[inference/model]Adapted to the baichuan2-7B model (#5591)

* Adapted to the baichuan2-7B model

* modified according to the review comments.

* Modified the method of obtaining random weights.

* modified according to the review comments.

* change mlp layewr 'NOTE'

tests/test_infer/test_models/test_baichuan.py

@@ -0,0 +1,97 @@
+import os
+import random
+
+import numpy as np
+import pytest
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+import colossalai
+from colossalai.inference.config import _DEFAULT_PROMPT_TEMPLATES, InferenceConfig
+from colossalai.inference.core.engine import InferenceEngine
+from colossalai.inference.flash_decoding_utils import FDIntermTensors
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+
+BAICHUAN_MODEL_NAME_OR_PATH = "baichuan-inc/Baichuan2-7B-Base"
+
+
+def setup_seed(seed):
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+
+
+def check_inference_engine(use_engine=False, prompt_template=None):
+    setup_seed(20)
+    tokenizer = AutoTokenizer.from_pretrained(BAICHUAN_MODEL_NAME_OR_PATH, use_fast=False, trust_remote_code=True)
+    model = AutoModelForCausalLM.from_pretrained(
+        BAICHUAN_MODEL_NAME_OR_PATH, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True
+    ).cuda()
+    model = model.eval()
+
+    inputs = [
+        "介绍一下今天的北京,比如故宫，天安门，长城或者其他的一些景点,",
+    ]
+
+    output_len = 38
+    do_sample = False
+
+    if use_engine:
+        inference_config = InferenceConfig(
+            max_output_len=output_len, prompt_template=prompt_template, dtype="fp32", use_cuda_kernel=True
+        )
+        inference_engine = InferenceEngine(model, tokenizer, inference_config, verbose=True)
+        assert inference_engine.generation_config.max_new_tokens == output_len
+        inference_engine.add_request(prompts=inputs)
+        assert inference_engine.request_handler._has_waiting()
+        generation_config = GenerationConfig(do_sample=do_sample)
+        outputs = inference_engine.generate(generation_config=generation_config)
+    else:
+        if prompt_template:
+            # apply prompt template
+            inputs = [_DEFAULT_PROMPT_TEMPLATES[prompt_template].format(input_text=input_text) for input_text in inputs]
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+        inputs = tokenizer.batch_encode_plus(inputs, padding=True, return_tensors="pt")["input_ids"]
+        inputs = inputs.cuda()
+        generation_config = GenerationConfig(
+            do_sample=do_sample,
+            pad_token_id=tokenizer.pad_token_id,
+            max_new_tokens=output_len,
+        )
+        outputs = model.generate(inputs, generation_config=generation_config)
+        outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+
+    return outputs
+
+
+@parameterize("prompt_template", [None, "baichuan"])
+def check_output_consistency(prompt_template):
+    cai_outputs = check_inference_engine(use_engine=True, prompt_template=prompt_template)
+    transformer_outputs = check_inference_engine(use_engine=False, prompt_template=prompt_template)
+
+    for s1, s2 in zip(cai_outputs, transformer_outputs):
+        assert s1 == s2, f"\nColossalAI Output: {s1}\nTransformers Output: {s2}"
+
+    # clear singleton flash decoding tensors
+    FDIntermTensors._instances = {}
+
+
+def run_dist(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, port=port, host="localhost")
+    check_output_consistency()
+
+
+@pytest.mark.skipif(
+    not os.path.exists(BAICHUAN_MODEL_NAME_OR_PATH),
+    reason="There is no local model address included, please replace this address with a valid one.",
+)
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_inference_engine():
+    spawn(run_dist, 1)
+
+
+if __name__ == "__main__":
+    test_inference_engine()