_id - self.end_id = end_id - self.max_attention_window_size = max_attention_window_size - self.output_len = 2 - self.is_enc_dec = is_enc_dec - self.decoder_start_token_id = None - self.engine_dir = engine_dir - if self.is_enc_dec: - self.decoder_start_token_id = AutoConfig.from_pretrained( - hf_model_dir).decoder_start_token_id - - def __call__(self, prompt): - rank = tensorrt_llm.mpi_rank() - # Run the model in batch size 1 and beam size 1 - inputs = self.tokenizer.encode(prompt, return_tensors="pt").squeeze(0) - batch_input_ids = [inputs] - - # For multi-choice tasks like MMLU, we don't need to adjust following parameters - output_len = self.output_len - top_k = 1 - top_p = 0.0 - - input_lengths = [x.size(0) for x in batch_input_ids] - - with torch.no_grad(): - if isinstance(self.model, nn.Module): - # Left padding for HF - max_length = max(input_lengths) - paddings = [ - torch.ones(max_length - l, dtype=torch.int32) * self.pad_id - for l in input_lengths - ] - batch_input_ids = [ - torch.cat([pad, x]) - for x, pad in zip(batch_input_ids, paddings) - ] - batch_input_ids = torch.stack(batch_input_ids) - batch_input_ids = batch_input_ids.cuda() - if self.is_enc_dec: - batch_decoder_input_ids = torch.IntTensor( - [[self.decoder_start_token_id]]).to('cuda') - batch_decoder_input_ids = batch_decoder_input_ids.repeat( - (batch_input_ids.shape[0], 1)) - - with torch.no_grad(): - # Use default temperature and top_k - outputs = self.model.generate( - batch_input_ids, - max_new_tokens=output_len, - top_k=top_k, - decoder_input_ids=batch_decoder_input_ids - if self.is_enc_dec else None) - if not self.is_enc_dec: - output_ids = outputs[0, input_lengths[0]:] - else: - output_ids = outputs[0] - - elif isinstance(self.model, ModelRunnerCpp) or isinstance( - self.model, ModelRunner): - if self.is_enc_dec: - encoder_input_ids, encoder_input_features, encoder_output_lengths, decoder_input_ids = prepare_enc_dec_inputs( - batch_input_ids, self.model_name, self.engine_dir, None) - - outputs = self.model.generate( - batch_input_ids=decoder_input_ids - if self.is_enc_dec else batch_input_ids, - encoder_input_ids=encoder_input_ids - if self.is_enc_dec else None, - encoder_input_features=encoder_input_features - if self.is_enc_dec else None, - encoder_output_lengths=encoder_output_lengths - if self.is_enc_dec else None, - max_new_tokens=output_len, - max_attention_window_size=self.max_attention_window_size, - end_id=self.end_id, - pad_id=self.pad_id, - top_k=top_k, - top_p=top_p, - ) - torch.cuda.synchronize() - if rank == 0: - if not self.is_enc_dec: - output_ids = outputs[0, 0, input_lengths[0]:] - else: - output_ids = outputs[0, 0] - if rank == 0: - return self.tokenizer.decode(output_ids, skip_special_tokens=True) - else: - return None - - def check_valid_length(self, prompt): - if isinstance(self.model, nn.Module): - return True - input_len = len(self.tokenizer.encode(prompt)) - return input_len <= self.model.max_input_len and input_len + self.output_len <= self.model.max_seq_len - - -def parse_args(): - parser = argparse.ArgumentParser() - parser.add_argument( - "--data_dir", - type=str, - default="data/mmlu", - help=("Path to the data directory. If not available, " - "download https://people.eecs.berkeley.edu/~hendrycks/data.tar"), - ) - parser.add_argument("--ntrain", type=int, default=5) - parser.add_argument("--max_input_length", type=int, default=2048) - parser.add_argument("--test_trt_llm", action="store_true") - parser.add_argument("--test_hf", action="store_true") - parser.add_argument('--check_accuracy', action='store_true') - parser.add_argument('--accuracy_threshold', type=float, default=30) - parser.add_argument('--max_ite', type=int, default=10000000) - parser = add_common_args(parser) - - args = parser.parse_args() - - return args - - -def main(): - args = parse_args() - if args.tokenizer_dir is None: - args.tokenizer_dir = args.hf_model_dir - random.seed(RAND_SEED) - np.random.seed(RAND_SEED) - runtime_rank = tensorrt_llm.mpi_rank() - - os.path.dirname(os.path.abspath(__file__)) - data_fullpath = os.path.join(args.data_dir, "test") - - subjects = sorted([ - f.split("_test.csv")[0] for f in os.listdir(data_fullpath) - if "_test.csv" in f - ]) - - all_cors = [] - subcat_cors = { - subcat: [] - for subcat_lists in get_subcategories().values() - for subcat in subcat_lists - } - cat_cors = {cat: [] for cat in get_categories()} - - # different handling if encoder-decoder models - is_enc_dec = read_is_enc_dec( - args.engine_dir if not args.test_hf else args.hf_model_dir, - args.test_hf) - - model_name, model_version = read_model_name( - (args.engine_dir if not is_enc_dec else os.path.join( - args.engine_dir, 'encoder')) - if not args.test_hf else args.hf_model_dir, args.test_hf) - - tokenizer, pad_id, end_id = load_tokenizer( - tokenizer_dir=args.tokenizer_dir, - vocab_file=args.vocab_file, - model_name=model_name, - model_version=model_version, - ) - - if args.test_trt_llm: - assert not args.test_hf, "Cannot test both TRT-LLM and HF" - runner_cls = ModelRunner if not PYTHON_BINDINGS else ModelRunnerCpp - runner_kwargs = {} - if PYTHON_BINDINGS: - runner_kwargs.update(max_beam_width=1) - runner_kwargs.update( - is_enc_dec=is_enc_dec, - max_tokens_in_paged_kv_cache=args.max_tokens_in_paged_kv_cache, - kv_cache_enable_block_reuse=args.kv_cache_enable_block_reuse, - kv_cache_free_gpu_memory_fraction=args. - kv_cache_free_gpu_memory_fraction, - cross_kv_cache_fraction=args.cross_kv_cache_fraction - if is_enc_dec else None, - enable_chunked_context=args.enable_chunked_context, - multi_block_mode=args.multi_block_mode) - model = runner_cls.from_dir(engine_dir=args.engine_dir, - rank=runtime_rank, - **runner_kwargs) - else: - assert args.test_hf, "Must test either TRT-LLM or HF" - if 'GLM' in model_name and model_version == 'glm': - auto_model_cls = AutoModelForSeq2SeqLM - elif 'GLM' in model_name and model_version == 'chatglm': - auto_model_cls = AutoModel - elif is_enc_dec: - auto_model_cls = AutoModelForSeq2SeqLM - else: - auto_model_cls = AutoModelForCausalLM - model = auto_model_cls.from_pretrained( - args.hf_model_dir, - trust_remote_code=True, - dtype=DTYPE_STR_MAPPING[args.hf_data_type], - device_map="auto" if args.hf_device_map_auto else None, - ) - if not args.hf_device_map_auto: - model.cuda() - if model_name == "qwen": - model.generation_config = GenerationConfig.from_pretrained( - args.hf_model_dir, trust_remote_code=True) - - pipeline = Pipeline(tokenizer, model, model_name, pad_id, end_id, - args.max_attention_window_size, is_enc_dec, - args.hf_model_dir, args.engine_dir) - - for subject in tqdm(subjects): - dev_df = pd.read_csv(os.path.join(args.data_dir, "dev", - subject + "_dev.csv"), - header=None)[:args.ntrain] - test_df = pd.read_csv(os.path.join(args.data_dir, "test", - subject + "_test.csv"), - header=None) - - cors, acc, probs = evaluate(args, subject, pipeline, dev_df, test_df) - subcats = get_subcategories()[subject] - for subcat in subcats: - subcat_cors[subcat].append(cors) - for key in get_categories().keys(): - if subcat in get_categories()[key]: - cat_cors[key].append(cors) - all_cors.append(cors) - - if runtime_rank == 0: - for subcat in subcat_cors: - acc = np.mean(np.concatenate(subcat_cors[subcat])) * 100 - print(f"Average accuracy {acc:.2f} - {subcat}") - - for cat in cat_cors: - acc = np.mean(np.concatenate(cat_cors[cat])) * 100 - print(f"Average accuracy {acc:.2f} - {cat}") - - weighted_acc = np.mean(np.concatenate(all_cors)) * 100 - print(f"MMLU weighted average accuracy: {weighted_acc:.2f}") - - if args.check_accuracy: - assert weighted_acc >= args.accuracy_threshold, f"Expected accuracy >= {args.accuracy_threshold} while got {weighted_acc}" - return weighted_acc - - -if __name__ == "__main__": - main() diff --git a/examples/models/contrib/arctic/README.md b/examples/models/contrib/arctic/README.md deleted file mode 100644 index d346a3644517..000000000000 --- a/examples/models/contrib/arctic/README.md +++ /dev/null @@ -1,94 +0,0 @@ -# Arctic - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document shows how to build and run a [Arctic](https://huggingface.co/Snowflake/snowflake-arctic-instruct) model in TensorRT-LLM. - -The TensorRT LLM Arctic implementation is based on the LLaMA model, with Mixture of Experts (MoE) enabled. The implementation can -be found in [llama/model.py](../../../../tensorrt_llm/models/llama/model.py). -See the LLaMA example [`examples/models/core/llama`](../../core/llama) for details. - -- [Arctic](#arctic) - - [Download model checkpoints](#download-model-checkpoints) - - [TensorRT LLM workflow](#tensorrt-llm-workflow) - - [Apply FP8 PTQ](#apply-fp8-ptq) - - [Build TensorRT engine](#build-tensorrt-engine) - - [Run Engine](#run-engine) - - [OOTB](#ootb) - -## Download model checkpoints - -First, download the HuggingFace BF16 checkpoints of Arctic model. - -**CAVEAT: this model is a pretty large Mixture-of-Experts (MoE) model, which has nearly 500B parameters and requires around 900GB disk space for storage. Please make sure you have enough space before proceeding.** - -```bash -HF_MODEL="arctic" -git clone https://huggingface.co/Snowflake/snowflake-arctic-instruct tmp/hf_checkpoints/${HF_MODEL} - -``` - -## TensorRT LLM workflow -Next, we use the general quantization script `quantize.py` to convert the checkpoints in FP8, and build the model with `trtllm-build` on multi-GPUs. In the example below, we use Tensor Parallelism (TP) across 8 GPUs. - -**Note: for such large model, it is deemed necessary to apply Post-Training Quantization (PTQ) methods on the model weights to deploy it on a cluster node, e.g., 8xH100 GPUs. In this example, we demonstrate the FP8 quantization workflow, which is supported on Hopper-and-next GPU architectures. For instructions of other PTQ methods other than FP8, please refer to the LLaMA or Mixtral examples.** - - -Set environment variables and necessary directory: - -```bash -PREC_RAW="bfloat16" -PREC_QUANT="fp8" -TP=8 -ENGINE="${HF_MODEL}_${PREC_QUANT}_tp${TP}" - -mkdir -p tmp/trt_engines -``` - -### Apply FP8 PTQ - -Notes: -- currently quantize.py does not support for Expert Parallelism (EP) mode yet. User should use `../../core/llama/convert_checkpoint.py` and specify `--moe_ep_size 1` instead, if needed. -- TensorRT LLM uses static quantization methods, which is expected to be faster at runtime as compared to dynamic quantization methods. This comes at a cost of an offline calibration step during quantization. `batch_size` and `calib_size` can be adjusted to shorten the calibration time. Please refer to ../quantization/README.md for explanation. -- **due to the large model size and the calibration step (which has to load the HuggingFace model and run forward passes), it is likely that you will need more number of GPUs during quantization step than the number of GPUs for engine building and final deployment. For example, using 16xH100 or 8xH200 for quantization & 8xH100 for deployment.** - -```bash -python ../../../quantization/quantize.py --model_dir tmp/hf_checkpoints/${HF_MODEL} \ - --dtype ${PREC_RAW} \ - --qformat ${PREC_QUANT} \ - --kv_cache_dtype ${PREC_QUANT} \ - --output_dir tmp/tllm_checkpoints/${ENGINE} \ - --batch_size 1 \ - --calib_size 128 \ - --tp_size ${TP} |& tee tmp/trt_engines/${ENGINE}_quantize.log - -``` - -### Build TensorRT engine -```bash -# Enable fp8 context fmha to get further acceleration by setting `--use_fp8_context_fmha enable` -# Use --workers to enable parallel build -trtllm-build --checkpoint_dir ./tmp/tllm_checkpoints/${ENGINE} \ - --output_dir ./tmp/trt_engines/${ENGINE} \ - --gpt_attention_plugin ${PREC_RAW} \ - --gemm_plugin ${PREC_RAW} \ - --workers ${TP} |& tee tmp/trt_engines/${ENGINE}_build.log -``` - -### Run Engine -Test your engine with the [run.py](../../../run.py) script: - -```bash -mpirun -n ${TP} --allow-run-as-root python ../../../run.py --engine_dir ./tmp/trt_engines/${ENGINE} --tokenizer_dir tmp/hf_checkpoints/${HF_MODEL} --max_output_len 20 --input_text "The future of AI is" |& tee tmp/trt_engines/${ENGINE}_run.log -``` - -For more examples see [`examples/models/core/llama/README.md`](../../core/llama/README.md) - - -### OOTB - -Arctic supports OOTB operation without the plugin, however this comes at a significant performance cost. Users should prefer using the plugin path whenever possible. diff --git a/examples/models/contrib/blip2/README.md b/examples/models/contrib/blip2/README.md deleted file mode 100644 index 0e80ed4fd81b..000000000000 --- a/examples/models/contrib/blip2/README.md +++ /dev/null @@ -1,4 +0,0 @@ -This example has been moved to [`../multimodal`](../../../multimodal) and merged with other multimodal examples. -Please follow [`../multimodal/README.md`](../../../multimodal/README.md) for new instructions. - -**NOTICE:** This folder will be removed in v1.0 release. diff --git a/examples/models/contrib/chatglm-6b/README.md b/examples/models/contrib/chatglm-6b/README.md deleted file mode 100644 index 00a8c2424665..000000000000 --- a/examples/models/contrib/chatglm-6b/README.md +++ /dev/null @@ -1,112 +0,0 @@ -# ChatGLM - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document explains how to build the [ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) models using TensorRT LLM and run on a single GPU, a single node with multiple GPUs or multiple nodes with multiple GPUs. - -- [ChatGLM](#chatglm) - - [Overview](#overview) - - [Support Matrix](#support-matrix) - - [Model comparison](#model-comparison) - - [Tokenizer and special tokens comparison](#tokenizer-and-special-tokens-comparison) - - [Usage](#usage) - - [1. Download repo and weights from HuggingFace Transformers](#1-download-repo-and-weights-from-huggingface-transformers) - - [2. Convert weights from HF Transformers to TensorRT LLM format](#2-convert-weights-from-hf-transformers-to-tensorrt-llm-format) - - [3. Build TensorRT engine(s)](#3-build-tensorrt-engines) - - [Enable plugins](#enable-plugins) - - [In-flight batching](#in-flight-batching) - - [4. Run inference](#4-run-inference) - - [Single node, single GPU](#single-node-single-gpu) - - [Single node, multi GPU](#single-node-multi-gpu) - - [5. Run summarization task](#5-run-summarization-task) - - [Weight Only quantization](#weight-only-quantization) - - [Smooth Quantization (SQ)](#smooth-quantization-sq) - - [Activation-aware Weight Quantization (AWQ)](#activation-aware-weight-quantization-awq) - - [FP8 Quantization](#fp8-quantization) - - [Benchmark](#benchmark) - - -## Overview - -The TensorRT LLM ChatGLM implementation can be found in [`tensorrt_llm/models/chatglm/model.py`](../../tensorrt_llm/models/chatglm/model.py). -The TensorRT LLM ChatGLM example code is located in [`examples/models/contrib/chatglm-6b`](./). There is one main file: - -* [`examples/models/core/glm-4-9b/convert_checkpoint.py`](../../../glm-4-9b/convert_checkpoint.py) to convert a checkpoint from the [HuggingFace (HF) Transformers](https://github.com/huggingface/transformers) format to the TensorRT LLM format. - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - -| Model Name | FP16 | FMHA | WO | SQ | AWQ | FP8 | TP | PP | ST | C++ | benchmark | IFB | -| :--------------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :-------: | :---: | -| chatglm_6b | Y | | Y | | | | Y | | Y | Y | Y | Y | -| glm_10b | Y | Y | Y | | Y | Y | Y | | Y | Y | Y | Y | - -* Model Name: the name of the model, the same as the name on HuggingFace -* FMHA: Fused MultiHead Attention (see introduction below) -* WO: Weight Only Quantization (int8 / int4) -* SQ: Smooth Quantization (int8) -* AWQ: Activation Aware Weight Quantization (int4) -* FP8: FP8 Quantization -* TP: Tensor Parallel -* PP: Pipeline Parallel -* ST: Strongly Typed -* C++: C++ Runtime -* benchmark: benchmark by python / C++ Runtime -* IFB: In-flight Batching (see introduction below) - -## Model comparison - -| Name | nL | nAH | nKH | nHW | nH | nF | nMSL | nV | bP2D | bBQKV | bBDense | Comments | -| :--------------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :----: | :---: | :---: | :-----: | :----------------------------------------------------------------- | -| chatglm_6b | 28 | 32 | 32 | 128 | 4096 | 16384 | 2048 | 130528 | Y | Y | Y | | -| glm_10b | 48 | 64 | 32 | 64 | 4096 | 16384 | 1024 | 50304 | Y | Y | Y | | - -* nL: number of layers -* nAH: number of attention heads -* nKH: number of kv heads (less than nAH if multi_query_attention is used) -* nHW: head width -* nH: hidden size -* nF: FFN hidden size -* nMSL: max sequence length (input + output) -* nV: vocabulary size -* bP2D: use position_encoding_2d (Y: Yes, N: No) -* bBQKV: use bias for QKV multiplication in self-attention -* bBDense: use bias for Dense multiplication in self-attention - -## Tokenizer and special tokens comparison - -| Name | Tokenizer | bos | eos | pad | cls | startofpiece | endofpiece | mask | smask | gmask | -| :--------------: | :--------------: | :----: | :----: | :---: | :---: | :----------: | :--------: | :----: | :---: | :----: | -| chatglm_6b | ChatGLMTokenizer | 130004 | 130005 | 3 | | 130004 | 130005 | 130000 | | 130001 | -| glm_10b | GLMGPT2Tokenizer | 50257 | 50256 | 50256 | 50259 | 50257 | 50258 | 50260 | 50264 | 50263 | - -## Usage - -The next section describe how to build the engine and run the inference demo. - -### 1. Download repo and weights from HuggingFace Transformers - -```bash -pip install -r requirements.txt -apt-get update -apt-get install git-lfs -rm -rf chatglm* - -# clone one or more models we want to build -git clone https://huggingface.co/THUDM/chatglm-6b chatglm_6b -git clone https://huggingface.co/THUDM/glm-10b glm_10b - -# replace tokenization file if using transformers-4.36.1 for model ChatGLM-6B (this might be needless in the future) -cp chatglm_6b/tokenization_chatglm.py chatglm_6b/tokenization_chatglm.py-backup -cp tokenization_chatglm.py chatglm_6b -``` - -For more example codes, please refer to the [examples/models/core/glm-4-9b/README.md](../../../glm-4-9b/README.md). diff --git a/examples/models/contrib/chatglm-6b/requirements.txt b/examples/models/contrib/chatglm-6b/requirements.txt deleted file mode 100644 index cdc65bf2bb38..000000000000 --- a/examples/models/contrib/chatglm-6b/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -evaluate -protobuf -rouge_score -sentencepiece -tiktoken diff --git a/examples/models/contrib/chatglm-6b/tokenization_chatglm.py b/examples/models/contrib/chatglm-6b/tokenization_chatglm.py deleted file mode 100755 index 8ae124909e98..000000000000 --- a/examples/models/contrib/chatglm-6b/tokenization_chatglm.py +++ /dev/null @@ -1,467 +0,0 @@ -"""Tokenization classes for ChatGLM.""" -import os -from typing import Dict, List, Optional, Union - -import numpy as np -import sentencepiece as spm -from transformers.tokenization_utils import PreTrainedTokenizer -from transformers.tokenization_utils_base import BatchEncoding, EncodedInput -from transformers.utils import PaddingStrategy, logging - -logger = logging.get_logger(__name__) - -PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - "THUDM/chatglm-6b": 2048, -} - - -class TextTokenizer: - - def __init__(self, model_path): - self.sp = spm.SentencePieceProcessor() - self.sp.Load(model_path) - self.num_tokens = self.sp.vocab_size() - - def encode(self, text): - return self.sp.EncodeAsIds(text) - - def decode(self, ids: List[int]): - return self.sp.DecodeIds(ids) - - def tokenize(self, text): - return self.sp.EncodeAsPieces(text) - - def convert_tokens_to_string(self, tokens): - return self.sp.DecodePieces(tokens) - - def convert_tokens_to_ids(self, tokens): - return [self.sp.PieceToId(token) for token in tokens] - - def convert_token_to_id(self, token): - return self.sp.PieceToId(token) - - def convert_id_to_token(self, idx): - return self.sp.IdToPiece(idx) - - def __len__(self): - return self.num_tokens - - -class SPTokenizer: - - def __init__( - self, - vocab_file, - num_image_tokens=20000, - max_blank_length=80, - byte_fallback=True, - ): - assert vocab_file is not None - self.vocab_file = vocab_file - self.num_image_tokens = num_image_tokens - self.special_tokens = [ - "[MASK]", "[gMASK]", "[sMASK]", "", "", "", - "", "" - ] - self.max_blank_length = max_blank_length - self.byte_fallback = byte_fallback - self.text_tokenizer = TextTokenizer(vocab_file) - - def _get_text_tokenizer(self): - return self.text_tokenizer - - @staticmethod - def get_blank_token(length: int): - assert length >= 2 - return f"<|blank_{length}|>" - - @staticmethod - def get_tab_token(): - return f"<|tab|>" - - @property - def num_text_tokens(self): - return self.text_tokenizer.num_tokens - - @property - def num_tokens(self): - return self.num_image_tokens + self.num_text_tokens - - @staticmethod - def _encode_whitespaces(text: str, max_len: int = 80): - text = text.replace("\t", SPTokenizer.get_tab_token()) - for i in range(max_len, 1, -1): - text = text.replace(" " * i, SPTokenizer.get_blank_token(i)) - return text - - def _preprocess(self, text: str, linebreak=True, whitespaces=True): - if linebreak: - text = text.replace("\n", "") - if whitespaces: - text = self._encode_whitespaces(text, max_len=self.max_blank_length) - return text - - def encode(self, - text: str, - linebreak=True, - whitespaces=True, - add_dummy_prefix=True) -> List[int]: - """ - @param text: Text to encode. - @param linebreak: Whether to encode newline (\n) in text. - @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding. - @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text. - @param add_dummy_prefix: Whether to add dummy blank space in the beginning. - """ - text = self._preprocess(text, linebreak, whitespaces) - if not add_dummy_prefix: - text = "" + text - tmp = self._get_text_tokenizer().encode(text) - tokens = [x + self.num_image_tokens for x in tmp] - return tokens if add_dummy_prefix else tokens[2:] - - def postprocess(self, text): - text = text.replace("", "\n") - text = text.replace(SPTokenizer.get_tab_token(), "\t") - for i in range(2, self.max_blank_length + 1): - text = text.replace(self.get_blank_token(i), " " * i) - return text - - def decode(self, text_ids: List[int]) -> str: - ids = [int(_id) - self.num_image_tokens for _id in text_ids] - ids = [_id for _id in ids if _id >= 0] - text = self._get_text_tokenizer().decode(ids) - text = self.postprocess(text) - return text - - def decode_tokens(self, tokens: List[str]) -> str: - text = self._get_text_tokenizer().convert_tokens_to_string(tokens) - text = self.postprocess(text) - return text - - def tokenize(self, - text: str, - linebreak=True, - whitespaces=True, - add_dummy_prefix=True) -> List[str]: - """ - @param text: Text to encode. - @param linebreak: Whether to encode newline (\n) in text. - @param whitespaces: Whether to encode multiple whitespaces or tab in text, useful for source code encoding. - @param special_tokens: Whether to encode special token ([MASK], [gMASK], etc.) in text. - @param add_dummy_prefix: Whether to add dummy blank space in the beginning. - """ - text = self._preprocess(text, linebreak, whitespaces) - if not add_dummy_prefix: - text = "" + text - tokens = self._get_text_tokenizer().tokenize(text) - return tokens if add_dummy_prefix else tokens[2:] - - def __getitem__(self, x: Union[int, str]): - if isinstance(x, int): - if x < self.num_image_tokens: - return "".format(x) - else: - return self.text_tokenizer.convert_id_to_token( - x - self.num_image_tokens) - elif isinstance(x, str): - if x.startswith("") and x[7:-1].isdigit(): - return int(x[7:-1]) - else: - return self.text_tokenizer.convert_token_to_id( - x) + self.num_image_tokens - else: - raise ValueError("The key should be str or int.") - - -class ChatGLMTokenizer(PreTrainedTokenizer): - """ - Construct a ChatGLM tokenizer. Based on byte-level Byte-Pair-Encoding. - - Args: - vocab_file (`str`): - Path to the vocabulary file. - """ - - vocab_files_names = {"vocab_file": "ice_text.model"} - max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES - model_input_names = ["input_ids", "attention_mask", "position_ids"] - - def __init__(self, - vocab_file, - do_lower_case=False, - remove_space=False, - bos_token='', - eos_token='', - end_token='', - mask_token='[MASK]', - gmask_token='[gMASK]', - padding_side="left", - pad_token="", - unk_token="", - num_image_tokens=20000, - **kwargs) -> None: # Fix for new transformers - - self.do_lower_case = do_lower_case - self.remove_space = remove_space - self.vocab_file = vocab_file - - self.bos_token = bos_token - self.eos_token = eos_token - self.end_token = end_token - self.mask_token = mask_token - self.gmask_token = gmask_token - - self.sp_tokenizer = SPTokenizer(vocab_file, - num_image_tokens=num_image_tokens) - - super().__init__( - do_lower_case=do_lower_case, # Fix for new transformers - remove_space=remove_space, - padding_side=padding_side, - bos_token=bos_token, - eos_token=eos_token, - end_token=end_token, - mask_token=mask_token, - gmask_token=gmask_token, - pad_token=pad_token, - unk_token=unk_token, - num_image_tokens=num_image_tokens, - **kwargs) - """ Initialization """ - - @property - def gmask_token_id(self) -> Optional[int]: - if self.gmask_token is None: - return None - return self.convert_tokens_to_ids(self.gmask_token) - - @property - def end_token_id(self) -> Optional[int]: - """ - `Optional[int]`: Id of the end of context token in the vocabulary. Returns `None` if the token has not been - set. - """ - if self.end_token is None: - return None - return self.convert_tokens_to_ids(self.end_token) - - @property - def vocab_size(self): - """ Returns vocab size """ - return self.sp_tokenizer.num_tokens - - def get_vocab(self): - """ Returns vocab as a dict """ - vocab = { - self._convert_id_to_token(i): i - for i in range(self.vocab_size) - } - vocab.update(self.added_tokens_encoder) - return vocab - - def preprocess_text(self, inputs): - if self.remove_space: - outputs = " ".join(inputs.strip().split()) - else: - outputs = inputs - - if self.do_lower_case: - outputs = outputs.lower() - - return outputs - - def _tokenize(self, text, **kwargs): - """ Returns a tokenized string. """ - text = self.preprocess_text(text) - - seq = self.sp_tokenizer.tokenize(text) - - return seq - - def convert_tokens_to_string(self, tokens: List[str]) -> str: - return self.sp_tokenizer.decode_tokens(tokens) - - def _decode(self, token_ids: Union[int, List[int]], **kwargs) -> str: - if isinstance(token_ids, int): - token_ids = [token_ids] - if len(token_ids) == 0: - return "" - if self.pad_token_id in token_ids: # remove pad - token_ids = list(filter((self.pad_token_id).__ne__, token_ids)) - return super()._decode(token_ids, **kwargs) - - def _convert_token_to_id(self, token): - """ Converts a token (str) in an id using the vocab. """ - return self.sp_tokenizer[token] - - def _convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - return self.sp_tokenizer[index] - - def save_vocabulary(self, save_directory, filename_prefix=None): - """ - Save the vocabulary and special tokens file to a directory. - - Args: - save_directory (`str`): - The directory in which to save the vocabulary. - filename_prefix (`str`, *optional*): - An optional prefix to add to the named of the saved files. - - Returns: - `Tuple(str)`: Paths to the files saved. - """ - if os.path.isdir(save_directory): - vocab_file = os.path.join(save_directory, - self.vocab_files_names["vocab_file"]) - else: - vocab_file = save_directory - - with open(self.vocab_file, 'rb') as fin: - proto_str = fin.read() - - with open(vocab_file, "wb") as writer: - writer.write(proto_str) - - return (vocab_file, ) - - def build_inputs_with_special_tokens( - self, - token_ids_0: List[int], - token_ids_1: Optional[List[int]] = None) -> List[int]: - """ - Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and - adding special tokens. A BERT sequence has the following format: - - - single sequence: `[CLS] X [SEP]` - - pair of sequences: `[CLS] A [SEP] B [SEP]` - - Args: - token_ids_0 (`List[int]`): - List of IDs to which the special tokens will be added. - token_ids_1 (`List[int]`, *optional*): - Optional second list of IDs for sequence pairs. - - Returns: - `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. - """ - gmask_id = self.sp_tokenizer[self.gmask_token] - eos_id = self.sp_tokenizer[self.eos_token] - token_ids_0 = token_ids_0 + [ - gmask_id, self.sp_tokenizer[self.bos_token] - ] - if token_ids_1 is not None: - token_ids_0 = token_ids_0 + token_ids_1 + [eos_id] - return token_ids_0 - - def _pad( - self, - encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], - max_length: Optional[int] = None, - padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, - pad_to_multiple_of: Optional[int] = None, - return_attention_mask: Optional[bool] = None, - padding_side: str = "left", # Fix for new transformers - ) -> dict: - """ - Pad encoded inputs (on left/right and up to predefined length or max length in the batch) - - Args: - encoded_inputs: - Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). - max_length: maximum length of the returned list and optionally padding length (see below). - Will truncate by taking into account the special tokens. - padding_strategy: PaddingStrategy to use for padding. - - - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - - PaddingStrategy.DO_NOT_PAD: Do not pad - The tokenizer padding sides are defined in self.padding_side: - - - 'left': pads on the left of the sequences - - 'right': pads on the right of the sequences - pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. - This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability - `>= 7.5` (Volta). - return_attention_mask: - (optional) Set to False to avoid returning attention mask (default: set to model specifics) - """ - # Load from model defaults - bos_token_id = self.sp_tokenizer[self.bos_token] - mask_token_id = self.sp_tokenizer[self.mask_token] - gmask_token_id = self.sp_tokenizer[self.gmask_token] - assert self.padding_side == "left" - - required_input = encoded_inputs[self.model_input_names[0]] - seq_length = len(required_input) - - if padding_strategy == PaddingStrategy.LONGEST: - max_length = len(required_input) - - if max_length is not None and pad_to_multiple_of is not None and ( - max_length % pad_to_multiple_of != 0): - max_length = ( - (max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of - - needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len( - required_input) != max_length - - # Initialize attention mask if not present. - if max_length is not None: - if "attention_mask" not in encoded_inputs: - if bos_token_id in required_input: - context_length = required_input.index(bos_token_id) - else: - context_length = seq_length - attention_mask = np.ones((1, seq_length, seq_length)) - attention_mask = np.tril(attention_mask) - attention_mask[:, :, :context_length] = 1 - attention_mask = np.bool_(attention_mask < 0.5) - encoded_inputs["attention_mask"] = attention_mask - - if "position_ids" not in encoded_inputs: - if bos_token_id in required_input: - context_length = required_input.index(bos_token_id) - else: - context_length = seq_length - position_ids = np.arange(seq_length, dtype=np.int64) - mask_token = mask_token_id if mask_token_id in required_input else gmask_token_id - if mask_token in required_input: - mask_position = required_input.index(mask_token) - position_ids[context_length:] = mask_position - block_position_ids = np.concatenate([ - np.zeros(context_length, dtype=np.int64), - np.arange(1, - seq_length - context_length + 1, - dtype=np.int64) - ]) - encoded_inputs["position_ids"] = np.stack( - [position_ids, block_position_ids], axis=0) - - if needs_to_be_padded: - difference = max_length - len(required_input) - - if "attention_mask" in encoded_inputs: - encoded_inputs["attention_mask"] = np.pad( - encoded_inputs["attention_mask"], - pad_width=[(0, 0), (difference, 0), (difference, 0)], - mode='constant', - constant_values=True) - if "token_type_ids" in encoded_inputs: - encoded_inputs["token_type_ids"] = [ - self.pad_token_type_id - ] * difference + encoded_inputs["token_type_ids"] - if "special_tokens_mask" in encoded_inputs: - encoded_inputs["special_tokens_mask"] = [ - 1 - ] * difference + encoded_inputs["special_tokens_mask"] - if "position_ids" in encoded_inputs: - encoded_inputs["position_ids"] = np.pad( - encoded_inputs["position_ids"], - pad_width=[(0, 0), (difference, 0)]) - encoded_inputs[self.model_input_names[ - 0]] = [self.pad_token_id] * difference + required_input - - return encoded_inputs diff --git a/examples/models/contrib/chatglm2-6b/README.md b/examples/models/contrib/chatglm2-6b/README.md deleted file mode 100644 index c25818e3f95c..000000000000 --- a/examples/models/contrib/chatglm2-6b/README.md +++ /dev/null @@ -1,108 +0,0 @@ -# ChatGLM - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document explains how to build the [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b), [ChatGLM2-6B-32k](https://huggingface.co/THUDM/chatglm2-6b-32k) models using TensorRT LLM and run on a single GPU, a single node with multiple GPUs or multiple nodes with multiple GPUs. - -- [ChatGLM](#chatglm) - - [Overview](#overview) - - [Support Matrix](#support-matrix) - - [Model comparison](#model-comparison) - - [Tokenizer and special tokens comparison](#tokenizer-and-special-tokens-comparison) - - [Usage](#usage) - - [1. Download repo and weights from HuggingFace Transformers](#1-download-repo-and-weights-from-huggingface-transformers) - - [2. Convert weights from HF Transformers to TensorRT LLM format](#2-convert-weights-from-hf-transformers-to-tensorrt-llm-format) - - [3. Build TensorRT engine(s)](#3-build-tensorrt-engines) - - [Enable plugins](#enable-plugins) - - [In-flight batching](#in-flight-batching) - - [4. Run inference](#4-run-inference) - - [Single node, single GPU](#single-node-single-gpu) - - [Single node, multi GPU](#single-node-multi-gpu) - - [5. Run summarization task](#5-run-summarization-task) - - [Weight Only quantization](#weight-only-quantization) - - [Smooth Quantization (SQ)](#smooth-quantization-sq) - - [Activation-aware Weight Quantization (AWQ)](#activation-aware-weight-quantization-awq) - - [FP8 Quantization](#fp8-quantization) - - [Benchmark](#benchmark) - - -## Overview - -The TensorRT LLM ChatGLM implementation can be found in [`tensorrt_llm/models/chatglm/model.py`](../../tensorrt_llm/models/chatglm/model.py). -The TensorRT LLM ChatGLM example code is located in [`examples/models/contrib/chatglm2-6b`](./). There is one main file: - -* [`examples/models/core/glm-4-9b/convert_checkpoint.py`](../../../glm-4-9b/convert_checkpoint.py) to convert a checkpoint from the [HuggingFace (HF) Transformers](https://github.com/huggingface/transformers) format to the TensorRT LLM format. - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - -| Model Name | FP16 | FMHA | WO | SQ | AWQ | FP8 | TP | PP | ST | C++ | benchmark | IFB | -| :--------------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :-------: | :---: | -| chatglm2_6b | Y | Y | Y | Y | Y | Y | Y | | Y | Y | Y | Y | -| chatglm2_6b_32k | Y | Y | Y | | Y | Y | Y | | Y | Y | Y | Y | - -* Model Name: the name of the model, the same as the name on HuggingFace -* FMHA: Fused MultiHead Attention (see introduction below) -* WO: Weight Only Quantization (int8 / int4) -* SQ: Smooth Quantization (int8) -* AWQ: Activation Aware Weight Quantization (int4) -* FP8: FP8 Quantization -* TP: Tensor Parallel -* PP: Pipeline Parallel -* ST: Strongly Typed -* C++: C++ Runtime -* benchmark: benchmark by python / C++ Runtime -* IFB: In-flight Batching (see introduction below) - -## Model comparison - -| Name | nL | nAH | nKH | nHW | nH | nF | nMSL | nV | bP2D | bBQKV | bBDense | Comments | -| :--------------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :----: | :---: | :---: | :-----: | :----------------------------------------------------------------- | -| chatglm2_6b | 28 | 32 | 2 | 128 | 4096 | 13696 | 32768 | 65024 | N | Y | N | Multi_query_attention, RMSNorm rather than LayerNorm in chatglm_6b | -| chatglm2_6b_32k | 28 | 32 | 2 | 128 | 4096 | 13696 | 32768 | 65024 | N | Y | N | RoPE base=160000 rather than 10000 in chatglm2_6b | - -* nL: number of layers -* nAH: number of attention heads -* nKH: number of kv heads (less than nAH if multi_query_attention is used) -* nHW: head width -* nH: hidden size -* nF: FFN hidden size -* nMSL: max sequence length (input + output) -* nV: vocabulary size -* bP2D: use position_encoding_2d (Y: Yes, N: No) -* bBQKV: use bias for QKV multiplication in self-attention -* bBDense: use bias for Dense multiplication in self-attention - -## Tokenizer and special tokens comparison - -| Name | Tokenizer | bos | eos | pad | cls | startofpiece | endofpiece | mask | smask | gmask | -| :--------------: | :--------------: | :----: | :----: | :---: | :---: | :----------: | :--------: | :----: | :---: | :----: | -| chatglm2_6b | ChatGLMTokenizer | 1 | 2 | 0 | | | | | | | -| chatglm2_6b_32k | ChatGLMTokenizer | 1 | 2 | 0 | | | | | | | - -## Usage - -The next section describe how to build the engine and run the inference demo. - -### 1. Download repo and weights from HuggingFace Transformers - -```bash -pip install -r requirements.txt -apt-get update -apt-get install git-lfs -rm -rf chatglm* - -# clone one or more models we want to build -git clone https://huggingface.co/THUDM/chatglm2-6b chatglm2_6b -git clone https://huggingface.co/THUDM/chatglm2-6b-32k chatglm2_6b_32k -``` - -For more example codes, please refer to the [examples/models/core/glm-4-9b/README.md](../../../glm-4-9b/README.md). diff --git a/examples/models/contrib/chatglm2-6b/requirements.txt b/examples/models/contrib/chatglm2-6b/requirements.txt deleted file mode 100644 index cdc65bf2bb38..000000000000 --- a/examples/models/contrib/chatglm2-6b/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -evaluate -protobuf -rouge_score -sentencepiece -tiktoken diff --git a/examples/models/contrib/chatglm2-6b/tokenization_chatglm.py b/examples/models/contrib/chatglm2-6b/tokenization_chatglm.py deleted file mode 100644 index dea5fdc7c7dc..000000000000 --- a/examples/models/contrib/chatglm2-6b/tokenization_chatglm.py +++ /dev/null @@ -1,282 +0,0 @@ -import os -from typing import Dict, List, Optional, Union - -from sentencepiece import SentencePieceProcessor -from transformers import PreTrainedTokenizer -from transformers.tokenization_utils_base import BatchEncoding, EncodedInput -from transformers.utils import PaddingStrategy - - -class SPTokenizer: - - def __init__(self, model_path: str): - # reload tokenizer - assert os.path.isfile(model_path), model_path - self.sp_model = SentencePieceProcessor(model_file=model_path) - - # BOS / EOS token IDs - self.n_words: int = self.sp_model.vocab_size() - self.bos_id: int = self.sp_model.bos_id() - self.eos_id: int = self.sp_model.eos_id() - self.pad_id: int = self.sp_model.unk_id() - assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() - - special_tokens = ["[MASK]", "[gMASK]", "[sMASK]", "sop", "eop"] - self.special_tokens = {} - self.index_special_tokens = {} - for token in special_tokens: - self.special_tokens[token] = self.n_words - self.index_special_tokens[self.n_words] = token - self.n_words += 1 - - def tokenize(self, s: str): - return self.sp_model.EncodeAsPieces(s) - - def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: - assert type(s) is str - t = self.sp_model.encode(s) - if bos: - t = [self.bos_id] + t - if eos: - t = t + [self.eos_id] - return t - - def decode(self, t: List[int]) -> str: - return self.sp_model.decode(t) - - def decode_tokens(self, tokens: List[str]) -> str: - text = self.sp_model.DecodePieces(tokens) - return text - - def convert_token_to_id(self, token): - """ Converts a token (str) in an id using the vocab. """ - if token in self.special_tokens: - return self.special_tokens[token] - return self.sp_model.PieceToId(token) - - def convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - if index in self.index_special_tokens or index in [ - self.eos_id, self.bos_id, self.pad_id - ] or index < 0: - return "" - return self.sp_model.IdToPiece(index) - - -class ChatGLMTokenizer(PreTrainedTokenizer): - vocab_files_names = {"vocab_file": "tokenizer.model"} - - model_input_names = ["input_ids", "attention_mask", "position_ids"] - - def __init__(self, - vocab_file, - padding_side="left", - clean_up_tokenization_spaces=False, - **kwargs): - self.name = "GLMTokenizer" - - self.vocab_file = vocab_file - self.tokenizer = SPTokenizer(vocab_file) - self.special_tokens = { - "": self.tokenizer.bos_id, - "": self.tokenizer.eos_id, - "": self.tokenizer.pad_id - } - super().__init__( - padding_side=padding_side, - clean_up_tokenization_spaces=clean_up_tokenization_spaces, - **kwargs) - - def get_command(self, token): - if token in self.special_tokens: - return self.special_tokens[token] - assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" - return self.tokenizer.special_tokens[token] - - @property - def unk_token(self) -> str: - return "" - - @property - def pad_token(self) -> str: - return "" - - @property - def pad_token_id(self): - return self.get_command("") - - @property - def eos_token(self) -> str: - return "" - - @property - def eos_token_id(self): - return self.get_command("") - - @property - def vocab_size(self): - return self.tokenizer.n_words - - def get_vocab(self): - """ Returns vocab as a dict """ - vocab = { - self._convert_id_to_token(i): i - for i in range(self.vocab_size) - } - vocab.update(self.added_tokens_encoder) - return vocab - - def _tokenize(self, text, **kwargs): - return self.tokenizer.tokenize(text) - - def _convert_token_to_id(self, token): - """ Converts a token (str) in an id using the vocab. """ - return self.tokenizer.convert_token_to_id(token) - - def _convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - return self.tokenizer.convert_id_to_token(index) - - def convert_tokens_to_string(self, tokens: List[str]) -> str: - return self.tokenizer.decode_tokens(tokens) - - def save_vocabulary(self, save_directory, filename_prefix=None): - """ - Save the vocabulary and special tokens file to a directory. - - Args: - save_directory (`str`): - The directory in which to save the vocabulary. - filename_prefix (`str`, *optional*): - An optional prefix to add to the named of the saved files. - - Returns: - `Tuple(str)`: Paths to the files saved. - """ - if os.path.isdir(save_directory): - vocab_file = os.path.join(save_directory, - self.vocab_files_names["vocab_file"]) - else: - vocab_file = save_directory - - with open(self.vocab_file, 'rb') as fin: - proto_str = fin.read() - - with open(vocab_file, "wb") as writer: - writer.write(proto_str) - - return (vocab_file, ) - - def get_prefix_tokens(self): - prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] - return prefix_tokens - - def build_prompt(self, query, history=None): - if history is None: - history = [] - prompt = "" - for i, (old_query, response) in enumerate(history): - prompt += "[Round {}]\n\n问：{}\n\n答：{}\n\n".format( - i + 1, old_query, response) - prompt += "[Round {}]\n\n问：{}\n\n答：".format(len(history) + 1, query) - return prompt - - def build_inputs_with_special_tokens( - self, - token_ids_0: List[int], - token_ids_1: Optional[List[int]] = None) -> List[int]: - """ - Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and - adding special tokens. A BERT sequence has the following format: - - - single sequence: `[CLS] X [SEP]` - - pair of sequences: `[CLS] A [SEP] B [SEP]` - - Args: - token_ids_0 (`List[int]`): - List of IDs to which the special tokens will be added. - token_ids_1 (`List[int]`, *optional*): - Optional second list of IDs for sequence pairs. - - Returns: - `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. - """ - prefix_tokens = self.get_prefix_tokens() - token_ids_0 = prefix_tokens + token_ids_0 - if token_ids_1 is not None: - token_ids_0 = token_ids_0 + token_ids_1 + [ - self.get_command("") - ] - return token_ids_0 - - def _pad( - self, - encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], - max_length: Optional[int] = None, - padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, - pad_to_multiple_of: Optional[int] = None, - return_attention_mask: Optional[bool] = None, - padding_side: str = "left", # Fix for new transformers - ) -> dict: - """ - Pad encoded inputs (on left/right and up to predefined length or max length in the batch) - - Args: - encoded_inputs: - Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). - max_length: maximum length of the returned list and optionally padding length (see below). - Will truncate by taking into account the special tokens. - padding_strategy: PaddingStrategy to use for padding. - - - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - - PaddingStrategy.DO_NOT_PAD: Do not pad - The tokenizer padding sides are defined in self.padding_side: - - - 'left': pads on the left of the sequences - - 'right': pads on the right of the sequences - pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. - This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability - `>= 7.5` (Volta). - return_attention_mask: - (optional) Set to False to avoid returning attention mask (default: set to model specifics) - """ - # Load from model defaults - assert self.padding_side == "left" - - required_input = encoded_inputs[self.model_input_names[0]] - seq_length = len(required_input) - - if padding_strategy == PaddingStrategy.LONGEST: - max_length = len(required_input) - - if max_length is not None and pad_to_multiple_of is not None and ( - max_length % pad_to_multiple_of != 0): - max_length = ( - (max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of - - needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len( - required_input) != max_length - - # Initialize attention mask if not present. - if "attention_mask" not in encoded_inputs: - encoded_inputs["attention_mask"] = [1] * seq_length - - if "position_ids" not in encoded_inputs: - encoded_inputs["position_ids"] = list(range(seq_length)) - - if needs_to_be_padded: - difference = max_length - len(required_input) - - if "attention_mask" in encoded_inputs: - encoded_inputs["attention_mask"] = [ - 0 - ] * difference + encoded_inputs["attention_mask"] - if "position_ids" in encoded_inputs: - encoded_inputs["position_ids"] = [ - 0 - ] * difference + encoded_inputs["position_ids"] - encoded_inputs[self.model_input_names[ - 0]] = [self.pad_token_id] * difference + required_input - - return encoded_inputs diff --git a/examples/models/contrib/chatglm3-6b-32k/README.md b/examples/models/contrib/chatglm3-6b-32k/README.md deleted file mode 100644 index 0636cba34f9c..000000000000 --- a/examples/models/contrib/chatglm3-6b-32k/README.md +++ /dev/null @@ -1,112 +0,0 @@ -# ChatGLM - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document explains how to build the [ChatGLM3-6B](https://huggingface.co/THUDM/chatglm3-6b), [ChatGLM3-6B-Base](https://huggingface.co/THUDM/chatglm3-6b-base), [ChatGLM3-6B-32k](https://huggingface.co/THUDM/chatglm3-6b-32k) models using TensorRT LLM and run on a single GPU, a single node with multiple GPUs or multiple nodes with multiple GPUs. - -- [ChatGLM](#chatglm) - - [Overview](#overview) - - [Support Matrix](#support-matrix) - - [Model comparison](#model-comparison) - - [Tokenizer and special tokens comparison](#tokenizer-and-special-tokens-comparison) - - [Usage](#usage) - - [1. Download repo and weights from HuggingFace Transformers](#1-download-repo-and-weights-from-huggingface-transformers) - - [2. Convert weights from HF Transformers to TensorRT LLM format](#2-convert-weights-from-hf-transformers-to-tensorrt-llm-format) - - [3. Build TensorRT engine(s)](#3-build-tensorrt-engines) - - [Enable plugins](#enable-plugins) - - [In-flight batching](#in-flight-batching) - - [4. Run inference](#4-run-inference) - - [Single node, single GPU](#single-node-single-gpu) - - [Single node, multi GPU](#single-node-multi-gpu) - - [5. Run summarization task](#5-run-summarization-task) - - [Weight Only quantization](#weight-only-quantization) - - [Smooth Quantization (SQ)](#smooth-quantization-sq) - - [Activation-aware Weight Quantization (AWQ)](#activation-aware-weight-quantization-awq) - - [FP8 Quantization](#fp8-quantization) - - [Benchmark](#benchmark) - - -## Overview - -The TensorRT LLM ChatGLM implementation can be found in [`tensorrt_llm/models/chatglm/model.py`](../../tensorrt_llm/models/chatglm/model.py). -The TensorRT LLM ChatGLM example code is located in [`examples/models/contrib/chatglm3-6b-32k`](./). There is one main file: - -* [`examples/models/core/glm-4-9b/convert_checkpoint.py`](../../../glm-4-9b/convert_checkpoint.py) to convert a checkpoint from the [HuggingFace (HF) Transformers](https://github.com/huggingface/transformers) format to the TensorRT LLM format. - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - -| Model Name | FP16 | FMHA | WO | SQ | AWQ | FP8 | TP | PP | ST | C++ | benchmark | IFB | -| :--------------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :-------: | :---: | -| chatglm3_6b | Y | Y | Y | Y | Y | Y | Y | | Y | Y | Y | Y | -| chatglm3_6b_base | Y | Y | Y | Y | Y | Y | Y | | Y | Y | Y | Y | -| chatglm3_6b_32k | Y | Y | Y | Y | Y | Y | Y | | Y | Y | Y | Y | - -* Model Name: the name of the model, the same as the name on HuggingFace -* FMHA: Fused MultiHead Attention (see introduction below) -* WO: Weight Only Quantization (int8 / int4) -* SQ: Smooth Quantization (int8) -* AWQ: Activation Aware Weight Quantization (int4) -* FP8: FP8 Quantization -* TP: Tensor Parallel -* PP: Pipeline Parallel -* ST: Strongly Typed -* C++: C++ Runtime -* benchmark: benchmark by python / C++ Runtime -* IFB: In-flight Batching (see introduction below) - -## Model comparison - -| Name | nL | nAH | nKH | nHW | nH | nF | nMSL | nV | bP2D | bBQKV | bBDense | Comments | -| :--------------: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :----: | :---: | :---: | :-----: | :----------------------------------------------------------------- | -| chatglm3_6b | 28 | 32 | 2 | 128 | 4096 | 13696 | 8192 | 65024 | N | Y | N | Different in preprocess and postprocess than chatglm2_6b | -| chatglm3_6b_base | 28 | 32 | 2 | 128 | 4096 | 13696 | 32768 | 65024 | N | Y | N | | -| chatglm3_6b_32k | 28 | 32 | 2 | 128 | 4096 | 13696 | 32768 | 65024 | N | Y | N | RoPE base=500000 rather than 10000 in chatglm3_6b | - -* nL: number of layers -* nAH: number of attention heads -* nKH: number of kv heads (less than nAH if multi_query_attention is used) -* nHW: head width -* nH: hidden size -* nF: FFN hidden size -* nMSL: max sequence length (input + output) -* nV: vocabulary size -* bP2D: use position_encoding_2d (Y: Yes, N: No) -* bBQKV: use bias for QKV multiplication in self-attention -* bBDense: use bias for Dense multiplication in self-attention - -## Tokenizer and special tokens comparison - -| Name | Tokenizer | bos | eos | pad | cls | startofpiece | endofpiece | mask | smask | gmask | -| :--------------: | :--------------: | :----: | :----: | :---: | :---: | :----------: | :--------: | :----: | :---: | :----: | -| chatglm3_6b | ChatGLMTokenizer | 1 | 2 | 0 | | | | 130000 | | | -| chatglm3_6b_base | ChatGLMTokenizer | 1 | 2 | 0 | | | | 130000 | | | -| chatglm3_6b_32k | ChatGLMTokenizer | 1 | 2 | 0 | | | | 130000 | | | - -## Usage - -The next section describe how to build the engine and run the inference demo. - -### 1. Download repo and weights from HuggingFace Transformers - -```bash -pip install -r requirements.txt -apt-get update -apt-get install git-lfs -rm -rf chatglm* - -# clone one or more models we want to build -git clone https://huggingface.co/THUDM/chatglm3-6b chatglm3_6b -git clone https://huggingface.co/THUDM/chatglm3-6b-base chatglm3_6b_base -git clone https://huggingface.co/THUDM/chatglm3-6b-32k chatglm3_6b_32k -``` - -For more example codes, please refer to the [examples/models/core/glm-4-9b/README.md](../../../glm-4-9b/README.md). diff --git a/examples/models/contrib/chatglm3-6b-32k/requirements.txt b/examples/models/contrib/chatglm3-6b-32k/requirements.txt deleted file mode 100644 index cdc65bf2bb38..000000000000 --- a/examples/models/contrib/chatglm3-6b-32k/requirements.txt +++ /dev/null @@ -1,8 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -evaluate -protobuf -rouge_score -sentencepiece -tiktoken diff --git a/examples/models/contrib/chatglm3-6b-32k/tokenization_chatglm.py b/examples/models/contrib/chatglm3-6b-32k/tokenization_chatglm.py deleted file mode 100644 index 67a3d52442a6..000000000000 --- a/examples/models/contrib/chatglm3-6b-32k/tokenization_chatglm.py +++ /dev/null @@ -1,313 +0,0 @@ -import json -import os -from typing import Dict, List, Optional, Union - -from sentencepiece import SentencePieceProcessor -from transformers import PreTrainedTokenizer -from transformers.tokenization_utils_base import BatchEncoding, EncodedInput -from transformers.utils import PaddingStrategy - - -class SPTokenizer: - - def __init__(self, model_path: str): - # reload tokenizer - assert os.path.isfile(model_path), model_path - self.sp_model = SentencePieceProcessor(model_file=model_path) - - # BOS / EOS token IDs - self.n_words: int = self.sp_model.vocab_size() - self.bos_id: int = self.sp_model.bos_id() - self.eos_id: int = self.sp_model.eos_id() - self.pad_id: int = self.sp_model.unk_id() - assert self.sp_model.vocab_size() == self.sp_model.get_piece_size() - - special_tokens = [ - "[MASK]", "[gMASK]", "[sMASK]", "sop", "eop", "<|system|>", - "<|user|>", "<|assistant|>", "<|observation|>" - ] - self.special_tokens = {} - self.index_special_tokens = {} - for token in special_tokens: - self.special_tokens[token] = self.n_words - self.index_special_tokens[self.n_words] = token - self.n_words += 1 - - def tokenize(self, s: str): - return self.sp_model.EncodeAsPieces(s) - - def encode(self, s: str, bos: bool = False, eos: bool = False) -> List[int]: - assert type(s) is str - t = self.sp_model.encode(s) - if bos: - t = [self.bos_id] + t - if eos: - t = t + [self.eos_id] - return t - - def decode(self, t: List[int]) -> str: - text, buffer = "", [] - for token in t: - if token in self.index_special_tokens: - if buffer: - text += self.sp_model.decode(buffer) - buffer = [] - text += self.index_special_tokens[token] - else: - buffer.append(token) - if buffer: - text += self.sp_model.decode(buffer) - return text - - def decode_tokens(self, tokens: List[str]) -> str: - text = self.sp_model.DecodePieces(tokens) - return text - - def convert_token_to_id(self, token): - """ Converts a token (str) in an id using the vocab. """ - if token in self.special_tokens: - return self.special_tokens[token] - return self.sp_model.PieceToId(token) - - def convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - if index in self.index_special_tokens: - return self.index_special_tokens[index] - if index in [self.eos_id, self.bos_id, self.pad_id] or index < 0: - return "" - return self.sp_model.IdToPiece(index) - - -class ChatGLMTokenizer(PreTrainedTokenizer): - vocab_files_names = {"vocab_file": "tokenizer.model"} - - model_input_names = ["input_ids", "attention_mask", "position_ids"] - - def __init__(self, - vocab_file, - padding_side="left", - clean_up_tokenization_spaces=False, - **kwargs): - self.name = "GLMTokenizer" - - self.vocab_file = vocab_file - self.tokenizer = SPTokenizer(vocab_file) - self.special_tokens = { - "": self.tokenizer.bos_id, - "": self.tokenizer.eos_id, - "": self.tokenizer.pad_id - } - super().__init__( - padding_side=padding_side, - clean_up_tokenization_spaces=clean_up_tokenization_spaces, - **kwargs) - - def get_command(self, token): - if token in self.special_tokens: - return self.special_tokens[token] - assert token in self.tokenizer.special_tokens, f"{token} is not a special token for {self.name}" - return self.tokenizer.special_tokens[token] - - @property - def unk_token(self) -> str: - return "" - - @property - def pad_token(self) -> str: - return "" - - @property - def pad_token_id(self): - return self.get_command("") - - @property - def eos_token(self) -> str: - return "" - - @property - def eos_token_id(self): - return self.get_command("") - - @property - def vocab_size(self): - return self.tokenizer.n_words - - def get_vocab(self): - """ Returns vocab as a dict """ - vocab = { - self._convert_id_to_token(i): i - for i in range(self.vocab_size) - } - vocab.update(self.added_tokens_encoder) - return vocab - - def _tokenize(self, text, **kwargs): - return self.tokenizer.tokenize(text) - - def _convert_token_to_id(self, token): - """ Converts a token (str) in an id using the vocab. """ - return self.tokenizer.convert_token_to_id(token) - - def _convert_id_to_token(self, index): - """Converts an index (integer) in a token (str) using the vocab.""" - return self.tokenizer.convert_id_to_token(index) - - def convert_tokens_to_string(self, tokens: List[str]) -> str: - return self.tokenizer.decode_tokens(tokens) - - def save_vocabulary(self, save_directory, filename_prefix=None): - """ - Save the vocabulary and special tokens file to a directory. - - Args: - save_directory (`str`): - The directory in which to save the vocabulary. - filename_prefix (`str`, *optional*): - An optional prefix to add to the named of the saved files. - - Returns: - `Tuple(str)`: Paths to the files saved. - """ - if os.path.isdir(save_directory): - vocab_file = os.path.join(save_directory, - self.vocab_files_names["vocab_file"]) - else: - vocab_file = save_directory - - with open(self.vocab_file, 'rb') as fin: - proto_bytes = fin.read() - - with open(vocab_file, "wb") as writer: - writer.write(proto_bytes) - - return (vocab_file, ) - - def get_prefix_tokens(self): - prefix_tokens = [self.get_command("[gMASK]"), self.get_command("sop")] - return prefix_tokens - - def build_single_message(self, role, metadata, message): - assert role in ["system", "user", "assistant", "observation"], role - role_tokens = [self.get_command(f"<|{role}|>") - ] + self.tokenizer.encode(f"{metadata}\n") - message_tokens = self.tokenizer.encode(message) - tokens = role_tokens + message_tokens - return tokens - - def build_chat_input(self, query, history=None, role="user"): - if history is None: - history = [] - input_ids = [] - for item in history: - content = item["content"] - if item["role"] == "system" and "tools" in item: - content = content + "\n" + json.dumps( - item["tools"], indent=4, ensure_ascii=False) - input_ids.extend( - self.build_single_message(item["role"], - item.get("metadata", ""), content)) - input_ids.extend(self.build_single_message(role, "", query)) - input_ids.extend([self.get_command("<|assistant|>")]) - return self.batch_encode_plus([input_ids], - return_tensors="pt", - is_split_into_words=True) - - def build_inputs_with_special_tokens( - self, - token_ids_0: List[int], - token_ids_1: Optional[List[int]] = None) -> List[int]: - """ - Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and - adding special tokens. A BERT sequence has the following format: - - - single sequence: `[CLS] X [SEP]` - - pair of sequences: `[CLS] A [SEP] B [SEP]` - - Args: - token_ids_0 (`List[int]`): - List of IDs to which the special tokens will be added. - token_ids_1 (`List[int]`, *optional*): - Optional second list of IDs for sequence pairs. - - Returns: - `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens. - """ - prefix_tokens = self.get_prefix_tokens() - token_ids_0 = prefix_tokens + token_ids_0 - if token_ids_1 is not None: - token_ids_0 = token_ids_0 + token_ids_1 + [ - self.get_command("") - ] - return token_ids_0 - - def _pad( - self, - encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding], - max_length: Optional[int] = None, - padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD, - pad_to_multiple_of: Optional[int] = None, - return_attention_mask: Optional[bool] = None, - padding_side: str = "left", # Fix for new transformers - ) -> dict: - """ - Pad encoded inputs (on left/right and up to predefined length or max length in the batch) - - Args: - encoded_inputs: - Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`). - max_length: maximum length of the returned list and optionally padding length (see below). - Will truncate by taking into account the special tokens. - padding_strategy: PaddingStrategy to use for padding. - - - PaddingStrategy.LONGEST Pad to the longest sequence in the batch - - PaddingStrategy.MAX_LENGTH: Pad to the max length (default) - - PaddingStrategy.DO_NOT_PAD: Do not pad - The tokenizer padding sides are defined in self.padding_side: - - - 'left': pads on the left of the sequences - - 'right': pads on the right of the sequences - pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value. - This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability - `>= 7.5` (Volta). - return_attention_mask: - (optional) Set to False to avoid returning attention mask (default: set to model specifics) - """ - # Load from model defaults - assert self.padding_side == "left" - - required_input = encoded_inputs[self.model_input_names[0]] - seq_length = len(required_input) - - if padding_strategy == PaddingStrategy.LONGEST: - max_length = len(required_input) - - if max_length is not None and pad_to_multiple_of is not None and ( - max_length % pad_to_multiple_of != 0): - max_length = ( - (max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of - - needs_to_be_padded = padding_strategy != PaddingStrategy.DO_NOT_PAD and len( - required_input) != max_length - - # Initialize attention mask if not present. - if "attention_mask" not in encoded_inputs: - encoded_inputs["attention_mask"] = [1] * seq_length - - if "position_ids" not in encoded_inputs: - encoded_inputs["position_ids"] = list(range(seq_length)) - - if needs_to_be_padded: - difference = max_length - len(required_input) - - if "attention_mask" in encoded_inputs: - encoded_inputs["attention_mask"] = [ - 0 - ] * difference + encoded_inputs["attention_mask"] - if "position_ids" in encoded_inputs: - encoded_inputs["position_ids"] = [ - 0 - ] * difference + encoded_inputs["position_ids"] - encoded_inputs[self.model_input_names[ - 0]] = [self.pad_token_id] * difference + required_input - - return encoded_inputs diff --git a/examples/models/contrib/internlm/.gitignore b/examples/models/contrib/internlm/.gitignore deleted file mode 100644 index 7ce339719a3e..000000000000 --- a/examples/models/contrib/internlm/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -internlm* -tokenizer.model diff --git a/examples/models/contrib/internlm/README.md b/examples/models/contrib/internlm/README.md deleted file mode 100644 index f44180f01aad..000000000000 --- a/examples/models/contrib/internlm/README.md +++ /dev/null @@ -1,320 +0,0 @@ -# InternLM - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document shows how to build and run InternLM 7B / 20B models in TensorRT LLM on both single GPU, single node multi-GPU and multi-node multi-GPU. - -- [InternLM](#internlm) - - [Overview](#overview) - - [Support Matrix](#support-matrix) - - [Usage](#usage) - - [Build TensorRT engine(s)](#build-tensorrt-engines) - - [INT8 weight only + INT8 KV cache](#int8-weight-only--int8-kv-cache) - - [SmoothQuant](#smoothquant) - - [Run](#run) - - [Summarization using the InternLM model](#summarization-using-the-internlm-model) - -## Overview - -The TensorRT LLM InternLM implementation is based on the LLaMA model. The implementation can -be found in [tensorrt_llm/models/llama/model.py](../../../../tensorrt_llm/models/llama/model.py). -The TensorRT LLM InternLM example code lies in [`examples/models/contrib/internlm`](./): - -* [`convert_checkpoint.py`](../../core/llama/convert_checkpoint.py) converts the Huggingface Model of InternLM into TensorRT LLM checkpoint. -* [`convert_checkpoint.py`] to to convert a checkpoint from the [HuggingFace (HF) Transformers](https://github.com/huggingface/transformers) format to the TensorRT LLM format - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - * FP16 / BF16 - * INT8 & INT4 Weight-Only - * Smooth Quant - * INT8 KV Cache - * Tensor Parallel & Pipeline Parallel - -## Usage - -The TensorRT LLM InternLM example code locates at [examples/models/contrib/internlm](./). It takes HF weights as input, and builds the corresponding TensorRT engines. The number of TensorRT engines depends on the number of GPUs used to run inference. - -### Build TensorRT engine(s) - -Please install required packages first: - -```bash -pip install -r requirements.txt -``` - -TensorRT LLM InternLM builds TensorRT engine(s) from HF checkpoint. If no checkpoint directory is specified, TensorRT LLM will build engine(s) with dummy weights. - -InternLM has released several checkpoints of different size or capabilities under https://huggingface.co/internlm. Users can pick any one repository and follow instructions to prepare the checkpoint. - -Below examples use [internlm-chat-7b](https://huggingface.co/internlm/internlm-chat-7b) and [internlm-chat-20b](https://huggingface.co/internlm/internlm-chat-20b) and assume these repositories are cloned or linked under this directory, for example `./internlm-chat-7b/`. - -Normally `trtllm-build` only requires single GPU, but if you've already got all the GPUs needed for inference, you could enable parallel building to make the engine building process faster by adding `--workers` argument. Please note that currently `--workers` feature only supports single node. - -Here're some examples: - -```bash -# Build a single-GPU float16 engine from HF weights. -# gpt_attention_plugin is necessary in InternLM. -# Try use_gemm_plugin to prevent accuracy issue. -cd examples/models/core/llama - -# Convert the InternLM 7B model using a single GPU and FP16. -python convert_checkpoint.py --model_dir ./internlm-chat-7b/ \ - --dtype float16 \ - --output_dir ./internlm-chat-7b/trt_engines/fp16/1-gpu/ -# Note: setting `--dtype bfloat16` to use bfloat16 precision. - -# BUild the InternLM 7B model using a single GPU -trtllm-build --checkpoint_dir ./internlm-chat-7b/trt_engines/fp16/1-gpu/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 - -# Convert the InternLM 7B model using a single GPU and apply INT8 weight-only quantization.. -python convert_checkpoint.py --model_dir ./internlm-chat-7b/ \ - --dtype float16 \ - --output_dir ./internlm-chat-7b/trt_engines/int8/1-gpu/ \ - --use_weight_only \ - --weight_only_precision int8 - -trtllm-build --checkpoint_dir ./internlm-chat-7b/trt_engines/int8/1-gpu/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 - -# Note: setting `--weight_only_precision int4` to use INT4 weight-only quantization - -# Build InternLM 7B using 2-way tensor parallelism. -python convert_checkpoint.py --model_dir ./internlm-chat-7b/ \ - --dtype float16 \ - --output_dir ./internlm-chat-7b/trt_engines/fp16/2-gpu/ \ - --tp_size 2 - -trtllm-build --checkpoint_dir ./internlm-chat-7b/trt_engines/fp16/2-gpu/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 - -# Build InternLM 20B using 2-way tensor parallelism. -python convert_checkpoint.py --model_dir ./internlm-chat-20b/ \ - --dtype bfloat16 \ - --output_dir ./internlm-chat-20b/trt_engines/bf16/2-gpu/ \ - --tp_size 2 --workers 2 - -trtllm-build --checkpoint_dir ./internlm-chat-7b/trt_engines/bf16/2-gpu/ \ - --output_dir ./engine_outputs \ - --gpt_attention_plugin bfloat16 \ - --gemm_plugin bfloat16 -``` - -#### INT8 weight only + INT8 KV cache - -For INT8 KV cache, [`convert_checkpoint.py`](./convert_checkpoint.py) features a -`--int8_kv_cache` option. Setting `--int8_kv_cache` will calibrate the model, -and then export the scaling factors needed for INT8 KV cache inference. - - -Example: - -```bash -cd examples/models/core/llama - -# For 7B models -python convert_checkpoint.py --model_dir ./internlm-chat-7b \ - --output_dir ./internlm-chat-7b/smooth_internlm/int8_kv_cache/ \ - --dtype float16 \ - --use_weight_only \ - --weight_only_precision int8 \ - --int8_kv_cache - -# Build 7B model with both INT8 weight-only and INT8 KV cache enabled -trtllm-build --checkpoint_dir ./internlm-chat-7b/smooth_internlm/int8_kv_cache/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 \ -``` - - -```bash -cd examples/models/core/llama - -# For 20B models -python convert_checkpoint.py --model_dir ./internlm-chat-20b \ - --output_dir ./internlm-chat-20b/smooth_internlm/int8_kv_cache/ \ - --dtype float16 \ - --use_weight_only \ - --weight_only_precision int8 \ - --int8_kv_cache - -# Build 20B model with both INT8 weight-only and INT8 KV cache enabled -trtllm-build --checkpoint_dir ./internlm-chat-20b/smooth_internlm/int8_kv_cache/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 \ -``` - - -Test with `../../../run.py` or `../../../summarize.py`: - -```bash -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir ./internlm-chat-7b/trt_engines/int8_kv_cache_weight_only/1-gpu - -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-20b/ \ - --engine_dir ./internlm-chat-20b/trt_engines/int8_kv_cache_weight_only/1-gpu - -python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-7b \ - --data_type fp16 \ - --engine_dir ./internlm-chat-7b/trt_engines/int8_kv_cache_weight_only/1-gpu - -python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-20b \ - --data_type fp16 \ - --engine_dir ./internlm-chat-20b/trt_engines/int8_kv_cache_weight_only/1-gpu -``` - -#### SmoothQuant - -Unlike the FP16 build where the HF weights are processed and loaded into the TensorRT LLM directly, the SmoothQuant needs to load INT8 weights which should be pre-processed before building an engine. - -Example: -```bash -cd examples/models/core/llama - -# For 7B models -python convert_checkpoint.py --model_dir ./internlm-chat-7b --output_dir ./internlm-chat-7b/smooth_internlm/sq0.5/ --dtype float16 --smoothquant 0.5 -# Build the engine -trtllm-build --checkpoint_dir ./internlm-chat-7b/smooth_internlm/sq0.5/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 - -# For 20B models -cd examples/models/core/llama - -python convert_checkpoint.py --model_dir ./internlm-chat-20b --output_dir ./internlm-chat-20b/smooth_internlm/sq0.5/ --dtype float16 --smoothquant 0.5 -trtllm-build --checkpoint_dir ./internlm-chat-20b/smooth_internlm/sq0.5/ \ - --output_dir ./engine_outputs \ - --gemm_plugin float16 -``` - -[`convert_checkpoint.py`](./convert_checkpoint.py) add new options for the support of INT8 inference of SmoothQuant models. - -`--smoothquant` is the starting point of INT8 inference. By default, it -will run the model in the _per-tensor_ mode. - -Then, you can add any combination of `--per-token` and `--per-channel` to get the corresponding behaviors. - -Examples of build invocations: - -```bash -# Build model for SmoothQuant in the _per_token_ + _per_channel_ mode -cd examples/models/core/llama - -# 7B model -python convert_checkpoint.py --model_dir ./internlm-chat-7b --output_dir ./internlm-chat-7b/smooth_internlm/sq0.5/ --dtype float16 --smoothquant 0.5 --per_channel --per_token - -# 20B model -python convert_checkpoint.py --model_dir ./internlm-chat-20b --output_dir ./internlm-chat-20b/smooth_internlm/sq0.5/ --dtype float16 --smoothquant 0.5 --per_channel --per_token -``` - - -Test with `../../../run.py` or `../../../summarize.py`: - -```bash -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir ./internlm-chat-7b/smooth_internlm/sq0.5/ - -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-20b/ \ - --engine_dir ./internlm-chat-20b/smooth_internlm/sq0.5/ - -python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-7b \ - --data_type fp16 \ - --engine_dir ./internlm-chat-7b/smooth_internlm/sq0.5/ - -python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-20b \ - --data_type fp16 \ - --engine_dir ./internlm-chat-20b/smooth_internlm/sq0.5/ -``` - -### Run - -To run a TensorRT LLM InternLM model using the engines generated by `trtllm-build` - -```bash -# InternLM 7B with fp16 -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir=./internlm-chat-7b/trt_engines/fp16/1-gpu/ - -# InternLM 7B with bf16 -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir=./internlm-chat-7b/trt_engines/bf16/1-gpu/ - -# InternLM 7B with int8 weight only quantization -python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir=./internlm-chat-7b/trt_engines/weight_only/1-gpu/ - -# InternLM 7B with fp16 and tensor parallelism -mpirun -n 2 --allow-run-as-root \ - python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir=./internlm-chat-7b/trt_engines/fp16/2-gpu/ - -# InternLM 20B with fp16 and tensor parallelism and pipeline parallelism -mpirun -n 4 --allow-run-as-root \ - python ../../../run.py --max_output_len=120 \ - --input_text 'Tell me about yourself.' \ - --tokenizer_dir ./internlm-chat-7b/ \ - --engine_dir=./internlm-chat-7b/trt_engines/bf16/4-gpu/ -``` - -### Summarization using the InternLM model - -```bash -# Run summarization using the InternLM 7B model in FP16. -python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-7b/ \ - --data_type fp16 \ - --engine_dir ./engine_outputs - -# Run summarization using the InternLM 7B model quantized to INT8. -python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-7b/ \ - --data_type fp16 \ - --engine_dir ./engine_outputs - -# Run summarization using the InternLM 7B model in FP16 using two GPUs. -mpirun -n 2 --allow-run-as-root \ - python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-7b/ \ - --data_type fp16 \ - --engine_dir ./internlm-chat-7b/trt_engines/fp16/2-gpu/ - -# Run summarization using the InternLM 20B model in BF16 using 4 GPUs. -mpirun -n 4 --allow-run-as-root \ - python ../../../summarize.py --test_trt_llm --test_hf \ - --hf_model_dir ./internlm-chat-20b/ \ - --data_type bf16 \ - --engine_dir ./internlm-chat-20b/trt_engines/bf16/4-gpu/ -``` diff --git a/examples/models/contrib/internlm/requirements.txt b/examples/models/contrib/internlm/requirements.txt deleted file mode 100644 index d9354a133c65..000000000000 --- a/examples/models/contrib/internlm/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -rouge_score -sentencepiece>=0.1.99 -evaluate diff --git a/examples/models/contrib/jais/README.md b/examples/models/contrib/jais/README.md deleted file mode 100644 index d15cfd116962..000000000000 --- a/examples/models/contrib/jais/README.md +++ /dev/null @@ -1,125 +0,0 @@ -# Jais - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document elaborates how to build Jais model to runnable engines on multi-GPU node and perform a summarization task using these engines. - -Currently it has been tested on -- [Jais-13b-chat](https://huggingface.co/core42/jais-13b-chat) -- [Jais-30b-chat-v3](https://huggingface.co/core42/jais-30b-chat-v3) - - -- [Jais](#jais) - - [Overview](#overview) - - [Support Matrix](#support-matrix) - - [Usage](#usage) - - [Build TensorRT engine(s)](#build-tensorrt-engines) - - [Run inference](#run) - -## Overview - -The TensorRT LLM support for Jais is based on the GPT model, the implementation can be found in [tensorrt_llm/models/gpt/model.py](../../../../tensorrt_llm/models/gpt/model.py). Jais model resembles GPT very much except it uses alibi embedding, embedding scale, swiglu, and logits scale, we therefore reuse the [GPT example code](../../../gpt) for Jais, - -* [`convert_checkpoint.py`](../../../gpt/convert_checkpoint.py) to convert the Jais model into TensorRT LLM checkpoint format. - -In addition, there are two shared files in the parent folder [`examples`](../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix -The tested configurations are: - * FP16 - * FP8 - * Inflight Batching - * Tensor Parallel - -## Usage - -This section gives a whole process where we convert HF models, build TensorRT LLM engines and ultimately perform summarization. - -### Build TensorRT engine(s) - -Run the following commands and TRT-LLM will first transforms a HF model into its own checkpoint format, then builds a TRT engine based on the checkpoint - -```bash -# single gpu, dtype float16 for jais-13b-chat -python3 ../../../gpt/convert_checkpoint.py --model_dir core42/jais-13b-chat \ - --dtype float16 \ - --output_dir jais-13b-chat/trt_ckpt/fp16/1-gpu - -# 2-way tensor parallelism for jais-30b-chat-v3 -python3 ../../../gpt/convert_checkpoint.py --model_dir core42/jais-30b-chat-v3 \ - --dtype float16 \ - --tp_size 2 \ - --output_dir jais-30b-chat-v3/trt_ckpt/fp16/2-gpu -``` - -```bash -# Build a single-GPU float16 engine from TensorRT LLM checkpoint for jais-13b-chat -# Enable the special TensorRT LLM GPT Attention plugin (--gpt_attention_plugin) to increase runtime performance. -# It is recommend to use --remove_input_padding along with --gpt_attention_plugin for better performance -trtllm-build --checkpoint_dir jais-13b-chat/trt_ckpt/fp16/1-gpu \ - --gpt_attention_plugin float16 \ - --remove_input_padding enable \ - --output_dir jais-13b-chat/trt_engines/fp16/1-gpu - -# Build 2-way tensor parallelism engines from TensorRT LLM checkpoint for jais-30b-chat-v3 -trtllm-build --checkpoint_dir jais-30b-chat-v3/trt_ckpt/fp16/2-gpu \ - --gpt_attention_plugin float16 \ - --remove_input_padding enable \ - --output_dir jais-30b-chat-v3/trt_engines/fp16/2-gpu -``` - - -### Run - -The [`../../../run.py`](../../../run.py) script can be used to run inference with the built engine(s). - -```bash -python3 ../../../run.py --engine_dir jais-13b-chat/trt_engines/fp16/1-gpu \ - --tokenizer_dir core42/jais-13b-chat \ - --max_output_len 10 -``` - -If the engines are run successfully, you will see output like: -``` -...... -Input [Text 0]: "Born in north-east France, Soyer trained as a" -Output [Text 0 Beam 0]: " chef in Paris before moving to England in 1816" -``` - -```bash -python3 ../../../run.py --engine_dir jais-13b-chat/trt_engines/fp16/1-gpu \ - --tokenizer_dir core42/jais-13b-chat \ - --max_output_len 8 \ - --input_text "ولد في 1304 ميلادياً ابن بطوطه, لقد ذهب" -``` - -If the engines are run successfully, you will see output like: -``` -..... -Input [Text 0]: "ولد في 1304 ميلادياً ابن بطوطه, لقد ذهب" -Output [Text 0 Beam 0]: " في جميع أنحاء العالم المعروف في ذلك الوقت" -``` - - -To run a 2 TP model you can do the following -```bash -mpirun -np 2 \ - python3 ../../../run.py --engine_dir jais-30b-chat-v3/trt_engines/fp16/2-gpu \ - --tokenizer_dir core42/jais-30b-chat-v3 \ - --max_output_len 30 -``` - -If the engines are run successfully, you will see output like: -``` -Input [Text 0]: "Born in north-east France, Soyer trained as a" -Output [Text 0 Beam 0]: " chef, working in a series of high-end establishments. - -Soyer's career took him to work in a number of establishments across Europe," -``` diff --git a/examples/models/contrib/jais/requirements.txt b/examples/models/contrib/jais/requirements.txt deleted file mode 100644 index 592e01e5ba6d..000000000000 --- a/examples/models/contrib/jais/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -evaluate -rouge_score -SentencePiece>=0.1.99 diff --git a/examples/models/contrib/sdxl/README.md b/examples/models/contrib/sdxl/README.md deleted file mode 100644 index 05d874f5f27a..000000000000 --- a/examples/models/contrib/sdxl/README.md +++ /dev/null @@ -1,42 +0,0 @@ -# Stable Diffusion XL - -This document showcases how to build and run the [Stable Diffusion XL (SDXL)](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) model on multiple GPUs using TensorRT-LLM. The community-contributed SDXL example in TRT-LLM is intended solely to showcase distributed inference for high-resolution use cases. For an optimized single-GPU setup in Stable Diffusion inference, please refer to the [TensorRT DemoDiffusion example](https://github.com/NVIDIA/TensorRT/tree/main/demo/Diffusion). - -The design of distributed parallel inference comes from the CVPR 2024 paper [DistriFusion](https://github.com/mit-han-lab/distrifuser) from [MIT HAN Lab](https://hanlab.mit.edu/). To simplify the implementation, all communications in this example are handled synchronously. - -## Usage - -### 1. Build TensorRT Engine - -```bash -# 1 gpu -python build_sdxl_unet.py --size 1024 - -# 2 gpus -mpirun -n 2 --allow-run-as-root python build_sdxl_unet.py --size 1024 -``` - -### 2. Generate images using the engine - - -```bash -# 1 gpu -python run_sdxl.py --size 1024 --prompt "flowers, rabbit" - -# 2 gpus -mpirun -n 2 --allow-run-as-root python run_sdxl.py --size 1024 --prompt "flowers, rabbit" -``` - -## Latency Benchmark -This benchmark is provided as reference points and should not be considered as the peak inference speed that can be delivered by TensorRT-LLM. - -| Framework | Resolution | n_gpu | A100 latency (s) | A100 speedup | H100 latency (s) | H100 speedup | -|:---------:|:----------:|:-----:|:---------------:|:-------------:|:---------------:|:-------------:| -| Torch | 1024x1024 | 1 | 6.280 | 1 | 5.820 | 1 | -| TRT-LLM | 1024x1024 | 2 | 2.803 | **2.24x** | 1.719 | **3.39x** | -| TRT-LLM | 1024x1024 | 4 | 2.962 | **2.12x** | 2.592 | **2.25x** | -| Torch | 2048x2048 | 1 | 27.865 | 1 | 18.330 | 1 | -| TRT-LLM | 2048x2048 | 2 | 13.152 | **2.12x** | 7.943 | **2.31x** | -| TRT-LLM | 2048x2048 | 4 | 9.781 | **2.85x** | 7.596 | **2.41x** | - -torch v2.5.0. TRT-LLM v0.15.0.dev2024102900, `--num-warmup-runs=5; --avg-runs=20`. All communications are synchronous. diff --git a/examples/models/contrib/sdxl/build_sdxl_unet.py b/examples/models/contrib/sdxl/build_sdxl_unet.py deleted file mode 100755 index e2893859f731..000000000000 --- a/examples/models/contrib/sdxl/build_sdxl_unet.py +++ /dev/null @@ -1,148 +0,0 @@ -import argparse -import os - -import tensorrt as trt -import torch -from diffusers import DiffusionPipeline - -import tensorrt_llm -from tensorrt_llm.builder import Builder -from tensorrt_llm.mapping import Mapping -from tensorrt_llm.models.unet.pp.unet_pp import DistriUNetPP -from tensorrt_llm.models.unet.unet_2d_condition import UNet2DConditionModel -from tensorrt_llm.models.unet.weights import load_from_hf_unet -from tensorrt_llm.network import net_guard - -parser = argparse.ArgumentParser(description='build the UNet TensorRT engine.') -parser.add_argument('--model_dir', - type=str, - default='stabilityai/stable-diffusion-xl-base-1.0') -parser.add_argument('--size', type=int, default=1024, help='image size') -parser.add_argument('--output_dir', - type=str, - default=None, - help='output directory') - -args = parser.parse_args() - -model_dir = args.model_dir -size = args.size -sample_size = size // 8 - -world_size = tensorrt_llm.mpi_world_size() -rank = tensorrt_llm.mpi_rank() -output_dir = f'sdxl_s{size}_w{world_size}' if args.output_dir is None else args.output_dir -if rank == 0 and not os.path.exists(output_dir): - os.makedirs(output_dir) - -device_per_batch = world_size // 2 if world_size > 1 else 1 -batch_group = 2 if world_size > 1 else 1 - -# Use tp_size to indicate the size of patch parallelism -# Use pp_size to indicate the size of batch parallelism -mapping = Mapping(world_size=world_size, - rank=rank, - tp_size=device_per_batch, - pp_size=batch_group) - -torch.cuda.set_device(tensorrt_llm.mpi_rank()) - -tensorrt_llm.logger.set_level('verbose') -builder = Builder() -builder_config = builder.create_builder_config( - name='UNet2DConditionModel', - precision='float16', - timing_cache='model.cache', - profiling_verbosity='detailed', - tensor_parallel=world_size, - precision_constraints= - None, # do not use obey or the precision error will be too large -) - -pipeline = DiffusionPipeline.from_pretrained(model_dir, - torch_dtype=torch.float16) -model = UNet2DConditionModel( - sample_size=sample_size, - in_channels=4, - out_channels=4, - center_input_sample=False, - flip_sin_to_cos=True, - freq_shift=0, - down_block_types=("DownBlock2D", "CrossAttnDownBlock2D", - "CrossAttnDownBlock2D"), - up_block_types=("CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "UpBlock2D"), - block_out_channels=(320, 640, 1280), - layers_per_block=2, - downsample_padding=1, - mid_block_scale_factor=1.0, - act_fn="silu", - norm_num_groups=32, - norm_eps=1e-5, - cross_attention_dim=2048, - attention_head_dim=[5, 10, 20], - addition_embed_type="text_time", - addition_time_embed_dim=256, - projection_class_embeddings_input_dim=2816, - transformer_layers_per_block=[1, 2, 10], - use_linear_projection=True, - dtype=trt.float16, -) - -load_from_hf_unet(pipeline.unet, model) -model = DistriUNetPP(model, mapping) - -# Module -> Network -network = builder.create_network() -network.plugin_config.to_legacy_setting() -if mapping.world_size > 1: - network.plugin_config.set_nccl_plugin('float16') - -with net_guard(network): - # Prepare - network.set_named_parameters(model.named_parameters()) - - # Forward - sample = tensorrt_llm.Tensor( - name='sample', - dtype=trt.float16, - shape=[2, 4, sample_size, sample_size], - ) - timesteps = tensorrt_llm.Tensor( - name='timesteps', - dtype=trt.float16, - shape=[ - 1, - ], - ) - encoder_hidden_states = tensorrt_llm.Tensor( - name='encoder_hidden_states', - dtype=trt.float16, - shape=[2, 77, 2048], - ) - text_embeds = tensorrt_llm.Tensor( - name='text_embeds', - dtype=trt.float16, - shape=[2, 1280], - ) - time_ids = tensorrt_llm.Tensor( - name='time_ids', - dtype=trt.float16, - shape=[2, 6], - ) - - output = model(sample, timesteps, encoder_hidden_states, text_embeds, - time_ids) - - # Mark outputs - output_dtype = trt.float16 - output.mark_output('pred', output_dtype) - -# Network -> Engine -engine = builder.build_engine(network, builder_config) -assert engine is not None, 'Failed to build engine.' - -engine_name = f'sdxl_unet_s{size}_w{world_size}_r{rank}.engine' -engine_path = os.path.join(output_dir, engine_name) -with open(engine_path, 'wb') as f: - f.write(engine) -builder.save_config(builder_config, os.path.join(output_dir, 'config.json')) diff --git a/examples/models/contrib/sdxl/pipeline_stable_diffusion_xl.py b/examples/models/contrib/sdxl/pipeline_stable_diffusion_xl.py deleted file mode 100755 index 018f3fad783f..000000000000 --- a/examples/models/contrib/sdxl/pipeline_stable_diffusion_xl.py +++ /dev/null @@ -1,1365 +0,0 @@ -# Copyright 2023 The HuggingFace Team. All rights reserved. -# -# 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 inspect -import json -import os -from typing import Any, Callable, Dict, List, Optional, Tuple, Union - -import tensorrt as trt -import torch -from diffusers.image_processor import PipelineImageInput, VaeImageProcessor -from diffusers.loaders import (FromSingleFileMixin, IPAdapterMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin) -from diffusers.models import AutoencoderKL, UNet2DConditionModel -from diffusers.models.attention_processor import (AttnProcessor2_0, - LoRAAttnProcessor2_0, - LoRAXFormersAttnProcessor, - XFormersAttnProcessor) -from diffusers.models.lora import adjust_lora_scale_text_encoder -from diffusers.pipelines.pipeline_utils import DiffusionPipeline -from diffusers.pipelines.stable_diffusion_xl.pipeline_output import \ - StableDiffusionXLPipelineOutput -from diffusers.schedulers import KarrasDiffusionSchedulers -from diffusers.utils import (USE_PEFT_BACKEND, deprecate, - is_invisible_watermark_available, - is_torch_xla_available, logging, - replace_example_docstring, scale_lora_layers, - unscale_lora_layers) -from diffusers.utils.torch_utils import randn_tensor -from transformers import (CLIPImageProcessor, CLIPTextModel, - CLIPTextModelWithProjection, CLIPTokenizer, - CLIPVisionModelWithProjection) - -import tensorrt_llm -from tensorrt_llm.runtime import Session, TensorInfo - -if is_invisible_watermark_available(): - from diffusers.pipelines.stable_diffusion_xl.watermark import \ - StableDiffusionXLWatermarker - -if is_torch_xla_available(): - import torch_xla.core.xla_model as xm - - XLA_AVAILABLE = True -else: - XLA_AVAILABLE = False - -logger = logging.get_logger(__name__) # pylint: disable=invalid-name - -EXAMPLE_DOC_STRING = """ - Examples: - ```py - >>> import torch - >>> from diffusers import StableDiffusionXLPipeline - - >>> pipe = StableDiffusionXLPipeline.from_pretrained( - ... "stabilityai/stable-diffusion-xl-base-1.0", torch_dtype=torch.float16 - ... ) - >>> pipe = pipe.to("cuda") - - >>> prompt = "a photo of an astronaut riding a horse on mars" - >>> image = pipe(prompt).images[0] - ``` -""" - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.rescale_noise_cfg -def rescale_noise_cfg(noise_cfg, noise_pred_text, guidance_rescale=0.0): - """ - Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and - Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). See Section 3.4 - """ - std_text = noise_pred_text.std(dim=list(range(1, noise_pred_text.ndim)), - keepdim=True) - std_cfg = noise_cfg.std(dim=list(range(1, noise_cfg.ndim)), keepdim=True) - # rescale the results from guidance (fixes overexposure) - noise_pred_rescaled = noise_cfg * (std_text / std_cfg) - # mix with the original results from guidance by factor guidance_rescale to avoid "plain looking" images - noise_cfg = guidance_rescale * noise_pred_rescaled + \ - (1 - guidance_rescale) * noise_cfg - return noise_cfg - - -# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps -def retrieve_timesteps( - scheduler, - num_inference_steps: Optional[int] = None, - device: Optional[Union[str, torch.device]] = None, - timesteps: Optional[List[int]] = None, - **kwargs, -): - """ - Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles - custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. - - Args: - scheduler (`SchedulerMixin`): - The scheduler to get timesteps from. - num_inference_steps (`int`): - The number of diffusion steps used when generating samples with a pre-trained model. If used, - `timesteps` must be `None`. - device (`str` or `torch.device`, *optional*): - The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. - timesteps (`List[int]`, *optional*): - Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default - timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` - must be `None`. - - Returns: - `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the - second element is the number of inference steps. - """ - if timesteps is not None: - accepts_timesteps = "timesteps" in set( - inspect.signature(scheduler.set_timesteps).parameters.keys()) - if not accepts_timesteps: - raise ValueError( - f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" - f" timestep schedules. Please check whether you are using the correct scheduler." - ) - scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) - timesteps = scheduler.timesteps - num_inference_steps = len(timesteps) - else: - scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) - timesteps = scheduler.timesteps - return timesteps, num_inference_steps - - -class StableDiffusionXLPipeline( - DiffusionPipeline, - FromSingleFileMixin, - StableDiffusionXLLoraLoaderMixin, - TextualInversionLoaderMixin, - IPAdapterMixin, -): - r""" - Pipeline for text-to-image generation using Stable Diffusion XL. - - This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods the - library implements for all the pipelines (such as downloading or saving, running on a particular device, etc.) - - In addition the pipeline inherits the following loading methods: - - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.load_lora_weights`] - - *Ckpt*: [`loaders.FromSingleFileMixin.from_single_file`] - - as well as the following saving methods: - - *LoRA*: [`loaders.StableDiffusionXLLoraLoaderMixin.save_lora_weights`] - - Args: - vae ([`AutoencoderKL`]): - Variational Auto-Encoder (VAE) Model to encode and decode images to and from latent representations. - text_encoder ([`CLIPTextModel`]): - Frozen text-encoder. Stable Diffusion XL uses the text portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel), specifically - the [clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14) variant. - text_encoder_2 ([` CLIPTextModelWithProjection`]): - Second frozen text-encoder. Stable Diffusion XL uses the text and pool portion of - [CLIP](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModelWithProjection), - specifically the - [laion/CLIP-ViT-bigG-14-laion2B-39B-b160k](https://huggingface.co/laion/CLIP-ViT-bigG-14-laion2B-39B-b160k) - variant. - tokenizer (`CLIPTokenizer`): - Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - tokenizer_2 (`CLIPTokenizer`): - Second Tokenizer of class - [CLIPTokenizer](https://huggingface.co/docs/transformers/v4.21.0/en/model_doc/clip#transformers.CLIPTokenizer). - unet ([`UNet2DConditionModel`]): Conditional U-Net architecture to denoise the encoded image latents. - scheduler ([`SchedulerMixin`]): - A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of - [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. - force_zeros_for_empty_prompt (`bool`, *optional*, defaults to `"True"`): - Whether the negative prompt embeddings shall be forced to always be set to 0. Also see the config of - `stabilityai/stable-diffusion-xl-base-1-0`. - add_watermarker (`bool`, *optional*): - Whether to use the [invisible_watermark library](https://github.com/ShieldMnt/invisible-watermark/) to - watermark output images. If not defined, it will default to True if the package is installed, otherwise no - watermarker will be used. - """ - - model_cpu_offload_seq = "text_encoder->text_encoder_2->unet->vae" - _optional_components = [ - "tokenizer", - "tokenizer_2", - "text_encoder", - "text_encoder_2", - "image_encoder", - "feature_extractor", - ] - _callback_tensor_inputs = [ - "latents", - "prompt_embeds", - "negative_prompt_embeds", - "add_text_embeds", - "add_time_ids", - "negative_pooled_prompt_embeds", - "negative_add_time_ids", - ] - - def __init__( - self, - vae: AutoencoderKL, - text_encoder: CLIPTextModel, - text_encoder_2: CLIPTextModelWithProjection, - tokenizer: CLIPTokenizer, - tokenizer_2: CLIPTokenizer, - unet: UNet2DConditionModel, - scheduler: KarrasDiffusionSchedulers, - image_encoder: CLIPVisionModelWithProjection = None, - feature_extractor: CLIPImageProcessor = None, - force_zeros_for_empty_prompt: bool = True, - add_watermarker: Optional[bool] = None, - ): - super().__init__() - - self.register_modules( - vae=vae, - text_encoder=text_encoder, - text_encoder_2=text_encoder_2, - tokenizer=tokenizer, - tokenizer_2=tokenizer_2, - unet=unet, - scheduler=scheduler, - image_encoder=image_encoder, - feature_extractor=feature_extractor, - ) - self.register_to_config( - force_zeros_for_empty_prompt=force_zeros_for_empty_prompt) - self.vae_scale_factor = 2**(len(self.vae.config.block_out_channels) - 1) - self.image_processor = VaeImageProcessor( - vae_scale_factor=self.vae_scale_factor) - - self.default_sample_size = self.unet.config.sample_size - - add_watermarker = add_watermarker if add_watermarker is not None else is_invisible_watermark_available( - ) - - if add_watermarker: - self.watermark = StableDiffusionXLWatermarker() - else: - self.watermark = None - - self.execution_device = torch.device('cpu') - self.engine = {} - - def to( - self, - torch_device: Optional[Union[str, torch.device]] = None, - torch_dtype: Optional[torch.dtype] = None, - silence_dtype_warnings: bool = False, - ): - super().to(torch_device) - if isinstance(torch_device, str): - torch_device = torch.device(torch_device) - self.execution_device = torch_device - return self - - def prepare(self, path, size): - self.unet.cpu() - torch.cuda.empty_cache() - - def trt_dtype_to_torch(dtype): - if dtype == trt.float16: - return torch.float16 - elif dtype == trt.float32: - return torch.float32 - elif dtype == trt.int32: - return torch.int32 - else: - raise TypeError("%s is not supported" % dtype) - - config_path = os.path.join(path, 'config.json') - with open(config_path, 'r') as f: - config = json.load(f) - config['builder_config']['precision'] - world_size = config['builder_config']['tensor_parallel'] - - runtime_world_size = tensorrt_llm.mpi_world_size() - assert world_size == runtime_world_size, f'Engine world size ({world_size}) != Runtime world size ({runtime_world_size})' - runtime_rank = tensorrt_llm.mpi_rank() if world_size > 1 else 0 - torch.cuda.set_device(runtime_rank) - - serialize_file = f'sdxl_unet_s{size}_w{world_size}_r{runtime_rank}.engine' - serialize_path = os.path.join(path, serialize_file) - self.stream = torch.cuda.current_stream().cuda_stream - print(f'Loading engine from {serialize_path}') - with open(serialize_path, 'rb') as f: - engine_buffer = f.read() - print(f'Creating session from engine') - self.session = Session.from_serialized_engine(engine_buffer) - - output_info = self.session.infer_shapes([ - TensorInfo('sample', trt.DataType.HALF, - [2, 4, size // 8, size // 8]), - TensorInfo('timesteps', trt.DataType.HALF, [ - 1, - ]), - TensorInfo('encoder_hidden_states', trt.DataType.HALF, - [2, 77, 2048]), - TensorInfo('text_embeds', trt.DataType.HALF, [2, 1280]), - TensorInfo('time_ids', trt.DataType.HALF, [2, 6]), - ]) - self.outputs = { - t.name: - torch.empty(tuple(t.shape), - dtype=trt_dtype_to_torch(t.dtype), - device='cuda') - for t in output_info - } - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_slicing - def enable_vae_slicing(self): - r""" - Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to - compute decoding in several steps. This is useful to save some memory and allow larger batch sizes. - """ - self.vae.enable_slicing() - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_slicing - def disable_vae_slicing(self): - r""" - Disable sliced VAE decoding. If `enable_vae_slicing` was previously enabled, this method will go back to - computing decoding in one step. - """ - self.vae.disable_slicing() - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_vae_tiling - def enable_vae_tiling(self): - r""" - Enable tiled VAE decoding. When this option is enabled, the VAE will split the input tensor into tiles to - compute decoding and encoding in several steps. This is useful for saving a large amount of memory and to allow - processing larger images. - """ - self.vae.enable_tiling() - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_vae_tiling - def disable_vae_tiling(self): - r""" - Disable tiled VAE decoding. If `enable_vae_tiling` was previously enabled, this method will go back to - computing decoding in one step. - """ - self.vae.disable_tiling() - - def encode_prompt( - self, - prompt: str, - prompt_2: Optional[str] = None, - device: Optional[torch.device] = None, - num_images_per_prompt: int = 1, - do_classifier_free_guidance: bool = True, - negative_prompt: Optional[str] = None, - negative_prompt_2: Optional[str] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - lora_scale: Optional[float] = None, - clip_skip: Optional[int] = None, - ): - r""" - Encodes the prompt into text encoder hidden states. - - Args: - prompt (`str` or `List[str]`, *optional*): - prompt to be encoded - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - device: (`torch.device`): - torch device - num_images_per_prompt (`int`): - number of images that should be generated per prompt - do_classifier_free_guidance (`bool`): - whether to use classifier free guidance or not - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is - less than `1`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - lora_scale (`float`, *optional*): - A lora scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. - clip_skip (`int`, *optional*): - Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that - the output of the pre-final layer will be used for computing the prompt embeddings. - """ - device = self.execution_device - - # set lora scale so that monkey patched LoRA - # function of text encoder can correctly access it - if lora_scale is not None and isinstance( - self, StableDiffusionXLLoraLoaderMixin): - self._lora_scale = lora_scale - - # dynamically adjust the LoRA scale - if self.text_encoder is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder, - lora_scale) - else: - scale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if not USE_PEFT_BACKEND: - adjust_lora_scale_text_encoder(self.text_encoder_2, - lora_scale) - else: - scale_lora_layers(self.text_encoder_2, lora_scale) - - prompt = [prompt] if isinstance(prompt, str) else prompt - - if prompt is not None: - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - # Define tokenizers and text encoders - tokenizers = [self.tokenizer, self.tokenizer_2 - ] if self.tokenizer is not None else [self.tokenizer_2] - text_encoders = ([ - self.text_encoder, self.text_encoder_2 - ] if self.text_encoder is not None else [self.text_encoder_2]) - - if prompt_embeds is None: - prompt_2 = prompt_2 or prompt - prompt_2 = [prompt_2] if isinstance(prompt_2, str) else prompt_2 - - # textual inversion: procecss multi-vector tokens if necessary - prompt_embeds_list = [] - prompts = [prompt, prompt_2] - for prompt, tokenizer, text_encoder in zip(prompts, tokenizers, - text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - prompt = self.maybe_convert_prompt(prompt, tokenizer) - - text_inputs = tokenizer( - prompt, - padding="max_length", - max_length=tokenizer.model_max_length, - truncation=True, - return_tensors="pt", - ) - - text_input_ids = text_inputs.input_ids - untruncated_ids = tokenizer(prompt, - padding="longest", - return_tensors="pt").input_ids - - if untruncated_ids.shape[ - -1] >= text_input_ids.shape[-1] and not torch.equal( - text_input_ids, untruncated_ids): - removed_text = tokenizer.batch_decode( - untruncated_ids[:, tokenizer.model_max_length - 1:-1]) - logger.warning( - "The following part of your input was truncated because CLIP can only handle sequences up to" - f" {tokenizer.model_max_length} tokens: {removed_text}") - - prompt_embeds = text_encoder(text_input_ids.to(device), - output_hidden_states=True) - - # We are only ALWAYS interested in the pooled output of the final text encoder - pooled_prompt_embeds = prompt_embeds[0] - if clip_skip is None: - prompt_embeds = prompt_embeds.hidden_states[-2] - else: - # "2" because SDXL always indexes from the penultimate layer. - prompt_embeds = prompt_embeds.hidden_states[-(clip_skip + - 2)] - - prompt_embeds_list.append(prompt_embeds) - - prompt_embeds = torch.concat(prompt_embeds_list, dim=-1) - - # get unconditional embeddings for classifier free guidance - zero_out_negative_prompt = negative_prompt is None and self.config.force_zeros_for_empty_prompt - if do_classifier_free_guidance and negative_prompt_embeds is None and zero_out_negative_prompt: - negative_prompt_embeds = torch.zeros_like(prompt_embeds) - negative_pooled_prompt_embeds = torch.zeros_like( - pooled_prompt_embeds) - elif do_classifier_free_guidance and negative_prompt_embeds is None: - negative_prompt = negative_prompt or "" - negative_prompt_2 = negative_prompt_2 or negative_prompt - - # normalize str to list - negative_prompt = batch_size * \ - [negative_prompt] if isinstance( - negative_prompt, str) else negative_prompt - negative_prompt_2 = (batch_size * [negative_prompt_2] if isinstance( - negative_prompt_2, str) else negative_prompt_2) - - uncond_tokens: List[str] - if prompt is not None and type(prompt) is not type(negative_prompt): - raise TypeError( - f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" - f" {type(prompt)}.") - elif batch_size != len(negative_prompt): - raise ValueError( - f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" - f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" - " the batch size of `prompt`.") - else: - uncond_tokens = [negative_prompt, negative_prompt_2] - - negative_prompt_embeds_list = [] - for negative_prompt, tokenizer, text_encoder in zip( - uncond_tokens, tokenizers, text_encoders): - if isinstance(self, TextualInversionLoaderMixin): - negative_prompt = self.maybe_convert_prompt( - negative_prompt, tokenizer) - - max_length = prompt_embeds.shape[1] - uncond_input = tokenizer( - negative_prompt, - padding="max_length", - max_length=max_length, - truncation=True, - return_tensors="pt", - ) - - negative_prompt_embeds = text_encoder( - uncond_input.input_ids.to(device), - output_hidden_states=True, - ) - # We are only ALWAYS interested in the pooled output of the final text encoder - negative_pooled_prompt_embeds = negative_prompt_embeds[0] - negative_prompt_embeds = negative_prompt_embeds.hidden_states[ - -2] - - negative_prompt_embeds_list.append(negative_prompt_embeds) - - negative_prompt_embeds = torch.concat(negative_prompt_embeds_list, - dim=-1) - - if self.text_encoder_2 is not None: - prompt_embeds = prompt_embeds.to(dtype=self.text_encoder_2.dtype, - device=device) - else: - prompt_embeds = prompt_embeds.to(dtype=self.unet.dtype, - device=device) - - bs_embed, seq_len, _ = prompt_embeds.shape - # duplicate text embeddings for each generation per prompt, using mps friendly method - prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) - prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, - seq_len, -1) - - if do_classifier_free_guidance: - # duplicate unconditional embeddings for each generation per prompt, using mps friendly method - seq_len = negative_prompt_embeds.shape[1] - - if self.text_encoder_2 is not None: - negative_prompt_embeds = negative_prompt_embeds.to( - dtype=self.text_encoder_2.dtype, device=device) - else: - negative_prompt_embeds = negative_prompt_embeds.to( - dtype=self.unet.dtype, device=device) - - negative_prompt_embeds = negative_prompt_embeds.repeat( - 1, num_images_per_prompt, 1) - negative_prompt_embeds = negative_prompt_embeds.view( - batch_size * num_images_per_prompt, seq_len, -1) - - pooled_prompt_embeds = pooled_prompt_embeds.repeat( - 1, num_images_per_prompt).view(bs_embed * num_images_per_prompt, -1) - if do_classifier_free_guidance: - negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.repeat( - 1, num_images_per_prompt).view(bs_embed * num_images_per_prompt, - -1) - - if self.text_encoder is not None: - if isinstance( - self, - StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder, lora_scale) - - if self.text_encoder_2 is not None: - if isinstance( - self, - StableDiffusionXLLoraLoaderMixin) and USE_PEFT_BACKEND: - # Retrieve the original scale by scaling back the LoRA layers - unscale_lora_layers(self.text_encoder_2, lora_scale) - - return prompt_embeds, negative_prompt_embeds, pooled_prompt_embeds, negative_pooled_prompt_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image - def encode_image(self, image, device, num_images_per_prompt): - dtype = next(self.image_encoder.parameters()).dtype - - if not isinstance(image, torch.Tensor): - image = self.feature_extractor(image, - return_tensors="pt").pixel_values - - image = image.to(device=device, dtype=dtype) - image_embeds = self.image_encoder(image).image_embeds - image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, - dim=0) - - uncond_image_embeds = torch.zeros_like(image_embeds) - return image_embeds, uncond_image_embeds - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs - def prepare_extra_step_kwargs(self, generator, eta): - # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature - # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. - # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 - # and should be between [0, 1] - - accepts_eta = "eta" in set( - inspect.signature(self.scheduler.step).parameters.keys()) - extra_step_kwargs = {} - if accepts_eta: - extra_step_kwargs["eta"] = eta - - # check if the scheduler accepts generator - accepts_generator = "generator" in set( - inspect.signature(self.scheduler.step).parameters.keys()) - if accepts_generator: - extra_step_kwargs["generator"] = generator - return extra_step_kwargs - - def check_inputs( - self, - prompt, - prompt_2, - height, - width, - callback_steps, - negative_prompt=None, - negative_prompt_2=None, - prompt_embeds=None, - negative_prompt_embeds=None, - pooled_prompt_embeds=None, - negative_pooled_prompt_embeds=None, - callback_on_step_end_tensor_inputs=None, - ): - if height % 8 != 0 or width % 8 != 0: - raise ValueError( - f"`height` and `width` have to be divisible by 8 but are {height} and {width}." - ) - - if callback_steps is not None and (not isinstance(callback_steps, int) - or callback_steps <= 0): - raise ValueError( - f"`callback_steps` has to be a positive integer but is {callback_steps} of type" - f" {type(callback_steps)}.") - - if callback_on_step_end_tensor_inputs is not None and not all( - k in self._callback_tensor_inputs - for k in callback_on_step_end_tensor_inputs): - raise ValueError( - f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" - ) - - if prompt is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two.") - elif prompt_2 is not None and prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `prompt_2`: {prompt_2} and `prompt_embeds`: {prompt_embeds}. Please make sure to" - " only forward one of the two.") - elif prompt is None and prompt_embeds is None: - raise ValueError( - "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." - ) - elif prompt is not None and (not isinstance(prompt, str) - and not isinstance(prompt, list)): - raise ValueError( - f"`prompt` has to be of type `str` or `list` but is {type(prompt)}" - ) - elif prompt_2 is not None and (not isinstance(prompt_2, str) - and not isinstance(prompt_2, list)): - raise ValueError( - f"`prompt_2` has to be of type `str` or `list` but is {type(prompt_2)}" - ) - - if negative_prompt is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - elif negative_prompt_2 is not None and negative_prompt_embeds is not None: - raise ValueError( - f"Cannot forward both `negative_prompt_2`: {negative_prompt_2} and `negative_prompt_embeds`:" - f" {negative_prompt_embeds}. Please make sure to only forward one of the two." - ) - - if prompt_embeds is not None and negative_prompt_embeds is not None: - if prompt_embeds.shape != negative_prompt_embeds.shape: - raise ValueError( - "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" - f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" - f" {negative_prompt_embeds.shape}.") - - if prompt_embeds is not None and pooled_prompt_embeds is None: - raise ValueError( - "If `prompt_embeds` are provided, `pooled_prompt_embeds` also have to be passed. Make sure to generate `pooled_prompt_embeds` from the same text encoder that was used to generate `prompt_embeds`." - ) - - if negative_prompt_embeds is not None and negative_pooled_prompt_embeds is None: - raise ValueError( - "If `negative_prompt_embeds` are provided, `negative_pooled_prompt_embeds` also have to be passed. Make sure to generate `negative_pooled_prompt_embeds` from the same text encoder that was used to generate `negative_prompt_embeds`." - ) - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents - def prepare_latents(self, - batch_size, - num_channels_latents, - height, - width, - dtype, - device, - generator, - latents=None): - shape = (batch_size, num_channels_latents, - height // self.vae_scale_factor, - width // self.vae_scale_factor) - if isinstance(generator, list) and len(generator) != batch_size: - raise ValueError( - f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" - f" size of {batch_size}. Make sure the batch size matches the length of the generators." - ) - - if latents is None: - latents = randn_tensor(shape, - generator=generator, - device=device, - dtype=dtype) - else: - latents = latents.to(device) - - # scale the initial noise by the standard deviation required by the scheduler - latents = latents * self.scheduler.init_noise_sigma - return latents - - def _get_add_time_ids(self, - original_size, - crops_coords_top_left, - target_size, - dtype, - text_encoder_projection_dim=None): - add_time_ids = list(original_size + crops_coords_top_left + target_size) - - passed_add_embed_dim = ( - self.unet.config.addition_time_embed_dim * len(add_time_ids) + - text_encoder_projection_dim) - expected_add_embed_dim = self.unet.add_embedding.linear_1.in_features - - if expected_add_embed_dim != passed_add_embed_dim: - raise ValueError( - f"Model expects an added time embedding vector of length {expected_add_embed_dim}, but a vector of {passed_add_embed_dim} was created. The model has an incorrect config. Please check `unet.config.time_embedding_type` and `text_encoder_2.config.projection_dim`." - ) - - add_time_ids = torch.tensor([add_time_ids], dtype=dtype) - return add_time_ids - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale.StableDiffusionUpscalePipeline.upcast_vae - def upcast_vae(self): - dtype = self.vae.dtype - self.vae.to(dtype=torch.float32) - use_torch_2_0_or_xformers = isinstance( - self.vae.decoder.mid_block.attentions[0].processor, - ( - AttnProcessor2_0, - XFormersAttnProcessor, - LoRAXFormersAttnProcessor, - LoRAAttnProcessor2_0, - ), - ) - # if xformers or torch_2_0 is used attention block does not need - # to be in float32 which can save lots of memory - if use_torch_2_0_or_xformers: - self.vae.post_quant_conv.to(dtype) - self.vae.decoder.conv_in.to(dtype) - self.vae.decoder.mid_block.to(dtype) - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.enable_freeu - def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): - r"""Enables the FreeU mechanism as in https://arxiv.org/abs/2309.11497. - - The suffixes after the scaling factors represent the stages where they are being applied. - - Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of the values - that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. - - Args: - s1 (`float`): - Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to - mitigate "oversmoothing effect" in the enhanced denoising process. - s2 (`float`): - Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to - mitigate "oversmoothing effect" in the enhanced denoising process. - b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. - b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. - """ - if not hasattr(self, "unet"): - raise ValueError("The pipeline must have `unet` for using FreeU.") - self.unet.enable_freeu(s1=s1, s2=s2, b1=b1, b2=b2) - - # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.disable_freeu - def disable_freeu(self): - """Disables the FreeU mechanism if enabled.""" - self.unet.disable_freeu() - - # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding - def get_guidance_scale_embedding(self, - w, - embedding_dim=512, - dtype=torch.float32): - """ - See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 - - Args: - timesteps (`torch.Tensor`): - generate embedding vectors at these timesteps - embedding_dim (`int`, *optional*, defaults to 512): - dimension of the embeddings to generate - dtype: - data type of the generated embeddings - - Returns: - `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` - """ - assert len(w.shape) == 1 - w = w * 1000.0 - - half_dim = embedding_dim // 2 - emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) - emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) - emb = w.to(dtype)[:, None] * emb[None, :] - emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) - if embedding_dim % 2 == 1: # zero pad - emb = torch.nn.functional.pad(emb, (0, 1)) - assert emb.shape == (w.shape[0], embedding_dim) - return emb - - @property - def guidance_scale(self): - return self._guidance_scale - - @property - def guidance_rescale(self): - return self._guidance_rescale - - @property - def clip_skip(self): - return self._clip_skip - - # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) - # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` - # corresponds to doing no classifier free guidance. - @property - def do_classifier_free_guidance(self): - return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None - - @property - def cross_attention_kwargs(self): - return self._cross_attention_kwargs - - @property - def denoising_end(self): - return self._denoising_end - - @property - def num_timesteps(self): - return self._num_timesteps - - @torch.no_grad() - @replace_example_docstring(EXAMPLE_DOC_STRING) - def __call__( - self, - prompt: Union[str, List[str]] = None, - prompt_2: Optional[Union[str, List[str]]] = None, - height: Optional[int] = None, - width: Optional[int] = None, - num_inference_steps: int = 50, - timesteps: List[int] = None, - denoising_end: Optional[float] = None, - guidance_scale: float = 5.0, - negative_prompt: Optional[Union[str, List[str]]] = None, - negative_prompt_2: Optional[Union[str, List[str]]] = None, - num_images_per_prompt: Optional[int] = 1, - eta: float = 0.0, - generator: Optional[Union[torch.Generator, - List[torch.Generator]]] = None, - latents: Optional[torch.FloatTensor] = None, - prompt_embeds: Optional[torch.FloatTensor] = None, - negative_prompt_embeds: Optional[torch.FloatTensor] = None, - pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - negative_pooled_prompt_embeds: Optional[torch.FloatTensor] = None, - ip_adapter_image: Optional[PipelineImageInput] = None, - output_type: Optional[str] = "pil", - return_dict: bool = True, - cross_attention_kwargs: Optional[Dict[str, Any]] = None, - guidance_rescale: float = 0.0, - original_size: Optional[Tuple[int, int]] = None, - crops_coords_top_left: Tuple[int, int] = (0, 0), - target_size: Optional[Tuple[int, int]] = None, - negative_original_size: Optional[Tuple[int, int]] = None, - negative_crops_coords_top_left: Tuple[int, int] = (0, 0), - negative_target_size: Optional[Tuple[int, int]] = None, - clip_skip: Optional[int] = None, - callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, - callback_on_step_end_tensor_inputs: List[str] = ["latents"], - **kwargs, - ): - r""" - Function invoked when calling the pipeline for generation. - - Args: - prompt (`str` or `List[str]`, *optional*): - The prompt or prompts to guide the image generation. If not defined, one has to pass `prompt_embeds`. - instead. - prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts to be sent to the `tokenizer_2` and `text_encoder_2`. If not defined, `prompt` is - used in both text-encoders - height (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The height in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - width (`int`, *optional*, defaults to self.unet.config.sample_size * self.vae_scale_factor): - The width in pixels of the generated image. This is set to 1024 by default for the best results. - Anything below 512 pixels won't work well for - [stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0) - and checkpoints that are not specifically fine-tuned on low resolutions. - num_inference_steps (`int`, *optional*, defaults to 50): - The number of denoising steps. More denoising steps usually lead to a higher quality image at the - expense of slower inference. - timesteps (`List[int]`, *optional*): - Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument - in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is - passed will be used. Must be in descending order. - denoising_end (`float`, *optional*): - When specified, determines the fraction (between 0.0 and 1.0) of the total denoising process to be - completed before it is intentionally prematurely terminated. As a result, the returned sample will - still retain a substantial amount of noise as determined by the discrete timesteps selected by the - scheduler. The denoising_end parameter should ideally be utilized when this pipeline forms a part of a - "Mixture of Denoisers" multi-pipeline setup, as elaborated in [**Refining the Image - Output**](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion/stable_diffusion_xl#refining-the-image-output) - guidance_scale (`float`, *optional*, defaults to 5.0): - Guidance scale as defined in [Classifier-Free Diffusion Guidance](https://arxiv.org/abs/2207.12598). - `guidance_scale` is defined as `w` of equation 2. of [Imagen - Paper](https://arxiv.org/pdf/2205.11487.pdf). Guidance scale is enabled by setting `guidance_scale > - 1`. Higher guidance scale encourages to generate images that are closely linked to the text `prompt`, - usually at the expense of lower image quality. - negative_prompt (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation. If not defined, one has to pass - `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is - less than `1`). - negative_prompt_2 (`str` or `List[str]`, *optional*): - The prompt or prompts not to guide the image generation to be sent to `tokenizer_2` and - `text_encoder_2`. If not defined, `negative_prompt` is used in both text-encoders - num_images_per_prompt (`int`, *optional*, defaults to 1): - The number of images to generate per prompt. - eta (`float`, *optional*, defaults to 0.0): - Corresponds to parameter eta (η) in the DDIM paper: https://arxiv.org/abs/2010.02502. Only applies to - [`schedulers.DDIMScheduler`], will be ignored for others. - generator (`torch.Generator` or `List[torch.Generator]`, *optional*): - One or a list of [torch generator(s)](https://pytorch.org/docs/stable/generated/torch.Generator.html) - to make generation deterministic. - latents (`torch.FloatTensor`, *optional*): - Pre-generated noisy latents, sampled from a Gaussian distribution, to be used as inputs for image - generation. Can be used to tweak the same generation with different prompts. If not provided, a latents - tensor will ge generated by sampling using the supplied random `generator`. - prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not - provided, text embeddings will be generated from `prompt` input argument. - negative_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input - argument. - pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. - If not provided, pooled text embeddings will be generated from `prompt` input argument. - negative_pooled_prompt_embeds (`torch.FloatTensor`, *optional*): - Pre-generated negative pooled text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt - weighting. If not provided, pooled negative_prompt_embeds will be generated from `negative_prompt` - input argument. - ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. - output_type (`str`, *optional*, defaults to `"pil"`): - The output format of the generate image. Choose between - [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`. - return_dict (`bool`, *optional*, defaults to `True`): - Whether or not to return a [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] instead - of a plain tuple. - cross_attention_kwargs (`dict`, *optional*): - A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under - `self.processor` in - [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). - guidance_rescale (`float`, *optional*, defaults to 0.0): - Guidance rescale factor proposed by [Common Diffusion Noise Schedules and Sample Steps are - Flawed](https://arxiv.org/pdf/2305.08891.pdf) `guidance_scale` is defined as `φ` in equation 16. of - [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). - Guidance rescale factor should fix overexposure when using zero terminal SNR. - original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - If `original_size` is not the same as `target_size` the image will appear to be down- or upsampled. - `original_size` defaults to `(height, width)` if not specified. Part of SDXL's micro-conditioning as - explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - `crops_coords_top_left` can be used to generate an image that appears to be "cropped" from the position - `crops_coords_top_left` downwards. Favorable, well-centered images are usually achieved by setting - `crops_coords_top_left` to (0, 0). Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - For most cases, `target_size` should be set to the desired height and width of the generated image. If - not specified it will default to `(height, width)`. Part of SDXL's micro-conditioning as explained in - section 2.2 of [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). - negative_original_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a specific image resolution. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_crops_coords_top_left (`Tuple[int]`, *optional*, defaults to (0, 0)): - To negatively condition the generation process based on a specific crop coordinates. Part of SDXL's - micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - negative_target_size (`Tuple[int]`, *optional*, defaults to (1024, 1024)): - To negatively condition the generation process based on a target image resolution. It should be as same - as the `target_size` for most cases. Part of SDXL's micro-conditioning as explained in section 2.2 of - [https://huggingface.co/papers/2307.01952](https://huggingface.co/papers/2307.01952). For more - information, refer to this issue thread: https://github.com/huggingface/diffusers/issues/4208. - callback_on_step_end (`Callable`, *optional*): - A function that calls at the end of each denoising steps during the inference. The function is called - with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, - callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by - `callback_on_step_end_tensor_inputs`. - callback_on_step_end_tensor_inputs (`List`, *optional*): - The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list - will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the - `._callback_tensor_inputs` attribute of your pipeline class. - - Examples: - - Returns: - [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] or `tuple`: - [`~pipelines.stable_diffusion_xl.StableDiffusionXLPipelineOutput`] if `return_dict` is True, otherwise a - `tuple`. When returning a tuple, the first element is a list with the generated images. - """ - - callback = kwargs.pop("callback", None) - callback_steps = kwargs.pop("callback_steps", None) - - if callback is not None: - deprecate( - "callback", - "1.0.0", - "Passing `callback` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", - ) - if callback_steps is not None: - deprecate( - "callback_steps", - "1.0.0", - "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider use `callback_on_step_end`", - ) - - # 0. Default height and width to unet - height = height or self.default_sample_size * self.vae_scale_factor - width = width or self.default_sample_size * self.vae_scale_factor - - original_size = original_size or (height, width) - target_size = target_size or (height, width) - - # 1. Check inputs. Raise error if not correct - self.check_inputs( - prompt, - prompt_2, - height, - width, - callback_steps, - negative_prompt, - negative_prompt_2, - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - callback_on_step_end_tensor_inputs, - ) - - self._guidance_scale = guidance_scale - self._guidance_rescale = guidance_rescale - self._clip_skip = clip_skip - self._cross_attention_kwargs = cross_attention_kwargs - self._denoising_end = denoising_end - - # 2. Define call parameters - if prompt is not None and isinstance(prompt, str): - batch_size = 1 - elif prompt is not None and isinstance(prompt, list): - batch_size = len(prompt) - else: - batch_size = prompt_embeds.shape[0] - - device = self.execution_device - - # 3. Encode input prompt - lora_scale = (self.cross_attention_kwargs.get("scale", None) - if self.cross_attention_kwargs is not None else None) - - ( - prompt_embeds, - negative_prompt_embeds, - pooled_prompt_embeds, - negative_pooled_prompt_embeds, - ) = self.encode_prompt( - prompt=prompt, - prompt_2=prompt_2, - device=device, - num_images_per_prompt=num_images_per_prompt, - do_classifier_free_guidance=self.do_classifier_free_guidance, - negative_prompt=negative_prompt, - negative_prompt_2=negative_prompt_2, - prompt_embeds=prompt_embeds, - negative_prompt_embeds=negative_prompt_embeds, - pooled_prompt_embeds=pooled_prompt_embeds, - negative_pooled_prompt_embeds=negative_pooled_prompt_embeds, - lora_scale=lora_scale, - clip_skip=self.clip_skip, - ) - - # 4. Prepare timesteps - timesteps, num_inference_steps = retrieve_timesteps( - self.scheduler, num_inference_steps, device, timesteps) - - # 5. Prepare latent variables - num_channels_latents = self.unet.config.in_channels - latents = self.prepare_latents( - batch_size * num_images_per_prompt, - num_channels_latents, - height, - width, - prompt_embeds.dtype, - device, - generator, - latents, - ) - - # 6. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline - extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) - - # 7. Prepare added time ids & embeddings - add_text_embeds = pooled_prompt_embeds - if self.text_encoder_2 is None: - text_encoder_projection_dim = int(pooled_prompt_embeds.shape[-1]) - else: - text_encoder_projection_dim = self.text_encoder_2.config.projection_dim - - add_time_ids = self._get_add_time_ids( - original_size, - crops_coords_top_left, - target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - if negative_original_size is not None and negative_target_size is not None: - negative_add_time_ids = self._get_add_time_ids( - negative_original_size, - negative_crops_coords_top_left, - negative_target_size, - dtype=prompt_embeds.dtype, - text_encoder_projection_dim=text_encoder_projection_dim, - ) - else: - negative_add_time_ids = add_time_ids - - if self.do_classifier_free_guidance: - prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds], - dim=0) - add_text_embeds = torch.cat( - [negative_pooled_prompt_embeds, add_text_embeds], dim=0) - add_time_ids = torch.cat([negative_add_time_ids, add_time_ids], - dim=0) - - prompt_embeds = prompt_embeds.to(device) - add_text_embeds = add_text_embeds.to(device) - add_time_ids = add_time_ids.to(device).repeat( - batch_size * num_images_per_prompt, 1) - - if ip_adapter_image is not None: - image_embeds, negative_image_embeds = self.encode_image( - ip_adapter_image, device, num_images_per_prompt) - if self.do_classifier_free_guidance: - image_embeds = torch.cat([negative_image_embeds, image_embeds]) - image_embeds = image_embeds.to(device) - - # 8. Denoising loop - num_warmup_steps = max( - len(timesteps) - num_inference_steps * self.scheduler.order, 0) - - # 8.1 Apply denoising_end - if (self.denoising_end is not None - and isinstance(self.denoising_end, float) - and self.denoising_end > 0 and self.denoising_end < 1): - discrete_timestep_cutoff = int( - round(self.scheduler.config.num_train_timesteps - - (self.denoising_end * - self.scheduler.config.num_train_timesteps))) - num_inference_steps = len( - list( - filter(lambda ts: ts >= discrete_timestep_cutoff, - timesteps))) - timesteps = timesteps[:num_inference_steps] - - # 9. Optionally get Guidance Scale Embedding - timestep_cond = None - if self.unet.config.time_cond_proj_dim is not None: - guidance_scale_tensor = torch.tensor(self.guidance_scale - - 1).repeat( - batch_size * - num_images_per_prompt) - timestep_cond = self.get_guidance_scale_embedding( - guidance_scale_tensor, - embedding_dim=self.unet.config.time_cond_proj_dim).to( - device=device, dtype=latents.dtype) - - self._num_timesteps = len(timesteps) - with self.progress_bar(total=num_inference_steps) as progress_bar: - for i, t in enumerate(timesteps): - # expand the latents if we are doing classifier free guidance - latent_model_input = torch.cat( - [latents] * - 2) if self.do_classifier_free_guidance else latents - - latent_model_input = self.scheduler.scale_model_input( - latent_model_input, t) - - # predict the noise residual - added_cond_kwargs = { - "text_embeds": add_text_embeds, - "time_ids": add_time_ids - } - if ip_adapter_image is not None: - added_cond_kwargs["image_embeds"] = image_embeds - - t = t.to(latent_model_input.dtype) - feed_dict = { - 'sample': latent_model_input, - 'timesteps': t.unsqueeze(0), - 'encoder_hidden_states': prompt_embeds, - 'text_embeds': add_text_embeds, - 'time_ids': add_time_ids, - } - ok = self.session.run(feed_dict, self.outputs, self.stream) - assert ok, "Runtime execution failed" - noise_pred = self.outputs['pred'] - torch.cuda.synchronize() - - # perform guidance - if self.do_classifier_free_guidance: - noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) - noise_pred = noise_pred_uncond + self.guidance_scale * \ - (noise_pred_text - noise_pred_uncond) - - if self.do_classifier_free_guidance and self.guidance_rescale > 0.0: - # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf - noise_pred = rescale_noise_cfg( - noise_pred, - noise_pred_text, - guidance_rescale=self.guidance_rescale) - - # compute the previous noisy sample x_t -> x_t-1 - latents = self.scheduler.step(noise_pred, - t, - latents, - **extra_step_kwargs, - return_dict=False)[0] - - if callback_on_step_end is not None: - callback_kwargs = {} - for k in callback_on_step_end_tensor_inputs: - callback_kwargs[k] = locals()[k] - callback_outputs = callback_on_step_end( - self, i, t, callback_kwargs) - - latents = callback_outputs.pop("latents", latents) - prompt_embeds = callback_outputs.pop( - "prompt_embeds", prompt_embeds) - negative_prompt_embeds = callback_outputs.pop( - "negative_prompt_embeds", negative_prompt_embeds) - add_text_embeds = callback_outputs.pop( - "add_text_embeds", add_text_embeds) - negative_pooled_prompt_embeds = callback_outputs.pop( - "negative_pooled_prompt_embeds", - negative_pooled_prompt_embeds) - add_time_ids = callback_outputs.pop("add_time_ids", - add_time_ids) - negative_add_time_ids = callback_outputs.pop( - "negative_add_time_ids", negative_add_time_ids) - - # call the callback, if provided - if i == len(timesteps) - 1 or ( - (i + 1) > num_warmup_steps and - (i + 1) % self.scheduler.order == 0): - progress_bar.update() - if callback is not None and i % callback_steps == 0: - step_idx = i // getattr(self.scheduler, "order", 1) - callback(step_idx, t, latents) - - if XLA_AVAILABLE: - xm.mark_step() - - if not output_type == "latent": - # make sure the VAE is in float32 mode, as it overflows in float16 - needs_upcasting = self.vae.dtype == torch.float16 and self.vae.config.force_upcast - - if needs_upcasting: - self.upcast_vae() - latents = latents.to( - next(iter(self.vae.post_quant_conv.parameters())).dtype) - - image = self.vae.decode(latents / self.vae.config.scaling_factor, - return_dict=False)[0] - - # cast back to fp16 if needed - if needs_upcasting: - self.vae.to(dtype=torch.float16) - else: - image = latents - - if not output_type == "latent": - # apply watermark if available - if self.watermark is not None: - image = self.watermark.apply_watermark(image) - - image = self.image_processor.postprocess(image, - output_type=output_type) - - # Offload all models - self.maybe_free_model_hooks() - - if not return_dict: - return (image, ) - - return StableDiffusionXLPipelineOutput(images=image) diff --git a/examples/models/contrib/sdxl/run_sdxl.py b/examples/models/contrib/sdxl/run_sdxl.py deleted file mode 100755 index 077b93f9ab7e..000000000000 --- a/examples/models/contrib/sdxl/run_sdxl.py +++ /dev/null @@ -1,96 +0,0 @@ -import argparse -import time - -import numpy as np -import torch -from pipeline_stable_diffusion_xl import StableDiffusionXLPipeline - -import tensorrt_llm - -world_size = tensorrt_llm.mpi_world_size() -rank = tensorrt_llm.mpi_rank() - - -def parseArgs(): - parser = argparse.ArgumentParser( - description='run SDXL with the UNet TensorRT engine.') - parser.add_argument('--model_dir', - type=str, - default='stabilityai/stable-diffusion-xl-base-1.0') - parser.add_argument('--size', type=int, default=1024) - parser.add_argument('--seed', type=int, default=233) - parser.add_argument('--num_inference_steps', type=int, default=50) - parser.add_argument( - '--prompt', - type=str, - default= - "masterpiece, gouache painting, 1girl, distant view, lone boat, willow trees" - ) - parser.add_argument('--engine_dir', - type=str, - default=None, - help='engine directory') - parser.add_argument('--num-warmup-runs', type=int, default=3) - parser.add_argument('--avg-runs', type=int, default=10) - parser.add_argument("--ignore_ratio", - type=float, - default=0.2, - help="Ignored ratio of the slowest and fastest steps") - parser.add_argument("--output", - type=str, - default="output.png", - help="Output file name") - return parser.parse_args() - - -if __name__ == "__main__": - args = parseArgs() - model_dir = args.model_dir - size = args.size - seed = args.seed - prompt = args.prompt - num_inference_steps = args.num_inference_steps - engine_dir = f'sdxl_s{size}_w{world_size}' if args.engine_dir is None else args.engine_dir - num_warmup_runs = args.num_warmup_runs - avg_runs = args.avg_runs - output_file = args.output - - pipeline = StableDiffusionXLPipeline.from_pretrained( - model_dir, - torch_dtype=torch.float16, - use_safetensors=True, - ) - pipeline.set_progress_bar_config(disable=rank != 0) - pipeline.prepare(engine_dir, size) - pipeline.to('cuda') - - # warm up - for i in range(num_warmup_runs): - image = pipeline( - num_inference_steps=num_inference_steps, - prompt=prompt, - generator=torch.Generator(device="cuda").manual_seed(seed), - height=size, - width=size).images[0] - - latency_list = [] - for i in range(avg_runs): - st = time.time() - image = pipeline( - num_inference_steps=num_inference_steps, - prompt=prompt, - generator=torch.Generator(device="cuda").manual_seed(seed), - height=size, - width=size, - ).images[0] - ed = time.time() - latency_list.append(ed - st) - - latency_list = sorted(latency_list) - ignored_count = int(args.ignore_ratio * len(latency_list) / 2) - if ignored_count > 0: - latency_list = latency_list[ignored_count:-ignored_count] - - if rank == 0: - print(f"Avg latency: {np.sum(latency_list) / len(latency_list):.5f} s") - image.save(output_file) diff --git a/examples/models/contrib/skywork/README.md b/examples/models/contrib/skywork/README.md deleted file mode 100644 index 59b22a861987..000000000000 --- a/examples/models/contrib/skywork/README.md +++ /dev/null @@ -1,113 +0,0 @@ -# Skywork - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document elaborates how to build the [Skywork](https://huggingface.co/Skywork/) model to runnable engines on single GPU node and perform a summarization task using these engines. - -## Overview -The TensorRT LLM Skywork implementation is based on the LLaMA model. The implementation can -be found in [tensorrt_llm/models/llama/model.py](../../../../tensorrt_llm/models/llama/model.py). -The TensorRT LLM Skywork example code lies in [`examples/models/contrib/skywork`](./): - -* [`convert_checkpoint.py`](../../core/llama/convert_checkpoint.py) converts the Huggingface Model of Skywork into TensorRT LLM checkpoint. - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - * FP16 & BF16 - -## Usage - -This section gives a whole process where we convert HF models, build TensorRT LLM engines and ultimately perform summarization. - -### 1. Clone Code and Weights from Huggingface - -To download checkpoints from HF, you need to have `git-lfs` installed in your machine: - -```bash -pip install -r requirements.txt && sudo apt-get install git-lfs -``` - -Then clone the HF repository with: - -```bash -# Skywork 13B Base Model -git clone https://huggingface.co/Skywork/Skywork-13B-base -``` - -### 2. Convert HF Model to TRT Checkpoint - -```bash -cd examples/models/core/llama - -# fp16 model -python3 convert_checkpoint.py --model_dir ./Skywork-13B-base \ - --dtype float16 \ - --output_dir ./skywork-13b-base/trt_ckpt/fp16 - -# bf16 model -python3 convert_checkpoint.py --model_dir ./Skywork-13B-base \ - --dtype bfloat16 \ - --output_dir ./skywork-13b-base/trt_ckpt/bf16 -``` - -### 3. Build TensorRT Engine(s) - -```bash -# fp16 -trtllm-build --checkpoint_dir ./skywork-13b-base/trt_ckpt/fp16 \ - --gemm_plugin float16 \ - --gpt_attention_plugin float16 \ - --context_fmha enable \ - --max_batch_size 32 \ - --max_input_len 512 \ - --max_seq_len 1024 \ - --output_dir ./skywork-13b-base/trt_engine/fp16 - -# bf16 -trtllm-build --checkpoint_dir ./skywork-13b-base/trt_ckpt/bf16 \ - --gemm_plugin bfloat16 \ - --gpt_attention_plugin bfloat16 \ - --context_fmha enable \ - --max_batch_size 32 \ - --max_input_len 512 \ - --max_seq_len 1024 \ - --output_dir ./skywork-13b-base/trt_engine/bf16 -``` - -### 4. Summarization using the Engines - -After building TRT engines, we can use them to perform various tasks. TensorRT LLM provides handy code to run summarization on [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset and get [ROUGE](https://en.wikipedia.org/wiki/ROUGE_(metric)) scores. The `ROUGE-1` score can be used to validate model implementations. - -```bash -# fp16 -python ../../../summarize.py --hf_model_dir ./Skywork-13B-base \ - --test_hf \ - --batch_size 32 \ - --max_input_length 512 \ - --output_len 512 \ - --test_trt_llm \ - --engine_dir ./skywork-13b-base/trt_engine/fp16 \ - --data_type fp16 \ - --check_accuracy \ - --tensorrt_llm_rouge1_threshold=14 - -# bf16 -python ../../../summarize.py --hf_model_dir ./Skywork-13B-base \ - --test_hf \ - --batch_size 32 \ - --max_input_length 512 \ - --output_len 512 \ - --test_trt_llm \ - --engine_dir ./skywork-13b-base/trt_engine/bf16 \ - --data_type bf16 \ - --check_accuracy \ - --tensorrt_llm_rouge1_threshold=14 -``` diff --git a/examples/models/contrib/skywork/requirements.txt b/examples/models/contrib/skywork/requirements.txt deleted file mode 100644 index 88232baef811..000000000000 --- a/examples/models/contrib/skywork/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -evaluate -rouge_score -sentencepiece>=0.1.99 diff --git a/examples/models/contrib/smaug/README.md b/examples/models/contrib/smaug/README.md deleted file mode 100644 index 2d96da5854e1..000000000000 --- a/examples/models/contrib/smaug/README.md +++ /dev/null @@ -1,62 +0,0 @@ -# Smaug - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document elaborates how to build the [Smaug-72B-v0.1](https://huggingface.co/abacusai/Smaug-72B-v0.1) model to runnable engines on multi-GPU node and perform a summarization task using these engines. - -## Overview - -The TensorRT LLM support for Smaug-72B-v0.1 is based on the LLaMA model, the implementation can be found in [tensorrt_llm/models/llama/model.py](../../../../tensorrt_llm/models/llama/model.py). Smaug model resembles LLaMA very much except it uses bias term in its attention module, we therefore reuse the [LLaMA example code](../../core/llama) for Smaug, - -* [`convert_checkpoint.py`](./convert_checkpoint.py) to convert the LLaMA model into TensorRT LLM checkpoint format. - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`../../../run.py`](../../../run.py) to run the inference on an input text; -* [`../../../summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - -* FP16 - -## Usage - -This section gives a whole process where we convert HF models, build TensorRT LLM engines and ultimately perform summarization. - -### Build TensorRT engine(s) - -Run the following commands and TRT-LLM will first transforms a HF model into its own checkpoint format, then builds a TRT engine based on the checkpoint - -```bash -python ../../../llama/convert_checkpoint.py \ - --model_dir ./Smaug-72B-v0.1 \ - --output_dir ./tllm_checkpoint_8gpu_tp8 \ - --dtype float16 \ - --tp_size 8 - -trtllm-build --checkpoint_dir ./tllm_checkpoint_8gpu_tp8 \ - --output_dir ./Smaug_72B_tp8 \ - --gemm_plugin float16 \ - --gpt_attention_plugin float16 \ - --context_fmha=enable \ - --max_batch_size 64 \ - --remove_input_padding=enable -``` - -### Run Summarization - -After building TRT engine, we can use it to perform various tasks. TensorRT LLM provides handy code to run summarization on [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset and get [ROUGE](https://en.wikipedia.org/wiki/ROUGE_(metric)) scores. The `ROUGE-1` score can be used to validate model implementations. - -```bash -mpirun -n 8 -allow-run-as-root python ../../../summarize.py \ - --hf_model_dir ../Smaug-72B-v0.1 \ - --engine_dir ./Smaug_72B_tp8 \ - --data_type fp16 \ - --test_hf \ - --hf_device_map_auto \ - --test_trt_llm -``` diff --git a/examples/models/contrib/smaug/requirements.txt b/examples/models/contrib/smaug/requirements.txt deleted file mode 100644 index 88232baef811..000000000000 --- a/examples/models/contrib/smaug/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -datasets==3.1.0 -evaluate -rouge_score -sentencepiece>=0.1.99 diff --git a/examples/models/core/granite/README.md b/examples/models/core/granite/README.md deleted file mode 100644 index b6cf34c50c5f..000000000000 --- a/examples/models/core/granite/README.md +++ /dev/null @@ -1,89 +0,0 @@ -# Granite - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document shows how to build and run a [Granite 3.0](https://huggingface.co/collections/ibm-granite/granite-30-language-models-66fdb59bbb54785c3512114f) model in TensorRT-LLM. - -The TensorRT LLM Granite implementation is based on the LLaMA model, with Mixture of Experts (MoE) enabled. The implementation can be found in [`llama/model.py`](../../../../tensorrt_llm/models/llama/model.py). See the LLaMA example [`examples/models/core/llama`](../llama) for details. - -- [Granite 3.0](#Granite) - - [Download model checkpoints](#download-model-checkpoints) - - [Convert weights from HF Transformers to TensorRT LLM format](#Convert-weights-from-HF-Transformers-to-TensorRT-LLM-format) - - [Build TensorRT engine](#build-tensorrt-engine) - - [Run Engine](#run-engine) - -## Download model checkpoints - -First, download the HuggingFace BF16 checkpoints of Granite 3.0 model. - -```bash -HF_MODEL="granite-3.0-8b-instruct" # or granite-3.0-3b-a800m-instruct -# clone the model we want to build -git clone https://huggingface.co/ibm-granite/${HF_MODEL} tmp/hf_checkpoints/${HF_MODEL} -``` - -## Convert weights from HF Transformers to TensorRT LLM format -Set environment variables and necessary directory: - -```bash -PREC_RAW="bfloat16" -TP=1 -mkdir -p tmp/trt_engines -``` - -### BF16 -Convert the weights using the `convert_checkpoint.py` script: - -```bash -ENGINE="${HF_MODEL}_${PREC_RAW}_tp${TP}" -export TRTLLM_DISABLE_UNIFIED_CONVERTER=1 # The current checkpoint conversion code requires legacy path -python3 ../llama/convert_checkpoint.py --model_dir tmp/hf_checkpoints/${HF_MODEL} \ - --output_dir tmp/tllm_checkpoints/${ENGINE} \ - --dtype ${PREC_RAW} \ - --tp_size ${TP} \ - --use_embedding_sharing - - -``` -### FP8 PTQ -Notes: -- Currently quantize.py does not support Expert Parallelism (EP) mode yet. User should use `../llama/convert_checkpoint.py` and specify `--moe_ep_size 1` instead, if needed. -- TensorRT LLM uses static quantization methods, which is expected to be faster at runtime as compared to dynamic quantization methods. This comes at a cost of an offline calibration step during quantization. `batch_size` and `calib_size` can be adjusted to shorten the calibration time. Please refer to `../../../quantization/README.md` for explanation. - -```bash -PREC_QUANT="fp8" -ENGINE="${HF_MODEL}_${PREC_QUANT}_tp${TP}" -python ../../../quantization/quantize.py --model_dir tmp/hf_checkpoints/${HF_MODEL} \ - --dtype ${PREC_RAW} \ - --qformat ${PREC_QUANT} \ - --kv_cache_dtype ${PREC_QUANT} \ - --output_dir tmp/tllm_checkpoints/${ENGINE} \ - --batch_size 1 \ - --calib_size 128 \ - --tp_size ${TP} - -``` - -## Build TensorRT engine -```bash -# Enable fp8 context fmha to get further acceleration by setting `--use_fp8_context_fmha enable` -# Use --workers to enable parallel build -trtllm-build --checkpoint_dir ./tmp/tllm_checkpoints/${ENGINE} \ - --output_dir ./tmp/trt_engines/${ENGINE} \ - --gpt_attention_plugin ${PREC_RAW} \ - --gemm_plugin ${PREC_RAW} \ - --workers ${TP} -``` - -## Run Engine -Test your engine with the [run.py](../../../run.py) script: - -```bash -mpirun -n ${TP} --allow-run-as-root python ../../../run.py --engine_dir ./tmp/trt_engines/${ENGINE} --tokenizer_dir tmp/hf_checkpoints/${HF_MODEL} --max_output_len 20 --input_text "The future of AI is" -``` - -For more usage examples see [`examples/models/core/llama/README.md`](../llama/README.md) diff --git a/examples/models/core/mixtral/README.md b/examples/models/core/mixtral/README.md deleted file mode 100644 index 079753f88c92..000000000000 --- a/examples/models/core/mixtral/README.md +++ /dev/null @@ -1,219 +0,0 @@ -# Mixtral - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -This document shows how to build and run a Mixtral model in TensorRT LLM on both single GPU, single node multi-GPU and -multi-node multi-GPU. Mixtral 8x22B is also supported and can be replace Mixtral 8x7B below as long as GPU memory is -sufficient. - -## Overview - -The TensorRT LLM Mixtral implementation is based on the LLaMA model, with Mixture of Experts enabled. The implementation can -be found in [tensorrt_llm/models/llama/model.py](../../../../tensorrt_llm/models/llama/model.py). -See the LLaMA example [`examples/models/core/llama`](../llama) for details. - -### Build TensorRT engine(s) - -#### Download Mixtral 8x7b weights -Get the weights by downloading from HF https://huggingface.co/mistralai/Mixtral-8x7B-v0.1. -See also https://huggingface.co/docs/transformers/main/en/model_doc/mixtral - -```bash -git lfs install -git clone https://huggingface.co/mistralai/Mixtral-8x7B-v0.1 -``` - -#### Download Mixtral 8x22b weights -Get the weights by downloading from HF https://huggingface.co/mistralai/Mixtral-8x22B-v0.1. -See also https://huggingface.co/docs/transformers/main/en/model_doc/mixtral - -```bash -git lfs install -git clone https://huggingface.co/mistralai/Mixtral-8x22B-v0.1 -``` - -We use the LLaMA `convert_checkpoint.py` script to convert and build the model. TensorRT LLM LLaMA builds TensorRT engine(s) from HF checkpoint provided by `--model_dir`. -If no checkpoint directory is specified, TensorRT LLM will build engine(s) with dummy weights. - -`trtllm-build` uses one GPU by default, but if you have already more GPUs available at build time, -you may enable parallel builds to make the engine building process faster by adding the `--workers` argument. - -Here are some examples: - -```bash -# Build Mixtral8x7B with pipeline parallelism -python ../llama/convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \ - --output_dir ./tllm_checkpoint_mixtral_2gpu \ - --dtype float16 \ - --pp_size 2 -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \ - --output_dir ./trt_engines/mixtral/pp2 \ - --gemm_plugin float16 - -``` - -```bash -# Build Mixtral8x7B with tensor parallelism -python ../llama/convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \ - --output_dir ./tllm_checkpoint_mixtral_2gpu \ - --dtype float16 \ - --tp_size 2 \ - --moe_tp_size 2 -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \ - --output_dir ./trt_engines/mixtral/tp2 \ - --gemm_plugin float16 - - -# Build Mixtral8x22B with tensor parallelism and expert parallelism -python ../llama/convert_checkpoint.py --model_dir ./Mixtral-8x22B-v0.1 \ - --output_dir ./tllm_checkpoint_mixtral_8gpu \ - --dtype float16 \ - --tp_size 8 \ - --moe_tp_size 2 \ - --moe_ep_size 4 -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_8gpu \ - --output_dir ./trt_engines/mixtral/tp2ep4 \ - --gemm_plugin float16 -``` - -Then, you can test your engine with the [run.py](../../../run.py) script: - -```bash -mpirun -n 2 python3 ../../../run.py --engine_dir ./trt_engines/mixtral/tp2 --tokenizer_dir ./Mixtral-8x7B-v0.1 --max_output_len 8 --input_text "I love french quiche" -``` - -For more examples see [`examples/models/core/llama/README.md`](../llama/README.md) - -### Parallelism Modes - -Mixture of Experts supports 3 parallelism modes, these are Expert Parallelism (EP), Tensor Parallelism (TP), and the hybrid of the two (TP+EP). - -In TP mode (default) expert weight matrices are sliced evenly between all GPUs, so that all GPUs work together to calculate the result for each expert. - -In EP mode each GPU is assigned a subset of the expert weights matrices, so each GPU works independently to calculate the result for its assigned experts. This may cause load balancing issues where some GPUs have more work than others, thus increasing latency. - -In TP+EP mode, both strategies are used simultaneously. This means each GPU handles a portion of the expert weights matrices (as in EP mode) and these weights are further sliced across multiple GPUs (as in TP mode). This hybrid approach aims to balance the workload more evenly across GPUs, enhancing efficiency and reducing the likelihood of bottlenecks associated with EP mode alone. - -You can enable Expert Parallel or hybrid parallel by setting `--moe_tp_size` and `--moe_ep_size` when calling `convert_coneckpoint.py`. If only `--moe_tp_size` is provided, TRT-LLM will use Tensor Parallel for the MoE model; if only `--moe_ep_size` is provided, TRT-LLM will use Expert Parallel; if both are provided, the hybrid parallel will be used. - -Be sure that the product of `moe_tp_size` and `moe_ep_size` should equal to `tp_size`, since the total number of MoE parallelism across all GPUs must match the total number of parallelism in other parts of the model. - -```bash -# Build Mixtral8x7B with Expert Parallelism -python ../llama/convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \ - --output_dir ./tllm_checkpoint_mixtral_2gpu \ - --dtype float16 \ - --tp_size 2 \ - --moe_ep_size 2 -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \ - --output_dir ./trt_engines/mixtral/ep2 \ - --gemm_plugin float16 - -# Build Mixtral8x7B with Expert Parallelism and Tensor Parallelism -python ../llama/convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \ - --output_dir ./tllm_checkpoint_mixtral_4gpu \ - --dtype float16 \ - --tp_size 4 \ - --moe_tp_size 2 \ - --moe_ep_size 2 -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_4gpu \ - --output_dir ./trt_engines/mixtral/tp2ep2 \ - --gemm_plugin float16 -``` - -### Normalization Modes - -MOE Supports different normalization modes which influence how the scales are calculated for the final weighted sum in -of the different top-k values. - -- 0 (NONE) corresponds to: `scales = topk(softmax(routing values))` -- 1 (RENORM) corresponds to: `scales = softmax(topk(routing values))` -- 2 (SPARSE_MIXER) corresponds to: `scales = sparsemixer(routing values)` - -Mixtral uses `RENORM` mode, this is set as the default. To use a different mode use the `--moe_normalization_mode` flag. -See [tensorrt_llm/layers/moe.py](../../../../tensorrt_llm/layers/moe.py#L56) for available values - - -## Quantization - -### Weight-only Quantization - -Mixtral supports weight only quantization - -```bash -# Build Mixtral8x7B with weight only -python ../llama/convert_checkpoint.py --model_dir ./Mixtral-8x7B-v0.1 \ - --output_dir ./tllm_checkpoint_mixtral_2gpu \ - --dtype float16 \ - --tp_size 2 \ - --use_weight_only \ - --weight_only_precision int8 -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \ - --output_dir ./trt_engines/mixtral/tp2 \ - --gemm_plugin float16 -``` - -### FP8 Post-Training Quantization - -Mixtral supports FP8 quantization, using Modelopt. See [`examples/models/core/llama/README.md`](../llama/README.md#fp8-post-training-quantization) for full details on installing Modelopt - -```bash -# Quantize HF Mixtral into FP8 and export trtllm checkpoint -python ../../../quantization/quantize.py --model_dir ./Mixtral-8x7B-v0.1 \ - --dtype float16 \ - --qformat fp8 \ - --kv_cache_dtype fp8 \ - --output_dir ./tllm_checkpoint_mixtral_2gpu \ - --calib_size 512 \ - --tp_size 2 - -# Build trtllm engines from the trtllm checkpoint -# Enable fp8 context fmha to get further acceleration by setting `--use_fp8_context_fmha enable` -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_2gpu \ - --output_dir ./engine_outputs \ - --workers 2 -``` - -### AWQ Quantization - -Mixtral supports AWQ quantization using [AutoAWQ](https://github.com/casper-hansen/AutoAWQ). - -```bash -# Convert AutoAWQ HF checkpoints into TRT-LLM checkpoint -python ../llama/convert_checkpoint.py --model_dir ./tmp/mixtral-8x7b-v0.1-AWQ/ \ - --output_dir ./tllm_checkpoint_mixtral_awq_1gpu - -# Build trtllm engines from the trtllm checkpoint -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_awq_1gpu \ - --output_dir ./engine_outputs -``` - -You may found `quant_algo = W4A16_GPTQ` in the configuration file of the converted checkpoints, and that's because AutoAWQ is using exactly the same components as GPTQ. - -### NVFP4 Post-Training Quantization - -Mixtral supports NVFP4 quantization. - -```bash -# Quantize HF Mixtral into FP8 and export trtllm checkpoint -python ../../../quantization/quantize.py --model_dir ./Mixtral-8x7B-v0.1 \ - --dtype float16 \ - --qformat nvfp4 \ - --kv_cache_dtype fp8 \ - --output_dir ./tllm_checkpoint_mixtral_nvfp4_1gpu \ - --calib_size 512 \ - --tp_size 1 - -# Build trtllm engines from the trtllm checkpoint -# Enable fp8 context fmha to get further acceleration by setting `--use_fp8_context_fmha enable` -trtllm-build --checkpoint_dir ./tllm_checkpoint_mixtral_nvfp4_1gpu \ - --output_dir ./engine_outputs -``` - -## OOTB - -Mixtral supports OOTB operation without the plugin, however this comes at a significant performance cost. Users should prefer using the plugin path whenever possible diff --git a/examples/models/core/mixtral/requirements.txt b/examples/models/core/mixtral/requirements.txt deleted file mode 100644 index c1b98e5d3f7a..000000000000 --- a/examples/models/core/mixtral/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -transformers==4.56.0 -accelerate==0.25.0 diff --git a/examples/models/core/multimodal/README.md b/examples/models/core/multimodal/README.md index 159e26d4c7d8..924561126cfb 100644 --- a/examples/models/core/multimodal/README.md +++ b/examples/models/core/multimodal/README.md @@ -1,1169 +1,17 @@ # Multi-Modal -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. +The engine-build multimodal workflow that used to live here +(`build_multimodal_engine.py` / `run.py` / `eval.py` on top of +`trtllm-build`) was removed together with the legacy TensorRT backend. -This document shows how to run multimodal pipelines with TensorRT-LLM, e.g. from image+text input modalities to text output. +Multimodal models are supported on the PyTorch backend. See: -Multimodal models' LLM part has an additional parameter `--max_multimodal_len` compared to LLM-only build commands. Under the hood, `max_multimodal_len` and `max_prompt_embedding_table_size` are effectively the same concept, i.e., prepended/concatenated embeddings (either multimodal feature embeddings or prompt tuning embeddings) to the LLM input embeddings. The multimodal features from the visual encoder of shape `[batch_size, num_visual_features, visual_hidden_dim]` is flattened as `[batch_size * num_visual_features, visual_hidden_dim]` and passed like a prompt embedding table. - -We first describe three runtime modes for running multimodal models and how to run each model on a single GPU. We then provide general guidelines on using tensor parallelism for the LLM part of the pipeline. - -- [Runtime Mode](#runtime-modes) -- [BLIP2](#blip2) -- [CogVLM](#cogvlm) -- [Deplot](#deplot) -- [Fuyu](#fuyu) -- [Gemma3](#gemma3) -- [InternLM-XComposer2](#internlm-xcomposer2) -- [InternVL2](#internvl2) -- [Kosmos-2](#kosmos-2) -- [LLaVA, LLaVa-NeXT, LLaVA-OneVision and VILA](#llava-llava-next-llava-onevision-and-vila) -- [MLLaMA](#mllama) -- [NeVA](#neva) -- [Nougat](#nougat) -- [Phi-3-vision](#phi-3-vision) -- [Phi-4-multimodal](#phi-4-multimodal) -- [Qwen2-VL](#qwen2-vl) -- [Qwen-Image-Bench Evaluator](#qwen-image-bench-evaluator) -- [Video NeVA](#video-neva) -- [Dataset Evaluation](#dataset-evaluation) -- [Enabling Tensor Parallelism for multi-GPU](#enabling-tensor-parallelism-for-multi-gpu) -- [Enabling Embedding Table Offloading](#enabling-embedding-table-offloading) - -## Runtime Modes -TensorRT LLM supports three runtime modes for running multimodal models. -- `cpp_llm_only` (default): vision engine runs in python runtime, LLM in pybind C++ runtime -- `python`: everything runs in python runtime -- `cpp`: everything runs in C++ runtime - -This can be specified by the `--session RUNTIME_MODE` argument in `run.py` (see instructions of each model below). -Not all models supports end-to-end `cpp` mode, the checked ones below are supported. See footnotes for reasons models are unsupported -- [ ] BLIP-2-T5 [^1] -- [x] BLIP-2-OPT -- [x] CogVLM -- [ ] Deplot [^1] -- [ ] Pix2Struct [^1] -- [x] Fuyu -- [x] InternVL2-2b -- [x] Kosmos-2 -- [x] LLaVA -- [ ] LLaVA-NeXT / OneVision [^2] -- [x] VILA [^3] -- [ ] Mllama [^1] -- [x] NeVA -- [ ] Nougat [^1] -- [ ] Phi-3-Vision [^2] -- [ ] Phi-4-multimodal -- [ ] Qwen2-VL [^4] -- [x] Video-NeVA - - -[^1]: Model uses cross attention to feed visiual features to LLM decoder, which is not supported -[^2]: Model requires post processing its encoder output features, which is not supported -[^3]: Currently C++ runtime only supports single image per request (VILA mode 2) -[^4]: Vision encoder requires additional inputs not supported by the C++ runtime - -## BLIP2 - -This BLIP section covers both BLIP2-OPT and BLIP2-T5, with minor changes needed when switching the LLM backbone. - -1. Download Huggingface weights and convert original checkpoint to TRT-LLM checkpoint format - following example in `examples/models/contrib/opt/README.md` and `examples/models/core/enc_dec/README.md`. - - ```bash - export MODEL_NAME="blip2-opt-2.7b" # options: blip2-opt-6.7b, blip2-flan-t5-xl, blip2-flan-t5-xxl - git clone https://huggingface.co/Salesforce/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - - For BLIP2-OPT family, - ```bash - python ../../contrib/opt/convert_checkpoint.py --model_type blip2 \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 - ``` - - For BLIP2-T5 family, - ```bash - python ../enc_dec/convert_checkpoint.py --model_type blip2 \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/bfloat16 \ - --tp_size 1 \ - --pp_size 1 \ - --dtype bfloat16 - ``` - -2. Build TRT-LLM engine from TRT-LLM checkpoint - - For BLIP2-OPT family, - ```bash - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin float16 \ - --max_beam_width 1 \ - --max_batch_size 8 \ - --max_seq_len 1024 \ - --max_input_len 924 \ - --max_multimodal_len 256 # 8 (max_batch_size) * 32 (num_visual_features) - ``` - - For BLIP2-T5 family, - ```bash - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/encoder \ - --output_dir tmp/trt_engines/${MODEL_NAME}/bfloat16/llm/encoder \ - --paged_kv_cache disable \ - --moe_plugin disable \ - --gemm_plugin bfloat16 \ - --bert_attention_plugin bfloat16 \ - --gpt_attention_plugin bfloat16 \ - --remove_input_padding enable \ - --context_fmha disable \ - --max_beam_width 1 \ - --max_batch_size 8 \ - --max_input_len 924 \ - --max_multimodal_len 256 # 8 (max_batch_size) * 32 (num_visual_features) - - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/decoder \ - --output_dir tmp/trt_engines/${MODEL_NAME}/bfloat16/llm/decoder \ - --paged_kv_cache disable \ - --moe_plugin disable \ - --gemm_plugin bfloat16 \ - --bert_attention_plugin bfloat16 \ - --gpt_attention_plugin bfloat16 \ - --remove_input_padding enable \ - --context_fmha disable \ - --max_beam_width 1 \ - --max_batch_size 8 \ - --max_seq_len 1024 \ - --max_encoder_input_len 924 \ - --max_input_len 1 # Same command for decoder but don't set --max_multimodal_len - ``` - - **NOTE**: `max_multimodal_len = max_batch_size * num_visual_features`, so if you change max_batch_size, max multimodal length **MUST** be changed accordingly. - -3. Build TensorRT engines for vision encoders - - ```bash - python build_multimodal_engine.py --model_type blip2 --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/bfloat16/vision --max_batch_size 8 - ``` - - The built engines are located in `tmp/trt_engines/${MODEL_NAME}/bfloat16/vision` for BLIP2-T5, similarly for BLIP-OPT. - - To run the BLIP2 pipeline with batch size > 1, change `--max_batch_size` argument to `build_multimodal_engine.py` accordingly. - -4. Assemble everything into BLIP2 pipeline - - For BLIP2-OPT family, - ```bash - python run.py \ - --max_new_tokens 30 \ - --input_text "Question: which city is this? Answer:" \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu - ``` - - For BLIP2-T5 family, - ```bash - python run.py \ - --max_new_tokens 30 \ - --input_text "Question: which city is this? Answer:" \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/bfloat16 - ``` - -5. (Optional) INT8/INT4 weight-only quantization for OPT can be enabled using commands as follows (take `INT4` as an example, while `INT8` is the default precision for weight-only quantization): - ```bash - python ../../contrib/opt/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --dtype float16 \ - --output_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \ - --use_weight_only \ - --weight_only_precision int4 - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/int4_weightonly/1-gpu/llm \ - --gemm_plugin float16 \ - --max_beam_width 1 \ - --max_batch_size 8 \ - --max_multimodal_len 256 \ - --max_input_len 924 \ - --max_seq_len 1024 - ``` - - The built OPT engines lie in `tmp/trt_engines/${MODEL_NAME}/int4_weightonly/1-gpu/llm`. - You should use this directory without the `llm` part as `--engine_dir` argument to `run.py` - - **NOTE:** INT8/INT4 option is not supported for BLIP2-T5, because quantization support has not been - added for encoder-decoder models yet. - -## CogVLM - -Currently, CogVLM only support bfloat16 precision. - -1. Download Huggingface weights - - ```bash - export MODEL_NAME="cogvlm-chat-hf" - git clone https://huggingface.co/THUDM/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - export TOKENIZER_NAME="vicuna-7b-v1.5" - git clone https://huggingface.co/lmsys/${TOKENIZER_NAME} tmp/hf_models/${TOKENIZER_NAME} - ``` - - Because currently onnx doesn't support `xops.memory_efficient_attention`, we need to modify some source code of the huggingface CogVLM. - ``` - cd tmp/hf_models/${MODEL_NAME} - sed -i '4s/.*//;40s/.*/ out = self.attention(q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2)).transpose(1, 2).contiguous()/;41s/.*//;42s/.*//' visual.py # It will replace memory_efficient_attention with some basic ops - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in `examples/cogvlm` - - CogVLM uses a Vit encoder as LLM encoder and a modified Llama as decoder. - - ```bash - python ../../contrib/cogvlm/convert_checkpoint.py --model_dir tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_models/${MODEL_NAME} --dtype bfloat16 --use_prompt_tuning - - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME} \ - --output_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu/llm \ - --gemm_plugin bfloat16 \ - --gpt_attention_plugin bfloat16 \ - --remove_input_padding enable \ - --max_batch_size 48 \ - --max_input_len 2048 \ - --max_seq_len 3076 \ - --paged_kv_cache enable \ - --bert_attention_plugin disable \ - --moe_plugin disable \ - --max_multimodal_len 61440 # 48 (max_batch_size) * 1280 (max_num_visual_features) - ``` - -3. Generate TensorRT engines for visual components and combine everything into final pipeline. - - ```bash - python build_multimodal_engine.py --model_type cogvlm --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 48 --output_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu/vision - - python run.py \ - --max_new_tokens 1000 \ - --input_text " [INST] please describe this image in detail [/INST] " \ - --hf_model_dir tmp/hf_models/${TOKENIZER_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu \ - --batch_size 1 \ - --top_p 0.4 \ - --top_k 1 \ - --temperature 0.2 \ - --repetition_penalty 1.2 \ - --enable_context_fmha_fp32_acc - - CogVLM uses model_runner_cpp for its LLM decoder by default. To switch to model_runner, set `--session python` in the command mentioned above. - ``` - -## Deplot - -1. Download Huggingface weights and convert original checkpoint to TRT-LLM checkpoint format - following example in `examples/models/core/enc_dec/README.md`. - - ```bash - export MODEL_NAME="deplot" - git clone https://huggingface.co/google/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - - python ../enc_dec/convert_checkpoint.py --model_type pix2struct \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/float16 \ - --tp_size 1 \ - --pp_size 1 \ - --dtype float16 - ``` - -2. Build TRT-LLM engine from TRT-LLM checkpoint - - ```bash - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/float16/decoder \ - --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/float16/llm/decoder \ - --paged_kv_cache disable \ - --moe_plugin disable \ - --gemm_plugin float16 \ - --bert_attention_plugin float16 \ - --gpt_attention_plugin float16 \ - --remove_input_padding enable \ - --context_fmha disable \ - --max_beam_width 1 \ - --max_batch_size 8 \ - --max_seq_len 2558 \ - --max_encoder_input_len 2048 \ - --max_input_len 1 - ``` - - The built deplot engines are located in `tmp/trt_engines/${MODEL_NAME}/1-gpu/float16`. - -3. Build TensorRT engines for visual components - - ```bash - python build_multimodal_engine.py --model_type pix2struct --model_path tmp/hf_models/${MODEL_NAME} --max_batch_size 8 --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/float16/vision - ``` - - The built visual engines are located in `tmp/trt_engines/${MODEL_NAME}/1-gpu/float16/vision`. - - To run the deplot pipeline with batch size > 1, change `--max_batch_size` argument to `build_multimodal_engine.py` accordingly. - -4. Assemble everything into deplot pipeline - - ```bash - python run.py \ - --max_new_tokens 100 \ - --input_text "" \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/float16 - ``` - -## Fuyu - -1. Download Huggingface weights - - ```bash - export MODEL_NAME="fuyu-8b" - git clone https://huggingface.co/adept/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in `examples/models/core/gpt`. - The LLM portion of Fuyu uses a Persimmon model - ```bash - python ../gpt/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 \ - --gpt_variant persimmon - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin float16 \ - --use_fused_mlp=enable \ - --max_batch_size 1 \ - --max_input_len 2048 \ - --max_seq_len 2560 \ - --max_multimodal_len 2048 - ``` - -3. Generate TensorRT engines for visual components and combine everything into final pipeline. - - ```bash - python build_multimodal_engine.py --model_type fuyu --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision - - python run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu - ``` - -## Gemma3 - -**NOTE: We only support Gemma3 VLMs in Pytorch workflow.** - -Gemma3VL decoder requires a custom attention mask while processing images. During the context phase: -- Text tokens attend to other tokens in a causal fashion (standard autoregressive behavior) -- Image tokens attend to other tokens in a causal fashion AND attend to other tokens from the same image in a bidirectional manner - -**Reference:** [Gemma3 Model Documentation](https://huggingface.co/docs/transformers/en/model_doc/gemma3) - -We support this custom mask with FlashInfer attention backend. - -### Requirements - -To ensure expected behavior with Gemma3VL, the following configurations are **required**: -- **Attention Backend**: Use the FlashInfer attention backend -- **Chunked Prefill**: Must be disabled -- **KV Cache Reuse**: Must be disabled - -### Quick Start - -#### 1. Download Model Weights - -```bash -export MODEL_NAME="gemma-3-27b-it" -git clone https://huggingface.co/google/${MODEL_NAME} -``` - -#### 2. Interactive Testing - -Use the `quickstart_multimodal.py` script for quick testing: - -```bash -python3 examples/llm-api/quickstart_multimodal.py \ - --model_dir ${MODEL_NAME}/ \ - --modality image \ - --image_format pil \ - --attention_backend FLASHINFER \ - --disable_kv_cache_reuse -``` - -#### 3. Model Serving - -Serve the model using `trtllm-serve` with the required llmapi arguments mentioned in a yaml file: - -```bash -# Create the configuration file -cat > extra-llm-api-options.yaml << 'EOF' -cuda_graph_config: null -attn_backend: "FLASHINFER" -enable_chunked_prefill: false -kv_cache_config: - enable_block_reuse: false -EOF - -# Serve the model -trtllm-serve ${MODEL_NAME}/ \ - --backend pytorch \ - --tp_size 1 \ - --port 8000 \ - --max_batch_size 4 \ - --config extra-llm-api-options.yaml -``` - -### Supported Model Variants - -Currently supported Gemma3 variants: 4B, 12B, 27B - - -## InternLM-XComposer2 - -**NOTE: We only support InternLM-XComposer-VL-7b for now** - -Firstly, please install transformers with 4.45.2 -```bash - pip install -r requirements-internlm-xcomposer2.txt -``` - -1. Convert Huggingface weights to TRT-LLM checkpoint format using `examples/models/contrib/internlm/README.md`. - -2. Use `trtllm-build` command to build TRT-LLM engine for OPT. - -3. The full list of commands is as follows: - - ```bash - export MODEL_NAME=internlm-xcomposer2-vl-7b - git lfs clone https://huggingface.co/internlm/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - - python ../internlm2/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --dtype float16 \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin float16 \ - --lora_plugin float16 \ - --lora_dir . \ - --max_lora_rank 256 \ - --max_input_len 1536 \ - --max_batch_size 48 \ - --max_multimodal_len 58800 # 58800 = 1225(visual token/img) * 48 (max_batch_size), as each image corresponds to 1225 visual tokens in the ViT here - - python build_multimodal_engine.py \ - --model_type internlm-xcomposer2 \ - --model_path tmp/hf_models/${MODEL_NAME} \ - --output_dir trt_engines/${MODEL_NAME}/fp16/1-gpu/vision \ - --max_batch_size 48 - - python run.py \ - --max_new_tokens 200 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --batch_size 1 - ``` - -## InternVL2 - -[InternVL Family](https://github.com/OpenGVLab/InternVL): Closing the Gap to Commercial Multimodal Models with Open-Source Suites —— A Pioneering Open-Source Alternative to GPT-4o. Here we show how to deploy InternVL2‑1B/InternVL2‑2B/InternVL2‑4B/InternVL2‑8B/InternVL2‑26B in TensorRT-LLM. - -Firstly, please install transformers with 4.37.2 -```bash - pip install transformers==4.37.2 -``` - -1. Download Huggingface weights - - For InternVL2-1B - ```bash - export MODEL_NAME="InternVL2-1B" - git clone https://huggingface.co/OpenGVLab/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - export LLM_MODEL_NAME="qwen" - ``` - - - For InternVL2-2B/InternVL2‑8B/InternVL2‑26B - ```bash - export MODEL_NAME="InternVL2-2B" # or InternVL2‑8B, InternVL2‑26B - git clone https://huggingface.co/OpenGVLab/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - export LLM_MODEL_NAME="internlm2" - ``` - - - For InternVL2-4B - ```bash - export MODEL_NAME="InternVL2-4B" - git clone https://huggingface.co/OpenGVLab/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - export LLM_MODEL_NAME="phi" - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints - ```bash - python ../${LLM_MODEL_NAME}/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 - ``` - -3. Build TRT engines - ```bash - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin auto \ - --max_batch_size 1 \ - --max_input_len 4096 \ - --max_seq_len 4608 \ - --max_multimodal_len 3328 - ``` - -4. Generate TensorRT engines for visual components and combine everything into final pipeline. - ```bash - python build_multimodal_engine.py --model_type internvl --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision - python run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/ \ - --image_path tmp/hf_models/${MODEL_NAME}/examples/image1.jpg - ``` - -5. (Optional) FP8 and INT8 SmoothQuant quantization is supported for the InternVL2-4B variant (LLM model only). - - ```bash - # FP8 quantization - python ../../../quantization/quantize.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp8/1-gpu \ - --dtype bfloat16 \ - --qformat fp8 \ - --kv_cache_dtype fp8 - - # INT8 SmoothQuant quantization - python ../../../quantization/quantize.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/int8/1-gpu \ - --dtype bfloat16 \ - --qformat int8_sq - ``` - - Then follow the same `trtllm-build`, `build_multimodal_engine.py` and `run.py` steps as before. - - -## Kosmos-2 - -1. Download Huggingface weights - - ```bash - export MODEL_NAME="kosmos-2" - git clone https://huggingface.co/microsoft/kosmos-2-patch14-224 tmp/hf_models/${MODEL_NAME} - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in `examples/models/core/gpt`. - ```bash - python ../gpt/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 \ - --gpt_variant ${MODEL_NAME} - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gpt_attention_plugin float16 \ - --gemm_plugin float16 \ - --max_batch_size 1 \ - --max_input_len 512 \ - --max_seq_len 1024 \ - --max_multimodal_len 64 # 1 (max_batch_size) * 64 (num_visual_features) - ``` - -3. Generate TensorRT engines for visual components and combine everything into final pipeline. - - ```bash - python build_multimodal_engine.py --model_type kosmos-2 --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision - - python run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu - ``` - -## LLaVA, LLaVa-NeXT, LLaVA-OneVision and VILA - -[LLaVA](https://github.com/haotian-liu/LLaVA) and [VILA](https://github.com/Efficient-Large-Model/VILA) are both visual language models (VLM) that can be deployed in TensorRT LLM with many quantization options. [LLaVA-NeXT](https://huggingface.co/collections/llava-hf/llava-next-65f75c4afac77fd37dbbe6cf) is an extension of LLaVA. TRT-LLM currently supports [Mistral-7b](https://huggingface.co/llava-hf/llava-v1.6-mistral-7b-hf) and [ Nous-Hermes-2-Yi-34B](https://huggingface.co/llava-hf/llava-v1.6-34b-hf) variant of LLaVA-NeXT. [LLaVA-OneVision](https://huggingface.co/collections/llava-hf/llava-onevision-66bb1e9ce8856e210a7ed1fe) is another extension of LLaVA. - -1. Download Huggingface model weights. These models have both visual and LLM components - unlike BLIP2 example which downloads only LLM components from Huggingface. - - For LLaVA, - - ```bash - export MODEL_NAME="llava-1.5-7b-hf" # also llava-1.5-13b-hf - git clone https://huggingface.co/llava-hf/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - For LLaVA-NeXT, - - ```bash - export MODEL_NAME="llava-v1.6-mistral-7b-hf" #for 34b variant "llava-v1.6-34b-hf" - git clone https://huggingface.co/llava-hf/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - - For LLaVA-OneVision, - - ```bash - export MODEL_NAME="llava-onevision-qwen2-7b-ov-hf" # also llava-onevision-qwen2-0.5b-ov-hf, llava-onevision-qwen2-72b-ov-hf, etc - git clone https://huggingface.co/llava-hf/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - - For VILA, we need a few more steps until it is added to HF model zoo - - ```bash - # install the following dependency - pip install -r requirements-vila.txt - - # clone original VILA repo - export VILA_PATH="tmp/hf_models/VILA" - git clone https://github.com/Efficient-Large-Model/VILA.git ${VILA_PATH} - - # download VILA checkpoints - export MODEL_NAME="vila1.5-3b" # NOTE: name must contain vila or VILA! it's used to identify whether we need to register the non-HF VILA codebase in HF Auto class - git clone https://huggingface.co/Efficient-Large-Model/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - -2. Generate TRT-LLM engine for LLaMA following example in `examples/models/core/llama/README.md` and `examples/models/core/qwen/README.md` - - ```bash - python ../llama/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 - - # for LLaVA - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin float16 \ - --use_fused_mlp=enable \ - --max_batch_size 1 \ - --max_input_len 2048 \ - --max_seq_len 2560 \ - --max_multimodal_len 576 # 1 (max_batch_size) * 576 (num_visual_features) - - # for LLaVA-NeXT - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gpt_attention_plugin float16 \ - --gemm_plugin float16 \ - --use_fused_mlp=enable \ - --max_batch_size 1 \ - --max_input_len 4096 \ - --max_seq_len 5120 \ - --max_num_tokens 4096 \ - --max_multimodal_len 4096 # 1 (max_batch_size) * 4096 (max_input_len) - - # for VILA - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin float16 \ - --use_fused_mlp=enable \ - --max_batch_size 1 \ - --max_input_len 2048 \ - --max_seq_len 2560 \ - --max_multimodal_len 196 # 1 (max_batch_size) * 196 (num_visual_features) - - # for LLaVA-OneVision - python ../qwen/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin float16 \ - --use_fused_mlp=enable \ - --max_batch_size 1 \ - --max_input_len 7228 \ - --max_seq_len 7328 \ - --max_multimodal_len 7128 # max_batch_size * num_visual_features(depends on the image size or the specified video num frame) - ``` - -3. Build TensorRT engines for visual components - - ```bash - python build_multimodal_engine.py --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision --model_type llava # for LLaVA - - python build_multimodal_engine.py --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision --model_type llava_next --max_batch_size 5 # 1 (max_batch_size) * 5 (because LLAVA-NeXT visual encoder can have at most 5 patches) # for LLaVA-NeXT - - python build_multimodal_engine.py --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision --model_type llava_onevision --max_batch_size 32 # max_batch_size * patch for image or frame for video # for LLaVA-OneVision - - python build_multimodal_engine.py --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision --model_type vila --vila_path ${VILA_PATH} # for VILA - ``` - - ```bash - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --input_text "\n Which city is this?" # for LLaVA and for LLaVA-NeXT - - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --image_path=https://github.com/Efficient-Large-Model/VILA/raw/main/demo_images/av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \ - --input_text "\n Please elaborate what you see in the images?","\n Which city is this?" \ - --batch_size=2 # for LLaVA - ``` - - Note that Llava can support N pairs inference batching, `--batch_size=N` should be used. There should be N images listed under `--image_path` and N text prompts listed under `--input_text`. Don't forget to set the `--max_batch_size` and `--max_multimodal_len` during engine building. - - For LLaVA-OneVision, you can use either image or video as inputs. - ```bash - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --input_text "What is shown in this image?" \ - --image_path image.png - - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --input_text "Why is this video funny?" \ - --video_path video.mp4 - --video_num_frames 8 # sample uniformly 8 frames from the video, up to 32 frames - ``` - - For VILA, you can use either local file or web url as input images. - Suppose you have a local image `av.png` downloaded from `https://github.com/Efficient-Large-Model/VILA/blob/main/demo_trt_llm/av.png` and the url of `merlion.png` - ```bash - wget -O av.png https://raw.githubusercontent.com/Efficient-Large-Model/VILA/main/demo_images/av.png - - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --image_path=av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \ - --input_text="\n\n Please elaborate what you see in the images?" \ - --batch_size=1 # for VILA mode 1 - - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu \ - --image_path=av.png,https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png \ - --input_text="\n Please elaborate what you see in the images?","\n Which city is this?" \ - --batch_size=2 \ - --check_accuracy # for VILA mode 2 - ``` - - Note that VILA can support different modes in terms of batching: - - Mode 1: if you want to query N images as a whole using a prompt, `--batch_size=1` should be used (which is the default value). Example is given above. - - Mode 2: if you want to query N pairs, `--batch_size=N` should be used. There should be N images listed under `--image_path` and N text prompts listed under `--input_text`. Don't forget to set the `--max_batch_size` and `--max_multimodal_len` during engine building. - - Note: use `--run_profiling` for performance measurement, use `--check_accuracy` for accuracy check. - -4. (Optional) Different quantization methods supported in LLaMA and Qwen can be applied to LLaVA/VILA/LLaVA-OneVision as well, such as INT4/INT8 weight-only, SmoothQuant, and INT4 Activation-Aware Quantization (AWQ). Detailed instructions can be found in LLaMA [README](../llama/README.md) and Qwen [README](../qwen/README.md). - - For example, - - ```bash - # INT4 weight only - python ../llama/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --dtype float16 \ - --output_dir tmp/trt_models/${MODEL_NAME}/int4_weightonly/1-gpu \ - --use_weight_only \ - --weight_only_precision int4 - - # INT4 AWQ - python ../../../quantization/quantize.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/int4_awq/1-gpu \ - --dtype float16 \ - --qformat int4_awq \ - --calib_size 32 - ``` - - Then follow the same `trtllm-build` and `run.py` steps as before. NOTE: for `trtllm-build` command, do not use `--use_fused_mlp=enable` in these quantization modes. - -## MLLaMA - -This section shows how to build and run a LLaMA-3.2 Vision model in TensorRT-LLM. We use [Llama-3.2-11B-Vision/](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision) as an example. - -For LLaMA-3.2 text model, please refer to the [examples/models/core/llama/README.md](../llama/README.md) because it shares the model architecture of llama. - -### Support data types - * BF16 - * Tensor Parallel - * INT8 & INT4 Weight-Only - * FP8 - -### Build and run vision model - -* build engine of vision encoder model - -```bash -python examples/models/core/multimodal/build_multimodal_engine.py --model_type mllama \ - --model_path Llama-3.2-11B-Vision/ \ - --output_dir /tmp/mllama/trt_engines/vision/ -``` - -* build engine of decoder model - -```bash -python examples/models/core/mllama/convert_checkpoint.py --model_dir Llama-3.2-11B-Vision/ \ - --output_dir /tmp/mllama/trt_ckpts \ - --dtype bfloat16 - -trtllm-build --checkpoint_dir /tmp/mllama/trt_ckpts \ - --output_dir /tmp/mllama/trt_engines/llm/ \ - --max_num_tokens 4096 \ - --max_seq_len 2048 \ - --workers 1 \ - --gemm_plugin auto \ - --max_batch_size 4 \ - --max_encoder_input_len 4100 \ - --input_timing_cache model.cache -``` - -Note that for instruct Vision model, please set the `max_encoder_input_len` as `6404`. - -* Run test on multimodal/run.py with C++ runtime (LLM part only) - -```bash -python3 examples/models/core/multimodal/run.py --engine_dir /tmp/mllama/trt_engines/ \ - --hf_model_dir Llama-3.2-11B-Vision/ \ - --image_path https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg \ - --input_text "<|image|><|begin_of_text|>If I had to write a haiku for this one" \ - --max_new_tokens 50 \ - --batch_size 2 - -Use model_runner_cpp by default. To switch to model_runner, set `--session python` in the command mentioned above. - -python3 examples/models/core/multimodal/eval.py \ - --engine_dir /tmp/mllama/trt_engines/ \ - --hf_model_dir Llama-3.2-11B-Vision/ \ - --test_trtllm \ - --accuracy_threshold 65 \ - --eval_task lmms-lab/ai2d -``` - -### Run MLLaMA decoder part by FP8 - -```bash -# install modelopt 0.21.0 -pip install nvidia-modelopt[torch]~=0.21.0 - -python ./examples/quantization/quantize.py --model_dir Llama-3.2-11B-Vision/ \ - --dtype bfloat16 \ - --qformat fp8 \ - --output_dir /tmp/llama-3.2-11B-Vision/fp8/ \ - --kv_cache_dtype fp8 \ - --calib_size 512 \ - --calib_dataset scienceqa - -trtllm-build --checkpoint_dir /tmp/llama-3.2-11B-Vision/fp8/ \ - --output_dir /tmp/trt_engines/llama-3.2-11B-Vision/fp8/llm \ - --max_num_tokens 4096 \ - --max_seq_len 2048 \ - --workers 1 \ - --gemm_plugin auto \ - --max_batch_size 4 \ - --max_encoder_input_len 4100 \ - --input_timing_cache model.cache \ - --use_paged_context_fmha enable \ - --use_fp8_context_fmha enable - -# copy visiual engine directory `/tmp/mllama/trt_engines/vision/` to fp8 engine directory `/tmp/trt_engines/llama-3.2-11B-Vision/fp8/vision` - -python3 examples/models/core/multimodal/run.py --engine_dir /tmp/trt_engines/llama-3.2-11B-Vision/fp8/ \ - --hf_model_dir Llama-3.2-11B-Vision/ \ - --image_path https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg \ - --input_text "<|image|><|begin_of_text|>If I had to write a haiku for this one" \ - --max_new_tokens 50 \ - --batch_size 2 - -python3 examples/models/core/multimodal/eval.py --engine_dir /tmp/trt_engines/llama-3.2-11B-Vision/fp8/ \ - --hf_model_dir Llama-3.2-11B-Vision/ \ - --test_trtllm \ - --accuracy_threshold 65 \ - --eval_task lmms-lab/ai2d -``` - -Note that for instruct Vision model, please set the `max_encoder_input_len` as `6404`. - -## NeVA - -[NeVA](https://docs.nvidia.com/nemo-framework/user-guide/24.12/nemotoolkit/multimodal/mllm/neva.html) is a groundbreaking addition to the NeMo Multimodal ecosystem. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM. - -1. Generate TRT-LLM engine for NVGPT following example in `examples/models/core/gpt/README.md`. To adhere to the NVGPT conventions of the conversion script, some layer keys have to be remapped using `--nemo_rename_key`. - - ```bash - export MODEL_NAME="neva" - python ../gpt/convert_checkpoint.py \ - --nemo_ckpt_path ./${MODEL_NAME}.nemo \ - --dtype bfloat16 \ - --output_dir tmp/trt_models/${MODEL_NAME} \ - --nemo_rename_key model:model.language_model \ - attention.linear_qkv.layer_norm_bias:input_layernorm.bias \ - attention.linear_qkv.layer_norm_weight:input_layernorm.weight \ - mlp.linear_fc1.layer_norm_bias:post_attention_layernorm.bias \ - mlp.linear_fc1.layer_norm_weight:post_attention_layernorm.weight \ - linear_qkv:query_key_value \ - linear_fc1:dense_h_to_4h \ - linear_fc2:dense_4h_to_h \ - linear_proj:dense \ - decoder:encoder - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME} \ - --output_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu/llm \ - --gpt_attention_plugin bfloat16 \ - --gemm_plugin bfloat16 \ - --max_batch_size 1 \ - --max_input_len 2048 \ - --max_seq_len 2560 \ - --max_multimodal_len 729 # 1 (max_batch_size) * 729 (num_visual_features) - ``` - -2. Build TensorRT engines for visual components - - ```bash - python build_multimodal_engine.py --model_path ./${MODEL_NAME}.nemo --model_type neva --output_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu/vision - ``` - - ```bash - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/trt_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/bf16/1-gpu \ - --input_text "Question: which city is this? Answer:" - ``` - - Note: use `--run_profiling` for performance measurement, use `--check_accuracy` for accuracy check. - -## Nougat - -1. Download Huggingface weights - - ```bash - export MODEL_NAME="nougat-base" # also nougat-small - git clone https://huggingface.co/facebook/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in `examples/models/core/enc_dec` - - Nougat uses mBART architecture but replaces the LLM encoder with a Swin Transformer encoder. - To achieve this, we add an extra `--nougat` flag (over mBART example) to - `convert_checkpoint.py` in `examples/models/core/enc_dec` and `trtllm-build`. - - ```bash - python ../enc_dec/convert_checkpoint.py --model_type bart \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/bfloat16 \ - --tp_size 1 \ - --pp_size 1 \ - --dtype bfloat16 \ - --nougat - - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/bfloat16/decoder \ - --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/llm/decoder \ - --paged_kv_cache disable \ - --moe_plugin disable \ - --gemm_plugin bfloat16 \ - --bert_attention_plugin bfloat16 \ - --gpt_attention_plugin bfloat16 \ - --remove_input_padding enable \ - --max_beam_width 1 \ - --max_batch_size 1 \ - --max_seq_len 101 \ - --max_input_len 1 \ - --max_encoder_input_len 588 # 1 (max_batch_size) * 588 (num_visual_features) - ``` - -3. Generate TensorRT engines for visual components and combine everything into final pipeline. - - ```bash - python build_multimodal_engine.py --model_type nougat --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16/vision - - python run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/1-gpu/bfloat16 - ``` - - Note: Nougat models usually do not need a text prompt. - - -## Phi-3-vision - -1. Download Huggingface weights - - ```bash - export MODEL_NAME="Phi-3-vision-128k-instruct" # or Phi-3.5-vision-instruct - git clone https://huggingface.co/microsoft/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in `examples/models/core/phi`. - ```bash - python ../phi/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gpt_attention_plugin float16 \ - --gemm_plugin float16 \ - --max_batch_size 1 \ - --max_input_len 4096 \ - --max_seq_len 4608 \ - --max_multimodal_len 4096 - ``` - -3. Generate TensorRT engines for visual components and combine everything into final pipeline. - - ```bash - python build_multimodal_engine.py --model_type phi-3-vision --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision - - python run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --kv_cache_free_gpu_memory_fraction 0.7 \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/ \ - --image_path=https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png - ``` -## Phi-4-multimodal -Navigate to the folder `TensorRT-LLM/examples/models/core/multimodal` - -1. Download Huggingface weights - - ```bash - export MODEL_NAME="Phi-4-multimodal-instruct" - export HF_DIR="tmp/hf_models/${MODEL_NAME}" - export CKPT_DIR="tmp/trt_models/${MODEL_NAME}/fp16/1-gpu" - export ENGINE_DIR="tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu" - git clone https://huggingface.co/microsoft/${MODEL_NAME} ${HF_DIR} - - ``` - -2. Convert Huggingface weights into TRT-LLM checkpoints and build TRT engines using scripts in `examples/models/core/phi`. - ```bash - python ../phi/convert_checkpoint.py \ - --model_dir ${HF_DIR} \ - --output_dir ${CKPT_DIR} \ - --dtype float16 - - trtllm-build \ - --checkpoint_dir ${CKPT_DIR} \ - --output_dir ${ENGINE_DIR} \ - --gpt_attention_plugin float16 \ - --gemm_plugin float16 \ - --max_batch_size 1 \ - --max_input_len 4096 \ - --max_seq_len 4608 \ - --max_multimodal_len 4096 - ``` - -3. Generate TensorRT engines for visual components and combine everything into final pipeline. -*Note: the encoders are not the TRT engines but are pure Pytorch ones* - - ```bash - python build_multimodal_engine.py --model_type phi-4-multimodal --model_path ${HF_DIR} --output_dir ${ENGINE_DIR} - - python run.py \ - --hf_model_dir ${HF_DIR} \ - --kv_cache_free_gpu_memory_fraction 0.7 \ - --engine_dir ${ENGINE_DIR} \ - --image_path=https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png - --audio_path=${HF_DIR}/examples/what_is_shown_in_this_image.wav - ``` -## Qwen2-VL -[Qwen2-VL Family](https://github.com/QwenLM/Qwen2-VL): is the latest version of the vision language models in the Qwen model families. Here we show how to deploy Qwen2-VL 2B and 7B in TensorRT-LLM. - -Firstly, please install transformers and qwen-vl-utils -```bash -pip install -r requirements-qwen2vl.txt -``` -### Support data types - * FP16 - * FP8 - -### Build and run vision model -* Download Huggingface weights - ```bash - export MODEL_NAME="Qwen2-VL-7B-Instruct" # or Qwen2-VL-2B-Instruct - git clone https://huggingface.co/Qwen/${MODEL_NAME} tmp/hf_models/${MODEL_NAME} - ``` -* Build engine of decoder model - - ```bash - python3 ../qwen/convert_checkpoint.py \ - --model_dir=tmp/hf_models/${MODEL_NAME} \ - --output_dir=tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --dtype float16 - - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/llm \ - --gemm_plugin=float16 \ - --gpt_attention_plugin=float16 \ - --max_batch_size=4 \ - --max_input_len=2048 \ - --max_seq_len=3072 \ - --max_multimodal_len=1296 #(max_batch_size) * 324 (num_visual_features), this's for image_shape=[504,504] - ``` - -* Build engine of vision encoder model - ```bash - python build_multimodal_engine.py --model_type qwen2_vl --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision - ``` - -* Run test on multimodal/run.py with C++ runtime (LLM part only) - ```bash - python3 run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/ - ``` -### Run Qwen2-VL decoder part by FP8 -* Build engine - ```bash - python ./examples/quantization/quantize.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --dtype float16 \ - --qformat fp8 \ - --kv_cache_dtype fp8 \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp8/1-gpu \ - --calib_size 512 - - trtllm-build --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp8/1-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp8/1-gpu/llm \ - --max_input_len=2048 \ - --max_seq_len 3072 \ - --gemm_plugin auto \ - --max_batch_size 4 \ - --max_multimodal_len=1296 - - # copy visiual engine directory `tmp/trt_engines/${MODEL_NAME}/fp16/1-gpu/vision/` to fp8 engine directory `tmp/trt_engines/${MODEL_NAME}/fp8/1-gpu/vision` - ``` -* Run test on multimodal/run.py with C++ runtime (LLM part only) - ```bash - python3 run.py \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp8/1-gpu/ - ``` +- [Supported models](https://nvidia.github.io/TensorRT-LLM/models/supported-models.html) +- [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) and the + [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) +- Multimodal serving examples under `examples/llm-api/` and + `examples/serve/` ## Qwen-Image-Bench Evaluator @@ -1183,153 +31,3 @@ python examples/models/core/multimodal/qwen_image_bench_eval.py \ The script evaluates all five Qwen-Image-Bench level-1 dimensions by default: Quality, Aesthetics, Alignment, Real-world Fidelity, and Creative Generation. - -## Video NeVA - -[Video NeVA](https://github.com/NVIDIA/NeMo/blob/main/docs/source/multimodal/mllm/video_neva.rst) is a groundbreaking addition to the NeMo Multimodal ecosystem that could work with video modality. This model seamlessly integrates large language-centric models with a vision encoder, that can be deployed in TensorRT-LLM. - -1. Generate TRT-LLM engine for Nemotron model following example in `examples/models/core/nemotron/README.md`. To adhere to the NVGPT conventions of the conversion script. This will be used as our base LM for inference. - - ```bash - pip install decord # used for loading video - - python3 ../../../quantization/quantize.py \ - --nemo_ckpt_path /path/to/nemotron/model.nemo \ - --dtype bfloat16 \ - --batch_size 64 \ - --qformat full_prec \ - --output_dir nemotron-3/trt_ckpt/bf16/1-gpu - - - trtllm-build \ - --checkpoint_dir nemotron-3/trt_ckpt/bf16/1-gpu \ - --output_dir tmp/trt_engines/nemotron-3/bf16/1-gpu/llm \ - --gpt_attention_plugin bfloat16 \ - --gemm_plugin bfloat16 \ - --max_batch_size 1 \ - --max_input_len 4096 \ - --max_seq_len 4352 \ - --max_multimodal_len 3072 # 1 (max_batch_size) * (12 num_frames) * (256 image_token_len) - ``` - -2. Build TensorRT engines for visual components - - ```bash - python build_multimodal_engine.py --model_path /path/to/video/neva/projector.nemo --model_type video-neva --output_dir tmp/trt_engines/nemotron-3/visual_encoder --output_dir tmp/trt_engines/nemotron-3/bf16/1-gpu/vision - ``` - - ```bash - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir nemotron-3/trt_ckpt/bf16/1-gpu \ - --engine_dir tmp/trt_engines/nemotron-3/bf16/1-gpu \ - --input_text "Question: what is in the video? Answer:" \ - --video_path /path/to/your/local/video/file - ``` - - Note: use `--run_profiling` for performance measurement, use `--check_accuracy` for accuracy check. - -## Dataset Evaluation - -This section explains how to evaluate datasets using our provided script, including supported models and configurations. - -### Evaluation Command -To run an evaluation, use the following command: - -```bash -python ./examples/models/core/multimodal/eval.py \ - --model_type \ - --engine_dir \ - --hf_model_dir \ - --dataset_dir \ - --test_trtllm (or --test_hf, or both) \ - --accuracy_threshold \ - --eval_task \ - --max_ite 20 \ - --visual_engine_name -``` - -### Parameters -- `--model_type`: Specify the model type to evaluate. -- `--engine_dir`: Path to the model engines directory. -- `--hf_model_dir`: Path to the Hugging Face model directory. -- `--dataset_dir`: Path to the dataset directory. If not specified, will load the dataset from HF with the `--eval_task` as dataset tag. -- `--test_trtllm` or `--test_hf`: Specify which evaluation framework to use. Both can be used simultaneously. -- `--accuracy_threshold`: Set the accuracy threshold for evaluation. -- `--eval_task`: Specify the evaluation task. Supported tasks: `['lmms-lab/ai2d', 'lmms-lab/VQAv2', 'lmms-lab/MME']`. Default to `'lmms-lab/VQAv2'`. -- `--max_ite`: Maximum number of iterations, default to 20. -- `--visual_engine_name`: Name of the visual engine. - -### Supported Evaluation Tasks -The following evaluation tasks are supported: -- `lmms-lab/ai2d` -- `lmms-lab/VQAv2` -- `lmms-lab/MME` - -### Supported Model Types -The script supports the following model types: -- `blip2` -- `fuyu` -- `kosmos-2` -- `llava` -- `llava_next` -- `llava_onevision` -- `phi-3-vision` -- `qwen2_vl` -- `mllama` -- `vila` -- `cogvlm` -- `neva` -- `internvl` - -**Note:** The models `vila`, `cogvlm`, `neva`, and `internvl` do not support the `--test_hf` evaluation framework. - -## Enabling Tensor Parallelism for multi-GPU - -The LLM part of the pipeline can be run on multiple GPUs using tensor parallelism. -The visual encoder will be replicated on each GPU and operate in a data parallel fashion. - -To enable tensor parallelism, both weight conversion step (from Huggingface to FT format) -and engine building step should use additional arguments. Finally `run.py` should be prefixed -with `mpirun -n NUM_GPUS --allow-run-as-root`. - -The full set of commands to enable 2-way tensor parallelism for LLaVA is: - - ```bash - export MODEL_NAME="llava-1.5-7b-hf" - - python ../llama/convert_checkpoint.py \ - --model_dir tmp/hf_models/${MODEL_NAME} \ - --output_dir tmp/trt_models/${MODEL_NAME}/fp16/2-gpu \ - --dtype float16 --tp_size 2 - - trtllm-build \ - --checkpoint_dir tmp/trt_models/${MODEL_NAME}/fp16/2-gpu \ - --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/2-gpu/llm \ - --gemm_plugin float16 \ - --max_batch_size 1 \ - --max_input_len 2048 \ - --max_seq_len 2560 \ - --max_multimodal_len 576 - - python build_multimodal_engine.py --model_type llava --model_path tmp/hf_models/${MODEL_NAME} --output_dir tmp/trt_engines/${MODEL_NAME}/fp16/2-gpu/vision - - mpirun -n 2 --allow-run-as-root \ - python run.py \ - --max_new_tokens 30 \ - --hf_model_dir tmp/hf_models/${MODEL_NAME} \ - --engine_dir tmp/trt_engines/${MODEL_NAME}/fp16/2-gpu \ - ``` -## Enabling Embedding Table Offloading - -Embedding Table Offloading is a memory optimization technique that helps manage large embedding tables more efficiently. It offloads the embedding table to CPU memory and uses a chunked prefetching mechanism during processing. This approach is only available when operating in context chunk mode. - -To enable this feature, use the `--mm_embedding_offloading` argument: -```bash -python run.py \ - --enable_chunked_context \ - --mm_embedding_offloading true \ - --hf_model_dir ${HF_MODEL_PATH} \ - --engine_dir ${ENGINE_PATH} -``` -When not explicitly specified, this feature automatically enables if you're using a multimodal model along with context chunking enabled. diff --git a/examples/models/core/multimodal/__init__.py b/examples/models/core/multimodal/__init__.py deleted file mode 100644 index e69de29bb2d1..000000000000 diff --git a/examples/models/core/multimodal/build_multimodal_engine.py b/examples/models/core/multimodal/build_multimodal_engine.py deleted file mode 100644 index 59e8bb4ffa25..000000000000 --- a/examples/models/core/multimodal/build_multimodal_engine.py +++ /dev/null @@ -1,12 +0,0 @@ -import argparse - -from tensorrt_llm.tools.multimodal_builder import (MultimodalEngineBuilder, - add_multimodal_arguments) - -if __name__ == '__main__': - parser = argparse.ArgumentParser() - parser = add_multimodal_arguments(parser) - args = parser.parse_args() - - builder = MultimodalEngineBuilder(args) - builder.build() diff --git a/examples/models/core/multimodal/eval.py b/examples/models/core/multimodal/eval.py deleted file mode 100644 index 01804d290dee..000000000000 --- a/examples/models/core/multimodal/eval.py +++ /dev/null @@ -1,317 +0,0 @@ -# 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 argparse -import os - -import aiohttp -import datasets -import torch -from transformers import AutoProcessor -from utils import add_common_args - -import tensorrt_llm -import tensorrt_llm.profiler as profiler -from tensorrt_llm import logger -from tensorrt_llm.runtime import MultimodalModelRunner - -SUPPORTED_MODEL_TYPES = { - 'blip2': 'Blip2ForConditionalGeneration', - 'fuyu': 'FuyuForCausalLM', - 'kosmos-2': 'Kosmos2ForConditionalGeneration', - 'llava': 'LlavaForConditionalGeneration', - 'llava_next': 'LlavaNextForConditionalGeneration', - 'llava_onevision': 'LlavaOnevisionForConditionalGeneration', - 'phi-3-vision': 'AutoModelForCausalLM', - 'qwen2_vl': 'Qwen2VLForConditionalGeneration', # not tested for TRT-LLM yet - 'mllama': 'MllamaForConditionalGeneration', - 'vila': None, - 'cogvlm': None, # not tested for TRT-LLM yet - 'neva': None, # not tested for TRT-LLM yet - 'internvl': None, -} -EVAL_TASKS = ['lmms-lab/ai2d', 'lmms-lab/VQAv2', 'lmms-lab/MME'] - - -def parse_arguments(args=None): - parser = argparse.ArgumentParser() - parser = add_common_args(parser) - parser.add_argument('--test_trtllm', - action='store_true', - default=None, - help="Evaluate the TensorRT-LLM.") - parser.add_argument('--test_hf', - action='store_true', - default=None, - help="Evaluate the Huggingface.") - parser.add_argument('--max_ite', type=int, default=20) - parser.add_argument('--eval_task', - type=str, - choices=EVAL_TASKS, - default='lmms-lab/VQAv2') - parser.add_argument('--model_type', - type=str, - default=None, - choices=SUPPORTED_MODEL_TYPES.keys()) - parser.add_argument( - '--accuracy_threshold', - type=float, - default=None, - help= - 'used to check the accuracy of test_trtllm. Should be between 0 and 100.' - ) - parser.add_argument( - '--dataset_dir', - type=str, - default=None, - help="The local directory of the dataset for evaluation; " - "will download the dataset from huggingface hub if not specified.") - parser.add_argument( - '--dataset_cache_dir', - type=str, - default=None, - help="The local cache directory for dataset; " - "will use `~/.cache/huggingface/datasets` if not specified.") - return parser.parse_args(args=args) - - -def load_dataset(args) -> datasets.Dataset: - split_name = 'validation' if 'VQAv2' in args.eval_task else 'test' - - if args.dataset_dir is not None and os.path.exists( - os.path.join(args.dataset_dir, "dataset_info.json")): - logger.info(f"load dataset by load_from_disk from {args.dataset_dir}") - dataset = datasets.load_from_disk(args.dataset_dir) - - else: - logger.info( - f"load dataset by load_dataset from {args.dataset_dir or args.eval_task}" - ) - dataset = datasets.load_dataset( - args.dataset_dir or args.eval_task, - cache_dir=args.dataset_cache_dir, - split=split_name, - storage_options={ - 'client_kwargs': { - 'timeout': aiohttp.ClientTimeout(total=3600) - } - }, - trust_remote_code=True, - ) - return dataset - - -def load_hf_model(args): - if SUPPORTED_MODEL_TYPES[args.model_type] is None: - raise ValueError(f"Unsupported HF model_type: {args.model_type}") - profiler.start('load HF model') - model_class = getattr(__import__('transformers'), - SUPPORTED_MODEL_TYPES[args.model_type]) - hf_model = model_class.from_pretrained(args.hf_model_dir, - dtype=torch.float16, - device_map="cuda:0", - trust_remote_code=True) - profiler.stop('load HF model') - - logger.info( - f'Load HF model takes: {profiler.elapsed_time_in_sec("load HF model")} sec' - ) - return hf_model - - -def load_trtllm_model(args): - profiler.start('load TensorRT LLM model') - trtllm_model = MultimodalModelRunner(args) - profiler.stop('load TensorRT LLM model') - logger.info( - f'Load TensorRT LLM model takes: {profiler.elapsed_time_in_sec("load TensorRT LLM model")} sec' - ) - return trtllm_model - - -def prepare_prompts(task, data, model_type, processor) -> str: - prompts = None - question = data['question'] - if question[-1] != '?': - question += '?' - - if task == 'lmms-lab/ai2d': - for j, option in enumerate(data['options']): - question += f" ({j}) {option}" - - if model_type in ['blip2', 'neva']: - prompts = f"Question: {question} Answer: " - elif model_type == 'fuyu': - prompts = f"Answer the following {task} question based on the image: {question}" - elif model_type == 'kosmos-2': - prompts = f" Question: {question} Answer: " - elif model_type == 'cogvlm': - prompts = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {question} ASSISTANT:" - elif model_type in ['llava', 'llava_next', 'llava_onevision']: - conversation = [ - { - "role": - "user", - "content": [ - { - "type": "image" - }, - { - "type": "text", - "text": question - }, - ], - }, - ] - prompts = processor.apply_chat_template(conversation, - add_generation_prompt=True) - elif model_type in ['vila', 'internvl']: - prompts = f"\n{question}" - elif model_type == 'phi-3-vision': - messages = [ - { - "role": "user", - "content": f"<|image_1|>\n{question}" - }, - ] - prompts = processor.tokenizer.apply_chat_template( - messages, tokenize=False, add_generation_prompt=True) - elif model_type == 'qwen2_vl': - conversation = [{ - "role": - "user", - "content": [{ - "type": "image", - }, { - "type": "text", - "text": question - }] - }] - prompts = processor.apply_chat_template(conversation, - add_generation_prompt=True) - elif model_type == 'mllama': - prompts = processor.apply_chat_template(images=data['image'], - text=question + "; answer: ") - else: - raise ValueError(f"Unsupported model_type: {model_type}") - - return prompts - - -def eval(output, task, data) -> bool: - output = output.strip().lower() - if task == 'lmms-lab/VQAv2': - return any(answer['answer'] in output for answer in data['answers']) - else: - return data['answer'].lower() in output - - -os.environ["TOKENIZERS_PARALLELISM"] = "false" -args = parse_arguments() -if args.model_type not in SUPPORTED_MODEL_TYPES: - raise ValueError(f"Unsupported model_type: {args.model_type}") - -logger.set_level(args.log_level) - -runtime_rank = tensorrt_llm.mpi_rank() -dataset = load_dataset(args) -hf_model = load_hf_model(args) if args.test_hf else None -trtllm_model = load_trtllm_model(args) if args.test_trtllm else None -if SUPPORTED_MODEL_TYPES[args.model_type] is None: - hf_processor = None -else: - hf_processor = AutoProcessor.from_pretrained(args.hf_model_dir, - trust_remote_code=True) -hf_correct = trtllm_correct = 0 - -if args.model_type == 'mllama': - from tensorrt_llm.runtime.processor_wrapper import MllamaProcessorWrapper - hf_processor = MllamaProcessorWrapper(hf_processor, logger) - -torch.random.manual_seed(0) -profiler.start('evaluation') -if args.test_trtllm or args.test_hf: - for i in range(args.max_ite): - logger.debug(f"Ite: {i:3d}") - data = dataset[i] - if i > len(dataset): - break - prompts = prepare_prompts(args.eval_task, data, args.model_type, - hf_processor) - image = data['image'] - - if args.test_hf: - assert hf_model is not None, f"Unsupported HF model_type: {args.model_type}" - profiler.start('hf') - inputs = hf_processor( - images=image, - text=prompts, - return_tensors="pt", - ).to(hf_model.device, - torch.float16) # add torch.float16 for llava-onevision - input_length = inputs.input_ids.shape[-1] - hf_output = hf_model.generate(**inputs, - max_new_tokens=args.max_new_tokens) - hf_result = (hf_processor.batch_decode( - hf_output, skip_special_tokens=True)[0] if args.model_type in [ - 'blip2' - ] else hf_processor.decode(hf_output[0][input_length:], - skip_special_tokens=True)) - hf_correct += eval(hf_result, args.eval_task, data) - profiler.stop('hf') - - if args.test_trtllm: - profiler.start('tensorrt_llm') - _, output_text = trtllm_model.run( - input_text=prompts, - input_image=image, - input_audio=None, - max_new_tokens=args.max_new_tokens) - if runtime_rank == 0: - trtllm_result = output_text[0][0] - trtllm_correct += eval(trtllm_result, args.eval_task, data) - profiler.stop('tensorrt_llm') - - if runtime_rank == 0: - if args.eval_task == 'lmms-lab/VQAv2': - answer = data['answers'] - else: - answer = data['answer'] - logger.debug(f"prompts: {prompts}") - logger.debug(f"reference answer: {answer}") - if args.test_hf: - logger.debug(f"HF's answer: {hf_result}") - if args.test_trtllm: - logger.debug(f"TRT-LLM's answer: {trtllm_result}") - - if runtime_rank == 0: - logger.info(f"total iterations: {args.max_ite}") - if args.test_hf: - logger.info( - f"HF's accuracy: {100 * hf_correct / args.max_ite:4.2f}%") - if args.test_trtllm: - logger.info( - f"TRT-LLM's accuracy: {100 * trtllm_correct / args.max_ite:4.2f}%" - ) - # check if the accuracy is above the threshold - if args.accuracy_threshold is not None and args.test_trtllm: - assert trtllm_correct / args.max_ite >= args.accuracy_threshold / 100, \ - f"TRT-LLM's accuracy is below the threshold: {args.accuracy_threshold}%." -else: - logger.info("Neither enable test_trtllm nor enable test_hf") - -profiler.stop('evaluation') -logger.info( - f'Evaluation takes: {profiler.elapsed_time_in_sec("evaluation")} sec') diff --git a/examples/models/core/multimodal/requirements-eclair.txt b/examples/models/core/multimodal/requirements-eclair.txt deleted file mode 100644 index 281c8ae93a91..000000000000 --- a/examples/models/core/multimodal/requirements-eclair.txt +++ /dev/null @@ -1 +0,0 @@ -timm diff --git a/examples/models/core/multimodal/requirements-internlm-xcomposer2.txt b/examples/models/core/multimodal/requirements-internlm-xcomposer2.txt deleted file mode 100644 index 5d27a97ebb68..000000000000 --- a/examples/models/core/multimodal/requirements-internlm-xcomposer2.txt +++ /dev/null @@ -1 +0,0 @@ -transformers==4.56.0 diff --git a/examples/models/core/multimodal/requirements-llava_onevision.txt b/examples/models/core/multimodal/requirements-llava_onevision.txt deleted file mode 100644 index 126cbda08e15..000000000000 --- a/examples/models/core/multimodal/requirements-llava_onevision.txt +++ /dev/null @@ -1,4 +0,0 @@ -git+https://github.com/LLaVA-VL/LLaVA-NeXT.git -transformers>=4.56.0 -einops -av diff --git a/examples/models/core/multimodal/requirements-qwen2vl.txt b/examples/models/core/multimodal/requirements-qwen2vl.txt deleted file mode 100644 index 50f14d1d8095..000000000000 --- a/examples/models/core/multimodal/requirements-qwen2vl.txt +++ /dev/null @@ -1,2 +0,0 @@ -accelerate -qwen-vl-utils==0.0.8 # 0.0.9 has bug https://github.com/QwenLM/Qwen2-VL/pull/673, rollback until a newer version is released diff --git a/examples/models/core/multimodal/requirements-vila.txt b/examples/models/core/multimodal/requirements-vila.txt deleted file mode 100644 index 00775aa0cdfa..000000000000 --- a/examples/models/core/multimodal/requirements-vila.txt +++ /dev/null @@ -1,2 +0,0 @@ -git+https://github.com/bfshi/scaling_on_scales.git -transformers==4.56.0 diff --git a/examples/models/core/multimodal/run.py b/examples/models/core/multimodal/run.py deleted file mode 100644 index fb13554848e7..000000000000 --- a/examples/models/core/multimodal/run.py +++ /dev/null @@ -1,128 +0,0 @@ -import argparse -import os - -from utils import add_common_args, compute_str_match_rate - -import tensorrt_llm -import tensorrt_llm.profiler as profiler -from tensorrt_llm import logger -from tensorrt_llm.runtime import MultimodalModelRunner - - -def print_result(model, input_text, output_text, args): - logger.info("---------------------------------------------------------") - if model.model_type != 'nougat': - logger.info(f"\n[Q] {input_text}") - for i in range(len(output_text)): - logger.info(f"\n[A]: {output_text[i]}") - - if args.num_beams == 1: - output_ids = model.tokenizer(output_text[0][0], - add_special_tokens=False)['input_ids'] - logger.info(f"Generated {len(output_ids)} tokens") - - if args.check_accuracy: - if model.model_type != 'nougat': - if model.model_type == "vila": - for i in range(len(args.image_path.split(args.path_sep))): - if i % 2 == 0: - assert output_text[i][0].lower( - ) == "the image captures a bustling city intersection teeming with life. from the perspective of a car's dashboard camera, we see" - else: - assert output_text[i][0].lower( - ) == "the image captures the iconic merlion statue in singapore, a renowned worldwide landmark. the merlion, a mythical" - elif model.model_type == "llava": - for i in range(len(args.image_path.split(args.path_sep))): - assert output_text[i][0].lower() == 'singapore' - elif model.model_type == 'fuyu': - assert output_text[0][0].lower() == '4' - elif model.model_type == "pix2struct": - assert "characteristic | cat food, day | cat food, wet | cat treats" in output_text[ - 0][0].lower() - elif model.model_type in [ - 'blip2', 'neva', 'phi-3-vision', 'llava_next', - 'phi-4-multimodal', 'pixtral' - ]: - assert 'singapore' in output_text[0][0].lower() - elif model.model_type == 'video-neva': - assert 'robot' in output_text[0][0].lower() - elif model.model_type == 'kosmos-2': - assert 'snowman' in output_text[0][0].lower() - elif model.model_type == "mllama": - if "If I had to write a haiku for this one" in input_text: - ref_1 = ", it would be:.\\nPeter Rabbit is a rabbit.\\nHe lives in a cozy little house.\\nHe's a very good rabbit.\\" - ref_2 = "Here is a haiku for the image:\n\n" - - elif "Answer:" in input_text: - ref_1 = "2,173. A 1 2 3 4 5 6 Date Income 2005-12-17" - ref_2 = "Answer: 2,173. 1 2 3 4 5 6 Date Income 2005-12-17" - - elif "The key to life is" in input_text: - ref_1 = "to find your passion and pursue it with all your heart. For me, that passion is photography. I love capturing the beauty of the world around me" - ref_2 = "not to be found in the external world," - output = output_text[0][0] - match_rate = max(compute_str_match_rate(ref_1, output), - compute_str_match_rate(ref_2, output)) - logger.info(f"match rate: {match_rate}") - assert match_rate >= 50, \ - f"expected results: '{ref_1}' or '{ref_2}', generated results: '{output}'" - - elif model.model_type == 'llava_onevision': - if args.video_path is None: - assert 'singapore' in output_text[0][0].lower() - else: - assert 'the video is funny because the child\'s actions are' in output_text[ - 0][0].lower() - elif model.model_type == "qwen2_vl": - assert 'dog' in output_text[0][0].lower() - else: - assert output_text[0][0].lower() == 'singapore' - - if args.run_profiling: - msec_per_batch = lambda name: 1000 * profiler.elapsed_time_in_sec( - name) / args.profiling_iterations - logger.info('Latencies per batch (msec)') - logger.info('e2e generation: %.1f' % (msec_per_batch('Generate'))) - logger.info(' ' * 2 + 'Preprocessing: %.1f' % - (msec_per_batch('Preprocess'))) - logger.info(' ' * 4 + 'Vision encoder: %.1f' % - (msec_per_batch('Vision encoder'))) - if profiler.elapsed_time_in_sec('Feature transform') is not None: - logger.info(' ' * 4 + 'Feature transform: %.1f' % - (msec_per_batch('Feature transform'))) - logger.info(' ' * 2 + 'LLM generate: %.1f' % (msec_per_batch('LLM'))) - logger.info(' ' * 2 + 'Tokenizer decode: %.1f' % - (msec_per_batch('Tokenizer decode'))) - - logger.info("---------------------------------------------------------") - - -if __name__ == '__main__': - os.environ["TOKENIZERS_PARALLELISM"] = "false" - parser = argparse.ArgumentParser() - parser = add_common_args(parser) - args = parser.parse_args() - logger.set_level(args.log_level) - - model = MultimodalModelRunner(args) - visual_data = model.load_test_data(args.image_path, args.video_path) - audio_data = model.load_test_audio(args.audio_path) - - if args.run_profiling: - num_warmup_iters = 3 # Multiple iterations to load both vision and LLM engines into memory - for _ in range(num_warmup_iters): - input_text, output_text = model.run(args.input_text, visual_data, - audio_data, args.max_new_tokens) - profiler.reset() - - num_iters = args.profiling_iterations if args.run_profiling else 1 - - for _ in range(num_iters): - input_text, output_text = model.run(args.input_text, visual_data, - audio_data, args.max_new_tokens) - - runtime_rank = tensorrt_llm.mpi_rank() - if runtime_rank == 0: - print_result(model, input_text, output_text, args) - -# TODO: raise error if VILA mode 1 with C++ runtime diff --git a/examples/models/core/multimodal/utils.py b/examples/models/core/multimodal/utils.py deleted file mode 100644 index 6d13fb600f40..000000000000 --- a/examples/models/core/multimodal/utils.py +++ /dev/null @@ -1,163 +0,0 @@ -def add_common_args(parser): - parser.add_argument('--max_new_tokens', type=int, default=128) - parser.add_argument('--batch_size', type=int, default=1) - parser.add_argument('--log_level', type=str, default='info') - parser.add_argument('--engine_dir', - type=str, - default=None, - help='Directory containing visual and LLM TRT engines') - parser.add_argument('--visual_engine_name', - type=str, - default='model.engine', - help='Name of visual TRT engine') - parser.add_argument('--audio_engine_name', - type=str, - default='model.engine', - help='Name of audio TRT engine') - parser.add_argument('--hf_model_dir', - type=str, - default=None, - help="Directory containing tokenizer") - parser.add_argument('--input_text', - type=str, - nargs='+', - default=None, - help='Text prompt to LLM') - parser.add_argument('--num_beams', - type=int, - help="Use beam search if num_beams >1", - default=1) - parser.add_argument('--top_k', type=int, default=1) - parser.add_argument('--top_p', type=float, default=0.0) - parser.add_argument('--temperature', type=float, default=1.0) - parser.add_argument('--repetition_penalty', type=float, default=1.0) - parser.add_argument('--run_profiling', - action='store_true', - help='Profile runtime over several iterations') - parser.add_argument('--profiling_iterations', - type=int, - help="Number of iterations to run profiling", - default=20) - parser.add_argument('--check_accuracy', - action='store_true', - help='Check correctness of text output') - parser.add_argument( - '--video_path', - type=str, - default=None, - help= - 'Path to your local video file, using \'llava-onevision-accuracy\' to check the Llava-OneVision model accuracy' - ) - parser.add_argument( - '--video_num_frames', - type=int, - help= - "The number of frames sampled from the video in the Llava-OneVision model.", - default=None) - parser.add_argument("--image_path", - type=str, - nargs='+', - default=None, - help='List of input image paths, separated by symbol') - parser.add_argument("--audio_path", - type=str, - default=None, - help='input audio path') - parser.add_argument("--path_sep", - type=str, - default=",", - help='Path separator symbol') - parser.add_argument("--prompt_sep", - type=str, - default=",", - help="Prompt separator symbol") - parser.add_argument('--enable_context_fmha_fp32_acc', - action='store_true', - default=None, - help="Enable FMHA runner FP32 accumulation.") - parser.add_argument( - '--enable_chunked_context', - action='store_true', - help='Enables chunked context (only available with cpp session).', - ) - parser.add_argument( - '--mm_embedding_offloading', - type=lambda s: s.lower() == "true", - default=None, - help= - 'Enable position table offloading. When not specified, defaults to True if using a multimodal model with chunked context.' - ) - parser.add_argument( - '--session', - default='cpp_llm_only', - type=str, - choices=['python', 'cpp_llm_only', 'cpp'], - help= - 'Rumtime used to run the models. \n`cpp_llm_only`: vision engine run in python runtime, but LLM in pybind cpp runtime\n`python`: everything runs in python runtime\n`cpp`: everything runs in C++ runtime' - ) - parser.add_argument( - '--kv_cache_free_gpu_memory_fraction', - default=0.7, - type=float, - help='Specify the free gpu memory fraction.', - ) - parser.add_argument( - '--cross_kv_cache_fraction', - default=0.5, - type=float, - help= - 'Specify the kv cache fraction reserved for cross attention. Only applicable for encoder-decoder models. By default 0.5 for self and 0.5 for cross.', - ) - parser.add_argument( - '--multi_block_mode', - type=lambda s: s.lower() in - ("yes", "true", "t", "1" - ), # custom boolean function to convert input string to boolean - default=True, - help= - "Distribute the work across multiple CUDA thread-blocks on the GPU for masked MHA kernel." - ) - parser.add_argument( - '--lora_task_uids', - type=str, - default=None, - nargs="+", - help="The list of LoRA task uids; use -1 to disable the LoRA module") - parser.add_argument('--debug_mode', - default=False, - action='store_true', - help="Whether or not to turn on the debug mode") - parser.add_argument( - '--trust_remote_code', - action='store_true', - default=False, - help='Allow loading models with custom remote code from HuggingFace Hub. ' - 'Only enable this for models from trusted sources.') - return parser - - -def levenshtein_distance(s1, s2): - if len(s1) < len(s2): - return levenshtein_distance(s2, s1) - - if len(s2) == 0: - return len(s1) - - previous_row = range(len(s2) + 1) - for i, c1 in enumerate(s1): - current_row = [i + 1] - for j, c2 in enumerate(s2): - insertions = previous_row[j + 1] + 1 - deletions = current_row[j] + 1 - substitutions = previous_row[j] + (c1 != c2) - current_row.append(min(insertions, deletions, substitutions)) - previous_row = current_row - - return previous_row[-1] - - -def compute_str_match_rate(s1, s2): - distance = levenshtein_distance(s1, s2) - max_length = max(len(s1), len(s2)) - match_rate = (1 - distance / max_length) * 100 - return match_rate diff --git a/examples/models/core/nemotron/README_nemotron-3.md b/examples/models/core/nemotron/README_nemotron-3.md deleted file mode 100644 index 0816eb995fd8..000000000000 --- a/examples/models/core/nemotron/README_nemotron-3.md +++ /dev/null @@ -1,214 +0,0 @@ -# Nemotron-3 - -This document demonstrates how to build the Nemotron models using TensorRT LLM and run on a single GPU or multiple GPUs. - -- [Nemotron](#nemotron) - - [Overview](#overview) - - [Support Matrix](#support-matrix) - - [Usage](#usage) - - [Download weights from HuggingFace Transformers](#download-weights-from-huggingface-transformers) - - [Build TensorRT engine(s)](#build-tensorrt-engines) - - [FP8 Quantization](#fp8-quantization) - - [INT4 AWQ Quantization](#int4-awq-quantization) - - [Run Inference](#run-inference) - -## Overview - -The TensorRT LLM Nemotron implementation is based on the GPT model, which can be found in [`tensorrt_llm/models/gpt/model.py`](../../../../tensorrt_llm/models/gpt/model.py). The TensorRT LLM Nemotron example is located in [`examples/models/core/nemotron`](./). - -In addition, there are two shared files in the parent folder [`examples`](../../../) for inference and evaluation: - -* [`run.py`](../../../run.py) to run the inference on an input text; -* [`summarize.py`](../../../summarize.py) to summarize the articles in the [cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -## Support Matrix - * FP16/BF16 - * FP8 - * INT4 AWQ - * Tensor Parallel - * Pipeline Parallel - * Inflight Batching - * PAGED_KV_CACHE - * STRONGLY TYPED - * checkpoint type: Nemo, Huggingface (HF) - -## Nemo checkpoint - Usage - -### Download weights from HuggingFace Transformers - - -Install the dependencies and setup `git-lfs`. - -```bash -# Install dependencies -pip install -r requirements.txt - -# Setup git-lfs -git lfs install -``` - -Download one or more Nemotron models that you would like to build to TensorRT LLM engines. You can download from the [HuggingFace](https://huggingface.co) hub: - -```bash -# Download nemotron-3-8b-base-4k -git clone https://huggingface.co/nvidia/nemotron-3-8b-base-4k - -# Download nemotron-3-8b-chat-4k-sft -git clone https://huggingface.co/nvidia/nemotron-3-8b-chat-4k-sft - -# Download nemotron-3-8b-chat-4k-rlhf -git clone https://huggingface.co/nvidia/nemotron-3-8b-chat-4k-rlhf -``` - -### Build TensorRT engine(s) -The [`examples/quantization/quantize.py`](../../../quantization/quantize.py) script can quantize the Nemotron models and export to TensorRT LLM checkpoints. You may optionally skip the quantization step by specifying `--qformat full_prec` and thus export float16 or bfloat16 TensorRT LLM checkpoints. - -The `trtllm-build` command builds TensorRT LLM engines from TensorRT LLM checkpoints. The number of engine files is same to the number of GPUs used to run inference. Normally, `trtllm-build` uses one GPU by default, but if you have already more GPUs available at build time, you may enable parallel builds to make the engine building process faster by adding the `--workers` argument. - -Here are some examples: - -```bash -# single gpu, dtype bfloat16 -python3 ../../../quantization/quantize.py \ - --nemo_ckpt_path nemotron-3-8b-base-4k/Nemotron-3-8B-Base-4k.nemo \ - --dtype bfloat16 \ - --batch_size 64 \ - --qformat full_prec \ - --output_dir nemotron-3-8b/trt_ckpt/bf16/1-gpu - -trtllm-build --checkpoint_dir nemotron-3-8b/trt_ckpt/bf16/1-gpu \ - --gpt_attention_plugin bfloat16 \ - --gemm_plugin bfloat16 \ - --output_dir nemotron-3-8b/trt_engines/bf16/1-gpu -``` - -```bash -# 2-way tensor parallelism -python3 ../../../quantization/quantize.py \ - --nemo_ckpt_path nemotron-3-8b-base-4k/Nemotron-3-8B-Base-4k.nemo \ - --dtype bfloat16 \ - --batch_size 64 \ - --qformat full_prec \ - --tp_size 2 \ - --output_dir nemotron-3-8b/trt_ckpt/bf16/tp2 - -trtllm-build --checkpoint_dir nemotron-3-8b/trt_ckpt/bf16/tp2 \ - --gpt_attention_plugin bfloat16 \ - --gemm_plugin bfloat16 \ - --workers 2 \ - --output_dir nemotron-3-8b/trt_engines/bf16/tp2 -``` - -```bash -# 2-way tensor parallelism for both calibration and inference -mpirun -np 2 \ - python3 ../../../quantization/quantize.py \ - --nemo_ckpt_path nemotron-3-8b-base-4k/Nemotron-3-8B-Base-4k.nemo \ - --dtype bfloat16 \ - --batch_size 64 \ - --qformat full_prec \ - --calib_tp_size 2 \ - --tp_size 2 \ - --output_dir nemotron-3-8b/trt_ckpt/bf16/tp2 - -trtllm-build --checkpoint_dir nemotron-3-8b/trt_ckpt/bf16/tp2 \ - --gpt_attention_plugin bfloat16 \ - --gemm_plugin bfloat16 \ - --workers 2 \ - --output_dir nemotron-3-8b/trt_engines/bf16/tp2 -``` - -#### FP8 Quantization - -Quantize the Nemotron models to FP8 by specifying `--qformat fp8` to `quantize.py`. - -```bash -# single gpu, fp8 quantization -python3 ../../../quantization/quantize.py \ - --nemo_ckpt_path nemotron-3-8b-base-4k/Nemotron-3-8B-Base-4k.nemo \ - --dtype bfloat16 \ - --batch_size 64 \ - --qformat fp8 \ - --output_dir nemotron-3-8b/trt_ckpt/fp8/1-gpu - -trtllm-build --checkpoint_dir nemotron-3-8b/trt_ckpt/fp8/1-gpu \ - --gpt_attention_plugin bfloat16 \ - --output_dir nemotron-3-8b/trt_engines/fp8/1-gpu -``` - -#### INT4 AWQ Quantization - -Quantize the Nemotron models using INT4 AWQ by specifying `--qformat int4_awq` to `quantize.py`. - -```bash -# single gpu, int4 awq quantization -python3 ../../../quantization/quantize.py \ - --nemo_ckpt_path nemotron-3-8b-base-4k/Nemotron-3-8B-Base-4k.nemo \ - --dtype bfloat16 \ - --batch_size 64 \ - --qformat int4_awq \ - --output_dir nemotron-3-8b/trt_ckpt/int4_awq/1-gpu - -trtllm-build --checkpoint_dir nemotron-3-8b/trt_ckpt/int4_awq/1-gpu \ - --gpt_attention_plugin bfloat16 \ - --output_dir nemotron-3-8b/trt_engines/int4_awq/1-gpu -``` - -### Run Inference - -The `summarize.py` script can run the built engines to summarize the articles from the -[cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) dataset. - -```bash -# single gpu -python3 ../../../summarize.py --test_trt_llm \ - --no_add_special_tokens \ - --engine_dir nemotron-3-8b/trt_engines/bf16/1-gpu \ - --vocab_file nemotron-3-8b/trt_ckpt/bf16/1-gpu/tokenizer.model - -# multiple gpus -mpirun -np 2 \ - python3 ../../../summarize.py --test_trt_llm \ - --no_add_special_tokens \ - --engine_dir nemotron-3-8b/trt_engines/bf16/tp2 \ - --vocab_file nemotron-3-8b/trt_ckpt/bf16/tp2/tokenizer.model -``` - -If the engines are run successfully, you will see output like: -``` -...... -[04/23/2024-09:55:54] [TRT-LLM] [I] TensorRT LLM (total latency: 14.926485538482666 sec) -[04/23/2024-09:55:54] [TRT-LLM] [I] TensorRT LLM (total output tokens: 2000) -[04/23/2024-09:55:54] [TRT-LLM] [I] TensorRT LLM (tokens per second: 133.99001357980129) -[04/23/2024-09:55:54] [TRT-LLM] [I] TensorRT LLM beam 0 result -[04/23/2024-09:55:54] [TRT-LLM] [I] rouge1 : 19.48743720965424 -[04/23/2024-09:55:54] [TRT-LLM] [I] rouge2 : 6.272381295466071 -[04/23/2024-09:55:54] [TRT-LLM] [I] rougeL : 15.011005943152721 -[04/23/2024-09:55:54] [TRT-LLM] [I] rougeLsum : 17.76145734406502 -``` - -## HF checkpoint - Usage -Support for Nemotron models was added with transformers 4.44.0 release. - -```bash -# install transformers library -pip install transformers>=4.44.0 -# Download hf minitron model -git clone https://huggingface.co/nvidia/Minitron-4B-Base - -# Convert to TensorRT LLM checkpoint -python3 ../gpt/convert_checkpoint.py --model_dir Minitron-4B-Base \ - --dtype bfloat16 \ - --output_dir minitron/trt_ckpt/bf16/1-gpu - -# Build TensorRT LLM engines -trtllm-build --checkpoint_dir minitron/trt_ckpt/bf16/1-gpu \ - --gemm_plugin auto \ - --output_dir minitron/trt_engines/bf16/1-gpu - -# Run inference -python3 ../../../run.py --engine_dir minitron/trt_engines/bf16/1-gpu \ - --tokenizer_dir Minitron-4B-Base \ - --input_text "def print_hello_world():" \ - --max_output_len 20 -``` diff --git a/examples/models/core/nemotron/requirements.txt b/examples/models/core/nemotron/requirements.txt deleted file mode 100644 index f8d1f8e1abf4..000000000000 --- a/examples/models/core/nemotron/requirements.txt +++ /dev/null @@ -1,7 +0,0 @@ --c ../../../constraints.txt -tensorrt_llm>=0.0.0.dev0 -nemo-toolkit[all]==2.0.0rc1 -megatron-core @ git+https://github.com/NVIDIA/Megatron-LM@core_r0.8.0 -datasets==3.1.0 -evaluate -rouge_score diff --git a/examples/models/core/qwen2audio/README.md b/examples/models/core/qwen2audio/README.md deleted file mode 100644 index 92115345077e..000000000000 --- a/examples/models/core/qwen2audio/README.md +++ /dev/null @@ -1,80 +0,0 @@ -# Guide to Qwen2-Audio deployment pipeline - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -1. Download the Qwen2-Audio model. - ```bash - git lfs install - export MODEL_PATH="tmp/Qwen2-Audio-7B-Instruct" - git clone https://huggingface.co/Qwen/Qwen2-Audio-7B-Instruct $MODEL_PATH - ``` -2. Generate the TensorRT engine of audio encoder. - ```bash - export ENGINE_DIR="./trt_engines/qwen2audio/fp16" - python3 ../multimodal/build_multimodal_engine.py --model_type qwen2_audio --model_path $MODEL_PATH --max_batch_size 32 --output_dir ${ENGINE_DIR}/audio - ``` - - The TensorRT engine will be generated under `${ENGINE_DIR}/audio`. - -3. Build Qwen2 LLM TensorRT engine. -- Convert checkpoint - 1. Install packages - ```bash - pip install -r requirements.txt - ``` - 2. Convert - 2.1 FP16 checkpoint - ```bash - python3 ../qwen/convert_checkpoint.py --model_dir=$MODEL_PATH \ - --dtype=float16 \ - --output_dir=./tllm_checkpoint_1gpu_fp16 - ``` - 2.2 (Optional) INT8 Weight Only checkpoint - ```bash - python3 ../qwen/convert_checkpoint.py --model_dir=$MODEL_PATH \ - --dtype=float16 \ - --use_weight_only \ - --weight_only_precision=int8 \ - --output_dir=./tllm_checkpoint_1gpu_fp16_wo8 - ``` - -- Build TensorRT LLM engine - - NOTE: `max_prompt_embedding_table_size = query_token_num * max_batch_size`, therefore, if you change `max_batch_size`, `--max_prompt_embedding_table_size` must be reset accordingly. - ```bash - trtllm-build --checkpoint_dir=./tllm_checkpoint_1gpu_fp16 \ - --gemm_plugin=float16 --gpt_attention_plugin=float16 \ - --max_batch_size=1 --max_prompt_embedding_table_size=4096 \ - --output_dir=${ENGINE_DIR}/llm - ``` - The built Qwen engines are located in `${ENGINE_DIR}/llm`. - - You can replace the `--checkpoint_dir` with INT8 Weight Only checkpoint to build INT8 Weight Only engine as well. - For more information about Qwen, refer to the README.md in [`example/models/core/qwen`](../qwen). - -4. Assemble everything into the Qwen2-Audio pipeline. - - 4.1 Run with FP16 LLM engine - ```bash - python3 run.py \ - --tokenizer_dir=$MODEL_PATH \ - --engine_dir=${ENGINE_DIR}/llm \ - --audio_engine_path=${ENGINE_DIR}/audio/model.engine \ - --audio_url='./audio/glass-breaking-151256.mp3' - ``` - 4.2 (Optional) For multiple rounds of dialogue, you can run: - ```bash - python3 run_chat.py \ - --tokenizer_dir=$MODEL_PATH \ - --engine_dir=${ENGINE_DIR}/llm \ - --audio_engine_path=${ENGINE_DIR}/audio/model.engine \ - --max_new_tokens=256 - ``` - - Note: - - This example supports reusing the KV Cache for audio segments by assigning unique audio IDs. - - To further optimize performance, users can also cache the audio features (encoder output) to bypass the audio encoder if the original audio data remains unchanged. diff --git a/examples/models/core/qwen2audio/audio/glass-breaking-151256.mp3 b/examples/models/core/qwen2audio/audio/glass-breaking-151256.mp3 deleted file mode 100644 index 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b/examples/models/core/qwen2audio/run.py deleted file mode 100644 index a0b0a68fb1c9..000000000000 --- a/examples/models/core/qwen2audio/run.py +++ /dev/null @@ -1,640 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2022-2025 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 argparse -import json -import os -from io import BytesIO -from urllib.request import urlopen - -import librosa -import tensorrt as trt -import torch -from transformers import AutoConfig, AutoProcessor, AutoTokenizer -from utils import add_common_args - -import tensorrt_llm -import tensorrt_llm.profiler as profiler -from tensorrt_llm import logger -from tensorrt_llm.llmapi.kv_cache_type import KVCacheType -from tensorrt_llm.quantization import QuantMode -from tensorrt_llm.runtime import (PYTHON_BINDINGS, ModelConfig, ModelRunner, - SamplingConfig, Session, TensorInfo) - -if PYTHON_BINDINGS: - from tensorrt_llm.runtime import ModelRunnerCpp - - -def get_engine_name(rank): - return "rank{}.engine".format(rank) - - -def trt_dtype_to_torch(dtype): - if dtype == trt.float16: - return torch.float16 - elif dtype == trt.float32: - return torch.float32 - elif dtype == trt.int32: - return torch.int32 - else: - raise TypeError("%s is not supported" % dtype) - - -class QWenInfer(object): - - def __init__(self, - audio_engine_path, - tokenizer_dir, - engine_dir, - log_level, - output_csv, - output_npy, - num_beams, - gpu_id=0): - self.audio_engine_path = audio_engine_path - self.tokenizer_dir = tokenizer_dir - self.engine_dir = engine_dir - self.log_level = log_level - self.max_seq_len = 0 - self.runner = None - self.hf_audio_tower = None - self.tokenizer = None - self.config = None - self.sampling_config = None - self.output_csv = output_csv - self.output_npy = output_npy - self.num_beams = num_beams - self.model_config = None - self.gpu_device = torch.device("cuda", gpu_id) - - def get_model(self): - # --load the tokenizer and engines # - tokenizer = AutoTokenizer.from_pretrained( - self.tokenizer_dir, - legacy=False, - trust_remote_code=True, - ) - processor = AutoProcessor.from_pretrained(self.tokenizer_dir, - trust_remote_code=True) - config_path = os.path.join(self.engine_dir, "config.json") - with open(config_path, "r") as f: - config = json.load(f) - self.max_seq_len = config["build_config"]["max_seq_len"] - assert self.max_seq_len > 0, "max_seq_len must be positive" - - gen_config_path = os.path.join(self.tokenizer_dir, - "generation_config.json") - with open(gen_config_path, "r") as f: - gen_config = json.load(f) - top_k = gen_config["top_k"] - top_p = gen_config["top_p"] - eos_token_id = tokenizer.pad_token_id - pad_token_id = tokenizer.pad_token_id - - use_gpt_attention_plugin = config["build_config"]["plugin_config"][ - "gpt_attention_plugin"] - remove_input_padding = config["build_config"]["plugin_config"][ - "remove_input_padding"] - dtype = config["pretrained_config"]["dtype"] - tp_size = config["pretrained_config"]["mapping"]["tp_size"] - pp_size = config["pretrained_config"]["mapping"]["pp_size"] - world_size = tp_size * pp_size - assert ( - world_size == tensorrt_llm.mpi_world_size() - ), f"Engine world size ({world_size}) != Runtime world size ({tensorrt_llm.mpi_world_size()})" - num_heads = config["pretrained_config"][ - "num_attention_heads"] // world_size - max_batch_size = config["build_config"]["max_batch_size"] - hidden_size = config["pretrained_config"]["hidden_size"] // world_size - vocab_size = config["pretrained_config"]["vocab_size"] - num_layers = config["pretrained_config"]["num_hidden_layers"] - num_kv_heads = config["pretrained_config"].get("num_key_value_heads", - num_heads) - if "kv_cache_type" in config["build_config"]: - kv_cache_type = KVCacheType(config["build_config"]["kv_cache_type"]) - else: - kv_cache_type = KVCacheType.CONTINUOUS - - tokens_per_block = config["build_config"]["plugin_config"][ - "tokens_per_block"] - max_prompt_embedding_table_size = config["build_config"].get( - "max_prompt_embedding_table_size", 0) - quant_mode = QuantMode.from_quant_algo( - config["pretrained_config"]["quantization"]["quant_algo"], - config["pretrained_config"]["quantization"]["kv_cache_quant_algo"], - ) - if config["pretrained_config"].get("multi_query_mode", False): - tensorrt_llm.logger.warning( - "`multi_query_mode` config is deprecated. Please rebuild the engine." - ) - num_kv_heads = 1 - - runtime_rank = tensorrt_llm.mpi_rank() - runtime_mapping = tensorrt_llm.Mapping(world_size=world_size, - rank=runtime_rank, - tp_size=tp_size, - pp_size=pp_size) - - model_config = ModelConfig( - max_batch_size=max_batch_size, - num_heads=num_heads, - num_kv_heads=num_kv_heads, - hidden_size=hidden_size, - vocab_size=vocab_size, - num_layers=num_layers, - gpt_attention_plugin=use_gpt_attention_plugin, - kv_cache_type=kv_cache_type, - tokens_per_block=tokens_per_block, - remove_input_padding=remove_input_padding, - dtype=dtype, - quant_mode=quant_mode, - max_prompt_embedding_table_size=max_prompt_embedding_table_size, - max_beam_width=self.num_beams, - ) - sampling_config = SamplingConfig( - end_id=eos_token_id, - pad_id=pad_token_id, - num_beams=self.num_beams, - top_k=top_k, - top_p=top_p, - temperature=1.0, - ) - - engine_name = get_engine_name(runtime_rank) - serialize_path = os.path.join(self.engine_dir, engine_name) - print(f"Loading engine from {serialize_path}") - return ( - model_config, - sampling_config, - runtime_mapping, - runtime_rank, - serialize_path, - tokenizer, - processor, - eos_token_id, - pad_token_id, - ) - - def qwen_model_init(self, args): - logger.info(f"Loading audio engine from {self.audio_engine_path}") - with open(self.audio_engine_path, "rb") as f: - engine_buffer = f.read() - logger.info(f"Creating session from engine {self.audio_engine_path}") - self.session_audio = Session.from_serialized_engine(engine_buffer) - - self.config, _ = AutoConfig.from_pretrained( - self.tokenizer_dir, - return_unused_kwargs=True, - trust_remote_code=True, - ) - - ( - model_config, - sampling_config, - runtime_mapping, - runtime_rank, - serialize_path, - tokenizer, - processor, - eos_token_id, - pad_token_id, - ) = self.get_model() - runner_cls = ModelRunner if args.use_py_session else ModelRunnerCpp - runner_kwargs = dict( - engine_dir=args.engine_dir, - lora_dir=args.lora_dir, - rank=runtime_rank, - debug_mode=args.debug_mode, - lora_ckpt_source=args.lora_ckpt_source, - gpu_weights_percent=args.gpu_weights_percent, - max_output_len=args.max_new_tokens, - ) - if not args.use_py_session: - runner_kwargs.update( - is_enc_dec=False, - max_batch_size=model_config.max_batch_size, - max_input_len=self.max_seq_len - args.max_new_tokens, - max_beam_width=model_config.max_beam_width, - max_attention_window_size=args.max_attention_window_size, - sink_token_length=args.sink_token_length, - max_tokens_in_paged_kv_cache=args.max_tokens_in_paged_kv_cache, - kv_cache_enable_block_reuse=args.kv_cache_enable_block_reuse, - kv_cache_free_gpu_memory_fraction=args. - kv_cache_free_gpu_memory_fraction, - cross_kv_cache_fraction=None, - enable_chunked_context=args.enable_chunked_context, - multi_block_mode=args.multi_block_mode, - cuda_graph_mode=args.cuda_graph_mode, - device_ids=[args.gpu_id]) - runner_kwargs.update( - enable_context_fmha_fp32_acc=args.enable_context_fmha_fp32_acc) - self.runner = runner_cls.from_dir(**runner_kwargs) - self.tokenizer = tokenizer - self.processor = processor - self.sampling_config = sampling_config - self.model_config = model_config - - def ptuning_setup(self, prompt_table, dtype, hidden_size, tasks, input_ids): - if prompt_table is not None: - task_vocab_size = torch.tensor([prompt_table.shape[0]], - dtype=torch.int32, - device=self.gpu_device) - prompt_table = prompt_table.to( - dtype=tensorrt_llm._utils.str_dtype_to_torch(dtype), - device=self.gpu_device) - else: - prompt_table = torch.empty([1, hidden_size], device=self.gpu_device) - task_vocab_size = torch.zeros([1], device=self.gpu_device) - - if tasks is not None: - tasks = torch.tensor([int(t) for t in tasks.split(",")], - dtype=torch.int32, - device=self.gpu_device) - assert (tasks.shape[0] == input_ids.shape[0] - ), "Number of supplied tasks must match input batch size" - else: - tasks = torch.zeros([input_ids.size(0)], - dtype=torch.int32, - device=self.gpu_device) - - return [prompt_table, tasks, task_vocab_size] - - def build_user_input(self, audio=None, text=None): - assert isinstance(audio, str) or isinstance( - text, str), "audio or text must be provided as user input" - content = [] - if audio: - content.append({'type': 'audio', 'audio_url': audio}) - if text: - content.append({'type': 'text', 'text': text}) - user_input = {'role': 'user', 'content': content} - return user_input - - def get_raw_audios(self, audio_url): - audios = [] - for url in audio_url: - if os.path.isfile(url): - audio_data, _ = librosa.load( - url, sr=self.processor.feature_extractor.sampling_rate) - else: - audio_data, _ = librosa.load( - BytesIO(urlopen(url).read()), - sr=self.processor.feature_extractor.sampling_rate) - audios.append(audio_data) - return audios - - def audio_tower(self, audios, mask, stream, run_time=1): - audios = audios.to(self.gpu_device) - mask = mask.to(self.gpu_device) - audio_inputs = {"input": audios.float(), "mask": mask} - audio_output_info = self.session_audio.infer_shapes([ - TensorInfo("input", trt.DataType.FLOAT, audios.shape), - TensorInfo("mask", trt.DataType.HALF, mask.shape) - ]) - audio_outputs = { - t.name: - torch.empty(tuple(t.shape), - dtype=trt_dtype_to_torch(t.dtype), - device=self.gpu_device) - for t in audio_output_info - } - profiler.start("Audio") - for _ in range(run_time): - ok = self.session_audio.run(audio_inputs, audio_outputs, - stream.cuda_stream) - stream.synchronize() - audio_time = profiler.stop("Audio") / run_time - logger.info(f"TensorRT LLM Audio latency: {audio_time:3f} sec ") - - assert ok, "Runtime execution failed for audio session" - - audio_features = audio_outputs["output"] - - return audio_features - - def generate_for_qwen_audio( - self, - input_tokens, - args, - prompt_table=None, - extra_ids=None, - run_time=1, - ): - input_ids = torch.as_tensor(input_tokens, - device=self.gpu_device, - dtype=torch.int32) - input_lengths = torch.tensor([input_ids.size(1)], - device=self.gpu_device, - dtype=torch.int32) - max_input_length = torch.max(input_lengths).item() - max_new_tokens = min(args.max_new_tokens, - self.max_seq_len - max_input_length) - - prompt_table = prompt_table.unsqueeze(0) - profiler.start("QWen") - for _ in range(run_time): - outputs = self.runner.generate( - batch_input_ids=input_ids, - max_new_tokens=max_new_tokens, - max_attention_window_size=args.max_attention_window_size, - sink_token_length=args.sink_token_length, - end_id=self.sampling_config.end_id, - pad_id=self.sampling_config.pad_id, - temperature=args.temperature, - top_k=args.top_k, - top_p=args.top_p, - num_beams=args.num_beams, - num_return_sequences=args.num_return_sequences, - length_penalty=args.length_penalty, - early_stopping=args.early_stopping, - repetition_penalty=args.repetition_penalty, - presence_penalty=args.presence_penalty, - frequency_penalty=args.frequency_penalty, - stop_words_list=[[[151643], [151645]]], - bad_words_list=self.sampling_config.bad_words_list, - random_seed=args.random_seed, - lora_uids=args.lora_task_uids, - prompt_table=prompt_table, - prompt_tasks="0", - output_sequence_lengths=True, - no_repeat_ngram_size=args.no_repeat_ngram_size, - return_dict=True, - return_all_generated_tokens=False, - input_token_extra_ids=extra_ids) - output_ids = outputs['output_ids'] - torch.cuda.synchronize() - Qwen_time = profiler.stop("QWen") / run_time - - return output_ids, Qwen_time - - def get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor): - """ - Computes the output length of the convolutional layers and the output length of the audio encoder - """ - input_lengths = (input_lengths - 1) // 2 + 1 - output_lengths = (input_lengths - 2) // 2 + 1 - return input_lengths, output_lengths - - def qwen_infer(self, - input_text, - audios, - audio_ids, - args, - stream, - history=None, - past_audio_features=None, - run_time=1): - assert input_text, "input_text must be provided" - assert torch.cuda.is_available(), "no gpu available" - # preprocess on CPU maybe faster - device = torch.device("cpu") - if isinstance(history, list): - history.append(input_text) - full_text = self.processor.apply_chat_template( - history, add_generation_prompt=True, tokenize=False) - else: - full_text = input_text - inputs = self.processor( - text=full_text, - audios=audios, - return_tensors="pt", - padding=True, - sampling_rate=self.processor.feature_extractor.sampling_rate) - inputs = inputs.to(device) - input_ids = inputs.input_ids - - if hasattr(inputs, - 'input_features') and inputs.input_features is not None: - # audio tower - batch_size, _, max_mel_seq_len = inputs.input_features.shape - feature_attention_mask = inputs.feature_attention_mask - - audio_feat_lengths, num_audio_tokens = self.get_feat_extract_output_lengths( - feature_attention_mask.sum(-1)) - - max_seq_len = (max_mel_seq_len - 2) // 2 + 1 - # Create a sequence tensor of shape (batch_size, max_seq_len) - seq_range = (torch.arange(0, - max_seq_len, - dtype=audio_feat_lengths.dtype, - device=device).unsqueeze(0).expand( - batch_size, max_seq_len)) - lengths_expand = audio_feat_lengths.unsqueeze(1).expand( - batch_size, max_seq_len) - # Create mask - padding_mask = seq_range >= lengths_expand - - audio_attention_mask_ = padding_mask.view( - batch_size, 1, 1, max_seq_len).expand(batch_size, 1, - max_seq_len, max_seq_len) - audio_attention_mask = audio_attention_mask_.to(dtype=torch.float16, - device=device) - audio_attention_mask[audio_attention_mask_] = float("-inf") - - audio_features = self.audio_tower(inputs.input_features, - audio_attention_mask, stream, - run_time) - - # merge audio features and input ids - num_audios, max_audio_tokens, embed_dim = audio_features.shape - audio_features_mask = torch.arange( - max_audio_tokens, device=device).expand( - num_audios, - max_audio_tokens) < num_audio_tokens.unsqueeze(1) - masked_audio_features = audio_features[audio_features_mask].view( - -1, embed_dim) - batch_size, _ = input_ids.shape - - # 1. Create a mask to know where special audio tokens are - special_audio_token_mask = input_ids == self.config.audio_token_index - special_audio_token_num = special_audio_token_mask.sum().item() - if past_audio_features is not None: - assert isinstance(past_audio_features, - list), f'past_audio_features should be a list' - assert ( - special_audio_token_num == len(past_audio_features) + - num_audios - ), f'special_audio_token_num {special_audio_token_num} should be equal to len(past_audio_features) + num_audios ({len(past_audio_features)} + {num_audios})' - # split to get current audio features - cur_audio_features = torch.split(masked_audio_features, - num_audio_tokens.tolist()) - if len(past_audio_features) > 0: - # concat past and current audio features - masked_audio_features = torch.cat( - (torch.cat(past_audio_features).to( - masked_audio_features.device), - masked_audio_features)) - # get past audio tokens number - past_num_audio_tokens = torch.tensor([ - past_feat.size(0) for past_feat in past_audio_features - ]) - # concat past and current audio tokens number - num_audio_tokens = torch.cat( - (past_num_audio_tokens.to(num_audio_tokens.device), - num_audio_tokens)) - # extend past audio features, cache them in CPU memory - past_audio_features.extend( - [cur_feat.cpu() for cur_feat in cur_audio_features]) - - batch_indices, non_audio_indices = torch.where( - input_ids != self.config.audio_token_index) - - # 2. Fill the final input ids based on the mask. - batch_indices, audio_indices = torch.where( - input_ids == self.config.audio_token_index) - - vocab_size = self.config.vocab_size - fake_prompt_id = torch.arange(vocab_size, - vocab_size + num_audio_tokens.sum(), - device=device) - - input_ids[batch_indices, audio_indices] = fake_prompt_id - input_lengths = torch.tensor(input_ids.size(1), - dtype=torch.int32, - device=self.gpu_device) - dtype = self.model_config.dtype - prompt_table, tasks, task_vocab_size = self.ptuning_setup( - masked_audio_features, dtype, embed_dim, None, input_ids) - - # build extra ids - assert isinstance(audio_ids, list), "audio_ids must be a list" - assert ( - len(audio_ids) == num_audio_tokens.size(0) - ), f"audio_ids length doesn't match with num_audio_tokens ({len(audio_ids)} != {num_audio_tokens.size(0)})" - for i in audio_ids: - assert isinstance( - i, int - ) and i > 0, "audio_id should be an integer greater than 0" - extra_ids = torch.zeros_like(input_ids, - dtype=torch.int64, - device=device) - seq_extra_ids = torch.cat([ - torch.full((n, ), audio_ids[i], dtype=torch.int64) - for i, n in enumerate(num_audio_tokens) - ]).to(device) - extra_ids[batch_indices, audio_indices] = seq_extra_ids - extra_ids = extra_ids.tolist() - else: - input_ids = input_ids.to(dtype=torch.int32, device=self.gpu_device) - input_lengths = torch.tensor(input_ids.size(1), - dtype=torch.int32, - device=self.gpu_device) - dtype = self.model_config.dtype - prompt_table, tasks, task_vocab_size = self.ptuning_setup( - None, dtype, self.model_config.hidden_size, None, input_ids) - extra_ids = torch.zeros_like(input_ids, dtype=torch.int64).tolist() - - # print(f"extra_ids: {extra_ids}") - output_ids, Qwen_time = self.generate_for_qwen_audio( - input_ids, args, prompt_table, extra_ids, run_time) - - runtime_rank = tensorrt_llm.mpi_rank() - input_lengths = torch.tensor([input_ids.size(1)], - device=self.gpu_device, - dtype=torch.int32) - effective_output_token = 0 - if runtime_rank == 0: - if self.output_csv is None and self.output_npy is None: - for b in range(input_lengths.size(0)): - inputs = input_ids[b] - if self.num_beams <= 1: - outputs = output_ids[b][0, len(inputs):].tolist() - try: - effective_output_token = (effective_output_token + - outputs.index(151643)) - except: - effective_output_token = 1 - output_text = self.tokenizer.decode( - outputs, skip_special_tokens=True) - print(f'Output: "{output_text}"') - else: - for beam in range(self.num_beams): - outputs = output_ids[b][beam, len(inputs):].tolist() - output_text = self.tokenizer.decode( - outputs, skip_special_tokens=True) - print(f'Output(beam: {beam}): "{output_text}"') - logger.info(f"Input length={input_lengths[b]}") - logger.info(f"Output length={output_ids.shape}") - logger.info(f"TensorRT LLM QWen time: {Qwen_time:3f} sec ") - if isinstance(history, list): - history.append({'role': 'assistant', 'content': output_text}) - return output_text, past_audio_features - - -def parse_arguments(): - parser = argparse.ArgumentParser() - parser.add_argument("--max_new_tokens", type=int, default=10) - parser.add_argument( - "--audio_engine_path", - type=str, - default="plan/audio_encoder/audio_encoder_fp16.plan", - ) - parser.add_argument( - "--input_text", - type=str, - default= - "<|audio_bos|><|AUDIO|><|audio_eos|>Generate the caption in English:") - parser.add_argument( - "--audio_url", - nargs="+", - type=str, - default=["./audio/glass-breaking-151256.mp3"], - ) - parser.add_argument( - "--input_tokens", - dest="input_file", - type=str, - help= - "CSV or Numpy file containing tokenized input. Alternative to text input.", - default=None, - ) - parser.add_argument( - "--output_csv", - type=str, - help="CSV file where the tokenized output is stored.", - default=None, - ) - parser.add_argument( - "--output_npy", - type=str, - help="Numpy file where the tokenized output is stored.", - default=None, - ) - parser.add_argument( - "--gpu_id", - type=int, - help= - "Specify GPU device index for running. Should be the index seen by torch, not original index", - default=0, - ) - parser = add_common_args(parser) - - return parser.parse_args() - - -if __name__ == "__main__": - args = parse_arguments() - tensorrt_llm.logger.set_level(args.log_level) - - # use cudaSetDevice before loading audio engine - torch.cuda.set_device(args.gpu_id) - qinfer = QWenInfer(args.audio_engine_path, args.tokenizer_dir, - args.engine_dir, args.log_level, args.output_csv, - args.output_npy, args.num_beams, args.gpu_id) - qinfer.qwen_model_init(args) - - audios = qinfer.get_raw_audios(args.audio_url) - gpu_device = torch.device("cuda", args.gpu_id) - stream = torch.cuda.current_stream(device=gpu_device) - qinfer.qwen_infer(args.input_text, audios, [1], args, stream, None, None, 1) diff --git a/examples/models/core/qwen2audio/run_chat.py b/examples/models/core/qwen2audio/run_chat.py deleted file mode 100644 index 00d58cdc862b..000000000000 --- a/examples/models/core/qwen2audio/run_chat.py +++ /dev/null @@ -1,81 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2022-2025 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. - -# isort: off -import torch -from run import QWenInfer, parse_arguments - -import tensorrt_llm -# isort: on - -if __name__ == '__main__': - args = parse_arguments() - stream = torch.cuda.current_stream() - tensorrt_llm.logger.set_level(args.log_level) - qinfer = QWenInfer( - args.audio_engine_path, - args.tokenizer_dir, - args.engine_dir, - args.log_level, - args.output_csv, - args.output_npy, - args.num_beams, - ) - qinfer.qwen_model_init(args) - - run_i = 0 - history = [] - audios = None - global_audio_id = 1 - audio_ids = [] - - while True: - input_text = None - try: - input_text = input( - "Text (type 'q' to quit, or 'audio_url:[url]' to input audio): " - ) - except: - continue - - if input_text == "clear history": - history = [] - audios = None - continue - - if input_text.lower() == 'q': - break - print('\n') - - if input_text.startswith('audio_url:'): - audio_url = input_text[len('audio_url:'):].strip() - if isinstance(audios, list): - audios.extend(qinfer.get_raw_audios([audio_url])) - else: - audios = qinfer.get_raw_audios([audio_url]) - user_input = qinfer.build_user_input(audio=audio_url) - audio_ids.append(global_audio_id) - global_audio_id += 1 - else: - user_input = qinfer.build_user_input(text=input_text) - - qinfer.qwen_infer( - user_input, - audios, - audio_ids, - args, - stream, - history, - ) diff --git a/examples/models/core/qwen2audio/utils.py b/examples/models/core/qwen2audio/utils.py deleted file mode 100644 index 3252beebbf7d..000000000000 --- a/examples/models/core/qwen2audio/utils.py +++ /dev/null @@ -1,130 +0,0 @@ -from argparse import BooleanOptionalAction - - -def add_common_args(parser): - # sampling arguments - parser.add_argument('--num_beams', - type=int, - help="Use beam search if num_beams > 1", - default=1) - parser.add_argument('--num_return_sequences', - type=int, - help="Number of sequences to generate for each input.", - default=None) - parser.add_argument('--temperature', type=float, default=1.0) - parser.add_argument('--top_k', type=int, default=1) - parser.add_argument('--top_p', type=float, default=0.0) - parser.add_argument('--length_penalty', type=float, default=1.0) - parser.add_argument('--repetition_penalty', type=float, default=1.0) - parser.add_argument('--presence_penalty', type=float, default=0.0) - parser.add_argument('--frequency_penalty', type=float, default=0.0) - parser.add_argument('--random_seed', type=int, default=0) - parser.add_argument('--early_stopping', - type=int, - help='Use early stopping if num_beams > 1, ' - '1 for early-stopping, 0 for non-early-stopping' - 'other values for stopping by length', - default=1) - parser.add_argument('--no_repeat_ngram_size', type=int, default=None) - - # common runtime arguments - parser.add_argument('--sink_token_length', - type=int, - default=None, - help='The sink token length.') - parser.add_argument( - '--max_attention_window_size', - type=int, - default=None, - nargs="+", - help= - 'The attention window size that controls the sliding window attention kv cache behavior' - ) - parser.add_argument( - '--multi_block_mode', - type=lambda s: s.lower() in - ("yes", "true", "t", "1" - ), # custom boolean function to convert input string to boolean - default=True, - help= - "Distribute the work across multiple CUDA thread-blocks on the GPU for masked MHA kernel." - ) - parser.add_argument('--enable_context_fmha_fp32_acc', - action='store_true', - help="Enable FMHA runner FP32 accumulation.") - parser.add_argument('--cuda_graph_mode', - action='store_true', - help="Enable cuda graphs in the inference.") - parser.add_argument( - '--log_level', - type=str, - choices=['verbose', 'info', 'warning', 'error', 'internal_error'], - default='info') - parser.add_argument('--use_py_session', - default=False, - action='store_true', - help="Whether or not to use Python runtime session") - parser.add_argument('--debug_mode', - default=False, - action='store_true', - help="Whether or not to turn on the debug mode") - parser.add_argument('--lora_dir', - type=str, - default=None, - nargs="+", - help="The directory of LoRA weights") - parser.add_argument('--lora_ckpt_source', - type=str, - default="hf", - choices=["hf", "nemo"], - help="The source of lora checkpoint.") - parser.add_argument( - '--lora_task_uids', - type=str, - default=None, - nargs="+", - help="The list of LoRA task uids; use -1 to disable the LoRA module") - - # model arguments - parser.add_argument('--engine_dir', type=str, default='engine_outputs') - parser.add_argument('--hf_model_dir', '--model_dir', type=str, default=None) - parser.add_argument( - '--tokenizer_dir', - default=None, - help='tokenizer path; defaults to hf_model_dir if left unspecified') - - # memory argument - parser.add_argument( - '--gpu_weights_percent', - default=1, - type=float, - help= - 'Specify the percentage of weights that reside on GPU instead of CPU and streaming load during runtime.', - ) - parser.add_argument( - '--max_tokens_in_paged_kv_cache', - default=None, - type=int, - help= - 'Specify the maximum number of tokens in a kv cache page (only available with cpp session).', - ) - parser.add_argument( - '--kv_cache_enable_block_reuse', - default=True, - action=BooleanOptionalAction, - help= - 'Enables block reuse in kv cache (only available with cpp session).', - ) - parser.add_argument( - '--kv_cache_free_gpu_memory_fraction', - default=0.9, - type=float, - help='Specify the free gpu memory fraction.', - ) - parser.add_argument( - '--enable_chunked_context', - action='store_true', - help='Enables chunked context (only available with cpp session).', - ) - - return parser diff --git a/examples/models/core/qwenvl/README.md b/examples/models/core/qwenvl/README.md deleted file mode 100644 index 27fc5d1dfce0..000000000000 --- a/examples/models/core/qwenvl/README.md +++ /dev/null @@ -1,114 +0,0 @@ -# Guide to Qwen-VL deployment pipeline - -> [!WARNING] -> The `convert_checkpoint.py` / `trtllm-build` / `run.py` workflow described -> below is **legacy** and will not receive new features. New projects should use -> [`trtllm-serve`](https://nvidia.github.io/TensorRT-LLM/quick-start-guide.html) -> or the [LLM Python API](https://nvidia.github.io/TensorRT-LLM/llm-api/index.html) instead. - -1. Download the Qwen vision-language model (Qwen-VL). - ```bash - git lfs install - git clone https://huggingface.co/Qwen/Qwen-VL-Chat - ``` -2. Generate the Vision Transformer (ViT) ONNX model and the TensorRT engine. -- If you don't have ONNX file, run: - ```bash - python3 vit_onnx_trt.py --pretrained_model_path ./Qwen-VL-Chat - ``` - The ONNX and TensorRT engine will be generated under `./onnx/visual_encoder` and `./plan/visual_encoder` respectively. - -- If you already have an ONNX file under `./onnx/visual_encoder` and want to build a TensorRT engine with it, run: - ```bash - python3 vit_onnx_trt.py --pretrained_model_path ./Qwen-VL-Chat --only_trt - ``` - This command saves the test image tensor to `image.pt` for later pipeline inference. - -3. Build Qwen TensorRT engine. -- Convert checkpoint - 1. Install packages - ```bash - pip install -r requirements.txt - ``` - 2. Convert - ```bash - python3 ./examples/models/core/qwen/convert_checkpoint.py --model_dir=./Qwen-VL-Chat \ - --output_dir=./tllm_checkpoint_1gpu \ - --dtype float16 - ``` - -- Build TensorRT LLM engine - - NOTE: `max_prompt_embedding_table_size = query_token_num * max_batch_size`, therefore, if you change `max_batch_size`, `--max_prompt_embedding_table_size` must be reset accordingly. - ```bash - trtllm-build --checkpoint_dir=./tllm_checkpoint_1gpu \ - --gemm_plugin=float16 --gpt_attention_plugin=float16 \ - --max_input_len=2048 --max_seq_len=3072 \ - --max_batch_size=8 --max_prompt_embedding_table_size=2048 \ - --remove_input_padding=enable \ - --output_dir=./trt_engines/Qwen-VL-7B-Chat - ``` - The built Qwen engines are located in `./trt_engines/Qwen-VL-7B-Chat`. - For more information about Qwen, refer to the README.md in [`example/qwen`](../qwen). - -4. Assemble everything into the Qwen-VL pipeline. - - 4.1 Run with INT4 GPTQ weight-only quantization engine - ```bash - python3 run.py \ - --tokenizer_dir=./Qwen-VL-Chat \ - --qwen_engine_dir=./trt_engines/Qwen-VL-7B-Chat \ - --vit_engine_path=./plan/visual_encoder/visual_encoder_fp16.plan \ - --images_path='{"image": "./pics/demo.jpeg"}' - ``` - 4.2 (Optional) For multiple rounds of dialogue, you can run: - ```bash - python3 run_chat.py \ - --tokenizer_dir=./Qwen-VL-Chat \ - --qwen_engine_dir=./trt_engines/Qwen-VL-7B-Chat \ - --vit_engine_path=./plan/visual_encoder/visual_encoder_fp16.plan \ - --images_path='{"image": "./pics/demo.jpeg"}' - ``` - 4.3 (Optional) To show the bounding box result in the demo picture, install OpenCV, ZMQ, and request: - ```bash - pip install opencv-python==4.5.5.64 - pip install opencv-python-headless==4.5.5.64 - pip install zmq - pip install request - ``` - -   4.3.1 If the current program is executed on a remote machine, run the following command on a local machine: - - ```bash - python3 show_pic.py --ip=127.0.0.1 --port=8006 - ``` - -   Replace the `ip` and `port` values, where `ip` is your remote machine IP address. - -   Run the following command on the remote machine: - - ```bash - python3 run_chat.py \ - --tokenizer_dir=./Qwen-VL-Chat \ - --qwen_engine_dir=./trt_engines/Qwen-VL-7B-Chat \ - --vit_engine_path=./plan/visual_encoder/visual_encoder_fp16.plan \ - --display \ - --port=8006 - ``` - -   Replace the `port` value. - -   4.3.2 If the current program is executed on the local machine, run the following command: - - ```bash - python3 run_chat.py \ - --tokenizer_dir=./Qwen-VL-Chat \ - --qwen_engine_dir=./trt_engines/Qwen-VL-7B-Chat \ - --vit_engine_path=./plan/visual_encoder/visual_encoder_fp16.plan \ - --display \ - --local_machine - ``` - -   The question "Print the bounding box of the girl" is displayed. 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