[Fix] Fix Inference Example, Tests, and Requirements (#5688)

* clean requirements

* modify example inference struct

* add test ci scripts

* mark test_infer as submodule

* rm deprecated cls & deps

* import of HAS_FLASH_ATTN

* prune inference tests to be run

* prune triton kernel tests

* increment pytest timeout mins

* revert import path in openmoe

examples/inference/llama/llama_generation.py

@@ -0,0 +1,81 @@
+import argparse
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+import colossalai
+from colossalai.cluster import DistCoordinator
+from colossalai.inference.config import InferenceConfig
+from colossalai.inference.core.engine import InferenceEngine
+from colossalai.inference.modeling.policy.nopadding_llama import NoPaddingLlamaModelInferPolicy
+
+# For Llama 3, we'll use the following configuration
+MODEL_CLS = AutoModelForCausalLM
+POLICY_CLS = NoPaddingLlamaModelInferPolicy
+
+
+def infer(args):
+    # ==============================
+    # Launch colossalai, setup distributed environment
+    # ==============================
+    colossalai.launch_from_torch()
+    coordinator = DistCoordinator()
+
+    # ==============================
+    # Load model and tokenizer
+    # ==============================
+    model_path_or_name = args.model
+    model = MODEL_CLS.from_pretrained(model_path_or_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_path_or_name)
+    tokenizer.pad_token = tokenizer.eos_token
+    coordinator.print_on_master(f"Model Config:\n{model.config}")
+
+    # ==============================
+    # Initialize InferenceEngine
+    # ==============================
+    inference_config = InferenceConfig(
+        dtype=args.dtype,
+        max_batch_size=args.max_batch_size,
+        max_input_len=args.max_input_len,
+        max_output_len=args.max_output_len,
+        prefill_ratio=1.2,
+        block_size=16,
+        tp_size=args.tp_size,
+        use_cuda_kernel=args.use_cuda_kernel,
+    )
+    coordinator.print_on_master(f"Initializing Inference Engine...")
+    engine = InferenceEngine(model, tokenizer, inference_config, model_policy=POLICY_CLS(), verbose=True)
+
+    # ==============================
+    # Generation
+    # ==============================
+    generation_config = GenerationConfig(
+        pad_token_id=tokenizer.eos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        max_length=args.max_length,
+        do_sample=True,
+    )
+    coordinator.print_on_master(f"Generating...")
+    out = engine.generate(prompts=[args.prompt], generation_config=generation_config)
+    coordinator.print_on_master(out[0])
+
+
+# colossalai run --nproc_per_node 1 llama_generation.py -m MODEL_PATH
+if __name__ == "__main__":
+    # ==============================
+    # Parse Arguments
+    # ==============================
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-m", "--model", type=str, help="Path to the model or model name")
+    parser.add_argument(
+        "-p", "--prompt", type=str, default="Introduce some landmarks in the United Kingdom, such as", help="Prompt"
+    )
+    parser.add_argument("-b", "--max_batch_size", type=int, default=1, help="Max batch size")
+    parser.add_argument("-i", "--max_input_len", type=int, default=128, help="Max input length")
+    parser.add_argument("-o", "--max_output_len", type=int, default=128, help="Max output length")
+    parser.add_argument("-t", "--tp_size", type=int, default=1, help="Tensor Parallelism size")
+    parser.add_argument("-d", "--dtype", type=str, default="fp16", help="Data type", choices=["fp16", "fp32", "bf16"])
+    parser.add_argument("--use_cuda_kernel", action="store_true", help="Use CUDA kernel, use Triton by default")
+    parser.add_argument("--max_length", type=int, default=32, help="Max length for generation")
+    args = parser.parse_args()
+
+    infer(args)