305 lines
11 KiB
Python
305 lines
11 KiB
Python
# Copyright 2023-2024 SGLang Team
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import multiprocessing as mp
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import unittest
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import torch
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from sglang.test.runners import HFRunner, SRTRunner
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from sglang.test.test_utils import CustomTestCase
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LORA_SETS = [
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# {
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# "base": "meta-llama/Llama-2-7b-hf",
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# "loras": ["RuterNorway/Llama-2-7b-chat-norwegian-LoRa"],
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# },
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{"base": "meta-llama/Llama-2-7b-hf", "loras": ["winddude/wizardLM-LlaMA-LoRA-7B"]},
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# {"base": "Qwen/Qwen2.5-14B-Instruct", "loras": ["mssongit/Qwen2.5-14B-SFT-LoRA"]},
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# {"base": "mistralai/Mistral-7B-Instruct-v0.3", "loras": ["/home/ying/test_lora"]},
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# {
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# "base": "mistralai/Mistral-7B-Instruct-v0.3",
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# "loras": [
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# "/home/ying/test_lora",
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# "/home/ying/test_lora_1",
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# "/home/ying/test_lora_2",
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# "/home/ying/test_lora_3",
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# "/home/ying/test_lora_4",
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# ],
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# },
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# {"base": "meta-llama/Llama-2-7b-hf", "loras": ["yard1/llama-2-7b-sql-lora-test"]},
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]
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TORCH_DTYPES = [torch.float16]
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PROMPTS = [
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"""
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### Instruction:
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Write a poem about the transformers Python library.
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Mention the word "large language models" in that poem.
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### Response:
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The Transformers are large language models,
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They're used to make predictions on text.
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""",
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"""
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### Instruction:
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Tell me about llamas and alpacas
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### Response:
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Llamas are large, long-necked animals with a woolly coat. They have two toes on each foot instead of three like other camelids (camels, dromedaries). Llamas live in the Andean mountains of South America where they graze on grasses and shrubs. Alpaca is another name for domesticated llama. The word "alpaca" comes from an Incan language meaning "golden fleece." Alpacas look very similar to llamas but are smaller than their wild relatives. Both species were used by ancient people as pack animals and for meat. Today both llamas and alpacas are raised primarily for their fiber which can be spun into yarn or knitted into clothing.
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### Question 2:
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What do you know about llamas?
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### Answer:
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""",
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]
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# import json
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#
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# with open("/home/ying/test_prompt/dialogue_choice_prompts.json", "r") as f:
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# samples = json.load(f)
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# for sample in samples[:5]:
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# assert sample[0]["role"] == "user"
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# PROMPTS.append(sample[0]["content"][:2000])
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class TestLoRA(CustomTestCase):
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def inference(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens):
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print("=================== testing inference =======================")
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base_path = lora_set["base"]
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all_lora_paths = lora_set["loras"]
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batch_lora_paths = [None]
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i = 0
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for _ in range(len(prompts) - 1):
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batch_lora_paths.append(all_lora_paths[i])
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i = (i + 1) % len(all_lora_paths)
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with SRTRunner(
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base_path,
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torch_dtype=torch_dtype,
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model_type="generation",
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tp_size=tp_size,
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lora_paths=all_lora_paths,
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max_loras_per_batch=3,
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disable_cuda_graph=True,
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disable_radix_cache=True,
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) as srt_runner:
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srt_outputs = srt_runner.forward(
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prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
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)
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srt_outputs_lora_path_none = srt_runner.forward(
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prompts,
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max_new_tokens=max_new_tokens,
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lora_paths=[None] * len(prompts),
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)
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with HFRunner(
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base_path, torch_dtype=torch_dtype, model_type="generation"
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) as hf_runner:
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hf_outputs = hf_runner.forward(
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prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
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)
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with HFRunner(
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base_path,
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torch_dtype=torch_dtype,
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model_type="generation",
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) as hf_runner:
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hf_no_lora_outputs = hf_runner.forward(
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prompts, max_new_tokens=max_new_tokens
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)
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with SRTRunner(
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base_path,
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tp_size=tp_size,
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torch_dtype=torch_dtype,
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model_type="generation",
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) as srt_runner:
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srt_no_lora_outputs = srt_runner.forward(
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prompts, max_new_tokens=max_new_tokens
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)
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for i in range(len(prompts)):
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# compare input logprobs
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hf_logprobs = torch.Tensor(hf_outputs.top_input_logprobs[i])
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srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])
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hf_no_lora_logprobs = torch.Tensor(hf_no_lora_outputs.top_input_logprobs[i])
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srt_no_lora_logprobs = torch.Tensor(
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srt_no_lora_outputs.top_input_logprobs[i]
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)
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print(
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"max input diff between hf_lora and srt_lora",
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torch.max(abs(hf_logprobs - srt_logprobs)),
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)
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print(
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"max input diff between srt_base and srt_lora",
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torch.max(abs(srt_no_lora_logprobs - srt_logprobs)),
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)
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print(
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"max input diff between srt_base and hf_base",
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torch.max(abs(srt_no_lora_logprobs - hf_no_lora_logprobs)),
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)
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print(
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"max input diff between hf_lora and hf_base",
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torch.max(abs(hf_logprobs - hf_no_lora_logprobs)),
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)
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# compare output logprobs
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hf_logprobs = torch.Tensor(hf_outputs.top_output_logprobs[i])
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srt_logprobs = torch.Tensor(srt_outputs.top_output_logprobs[i])
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# print(
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# "\noutput logprobs diff",
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# [
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# float(torch.max(abs(hf_logprobs[j] - srt_logprobs[j])))
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# for j in range(max_new_tokens)
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# ],
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# )
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print(
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"max output diff between hf_lora and srt_lora",
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torch.max(abs(hf_logprobs - srt_logprobs)),
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"\n",
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)
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# compare output strings
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print(f"{hf_outputs.output_strs=}")
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print(f"{srt_outputs.output_strs=}")
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print(f"{hf_no_lora_outputs.output_strs=}")
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print(f"{srt_no_lora_outputs.output_strs=}")
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print(f"{srt_outputs_lora_path_none.output_strs=}")
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for i in range(len(prompts)):
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assert srt_outputs.output_strs[i].strip(" ") == hf_outputs.output_strs[
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i
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].strip(" "), (
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srt_outputs.output_strs[i].strip(" "),
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hf_outputs.output_strs[i].strip(" "),
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)
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assert (
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srt_no_lora_outputs.output_strs[i].strip(" ")
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== hf_no_lora_outputs.output_strs[i]
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), (
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srt_no_lora_outputs.output_strs[i].strip(" "),
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hf_no_lora_outputs.output_strs[i],
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)
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# assert srt_outputs_lora_path_none == srt_no_lora_outputs
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def serving(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens):
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print("=================== testing serving =======================")
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# test batch forward
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base_path = lora_set["base"]
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all_lora_paths = lora_set["loras"]
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batch_lora_paths = [None]
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i = 0
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for _ in range(len(prompts) - 1):
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batch_lora_paths.append(all_lora_paths[i])
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i = (i + 1) % len(all_lora_paths)
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with SRTRunner(
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base_path,
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tp_size=tp_size,
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torch_dtype=torch_dtype,
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model_type="generation",
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lora_paths=all_lora_paths,
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max_loras_per_batch=3,
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disable_cuda_graph=True,
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disable_radix_cache=True,
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) as srt_runner:
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srt_outputs = srt_runner.batch_forward(
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prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
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)
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with HFRunner(
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base_path,
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torch_dtype=torch_dtype,
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model_type="generation",
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output_str_only=True,
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) as hf_runner:
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hf_outputs = hf_runner.forward(
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prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
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)
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# compare output strings
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print(f"{hf_outputs.output_strs=}")
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print(f"{srt_outputs.output_strs=}")
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for i in range(len(prompts)):
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assert srt_outputs.output_strs[i].strip(" ") == hf_outputs.output_strs[i], (
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srt_outputs.output_strs[i].strip(" "),
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hf_outputs.output_strs[i],
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)
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def base_inference(self, prompts, lora_set, tp_size, torch_dtype, max_new_tokens):
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print("=================== testing base inference =======================")
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base_path = lora_set["base"]
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all_lora_paths = lora_set["loras"]
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batch_lora_paths = [None] * len(prompts)
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with SRTRunner(
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base_path,
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tp_size=tp_size,
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torch_dtype=torch_dtype,
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model_type="generation",
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) as srt_runner:
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srt_no_lora_outputs = srt_runner.forward(
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prompts, max_new_tokens=max_new_tokens
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)
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with SRTRunner(
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base_path,
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tp_size=tp_size,
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torch_dtype=torch_dtype,
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model_type="generation",
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lora_paths=all_lora_paths,
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) as srt_runner:
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srt_outputs = srt_runner.forward(
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prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
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)
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for i in range(len(prompts)):
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srt_no_lora_logprobs = torch.Tensor(
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srt_no_lora_outputs.top_input_logprobs[i]
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)
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srt_logprobs = torch.Tensor(srt_outputs.top_input_logprobs[i])
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print("max_diff", torch.max(abs(srt_no_lora_logprobs - srt_logprobs)))
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print(f"{srt_no_lora_outputs.output_strs=}")
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print(f"{srt_outputs.output_strs=}")
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for i in range(len(prompts)):
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assert srt_outputs.output_strs[i].strip(" ") == hf_outputs.output_strs[i], (
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srt_outputs.output_strs[i].strip(" "),
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hf_outputs.output_strs[i],
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)
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assert (
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srt_no_lora_outputs[i].output_strs.strip(" ")
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== hf_no_lora_outputs[i].output_strs
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)
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def test_all(self):
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for lora_set in LORA_SETS:
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# self.load_lora_adapter(lora_set, 1)
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for torch_dtype in TORCH_DTYPES:
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tp_size = 1
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max_new_tokens = 32
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self.inference(PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens)
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# self.serving(PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens)
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# self.base_inference(
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# PROMPTS, lora_set, tp_size, torch_dtype, max_new_tokens
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# )
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if __name__ == "__main__":
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try:
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mp.set_start_method("spawn")
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except RuntimeError:
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pass
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unittest.main(warnings="ignore")
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