Add GPTQ-GPTJ examples

examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_weight_only/README.md

@@ -41,6 +41,16 @@ sh run_tuning.sh --topology=topology_name --input_model=model_name_or_path --wei
 >
 > `weight_only_bits`, `weight_only_group`, `weight_only_scheme`, and `weight_only_algorithm` can be modified by user. For details, please refer to [README](../../../../../../../docs/source/quantization_weight_only.md).
 
+### Run MLPerf on GPT-J-6B
+Use the following link to get
+[**CNN Daily Mail** datasets](https://github.com/intel-innersource/frameworks.ai.benchmarking.mlperf.submission.inference-submission-v3-1/tree/master/closed/Intel/code/gpt-j/pytorch-cpu#download-and-prepare-dataset)
+and [gpt-j-6B mlperf model](https://github.com/mlcommons/inference/tree/master/language/gpt-j#download-gpt-j-model)
+
+Then run following command to do quantization
+```shell
+sh run_gptj_mlperf_int4.sh
+```
+
 ## 2. Benchmark
 ```bash
 # int8
@@ -102,4 +112,4 @@ from neural_compressor.utils.pytorch import load
 quantized_model = load(tuned_checkpoint, model)
 ```
 --------
-For more details, please refer to the [sample code](./run_clm.py).
+For more details, please refer to the [sample code](./run_clm.py).

examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_weight_only/cnn_dm_dataset.py

@@ -0,0 +1,172 @@
+import sys
+import argparse
+import os
+import time
+import json
+import fnmatch
+
+import copy
+import logging
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Sequence
+
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from datasets import load_dataset, load_from_disk
+from torch.nn.functional import pad
+from torch.utils.data import DataLoader
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+import random
+random.seed(9973)
+
+# Bucketize sequence lengths
+MaxLens = range(0,64,1919)
+Buckets = dict()
+cutoff_step = 64
+min_cutoff = 64
+min_len = 1
+for cutoff in range(min_cutoff, 1921, cutoff_step): # All input sequences
+    Buckets[cutoff] = list(range(min_len, cutoff, 1))
+    min_len = cutoff
+
+#Buckets[1920] = list(range(min_len, 1921, 1))
+
+input_buckets = dict()
+for cutoff, seq_lens in Buckets.items():
+    for seq_len in seq_lens:
+        input_buckets[seq_len] = cutoff
+
+#print("Buckets: {}".format(input_buckets))
+
+IGNORE_INDEX = -100
+DEFAULT_PAD_TOKEN = "[PAD]"
+DEFAULT_EOS_TOKEN = "</s>"
+DEFAULT_BOS_TOKEN = "</s>"
+DEFAULT_UNK_TOKEN = "</s>"
+PROMPT_DICT = {
+    "prompt_input": (
+        "Below is an instruction that describes a task, paired with an input that provides further context. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
+    ),
+    "prompt_no_input": (
+        "Below is an instruction that describes a task. "
+        "Write a response that appropriately completes the request.\n\n"
+        "### Instruction:\n{instruction}\n\n### Response:"
+    ),
+}
+
+
+class CNNDAILYMAIL(object):
+    def __init__(self, model_path, data_path, device="cpu",is_calib=False, num_samples=20, max_len=1920):
+        self.model_path = model_path
+        self.data_path = data_path
+        self.device = device
+        self.num_samples = num_samples
+        self.is_calib = is_calib
+
+        self.padding = "max_length" if self.is_calib else False
+        self.max_len = 2048 if self.is_calib else max_len
+
+        self.calib_collator = self.collate_batch
+        self.pad_max = max_len
+        self.load_tokenizer()
+        self.load_dataset()
+    def load_dataset(self):
+        """ Loads dataset"""
+        with open(self.data_path, "r") as fid:
+            list_data_dict = json.load(fid)
+            self.list_data_dict = copy.deepcopy(list_data_dict)
+
+        if self.num_samples is not None:
+            self.num_samples = min(self.num_samples, len(list_data_dict))
+
+            if self.is_calib:
+                list_data_dict = list_data_dict[:self.num_samples]
+            else:
+                list_data_dict = random.choices(list_data_dict, k=self.num_samples)
+
+        prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"]
+        sources = [prompt_input.format_map(example) for example in list_data_dict]
+        targets = [f"{example['output']}" for example in list_data_dict]
+
+        self.input_ids = []
+        self.input_lens = []
+        for i in range(len(sources)):
+            tok_input = self.tokenize_function(sources[i])
+            self.input_ids.append(tok_input.input_ids)
+
+
+        #if self.num_samples is not None:
+        #    self.num_samples = min(self.num_samples, len(list_data_dict))
+        #    self.input_ids = random.choices(self.input_ids, k=self.num_samples)
+        #    print("Sources: {}".format(len(sources)))
+        #    print("Targets: {}".format(len(targets)))
+        #    sources = random.choices(sources, k=self.num_samples)
+        #    targets = random.choices(targets, k=self.num_samples)
+
+
+        self.sources = sources
+        self.targets = targets
+
+    def load_tokenizer(self):
+        """ Returns the tokenizer """
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            self.model_path,
+            model_max_length=2048,
+            padding_side="right",
+            use_fast=False,
+        )
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+    @torch.no_grad()
+    def tokenize_function(self, text):
+        example = self.tokenizer(text, truncation=True, max_length=self.max_len, return_tensors="pt", padding=self.padding)
+        return example
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, i) -> Dict[str, torch.Tensor]:
+        input_ids = self.input_ids[i]
+        input_len = input_ids.shape[-1]
+        #pad_size = input_buckets[input_len] - input_len
+        #input_ids = F.pad(input_ids, pad=(0, pad_size))
+        return (input_ids, input_len)
+
+    @torch.no_grad()
+    def collate_batch(self, batch):
+        input_ids_padded = []
+
+        for input_ids, input_lens in batch: # input_ids are returned by this dataset (see __getitem__)
+            pad_len = self.pad_max - input_ids.shape[0]
+            #input_ids = F.pad(input_ids, pad=(0, pad_size), value=self.tokenizer.pad_token_id)
+            input_ids_padded.append(input_ids)
+
+        input_ids_padded = torch.vstack(input_ids_padded)
+        return (input_ids_padded, input_ids_padded)
+
+    def get_warmup_samples(self):
+        cutoff_set = set(range(128, 1920, 64))
+        warmup_samples = []
+        prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"]
+        sources = [prompt_input.format_map(example) for example in self.list_data_dict]
+        for source in sources: #self.input_ids:
+            tok_input = self.tokenize_function(source)
+            input_ids = tok_input.input_ids
+            input_len = input_ids.shape[-1]
+            bucket = input_buckets[input_len]
+            if bucket in cutoff_set:
+                #print("inputlen: {}; Bucket: {}".format(input_len, bucket))
+                pad_size = bucket - input_len
+                input_ids = F.pad(input_ids, pad=(0, pad_size), value=0)
+                warmup_samples.append(input_ids)
+                cutoff_set.remove(bucket)
+                if len(cutoff_set)==0:
+                    break
+
+        return warmup_samples