[example] Adds document about how to trace gpt2 model (#3028)

examples/src/main/java/ai/djl/examples/inference/nlp/TextGeneration.java

@@ -59,6 +59,7 @@ public static String generateTextWithPyTorchGreedy()
         SearchConfig config = new SearchConfig();
         config.setMaxSeqLength(60);
 
+        // You can use src/main/python/trace_gpt2.py to trace gpt2 model
         String url = "https://djl-misc.s3.amazonaws.com/test/models/gpt2/gpt2_pt.zip";
 
         Criteria<NDList, CausalLMOutput> criteria =
@@ -160,6 +161,20 @@ public static String[] generateTextWithOnnxRuntimeBeam()
         long padTokenId = 220;
         config.setPadTokenId(padTokenId);
 
+        // The model is converted optimum:
+        // https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#exporting-a-model-using-past-keysvalues-in-the-decoder
+        /*
+         * optimum-cli export onnx --model gpt2 gpt2_onnx/
+         *
+         * from transformers import AutoTokenizer
+         * from optimum.onnxruntime import ORTModelForCausalLM
+         *
+         * tokenizer = AutoTokenizer.from_pretrained("./gpt2_onnx/")
+         * model = ORTModelForCausalLM.from_pretrained("./gpt2_onnx/")
+         * inputs = tokenizer("My name is Arthur and I live in", return_tensors="pt")
+         * gen_tokens = model.generate(**inputs)
+         * print(tokenizer.batch_decode(gen_tokens))
+         */
         String url = "https://djl-misc.s3.amazonaws.com/test/models/gpt2/gpt2_onnx.zip";
 
         Criteria<NDList, CausalLMOutput> criteria =

examples/src/main/python/trace_gpt2.py

@@ -0,0 +1,73 @@
+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+
+model_name = 'gpt2-large'
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+
+# add the EOS token as PAD token to avoid warnings
+model = GPT2LMHeadModel.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id, torchscript=True)
+
+# %% model_inputs
+output_attentions = False
+output_hidden_states = False
+model_inputs = {}
+
+model_inputs['past_key_values'] = torch.load(
+    "../data/nested_tuple_" + model_name + ".pt")
+past_seq = model_inputs['past_key_values'][0][0].shape[-2]
+model_inputs['input_ids'] = torch.tensor([[404]])
+model_inputs['position_ids'] = torch.tensor([[past_seq]])
+# |attention_mask| = `len(past_key_values) + len(input_ids)`
+model_inputs['attention_mask'] = torch.ones(past_seq + 1, dtype=torch.int64)
+
+model_inputs['use_cache'] = True
+model_inputs['token_type_ids'] = None
+
+model_inputs['return_dict'] = False
+model_inputs['output_attentions'] = False
+model_inputs['output_hidden_states'] = False
+
+# This is a testing of text generation
+outputs = model(**model_inputs)
+
+# %% Wrapper class of GPT2LMHeadModel
+from typing import Tuple
+
+class Tracable(torch.nn.Module):
+    def __init__(self, config: dict):
+        super().__init__()
+        self.model = GPT2LMHeadModel.from_pretrained(model_name, pad_token_id=tokenizer.eos_token_id, torchscript=True)
+        self.config = {'use_cache': config.get('use_cache', True),
+                       'token_type_ids': config.get('token_type_ids', None),
+                       'return_dict': config.get('return_dict', False),
+                       'output_attentions': config.get('output_attentions', False),
+                       'output_hidden_states': config.get('output_hidden_states', True)}
+
+    def forward(self, my_input_ids, position_ids, attention_mask, past_key_values):
+        return self.model(input_ids=my_input_ids,
+                          position_ids=position_ids,
+                          attention_mask=attention_mask,
+                          past_key_values=past_key_values,
+                          **self.config)  # return_tensor = True
+
+# %% create class
+config = {}
+tracable = Tracable(config)
+input = (model_inputs['input_ids'],
+         model_inputs['position_ids'],
+         model_inputs['attention_mask'],
+         model_inputs['past_key_values'])
+
+output = tracable(*input)
+
+# %% trace
+tracable.eval()
+
+traced_model = torch.jit.trace(tracable, input)
+torch.jit.save(traced_model, "../traced_GPT2_hidden.pt")
+
+out1 = traced_model(*input)
+
+# %% load back
+loaded_model = torch.jit.load("../traced_GPT2_hidden.pt")
+out2 = loaded_model(*input)