initial llama

.clang-format

+Language: Cpp
+AccessModifierOffset: -4
+AlignAfterOpenBracket: Align
+AllowShortEnumsOnASingleLine: false
+AlignConsecutiveAssignments: true
+AlignConsecutiveDeclarations: true
+AlignEscapedNewlines: Right
+AlignOperands: true
+AlignTrailingComments: true
+AllowAllParametersOfDeclarationOnNextLine: true
+AllowAllArgumentsOnNextLine: true
+AllowShortBlocksOnASingleLine: Empty
+AllowShortCaseLabelsOnASingleLine: false
+AllowShortFunctionsOnASingleLine: Empty
+AllowShortIfStatementsOnASingleLine: Never
+AllowShortLoopsOnASingleLine: false
+AlwaysBreakAfterReturnType: None
+AlwaysBreakBeforeMultilineStrings: false
+AlwaysBreakTemplateDeclarations: true
+BinPackArguments: false
+BinPackParameters: false
+BreakBeforeBinaryOperators: NonAssignment
+BreakBeforeBraces: Stroustrup
+BreakBeforeTernaryOperators: false
+BreakConstructorInitializers: AfterColon
+BreakInheritanceList: AfterColon
+BreakStringLiterals: false
+ColumnLimit: 120
+CompactNamespaces: false
+ConstructorInitializerAllOnOneLineOrOnePerLine: true
+ConstructorInitializerIndentWidth: 4
+ContinuationIndentWidth: 4
+Cpp11BracedListStyle: true
+DerivePointerAlignment: false
+FixNamespaceComments: true
+IndentCaseLabels: true
+IndentPPDirectives: None
+IndentWidth: 4
+IndentWrappedFunctionNames: false
+KeepEmptyLinesAtTheStartOfBlocks: true
+MaxEmptyLinesToKeep: 1
+NamespaceIndentation: None
+PointerAlignment: Left
+ReflowComments: true
+SortIncludes: true
+SortUsingDeclarations: false
+SpaceAfterCStyleCast: false
+SpaceAfterTemplateKeyword: false
+SpaceBeforeAssignmentOperators: true
+SpaceBeforeCtorInitializerColon: false
+SpaceBeforeInheritanceColon: false
+SpaceBeforeParens: ControlStatements
+SpaceInEmptyParentheses: false
+SpacesBeforeTrailingComments: 2
+SpacesInAngles: false
+SpacesInCStyleCastParentheses: false
+SpacesInContainerLiterals: false
+SpacesInParentheses: false
+SpacesInSquareBrackets: false
+Standard: Cpp11
+TabWidth: 4
+UseTab: Never

.dockerignore

+docker
+.dockerignore
+.gitlab
+.gitlab-ci.yml
+
+*build*
+./models
+__pycache__
+.vscode
+translation
+.cache
+*.npy
+*.pth
+*.o
+**/.ipynb_checkpoints
\ No newline at end of file

.flake8

+[flake8]
+ignore = W292
+exclude =
+    *migrations*,
+    # python related
+    *.pyc,
+    .git,
+    __pycache__,
+
+max-line-length=120
+max-complexity=12
+format=pylint
+show_source = True
+statistics = True
+count = True

.github/ISSUE_TEMPLATE/bug_report.yml

+name: "Bug Report"
+description: Submit a bug report
+labels: [ "bug" ]
+body:
+  - type: input
+    id: branch
+    attributes:
+      label: Branch/Tag/Commit
+      description:
+      placeholder: ex,. main
+    validations:
+      required: true
+  - type: input
+    id: docker_image_version
+    attributes:
+      label: Docker Image Version
+      description:
+      placeholder: ex,. nvcr.io/nvidia/pytorch:22.08-py3
+    validations:
+      required: true
+  - type: input
+    id: gpu_name
+    attributes:
+      label: GPU name
+      description:
+      placeholder: ex,. A100
+    validations:
+      required: true
+  - type: input
+    id: cuda_driver
+    attributes:
+      label: CUDA Driver
+      description:
+      placeholder: ex,. 515.65.01
+    validations:
+      required: true
+
+  - type: textarea
+    id: reproduced-steps
+    attributes:
+      label: Reproduced Steps
+      description: Please provide the step to reproduce the bugs
+      render: shell
+      placeholder: |
+        Steps to reproduce your bugs: (please list what script you run and don't say following xxx, otherwise, we will ask again and again)
+
+        1. docker run -ti --gpus all nvcr.io/nvidia/pytorch:22.03-py3 bash
+        2. git clone https://github.com/NVIDIA/FasterTransformer.git
+        3. cd FasterTransformer mkdir build && cd build
+        4. cmake -DSM=80 -DCMAKE_BUILD_TYPE=Release .. && make -j12
+        5. ./bin/bert_example 32 12 32 12 64 0 0
+        6. What error you see.
+  
+    validations:
+      required: true

.gitignore

+*~
+*.o
+*build*/
+./models/
+__pycache__/
+.vscode
+.idea
+./translation
+.cache
+*.npy
+*.pth
+!tests/data/**/*.npy
+/models
+/notebooks
+**/.ipynb_checkpoints/
+.DS_Store
+
+/3rdparty/NeMo/
+/3rdparty/apex/
+

.gitmodules

+[submodule "3rdparty/Megatron-LM"]
+	path = 3rdparty/Megatron-LM
+	url = https://github.com/NVIDIA/Megatron-LM.git
+	branch = v2.6
+[submodule "examples/tensorflow/bert/tensorflow_bert/bert"]
+	path = examples/tensorflow/bert/tensorflow_bert/bert
+	url = https://github.com/google-research/bert.git
+[submodule "examples/pytorch/swin/Swin-Transformer-Quantization/SwinTransformer"]
+	path = examples/pytorch/swin/Swin-Transformer-Quantization/SwinTransformer
+	url = https://github.com/microsoft/Swin-Transformer
+[submodule "examples/pytorch/vit/ViT-quantization/ViT-pytorch"]
+	path = examples/pytorch/vit/ViT-quantization/ViT-pytorch
+	url = https://github.com/jeonsworld/ViT-pytorch
+[submodule "3rdparty/cutlass"]
+	path = 3rdparty/cutlass
+	url = https://github.com/NVIDIA/cutlass.git

3rdparty/CMakeLists.txt

+# Copyright (c) 2019-2023, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+add_subdirectory(common)
+add_subdirectory(trt_fused_multihead_attention)
+if(ENABLE_FP8)
+add_subdirectory(fp8_qgmma_1x1)
+endif()
\ No newline at end of file

3rdparty/INIReader.h

+// Read an INI file into easy-to-access name/value pairs.
+
+// inih and INIReader are released under the New BSD license.
+// Go to the project home page for more info:
+//
+// https://github.com/benhoyt/inih (Initial repo)
+// https://github.com/jtilly/inih  (The reference of this header file)
+/* inih -- simple .INI file parser
+inih is released under the New BSD license (see LICENSE.txt). Go to the project
+home page for more info:
+https://github.com/benhoyt/inih
+https://github.com/jtilly/inih 
+*/
+
+#ifndef __INI_H__
+#define __INI_H__
+
+/* Make this header file easier to include in C++ code */
+#ifdef __cplusplus
+extern "C" {
+#endif
+
+#include <stdio.h>
+
+/* Typedef for prototype of handler function. */
+typedef int (*ini_handler)(void* user, const char* section,
+                           const char* name, const char* value);
+
+/* Typedef for prototype of fgets-style reader function. */
+typedef char* (*ini_reader)(char* str, int num, void* stream);
+
+/* Parse given INI-style file. May have [section]s, name=value pairs
+   (whitespace stripped), and comments starting with ';' (semicolon). Section
+   is "" if name=value pair parsed before any section heading. name:value
+   pairs are also supported as a concession to Python's configparser.
+   For each name=value pair parsed, call handler function with given user
+   pointer as well as section, name, and value (data only valid for duration
+   of handler call). Handler should return nonzero on success, zero on error.
+   Returns 0 on success, line number of first error on parse error (doesn't
+   stop on first error), -1 on file open error, or -2 on memory allocation
+   error (only when INI_USE_STACK is zero).
+*/
+int ini_parse(const char* filename, ini_handler handler, void* user);
+
+/* Same as ini_parse(), but takes a FILE* instead of filename. This doesn't
+   close the file when it's finished -- the caller must do that. */
+int ini_parse_file(FILE* file, ini_handler handler, void* user);
+
+/* Same as ini_parse(), but takes an ini_reader function pointer instead of
+   filename. Used for implementing custom or string-based I/O. */
+int ini_parse_stream(ini_reader reader, void* stream, ini_handler handler,
+                     void* user);
+
+/* Nonzero to allow multi-line value parsing, in the style of Python's
+   configparser. If allowed, ini_parse() will call the handler with the same
+   name for each subsequent line parsed. */
+#ifndef INI_ALLOW_MULTILINE
+#define INI_ALLOW_MULTILINE 1
+#endif
+
+/* Nonzero to allow a UTF-8 BOM sequence (0xEF 0xBB 0xBF) at the start of
+   the file. See http://code.google.com/p/inih/issues/detail?id=21 */
+#ifndef INI_ALLOW_BOM
+#define INI_ALLOW_BOM 1
+#endif
+
+/* Nonzero to allow inline comments (with valid inline comment characters
+   specified by INI_INLINE_COMMENT_PREFIXES). Set to 0 to turn off and match
+   Python 3.2+ configparser behaviour. */
+#ifndef INI_ALLOW_INLINE_COMMENTS
+#define INI_ALLOW_INLINE_COMMENTS 1
+#endif
+#ifndef INI_INLINE_COMMENT_PREFIXES
+#define INI_INLINE_COMMENT_PREFIXES ";"
+#endif
+
+/* Nonzero to use stack, zero to use heap (malloc/free). */
+#ifndef INI_USE_STACK
+#define INI_USE_STACK 1
+#endif
+
+/* Stop parsing on first error (default is to keep parsing). */
+#ifndef INI_STOP_ON_FIRST_ERROR
+#define INI_STOP_ON_FIRST_ERROR 0
+#endif
+
+/* Maximum line length for any line in INI file. */
+#ifndef INI_MAX_LINE
+#define INI_MAX_LINE 200
+#endif
+
+#ifdef __cplusplus
+}
+#endif
+
+/* inih -- simple .INI file parser
+inih is released under the New BSD license (see LICENSE.txt). Go to the project
+home page for more info:
+https://github.com/benhoyt/inih
+*/
+
+#if defined(_MSC_VER) && !defined(_CRT_SECURE_NO_WARNINGS)
+#define _CRT_SECURE_NO_WARNINGS
+#endif
+
+#include <stdio.h>
+#include <ctype.h>
+#include <string.h>
+
+#if !INI_USE_STACK
+#include <stdlib.h>
+#endif
+
+#define MAX_SECTION 50
+#define MAX_NAME 50
+
+/* Strip whitespace chars off end of given string, in place. Return s. */
+inline static char* rstrip(char* s)
+{
+    char* p = s + strlen(s);
+    while (p > s && isspace((unsigned char)(*--p)))
+        *p = '\0';
+    return s;
+}
+
+/* Return pointer to first non-whitespace char in given string. */
+inline static char* lskip(const char* s)
+{
+    while (*s && isspace((unsigned char)(*s)))
+        s++;
+    return (char*)s;
+}
+
+/* Return pointer to first char (of chars) or inline comment in given string,
+   or pointer to null at end of string if neither found. Inline comment must
+   be prefixed by a whitespace character to register as a comment. */
+inline static char* find_chars_or_comment(const char* s, const char* chars)
+{
+#if INI_ALLOW_INLINE_COMMENTS
+    int was_space = 0;
+    while (*s && (!chars || !strchr(chars, *s)) &&
+           !(was_space && strchr(INI_INLINE_COMMENT_PREFIXES, *s))) {
+        was_space = isspace((unsigned char)(*s));
+        s++;
+    }
+#else
+    while (*s && (!chars || !strchr(chars, *s))) {
+        s++;
+    }
+#endif
+    return (char*)s;
+}
+
+/* Version of strncpy that ensures dest (size bytes) is null-terminated. */
+inline static char* strncpy0(char* dest, const char* src, size_t size)
+{
+    strncpy(dest, src, size);
+    dest[size - 1] = '\0';
+    return dest;
+}
+
+/* See documentation in header file. */
+inline int ini_parse_stream(ini_reader reader, void* stream, ini_handler handler,
+                     void* user)
+{
+    /* Uses a fair bit of stack (use heap instead if you need to) */
+#if INI_USE_STACK
+    char line[INI_MAX_LINE];
+#else
+    char* line;
+#endif
+    char section[MAX_SECTION] = "";
+    char prev_name[MAX_NAME] = "";
+
+    char* start;
+    char* end;
+    char* name;
+    char* value;
+    int lineno = 0;
+    int error = 0;
+
+#if !INI_USE_STACK
+    line = (char*)malloc(INI_MAX_LINE);
+    if (!line) {
+        return -2;
+    }
+#endif
+
+    /* Scan through stream line by line */
+    while (reader(line, INI_MAX_LINE, stream) != NULL) {
+        lineno++;
+
+        start = line;
+#if INI_ALLOW_BOM
+        if (lineno == 1 && (unsigned char)start[0] == 0xEF &&
+                           (unsigned char)start[1] == 0xBB &&
+                           (unsigned char)start[2] == 0xBF) {
+            start += 3;
+        }
+#endif
+        start = lskip(rstrip(start));
+
+        if (*start == ';' || *start == '#') {
+            /* Per Python configparser, allow both ; and # comments at the
+               start of a line */
+        }
+#if INI_ALLOW_MULTILINE
+        else if (*prev_name && *start && start > line) {
+
+#if INI_ALLOW_INLINE_COMMENTS
+        end = find_chars_or_comment(start, NULL);
+        if (*end)
+            *end = '\0';
+        rstrip(start);
+#endif
+
+            /* Non-blank line with leading whitespace, treat as continuation
+               of previous name's value (as per Python configparser). */
+            if (!handler(user, section, prev_name, start) && !error)
+                error = lineno;
+        }
+#endif
+        else if (*start == '[') {
+            /* A "[section]" line */
+            end = find_chars_or_comment(start + 1, "]");
+            if (*end == ']') {
+                *end = '\0';
+                strncpy0(section, start + 1, sizeof(section));
+                *prev_name = '\0';
+            }
+            else if (!error) {
+                /* No ']' found on section line */
+                error = lineno;
+            }
+        }
+        else if (*start) {
+            /* Not a comment, must be a name[=:]value pair */
+            end = find_chars_or_comment(start, "=:");
+            if (*end == '=' || *end == ':') {
+                *end = '\0';
+                name = rstrip(start);
+                value = lskip(end + 1);
+#if INI_ALLOW_INLINE_COMMENTS
+                end = find_chars_or_comment(value, NULL);
+                if (*end)
+                    *end = '\0';
+#endif
+                rstrip(value);
+
+                /* Valid name[=:]value pair found, call handler */
+                strncpy0(prev_name, name, sizeof(prev_name));
+                if (!handler(user, section, name, value) && !error)
+                    error = lineno;
+            }
+            else if (!error) {
+                /* No '=' or ':' found on name[=:]value line */
+                error = lineno;
+            }
+        }
+
+#if INI_STOP_ON_FIRST_ERROR
+        if (error)
+            break;
+#endif
+    }
+
+#if !INI_USE_STACK
+    free(line);
+#endif
+
+    return error;
+}
+
+/* See documentation in header file. */
+inline int ini_parse_file(FILE* file, ini_handler handler, void* user)
+{
+    return ini_parse_stream((ini_reader)fgets, file, handler, user);
+}
+
+/* See documentation in header file. */
+inline int ini_parse(const char* filename, ini_handler handler, void* user)
+{
+    FILE* file;
+    int error;
+
+    file = fopen(filename, "r");
+    if (!file)
+        return -1;
+    error = ini_parse_file(file, handler, user);
+    fclose(file);
+    return error;
+}
+
+#endif /* __INI_H__ */
+
+
+#ifndef __INIREADER_H__
+#define __INIREADER_H__
+
+#include <map>
+#include <set>
+#include <string>
+
+// Read an INI file into easy-to-access name/value pairs. (Note that I've gone
+// for simplicity here rather than speed, but it should be pretty decent.)
+class INIReader
+{
+public:
+    // Empty Constructor
+    INIReader() {};
+
+    // Construct INIReader and parse given filename. See ini.h for more info
+    // about the parsing.
+    INIReader(std::string filename);
+
+    // Construct INIReader and parse given file. See ini.h for more info
+    // about the parsing.
+    INIReader(FILE *file);
+    ~INIReader();
+    // Return the result of ini_parse(), i.e., 0 on success, line number of
+    // first error on parse error, or -1 on file open error.
+    int ParseError() const;
+
+    // Return the list of sections found in ini file
+    const std::set<std::string>& Sections() const;
+
+    // Get a string value from INI file, returning default_value if not found.
+    std::string Get(std::string section, std::string name,
+                    std::string default_value) const;
+    std::string Get(std::string section, std::string name) const;
+
+    // Get an integer (long) value from INI file, returning default_value if
+    // not found or not a valid integer (decimal "1234", "-1234", or hex "0x4d2").
+    long GetInteger(std::string section, std::string name, long default_value) const;
+    long GetInteger(std::string section, std::string name) const;
+
+    // Get a real (floating point double) value from INI file, returning
+    // default_value if not found or not a valid floating point value
+    // according to strtod().
+    double GetReal(std::string section, std::string name, double default_value) const;
+
+    // Get a single precision floating point number value from INI file, returning
+    // default_value if not found or not a valid floating point value
+    // according to strtof().
+    float GetFloat(std::string section, std::string name, float default_value) const;
+    float GetFloat(std::string section, std::string name) const;
+  
+    // Get a boolean value from INI file, returning default_value if not found or if
+    // not a valid true/false value. Valid true values are "true", "yes", "on", "1",
+    // and valid false values are "false", "no", "off", "0" (not case sensitive).
+    bool GetBoolean(std::string section, std::string name, bool default_value) const;
+
+protected:
+    int _error;
+    std::map<std::string, std::string> _values;
+    std::set<std::string> _sections;
+    static std::string MakeKey(std::string section, std::string name);
+    static int ValueHandler(void* user, const char* section, const char* name,
+                            const char* value);
+};
+
+#endif  // __INIREADER_H__
+
+
+#ifndef __INIREADER__
+#define __INIREADER__
+
+#include <algorithm>
+#include <cctype>
+#include <cstdlib>
+
+inline INIReader::INIReader(std::string filename)
+{
+    _error = ini_parse(filename.c_str(), ValueHandler, this);
+}
+
+inline INIReader::INIReader(FILE *file)
+{
+    _error = ini_parse_file(file, ValueHandler, this);
+}
+
+inline int INIReader::ParseError() const
+{
+    return _error;
+}
+
+inline INIReader::~INIReader() { }
+
+inline const std::set<std::string>& INIReader::Sections() const
+{
+    return _sections;
+}
+
+inline std::string INIReader::Get(std::string section, std::string name, std::string default_value) const
+{
+    std::string key = MakeKey(section, name);
+    return _values.count(key) ? _values.at(key) : default_value;
+}
+
+inline std::string INIReader::Get(std::string section, std::string name) const
+{
+    std::string key = MakeKey(section, name);
+    if(_values.count(key)) return _values.at(key);
+    else
+    {
+        printf("[ERROR] Does not find the section %s with name %s. \n", section.c_str(), name.c_str());
+        exit(-1);
+    }
+}
+
+inline long INIReader::GetInteger(std::string section, std::string name, long default_value) const
+{
+    std::string valstr = Get(section, name, "");
+    const char* value = valstr.c_str();
+    char* end;
+    // This parses "1234" (decimal) and also "0x4D2" (hex)
+    long n = strtol(value, &end, 0);
+    return end > value ? n : default_value;
+}
+
+inline long INIReader::GetInteger(std::string section, std::string name) const
+{
+    std::string valstr = Get(section, name, "");
+    const char* value = valstr.c_str();
+    char* end;
+    // This parses "1234" (decimal) and also "0x4D2" (hex)
+    long n = strtol(value, &end, 0);
+    if(end <= value)
+    {
+        printf("[ERROR] Does not find the section %s with name %s. \n", section.c_str(), name.c_str());
+        exit(-1);
+    }
+    return n;
+}
+
+inline double INIReader::GetReal(std::string section, std::string name, double default_value) const
+{
+    std::string valstr = Get(section, name, "");
+    const char* value = valstr.c_str();
+    char* end;
+    double n = strtod(value, &end);
+    return end > value ? n : default_value;
+}
+
+inline float INIReader::GetFloat(std::string section, std::string name, float default_value) const
+{
+    std::string valstr = Get(section, name, "");
+    const char* value = valstr.c_str();
+    char* end;
+    float n = strtof(value, &end);
+    return end > value ? n : default_value;
+}
+
+inline float INIReader::GetFloat(std::string section, std::string name) const
+{
+    std::string valstr = Get(section, name, "");
+    const char* value = valstr.c_str();
+    char* end;
+    float n = strtof(value, &end);
+    if(end <= value)
+    {
+        printf("[ERROR] Does not find the section %s with name %s. \n", section.c_str(), name.c_str());
+        exit(-1);
+    }
+    return n;
+}
+
+inline bool INIReader::GetBoolean(std::string section, std::string name, bool default_value) const
+{
+    std::string valstr = Get(section, name, "");
+    // Convert to lower case to make string comparisons case-insensitive
+    std::transform(valstr.begin(), valstr.end(), valstr.begin(), ::tolower);
+    if (valstr == "true" || valstr == "yes" || valstr == "on" || valstr == "1")
+        return true;
+    else if (valstr == "false" || valstr == "no" || valstr == "off" || valstr == "0")
+        return false;
+    else
+        return default_value;
+}
+
+inline std::string INIReader::MakeKey(std::string section, std::string name)
+{
+    std::string key = section + "=" + name;
+    // Convert to lower case to make section/name lookups case-insensitive
+    std::transform(key.begin(), key.end(), key.begin(), ::tolower);
+    return key;
+}
+
+inline int INIReader::ValueHandler(void* user, const char* section, const char* name,
+                            const char* value)
+{
+    INIReader* reader = (INIReader*)user;
+    std::string key = MakeKey(section, name);
+    if (reader->_values[key].size() > 0)
+        reader->_values[key] += "\n";
+    reader->_values[key] += value;
+    reader->_sections.insert(section);
+    return 1;
+}
+
+#endif  // __INIREADER__
\ No newline at end of file

3rdparty/Megatron-LM/.gitignore

+__pycache__
+

3rdparty/Megatron-LM/.gitlab-ci.yml

+image: gitlab-master.nvidia.com/dl/dgx/pytorch:20.12-py3-devel
+
+test:
+  script:
+    - pytest --junitxml=report.xml tests
+  artifacts:
+    when: always
+    reports:
+      junit: report.xml
+    
\ No newline at end of file

3rdparty/Megatron-LM/LICENSE

+The following applies to all files unless otherwise noted:
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3rdparty/Megatron-LM/README.md

+Megatron ([1](https://arxiv.org/pdf/1909.08053.pdf) and [2](https://arxiv.org/pdf/2104.04473.pdf)) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor and pipeline), and multi-node pre-training of transformer based models such as [GPT](https://arxiv.org/abs/2005.14165), [BERT](https://arxiv.org/pdf/1810.04805.pdf), and [T5](https://arxiv.org/abs/1910.10683) using mixed precision.
+
+Below are some of the projects where we have directly used Megatron:
+* [BERT and GPT Studies Using Megatron](https://arxiv.org/pdf/1909.08053.pdf)
+* [BioMegatron: Larger Biomedical Domain Language Model](https://www.aclweb.org/anthology/2020.emnlp-main.379.pdf)
+* [End-to-End Training of Neural Retrievers for Open-Domain Question Answering](https://arxiv.org/abs/2101.00408)
+* [Large Scale Multi-Actor Generative Dialog Modeling](https://www.aclweb.org/anthology/2020.acl-main.8.pdf)
+* [Local Knowledge Powered Conversational Agents](https://arxiv.org/abs/2010.10150)
+* [MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models](https://www.aclweb.org/anthology/2020.emnlp-main.226.pdf)
+* [RACE Reading Comprehension Dataset Leaderboard](http://www.qizhexie.com/data/RACE_leaderboard.html)
+* [Scaling Language Model Training to a Trillion Parameters Using Megatron](https://arxiv.org/pdf/2104.04473.pdf)
+* [Training Question Answering Models From Synthetic Data](https://www.aclweb.org/anthology/2020.emnlp-main.468.pdf)
+
+Megatron is also used in [NeMo Megatron](https://developer.nvidia.com/nvidia-nemo#nemo-megatron), a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters.
+
+Our codebase is capable of efficiently training very large (hundreds of billions of parameters) language models with both model and data parallelism. To demonstrate how the code scales with multiple GPUs and model sizes, we consider GPT models from 1 billion all the way to 1 trillion parameters. All models use a vocabulary size of 51,200 and a sequence length of 2048. We vary hidden size, number of attention heads, and number of layers to arrive at a specifc model size. As the model size increases, we also modestly increase the batch size. We leverage [NVIDIA's Selene supercomputer](https://www.top500.org/system/179842/) to perform scaling studies and use up to 3072 [A100](https://www.nvidia.com/en-us/data-center/a100/) GPUs for the largest model. Each cluster node has 8 NVIDIA 80GB A100 GPUs. The table below shows the model configurations along with the achieved FLOPs (both per GPU and aggregate over all GPUs). Note that these results are from benchmark runs and these models were not trained to convergence; however, the FLOPs are measured for end-to-end training, i.e., includes all operations including data loading, optimization, and even logging.
+
+Additionally, the model parallel size column reports a combined tensor and pipeline parallelism degrees. For numbers larger than 8, typically tensor parallel of size 8 was used. So, for example, the 145B model reports the total model parallel size of 64, which means that this setup used TP=8 and PP=8.
+
+![Cases](images/cases_april2021.png)
+
+All the cases from 1 billion to 1 trillion parameters achieve more than 43% half precision utilization, which is high for an end-to-end application. We observe that initially the utilization remains constant but as hidden size increases for larger models, utilization starts increasing and reaches 52% for the largest model. We also note that achieved aggregate petaFLOPs across all GPUs increases almost linearly with number of GPUs, demonstrating good weak scaling.
+
+
+# Contents
+   * [Contents](#contents)
+   * [Setup](#setup)
+      * [Downloading Checkpoints](#downloading-checkpoints)
+   * [Usage](#usage)
+   * [Training](#training)
+      * [Data Preprocessing](#data-preprocessing)
+      * [BERT Pretraining](#bert-pretraining)
+      * [GPT Pretraining](#gpt-pretraining)
+      * [T5 Pretraining](#t5-pretraining)
+      * [Distributed Pretraining](#distributed-pretraining)
+      * [GPT-3 Example](#gpt-3-example)
+   * [Evaluation and Tasks](#evaluation-and-tasks)
+      * [GPT Text Generation](#gpt-text-generation)
+      * [GPT Evaluation](#gpt-evaluation)
+         * [WikiText Perplexity Evaluation](#wikitext-perplexity-evaluation)
+         * [LAMBADA Cloze Accuracy](#lambada-cloze-accuracy)
+      * [BERT Task Evaluation](#bert-task-evaluation)
+         * [RACE Evaluation](#race-evaluation)
+         * [MNLI Evaluation](#mnli-evaluation)
+   * [Datasets](#datasets)
+      * [Collecting Wikipedia Training Data](#collecting-wikipedia-training-data)
+      * [Collecting GPT Webtext Data](#collecting-gpt-webtext-data)
+
+# Setup
+We have tested Megatron with [NGC's PyTorch container](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) version 20.12, which uses python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3.
+
+To use this repository, please install the latest supported versions of PyTorch with GPU support (python 3.8, pytorch 1.8, cuda 11.1, and nccl 2.8.3 and above) and NVIDIA [APEX](https://github.com/NVIDIA/apex#quick-start). We strongly recommend using one of [NGC's recent PyTorch containers](https://ngc.nvidia.com/catalog/containers/nvidia:pytorch) (the latest compatible version at time of publication can be pulled with `docker pull nvcr.io/nvidia/pytorch:20.12-py3`). Data preprocessing requires [NLTK](https://www.nltk.org/install.html), though this is not required for training, evaluation, or downstream tasks.
+
+## Downloading Checkpoints
+We have provided pretrained [BERT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_bert_345m) and [GPT-345M](https://ngc.nvidia.com/catalog/models/nvidia:megatron_lm_345m) checkpoints for use to evaluate or finetuning downstream tasks. To access these checkpoints, first [sign up](https://ngc.nvidia.com/signup) for and [setup](https://ngc.nvidia.com/setup/installers/cli) the NVIDIA GPU Cloud (NGC) Registry CLI. Further documentation for downloading models can be found in the [NGC documentation](https://docs.nvidia.com/dgx/ngc-registry-cli-user-guide/index.html#topic_6_4_1).
+
+Alternatively, you can directly download the checkpoints using:
+
+<pre>
+BERT-345M-uncased: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_uncased/zip -O megatron_bert_345m_v0.1_uncased.zip
+BERT-345M-cased: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_bert_345m/versions/v0.1_cased/zip -O megatron_bert_345m_v0.1_cased.zip
+GPT-345M: wget --content-disposition https://api.ngc.nvidia.com/v2/models/nvidia/megatron_lm_345m/versions/v0.0/zip -O megatron_lm_345m_v0.0.zip
+</pre>
+
+The models require vocabulary files to run. The BERT  WordPiece vocab file can be extracted from Google's pretrained BERT models: [uncased](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt), [cased](https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt). The GPT [vocab file](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json) and [merge table](https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt) can be downloaded directly.
+
+# Usage
+
+After installation, there are several possible workflows. The most comprehensive is:
+1. Data preprocessing
+2. Pretraining
+3. Finetuning (Optional for zero-shot tasks)
+4. Downstream task evaluation or text generation
+
+However, steps 1 and 2 can be replaced by using one of the pretrained models mentioned above.
+
+We've provided several scripts for pretraining both BERT and GPT in [`examples`](./examples) directory, as well as scripts for both zero-shot and fine-tuned downstream tasks including MNLI, RACE, WikiText103, and LAMBADA evaluation. There is also a script for GPT interactive text generation.
+
+# Training
+## Data Preprocessing
+The training data requires preprocessing. First, place your training data in a loose json format, with one json containing a text sample per line. For example:
+<pre>
+{"src": "www.nvidia.com", "text": "The quick brown fox", "type": "Eng", "id": "0", "title": "First Part"}
+{"src": "The Internet", "text": "jumps over the lazy dog", "type": "Eng", "id": "42", "title": "Second Part"}
+</pre>
+
+The name of the `text` field of the json can be changed by using the `--json-key` flag in [`preprocess_data.py`](./tools/preprocess_data.py) The other metadata are optional and are not used in training.
+
+The loose json is then processed into a binary format for training. To convert the json into mmap, cached index file, or the lazy loader format use `preprocess_data.py`. Set the `--dataset-impl` flag to `mmap`, `cached`, or `lazy`, respectively (default is `mmap`). An example script to prepare data for BERT training is:
+<pre>
+python tools/preprocess_data.py \
+       --input my-corpus.json \
+       --output-prefix my-bert \
+       --vocab bert-vocab.txt \
+       --dataset-impl mmap \
+       --tokenizer-type BertWordPieceLowerCase \
+       --split-sentences
+</pre>
+
+The output will be two files named, in this case, `my-bert_text_sentence.bin` and `my-bert_text_sentence.idx`. The `--data-path` specified in later BERT training is the full path and new filename, but without the file extension.
+
+For T5 use the same preprocessing as BERT, perhaps renaming it to:
+<pre>
+       --output-prefix my-t5 \
+</pre>
+
+Some minor modifications are required for GPT data preprocessing, namely, the addition of a merge table, an end-of-document token, removal of sentence splitting, and a change to the tokenizer type:
+<pre>
+python tools/preprocess_data.py \
+       --input my-corpus.json \
+       --output-prefix my-gpt2 \
+       --vocab gpt2-vocab.json \
+       --dataset-impl mmap \
+       --tokenizer-type GPT2BPETokenizer \
+       --merge-file gpt2-merges.txt \
+       --append-eod
+</pre>
+
+Here the output files are named `my-gpt2_text_document.bin` and `my-gpt2_text_document.idx`. As before, in GPT training, use the longer name without the extension as `--data-path`.
+
+Further command line arguments are described in the source file [`preprocess_data.py`](./tools/preprocess_data.py).
+
+## BERT Pretraining
+
+
+The `examples/pretrain_bert.sh` script runs single GPU 345M parameter BERT pretraining. Debugging is the primary use for single GPU training, as the code base and command line arguments are optimized for highly distributed training. Most of the arguments are fairly self-explanatory. By default, the learning rate decays linearly over the training iterations starting at `--lr` to a minimum set by `--min-lr` over `--lr-decay-iters` iterations. The fraction of training iterations used for warmup is set by `--lr-warmup-fraction`. While this is single GPU training, the batch size specified by `--micro-batch-size` is a single forward-backward path batch-size and the code will perform gradient accumulation steps until it reaches `global-batch-size` which is the batch size per iteration. The data is partitioned into a 949:50:1 ratio for training/validation/test sets (default is 969:30:1). This partitioning happens on the fly, but is consistent across runs with the same random seed (1234 by default, or specified manually with `--seed`). We use `train-iters` as the training iterations requested. Alternatively, one can provide `--train-samples` which is total number of samples to train on. If this option is present, then instead of providing `--lr-decay-iters`, one will need to provide `--lr-decay-samples`.
+
+The logging, checkpoint-saving, and evaluation intervals are specified. Checkpointing the activations facilitates the training of larger models and/or batches. Note that the `--data-path` now includes the additional `_text_sentence` suffix added in preprocessing, but does not include the file extensions.
+
+<pre>
+CHECKPOINT_PATH=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+DATA_PATH=my-bert_text_sentence
+
+BERT_ARGS="--num-layers 24 \
+           --hidden-size 1024 \
+           --num-attention-heads 16 \
+           --seq-length 512 \
+           --max-position-embeddings 512 \
+           --lr 0.0001 \
+           --lr-decay-iters 990000 \
+           --train-iters 2000000 \
+           --min-lr 0.00001 \
+           --lr-warmup-fraction 0.01 \
+	   --micro-batch-size 4 \
+           --global-batch-size 8 \
+           --vocab-file $VOCAB_FILE \
+           --split 949,50,1 \
+           --fp16"
+
+OUTPUT_ARGS="--log-interval 10 \
+             --save-interval 500 \
+             --eval-interval 100 \
+             --eval-iters 10 \
+             --activations-checkpoint-method uniform"
+
+python pretrain_bert.py \
+       $BERT_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH
+</pre>
+
+Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
+
+
+## GPT Pretraining
+
+The `examples/pretrain_gpt.sh` script runs single GPU 345M parameter GPT pretraining. As mentioned above, single GPU training is primarily intended for debugging purposes, as the code is optimized for distributed training.
+
+It follows largely the same format as the previous BERT script with a few notable differences: the tokenization scheme used is BPE (which requires a merge table and a `json` vocabulary file) instead of WordPiece, the model architecture allows for longer sequences (note that the max position embedding must be greater than or equal to the maximum sequence length), and the `--lr-decay-style` has been set to cosine decay.  Note that the `--data-path` now includes the additional `_text_document` suffix added in preprocessing, but does not include the file extensions.
+
+<pre>
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+DATA_PATH=my-gpt2_text_document
+
+GPT_ARGS="--num-layers 24 \
+          --hidden-size 1024 \
+          --num-attention-heads 16 \
+          --seq-length 1024 \
+          --max-position-embeddings 1024 \
+          --micro-batch-size 4 \
+          --global-batch-size 8 \
+          --lr 0.00015 \
+          --train-iters 500000 \
+          --lr-decay-iters 320000 \
+          --lr-decay-style cosine \
+          --vocab-file $VOCAB_FILE \
+          --merge-file $MERGE_FILE \
+          --lr-warmup-fraction .01 \
+          --fp16"
+
+OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
+
+python pretrain_gpt.py \
+       $GPT_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH \
+</pre>
+
+Further command line arguments are described in the source file [`arguments.py`](./megatron/arguments.py).
+
+## T5 Pretraining
+
+Very similar to BERT and GPT, the `examples/pretrain_t5.sh` script runs single GPU "base" (~220M parameter) T5 pretraining. The primary difference from BERT and GPT is the addition of the following arguments to accommodate the T5 architecture:
+
+* `--kv-channels` sets the inner dimension of the "key" and "value" matrices of all attention mechanisms in the model. For BERT and GPT this defaults to the hidden size divided by the number of attention heads, but can be configured for T5.
+
+* `--ffn-hidden-size` sets the hidden size in the feed-forward networks within a transformer layer. For BERT and GPT this defaults to 4 times the transformer hidden size, but can be configured for T5.
+
+* `--encoder-seq-length` and `--decoder-seq-length` set the sequence length for the encoder and decoder separately.
+
+All of the other arguments remain as they were for BERT and GPT pretraining.
+
+<pre>
+CHECKPOINT_PATH=checkpoints/t5_base
+VOCAB_FILE=t5-vocab.txt
+DATA_PATH=my-t5_text_sentence
+
+T5_ARGS="--num-layers 24 \
+         --hidden-size 1024 \
+         --num-attention-heads 16 \
+         --kv-channels 64 \
+         --ffn-hidden-size 3072 \
+         --encoder-seq-length 512 \
+         --decoder-seq-length 128 \
+         --max-position-embeddings 512 \
+         --lr 0.0001 \
+         --lr-decay-iters 990000 \
+         --train-iters 2000000 \
+         --min-lr 0.00001 \
+         --lr-warmup-fraction 0.01 \
+         --micro-batch-size 16 \
+         --global-batch-size 2048 \
+         --vocab-file $VOCAB_FILE \
+         --vocab-extra-ids 100 \
+         --split 949,50,1 \
+         --fp16"
+
+OUTPUT_ARGS=&#60;same as those in <a href="#bert-pretraining">BERT pretraining</a> above&#62;
+
+python pretrain_t5.py \
+       $T5_ARGS \
+       $OUTPUT_ARGS \
+       --save $CHECKPOINT_PATH \
+       --load $CHECKPOINT_PATH \
+       --data-path $DATA_PATH
+</pre>
+
+
+## Distributed Pretraining
+
+The `examples/pretrain_{bert,gpt,t5}_distributed.sh` scripts use the PyTorch distributed launcher for distributed training. As such, multi-node training can be achieved by properly setting environment variables and using `init_method='env://'` in the launcher. See the official PyTorch [documentation](https://pytorch.org/docs/stable/distributed.html#launch-utility) for further description of these [environment variables](https://pytorch.org/docs/stable/distributed.html#environment-variable-initialization). By default, multi-node training uses the [nccl](https://developer.nvidia.com/nccl) distributed backend. A simple set of additional arguments and the use of the PyTorch distributed module with the Python flag `-m torch.distributed.launch`, detailed below, are the only additional requirements to adopt distributed training.
+
+We use two types of parallelism: data and model parallelism. We facilitate two distributed data parallel implementations: a simple one of our own that performs gradient all-reduce at the end of back propagation step, and Torch's distributed data parallel wrapper that overlaps gradient reduction with back propagation computation. To switch between these two options use `--DDP-impl local` or `--DDP-impl torch`, respectively. As expected, Torch distributed data parallelism is more efficient at larger model sizes. For example, for the 8.3 billion parameters model running on 512 GPUs, the scaling increases from 60% to 76% when Torch's distributed data parallel is used. However, the overlapping method requires more memory and for some configurations (e.g., 2.5 billion parameters using 2-way model parallel and 1.2 billion parameters with no model parallel) can make the overall training slower as a result. We empirically found that using a smaller model in those cases improves the training time.
+
+Second, we developed a simple and efficient two-dimensional model-parallel approach. To use tensor model parallelism (splitting execution of a single transformer module over multiple GPUs), add the `--tensor-model-parallel-size` flag to specify the number of GPUs among which to split the model, along with the arguments passed to the distributed launcher as mentioned above. To use pipeline model parallelism (sharding the transformer modules into stages with an equal number of transformer modules on each stage, and then pipelining execution by breaking the batch into smaller microbatches), use the `--pipeline-model-parallel-size` flag to specify the number of stages to split the model into (e.g., splitting a model with 24 transformer layers across 4 stages would mean each stage gets 6 transformer layers each).
+
+<!-- The number of microbatches in a per-pipeline minibatch is controlled by the `--num-microbatches-in-minibatch` argument. With `WORLD_SIZE` GPUs, `TENSOR_MP_SIZE` tensor-model-parallel size, `PIPELINE_MP_SIZE` pipeline-model-parallel-size, `WORLD_SIZE`/(`TENSOR_MP_SIZE` * `PIPELINE_MP_SIZE`) GPUs will be used for data parallelism. The default values for `--tensor-model-parallel-size` and `--pipeline-model-parallel-size` is 1, which will not implement either form of model parallelism. -->
+
+We have examples of how to use these two different forms of model parallelism the example scripts ending in `distributed_with_mp.sh`:
+
+Other than these minor changes, the distributed training is identical to the training on a single GPU.
+
+Distributed training:
+<pre>
+WORLD_SIZE=8
+TENSOR_MP_SIZE=2
+PIPELINE_MP_SIZE=2
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT_PATH=&#60;same as above&#62;
+VOCAB_FILE=&#60;same as above&#62;
+DATA_PATH=&#60;same as above&#62;
+MODEL_ARGS=&#60;same as above&#62;
+OUTPUT_ARGS=&#60;same as above&#62;
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./pretrain_<model>.py \
+                $MODEL_ARGS \
+                $OUTPUT_ARGS \
+                --save $CHECKPOINT_PATH \
+                --load $CHECKPOINT_PATH \
+                --data-path $DATA_PATH \
+                --tensor-model-parallel-size $TENSOR_MP_SIZE \
+                --pipeline-model-parallel-size $PIPELINE_MP_SIZE \
+                --DDP-impl torch
+</pre>
+
+The interleaved pipelining schedule (more details in Section 2.2.2 of [our paper](https://arxiv.org/pdf/2104.04473.pdf)) can be enabled using the `--num-layers-per-virtual-pipeline-stage` argument, which controls the number of transformer layers in a virtual stage (by default with the non-interleaved schedule, each GPU will execute a single virtual stage with `NUM_LAYERS / PIPELINE_MP_SIZE` transformer layers). The total number of layers in the transformer model should be divisible by this argument value. Additionally, the number of microbatches in the pipeline (computed as `GLOBAL_BATCH_SIZE / (DATA_PARALLEL_SIZE * MICRO_BATCH_SIZE)`) should be divisible by the `PIPELINE_MP_SIZE` when using this schedule (this condition is checked in an assertion in the code). The interleaved schedule is not supported for pipelines with 2 stages (`PIPELINE_MP_SIZE=2`).
+
+## Activation Checkpointing and Recomputation
+
+To reduce GPU memory usage so deploy a large model to a training system, we support activation checkpointing and recomputation. We use a Transformer layer as the unit of checkpointing because the activation size bloats in the middle of a Transformer layer so checkpointing the input of a Transformer layer is storage-efficient. We support two activation checkpointing methods: `uniform` and `block`.
+
+Uniform method uniformly divides the Transformer layers into groups of layers and stores the input activations of each group in the memory. The baseline group size is 1 and, in this case, the input activation of each Transformer layer is checkpointed. When the GPU memory is insufficient, increasing the number of layers per group reduces the memory usage thus enables running a bigger model. For example, when using the number of layers per group of 4, the input activation of each group of 4 Transformer layers is checkpointed.
+
+Block method checkpoints the input activations of a set number of individual Transformer layers per pipeline stage and do the rest of layers without any checkpointing. This method can be used to skip checkpointing some Transformer layers until the GPU memory is fully used, which is applicable only when there is unused GPU memory. Checkpointing fewer transformer layers avoids unnecessary activation recomputation in the backprop thus improves training performance. For example, when we specify 5 layers to checkpoint of 8 layers per pipeline stage, the input activations of only the first 5 Transformer layers are checkpointed and activation recomputation for the rest 3 layers is not needed in the backprop.
+
+
+## GPT-3 Example
+
+In `examples/pretrain_gpt3_175B.sh` we have provided an example of how to configure Megatron to run [GPT-3](https://arxiv.org/abs/2005.14165) with 175 billion parameters on 1024 GPUs. The script is designed for [slurm](https://slurm.schedmd.com/documentation.html) with [pyxis](https://github.com/NVIDIA/pyxis) plugin but can be easily adopted to any other scheduler. It uses 8-way and 16-way tensor and pipeline parallelism, respectively. With options `global-batch-size 1536` and `rampup-batch-size 16 16 5859375`, the training will start with global batch size 16 and linearly increase the global batch size to 1536 over 5,859,375 samples with incrmeental steps 16. The training dataset can be either a single set or a multiple datasets combined with a set of weights.
+
+With full global batch size of 1536 on 1024 A100 GPUs, each iteration takes around 32 seconds resulting in 138 teraFLOPs per GPU which is 44% of the theoretical peak FLOPs.
+
+
+<!--
+## REALM Pipeline
+We are working on implementing the [REALM](https://arxiv.org/pdf/2002.08909.pdf) system. The following sections (will) reflect the three stages of training it. For now it's just the ICT code.
+Loosely, they are pretraining the retriever modules, then jointly training the language model and the retriever, and then finetuning a question answering head on the language model with fixed retriever.
+
+### Inverse Cloze Task (ICT) Pretraining
+1. Have a corpus in loose JSON format with the intention of creating a collection of fixed-size blocks of text as the fundamental units of data. For a corpus like Wikipedia, this will mean multiple sentences per block but also multiple blocks per document.
+Run `tools/preprocess_data.py` to construct one or more indexed datasets with the `--split-sentences` argument to make sentences the basic unit. For the original REALM system, we construct two datasets, one with the title of every document, and another with the body.
+Refer to the following script
+<pre>
+python preprocess_data.py \
+    --input /path/to/corpus.json \
+    --json-keys text title \
+    --split-sentences \
+    --tokenizer-type BertWordPieceLowerCase \
+    --vocab-file /path/to/vocab.txt \
+    --output-prefix corpus_indexed \
+    --workers 5  # works well for 10 CPU cores. Scale up accordingly.
+</pre>
+
+2. Use a custom samples mapping function in place of `megatron/data/realm_dataset_utils.get_block_samples_mapping` if required. To do this, you will need to implement a new function in C++ inside of `megatron/data/helpers.cpp`. The samples mapping data structure is used to select the data that will constitute every training sample in advance of the training loop.
+ The samples mapping is responsible for holding all of the required metadata needed to construct the sample from one or more indexed datasets. In REALM, the samples mapping contains the start and end sentence indices, as well as the document index (to find the correct title for a body) and a unique ID for every block.
+3. Pretrain a BERT language model using `pretrain_bert.py`, with the sequence length equal to the block size in token ids. This model should be trained on the same indexed dataset that is used to supply the blocks for the information retrieval task.
+In REALM, this is an uncased bert base model trained with the standard hyperparameters.
+4. Use `pretrain_ict.py` to train an `ICTBertModel` which uses two BERT-based encoders to encode queries and blocks to perform retrieval with.
+The script below trains the ICT model from REALM. It refrences a pretrained BERT model (step 3) in the `--bert-load` argument. The batch size used in the paper is 4096, so this would need to be run with data parallel world size 32.
+<pre>
+python pretrain_ict.py \
+    --num-layers 12 \
+    --num-attention-heads 12 \
+    --hidden-size 768 \
+    --batch-size 128 \
+    --seq-length 256 \
+    --max-position-embeddings 256 \
+    --ict-head-size 128 \
+    --train-iters 100000 \
+    --activations-checkpoint-method uniform \
+    --bert-load /path/to/pretrained_bert \
+    --load checkpoints \
+    --save checkpoints \
+    --data-path /path/to/indexed_dataset \
+    --titles-data-path /path/to/titles_indexed_dataset \
+    --vocab-file /path/to/vocab.txt \
+    --lr 0.0001 \
+    --num-workers 2 \
+    --lr-decay-style linear \
+    --weight-decay 1e-2 \
+    --clip-grad 1.0 \
+    --lr-warmup-fraction .01 \
+    --save-interval 3000 \
+    --query-in-block-prob 0.1 \
+    --fp16
+
+</pre>
+
+### Building an Index of Block Embeddings
+After having trained an ICT model, you can now embed an entire dataset of blocks by creating a `BlockData` structure. After that has been saved, you can load it
+and wrap it with a `FaissMIPSIndex` to do fast similarity search which is key in the learned information retrieval pipeline. The initial index can be built with the following script, meant to be run in an interactive session. It can leverage multiple GPUs on multiple nodes to index large datasets much more quickly.
+
+<pre>
+python tools/create_doc_index.py \
+    --num-layers 12 \
+    --hidden-size 768 \
+    --ict-head-size 128 \
+    --num-attention-heads 12 \
+    --batch-size 128 \
+    --activations-checkpoint-method uniform \
+    --seq-length 256 \
+    --max-position-embeddings 256 \
+    --ict-load /path/to/pretrained_ict \
+    --data-path /path/to/indexed_dataset \
+    --titles-data-path /path/to/titles_indexed_dataset \
+    --block-data-path embedded_blocks.pkl \
+    --indexer-log-interval 1000 \
+    --indexer-batch-size 128 \
+    --vocab-file /path/to/vocab.txt \
+    --num-workers 2 \
+    --fp16
+</pre>
+
+-->
+
+# Evaluation and Tasks
+
+We provide several command line arguments, detailed in the scripts listed below, to handle various zero-shot and fine-tuned downstream tasks. However, you can also finetune your model from a pretrained checkpoint on other corpora as desired. To do so, simply add the `--finetune` flag and adjust the input files and training parameters within the original training script. The iteration count will be reset to zero, and the optimizer and internal state will be reinitialized. If the fine-tuning is interrupted for any reason, be sure to remove the `--finetune` flag before continuing, otherwise the training will start again from the beginning.
+
+Because evaluation requires substantially less memory than training, it may be advantageous to merge a model trained in parallel for use on a single GPU in downstream tasks. The following script accomplishes this. Currently only tensor model parallelism is supported on input and pipeline model parallelism on the output. This example reads in a model with 2-way tensor model parallelism and writes out a model with 2-way pipeline model parallelism.
+
+<pre>
+TENSOR_MODEL_PARALLEL_SIZE=2
+TARGET_PIPELINE_MODEL_PARALLEL_SIZE=2
+
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m
+
+WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
+        --model-type BERT \
+        --tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
+        --pipeline-model-parallel-size 1 \
+        --target-pipeline-model-parallel-size $TARGET_PIPELINE_MODEL_PARALLEL_SIZE \
+        --tokenizer-type BertWordPieceLowerCase \
+        --vocab-file $VOCAB_FILE \
+        --num-layers 24 \
+        --hidden-size 1024 \
+        --num-attention-heads 16 \
+        --seq-length 512 \
+        --max-position-embeddings 512 \
+        --load $CHECKPOINT_PATH
+        --save $CHECKPOINT_PATH/merged
+
+</pre>
+
+Several downstream tasks are described for both GPT and BERT models below. They can be run in distributed and model parallel modes with the same changes used in the training scripts.
+
+## GPT Text Generation
+
+We have included a simple REST server to use for text generation in `tools/run_text_generation_server.py`. You run it much like you would start a pretraining job, specifying an appropriate pretrained checkpoint. There are also few optional parameters: `temperature`, `top-k`and `top-p`. See `--help` or the source file for more information. See [examples/run_text_generation_server_345M.sh](examples/run_text_generation_server_345M.sh) for an example of how to run the server.
+
+Once the server is running you can use `tools/text_generation_cli.py` to query it, it takes one argument which is the host the server is running on.
+
+<pre>
+tools/text_generation_cli.py localhost
+</pre>
+
+You can also use CURL or any other tools to query the server directly:
+
+<pre>
+curl 'http://localhost:5000/api' -X 'PUT' -H 'Content-Type: application/json; charset=UTF-8'  -d '{"prompts":["Hello world"], "tokens_to_generate":1}'
+</pre>
+
+See [megatron/text_generation_server.py](megatron/text_generation_server.py) for more API options.
+
+## GPT Evaluation
+We include example scripts for GPT evaluation on WikiText perplexity evaluation and LAMBADA Cloze accuracy.
+
+### WikiText Perplexity Evaluation
+For even comparison with prior works, we evaluate perplexity on the word-level [WikiText-103 test dataset](https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip), and appropriately compute perplexity given the change in tokens when using our subword tokenizer.
+
+We use the following command to run WikiText-103 evaluation on a 345M parameter model.
+<pre>
+TASK="WIKITEXT103"
+
+VALID_DATA=&#60;wikitext path&#62;.txt
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+
+COMMON_TASK_ARGS="--num-layers 24 \
+                  --hidden-size 1024 \
+                  --num-attention-heads 16 \
+                  --seq-length 1024 \
+                  --max-position-embeddings 1024 \
+                  --fp16 \
+                  --vocab-file $VOCAB_FILE"
+
+python tasks/main.py \
+       --task $TASK \
+       $COMMON_TASK_ARGS \
+       --valid-data $VALID_DATA \
+       --tokenizer-type GPT2BPETokenizer \
+       --merge-file $MERGE_FILE \
+       --load $CHECKPOINT_PATH \
+       --micro-batch-size 8 \
+       --activations-checkpoint-method uniform \
+       --log-interval 10 \
+       --no-load-optim \
+       --no-load-rng
+</pre>
+
+
+### LAMBADA Cloze Accuracy
+To compute LAMBADA cloze accuracy (the accuracy of predicting the last token given the preceding tokens) we utilize a detokenized, processed version of the [LAMBADA dataset](https://github.com/cybertronai/bflm/blob/master/lambada_test.jsonl).
+
+We use the following command to run LAMBADA evaluation on a 345M parameter model. Note that the `--strict-lambada` flag should be used to require whole word matching. Make that `lambada` is part of the file path.
+
+<pre>
+TASK="LAMBADA"
+
+VALID_DATA=&#60;lambada path&#62;.json
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT_PATH=checkpoints/gpt2_345m
+COMMON_TASK_ARGS=&#60;same as those in <a href="#wikitext-perplexity-evaluation">WikiText Perplexity Evaluation</a> above&#62;
+
+python tasks/main.py \
+       --task $TASK \
+       $COMMON_TASK_ARGS \
+       --valid-data $VALID_DATA \
+       --tokenizer-type GPT2BPETokenizer \
+       --strict-lambada \
+       --merge-file $MERGE_FILE \
+       --load $CHECKPOINT_PATH \
+       --micro-batch-size 8 \
+       --activations-checkpoint-method uniform \
+       --log-interval 10 \
+       --no-load-optim \
+       --no-load-rng
+</pre>
+
+Further command line arguments are described in the source file [`main.py`](./tasks/main.py)
+
+## BERT Task Evaluation
+### RACE Evaluation
+The following script finetunes the BERT model for evaluation on the [RACE dataset](http://www.cs.cmu.edu/~glai1/data/race/). The `TRAIN_DATA` and `VALID_DATA` directory contain the RACE dataset as separate `.txt` files. Note that for RACE, the batch size is the number of RACE query's to evaluate. Since each RACE query has four samples, the effective batch size passed through the model will be four times the batch size specified on the command line.
+
+<pre>
+TRAIN_DATA="data/RACE/train/middle"
+VALID_DATA="data/RACE/dev/middle \
+            data/RACE/dev/high"
+VOCAB_FILE=bert-vocab.txt
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+CHECKPOINT_PATH=checkpoints/bert_345m_race
+COMMON_TASK_ARGS="--num-layers 24 \
+                  --hidden-size 1024 \
+                  --num-attention-heads 16 \
+                  --seq-length 512 \
+                  --max-position-embeddings 512 \
+                  --fp16 \
+                  --vocab-file $VOCAB_FILE"
+
+COMMON_TASK_ARGS_EXT="--train-data $TRAIN_DATA \
+                      --valid-data $VALID_DATA \
+                      --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+                      --activations-checkpoint-method uniform \
+                      --save-interval 10000 \
+                      --save $CHECKPOINT_PATH \
+                      --log-interval 100 \
+                      --eval-interval 1000 \
+                      --eval-iters 10 \
+                      --weight-decay 1.0e-1"
+
+python tasks/main.py \
+       --task RACE \
+       $COMMON_TASK_ARGS \
+       $COMMON_TASK_ARGS_EXT \
+       --tokenizer-type BertWordPieceLowerCase \
+       --epochs 3 \
+       --micro-batch-size 4 \
+       --lr 1.0e-5 \
+       --lr-warmup-fraction 0.06
+</pre>
+
+### MNLI Evaluation
+The following script finetunes the BERT model for evaluation with the [MultiNLI sentence pair corpus](https://www.nyu.edu/projects/bowman/multinli/). Because the matching tasks are quite similar, the script can be quickly tweaked to work with the [Quora Question Pairs](https://www.kaggle.com/quora/question-pairs-dataset) (QQP) dataset as well.
+
+<pre>
+
+TRAIN_DATA="data/glue_data/MNLI/train.tsv"
+VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
+            data/glue_data/MNLI/dev_mismatched.tsv"
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m_mnli
+COMMON_TASK_ARGS=&#60;same as those in <a href="#race-evaluation">RACE Evaluation</a> above&#62;
+COMMON_TASK_ARGS_EXT=&#60;same as those in <a href="#race-evaluation">RACE Evaluation</a> above&#62;
+
+python tasks/main.py \
+       --task MNLI \
+       $COMMON_TASK_ARGS \
+       $COMMON_TASK_ARGS_EXT \
+       --tokenizer-type BertWordPieceLowerCase \
+       --epochs 5 \
+       --micro-batch-size 8 \
+       --lr 5.0e-5 \
+       --lr-warmup-fraction 0.065
+</pre>
+
+# Datasets
+We do not host any datasets for GPT or BERT training, however, we detail their collection so that our results may be reproduced.
+
+## Collecting Wikipedia Training Data
+We recommend following the Wikipedia data extraction process specified by Google research: "the recommended pre-processing is to download [the latest dump](https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2), extract the text with [WikiExtractor.py](https://github.com/attardi/wikiextractor), and then apply any necessary cleanup to convert it into plain text."
+
+We recommend using the `--json` argument when using WikiExtractor, which will dump the Wikipedia data into loose json format (one json per line), making it more manageable on the file system and also readily consumable by our codebase. We recommend further preprocessing this json dataset by nltk punctuation standardization. For BERT training, use the `--split-sentences` flag to `preprocess_data.py` as described [above](#data-preprocessing) to include sentence breaks in the produced index. If you'd like to use Wikipedia data for GPT training you should still clean it with nltk/spacy/ftfy, but do not use the `--split-sentences` flag.
+
+## Collecting GPT Webtext Data
+We utilize the publicly available [OpenWebText](https://github.com/eukaryote31/openwebtext) library from [jcpeterson](https://github.com/jcpeterson/openwebtext) and [eukaryote31's](https://github.com/eukaryote31/openwebtext) work to download urls. We then filtered, cleaned, and deduplicated all downloaded content according to the procedure described in our [openwebtext](./tools/openwebtext) directory. For reddit URLs corresponding to content up to October 2018 we arrived at approximately 37GB of content.

3rdparty/Megatron-LM/examples/evaluate_retriever_nq.sh

+#!/bin/bash
+
+# Evaluate natural question test data given Wikipedia embeddings and pretrained
+# ICT model or a finetuned model for Natural Question task
+
+# Datasets can be downloaded from the following link:
+# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
+
+EVIDENCE_DATA_DIR=<Specify path of Wikipedia dataset>
+EMBEDDING_PATH=<Specify path of the embeddings>
+CHECKPOINT_PATH=<Specify path of pretrained ICT model or finetuned model>
+
+QA_FILE=<Path of the natural question dev or test dataset>
+
+python tasks/main.py \
+    --task RETRIEVER-EVAL \
+    --tokenizer-type BertWordPieceLowerCase \
+    --num-layers 12 \
+    --hidden-size 768 \
+    --num-attention-heads 12 \
+    --tensor-model-parallel-size 1 \
+    --micro-batch-size 128 \
+    --activations-checkpoint-method uniform \
+    --seq-length 512 \
+    --max-position-embeddings 512 \
+    --load ${CHECKPOINT_PATH} \
+    --evidence-data-path ${EVIDENCE_DATA_DIR} \
+    --embedding-path ${EMBEDDING_PATH} \
+    --retriever-seq-length 256 \
+    --vocab-file  bert-vocab.txt\
+    --qa-data-test ${QA_FILE} \
+    --faiss-use-gpu \
+    --retriever-report-topk-accuracies 1 5 20 100 \
+    --fp16 \
+    --indexer-log-interval 1000 \
+    --indexer-batch-size 128
+
+

3rdparty/Megatron-LM/examples/evaluate_zeroshot_gpt.sh

+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TASK="LAMBADA"
+
+VALID_DATA=<lambada path>
+VOCAB_FILE=gpt2-vocab.json
+MERGE_FILE=gpt2-merges.txt
+CHECKPOINT=checkpoints/gpt2_345m
+
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task $TASK \
+               --valid-data $VALID_DATA \
+               --tokenizer-type GPT2BPETokenizer \
+               --strict-lambada \
+               --vocab-file $VOCAB_FILE \
+               --merge-file $MERGE_FILE \
+               --load $CHECKPOINT \
+               --tensor-model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --batch-size 8 \
+               --activations-checkpoint-method uniform \
+               --seq-length 1024 \
+               --max-position-embeddings 1024 \
+               --log-interval 10 \
+               --fp16 \
+               --no-load-optim \
+               --no-load-rng

3rdparty/Megatron-LM/examples/finetune_mnli_distributed.sh

+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TRAIN_DATA="data/glue_data/MNLI/train.tsv"
+VALID_DATA="data/glue_data/MNLI/dev_matched.tsv \
+            data/glue_data/MNLI/dev_mismatched.tsv"
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m_mnli
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task MNLI \
+               --seed 1234 \
+               --train-data $TRAIN_DATA \
+               --valid-data $VALID_DATA \
+               --tokenizer-type BertWordPieceLowerCase \
+               --vocab-file $VOCAB_FILE \
+               --epochs 5 \
+               --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+               --tensor-model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --micro-batch-size 8 \
+               --activations-checkpoint-method uniform \
+               --lr 5.0e-5 \
+               --lr-decay-style linear \
+               --lr-warmup-fraction 0.065 \
+               --seq-length 512 \
+               --max-position-embeddings 512 \
+               --save-interval 500000 \
+               --save $CHECKPOINT_PATH \
+               --log-interval 10 \
+               --eval-interval 100 \
+               --eval-iters 50 \
+               --weight-decay 1.0e-1 \
+               --fp16

3rdparty/Megatron-LM/examples/finetune_race_distributed.sh

+#!/bin/bash
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+TRAIN_DATA="data/RACE/train/middle"
+VALID_DATA="data/RACE/dev/middle \
+            data/RACE/dev/high"
+VOCAB_FILE=bert-vocab.txt
+PRETRAINED_CHECKPOINT=checkpoints/bert_345m
+CHECKPOINT_PATH=checkpoints/bert_345m_race
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+               --task RACE \
+               --seed 1234 \
+               --train-data $TRAIN_DATA \
+               --valid-data $VALID_DATA \
+               --tokenizer-type BertWordPieceLowerCase \
+               --vocab-file $VOCAB_FILE \
+               --epochs 3 \
+               --pretrained-checkpoint $PRETRAINED_CHECKPOINT \
+               --tensor-model-parallel-size 1 \
+               --num-layers 24 \
+               --hidden-size 1024 \
+               --num-attention-heads 16 \
+               --micro-batch-size 4 \
+               --activations-checkpoint-method uniform \
+               --lr 1.0e-5 \
+               --lr-decay-style linear \
+               --lr-warmup-fraction 0.06 \
+               --seq-length 512 \
+               --max-position-embeddings 512 \
+               --save-interval 100000 \
+               --save $CHECKPOINT_PATH \
+               --log-interval 10 \
+               --eval-interval 100 \
+               --eval-iters 50 \
+               --weight-decay 1.0e-1 \
+               --clip-grad 1.0 \
+               --hidden-dropout 0.1 \
+               --attention-dropout 0.1 \
+               --fp16

3rdparty/Megatron-LM/examples/finetune_retriever_distributed.sh

+#!/bin/bash
+
+# Finetune a BERT or pretrained ICT model using Google natural question data 
+# Datasets can be downloaded from the following link:
+# https://github.com/facebookresearch/DPR/blob/master/data/download_data.py
+
+WORLD_SIZE=8
+
+DISTRIBUTED_ARGS="--nproc_per_node $WORLD_SIZE \
+                  --nnodes 1 \
+                  --node_rank 0 \
+                  --master_addr localhost \
+                  --master_port 6000"
+
+CHECKPOINT_PATH=<Specify path for the finetuned retriever model>
+
+# Load either of the below
+BERT_LOAD_PATH=<Path of BERT pretrained model>
+PRETRAINED_CHECKPOINT=<Path of Pretrained ICT model>
+
+python -m torch.distributed.launch $DISTRIBUTED_ARGS ./tasks/main.py \
+        --task RET-FINETUNE-NQ \
+        --train-with-neg \
+        --train-hard-neg 1 \
+        --pretrained-checkpoint ${PRETRAINED_CHECKPOINT} \
+        --num-layers 12 \
+        --hidden-size 768 \
+        --num-attention-heads 12 \
+        --tensor-model-parallel-size 1 \
+        --tokenizer-type BertWordPieceLowerCase \
+        --train-data nq-train.json \
+        --valid-data nq-dev.json \
+        --save ${CHECKPOINT_PATH} \
+        --load ${CHECKPOINT_PATH} \
+        --vocab-file bert-vocab.txt \
+        --bert-load ${BERT_LOAD_PATH} \
+        --save-interval 5000 \
+        --log-interval 10 \
+        --eval-interval 20000 \
+        --eval-iters 100 \
+        --indexer-log-interval 1000 \
+        --faiss-use-gpu \
+        --DDP-impl torch \
+        --fp16 \
+        --retriever-report-topk-accuracies 1 5 10 20 100 \
+        --seq-length 512 \
+        --retriever-seq-length 256 \
+        --max-position-embeddings 512 \
+        --retriever-score-scaling \
+        --epochs 80 \
+        --micro-batch-size 8 \
+        --eval-micro-batch-size 16 \
+        --indexer-batch-size 128 \
+        --lr 2e-5 \
+        --lr-warmup-fraction 0.01 \
+        --weight-decay 1e-1

3rdparty/Megatron-LM/examples/merge_mp_bert.sh

+#!/bin/bash
+
+TENSOR_MODEL_PARALLEL_SIZE=2
+
+VOCAB_FILE=bert-vocab.txt
+CHECKPOINT_PATH=checkpoints/bert_345m
+
+WORLD_SIZE=$TENSOR_MODEL_PARALLEL_SIZE python tools/merge_mp_partitions.py \
+                                --model-type BERT \
+                                --tensor-model-parallel-size $TENSOR_MODEL_PARALLEL_SIZE \
+                                --tokenizer-type BertWordPieceLowerCase \
+                                --vocab-file $VOCAB_FILE \
+                                --num-layers 24 \
+                                --hidden-size 1024 \
+                                --num-attention-heads 16 \
+                                --seq-length 512 \
+                                --max-position-embeddings 512 \
+                                --load $CHECKPOINT_PATH

3rdparty/Megatron-LM/examples/msdp/README.md

+
+# Multi-Stage Prompting for Knowledgeable Dialogue Generation
+
+This directory contains all the scripts of multi-stage prompting for knowledgeable dialogue generation that includes data preparation, and knowledge and response generations. More details are available on [`knowledgeable task directory`](../../tasks/msdp).
+

3rdparty/Megatron-LM/examples/msdp/data_processing.sh

+#!/bin/bash
+
+# Data preparation for our framework: preprocessing the WoW and WoI datasets
+# The datasets can be downloaded through the following links:
+# WoW: https://parl.ai/projects/wizard_of_wikipedia/
+# WoI: https://parl.ai/projects/sea/
+
+DIR=`pwd`
+# Before running the preprocessing, please download 
+# the wizard of wikipedia and wizard datasets
+WOW_DATA_FOLDER=<PATH_OF_WIZARD_OF_WIKIPEDIA_DATA_FOLDER>
+WOI_DATA_FOLDER=<PATH_OF_WIZARD_OF_INTERNET_DATA_FOLDER>
+
+# We provide examples for processing the raw data from Wizard of Wikipedia
+# Processing the train dataset (train.json)
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func process_wow_dataset \
+        --raw_file ${WOW_DATA_FOLDER}/train.json \
+        --processed_file ${WOW_DATA_FOLDER}/train_processed.txt
+
+# Processing test seen dataset (test_random_split.json)
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func process_wow_dataset \
+        --raw_file ${WOW_DATA_FOLDER}/test_random_split.json \
+        --processed_file ${WOW_DATA_FOLDER}/testseen_processed.txt \
+        --knwl_ref_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_reference.txt \
+        --resp_ref_file ${WOW_DATA_FOLDER}/output_testseen_response_reference.txt
+
+# processing test unseen dataset (test_topic_split.json)
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func process_wow_dataset \
+        --raw_file ${WOW_DATA_FOLDER}/test_topic_split.json \
+        --processed_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \
+        --knwl_ref_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_reference.txt \
+        --resp_ref_file ${WOW_DATA_FOLDER}/output_testunseen_response_reference.txt
+
+
+# We provide the following script to process the raw data from Wizard of Internet
+# Processing the test dataset (test.jsonl)
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func process_woi_dataset \
+        --raw_file ${WOI_DATA_FOLDER}/test.jsonl \
+        --processed_file ${WOI_DATA_FOLDER}/test_processed.txt \
+        --knwl_ref_file ${WOI_DATA_FOLDER}/output_test_knowledge_reference.txt \
+        --resp_ref_file ${WOI_DATA_FOLDER}/output_test_response_reference.txt
+
+
+# Get the knowledge generation prompts for the each test dataset in WoW and WoI
+MODEL_FILE=<PATH_OF_THE_FINETUNED_DPR_MODEL> 
+# WoW test seen
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func get_knwl_gen_prompts \
+        --test_file ${WOW_DATA_FOLDER}/testseen_processed.txt \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --model_file ${MODEL_FILE} \
+        --processed_file ${WOW_DATA_FOLDER}/output_testseen_knowledge_prompts.json \
+        --data_type wow_seen
+
+# WoW test unseen
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func get_knwl_gen_prompts \
+        --test_file ${WOW_DATA_FOLDER}/testunseen_processed.txt \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --model_file ${MODEL_FILE} \
+        --processed_file ${WOW_DATA_FOLDER}/output_testunseen_knowledge_prompts.json \
+        --data_type wow_unseen
+
+# WoI
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func get_knwl_gen_prompts \
+        --test_file ${WOI_DATA_FOLDER}/test_processed.txt \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --model_file ${MODEL_FILE} \
+        --processed_file ${WOI_DATA_FOLDER}/output_test_knowledge_prompts.json \
+        --data_type woi
+
+
+# Get the response generation prompts (can be applied for all the test datasets)
+python ${DIR}/tasks/msdp/preprocessing.py \
+        --func get_resp_gen_prompts \
+        --train_file ${WOW_DATA_FOLDER}/train_processed.txt \
+        --processed_file ${WOW_DATA_FOLDER}/output_response_prompts.txt
+