add triton solution

runtime/triton_trtllm/scripts/test_llm.py

@@ -0,0 +1,144 @@
+
+# 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 ast
+import csv
+import os
+from pathlib import Path
+from typing import List, Optional
+
+import numpy as np
+import torch
+
+import tensorrt_llm
+from tensorrt_llm.logger import logger
+
+from tensorrt_llm.runtime import ModelRunnerCpp
+from transformers import AutoTokenizer
+
+
+def parse_arguments(args=None):
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--input_text',
+        type=str,
+        nargs='+',
+        default=["Born in north-east France, Soyer trained as a"])
+    parser.add_argument('--tokenizer_dir', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct")
+    parser.add_argument('--engine_dir', type=str, default="meta-llama/Meta-Llama-3-8B-Instruct")
+    parser.add_argument('--log_level', type=str, default="debug")
+    parser.add_argument('--kv_cache_free_gpu_memory_fraction', type=float, default=0.6)
+    parser.add_argument('--temperature', type=float, default=0.8)
+    parser.add_argument('--top_k', type=int, default=50)
+    parser.add_argument('--top_p', type=float, default=0.95)
+
+
+    return parser.parse_args(args=args)
+
+
+def parse_input(tokenizer,
+                input_text=None,
+                prompt_template=None):
+    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)
+        batch_input_ids.append(input_ids)
+ 
+    batch_input_ids = [
+        torch.tensor(x, dtype=torch.int32) for x in batch_input_ids
+    ]
+
+    logger.debug(f"Input token ids (batch_size = {len(batch_input_ids)}):")
+    for i, input_ids in enumerate(batch_input_ids):
+        logger.debug(f"Request {i}: {input_ids.tolist()}")
+
+    return batch_input_ids
+
+
+def main(args):
+    runtime_rank = tensorrt_llm.mpi_rank()
+    logger.set_level(args.log_level)
+
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_dir)
+    prompt_template = "<|sos|>{input_text}<|task_id|>"
+    end_id = tokenizer.convert_tokens_to_ids("<|eos1|>")
+
+    batch_input_ids = parse_input(tokenizer=tokenizer,
+                                  input_text=args.input_text,
+                                  prompt_template=prompt_template)
+
+    input_lengths = [x.size(0) for x in batch_input_ids]
+
+    runner_kwargs = dict(
+        engine_dir=args.engine_dir,
+        rank=runtime_rank,
+        max_output_len=1024,
+        enable_context_fmha_fp32_acc=False,
+        max_batch_size=len(batch_input_ids),
+        max_input_len=max(input_lengths),
+        kv_cache_free_gpu_memory_fraction=args.kv_cache_free_gpu_memory_fraction,
+        cuda_graph_mode=False,
+        gather_generation_logits=False,
+    )
+
+    runner = ModelRunnerCpp.from_dir(**runner_kwargs)
+
+    with torch.no_grad():
+        outputs = runner.generate(
+            batch_input_ids=batch_input_ids,
+            max_new_tokens=1024,
+            end_id=end_id,
+            pad_id=end_id,
+            temperature=args.temperature,
+            top_k=args.top_k,
+            top_p=args.top_p,
+            num_return_sequences=1,
+            repetition_penalty=1.1,
+            random_seed=42,
+            streaming=False,
+            output_sequence_lengths=True,
+            output_generation_logits=False,
+            return_dict=True,
+            return_all_generated_tokens=False)
+        torch.cuda.synchronize()
+        output_ids, sequence_lengths = outputs["output_ids"], outputs["sequence_lengths"]
+        num_output_sents, num_beams, _ = output_ids.size()
+        assert num_beams == 1
+        beam = 0
+        batch_size = len(input_lengths)
+        num_return_sequences = num_output_sents // batch_size
+        assert num_return_sequences == 1
+        for i in range(batch_size * num_return_sequences):
+            batch_idx = i // num_return_sequences
+            seq_idx = i % num_return_sequences
+            inputs = output_ids[i][0][:input_lengths[batch_idx]].tolist()
+            input_text = tokenizer.decode(inputs)
+            print(f'Input [Text {batch_idx}]: \"{input_text}\"')
+            output_begin = input_lengths[batch_idx]
+            output_end = sequence_lengths[i][beam]
+            outputs = output_ids[i][beam][output_begin:output_end].tolist()
+            output_text = tokenizer.decode(outputs)
+            print(f'Output [Text {batch_idx}]: \"{output_text}\"')
+            logger.debug(str(outputs))
+
+
+if __name__ == '__main__':
+    args = parse_arguments()
+    main(args)