added handler.py

handler.py

@@ -0,0 +1,22 @@
+from typing import Dict, List, Any
+import pipeline
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # Preload all the elements you are going to need at inference.
+        # pseudo:
+        a = 1
+
+    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
+        """
+       data args:
+            inputs (:obj: `str` | `PIL.Image` | `np.array`)
+            kwargs
+      Return:
+            A :obj:`list` | `dict`: will be serialized and returned
+        """
+        inputs = data.pop("inputs", data)
+        pipeline.start = inputs
+        output = pipeline.infer()
+        # isinstance(output,str)
+        return {"image": output}

pipeline.py

@@ -0,0 +1,59 @@
+from contextlib import nullcontext
+import torch
+import tiktoken
+from model import GPTConfig, GPT
+
+init_from = 'resume'  # either 'resume' (from an out_dir) or a gpt2 variant (e.g. 'gpt2-xl')
+out_dir = 'out-stinfo'  # ignored if init_from is not 'resume'
+start = "\n"  # or "<|endoftext|>" or etc. Can also specify a file, use as: "FILE:prompt.txt"
+num_samples = 1  # number of samples to draw
+max_new_tokens = 100  # number of tokens generated in each sample
+temperature = 0.6  # 1.0 = no change, < 1.0 = less random, > 1.0 = more random, in predictions
+top_k = 200  # retain only the top_k most likely tokens, clamp others to have 0 probability
+seed = 1337
+device = 'cuda'  # examples: 'cpu', 'cuda', 'cuda:0', 'cuda:1', etc.
+dtype = 'float16'
+
+def infer():
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed(seed)
+    torch.backends.cuda.matmul.allow_tf32 = True  # allow tf32 on matmul
+    torch.backends.cudnn.allow_tf32 = True  # allow tf32 on cudnn
+    device_type = 'cuda' if 'cuda' in device else 'cpu'  # for later use in torch.autocast
+    ptdtype = {
+        'float32': torch.float32,
+        'bfloat16': torch.bfloat16,
+        'float16': torch.float16
+    }[dtype]
+    ctx = nullcontext() if device_type == 'cpu' else torch.amp.autocast(
+        device_type=device_type, dtype=ptdtype)
+
+    ckpt_path = out_dir + '/' + 'ckpt.pt'
+    checkpoint = torch.load(ckpt_path, map_location=device)
+    gptconf = GPTConfig(**checkpoint['model_args'])
+    model = GPT(gptconf)
+    state_dict = checkpoint['model']
+    unwanted_prefix = '_orig_mod.'
+    for k, v in list(state_dict.items()):
+        if k.startswith(unwanted_prefix):
+            state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+    model.load_state_dict(state_dict)
+
+    model.eval()
+    model.to(device)
+
+    enc = tiktoken.get_encoding("gpt2")
+    encode = lambda s: enc.encode(s, allowed_special={"<|endoftext|>"})
+    decode = lambda l: enc.decode(l)
+
+    start_ids = encode(start)
+    x = (torch.tensor(start_ids, dtype=torch.long, device=device)[None, ...])
+
+    with torch.no_grad():
+        with ctx:
+            for k in range(num_samples):
+                y = model.generate(x,
+                                max_new_tokens,
+                                temperature=temperature,
+                                top_k=top_k)
+                return decode(y[0].tolist())