New Upstream Release - pytorch-text
Ready changes
Summary
Merged new upstream version: 0.15.2 (was: 0.14.1).
Resulting package
Built on 2023-08-10T13:53 (took 31m34s)
The resulting binary packages can be installed (if you have the apt repository enabled) by running one of:
apt install -t fresh-releases python3-torchtext-dbgsymapt install -t fresh-releases python3-torchtext
Lintian Result
Diff
diff --git a/.circleci/config.yml b/.circleci/config.yml
index 23539e4..b273007 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -39,11 +39,15 @@ binary_common: &binary_common
build_version:
description: "version number of release binary; by default, build a nightly"
type: string
- default: "0.14.1"
+ default: "0.15.2"
pytorch_version:
description: "PyTorch version to build against; by default, use a nightly"
type: string
- default: "1.13.1"
+ default: "2.0.1"
+ torchdata_version:
+ description: "TorchData version to build against; by default, use a nightly"
+ type: string
+ default: "0.6.1"
# Don't edit these
python_version:
description: "Python version to build against (e.g., 3.8)"
@@ -52,6 +56,7 @@ binary_common: &binary_common
PYTHON_VERSION: << parameters.python_version >>
BUILD_VERSION: << parameters.build_version >>
PYTORCH_VERSION: << parameters.pytorch_version >>
+ TORCHDATA_VERSION: << parameters.torchdata_version >>
CU_VERSION: cpu
MACOSX_DEPLOYMENT_TARGET: 10.9
@@ -79,7 +84,7 @@ jobs:
lint_python_and_config:
docker:
- - image: circleci/python:3.7
+ - image: circleci/python:3.8
steps:
- checkout
- run:
@@ -98,7 +103,7 @@ jobs:
lint_c:
docker:
- - image: circleci/python:3.7
+ - image: circleci/python:3.8
steps:
- run:
name: Install additional system libraries
@@ -602,9 +607,6 @@ workflows:
- lint_c
build:
jobs:
- - binary_linux_wheel:
- name: binary_linux_wheel_py3.7
- python_version: '3.7'
- binary_linux_wheel:
filters:
branches:
@@ -613,27 +615,6 @@ workflows:
only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
name: binary_linux_wheel_py3.8
python_version: '3.8'
- - binary_linux_wheel:
- name: binary_linux_wheel_py3.9
- python_version: '3.9'
- - binary_linux_wheel:
- name: binary_linux_wheel_py3.10
- python_version: '3.10'
- - binary_macos_wheel:
- name: binary_macos_wheel_py3.7
- python_version: '3.7'
- - binary_macos_wheel:
- name: binary_macos_wheel_py3.8
- python_version: '3.8'
- - binary_macos_wheel:
- name: binary_macos_wheel_py3.9
- python_version: '3.9'
- - binary_macos_wheel:
- name: binary_macos_wheel_py3.10
- python_version: '3.10'
- - binary_windows_wheel:
- name: binary_windows_wheel_py3.7
- python_version: '3.7'
- binary_windows_wheel:
name: binary_windows_wheel_py3.8
python_version: '3.8'
@@ -643,33 +624,9 @@ workflows:
- binary_windows_wheel:
name: binary_windows_wheel_py3.10
python_version: '3.10'
- - binary_linux_conda:
- name: binary_linux_conda_py3.7
- python_version: '3.7'
- - binary_linux_conda:
- name: binary_linux_conda_py3.8
- python_version: '3.8'
- - binary_linux_conda:
- name: binary_linux_conda_py3.9
- python_version: '3.9'
- - binary_linux_conda:
- name: binary_linux_conda_py3.10
- python_version: '3.10'
- - binary_macos_conda:
- name: binary_macos_conda_py3.7
- python_version: '3.7'
- - binary_macos_conda:
- name: binary_macos_conda_py3.8
- python_version: '3.8'
- - binary_macos_conda:
- name: binary_macos_conda_py3.9
- python_version: '3.9'
- - binary_macos_conda:
- name: binary_macos_conda_py3.10
- python_version: '3.10'
- - binary_windows_conda:
- name: binary_windows_conda_py3.7
- python_version: '3.7'
+ - binary_windows_wheel:
+ name: binary_windows_wheel_py3.11
+ python_version: '3.11'
- binary_windows_conda:
name: binary_windows_conda_py3.8
python_version: '3.8'
@@ -679,6 +636,9 @@ workflows:
- binary_windows_conda:
name: binary_windows_conda_py3.10
python_version: '3.10'
+ - binary_windows_conda:
+ name: binary_windows_conda_py3.11
+ python_version: '3.11'
- build_docs:
filters:
branches:
@@ -702,21 +662,6 @@ workflows:
- build_docs
unittest:
jobs:
- - unittest_linux:
- name: unittest_linux_py3.7
- python_version: '3.7'
- - unittest_linux:
- name: unittest_linux_py3.8
- python_version: '3.8'
- - unittest_linux:
- name: unittest_linux_py3.9
- python_version: '3.9'
- - unittest_linux:
- name: unittest_linux_py3.10
- python_version: '3.10'
- - unittest_windows:
- name: unittest_windows_py3.7
- python_version: '3.7'
- unittest_windows:
name: unittest_windows_py3.8
python_version: '3.8'
@@ -726,52 +671,12 @@ workflows:
- unittest_windows:
name: unittest_windows_py3.10
python_version: '3.10'
- - unittest_macos:
- name: unittest_macos_py3.7
- python_version: '3.7'
- - unittest_macos:
- name: unittest_macos_py3.8
- python_version: '3.8'
- - unittest_macos:
- name: unittest_macos_py3.9
- python_version: '3.9'
- - unittest_macos:
- name: unittest_macos_py3.10
- python_version: '3.10'
nightly:
jobs:
- circleci_consistency:
filters:
branches:
only: nightly
- - binary_linux_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.7
- python_version: '3.7'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.7_upload
- requires:
- - nightly_binary_linux_wheel_py3.7
- - smoke_test_linux_pip:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.7_smoke_test_pip
- python_version: '3.7'
- requires:
- - nightly_binary_linux_wheel_py3.7_upload
- binary_linux_wheel:
filters:
branches:
@@ -780,182 +685,6 @@ workflows:
only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
name: nightly_binary_linux_wheel_py3.8
python_version: '3.8'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.8_upload
- requires:
- - nightly_binary_linux_wheel_py3.8
- - smoke_test_linux_pip:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.8_smoke_test_pip
- python_version: '3.8'
- requires:
- - nightly_binary_linux_wheel_py3.8_upload
- - binary_linux_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.9
- python_version: '3.9'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.9_upload
- requires:
- - nightly_binary_linux_wheel_py3.9
- - smoke_test_linux_pip:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.9_smoke_test_pip
- python_version: '3.9'
- requires:
- - nightly_binary_linux_wheel_py3.9_upload
- - binary_linux_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.10
- python_version: '3.10'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.10_upload
- requires:
- - nightly_binary_linux_wheel_py3.10
- - smoke_test_linux_pip:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_wheel_py3.10_smoke_test_pip
- python_version: '3.10'
- requires:
- - nightly_binary_linux_wheel_py3.10_upload
- - binary_macos_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.7
- python_version: '3.7'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.7_upload
- requires:
- - nightly_binary_macos_wheel_py3.7
- - binary_macos_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.8
- python_version: '3.8'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.8_upload
- requires:
- - nightly_binary_macos_wheel_py3.8
- - binary_macos_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.9
- python_version: '3.9'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.9_upload
- requires:
- - nightly_binary_macos_wheel_py3.9
- - binary_macos_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.10
- python_version: '3.10'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_wheel_py3.10_upload
- requires:
- - nightly_binary_macos_wheel_py3.10
- - binary_windows_wheel:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_windows_wheel_py3.7
- python_version: '3.7'
- - binary_wheel_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_windows_wheel_py3.7_upload
- requires:
- - nightly_binary_windows_wheel_py3.7
- - smoke_test_windows_pip:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_windows_wheel_py3.7_smoke_test_pip
- python_version: '3.7'
- requires:
- - nightly_binary_windows_wheel_py3.7_upload
- binary_windows_wheel:
filters:
branches:
@@ -1040,218 +769,34 @@ workflows:
python_version: '3.10'
requires:
- nightly_binary_windows_wheel_py3.10_upload
- - binary_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.7
- python_version: '3.7'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.7_upload
- requires:
- - nightly_binary_linux_conda_py3.7
- - smoke_test_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.7_smoke_test_conda
- python_version: '3.7'
- requires:
- - nightly_binary_linux_conda_py3.7_upload
- - binary_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.8
- python_version: '3.8'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.8_upload
- requires:
- - nightly_binary_linux_conda_py3.8
- - smoke_test_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.8_smoke_test_conda
- python_version: '3.8'
- requires:
- - nightly_binary_linux_conda_py3.8_upload
- - binary_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.9
- python_version: '3.9'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.9_upload
- requires:
- - nightly_binary_linux_conda_py3.9
- - smoke_test_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.9_smoke_test_conda
- python_version: '3.9'
- requires:
- - nightly_binary_linux_conda_py3.9_upload
- - binary_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.10
- python_version: '3.10'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.10_upload
- requires:
- - nightly_binary_linux_conda_py3.10
- - smoke_test_linux_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_linux_conda_py3.10_smoke_test_conda
- python_version: '3.10'
- requires:
- - nightly_binary_linux_conda_py3.10_upload
- - binary_macos_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.7
- python_version: '3.7'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.7_upload
- requires:
- - nightly_binary_macos_conda_py3.7
- - binary_macos_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.8
- python_version: '3.8'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.8_upload
- requires:
- - nightly_binary_macos_conda_py3.8
- - binary_macos_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.9
- python_version: '3.9'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.9_upload
- requires:
- - nightly_binary_macos_conda_py3.9
- - binary_macos_conda:
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.10
- python_version: '3.10'
- - binary_conda_upload:
- context: org-member
- filters:
- branches:
- only: nightly
- tags:
- only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_macos_conda_py3.10_upload
- requires:
- - nightly_binary_macos_conda_py3.10
- - binary_windows_conda:
+ - binary_windows_wheel:
filters:
branches:
only: nightly
tags:
only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_windows_conda_py3.7
- python_version: '3.7'
- - binary_conda_upload:
+ name: nightly_binary_windows_wheel_py3.11
+ python_version: '3.11'
+ - binary_wheel_upload:
context: org-member
filters:
branches:
only: nightly
tags:
only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_windows_conda_py3.7_upload
+ name: nightly_binary_windows_wheel_py3.11_upload
requires:
- - nightly_binary_windows_conda_py3.7
- - smoke_test_windows_conda:
+ - nightly_binary_windows_wheel_py3.11
+ - smoke_test_windows_pip:
filters:
branches:
only: nightly
tags:
only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
- name: nightly_binary_windows_conda_py3.7_smoke_test_conda
- python_version: '3.7'
+ name: nightly_binary_windows_wheel_py3.11_smoke_test_pip
+ python_version: '3.11'
requires:
- - nightly_binary_windows_conda_py3.7_upload
+ - nightly_binary_windows_wheel_py3.11_upload
- binary_windows_conda:
filters:
branches:
@@ -1336,6 +881,34 @@ workflows:
python_version: '3.10'
requires:
- nightly_binary_windows_conda_py3.10_upload
+ - binary_windows_conda:
+ filters:
+ branches:
+ only: nightly
+ tags:
+ only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
+ name: nightly_binary_windows_conda_py3.11
+ python_version: '3.11'
+ - binary_conda_upload:
+ context: org-member
+ filters:
+ branches:
+ only: nightly
+ tags:
+ only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
+ name: nightly_binary_windows_conda_py3.11_upload
+ requires:
+ - nightly_binary_windows_conda_py3.11
+ - smoke_test_windows_conda:
+ filters:
+ branches:
+ only: nightly
+ tags:
+ only: /v[0-9]+(\.[0-9]+)*-rc[0-9]+/
+ name: nightly_binary_windows_conda_py3.11_smoke_test_conda
+ python_version: '3.11'
+ requires:
+ - nightly_binary_windows_conda_py3.11_upload
docker_build:
triggers:
- schedule:
diff --git a/.circleci/config.yml.in b/.circleci/config.yml.in
index 9ffb96b..50211fa 100644
--- a/.circleci/config.yml.in
+++ b/.circleci/config.yml.in
@@ -39,11 +39,15 @@ binary_common: &binary_common
build_version:
description: "version number of release binary; by default, build a nightly"
type: string
- default: "0.14.1"
+ default: "0.15.2"
pytorch_version:
description: "PyTorch version to build against; by default, use a nightly"
type: string
- default: "1.13.1"
+ default: "2.0.1"
+ torchdata_version:
+ description: "TorchData version to build against; by default, use a nightly"
+ type: string
+ default: "0.6.1"
# Don't edit these
python_version:
description: "Python version to build against (e.g., 3.8)"
@@ -52,6 +56,7 @@ binary_common: &binary_common
PYTHON_VERSION: << parameters.python_version >>
BUILD_VERSION: << parameters.build_version >>
PYTORCH_VERSION: << parameters.pytorch_version >>
+ TORCHDATA_VERSION: << parameters.torchdata_version >>
CU_VERSION: cpu
MACOSX_DEPLOYMENT_TARGET: 10.9
@@ -79,7 +84,7 @@ jobs:
lint_python_and_config:
docker:
- - image: circleci/python:3.7
+ - image: circleci/python:3.8
steps:
- checkout
- run:
@@ -98,7 +103,7 @@ jobs:
lint_c:
docker:
- - image: circleci/python:3.7
+ - image: circleci/python:3.8
steps:
- run:
name: Install additional system libraries
diff --git a/.circleci/regenerate.py b/.circleci/regenerate.py
index 53e7e31..4c8b343 100755
--- a/.circleci/regenerate.py
+++ b/.circleci/regenerate.py
@@ -21,7 +21,7 @@ import yaml
from jinja2 import select_autoescape
-PYTHON_VERSIONS = ["3.7", "3.8", "3.9", "3.10"]
+PYTHON_VERSIONS = ["3.8", "3.9", "3.10", "3.11"]
DOC_VERSION = ("linux", "3.8")
@@ -35,6 +35,16 @@ def build_workflows(prefix="", upload=False, filter_branch=None, indentation=6):
if not fb and (os_type == "linux" and btype == "wheel" and python_version == "3.8"):
# the fields must match the build_docs "requires" dependency
fb = "/.*/"
+ # We'll stop building Linux Wheels from CircleCI. Keeping
+ # around the Python3.8 build just for docs builds until those
+ # are also migrated.
+ if os_type == "linux" and btype == "wheel" and python_version != "3.8":
+ continue
+ if os_type == "linux" and btype == "conda":
+ continue
+ if os_type == "macos":
+ continue
+
w += build_workflow_pair(btype, os_type, python_version, fb, prefix, upload)
if not filter_branch:
@@ -50,6 +60,9 @@ def build_workflow_pair(btype, os_type, python_version, filter_branch, prefix=""
base_workflow_name = f"{prefix}binary_{os_type}_{btype}_py{python_version}"
w.append(generate_base_workflow(base_workflow_name, python_version, filter_branch, os_type, btype))
+ if os_type == "linux" and btype == "wheel" and python_version == "3.8":
+ upload = False
+
if upload:
w.append(generate_upload_workflow(base_workflow_name, filter_branch, btype))
if filter_branch == "nightly" and os_type in ["linux", "windows"]:
@@ -147,8 +160,12 @@ def indent(indentation, data_list):
def unittest_workflows(indentation=6):
w = []
- for os_type in ["linux", "windows", "macos"]:
+ for os_type in ["windows"]:
for python_version in PYTHON_VERSIONS:
+ # Turn off unit tests for 3.11, unit test are not setup properly in circleci
+ if python_version == "3.11":
+ continue
+
w.append(
{
f"unittest_{os_type}": {
diff --git a/.circleci/smoke_test/docker/Dockerfile b/.circleci/smoke_test/docker/Dockerfile
index b43902d..ba6f4fb 100644
--- a/.circleci/smoke_test/docker/Dockerfile
+++ b/.circleci/smoke_test/docker/Dockerfile
@@ -21,14 +21,12 @@ RUN apt-get -qq update && apt-get -qq -y install curl bzip2 sox libsox-dev libso
ENV PATH /opt/conda/bin:$PATH
-RUN conda create -y --name python3.7 python=3.7
RUN conda create -y --name python3.8 python=3.8
RUN conda create -y --name python3.9 python=3.9
RUN conda create -y --name python3.10 python=3.10
SHELL [ "/bin/bash", "-c" ]
RUN echo "source /usr/local/etc/profile.d/conda.sh" >> ~/.bashrc
-RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.7 && conda install -y numpy
RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.8 && conda install -y numpy
RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.9 && conda install -y numpy
RUN source /usr/local/etc/profile.d/conda.sh && conda activate python3.10 && conda install -y numpy
diff --git a/.circleci/unittest/linux/scripts/install.sh b/.circleci/unittest/linux/scripts/install.sh
index d7bdb3b..b0fed7d 100755
--- a/.circleci/unittest/linux/scripts/install.sh
+++ b/.circleci/unittest/linux/scripts/install.sh
@@ -1,6 +1,7 @@
#!/usr/bin/env bash
unset PYTORCH_VERSION
+unset TORCHDATA_VERSION
# For unittest, nightly PyTorch is used as the following section,
# so no need to set PYTORCH_VERSION.
# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config.
@@ -29,7 +30,8 @@ printf "* Installing PyTorch\n"
)
-printf "Installing torchdata nightly\n"
+printf "Installing torchdata nightly with portalocker\n"
+pip install "portalocker>=2.0.0"
pip install --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu
printf "* Installing torchtext\n"
diff --git a/.circleci/unittest/windows/scripts/install.sh b/.circleci/unittest/windows/scripts/install.sh
index 452f4ac..4c8aec4 100644
--- a/.circleci/unittest/windows/scripts/install.sh
+++ b/.circleci/unittest/windows/scripts/install.sh
@@ -1,6 +1,7 @@
#!/usr/bin/env bash
unset PYTORCH_VERSION
+unset TORCHDATA_VERSION
# For unittest, nightly PyTorch is used as the following section,
# so no need to set PYTORCH_VERSION.
# In fact, keeping PYTORCH_VERSION forces us to hardcode PyTorch version in config.
@@ -18,7 +19,8 @@ conda activate ./env
printf "* Installing PyTorch\n"
conda install -y -c "pytorch-${UPLOAD_CHANNEL}" ${CONDA_CHANNEL_FLAGS} pytorch cpuonly
-printf "* Installing torchdata nightly\n"
+printf "* Installing torchdata nightly with portalocker\n"
+pip install "portalocker>=2.0.0"
pip install --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu
printf "* Installing pywin32_postinstall script\n"
diff --git a/.github/scripts/validate_binaries.sh b/.github/scripts/validate_binaries.sh
new file mode 100755
index 0000000..3233b6d
--- /dev/null
+++ b/.github/scripts/validate_binaries.sh
@@ -0,0 +1,8 @@
+
+if [[ ${MATRIX_PACKAGE_TYPE} = "conda" ]]; then
+ conda install -y torchtext -c ${PYTORCH_CONDA_CHANNEL}
+else
+ pip install ${PYTORCH_PIP_PREFIX} torchtext --extra-index-url ${PYTORCH_PIP_DOWNLOAD_URL}
+fi
+
+python ./test/smoke_tests/smoke_tests.py
diff --git a/.github/workflows/build-conda-linux.yml b/.github/workflows/build-conda-linux.yml
new file mode 100644
index 0000000..5fac4ca
--- /dev/null
+++ b/.github/workflows/build-conda-linux.yml
@@ -0,0 +1,55 @@
+name: Build Linux Conda
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ tags:
+ # NOTE: Binary build pipelines should only get triggered on release candidate builds
+ # Release candidate tags look like: v1.11.0-rc1
+ - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+ workflow_dispatch:
+
+jobs:
+ generate-matrix:
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ with:
+ package-type: conda
+ os: linux
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ with-cuda: disable
+ channel: test
+ build:
+ needs: generate-matrix
+ strategy:
+ fail-fast: false
+ matrix:
+ include:
+ - repository: pytorch/text
+ pre-script: packaging/install_torchdata.sh
+ post-script: ""
+ conda-package-directory: packaging/torchtext
+ smoke-test-script: test/smoke_tests/smoke_tests.py
+ package-name: torchtext
+ name: ${{ matrix.repository }}
+ uses: pytorch/test-infra/.github/workflows/build_conda_linux.yml@main
+ with:
+ conda-package-directory: ${{ matrix.conda-package-directory }}
+ repository: ${{ matrix.repository }}
+ ref: ""
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+ pre-script: ${{ matrix.pre-script }}
+ post-script: ${{ matrix.post-script }}
+ package-name: ${{ matrix.package-name }}
+ smoke-test-script: ${{ matrix.smoke-test-script }}
+ # Using "development" as trigger event so these binaries are not uploaded
+ # to official channels yet
+ trigger-event: ${{ github.event_name }}
+ secrets:
+ CONDA_PYTORCHBOT_TOKEN: ${{ secrets.CONDA_PYTORCHBOT_TOKEN }}
diff --git a/.github/workflows/build-conda-m1.yml b/.github/workflows/build-conda-m1.yml
new file mode 100644
index 0000000..51de124
--- /dev/null
+++ b/.github/workflows/build-conda-m1.yml
@@ -0,0 +1,55 @@
+name: Build M1 Conda
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ tags:
+ # NOTE: Binary build pipelines should only get triggered on release candidate builds
+ # Release candidate tags look like: v1.11.0-rc1
+ - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+ workflow_dispatch:
+
+jobs:
+ generate-matrix:
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ with:
+ package-type: conda
+ os: macos-arm64
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ channel: test
+ build:
+ needs: generate-matrix
+ strategy:
+ fail-fast: false
+ matrix:
+ include:
+ - repository: pytorch/text
+ pre-script: packaging/install_torchdata.sh
+ post-script: ""
+ conda-package-directory: packaging/torchtext
+ smoke-test-script: test/smoke_tests/smoke_tests.py
+ package-name: torchtext
+ name: ${{ matrix.repository }}
+ uses: pytorch/test-infra/.github/workflows/build_conda_macos.yml@main
+ with:
+ conda-package-directory: ${{ matrix.conda-package-directory }}
+ repository: ${{ matrix.repository }}
+ ref: ""
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+ pre-script: ${{ matrix.pre-script }}
+ post-script: ${{ matrix.post-script }}
+ package-name: ${{ matrix.package-name }}
+ smoke-test-script: ${{ matrix.smoke-test-script }}
+ runner-type: macos-m1-12
+ # Using "development" as trigger event so these binaries are not uploaded
+ # to official channels yet
+ trigger-event: ${{ github.event_name }}
+ secrets:
+ CONDA_PYTORCHBOT_TOKEN: ${{ secrets.CONDA_PYTORCHBOT_TOKEN }}
diff --git a/.github/workflows/build-conda-macos.yml b/.github/workflows/build-conda-macos.yml
new file mode 100644
index 0000000..d59f030
--- /dev/null
+++ b/.github/workflows/build-conda-macos.yml
@@ -0,0 +1,55 @@
+name: Build Macos Conda
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ tags:
+ # NOTE: Binary build pipelines should only get triggered on release candidate builds
+ # Release candidate tags look like: v1.11.0-rc1
+ - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+ workflow_dispatch:
+
+jobs:
+ generate-matrix:
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ with:
+ package-type: conda
+ os: macos
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ channel: test
+ build:
+ needs: generate-matrix
+ strategy:
+ fail-fast: false
+ matrix:
+ include:
+ - repository: pytorch/text
+ pre-script: packaging/install_torchdata.sh
+ post-script: ""
+ conda-package-directory: packaging/torchtext
+ smoke-test-script: test/smoke_tests/smoke_tests.py
+ package-name: torchtext
+ name: ${{ matrix.repository }}
+ uses: pytorch/test-infra/.github/workflows/build_conda_macos.yml@main
+ with:
+ conda-package-directory: ${{ matrix.conda-package-directory }}
+ repository: ${{ matrix.repository }}
+ ref: ""
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+ pre-script: ${{ matrix.pre-script }}
+ post-script: ${{ matrix.post-script }}
+ package-name: ${{ matrix.package-name }}
+ smoke-test-script: ${{ matrix.smoke-test-script }}
+ runner-type: macos-12
+ # Using "development" as trigger event so these binaries are not uploaded
+ # to official channels yet
+ trigger-event: ${{ github.event_name }}
+ secrets:
+ CONDA_PYTORCHBOT_TOKEN: ${{ secrets.CONDA_PYTORCHBOT_TOKEN }}
diff --git a/.github/workflows/build-m1-binaries.yml b/.github/workflows/build-m1-binaries.yml
deleted file mode 100644
index 6b04482..0000000
--- a/.github/workflows/build-m1-binaries.yml
+++ /dev/null
@@ -1,178 +0,0 @@
-name: Build on M1
-on:
- pull_request:
- paths:
- - .github/workflows/build-m1-binaries.yml
- push:
- branches:
- - nightly
- - release/*
- tags:
- # NOTE: Binary build pipelines should only get triggered on release candidate builds
- # Release candidate tags look like: v1.11.0-rc1
- - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
- workflow_dispatch:
-env:
- CHANNEL: "nightly"
-jobs:
- build_wheels:
- name: "Build TorchText M1 wheels"
- runs-on: macos-m1-12
- strategy:
- fail-fast: false
- matrix:
- py_vers: ["3.8", "3.9", "3.10"]
- steps:
- - name: Checkout repository
- uses: actions/checkout@v2
- - name: Set CHANNEL (only for tagged pushes)
- if: ${{ github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags/') }}
- run: |
- # reference ends with an RC suffix
- if [[ ${GITHUB_REF_NAME} = *-rc[0-9]* ]]; then
- echo "CHANNEL=test" >> "$GITHUB_ENV"
- fi
- - name: Set Release CHANNEL (for release)
- if:
- ${{ (github.event_name == 'pull_request' && startsWith(github.base_ref, 'release')) || startsWith(github.ref,
- 'refs/heads/release') }}
- run: |
- echo "CHANNEL=test" >> "$GITHUB_ENV"
- - name: Setup miniconda
- uses: pytorch/test-infra/.github/actions/setup-miniconda@main
- - name: Build TorchText M1 wheel
- shell: arch -arch arm64 bash {0}
- env:
- ENV_NAME: conda-env-${{ github.run_id }}
- PY_VERS: ${{ matrix.py_vers }}
- run: |
- set -ex
- . packaging/pkg_helpers.bash
- # if we are uploading to test channell, our version consist only of the base: 0.x.x - no date string or suffix added
- if [[ $CHANNEL == "test" ]]; then
- setup_base_build_version
- else
- setup_build_version
- fi
- git submodule update --init --recursive
- WHL_NAME=torchtext-${BUILD_VERSION}-cp${PY_VERS/.}-cp${PY_VERS/.}-macosx_11_0_arm64.whl
- conda create -yp ${ENV_NAME} python=${PY_VERS} numpy cmake ninja wheel pkg-config
- conda run -p ${ENV_NAME} python3 -mpip install torch --pre --extra-index-url=https://download.pytorch.org/whl/${CHANNEL}
- conda run -p ${ENV_NAME} python3 -mpip install delocate
- conda run -p ${ENV_NAME} python3 setup.py bdist_wheel
- export PYTORCH_VERSION="$(conda run -p ${ENV_NAME} python3 -mpip show torch | grep ^Version: | sed 's/Version: *//')"
- conda run -p ${ENV_NAME} DYLD_FALLBACK_LIBRARY_PATH="${ENV_NAME}/lib" delocate-wheel -v --ignore-missing-dependencies dist/*.whl
- conda env remove -p ${ENV_NAME}
- - name: Test wheel
- shell: arch -arch arm64 bash {0}
- env:
- ENV_NAME: conda-test-env-${{ github.run_id }}
- PY_VERS: ${{ matrix.py_vers }}
- run: |
- set -ex
- conda create -yp ${ENV_NAME} python=${PY_VERS} numpy
- conda run -p ${ENV_NAME} python3 -mpip install torch --pre --extra-index-url=https://download.pytorch.org/whl/${CHANNEL}
- conda run -p ${ENV_NAME} python3 -mpip install dist/*.whl
- # Test torch is importable, by changing cwd and running import commands
- conda run --cwd /tmp -p ${ENV_NAME} python3 -c "import torchtext;print('torchtext version is ', torchtext.__version__)"
- conda env remove -p ${ENV_NAME}
- - name: Upload wheel to GitHub
- if:
- ${{ github.event_name == 'push' && (github.event.ref == 'refs/heads/nightly' || startsWith(github.event.ref,
- 'refs/tags/')) }}
- uses: actions/upload-artifact@v3
- with:
- name: torchtext-py${{ matrix.py_vers }}-macos11-m1
- path: dist/
- - name: Upload wheel to S3
- if:
- ${{ github.event_name == 'push' && (github.event.ref == 'refs/heads/nightly' || startsWith(github.event.ref,
- 'refs/tags/')) }}
- shell: arch -arch arm64 bash {0}
- env:
- AWS_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
- AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}
- run: |
- for pkg in dist/*; do
- aws s3 cp "$pkg" "s3://pytorch/whl/${CHANNEL}/cpu/" --acl public-read
- done
- build_conda:
- name: "Build TorchText M1 conda packages"
- runs-on: macos-m1-12
- strategy:
- matrix:
- py_vers: ["3.8", "3.9", "3.10"]
- steps:
- - name: Checkout repository
- uses: actions/checkout@v2
- - name: Set CHANNEL (only for tagged pushes)
- if: ${{ github.event_name == 'push' && startsWith(github.event.ref, 'refs/tags/') }}
- run: |
- # reference ends with an RC suffix
- if [[ ${GITHUB_REF_NAME} = *-rc[0-9]* ]]; then
- echo "CHANNEL=test" >> "$GITHUB_ENV"
- fi
- - name: Set CHANNEL Release (for release)
- if:
- ${{ (github.event_name == 'pull_request' && startsWith(github.base_ref, 'release')) || startsWith(github.ref,
- 'refs/heads/release') }}
- run: |
- echo "CHANNEL=test" >> "$GITHUB_ENV"
- - name: Setup miniconda
- uses: pytorch/test-infra/.github/actions/setup-miniconda@main
- - name: Install conda-build and purge previous artifacts
- shell: arch -arch arm64 bash {0}
- run: |
- conda install -yq conda-build
- conda build purge-all
- - name: Build TorchText M1 conda package
- shell: arch -arch arm64 bash {0}
- env:
- ENV_NAME: conda-env-${{ github.run_id }}
- PYTHON_VERSION: ${{ matrix.py_vers }}
- CU_VERSION: cpu
- run: |
- set -ex
- . packaging/pkg_helpers.bash
-
- if [[ $CHANNEL == "test" ]]; then
- setup_base_build_version
- export CONDA_CHANNEL_FLAGS="-c pytorch-test"
- else
- setup_build_version
- fi
- export BUILD_TYPE="conda"
- export NO_CUDA_PACKAGE=1
- export SOURCE_ROOT_DIR="$PWD"
-
- setup_conda_pytorch_constraint
- export SOURCE_ROOT_DIR=$(pwd)
- conda build \
- -c defaults \
- $CONDA_CHANNEL_FLAGS \
- --no-anaconda-upload \
- --python "$PYTHON_VERSION" \
- --output-folder=dist/ \
- packaging/torchtext
- - name: Upload package to GitHub
- uses: actions/upload-artifact@v3
- with:
- name: torchtext-py${{ matrix.py_vers }}-macos11-m1-conda
- path: dist/
- - name: Upload package to conda
- if:
- ${{ github.event_name == 'push' && (github.event.ref == 'refs/heads/nightly' || startsWith(github.event.ref,
- 'refs/tags/')) }}
- shell: arch -arch arm64 bash {0}
- env:
- CONDA_PYTORCHBOT_TOKEN: ${{ secrets.CONDA_PYTORCHBOT_TOKEN }}
- run: |
- conda install -yq anaconda-client
- set -x
- anaconda -t "${CONDA_PYTORCHBOT_TOKEN}" upload dist/osx-arm64/*.tar.bz2 -u "pytorch-${CHANNEL}" --label main --no-progress --force
-
-concurrency:
- group:
- ${{ github.workflow }}-${{ github.event.pull_request.number || github.sha }}-${{ github.event_name ==
- 'workflow_dispatch' }}
- cancel-in-progress: true
diff --git a/.github/workflows/build-wheels-linux.yml b/.github/workflows/build-wheels-linux.yml
new file mode 100644
index 0000000..7352a1c
--- /dev/null
+++ b/.github/workflows/build-wheels-linux.yml
@@ -0,0 +1,54 @@
+name: Build Linux Wheels
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ tags:
+ # NOTE: Binary build pipelines should only get triggered on release candidate builds
+ # Release candidate tags look like: v1.11.0-rc1
+ - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+ workflow_dispatch:
+
+jobs:
+ generate-matrix:
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ with:
+ package-type: wheel
+ os: linux
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ with-cuda: disable
+ channel: test
+ build:
+ needs: generate-matrix
+ strategy:
+ fail-fast: false
+ matrix:
+ include:
+ - repository: pytorch/text
+ pre-script: packaging/install_torchdata.sh
+ post-script: ""
+ smoke-test-script: test/smoke_tests/smoke_tests.py
+ package-name: torchtext
+ name: ${{ matrix.repository }}
+ uses: pytorch/test-infra/.github/workflows/build_wheels_linux.yml@main
+ with:
+ repository: ${{ matrix.repository }}
+ ref: ""
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+ pre-script: ${{ matrix.pre-script }}
+ post-script: ${{ matrix.post-script }}
+ package-name: ${{ matrix.package-name }}
+ smoke-test-script: ${{ matrix.smoke-test-script }}
+ # Using "development" as trigger event so these binaries are not uploaded
+ # to official channels yet
+ trigger-event: ${{ github.event_name }}
+ secrets:
+ AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+ AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}
diff --git a/.github/workflows/build-wheels-m1.yml b/.github/workflows/build-wheels-m1.yml
new file mode 100644
index 0000000..25a2292
--- /dev/null
+++ b/.github/workflows/build-wheels-m1.yml
@@ -0,0 +1,54 @@
+name: Build M1 Wheels
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ tags:
+ # NOTE: Binary build pipelines should only get triggered on release candidate builds
+ # Release candidate tags look like: v1.11.0-rc1
+ - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+ workflow_dispatch:
+
+jobs:
+ generate-matrix:
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ with:
+ package-type: wheel
+ os: macos-arm64
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ channel: test
+ build:
+ needs: generate-matrix
+ strategy:
+ fail-fast: false
+ matrix:
+ include:
+ - repository: pytorch/text
+ pre-script: packaging/install_torchdata.sh
+ post-script: ""
+ package-name: torchtext
+ smoke-test-script: test/smoke_tests/smoke_tests.py
+ name: ${{ matrix.repository }}
+ uses: pytorch/test-infra/.github/workflows/build_wheels_macos.yml@main
+ with:
+ repository: ${{ matrix.repository }}
+ ref: ""
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+ pre-script: ${{ matrix.pre-script }}
+ post-script: ${{ matrix.post-script }}
+ package-name: ${{ matrix.package-name }}
+ smoke-test-script: ${{ matrix.smoke-test-script }}
+ runner-type: macos-m1-12
+ # Using "development" as trigger event so these binaries are not uploaded
+ # to official channels yet
+ trigger-event: ${{ github.event_name }}
+ secrets:
+ AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+ AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}
diff --git a/.github/workflows/build-wheels-macos.yml b/.github/workflows/build-wheels-macos.yml
new file mode 100644
index 0000000..99772a5
--- /dev/null
+++ b/.github/workflows/build-wheels-macos.yml
@@ -0,0 +1,54 @@
+name: Build Macos Wheels
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ tags:
+ # NOTE: Binary build pipelines should only get triggered on release candidate builds
+ # Release candidate tags look like: v1.11.0-rc1
+ - v[0-9]+.[0-9]+.[0-9]+-rc[0-9]+
+ workflow_dispatch:
+
+jobs:
+ generate-matrix:
+ uses: pytorch/test-infra/.github/workflows/generate_binary_build_matrix.yml@main
+ with:
+ package-type: wheel
+ os: macos
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ channel: test
+ build:
+ needs: generate-matrix
+ strategy:
+ fail-fast: false
+ matrix:
+ include:
+ - repository: pytorch/text
+ pre-script: packaging/install_torchdata.sh
+ post-script: ""
+ package-name: torchtext
+ smoke-test-script: test/smoke_tests/smoke_tests.py
+ name: ${{ matrix.repository }}
+ uses: pytorch/test-infra/.github/workflows/build_wheels_macos.yml@main
+ with:
+ repository: ${{ matrix.repository }}
+ ref: ""
+ test-infra-repository: pytorch/test-infra
+ test-infra-ref: main
+ build-matrix: ${{ needs.generate-matrix.outputs.matrix }}
+ pre-script: ${{ matrix.pre-script }}
+ post-script: ${{ matrix.post-script }}
+ package-name: ${{ matrix.package-name }}
+ smoke-test-script: ${{ matrix.smoke-test-script }}
+ runner-type: macos-12
+ # Using "development" as trigger event so these binaries are not uploaded
+ # to official channels yet
+ trigger-event: ${{ github.event_name }}
+ secrets:
+ AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID: ${{ secrets.AWS_PYTORCH_UPLOADER_ACCESS_KEY_ID }}
+ AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY: ${{ secrets.AWS_PYTORCH_UPLOADER_SECRET_ACCESS_KEY }}
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
index 5f7a38a..b6af768 100644
--- a/.github/workflows/codeql.yml
+++ b/.github/workflows/codeql.yml
@@ -31,11 +31,13 @@ jobs:
- name: Install Torch
run: |
python -m pip install cmake
- python -m pip install torch==1.8.1+cpu -f https://download.pytorch.org/whl/torch_stable.html
+ python -m pip install --quiet --pre torch torchdata -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
sudo ln -s /usr/bin/ninja /usr/bin/ninja-build
- name: Build TorchText
- run: python setup.py develop --user
+ run: |
+ python -m pip install setuptools==65.7.0
+ python setup.py develop --user
# If any code scanning alerts are found, they will be under Security -> CodeQL
# Link: https://github.com/pytorch/text/security/code-scanning
diff --git a/.github/workflows/integration-test.yml b/.github/workflows/integration-test.yml
index 0c005a1..1e507a4 100644
--- a/.github/workflows/integration-test.yml
+++ b/.github/workflows/integration-test.yml
@@ -7,25 +7,55 @@ on:
workflow_dispatch:
jobs:
- build:
- runs-on: ubuntu-18.04
+ tests:
strategy:
- fail-fast: false
matrix:
- python-version: [3.8]
-
- steps:
- - uses: actions/checkout@v2
- - name: Set up Python ${{ matrix.python-version }}
- uses: actions/setup-python@v2
- with:
- python-version: ${{ matrix.python-version }}
- - name: Install packages
- run: |
- python -m pip install --quiet --upgrade pip
- python -m pip install --quiet --pre torch -f https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html
- python -m pip install --quiet pytest requests cmake ninja sentencepiece parameterized tqdm expecttest
- python setup.py install
- - name: Run integration test
- run: |
- cd test && pytest integration_tests -v --use-tmp-hub-dir
+ python_version: ["3.8"]
+ fail-fast: false
+ uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+ with:
+ runner: linux.12xlarge
+ repository: pytorch/text
+ script: |
+ # Mark Build Directory Safe
+ git config --global --add safe.directory /__w/text/text
+ # Set up Environment Variables
+ export PYTHON_VERSION="${{ matrix.python_version }}"
+ export VERSION="cpu"
+ export CUDATOOLKIT="cpuonly"
+ # Set CHANNEL
+ if [[ (${GITHUB_EVENT_NAME} = 'pull_request' && (${GITHUB_BASE_REF} = 'release'*)) || (${GITHUB_REF} = 'refs/heads/release'*) ]]; then
+ export CHANNEL=test
+ else
+ export CHANNEL=nightly
+ fi
+ # Create Conda Env
+ conda create -yp ci_env python="${PYTHON_VERSION}"
+ conda activate /work/ci_env
+ python3 -m pip --quiet install cmake>=3.18.0 ninja
+ conda env update --file ".circleci/unittest/linux/scripts/environment.yml" --prune
+ # TorchText-specific Setup
+ printf "* Downloading SpaCy English models\n"
+ python -m spacy download en_core_web_sm
+ printf "* Downloading SpaCy German models\n"
+ python -m spacy download de_core_news_sm
+ # Install PyTorch, Torchvision, and TorchData
+ set -ex
+ conda install \
+ --yes \
+ -c "pytorch-${CHANNEL}" \
+ -c nvidia "pytorch-${CHANNEL}"::pytorch[build="*${VERSION}*"] \
+ "${CUDATOOLKIT}"
+ printf "Installing torchdata nightly\n"
+ python3 -m pip install --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+ python3 setup.py develop
+ # Install integration test dependencies
+ python3 -m pip --quiet install parameterized
+ python3 -m pip --quiet install requests
+ python3 -m pip --quiet install sentencepiece
+ python3 -m pip --quiet install tqdm
+ python3 -m pip --quiet install expecttest
+ # Run Tests
+ python3 -m torch.utils.collect_env
+ cd test
+ pytest integration_tests -v --use-tmp-hub-dir
diff --git a/.github/workflows/test-linux-cpu.yml b/.github/workflows/test-linux-cpu.yml
new file mode 100644
index 0000000..998c91e
--- /dev/null
+++ b/.github/workflows/test-linux-cpu.yml
@@ -0,0 +1,69 @@
+name: Unit-tests on Linux CPU
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ workflow_dispatch:
+
+env:
+ CHANNEL: "nightly"
+
+jobs:
+ tests:
+ strategy:
+ matrix:
+ python_version: ["3.8", "3.9", "3.10"]
+ fail-fast: false
+ uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+ with:
+ runner: linux.12xlarge
+ repository: pytorch/text
+ script: |
+ # Mark Build Directory Safe
+ git config --global --add safe.directory /__w/text/text
+
+ # Set up Environment Variables
+ export PYTHON_VERSION="${{ matrix.python_version }}"
+ export VERSION="cpu"
+ export CUDATOOLKIT="cpuonly"
+
+ # Set CHANNEL
+ if [[ (${GITHUB_BASE_REF} = 'release'*) || (${GITHUB_REF} = 'refs/heads/release'*) ]]; then
+ export CHANNEL=test
+ else
+ export CHANNEL=nightly
+ fi
+
+ # Create Conda Env
+ conda create -yp ci_env python="${PYTHON_VERSION}"
+ conda activate /work/ci_env
+ python3 -m pip --quiet install cmake>=3.18.0 ninja
+ conda env update --file ".circleci/unittest/linux/scripts/environment.yml" --prune
+
+ # TorchText-specific Setup
+ printf "* Downloading SpaCy English models\n"
+ python -m spacy download en_core_web_sm
+ printf "* Downloading SpaCy German models\n"
+ python -m spacy download de_core_news_sm
+
+ # Install PyTorch, Torchvision, and TorchData
+ set -ex
+ conda install \
+ --yes \
+ -c "pytorch-${CHANNEL}" \
+ -c nvidia "pytorch-${CHANNEL}"::pytorch[build="*${VERSION}*"] \
+ "${CUDATOOLKIT}"
+ printf "Installing torchdata nightly\n"
+ python3 -m pip install "portalocker>=2.0.0"
+ python3 -m pip install --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+ python3 setup.py develop
+ python3 -m pip install parameterized
+
+ # Run Tests
+ python3 -m torch.utils.collect_env
+ cd test
+ python3 -m pytest --cov=torchtext --junitxml=test-results/junit.xml -v --durations 20 torchtext_unittest
diff --git a/.github/workflows/test-linux-gpu.yml b/.github/workflows/test-linux-gpu.yml
new file mode 100644
index 0000000..0b60213
--- /dev/null
+++ b/.github/workflows/test-linux-gpu.yml
@@ -0,0 +1,74 @@
+name: Unit-tests on Linux GPU
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ workflow_dispatch:
+
+env:
+ CHANNEL: "nightly"
+
+jobs:
+ tests:
+ strategy:
+ matrix:
+ python_version: ["3.8"]
+ cuda_arch_version: ["11.7"]
+ fail-fast: false
+ uses: pytorch/test-infra/.github/workflows/linux_job.yml@main
+ with:
+ runner: linux.g5.4xlarge.nvidia.gpu
+ repository: pytorch/text
+ gpu-arch-type: cuda
+ gpu-arch-version: ${{ matrix.cuda_arch_version }}
+ timeout: 120
+ script: |
+ # Mark Build Directory Safe
+ git config --global --add safe.directory /__w/text/text
+
+ # Set up Environment Variables
+ export PYTHON_VERSION="${{ matrix.python_version }}"
+ export VERSION="${{ matrix.cuda_arch_version }}"
+ export CUDATOOLKIT="pytorch-cuda=${VERSION}"
+
+ # Set CHANNEL
+ if [[ (${GITHUB_BASE_REF} = 'release'*) || (${GITHUB_REF} = 'refs/heads/release'*) ]]; then
+ export CHANNEL=test
+ else
+ export CHANNEL=nightly
+ fi
+
+ # Create Conda Env
+ conda create --quiet -yp ci_env python="${PYTHON_VERSION}"
+ conda activate /work/ci_env
+ python3 -m pip --quiet install cmake>=3.18.0 ninja
+ conda env update --file ".circleci/unittest/linux/scripts/environment.yml" --prune
+
+ # TorchText-specific Setup
+ printf "* Downloading SpaCy English models\n"
+ python -m spacy download en_core_web_sm
+ printf "* Downloading SpaCy German models\n"
+ python -m spacy download de_core_news_sm
+
+ # Install PyTorch and TorchData
+ set -ex
+ conda install \
+ --yes \
+ --quiet \
+ -c "pytorch-${CHANNEL}" \
+ -c nvidia "pytorch-${CHANNEL}"::pytorch[build="*${VERSION}*"] \
+ "${CUDATOOLKIT}"
+ printf "Installing torchdata nightly\n"
+ python3 -m pip install "portalocker>=2.0.0"
+ python3 -m pip install --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu --quiet
+ python3 setup.py develop
+ python3 -m pip install parameterized --quiet
+
+ # Run Tests
+ python3 -m torch.utils.collect_env
+ cd test
+ python3 -m pytest --junitxml=test-results/junit.xml -v --durations 20 -m gpu_test torchtext_unittest
diff --git a/.github/workflows/test-macos-cpu.yml b/.github/workflows/test-macos-cpu.yml
new file mode 100644
index 0000000..eb5f4fe
--- /dev/null
+++ b/.github/workflows/test-macos-cpu.yml
@@ -0,0 +1,76 @@
+name: Unit-tests on Macos CPU
+
+on:
+ pull_request:
+ push:
+ branches:
+ - nightly
+ - main
+ - release/*
+ workflow_dispatch:
+
+env:
+ CHANNEL: "nightly"
+
+jobs:
+ tests:
+ strategy:
+ matrix:
+ python_version: ["3.8", "3.9", "3.10"]
+ fail-fast: false
+ uses: pytorch/test-infra/.github/workflows/macos_job.yml@main
+ with:
+ runner: macos-12
+ repository: pytorch/text
+ timeout: 60
+ script: |
+ # Mark Build Directory Safe
+ git config --global --add safe.directory /__w/text/text
+
+ # Set up Environment Variables
+ export PYTHON_VERSION="${{ matrix.python_version }}"
+ export VERSION="cpu"
+ export CUDATOOLKIT="cpuonly"
+
+ # Set CHANNEL
+ if [[ (${GITHUB_BASE_REF} = 'release'*) || (${GITHUB_REF} = 'refs/heads/release'*) ]]; then
+ export CHANNEL=test
+ else
+ export CHANNEL=nightly
+ fi
+
+ # TODO: this can be removed as soon as linking issue could be resolved
+ # see https://github.com/pytorch/pytorch/issues/62424 from details
+ export MKL_CONSTRAINT='mkl==2021.2.0'
+
+ # Create Conda Env
+ conda create -yp ./ci_env python="${PYTHON_VERSION}"
+ conda activate ./ci_env
+ python3 -m pip --quiet install cmake>=3.18.0 ninja
+ conda env update --file ".circleci/unittest/linux/scripts/environment.yml" --prune
+
+ # TorchText-specific Setup
+ printf "* Downloading SpaCy English models\n"
+ python -m spacy download en_core_web_sm
+ printf "* Downloading SpaCy German models\n"
+ python -m spacy download de_core_news_sm
+
+ # Install PyTorch, Torchvision, and TorchData
+ set -ex
+ conda install \
+ --yes \
+ -c "pytorch-${CHANNEL}" \
+ -c nvidia \
+ "${MKL_CONSTRAINT}" \
+ pytorch \
+ "${CUDATOOLKIT}"
+ printf "Installing torchdata nightly\n"
+ python3 -m pip install "portalocker>=2.0.0"
+ python3 -m pip install --pre torchdata --extra-index-url https://download.pytorch.org/whl/nightly/cpu
+ python3 setup.py develop
+ python3 -m pip install parameterized
+
+ # Run Tests
+ python3 -m torch.utils.collect_env
+ cd test
+ python3 -m pytest --cov=torchtext --junitxml=test-results/junit.xml -v --durations 20 torchtext_unittest
diff --git a/.github/workflows/validate-binaries.yml b/.github/workflows/validate-binaries.yml
new file mode 100644
index 0000000..6bebfd3
--- /dev/null
+++ b/.github/workflows/validate-binaries.yml
@@ -0,0 +1,55 @@
+name: Validate binaries
+
+on:
+ workflow_call:
+ inputs:
+ channel:
+ description: "Channel to use (nightly, test, release, all)"
+ required: false
+ type: string
+ default: release
+ os:
+ description: "Operating system to generate for (linux, windows, macos, macos-arm64)"
+ required: true
+ type: string
+ ref:
+ description: "Reference to checkout, defaults to empty"
+ default: ""
+ required: false
+ type: string
+ workflow_dispatch:
+ inputs:
+ channel:
+ description: "Channel to use (nightly, test, release, all)"
+ required: true
+ type: choice
+ options:
+ - release
+ - nightly
+ - test
+ - all
+ os:
+ description: "Operating system to generate for (linux, windows, macos)"
+ required: true
+ type: choice
+ default: all
+ options:
+ - windows
+ - linux
+ - macos
+ - all
+ ref:
+ description: "Reference to checkout, defaults to empty"
+ default: ""
+ required: false
+ type: string
+jobs:
+ validate-binaries:
+ uses: pytorch/test-infra/.github/workflows/validate-domain-library.yml@main
+ with:
+ package_type: "conda,wheel"
+ os: ${{ inputs.os }}
+ channel: ${{ inputs.channel }}
+ repository: "pytorch/text"
+ smoke_test: "source ./.github/scripts/validate_binaries.sh"
+ install_torch: true
diff --git a/.github/workflows/validate-nightly-binaries.yml b/.github/workflows/validate-nightly-binaries.yml
new file mode 100644
index 0000000..d49a9e8
--- /dev/null
+++ b/.github/workflows/validate-nightly-binaries.yml
@@ -0,0 +1,27 @@
+# Scheduled validation of the nightly binaries
+name: cron
+
+on:
+ schedule:
+ # At 5:30 pm UTC (7:30 am PDT)
+ - cron: "30 17 * * *"
+ # Have the ability to trigger this job manually through the API
+ workflow_dispatch:
+ push:
+ branches:
+ - main
+ paths:
+ - .github/workflows/validate-nightly-binaries.yml
+ - .github/workflows/validate-binaries.yml
+ - .github/scripts/validate_binaries.sh
+ pull_request:
+ paths:
+ - .github/workflows/validate-nightly-binaries.yml
+ - .github/workflows/validate-binaries.yml
+ - .github/scripts/validate_binaries.sh
+jobs:
+ nightly:
+ uses: ./.github/workflows/validate-binaries.yml
+ with:
+ channel: nightly
+ os: all
diff --git a/CMakeLists.txt b/CMakeLists.txt
index ed39e64..b789a32 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -24,10 +24,10 @@ string(FIND "${CMAKE_CXX_FLAGS}" "-std=c++" env_cxx_standard)
if(env_cxx_standard GREATER -1)
message(
WARNING "C++ standard version definition detected in environment variable."
- "PyTorch requires -std=c++14. Please remove -std=c++ settings in your environment.")
+ "PyTorch requires -std=c++17. Please remove -std=c++ settings in your environment.")
endif()
-set(CMAKE_CXX_STANDARD 14)
+set(CMAKE_CXX_STANDARD 17)
set(CMAKE_C_STANDARD 11)
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
diff --git a/README.rst b/README.rst
index 83d11d5..35399cb 100644
--- a/README.rst
+++ b/README.rst
@@ -1,3 +1,5 @@
+.. image:: docs/source/_static/img/torchtext_logo.png
+
.. image:: https://circleci.com/gh/pytorch/text.svg?style=svg
:target: https://circleci.com/gh/pytorch/text
@@ -29,13 +31,13 @@ We recommend Anaconda as a Python package management system. Please refer to `py
:header: "PyTorch version", "torchtext version", "Supported Python version"
:widths: 10, 10, 10
- nightly build, main, ">=3.7, <=3.10"
+ nightly build, main, ">=3.8, <=3.11"
+ 1.13.0, 0.14.0, ">=3.7, <=3.10"
1.12.0, 0.13.0, ">=3.7, <=3.10"
1.11.0, 0.12.0, ">=3.6, <=3.9"
1.10.0, 0.11.0, ">=3.6, <=3.9"
1.9.1, 0.10.1, ">=3.6, <=3.9"
1.9, 0.10, ">=3.6, <=3.9"
- 1.8.2 (LTS), 0.9.2 (LTS), ">=3.6, <=3.9"
1.8.1, 0.9.1, ">=3.6, <=3.9"
1.8, 0.9, ">=3.6, <=3.9"
1.7.1, 0.8.1, ">=3.6, <=3.9"
@@ -53,9 +55,6 @@ Using pip::
pip install torchtext
-**Note** LTS versions are distributed through a different channel than the other versioned releases.
-Please refer to https://pytorch.org/get-started/locally/ for details.
-
Optional requirements
---------------------
@@ -94,6 +93,9 @@ To build torchtext from source, you need ``git``, ``CMake`` and C++11 compiler s
When building from source, make sure that you have the same C++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext.
If you are using the nightly build of PyTorch, checkout the environment it was built with `conda (here) <https://github.com/pytorch/builder/tree/main/conda>`_ and `pip (here) <https://github.com/pytorch/builder/tree/main/manywheel>`_.
+Additionally, datasets in torchtext are implemented using the torchdata library. Please take a look at the
+`installation instructions <https://github.com/pytorch/data#installation>`_ to download the latest nightlies or install from source.
+
Documentation
=============
@@ -117,7 +119,10 @@ Models
The library currently consist of following pre-trained models:
* RoBERTa: `Base and Large Architecture <https://github.com/pytorch/fairseq/tree/main/examples/roberta#pre-trained-models>`_
+* `DistilRoBERTa <https://github.com/huggingface/transformers/blob/main/examples/research_projects/distillation/README.md>`_
* XLM-RoBERTa: `Base and Large Architure <https://github.com/pytorch/fairseq/tree/main/examples/xlmr#pre-trained-models>`_
+* T5: `Small, Base, Large, 3B, and 11B Architecture <https://github.com/google-research/text-to-text-transfer-transformer>`_
+* Flan-T5: `Base, Large, XL, and XXL Architecture <https://github.com/google-research/t5x>`_
Tokenizers
==========
diff --git a/debian/changelog b/debian/changelog
index 543e257..eb41ee9 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,3 +1,9 @@
+pytorch-text (0.15.2-1) UNRELEASED; urgency=low
+
+ * New upstream release.
+
+ -- Debian Janitor <janitor@jelmer.uk> Thu, 10 Aug 2023 13:47:00 -0000
+
pytorch-text (0.14.1-2) unstable; urgency=medium
* Team upload.
diff --git a/debian/patches/cmake.patch b/debian/patches/cmake.patch
index e5a3210..055161b 100644
--- a/debian/patches/cmake.patch
+++ b/debian/patches/cmake.patch
@@ -1,11 +1,11 @@
Description:
don't trigger cmake build in third_party subdir.
That's useless since we don't embed sources.
-Index: pytorch-text/setup.py
+Index: pytorch-text.git/setup.py
===================================================================
---- pytorch-text.orig/setup.py
-+++ pytorch-text/setup.py
-@@ -101,7 +101,6 @@ setup_info = dict(
+--- pytorch-text.git.orig/setup.py
++++ pytorch-text.git/setup.py
+@@ -119,7 +119,6 @@ setup_info = dict(
# See here: https://github.com/pytorch/vision/issues/2134"
ext_modules=setup_helpers.get_ext_modules(),
cmdclass={
diff --git a/debian/patches/pybuild_no_submodule.patch b/debian/patches/pybuild_no_submodule.patch
index f8b8cda..4633872 100644
--- a/debian/patches/pybuild_no_submodule.patch
+++ b/debian/patches/pybuild_no_submodule.patch
@@ -1,11 +1,11 @@
From: Aron Xu <aron@debian.org>
Date: 2023-02-02
Subject: Do not init submodules because we do not use them
-Index: pytorch-text/setup.py
+Index: pytorch-text.git/setup.py
===================================================================
---- pytorch-text.orig/setup.py
-+++ pytorch-text/setup.py
-@@ -89,7 +89,7 @@ class clean(distutils.command.clean.clea
+--- pytorch-text.git.orig/setup.py
++++ pytorch-text.git/setup.py
+@@ -93,7 +93,7 @@ class clean(distutils.command.clean.clea
shutil.rmtree(str(path), ignore_errors=True)
diff --git a/docs/requirements.txt b/docs/requirements.txt
index 2f20beb..45d955c 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -3,3 +3,4 @@ sphinx==5.1.1
-e git+https://github.com/pytorch/pytorch_sphinx_theme.git@cece053#egg=pytorch_sphinx_theme
sphinx_gallery==0.11.1
matplotlib
+regex
diff --git a/docs/source/_static/img/torchtext_logo.png b/docs/source/_static/img/torchtext_logo.png
new file mode 100644
index 0000000..21400cd
Binary files /dev/null and b/docs/source/_static/img/torchtext_logo.png differ
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 7de6b45..b03e603 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -1,5 +1,6 @@
torchtext
=========
+.. image:: _static/img/torchtext_logo.png
This library is part of the `PyTorch
<http://pytorch.org/>`_ project. PyTorch is an open source
@@ -27,11 +28,18 @@ Features described in this documentation are classified by release status:
The :mod:`torchtext` package consists of data processing utilities and
popular datasets for natural language.
+.. toctree::
+ :maxdepth: 1
+ :caption: Torchtext Documentation
+ :hidden:
+
+ Index <self>
+ logo
+
.. toctree::
:maxdepth: 2
:caption: Package Reference
- self
nn_modules
data_functional
data_metrics
diff --git a/docs/source/logo.rst b/docs/source/logo.rst
new file mode 100644
index 0000000..897174d
--- /dev/null
+++ b/docs/source/logo.rst
@@ -0,0 +1,54 @@
+TorchText Logo
+===============
+
+If you make your project using TorchText and you want to mention TorchText, you can use the TorchText logo. There are couple of variations. You can download them from `here <https://download.pytorch.org/torchtext/logo/v1/torchtext-logo.zip>`__.
+
+Please follow `the guideline <https://download.pytorch.org/torchtext/logo/v1/guidelines.pdf>`__ for the proper usage.
+
+.. warning::
+
+ Please do not alter the logo. The guideline lists examples of improper usages as well, so please check them out before using the logos.
+
+Icon
+----
+
+.. image:: https://download.pytorch.org/torchtext/logo/v1/TorchText_Symbol_fullColor_RGB.png
+ :width: 400
+
+Horizontal
+----------
+
+.. image:: https://download.pytorch.org/torchtext/logo/v1/TorchText_Horiz_fullColor_RGB.png
+ :width: 400
+
+|
+
+.. image:: https://download.pytorch.org/torchtext/logo/v1/TorchText_Horiz_black_RGB.png
+ :width: 400
+
+|
+
+.. raw:: html
+
+ <div style="background-color:black; width: 400px;">
+ <img src="https://download.pytorch.org/torchtext/logo/v1/TorchText_Horiz_white_RGB.png" width="400"></img>
+ </div>
+
+Vertical
+--------
+
+.. image:: https://download.pytorch.org/torchtext/logo/v1/TorchText_Vertical_fullColor_RGB.png
+ :width: 400
+
+|
+
+.. image:: https://download.pytorch.org/torchtext/logo/v1/TorchText_Vertical_black_RGB.png
+ :width: 400
+
+|
+
+.. raw:: html
+
+ <div style="background-color:black; width: 400px;">
+ <img src="https://download.pytorch.org/torchtext/logo/v1/TorchText_Vertical_white_RGB.png" width="400"></img>
+ </div>
diff --git a/docs/source/transforms.rst b/docs/source/transforms.rst
index 6b12d0c..65c2188 100644
--- a/docs/source/transforms.rst
+++ b/docs/source/transforms.rst
@@ -99,3 +99,10 @@ StrToIntTransform
.. autoclass:: StrToIntTransform
.. automethod:: forward
+
+CharBPETokenizer
+----------------
+
+.. autoclass:: CharBPETokenizer
+
+ .. automethod:: forward
diff --git a/examples/tutorials/t5_demo.py b/examples/tutorials/t5_demo.py
index 1e7166e..d6165a2 100644
--- a/examples/tutorials/t5_demo.py
+++ b/examples/tutorials/t5_demo.py
@@ -3,6 +3,7 @@ T5-Base Model for Summarization, Sentiment Classification, and Translation
==========================================================================
**Author**: `Pendo Abbo <pabbo@fb.com>`__
+**Author**: `Joe Cummings <jrcummings@fb.com>`__
"""
@@ -24,7 +25,6 @@ T5-Base Model for Summarization, Sentiment Classification, and Translation
# Common imports
# --------------
import torch
-import torch.nn.functional as F
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
@@ -47,7 +47,7 @@ DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cp
# the T5 model expects the input to be batched.
#
-from torchtext.prototype.models import T5Transform
+from torchtext.models import T5Transform
padding_idx = 0
eos_idx = 1
@@ -66,7 +66,7 @@ transform = T5Transform(
#
# ::
#
-# from torchtext.prototype.models import T5_BASE_GENERATION
+# from torchtext.models import T5_BASE_GENERATION
# transform = T5_BASE_GENERATION.transform()
#
@@ -81,7 +81,7 @@ transform = T5Transform(
# https://pytorch.org/text/main/models.html
#
#
-from torchtext.prototype.models import T5_BASE_GENERATION
+from torchtext.models import T5_BASE_GENERATION
t5_base = T5_BASE_GENERATION
@@ -92,146 +92,18 @@ model.to(DEVICE)
#######################################################################
-# Sequence Generator
+# GenerationUtils
# ------------------
#
-# We can define a sequence generator to produce an output sequence based on the input sequence provided. This calls on the
+# We can use torchtext's `GenerationUtils` to produce an output sequence based on the input sequence provided. This calls on the
# model's encoder and decoder, and iteratively expands the decoded sequences until the end-of-sequence token is generated
-# for all sequences in the batch. The `generate` method shown below uses a beam search to generate the sequences. Larger
-# beam sizes can result in better generation at the cost of computational complexity, and a beam size of 1 is equivalent to
-# a greedy decoder.
-#
-
-from torch import Tensor
-from torchtext.prototype.models import T5Model
-
-
-def beam_search(
- beam_size: int,
- step: int,
- bsz: int,
- decoder_output: Tensor,
- decoder_tokens: Tensor,
- scores: Tensor,
- incomplete_sentences: Tensor,
-):
- probs = F.log_softmax(decoder_output[:, -1], dim=-1)
- top = torch.topk(probs, beam_size)
-
- # N is number of sequences in decoder_tokens, L is length of sequences, B is beam_size
- # decoder_tokens has shape (N,L) -> (N,B,L)
- # top.indices has shape (N,B) - > (N,B,1)
- # x has shape (N,B,L+1)
- # note that when step == 1, N = batch_size, and when step > 1, N = batch_size * beam_size
- x = torch.cat([decoder_tokens.unsqueeze(1).repeat(1, beam_size, 1), top.indices.unsqueeze(-1)], dim=-1)
-
- # beams are first created for a given sequence
- if step == 1:
- # x has shape (batch_size, B, L+1) -> (batch_size * B, L+1)
- # new_scores has shape (batch_size,B)
- # incomplete_sentences has shape (batch_size * B) = (N)
- new_decoder_tokens = x.view(-1, step + 1)
- new_scores = top.values
- new_incomplete_sentences = incomplete_sentences
-
- # beams already exist, want to expand each beam into possible new tokens to add
- # and for all expanded beams beloning to the same sequences, choose the top k
- else:
- # scores has shape (batch_size,B) -> (N,1) -> (N,B)
- # top.values has shape (N,B)
- # new_scores has shape (N,B) -> (batch_size, B^2)
- new_scores = (scores.view(-1, 1).repeat(1, beam_size) + top.values).view(bsz, -1)
-
- # v, i have shapes (batch_size, B)
- v, i = torch.topk(new_scores, beam_size)
-
- # x has shape (N,B,L+1) -> (batch_size, B, L+1)
- # i has shape (batch_size, B) -> (batch_size, B, L+1)
- # new_decoder_tokens has shape (batch_size, B, L+1) -> (N, L)
- x = x.view(bsz, -1, step + 1)
- new_decoder_tokens = x.gather(index=i.unsqueeze(-1).repeat(1, 1, step + 1), dim=1).view(-1, step + 1)
-
- # need to update incomplete sentences in case one of the beams was kicked out
- # y has shape (N) -> (N, 1) -> (N, B) -> (batch_size, B^2)
- y = incomplete_sentences.unsqueeze(-1).repeat(1, beam_size).view(bsz, -1)
-
- # now can use i to extract those beams that were selected
- # new_incomplete_sentences has shape (batch_size, B^2) -> (batch_size, B) -> (N, 1) -> N
- new_incomplete_sentences = y.gather(index=i, dim=1).view(bsz * beam_size, 1).squeeze(-1)
-
- # new_scores has shape (batch_size, B)
- new_scores = v
-
- return new_decoder_tokens, new_scores, new_incomplete_sentences
-
-
-def generate(encoder_tokens: Tensor, eos_idx: int, model: T5Model, beam_size: int) -> Tensor:
-
- # pass tokens through encoder
- bsz = encoder_tokens.size(0)
- encoder_padding_mask = encoder_tokens.eq(model.padding_idx)
- encoder_embeddings = model.dropout1(model.token_embeddings(encoder_tokens))
- encoder_output = model.encoder(encoder_embeddings, tgt_key_padding_mask=encoder_padding_mask)[0]
-
- encoder_output = model.norm1(encoder_output)
- encoder_output = model.dropout2(encoder_output)
-
- # initialize decoder input sequence; T5 uses padding index as starter index to decoder sequence
- decoder_tokens = torch.ones((bsz, 1), dtype=torch.long) * model.padding_idx
- scores = torch.zeros((bsz, beam_size))
-
- # mask to keep track of sequences for which the decoder has not produced an end-of-sequence token yet
- incomplete_sentences = torch.ones(bsz * beam_size, dtype=torch.long)
-
- # iteratively generate output sequence until all sequences in the batch have generated the end-of-sequence token
- for step in range(model.config.max_seq_len):
-
- if step == 1:
- # duplicate and order encoder output so that each beam is treated as its own independent sequence
- new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1)
- new_order = new_order.to(encoder_tokens.device).long()
- encoder_output = encoder_output.index_select(0, new_order)
- encoder_padding_mask = encoder_padding_mask.index_select(0, new_order)
-
- # causal mask and padding mask for decoder sequence
- tgt_len = decoder_tokens.shape[1]
- decoder_mask = torch.triu(torch.ones((tgt_len, tgt_len), dtype=torch.float64), diagonal=1).bool()
- decoder_padding_mask = decoder_tokens.eq(model.padding_idx)
-
- # T5 implemention uses padding idx to start sequence. Want to ignore this when masking
- decoder_padding_mask[:, 0] = False
-
- # pass decoder sequence through decoder
- decoder_embeddings = model.dropout3(model.token_embeddings(decoder_tokens))
- decoder_output = model.decoder(
- decoder_embeddings,
- memory=encoder_output,
- tgt_mask=decoder_mask,
- tgt_key_padding_mask=decoder_padding_mask,
- memory_key_padding_mask=encoder_padding_mask,
- )[0]
-
- decoder_output = model.norm2(decoder_output)
- decoder_output = model.dropout4(decoder_output)
- decoder_output = decoder_output * (model.config.embedding_dim ** -0.5)
- decoder_output = model.lm_head(decoder_output)
-
- decoder_tokens, scores, incomplete_sentences = beam_search(
- beam_size, step + 1, bsz, decoder_output, decoder_tokens, scores, incomplete_sentences
- )
- # ignore newest tokens for sentences that are already complete
- decoder_tokens[:, -1] *= incomplete_sentences
-
- # update incomplete_sentences to remove those that were just ended
- incomplete_sentences = incomplete_sentences - (decoder_tokens[:, -1] == eos_idx).long()
-
- # early stop if all sentences have been ended
- if (incomplete_sentences == 0).all():
- break
-
- # take most likely sequence
- decoder_tokens = decoder_tokens.view(bsz, beam_size, -1)[:, 0, :]
- return decoder_tokens
+# for all sequences in the batch. The `generate` method shown below uses greedy search to generate the sequences. Beam search and
+# other decoding strategies are also supported.
+#
+#
+from torchtext.prototype.generate import GenerationUtils
+
+sequence_generator = GenerationUtils(model)
#######################################################################
@@ -343,16 +215,16 @@ multi_dataloader = DataLoader(multi_datapipe, batch_size=None)
# ------------------
#
# We can put all of the components together to generate summaries on the first batch of articles in the CNNDM test set
-# using a beam size of 3.
+# using a beam size of 1.
#
batch = next(iter(cnndm_dataloader))
input_text = batch["article"]
target = batch["abstract"]
-beam_size = 3
+beam_size = 1
model_input = transform(input_text)
-model_output = generate(model=model, encoder_tokens=model_input, eos_idx=eos_idx, beam_size=beam_size)
+model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, beam_size=beam_size)
output_text = transform.decode(model_output.tolist())
for i in range(cnndm_batch_size):
@@ -442,7 +314,7 @@ target = batch["label"]
beam_size = 1
model_input = transform(input_text)
-model_output = generate(model=model, encoder_tokens=model_input, eos_idx=eos_idx, beam_size=beam_size)
+model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, beam_size=beam_size)
output_text = transform.decode(model_output.tolist())
for i in range(imdb_batch_size):
@@ -536,7 +408,7 @@ target = batch["german"]
beam_size = 4
model_input = transform(input_text)
-model_output = generate(model=model, encoder_tokens=model_input, eos_idx=eos_idx, beam_size=beam_size)
+model_output = sequence_generator.generate(model_input, eos_idx=eos_idx, beam_size=beam_size)
output_text = transform.decode(model_output.tolist())
for i in range(multi_batch_size):
diff --git a/notebooks/hf_vs_tt_t5.ipynb b/notebooks/hf_vs_tt_t5.ipynb
new file mode 100644
index 0000000..119cb62
--- /dev/null
+++ b/notebooks/hf_vs_tt_t5.ipynb
@@ -0,0 +1,83 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Ensuring the TorchText T5 implementation matches other OSS implementations\n",
+ "\n",
+ "> In order to run this notebook, you will need to install the huggingface library with the following command: `pip install transformers`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from transformers import T5Model\n",
+ "from torchtext.prototype.models import T5_BASE\n",
+ "\n",
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "input_sentence = [\"translate to Spanish: My name is Joe\"]\n",
+ "output_sentence = [\"Me llamo Joe\"]\n",
+ "\n",
+ "transform = T5_BASE.transform()\n",
+ "tt_t5_model = T5_BASE.get_model()\n",
+ "\n",
+ "hf_t5_model = T5Model.from_pretrained(\"t5-base\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "tokenized_sentence = transform(input_sentence)\n",
+ "tokenized_output = transform(output_sentence)\n",
+ "\n",
+ "tt_output = tt_t5_model(encoder_tokens=tokenized_sentence, decoder_tokens=tokenized_output)\n",
+ "hf_output = hf_t5_model(input_ids=tokenized_sentence, decoder_input_ids=tokenized_output, return_dict=True)\n",
+ "\n",
+ "assert torch.all(tt_output[\"encoder_output\"].eq(hf_output[\"encoder_last_hidden_state\"]))\n",
+ "assert torch.all(tt_output[\"decoder_output\"].eq(hf_output[\"last_hidden_state\"]))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.9.13 ('torchtext39')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "63c8862cb56f124e3ee7674b73de745eeb216416a9b24f78d1fcb7c775bff1b7"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/hf_with_torchtext_gen.ipynb b/notebooks/hf_with_torchtext_gen.ipynb
new file mode 100644
index 0000000..0df74a4
--- /dev/null
+++ b/notebooks/hf_with_torchtext_gen.ipynb
@@ -0,0 +1,151 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In order to run this notebook, you will need to install the huggingface library with the following command: `pip install transformers`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/homebrew/Caskroom/miniforge/base/envs/torchtext39/lib/python3.9/site-packages/tqdm-4.64.0-py3.9.egg/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+ " from .autonotebook import tqdm as notebook_tqdm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from transformers import T5ForConditionalGeneration, T5Tokenizer, BartForConditionalGeneration, BartTokenizer, GPT2LMHeadModel, GPT2Tokenizer\n",
+ "from torchtext.prototype.generate import GenerationUtil"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "t5 = T5ForConditionalGeneration.from_pretrained(\"t5-base\")\n",
+ "bart = BartForConditionalGeneration.from_pretrained(\"facebook/bart-large-cnn\")\n",
+ "gpt2 = GPT2LMHeadModel.from_pretrained(\"gpt2\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/homebrew/Caskroom/miniforge/base/envs/torchtext39/lib/python3.9/site-packages/transformers/models/t5/tokenization_t5.py:164: FutureWarning: This tokenizer was incorrectly instantiated with a model max length of 512 which will be corrected in Transformers v5.\n",
+ "For now, this behavior is kept to avoid breaking backwards compatibility when padding/encoding with `truncation is True`.\n",
+ "- Be aware that you SHOULD NOT rely on t5-base automatically truncating your input to 512 when padding/encoding.\n",
+ "- If you want to encode/pad to sequences longer than 512 you can either instantiate this tokenizer with `model_max_length` or pass `max_length` when encoding/padding.\n",
+ "- To avoid this warning, please instantiate this tokenizer with `model_max_length` set to your preferred value.\n",
+ " warnings.warn(\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['owning a dog is good for you, according to studies. a dog is']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Testing Huggingface's T5\n",
+ "test_sequence = [\"summarize: studies have shown that owning a dog is good for you\"]\n",
+ "generative_hf_t5 = GenerationUtil(t5, is_encoder_decoder=True, is_huggingface_model=True)\n",
+ "t5_tokenizer = T5Tokenizer.from_pretrained(\"t5-base\")\n",
+ "test_sequence_tk = t5_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
+ "tokens = generative_hf_t5.generate(test_sequence_tk, max_len=20, pad_idx=t5.config.pad_token_id)\n",
+ "print(t5_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['PG. PG&E said it scheduled the blackouts in response to forecasts for high winds.']\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Testing Huggingface's BART\n",
+ "test_sequence = [\"PG&E stated it scheduled the blackouts in response to forecasts for high winds \"\n",
+ " \"amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were \"\n",
+ " \"scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.\"]\n",
+ "generative_hf_bart = GenerationUtil(bart, is_encoder_decoder=True, is_huggingface_model=True)\n",
+ "bart_tokenizer = BartTokenizer.from_pretrained(\"facebook/bart-large-cnn\")\n",
+ "test_sequence_tk = bart_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
+ "tokens = generative_hf_bart.generate(test_sequence_tk, max_len=20, pad_idx=bart.config.pad_token_id)\n",
+ "print(bart_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[\"I enjoy walking with my cute dog, but I'm not sure if I'll ever be able to\"]\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Testing Huggingface's GPT2\n",
+ "test_sequence = [\"I enjoy walking with my cute dog\"]\n",
+ "generative_hf_gpt2 = GenerationUtil(gpt2, is_encoder_decoder=False, is_huggingface_model=True)\n",
+ "gpt2_tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
+ "test_sequence_tk = gpt2_tokenizer(test_sequence, return_tensors=\"pt\").input_ids\n",
+ "tokens = generative_hf_gpt2.generate(test_sequence_tk, max_len=20, pad_idx=gpt2.config.pad_token_id)\n",
+ "print(gpt2_tokenizer.batch_decode(tokens, skip_special_tokens=True))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3.9.13 ('torchtext39')",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.9.13"
+ },
+ "orig_nbformat": 4,
+ "vscode": {
+ "interpreter": {
+ "hash": "63c8862cb56f124e3ee7674b73de745eeb216416a9b24f78d1fcb7c775bff1b7"
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/packaging/build_conda.sh b/packaging/build_conda.sh
index 98fc8b9..9ece559 100755
--- a/packaging/build_conda.sh
+++ b/packaging/build_conda.sh
@@ -10,4 +10,5 @@ setup_env
export SOURCE_ROOT_DIR="$PWD"
setup_conda_pytorch_constraint
setup_visual_studio_constraint
+
conda build $CONDA_CHANNEL_FLAGS --no-anaconda-upload --python "$PYTHON_VERSION" packaging/torchtext
diff --git a/packaging/install_torchdata.sh b/packaging/install_torchdata.sh
new file mode 100755
index 0000000..4363fcd
--- /dev/null
+++ b/packaging/install_torchdata.sh
@@ -0,0 +1,47 @@
+#!/bin/bash
+package_type="$PACKAGE_TYPE"
+channel="test"
+if [ -z "$package_type" ]; then
+ package_type="wheel"
+fi
+if [ -z "$channel" ]; then
+ channel="test"
+fi
+
+# Wrong values
+if [ "$package_type" != "wheel" ] && [ "$package_type" != "conda" ]; then
+ exit 1
+fi
+if [ "$channel" != "nightly" ] && [ "$channel" != "test" ]; then
+ exit 1
+fi
+
+
+if [ "$package_type" = "wheel" ]; then
+ install_cmd="pip install"
+ if [ "$channel" = "nightly" ]; then
+ install_cmd="${install_cmd} --pre"
+ fi
+ install_channel="--extra-index-url https://download.pytorch.org/whl/${channel}/cpu"
+else
+ install_cmd="conda install"
+ install_channel="-c pytorch-${channel}"
+fi
+
+$install_cmd torchdata $install_channel
+
+if [ "$package_type" = "wheel" ]; then
+ TORCHDATA_VERSION="$(pip show torchdata | grep ^Version: | sed 's/Version: *//' | sed 's/+.\+//')"
+else
+ TORCHDATA_VERSION="$(conda search --json 'torchdata[channel=pytorch-'"${channel}"] | \
+ python -c "import json, os, re, sys; \
+ cuver = 'cpu'; \
+ pyver = os.environ.get('PYTHON_VERSION').replace('.', ''); \
+ print(re.sub(r'\\+.*$', '',
+ [x['version'] for x in json.load(sys.stdin)['torchdata'] \
+ if 'py' + pyver in x['fn']][-1]))"
+ )"
+ echo "export CONDA_TORCHDATA_CONSTRAINT='- torchdata==${TORCHDATA_VERSION}'" >> "${BUILD_ENV_FILE}"
+fi
+
+echo "export TORCHDATA_VERSION=${TORCHDATA_VERSION}" >> "${BUILD_ENV_FILE}"
diff --git a/packaging/pkg_helpers.bash b/packaging/pkg_helpers.bash
index 900c35e..3d8a4ee 100644
--- a/packaging/pkg_helpers.bash
+++ b/packaging/pkg_helpers.bash
@@ -136,7 +136,7 @@ retry () {
}
# Inputs:
-# PYTHON_VERSION (2.7, 3.5, 3.6, 3.7)
+# PYTHON_VERSION (2.7, 3.8, 3.9, 3.10)
# UNICODE_ABI (bool)
#
# Outputs:
@@ -158,7 +158,6 @@ setup_wheel_python() {
python_abi=cp27-cp27m
fi
;;
- 3.7) python_abi=cp37-cp37m ;;
3.8) python_abi=cp38-cp38 ;;
3.9) python_abi=cp39-cp39 ;;
3.10) python_abi=cp310-cp310 ;;
@@ -191,6 +190,14 @@ setup_pip_pytorch_version() {
-f https://download.pytorch.org/whl/torch_stable.html \
-f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html"
fi
+ if [[ -z "$TORCHDATA_VERSION" ]]; then
+ pip_install --pre torchdata -f "https://download.pytorch.org/whl/test/cpu/torch_test.html"
+ export TORCHDATA_VERSION="$(pip show torchdata | grep ^Version: | sed 's/Version: *//' | sed 's/+.\+//')"
+ else
+ pip_install "torchdata==$TORCHDATA_VERSION" \
+ -f https://download.pytorch.org/whl/torch_stable.html \
+ -f "https://download.pytorch.org/whl/${UPLOAD_CHANNEL}/torch_${UPLOAD_CHANNEL}.html"
+ fi
}
# Fill PYTORCH_VERSION with the latest conda nightly version, and
@@ -200,13 +207,13 @@ setup_pip_pytorch_version() {
setup_conda_pytorch_constraint() {
CONDA_CHANNEL_FLAGS=${CONDA_CHANNEL_FLAGS:-}
if [[ -z "$PYTORCH_VERSION" ]]; then
- export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c pytorch-test"
+ export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c pytorch-${UPLOAD_CHANNEL}"
PYTHON="python"
# Check if we have python 3 instead and prefer that
if python3 --version >/dev/null 2>/dev/null; then
PYTHON="python3"
fi
- export PYTORCH_VERSION="$(conda search --json 'pytorch[channel=pytorch-test]' | ${PYTHON} -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")"
+ export PYTORCH_VERSION="$(conda search --json pytorch[channel=pytorch-${UPLOAD_CHANNEL}] | ${PYTHON} -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['pytorch'][-1]['version']))")"
else
export CONDA_CHANNEL_FLAGS="${CONDA_CHANNEL_FLAGS} -c pytorch -c pytorch-${UPLOAD_CHANNEL}"
fi
@@ -220,11 +227,15 @@ setup_conda_pytorch_constraint() {
# TODO: Remove me later, see https://github.com/pytorch/pytorch/issues/62424 for more details
if [[ "$(uname)" == Darwin ]]; then
arch_name="$(uname -m)"
- if [ "${arch_name}" != "arm64" ]; then
+ if [[ "${arch_name}" != "arm64" && "${PYTHON_VERSION}" != "3.11" ]]; then
# Use less than equal to avoid version conflict in python=3.6 environment
export CONDA_EXTRA_BUILD_CONSTRAINT="- mkl<=2021.2.0"
fi
fi
+ if [[ -z "$TORCHDATA_VERSION" ]]; then
+ export TORCHDATA_VERSION="$(conda search --json 'torchdata[channel=pytorch-test]' | ${PYTHON} -c "import sys, json, re; print(re.sub(r'\\+.*$', '', json.load(sys.stdin)['torchdata'][-1]['version']))")"
+ fi
+ export CONDA_TORCHDATA_CONSTRAINT="- torchdata==$TORCHDATA_VERSION"
}
# Translate CUDA_VERSION into CUDA_CUDATOOLKIT_CONSTRAINT
diff --git a/packaging/torchtext/meta.yaml b/packaging/torchtext/meta.yaml
index 9d75022..0322150 100644
--- a/packaging/torchtext/meta.yaml
+++ b/packaging/torchtext/meta.yaml
@@ -24,6 +24,7 @@ requirements:
- requests
- tqdm
{{ environ.get('CONDA_PYTORCH_CONSTRAINT') }}
+ {{ environ.get('CONDA_TORCHDATA_CONSTRAINT') }}
build:
string: py{{py}}
diff --git a/pytest.ini b/pytest.ini
index bece71b..c7ba710 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,3 +1,5 @@
[pytest]
testpaths = test/
python_paths = ./
+markers =
+ gpu_test: marks cuda tests
diff --git a/requirements.txt b/requirements.txt
index 245ecec..cbc13ee 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -19,7 +19,7 @@ Sphinx
pytest
expecttest
parameterized
-torchdata
+torchdata>0.5
# Lets pytest find our code by automatically modifying PYTHONPATH
pytest-pythonpath
diff --git a/setup.py b/setup.py
index d8afa7a..2e1f240 100644
--- a/setup.py
+++ b/setup.py
@@ -63,10 +63,14 @@ _export_version(VERSION, SHA)
print("-- Building version " + VERSION)
pytorch_package_version = os.getenv("PYTORCH_VERSION")
+torchdata_package_version = os.getenv("TORCHDATA_VERSION")
pytorch_package_dep = "torch"
if pytorch_package_version is not None:
pytorch_package_dep += "==" + pytorch_package_version
+torchdata_package_dep = "torchdata"
+if torchdata_package_version is not None:
+ torchdata_package_dep += "==" + torchdata_package_version
class clean(distutils.command.clean.clean):
@@ -100,12 +104,12 @@ setup_info = dict(
description="Text utilities and datasets for PyTorch",
long_description=read("README.rst"),
license="BSD",
- install_requires=["tqdm", "requests", pytorch_package_dep, "numpy"],
- python_requires=">=3.7",
+ install_requires=["tqdm", "requests", pytorch_package_dep, "numpy", torchdata_package_dep],
+ python_requires=">=3.8",
classifiers=[
- "Programming Language :: Python :: 3.7",
"Programming Language :: Python :: 3.8",
"Programming Language :: Python :: 3.9",
+ "Programming Language :: Python :: 3.10",
],
# Package info
packages=find_packages(exclude=("test*", "tools*")),
diff --git a/test/integration_tests/prototype/test_models.py b/test/integration_tests/prototype/test_models.py
deleted file mode 100644
index 7743807..0000000
--- a/test/integration_tests/prototype/test_models.py
+++ /dev/null
@@ -1,137 +0,0 @@
-import pytest # noqa: F401
-import torch
-from parameterized import parameterized, parameterized_class
-from torchtext.prototype.models import (
- T5_BASE_ENCODER,
- T5_BASE,
- T5_BASE_GENERATION,
- T5_SMALL_ENCODER,
- T5_SMALL,
- T5_SMALL_GENERATION,
- T5_LARGE_ENCODER,
- T5_LARGE,
- T5_LARGE_GENERATION,
- T5Conf,
- T5Transform,
-)
-from torchtext.prototype.models.t5.wrapper import T5Wrapper
-from torchtext_unittest.common.assets import get_asset_path
-from torchtext_unittest.common.parameterized_utils import nested_params
-from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
-
-
-BUNDLERS = {
- "base_model": T5_BASE,
- "base_encoder": T5_BASE_ENCODER,
- "base_generation": T5_BASE_GENERATION,
- "small_model": T5_SMALL,
- "small_encoder": T5_SMALL_ENCODER,
- "small_generation": T5_SMALL_GENERATION,
- "large_model": T5_LARGE,
- "large_encoder": T5_LARGE_ENCODER,
- "large_generation": T5_LARGE_GENERATION,
-}
-
-
-@parameterized_class(
- ("model_name",),
- [
- ("base_model",),
- ("base_encoder",),
- ("base_generation",),
- ("small_model",),
- ("small_encoder",),
- ("small_generation",),
- ("large_model",),
- ("large_encoder",),
- ("large_generation",),
- ],
-)
-class TestT5Model(TorchtextTestCase):
- def _t5_model(self, is_jit, t5_model, expected_asset_name, test_text):
- """Verify that pre-trained T5 models in torchtext produce
- the same output as the HuggingFace reference implementation.
- """
- expected_asset_path = get_asset_path(expected_asset_name)
- transform = t5_model.transform()
- model = t5_model.get_model()
- model = model.eval()
-
- if is_jit:
- transform = torch.jit.script(transform)
- model = torch.jit.script(model)
-
- model_input = transform(test_text)
- if model.encoder_only:
- actual = model(model_input)["encoder_output"]
- else:
- actual = model(model_input)["decoder_output"]
-
- expected = torch.load(expected_asset_path)
- torch.testing.assert_close(actual, expected, atol=1e-04, rtol=2.5e-06)
-
- @nested_params(["jit", "not_jit"])
- def test_t5_model(self, name) -> None:
- configuration, type = self.model_name.split("_")
-
- expected_asset_name = f"t5.{configuration}.{type}.output.pt"
- test_text = ["Hello world", "Attention rocks!"]
- is_jit = name == "jit"
- t5_model = BUNDLERS[configuration + "_" + type]
- self._t5_model(is_jit=is_jit, t5_model=t5_model, expected_asset_name=expected_asset_name, test_text=test_text)
-
-
-@parameterized_class(
- ("configuration",),
- [
- ("small",),
- ("base",),
- ("large",),
- ],
-)
-class TestT5Wrapper(TorchtextTestCase):
- @parameterized.expand(["jit", "not_jit"])
- def test_t5_wrapper(self, name) -> None:
- configuration = self.configuration
- test_text = ["translate English to French: I want to eat pizza for dinner."]
- if configuration == "small":
- expected_text = ["Je veux manger la pizza pour le dîner."]
- else:
- expected_text = ["Je veux manger de la pizza pour le dîner."]
-
- beam_size = 3
- max_seq_len = 512
- model = T5Wrapper(configuration=configuration)
- if name == "jit":
- model = torch.jit.script(model)
-
- output_text = model(test_text, beam_size, max_seq_len)
- self.assertEqual(output_text, expected_text)
-
-
-class TestT5WrapperCheckpoint(TorchtextTestCase):
- @parameterized.expand(["jit", "not_jit"])
- def test_t5_wrapper_checkpoint(self, name) -> None:
- test_text = ["translate English to French: I want to eat pizza for dinner."]
- expected_text = ["Je veux manger de la pizza pour le dîner."]
- beam_size = 3
- max_seq_len = 512
- config = T5Conf(encoder_only=False, linear_head=True)
- transform = T5Transform(
- "https://download.pytorch.org/models/text/t5_tokenizer_base.model",
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- )
- model = T5Wrapper(
- checkpoint="https://download.pytorch.org/models/text/t5.base.generation.pt",
- t5_config=config,
- transform=transform,
- freeze_model=True,
- strict=True,
- )
- if name == "jit":
- model = torch.jit.script(model)
-
- output_text = model(test_text, beam_size, max_seq_len)
- self.assertEqual(output_text, expected_text)
diff --git a/test/integration_tests/test_generate.py b/test/integration_tests/test_generate.py
new file mode 100644
index 0000000..80acd9d
--- /dev/null
+++ b/test/integration_tests/test_generate.py
@@ -0,0 +1,54 @@
+from unittest.mock import patch
+
+import torch
+from torchtext.models import T5_BASE_GENERATION
+from torchtext.prototype.generate import DEFAULT_MAX_SEQ_LEN, GenerationUtils
+from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
+
+
+class TestGenerationUtil(TorchtextTestCase):
+ def setUp(self) -> None:
+ super().setUp()
+ t5_base = T5_BASE_GENERATION
+ self.transform = t5_base.transform()
+ self.model = t5_base.get_model()
+ self.model.eval()
+ # Examples taken from T5 Paper and Huggingface
+ self.inputs = self.transform(
+ [
+ "summarize: studies have shown that owning a dog is good for you",
+ "translate English to German: That is good.",
+ "cola sentence: The course is jumping well.",
+ "stsb sentence1: The rhino grazed on the grass. sentence2: A rhino is grazing in a field.",
+ "summarize: state authorities dispatched emergency crews tuesday to survey the damage after an onslaught of severe weather in mississippi...",
+ ]
+ )
+ torch.manual_seed(0)
+
+ def test_greedy_generate_with_t5(self) -> None:
+ generation_model = GenerationUtils(self.model)
+
+ tokens = generation_model.generate(self.inputs, num_beams=1, max_length=30)
+ generated_text = self.transform.decode(tokens.tolist())
+
+ expected_generated_text = [
+ "a dog is good for you, according to studies . owning a dog is good for you, according to studies .",
+ "Das ist gut.",
+ "acceptable",
+ "4.0",
+ "mississippi authorities dispatch emergency crews to survey damage . severe weather in mississippi has caused extensive damage",
+ ]
+
+ self.assertEqual(generated_text, expected_generated_text)
+
+ def test_generate_errors_with_incorrect_beams(self) -> None:
+ generation_model = GenerationUtils(self.model, is_encoder_decoder=True)
+
+ with self.assertRaises(ValueError):
+ generation_model.generate(self.inputs, num_beams=0)
+
+ @patch("logging.Logger.warning")
+ def test_warns_when_no_max_len_provided(self, mock) -> None:
+ generation_model = GenerationUtils(self.model)
+ generation_model.generate(self.inputs)
+ mock.assert_called_with(f"`max_length` was not specified. Defaulting to {DEFAULT_MAX_SEQ_LEN} tokens.")
diff --git a/test/integration_tests/test_models.py b/test/integration_tests/test_roberta_models.py
similarity index 92%
rename from test/integration_tests/test_models.py
rename to test/integration_tests/test_roberta_models.py
index 03422ea..9c1e53f 100644
--- a/test/integration_tests/test_models.py
+++ b/test/integration_tests/test_roberta_models.py
@@ -4,6 +4,7 @@ from parameterized import parameterized, parameterized_class
from torchtext.models import (
ROBERTA_BASE_ENCODER,
ROBERTA_LARGE_ENCODER,
+ ROBERTA_DISTILLED_ENCODER,
XLMR_BASE_ENCODER,
XLMR_LARGE_ENCODER,
)
@@ -15,13 +16,7 @@ BUNDLERS = {
"xlmr_large": XLMR_LARGE_ENCODER,
"roberta_base": ROBERTA_BASE_ENCODER,
"roberta_large": ROBERTA_LARGE_ENCODER,
-}
-
-BUNDLERS = {
- "xlmr_base": XLMR_BASE_ENCODER,
- "xlmr_large": XLMR_LARGE_ENCODER,
- "roberta_base": ROBERTA_BASE_ENCODER,
- "roberta_large": ROBERTA_LARGE_ENCODER,
+ "roberta_distilled": ROBERTA_DISTILLED_ENCODER,
}
@@ -32,6 +27,7 @@ BUNDLERS = {
("xlmr_large",),
("roberta_base",),
("roberta_large",),
+ ("roberta_distilled",),
],
)
class TestRobertaEncoders(TorchtextTestCase):
diff --git a/test/integration_tests/test_t5_models.py b/test/integration_tests/test_t5_models.py
new file mode 100644
index 0000000..0bacca4
--- /dev/null
+++ b/test/integration_tests/test_t5_models.py
@@ -0,0 +1,165 @@
+import os
+import tempfile
+
+import pytest # noqa: F401
+import torch
+from parameterized import parameterized_class
+from torchtext import _TEXT_BUCKET
+from torchtext._download_hooks import _TEST_DOWNLOAD_MANAGER
+from torchtext.models import (
+ FLAN_T5_BASE,
+ FLAN_T5_BASE_ENCODER,
+ FLAN_T5_BASE_GENERATION,
+ T5_BASE,
+ T5_BASE_ENCODER,
+ T5_BASE_GENERATION,
+ T5_LARGE,
+ T5_LARGE_ENCODER,
+ T5_LARGE_GENERATION,
+ T5_SMALL,
+ T5_SMALL_ENCODER,
+ T5_SMALL_GENERATION,
+ T5Bundle,
+)
+from torchtext_unittest.common.assets import get_asset_path
+from torchtext_unittest.common.parameterized_utils import nested_params
+from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
+
+BUNDLERS = {
+ "base_model": T5_BASE,
+ "base_encoder": T5_BASE_ENCODER,
+ "base_generation": T5_BASE_GENERATION,
+ "small_model": T5_SMALL,
+ "small_encoder": T5_SMALL_ENCODER,
+ "small_generation": T5_SMALL_GENERATION,
+ "large_model": T5_LARGE,
+ "large_encoder": T5_LARGE_ENCODER,
+ "large_generation": T5_LARGE_GENERATION,
+ "flan_base_encoder": FLAN_T5_BASE_ENCODER,
+ "flan_base_model": FLAN_T5_BASE,
+ "flan_base_generation": FLAN_T5_BASE_GENERATION,
+}
+
+
+@parameterized_class(
+ ("model_name",),
+ [
+ ("base_model",),
+ ("base_encoder",),
+ ("base_generation",),
+ ("small_model",),
+ ("small_encoder",),
+ ("small_generation",),
+ ("large_model",),
+ ("large_encoder",),
+ ("large_generation",),
+ ("flan_base_encoder",),
+ ("flan_base_model",),
+ ("flan_base_generation",),
+ ],
+)
+class TestT5Model(TorchtextTestCase):
+ def _t5_model(self, is_jit, t5_model, expected_asset_name, test_text):
+ """Verify that pre-trained T5 models in torchtext produce
+ the same output as the HuggingFace reference implementation.
+ """
+ expected_asset_path = get_asset_path(expected_asset_name)
+ transform = t5_model.transform()
+ model = t5_model.get_model()
+ model = model.eval()
+
+ if is_jit:
+ transform = torch.jit.script(transform)
+ model = torch.jit.script(model)
+
+ model_input = transform(test_text)
+ if model.encoder_only:
+ actual = model(encoder_tokens=model_input)["encoder_output"]
+ if not is_jit:
+ self._t5_get_encoder(model, model_input, actual)
+ else:
+ actual = model(encoder_tokens=model_input)["decoder_output"]
+
+ expected = torch.load(expected_asset_path)
+ torch.testing.assert_close(actual, expected, atol=1e-04, rtol=2.5e-06)
+
+ def _t5_get_encoder(self, model, model_input, encoder_output):
+ encoder = model.get_encoder()
+ # Need to set the key_padding_mask to ensure the same results
+ encoder_padding_mask = model_input.eq(model.padding_idx)
+ output_from_get_encoder = encoder(model_input, src_key_padding_mask=encoder_padding_mask)["encoder_output"]
+ assert torch.all(output_from_get_encoder.eq(encoder_output))
+
+ @nested_params(["not_jit", "jit"])
+ def test_t5_model(self, name) -> None:
+ names = self.model_name.split("_")
+
+ num_names = len(names)
+
+ if num_names == 3:
+ # Handled slightly differently for Flan-T5 model naming
+ configuration = names[1]
+ type = names[2]
+ expected_asset_name = f"t5.flan.{configuration}.{type}.output.pt"
+ t5_model = BUNDLERS["flan_" + configuration + "_" + type]
+ elif num_names == 2:
+ configuration = names[0]
+ type = names[1]
+ expected_asset_name = f"t5.{configuration}.{type}.output.pt"
+ t5_model = BUNDLERS[configuration + "_" + type]
+ else:
+ raise RuntimeError(f"Unknown model name: {self.model_name}")
+
+ test_text = ["Hello world", "Attention rocks!"]
+ is_jit = name == "jit"
+ self._t5_model(is_jit=is_jit, t5_model=t5_model, expected_asset_name=expected_asset_name, test_text=test_text)
+
+
+@parameterized_class(
+ ("model",),
+ [
+ ("hf_t5_small_encoder",),
+ ("hf_t5_small",),
+ ("hf_t5_small_generation",),
+ ("hf_flan_base_encoder",),
+ ("hf_flan_base",),
+ ("hf_flan_base_generation",),
+ ],
+)
+class TestLoadFromHFCheckpoints(TorchtextTestCase):
+ def setUp(self) -> None:
+ super().setUp()
+ self.encoder_input_ids = torch.tensor([[1, 2, 3, 4, 5, 6], [7, 8, 9, 0, 0, 0]])
+ self.encoder_padding_mask = torch.tensor(
+ [[False, False, False, False, False, False], [False, False, False, True, True, True]]
+ )
+ self.decoder_input_ids = torch.tensor([[7, 8, 9, 0, 0, 0], [10, 11, 12, 0, 0, 0]])
+ self.decoder_padding_mask = torch.tensor(
+ [[False, False, False, True, True, True], [False, False, False, True, True, True]]
+ )
+
+ def test_t5_bundler_load_hf_ckpt_pretrained(self) -> None:
+ with tempfile.TemporaryDirectory() as tmp:
+ local_path = f"{tmp}/{self.model}"
+ remote_bucket = f"{_TEXT_BUCKET}test_models"
+
+ os.mkdir(local_path)
+
+ for f in {"config.json", "pytorch_model.bin"}:
+ destination = f"{local_path}/{f}"
+ remote_path = f"{remote_bucket}/{self.model}/{f}"
+ _TEST_DOWNLOAD_MANAGER.get_local_path(url=remote_path, destination=destination)
+
+ names = self.model.split("_")
+ is_encoder_only = names[-1] == "encoder"
+
+ model = T5Bundle.build_model_from_huggingface_ckpt(local_path, encoder_only=is_encoder_only)
+ if is_encoder_only:
+ model(self.encoder_input_ids, encoder_padding_mask=self.encoder_padding_mask)
+ else:
+ model(
+ self.encoder_input_ids,
+ self.decoder_input_ids,
+ encoder_padding_mask=self.encoder_padding_mask,
+ decoder_padding_mask=self.decoder_padding_mask,
+ )
diff --git a/test/smoke_tests/smoke_tests.py b/test/smoke_tests/smoke_tests.py
new file mode 100644
index 0000000..2fbaeec
--- /dev/null
+++ b/test/smoke_tests/smoke_tests.py
@@ -0,0 +1,28 @@
+"""Run smoke tests"""
+
+import os
+import re
+
+import torchdata
+import torchtext
+import torchtext.version # noqa: F401
+
+NIGHTLY_ALLOWED_DELTA = 3
+channel = os.getenv("MATRIX_CHANNEL")
+
+
+def validateTorchdataVersion():
+ from datetime import datetime
+
+ date_t_str = re.findall(r"dev\d+", torchdata.__version__)[0]
+ date_t_delta = datetime.now() - datetime.strptime(date_t_str[3:], "%Y%m%d")
+
+ if date_t_delta.days >= NIGHTLY_ALLOWED_DELTA:
+ raise RuntimeError(f"torchdata binary {torchdata.__version__} is more than {NIGHTLY_ALLOWED_DELTA} days old!")
+
+
+if channel == "nightly":
+ validateTorchdataVersion()
+
+print("torchtext version is ", torchtext.__version__)
+print("torchdata version is ", torchdata.__version__)
diff --git a/test/torchtext_unittest/asset/openai-gpt-merges.txt b/test/torchtext_unittest/asset/openai-gpt-merges.txt
new file mode 100644
index 0000000..0023a68
--- /dev/null
+++ b/test/torchtext_unittest/asset/openai-gpt-merges.txt
@@ -0,0 +1,40001 @@
+#version: 0.2
+t h
+i n
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index 0000000..670d9bc
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diff --git a/test/torchtext_unittest/asset/t5.flan.base.encoder.output.pt b/test/torchtext_unittest/asset/t5.flan.base.encoder.output.pt
new file mode 100644
index 0000000..75c8e3e
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diff --git a/test/torchtext_unittest/asset/t5.flan.base.generation.output.pt b/test/torchtext_unittest/asset/t5.flan.base.generation.output.pt
new file mode 100644
index 0000000..7412663
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diff --git a/test/torchtext_unittest/asset/t5.flan.base.model.output.pt b/test/torchtext_unittest/asset/t5.flan.base.model.output.pt
new file mode 100644
index 0000000..bd2a5aa
Binary files /dev/null and b/test/torchtext_unittest/asset/t5.flan.base.model.output.pt differ
diff --git a/test/torchtext_unittest/common/case_utils.py b/test/torchtext_unittest/common/case_utils.py
index 9ed9a1c..b4d0405 100644
--- a/test/torchtext_unittest/common/case_utils.py
+++ b/test/torchtext_unittest/common/case_utils.py
@@ -4,6 +4,7 @@ import tempfile
import unittest
from itertools import zip_longest
+import torch
from torchtext._internal.module_utils import is_module_available
@@ -37,6 +38,17 @@ class TempDirMixin:
return path
+class TestBaseMixin:
+ """Mixin to provide consistent way to define device/dtype/backend aware TestCase"""
+
+ dtype = None
+ device = None
+
+ def setUp(self):
+ super().setUp()
+ torch.random.manual_seed(2434)
+
+
def skipIfNoModule(module, display_name=None):
display_name = display_name or module
return unittest.skipIf(not is_module_available(module), f'"{display_name}" is not available')
diff --git a/test/torchtext_unittest/common/torchtext_test_case.py b/test/torchtext_unittest/common/torchtext_test_case.py
index b30eade..aeb4f81 100644
--- a/test/torchtext_unittest/common/torchtext_test_case.py
+++ b/test/torchtext_unittest/common/torchtext_test_case.py
@@ -5,6 +5,7 @@ import os
import shutil
import subprocess
import tempfile
+from urllib.error import HTTPError
import torch # noqa: F401
from torch.testing._internal.common_utils import TestCase
@@ -12,6 +13,16 @@ from torch.testing._internal.common_utils import TestCase
logger = logging.getLogger(__name__)
+def third_party_download(test_func):
+ def inner(*args, **kwargs):
+ try:
+ return test_func(*args, **kwargs)
+ except HTTPError as e:
+ logger.warning(f"Cannot access URL in {test_func.__name__}. Error message {e}")
+
+ return inner
+
+
class TorchtextTestCase(TestCase):
def setUp(self) -> None:
logging.basicConfig(format=("%(asctime)s - %(levelname)s - " "%(name)s - %(message)s"), level=logging.INFO)
diff --git a/test/torchtext_unittest/datasets/common.py b/test/torchtext_unittest/datasets/common.py
index 10071bd..0fa3ae2 100644
--- a/test/torchtext_unittest/datasets/common.py
+++ b/test/torchtext_unittest/datasets/common.py
@@ -1,7 +1,7 @@
import pickle
from parameterized import parameterized
-from torch.utils.data.graph import traverse
+from torch.utils.data.graph import traverse_dps
from torch.utils.data.graph_settings import get_all_graph_pipes
from torchdata.dataloader2.linter import _check_shuffle_before_sharding
from torchdata.datapipes.iter import Shuffler, ShardingFilter
@@ -37,7 +37,7 @@ class TestShuffleShardDatasetWrapper(TorchtextTestCase):
for dp_split in dp:
_check_shuffle_before_sharding(dp_split)
- dp_graph = get_all_graph_pipes(traverse(dp_split))
+ dp_graph = get_all_graph_pipes(traverse_dps(dp_split))
for annotation_dp_type in [Shuffler, ShardingFilter]:
if not any(isinstance(dp, annotation_dp_type) for dp in dp_graph):
raise AssertionError(f"The dataset doesn't contain a {annotation_dp_type.__name__}() datapipe.")
diff --git a/test/torchtext_unittest/datasets/test_cnndm.py b/test/torchtext_unittest/datasets/test_cnndm.py
index c3b5561..73fa794 100644
--- a/test/torchtext_unittest/datasets/test_cnndm.py
+++ b/test/torchtext_unittest/datasets/test_cnndm.py
@@ -29,7 +29,7 @@ def _get_mock_dataset(root_dir):
stories = []
for i in range(5):
url = "_".join([source, split, str(i)])
- h = hashlib.sha1()
+ h = hashlib.new("sha1", usedforsecurity=False)
h.update(url.encode())
filename = h.hexdigest() + ".story"
txt_file = os.path.join(source_dir, filename)
@@ -74,7 +74,7 @@ class TestCNNDM(TempDirMixin, TorchtextTestCase):
story_fnames = []
for i in range(5):
url = "_".join([source, split, str(i)])
- h = hashlib.sha1()
+ h = hashlib.new("sha1", usedforsecurity=False)
h.update(url.encode())
filename = h.hexdigest() + ".story"
story_fnames.append(filename)
diff --git a/test/torchtext_unittest/models/gpu_tests/models_gpu_test.py b/test/torchtext_unittest/models/gpu_tests/models_gpu_test.py
new file mode 100644
index 0000000..58faf6e
--- /dev/null
+++ b/test/torchtext_unittest/models/gpu_tests/models_gpu_test.py
@@ -0,0 +1,14 @@
+import unittest
+
+import pytest
+import torch
+from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
+from torchtext_unittest.models.roberta_models_test_impl import RobertaBaseTestModels
+from torchtext_unittest.models.t5_models_test_impl import T5BaseTestModels
+
+
+@pytest.mark.gpu_test
+@unittest.skipIf(not torch.cuda.is_available(), reason="CUDA is not available")
+class TestModels32GPU(RobertaBaseTestModels, T5BaseTestModels, TorchtextTestCase):
+ dtype = torch.float32
+ device = torch.device("cuda")
diff --git a/test/torchtext_unittest/models/models_cpu_test.py b/test/torchtext_unittest/models/models_cpu_test.py
new file mode 100644
index 0000000..6f130f8
--- /dev/null
+++ b/test/torchtext_unittest/models/models_cpu_test.py
@@ -0,0 +1,10 @@
+import torch
+
+from ..common.torchtext_test_case import TorchtextTestCase
+from .roberta_models_test_impl import RobertaBaseTestModels
+from .t5_models_test_impl import T5BaseTestModels
+
+
+class TestModels32CPU(RobertaBaseTestModels, T5BaseTestModels, TorchtextTestCase):
+ dtype = torch.float32
+ device = torch.device("cpu")
diff --git a/test/torchtext_unittest/models/test_models.py b/test/torchtext_unittest/models/roberta_models_test_impl.py
similarity index 85%
rename from test/torchtext_unittest/models/test_models.py
rename to test/torchtext_unittest/models/roberta_models_test_impl.py
index 0e92f6b..b5b25fe 100644
--- a/test/torchtext_unittest/models/test_models.py
+++ b/test/torchtext_unittest/models/roberta_models_test_impl.py
@@ -2,15 +2,27 @@ import copy
from unittest.mock import patch
import torch
-import torchtext
from torch.nn import functional as torch_F
-from ..common.torchtext_test_case import TorchtextTestCase
+from ..common.case_utils import TestBaseMixin
-class TestModels(TorchtextTestCase):
+class RobertaBaseTestModels(TestBaseMixin):
+ def get_model(self, encoder_conf, head=None, freeze_encoder=False, checkpoint=None, override_checkpoint_head=False):
+ from torchtext.models import RobertaBundle
+
+ model = RobertaBundle.build_model(
+ encoder_conf=encoder_conf,
+ head=head,
+ freeze_encoder=freeze_encoder,
+ checkpoint=checkpoint,
+ override_checkpoint_head=override_checkpoint_head,
+ )
+ model.to(device=self.device, dtype=self.dtype)
+ return model
+
def test_roberta_bundler_build_model(self) -> None:
- from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaModel, RobertaBundle
+ from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaModel
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
@@ -18,14 +30,14 @@ class TestModels(TorchtextTestCase):
# case: user provide encoder checkpoint state dict
dummy_encoder = RobertaModel(dummy_encoder_conf)
- model = RobertaBundle.build_model(encoder_conf=dummy_encoder_conf, checkpoint=dummy_encoder.state_dict())
+ model = self.get_model(encoder_conf=dummy_encoder_conf, checkpoint=dummy_encoder.state_dict())
self.assertEqual(model.state_dict(), dummy_encoder.state_dict())
# case: user provide classifier checkpoint state dict when head is given and override_head is False (by default)
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
another_dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
- model = RobertaBundle.build_model(
+ model = self.get_model(
encoder_conf=dummy_encoder_conf,
head=another_dummy_classifier_head,
checkpoint=dummy_classifier.state_dict(),
@@ -34,7 +46,7 @@ class TestModels(TorchtextTestCase):
# case: user provide classifier checkpoint state dict when head is given and override_head is set True
another_dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
- model = RobertaBundle.build_model(
+ model = self.get_model(
encoder_conf=dummy_encoder_conf,
head=another_dummy_classifier_head,
checkpoint=dummy_classifier.state_dict(),
@@ -48,13 +60,13 @@ class TestModels(TorchtextTestCase):
encoder_state_dict = {}
for k, v in dummy_classifier.encoder.state_dict().items():
encoder_state_dict["encoder." + k] = v
- model = torchtext.models.RobertaBundle.build_model(
+ model = self.get_model(
encoder_conf=dummy_encoder_conf, head=dummy_classifier_head, checkpoint=encoder_state_dict
)
self.assertEqual(model.state_dict(), dummy_classifier.state_dict())
def test_roberta_bundler_train(self) -> None:
- from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaModel, RobertaBundle
+ from torchtext.models import RobertaClassificationHead, RobertaEncoderConf, RobertaModel
dummy_encoder_conf = RobertaEncoderConf(
vocab_size=10, embedding_dim=16, ffn_dimension=64, num_attention_heads=2, num_encoder_layers=2
@@ -63,8 +75,8 @@ class TestModels(TorchtextTestCase):
def _train(model):
optim = SGD(model.parameters(), lr=1)
- model_input = torch.tensor([[0, 1, 2, 3, 4, 5]])
- target = torch.tensor([0])
+ model_input = torch.tensor([[0, 1, 2, 3, 4, 5]]).to(device=self.device)
+ target = torch.tensor([0]).to(device=self.device)
logits = model(model_input)
loss = torch_F.cross_entropy(logits, target)
loss.backward()
@@ -73,7 +85,7 @@ class TestModels(TorchtextTestCase):
# does not freeze encoder
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
- model = RobertaBundle.build_model(
+ model = self.get_model(
encoder_conf=dummy_encoder_conf,
head=dummy_classifier_head,
freeze_encoder=False,
@@ -91,7 +103,7 @@ class TestModels(TorchtextTestCase):
# freeze encoder
dummy_classifier_head = RobertaClassificationHead(num_classes=2, input_dim=16)
dummy_classifier = RobertaModel(dummy_encoder_conf, dummy_classifier_head)
- model = RobertaBundle.build_model(
+ model = self.get_model(
encoder_conf=dummy_encoder_conf,
head=dummy_classifier_head,
freeze_encoder=True,
diff --git a/test/torchtext_unittest/prototype/models/test_models.py b/test/torchtext_unittest/models/t5_models_test_impl.py
similarity index 90%
rename from test/torchtext_unittest/prototype/models/test_models.py
rename to test/torchtext_unittest/models/t5_models_test_impl.py
index 7d7fc9d..bd36f32 100644
--- a/test/torchtext_unittest/prototype/models/test_models.py
+++ b/test/torchtext_unittest/models/t5_models_test_impl.py
@@ -3,12 +3,12 @@ from unittest.mock import patch
import torch
from torch.nn import functional as F
-from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
+from torchtext_unittest.common.case_utils import TestBaseMixin
-class TestModels(TorchtextTestCase):
+class T5BaseTestModels(TestBaseMixin):
def test_t5_bundler_build_model(self) -> None:
- from torchtext.prototype.models import T5Conf, T5Model, T5Bundle
+ from torchtext.models import T5Conf, T5Model, T5Bundle
# case: user provides encoder checkpoint state dict
dummy_encoder_conf = T5Conf(
@@ -57,7 +57,7 @@ class TestModels(TorchtextTestCase):
@patch("logging.Logger.warning")
def test_t5_bundler_get_model(self, mock):
- from torchtext.prototype.models import T5Conf, T5Bundle
+ from torchtext.models import T5Conf, T5Bundle
# encoder-decoder with generation
dummy_t5_generation_conf = T5Conf(
@@ -77,7 +77,7 @@ class TestModels(TorchtextTestCase):
)
def test_t5_bundler_raise_checkpoint(self) -> None:
- from torchtext.prototype.models import T5Conf, T5Bundle
+ from torchtext.models import T5Conf, T5Bundle
# encoder-only
with self.assertRaises(TypeError):
@@ -132,7 +132,7 @@ class TestModels(TorchtextTestCase):
)
def test_t5_bundler_conf_property(self) -> None:
- from torchtext.prototype.models import T5Conf, T5Bundle
+ from torchtext.models import T5Conf, T5Bundle
dummy_t5_conf = T5Conf(
encoder_only=False,
@@ -148,12 +148,14 @@ class TestModels(TorchtextTestCase):
def test_t5_bundler_train(self) -> None:
from torch.optim import SGD
- from torchtext.prototype.models import T5Conf, T5Model, T5Bundle
+ from torchtext.models import T5Conf, T5Model, T5Bundle
+
+ torch.manual_seed(123)
def _train(model):
optim = SGD(model.parameters(), lr=1)
- model_input = torch.tensor([[1, 2, 3, 4, 5]])
- target = torch.tensor([1])
+ model_input = torch.tensor([[1, 2, 3, 4, 5]]).to(device=self.device)
+ target = torch.tensor([1]).to(device=self.device)
output = model(model_input)["decoder_output"]
logits = F.log_softmax(output[:, -1], dim=-1)
loss = F.cross_entropy(logits, target)
@@ -177,6 +179,7 @@ class TestModels(TorchtextTestCase):
freeze_model=False,
checkpoint=dummy_model.state_dict(),
)
+ model.to(device=self.device, dtype=self.dtype)
current_state_dict = copy.deepcopy(model.state_dict())
_train(model)
diff --git a/test/torchtext_unittest/prototype/models/test_transforms.py b/test/torchtext_unittest/models/t5_test_transforms.py
similarity index 97%
rename from test/torchtext_unittest/prototype/models/test_transforms.py
rename to test/torchtext_unittest/models/t5_test_transforms.py
index 82d70a4..a3d3a58 100644
--- a/test/torchtext_unittest/prototype/models/test_transforms.py
+++ b/test/torchtext_unittest/models/t5_test_transforms.py
@@ -1,5 +1,5 @@
import torch
-from torchtext.prototype.models import T5Transform
+from torchtext.models import T5Transform
from torchtext_unittest.common.assets import get_asset_path
from torchtext_unittest.common.torchtext_test_case import TorchtextTestCase
diff --git a/test/torchtext_unittest/prototype/models/__init__.py b/test/torchtext_unittest/prototype/models/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/test/torchtext_unittest/test_build.py b/test/torchtext_unittest/test_build.py
index 7435e8c..e5ddad4 100644
--- a/test/torchtext_unittest/test_build.py
+++ b/test/torchtext_unittest/test_build.py
@@ -1,11 +1,13 @@
#!/usr/bin/env python3
"""Tests that requires external resources (Network access to fetch dataset)"""
import os
+import platform
+import unittest
import torch
import torchtext.data
-from .common.torchtext_test_case import TorchtextTestCase
+from .common.torchtext_test_case import TorchtextTestCase, third_party_download
class TestDataUtils(TorchtextTestCase):
@@ -64,6 +66,9 @@ class TestVocab(TorchtextTestCase):
self.assertEqual(token_one_vec.shape[0], vec.dim)
self.assertEqual(vec[tokens[0].lower()], token_one_vec)
+ # TODO(Nayef211): remove decorator once https://github.com/pytorch/text/issues/1900 is closed
+ @unittest.skipIf("CI" in os.environ and platform.system() == "Linux", "Test is known to fail on Linux.")
+ @third_party_download
def test_download_charngram_vectors(self) -> None:
# Build a vocab and get vectors twice to test caching.
for _ in range(2):
diff --git a/test/torchtext_unittest/test_transforms.py b/test/torchtext_unittest/test_transforms.py
index 995f358..618cbca 100644
--- a/test/torchtext_unittest/test_transforms.py
+++ b/test/torchtext_unittest/test_transforms.py
@@ -336,6 +336,7 @@ class TestGPT2BPETokenizer(TorchtextTestCase):
"Hélló WoŕlḊ¿",
"Respublica superiorem",
"Avdija Vršajević în",
+ "multi space",
]
expected_tokens = [
@@ -343,12 +344,14 @@ class TestGPT2BPETokenizer(TorchtextTestCase):
["H", "é", "ll", "ó", "Ġ", "ĠWo", "Å", "ķ", "l", "á¸", "Ĭ", "Â", "¿"],
["Res", "public", "a", "Ġsuper", "i", "orem"],
["Av", "d", "ija", "ĠV", "r", "Å¡", "aj", "ev", "i", "Äĩ", "ĠÃ", "®", "n"],
+ ["multi", "Ġ", "Ġ", "Ġ", "Ġ", "Ġ", "Ġspace"],
]
expected_token_ids = [
["15496", "2159", "28265", "703", "389", "345", "30"],
["39", "2634", "297", "10205", "220", "22173", "129", "243", "75", "41585", "232", "126", "123"],
["4965", "11377", "64", "2208", "72", "29625"],
["7355", "67", "34655", "569", "81", "32790", "1228", "1990", "72", "38325", "6184", "106", "77"],
+ ["41684", "220", "220", "220", "220", "220", "2272"],
]
# test batch of sentences
@@ -364,11 +367,349 @@ class TestGPT2BPETokenizer(TorchtextTestCase):
else:
self.assertEqual(tokenizer(txt), expected_token_ids[idx])
+ def _gpt2_bpe_tokenizer_with_added_vocab(self, tokenizer):
+ sample_texts = [
+ "<|endoftext|> and <|endoftext|> are special <|endofline|> is not!",
+ "test ACCEPT <avail_actions> with DECLINE <|endoftext|> and NO_ACTION",
+ "none in vocab: <|endofline|> WALK_60M WALK_10M <state>",
+ "Respublica Vršajević în",
+ "some in vocab: <|endofline|> WALK_60M WALK_10M <state>",
+ "<|endoftext|> WALK_60M WALK_10M <reward> <state>",
+ ]
+
+ newly_added = tokenizer.add_special_tokens(
+ special_tokens_dict={
+ "unk_token": "<|endoftext|>",
+ "additional_special_tokens": [
+ "ACCEPT",
+ "DECLINE",
+ "NO_ACTION",
+ "WALK_10M",
+ "WALK_60M",
+ "<reward>",
+ ],
+ }
+ )
+ self.assertEqual(newly_added, 6)
+
+ newly_added = tokenizer.add_special_tokens(
+ special_tokens_dict={
+ "unk_token": "<|endoftext|>",
+ "sep_token": "<avail_actions>",
+ "additional_special_tokens": [
+ "ACCEPT",
+ "DECLINE",
+ "NO_ACTION",
+ "WALK_10M",
+ "WALK_60M",
+ "<reward>",
+ ],
+ }
+ )
+ self.assertEqual(newly_added, 1)
+
+ expected_tokens = [
+ [
+ "<|endoftext|>",
+ "and",
+ "<|endoftext|>",
+ "are",
+ "Ġspecial",
+ "Ġ<",
+ "|",
+ "end",
+ "of",
+ "line",
+ "|",
+ ">",
+ "Ġis",
+ "Ġnot",
+ "!",
+ ],
+ ["test", "ACCEPT", "<avail_actions>", "with", "DECLINE", "<|endoftext|>", "and", "NO_ACTION"],
+ [
+ "none",
+ "Ġin",
+ "Ġvoc",
+ "ab",
+ ":",
+ "Ġ<",
+ "|",
+ "end",
+ "of",
+ "line",
+ "|",
+ ">",
+ "WALK_60M",
+ "WALK_10M",
+ "<",
+ "state",
+ ">",
+ ],
+ ["Res", "public", "a", "ĠV", "r", "Å¡", "aj", "ev", "i", "Äĩ", "ĠÃ", "®", "n"],
+ [
+ "some",
+ "Ġin",
+ "Ġvoc",
+ "ab",
+ ":",
+ "Ġ<",
+ "|",
+ "end",
+ "of",
+ "line",
+ "|",
+ ">",
+ "WALK_60M",
+ "WALK_10M",
+ "<",
+ "state",
+ ">",
+ ],
+ ["<|endoftext|>", "WALK_60M", "WALK_10M", "<reward>", "<", "state", ">"],
+ ]
+ expected_token_ids = [
+ [
+ "50256",
+ "392",
+ "50256",
+ "533",
+ "2041",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "1370",
+ "91",
+ "29",
+ "318",
+ "407",
+ "0",
+ ],
+ ["9288", "50257", "50263", "4480", "50258", "50256", "392", "50259"],
+ [
+ "23108",
+ "287",
+ "12776",
+ "397",
+ "25",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "1370",
+ "91",
+ "29",
+ "50261",
+ "50260",
+ "27",
+ "5219",
+ "29",
+ ],
+ ["4965", "11377", "64", "569", "81", "32790", "1228", "1990", "72", "38325", "6184", "106", "77"],
+ [
+ "11246",
+ "287",
+ "12776",
+ "397",
+ "25",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "1370",
+ "91",
+ "29",
+ "50261",
+ "50260",
+ "27",
+ "5219",
+ "29",
+ ],
+ ["50256", "50261", "50260", "50262", "27", "5219", "29"],
+ ]
+
+ # test batch of sentences
+ if tokenizer._return_tokens:
+ self.assertEqual(tokenizer(sample_texts), expected_tokens)
+ else:
+ self.assertEqual(tokenizer(sample_texts), expected_token_ids)
+
+ # test individual sentences
+ for idx, txt in enumerate(sample_texts):
+ if tokenizer._return_tokens:
+ self.assertEqual(tokenizer(txt), expected_tokens[idx])
+ else:
+ self.assertEqual(tokenizer(txt), expected_token_ids[idx])
+
+ def _gpt2_bpe_decoder(self, tokenizer):
+ sample_ids = [
+ ["15496", "2159", "28265", "703", "389", "345", "30"],
+ ["39", "2634", "297", "10205", "220", "22173", "129", "243", "75", "41585", "232", "126", "123"],
+ ["4965", "11377", "64", "2208", "72", "29625"],
+ ["7355", "67", "34655", "569", "81", "32790", "1228", "1990", "72", "38325", "6184", "106", "77"],
+ ]
+
+ expected_texts = [
+ "Hello World!, how are you?",
+ "Hélló WoŕlḊ¿",
+ "Respublica superiorem",
+ "Avdija Vršajević în",
+ ]
+
+ for idx, ids in enumerate(sample_ids):
+ self.assertEqual(tokenizer.decode(ids), expected_texts[idx])
+
+ def _gpt2_bpe_decoder_with_special_tokens(self, tokenizer):
+ sample_ids = [
+ [
+ "27",
+ "91",
+ "437",
+ "1659",
+ "5239",
+ "91",
+ "29",
+ "290",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "5239",
+ "91",
+ "29",
+ "389",
+ "2041",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "1370",
+ "91",
+ "29",
+ "318",
+ "407",
+ "0",
+ ],
+ [
+ "9288",
+ "15859",
+ "8905",
+ "51",
+ "1279",
+ "615",
+ "603",
+ "62",
+ "4658",
+ "29",
+ "351",
+ "27196",
+ "24027",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "5239",
+ "91",
+ "29",
+ "290",
+ "8005",
+ "62",
+ "44710",
+ ],
+ ["7355", "67", "34655", "569", "81", "32790", "1228", "1990", "72", "38325", "6184", "106", "77"],
+ [
+ "40",
+ "423",
+ "281",
+ "16882",
+ "1359",
+ "428",
+ "318",
+ "257",
+ "1332",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "5239",
+ "91",
+ "29",
+ ],
+ ]
+
+ expected_texts = [
+ "<|endoftext|> and <|endoftext|> are special <|endofline|> is not!",
+ "test ACCEPT <avail_actions> with DECLINE <|endoftext|> and NO_ACTION",
+ "Avdija Vršajević în",
+ "I have an inkling this is a test <|endoftext|>",
+ ]
+
+ for idx, ids in enumerate(sample_ids):
+ self.assertEqual(tokenizer.decode(ids), expected_texts[idx])
+
+ newly_added = tokenizer.add_special_tokens(
+ special_tokens_dict={
+ "unk_token": "<|endoftext|>",
+ "sep_token": "<avail_actions>",
+ "additional_special_tokens": [
+ "ACCEPT",
+ "DECLINE",
+ "inkling",
+ ],
+ }
+ )
+ self.assertEqual(newly_added, 4)
+
+ sample_ids = [
+ [
+ "50256",
+ "392",
+ "50256",
+ "533",
+ "2041",
+ "1279",
+ "91",
+ "437",
+ "1659",
+ "1370",
+ "91",
+ "29",
+ "318",
+ "407",
+ "0",
+ ],
+ ["9288", "50258", "50257", "4480", "50259", "50256", "392", "8005", "62", "44710"],
+ ["7355", "67", "34655", "569", "81", "32790", "1228", "1990", "72", "38325", "6184", "106", "77"],
+ ["40", "423", "281", "50260", "5661", "318", "257", "1332", "50256"],
+ ]
+
+ expected_texts = [
+ "<|endoftext|> and <|endoftext|> are special <|endofline|> is not!",
+ "test ACCEPT <avail_actions> with DECLINE <|endoftext|> and NO_ACTION",
+ "Avdija Vršajević în",
+ "I have an inkling this is a test <|endoftext|>",
+ ]
+
+ for idx, ids in enumerate(sample_ids):
+ self.assertEqual(tokenizer.decode(ids), expected_texts[idx])
+
@nested_params([True, False], [True, False])
def test_gpt2_bpe_tokenizer(self, test_scripting, return_tokens):
"""test tokenization on single sentence input as well as batch on sentences"""
self._gpt2_bpe_tokenizer(self._load_tokenizer(test_scripting=test_scripting, return_tokens=return_tokens))
+ def test_gpt2_bpe_decoder(self):
+ """test string output returned by decoder given the token ids"""
+ self._gpt2_bpe_decoder(self._load_tokenizer(test_scripting=False, return_tokens=False))
+ self._gpt2_bpe_decoder_with_special_tokens(self._load_tokenizer(test_scripting=False, return_tokens=False))
+
+ @nested_params([True, False])
+ def test_gpt2_bpe_tokenizer_with_added_vocab(self, return_tokens):
+ self._gpt2_bpe_tokenizer_with_added_vocab(
+ self._load_tokenizer(test_scripting=False, return_tokens=return_tokens)
+ )
+
def test_gpt2_bpe_tokenizer_save_load_pybind(self) -> None:
tokenizer = self._load_tokenizer(test_scripting=False, return_tokens=False)
tokenizer_path = os.path.join(self.test_dir, "gpt2_tokenizer_pybind.pt")
@@ -386,6 +727,68 @@ class TestGPT2BPETokenizer(TorchtextTestCase):
self._gpt2_bpe_tokenizer((loaded_tokenizer))
+class TestCharBPETokenizer(TorchtextTestCase):
+ def _load_tokenizer(self, return_tokens: bool):
+ encoder_json = "openai-gpt-vocab.json"
+ merges_file = "openai-gpt-merges.txt"
+ tokenizer = transforms.CharBPETokenizer(
+ bpe_encoder_path=get_asset_path(encoder_json),
+ bpe_merges_path=get_asset_path(merges_file),
+ suffix="</w>",
+ return_tokens=return_tokens,
+ )
+ return tokenizer
+
+ def _char_tokenizer(self, tokenizer):
+ sample_texts = [
+ "pytorch is a machine learning framework",
+ "torch uses a method called automatic differentiation!",
+ "what are tensors (torch.tensor)?",
+ "self.linear_relu_stack(x)",
+ ]
+
+ expected_tokens = [
+ ["py", "torch</w>", "is</w>", "a</w>", "machine</w>", "learning</w>", "framework</w>"],
+ [
+ "torch</w>",
+ "uses</w>",
+ "a</w>",
+ "method</w>",
+ "called</w>",
+ "automatic</w>",
+ "differenti",
+ "ation",
+ "!</w>",
+ ],
+ ["what</w>", "are</w>", "ten", "sors</w>", "(", "tor", "ch", ".", "ten", "sor", ")", "?</w>"],
+ ["self", ".", "lin", "ear", "_", "re", "lu", "_", "st", "ack", "(", "x", ")</w>"],
+ ]
+ expected_token_ids = [
+ [5420, 9047, 544, 246, 4165, 6024, 31971],
+ [9047, 8411, 246, 11823, 1347, 10223, 31671, 7427, 267],
+ [599, 640, 821, 10824, 38, 1525, 573, 1, 821, 1050, 37, 257],
+ [20828, 1, 1446, 653, 41, 492, 717, 41, 495, 1170, 38, 34, 275],
+ ]
+
+ # test batch of sentences
+ if tokenizer._return_tokens:
+ self.assertEqual(tokenizer(sample_texts), expected_tokens)
+ else:
+ self.assertEqual(tokenizer(sample_texts), expected_token_ids)
+
+ # test individual sentences
+ for idx, txt in enumerate(sample_texts):
+ if tokenizer._return_tokens:
+ self.assertEqual(tokenizer(txt), expected_tokens[idx])
+ else:
+ self.assertEqual(tokenizer(txt), expected_token_ids[idx])
+
+ @nested_params([True, False])
+ def test_char_bpe_tokenizer(self, return_tokens):
+ """test tokenization on single sentence input as well as batch on sentences"""
+ self._char_tokenizer(self._load_tokenizer(return_tokens=return_tokens))
+
+
class TestCLIPTokenizer(TorchtextTestCase):
def _load_tokenizer(self, init_using_merge_only: bool, test_scripting: bool, return_tokens: bool):
encoder_json = "clip_encoder.json"
diff --git a/torchtext/_download_hooks.py b/torchtext/_download_hooks.py
index d740827..89baafa 100644
--- a/torchtext/_download_hooks.py
+++ b/torchtext/_download_hooks.py
@@ -4,12 +4,9 @@ import requests
# This is to allow monkey-patching in fbcode
from torch.hub import load_state_dict_from_url # noqa
-from torchtext._internal.module_utils import is_module_available
+from torchdata.datapipes.iter import HttpReader, GDriveReader # noqa F401
from tqdm import tqdm
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import HttpReader, GDriveReader # noqa F401
-
def _stream_response(r, chunk_size=16 * 1024):
total_size = int(r.headers.get("Content-length", 0))
@@ -62,3 +59,4 @@ class DownloadManager:
_DATASET_DOWNLOAD_MANAGER = DownloadManager()
+_TEST_DOWNLOAD_MANAGER = DownloadManager()
diff --git a/torchtext/csrc/gpt2_bpe_tokenizer.cpp b/torchtext/csrc/gpt2_bpe_tokenizer.cpp
index 7b85f5b..5b10fe4 100644
--- a/torchtext/csrc/gpt2_bpe_tokenizer.cpp
+++ b/torchtext/csrc/gpt2_bpe_tokenizer.cpp
@@ -2,7 +2,10 @@
#include <torchtext/csrc/regex.h> // @manual
#include <algorithm>
+#include <codecvt>
+#include <locale>
#include <sstream>
+#include <string>
#include <unordered_set>
#include <utility>
@@ -59,30 +62,126 @@ std::vector<std::string> gpt2_bpe_pre_tokenizer(std::string input) {
// - ELSE IF prepend_space == True, prepend a space to the token and add to
// return list
// - ELSE, add token to return list
- std::string token;
std::vector<std::string> tokens;
- re2::StringPiece inp(input);
bool prepend_space = false;
- while (kGPT2Regex.FindAndConsume(&inp, &token)) {
- if (is_whitespace(token)) {
- prepend_space = false;
- if (inp.empty()) { // token is last token
- tokens.push_back(token);
- } else {
- if (token.length() > 1) {
- tokens.push_back(token.substr(0, token.length() - 1));
+ std::vector<std::string> index_matches;
+
+ /* Notes on handling Special Tokens:
+ We use regex pattern to first identify the special tokens in the input text.
+ Other 'non-special' tokens go through pre-tokenization as usual, but special
+ tokens skip those steps.
+
+ Steps:
+ * Loop over the set containing user-supplied strings that are to be treated as
+ special tokens. This set gets created through the calls to
+ `add_special_tokens` API.
+ - form a regex pattern that helps in extracting special tokens from the
+ input text.
+ * Create a vector that contains chunks of input text, such that each chunk is
+ either a sequence of non-special token or a single special token. For example,
+ assuming <|special_tok|> and [SEP] are special tokens, the following text
+ "This is an example with <|special_tok|> and [SEP] and [SPAM]."
+ will get converted to a vector of strings:
+ ["This is an example with", "<|special_tok|>", "and", "[SEP]", "and
+ [SPAM]."]
+ - if the input does not contain any special tokens, the vector will just
+ contain a single token that is the whole original input text.
+ * For all of the tokens in the above vector, we proceed with BPE tokenization
+ as usual while skipping over certain steps as appropriate for special tokens.
+ */
+
+ if (bpe_never_split_set_.size() > 0) {
+ std::string pattern = "";
+ // Escape regex characters for matching special tokens. This is done to
+ // ensure that characters like '|' in certain special tokens such as
+ // <|endoftext|> don't get special regex treatment.
+ for (std::string token : bpe_never_split_set_) {
+ std::string::size_type pos = 0;
+ while ((pos = token.find_first_of("|[]", pos)) != std::string::npos) {
+ switch (token[pos]) {
+ case '|':
+ token.replace(pos, 1, "\\|");
+ pos += 2;
+ break;
+ case '[':
+ token.replace(pos, 1, "\\[");
+ pos += 2;
+ break;
+ case ']':
+ token.replace(pos, 1, "\\]");
+ pos += 2;
+ break;
}
- if (token[token.length() - 1] == ' ') { // last char is space
- prepend_space = true;
- } else { // push last whitespace char as a token if it is not a space
- tokens.push_back(token.substr(token.length() - 1));
+ }
+ if (pattern.length() != 0) {
+ pattern += "|";
+ }
+ pattern += token;
+ }
+
+ // break input into non-special and special parts
+ const Regex specialTokenRegex("(" + pattern + ")");
+ re2::StringPiece input_strp(input);
+ std::string match;
+ int64_t last_idx = 0;
+ while (specialTokenRegex.FindAndConsume(&input_strp, &match)) {
+ int64_t start_idx = input.size() - input_strp.size() - match.size();
+ if (start_idx > last_idx) {
+ if (isspace(input[start_idx - 1])) {
+ // strip space on the left of the special token
+ index_matches.push_back(
+ input.substr(last_idx, start_idx - last_idx - 1));
+ } else {
+ index_matches.push_back(input.substr(last_idx, start_idx - last_idx));
}
}
- } else if (prepend_space) {
- tokens.push_back(" " + token);
- prepend_space = false;
- } else {
- tokens.push_back(token);
+ index_matches.push_back(input.substr(start_idx, match.size()));
+ last_idx = start_idx + match.size();
+ if (isspace(input[last_idx])) {
+ // strip space on the right of the special token
+ last_idx++;
+ }
+ }
+ if (last_idx <= input.length() - 1) {
+ index_matches.push_back(
+ input.substr(last_idx, input.length() - last_idx));
+ }
+ } else {
+ // input does not have any special tokens
+ index_matches.push_back(input);
+ }
+
+ for (std::string index_token : index_matches) {
+ bool is_never_split_token =
+ bpe_never_split_set_.find(index_token) != bpe_never_split_set_.end();
+ if (is_never_split_token) {
+ // skip the rest of pre-tokenization work for special tokens
+ tokens.push_back(index_token);
+ continue;
+ }
+ re2::StringPiece inp(index_token);
+ std::string token;
+ while (kGPT2Regex.FindAndConsume(&inp, &token)) {
+ if (is_whitespace(token)) {
+ prepend_space = false;
+ if (inp.empty()) { // token is last token
+ tokens.push_back(token);
+ } else {
+ if (token.length() > 1) {
+ tokens.push_back(token.substr(0, token.length() - 1));
+ }
+ if (token[token.length() - 1] == ' ') { // last char is space
+ prepend_space = true;
+ } else { // push last whitespace char as a token if it is not a space
+ tokens.push_back(token.substr(token.length() - 1));
+ }
+ }
+ } else if (prepend_space) {
+ tokens.push_back(" " + token);
+ prepend_space = false;
+ } else {
+ tokens.push_back(token);
+ }
}
}
return tokens;
@@ -147,7 +246,15 @@ GPT2BPEEncoder::GPT2BPEEncoder(
bpe_merge_ranks_(std::move(bpe_merge_ranks)),
byte_encoder_(std::move(byte_encoder)),
seperator_(std::move(seperator)),
- caching_enabled_(caching_enabled) {}
+ caching_enabled_(caching_enabled) {
+ for (auto const& x : bpe_encoder_) {
+ bpe_decoder_.insert(x.value(), x.key());
+ }
+
+ for (auto const& x : byte_encoder_) {
+ byte_decoder_.insert(x.value(), x.key());
+ }
+}
GPT2BPEEncoder::GPT2BPEEncoder(
const std::unordered_map<std::string, int64_t>& bpe_encoder,
@@ -162,11 +269,17 @@ GPT2BPEEncoder::GPT2BPEEncoder(
_map_to_c10_dict<int64_t, std::string>(byte_encoder),
caching_enabled) {}
-std::vector<std::string> GPT2BPEEncoder::ByteEncode_(std::string token) {
+std::vector<std::string> GPT2BPEEncoder::ByteEncode_(
+ std::string token,
+ bool is_never_split_token) {
// Equivalent to: (self.byte_encoder[b] for b in token.encode('utf-8')
std::vector<std::string> encoded;
- for (auto& ch : token) {
- encoded.push_back(byte_encoder_.at((unsigned char)ch));
+ if (is_never_split_token) {
+ encoded.push_back(token);
+ } else {
+ for (auto& ch : token) {
+ encoded.push_back(byte_encoder_.at((unsigned char)ch));
+ }
}
return encoded;
}
@@ -271,7 +384,13 @@ std::vector<std::string> GPT2BPEEncoder::PreTokenize_(std::string input) {
std::vector<int64_t> GPT2BPEEncoder::Encode(const std::string& text) {
std::vector<int64_t> bpe_token_ids;
for (const auto& token : PreTokenize_(text)) {
- auto byte_encoded_token = ByteEncode_(token);
+ if (added_tokens_encoder_.contains(token)) {
+ bpe_token_ids.push_back(added_tokens_encoder_.at(token));
+ continue;
+ }
+ bool is_never_split_token =
+ bpe_never_split_set_.find(token) != bpe_never_split_set_.end();
+ auto byte_encoded_token = ByteEncode_(token, is_never_split_token);
for (const auto& bpe_token : BPE_(byte_encoded_token)) {
bpe_token_ids.push_back(bpe_encoder_.at(bpe_token));
}
@@ -279,10 +398,79 @@ std::vector<int64_t> GPT2BPEEncoder::Encode(const std::string& text) {
return bpe_token_ids;
}
+std::string GPT2BPEEncoder::Decode(const std::vector<int64_t>& tokens) {
+ std::string text;
+ bool is_prev_special = false;
+ bool is_current_special = false;
+ // setup converter for converting wide chars to/from chars
+ using convert_type = std::codecvt_utf8<wchar_t>;
+ std::wstring_convert<convert_type, wchar_t> converter;
+
+ for (int tok_idx = 0; tok_idx < tokens.size(); tok_idx++) {
+ const auto token = tokens[tok_idx];
+ std::string decoded_token;
+
+ if (added_tokens_decoder_.contains(token)) {
+ // string is a special token from extended vocab
+ decoded_token = added_tokens_decoder_.at(token);
+ is_current_special = true;
+ } else {
+ const std::string str = bpe_decoder_.at(token);
+ if (bpe_never_split_set_.find(str) != bpe_never_split_set_.end()) {
+ // string is a special token from known vocab
+ decoded_token = str;
+ is_current_special = true;
+ } else {
+ // string is a regular token from known vocab
+ is_current_special = false;
+ const std::wstring ws = converter.from_bytes(str);
+ for (wchar_t wchr : ws) {
+ // get output character from byte decoder for each wide character
+ unsigned char uchr = byte_decoder_.at(converter.to_bytes(wchr));
+ decoded_token.push_back(uchr);
+ }
+ }
+ }
+
+ /* Fixing leading/trailing space(s)
+
+ We need to ensure spaces before and after special tokens are removed
+ appropirately. Assuming <|endoftext|> and HELLO are special tokens:
+ string input: "<|endoftext|> <|endoftext|> and HELLO world !"
+ is to be tokenized as:
+ ['<|endoftext|>', '<|endoftext|>', 'and', 'HELLO', 'world', 'Ġ!']
+ whereas an input like:
+ "<|endoftext|> and anything else!", gets tokenized as:
+ ['<|endoftext|>', 'and', 'Ġanything', 'Ġelse', '!']
+
+ Hence while decoding the corresponding string tokens back to
+ the original string text, we will have to insert those spaces back again.
+ - Add empty space before a special token if it is not at the begining of the
+ sentence and if it is not following another special token.
+ - Add empty space after a special token if it is not at the end of the
+ sentence.
+ */
+
+ // fix left space(s) for special tokens
+ if (is_current_special && (tok_idx > 0 && !is_prev_special)) {
+ text.push_back(' ');
+ }
+ text.append(decoded_token);
+ // fix right space(s) for special tokens
+ if (is_current_special && tok_idx != tokens.size() - 1) {
+ text.push_back(' ');
+ }
+ is_prev_special = is_current_special;
+ }
+ return text;
+}
+
std::vector<std::string> GPT2BPEEncoder::Tokenize(const std::string& text) {
std::vector<std::string> bpe_tokens;
for (const auto& token : PreTokenize_(text)) {
- auto byte_encoded_token = ByteEncode_(token);
+ bool is_never_split_token =
+ bpe_never_split_set_.find(token) != bpe_never_split_set_.end();
+ auto byte_encoded_token = ByteEncode_(token, is_never_split_token);
for (const auto& bpe_token : BPE_(byte_encoded_token)) {
bpe_tokens.push_back(bpe_token);
}
@@ -290,6 +478,48 @@ std::vector<std::string> GPT2BPEEncoder::Tokenize(const std::string& text) {
return bpe_tokens;
}
+int64_t GPT2BPEEncoder::AddSpecialTokens(
+ const c10::Dict<std::string, std::string>& standard_special_tokens_dict,
+ const std::vector<std::string>& additional_special_tokens) {
+ int64_t newly_added = 0;
+
+ /* All special tokens get added to `bpe_never_split_set_` set to avoid being
+ * split during tokenization. Tokens are added to `added_tokens_encoder_` only
+ * if they are not already known (i.e. not already present in `bpe_encoder_`).
+ */
+
+ // Loop for standard tokens such as "bos_token", "eos_token", etc.
+ for (auto const& token : standard_special_tokens_dict) {
+ if (added_tokens_encoder_.contains(token.value())) {
+ continue;
+ }
+ bpe_never_split_set_.insert(token.value());
+ if (!bpe_encoder_.contains(token.value())) {
+ added_tokens_encoder_.insert(
+ token.value(), bpe_encoder_.size() + added_tokens_encoder_.size());
+ added_tokens_decoder_.insert(
+ bpe_decoder_.size() + added_tokens_decoder_.size(), token.value());
+ newly_added++;
+ }
+ }
+
+ // Loop for any additional tokens
+ for (auto const& token : additional_special_tokens) {
+ if (added_tokens_encoder_.contains(token))
+ continue;
+ bpe_never_split_set_.insert(token);
+ if (!bpe_encoder_.contains(token)) {
+ added_tokens_encoder_.insert(
+ token, bpe_encoder_.size() + added_tokens_encoder_.size());
+ added_tokens_decoder_.insert(
+ bpe_decoder_.size() + added_tokens_decoder_.size(), token);
+ newly_added++;
+ }
+ }
+
+ return newly_added;
+}
+
std::unordered_map<std::string, int64_t> GPT2BPEEncoder::GetBPEEncoder() const {
return _c10_dict_to_map(bpe_encoder_);
}
diff --git a/torchtext/csrc/gpt2_bpe_tokenizer.h b/torchtext/csrc/gpt2_bpe_tokenizer.h
index 4977780..8d7de4d 100644
--- a/torchtext/csrc/gpt2_bpe_tokenizer.h
+++ b/torchtext/csrc/gpt2_bpe_tokenizer.h
@@ -1,10 +1,10 @@
#ifndef GPT2_BPE_TOKENIZER_H_
#define GPT2_BPE_TOKENIZER_H_
-
#include <torch/script.h>
#include <torchtext/csrc/export.h>
#include <cstdint>
+#include <set>
#include <string>
#include <tuple>
#include <unordered_map>
@@ -12,6 +12,9 @@
namespace torchtext {
+// set to store tokens that are not to be split
+static std::set<std::string> bpe_never_split_set_;
+
typedef std::tuple<
std::unordered_map<std::string, int64_t>,
std::unordered_map<std::string, int64_t>,
@@ -55,8 +58,12 @@ struct GPT2BPEEncoder : torch::CustomClassHolder {
private:
const int64_t inf_;
// Encode byte into an unicode character.
- std::vector<std::string> ByteEncode_(std::string token);
+ std::vector<std::string> ByteEncode_(
+ std::string token,
+ bool is_never_split_token);
int64_t GetBPEMergeRank_(std::string pair);
+ c10::Dict<std::string, int64_t> added_tokens_encoder_;
+ c10::Dict<int64_t, std::string> added_tokens_decoder_;
protected:
c10::Dict<std::string, std::vector<std::string>> cache_;
@@ -69,8 +76,10 @@ struct GPT2BPEEncoder : torch::CustomClassHolder {
public:
const c10::Dict<std::string, int64_t> bpe_encoder_;
+ const c10::Dict<int64_t, std::string> bpe_decoder_;
const c10::Dict<std::string, int64_t> bpe_merge_ranks_;
const c10::Dict<int64_t, std::string> byte_encoder_;
+ const c10::Dict<std::string, int64_t> byte_decoder_;
const std::string seperator_;
const bool caching_enabled_;
explicit GPT2BPEEncoder(
@@ -99,7 +108,11 @@ struct GPT2BPEEncoder : torch::CustomClassHolder {
// --> result --> [707, 5927, 11, 707, 68]
//
TORCHTEXT_API std::vector<int64_t> Encode(const std::string& text);
+ TORCHTEXT_API std::string Decode(const std::vector<int64_t>& tokens);
TORCHTEXT_API std::vector<std::string> Tokenize(const std::string& text);
+ TORCHTEXT_API int64_t AddSpecialTokens(
+ const c10::Dict<std::string, std::string>& standard_special_tokens_dict,
+ const std::vector<std::string>& additional_special_tokens);
TORCHTEXT_API std::unordered_map<std::string, int64_t> GetBPEEncoder() const;
TORCHTEXT_API std::unordered_map<std::string, int64_t> GetBPEMergeRanks()
diff --git a/torchtext/csrc/regex.cpp b/torchtext/csrc/regex.cpp
index 65d20fa..00ea624 100644
--- a/torchtext/csrc/regex.cpp
+++ b/torchtext/csrc/regex.cpp
@@ -6,6 +6,10 @@ Regex::Regex(const std::string& re_str) : re_str_(re_str) {
compiled_pattern_ = new RE2(re_str_);
}
+Regex::~Regex() {
+ delete compiled_pattern_;
+}
+
std::string Regex::Sub(std::string str, const std::string& repl) const {
RE2::GlobalReplace(&str, *compiled_pattern_, repl);
return str;
diff --git a/torchtext/csrc/regex.h b/torchtext/csrc/regex.h
index 35e7e50..d868f94 100644
--- a/torchtext/csrc/regex.h
+++ b/torchtext/csrc/regex.h
@@ -13,6 +13,7 @@ struct Regex : torch::CustomClassHolder {
std::string re_str_;
TORCHTEXT_API Regex(const std::string& re_str);
+ TORCHTEXT_API ~Regex();
TORCHTEXT_API std::string Sub(std::string str, const std::string& repl) const;
TORCHTEXT_API bool FindAndConsume(re2::StringPiece* input, std::string* text)
const;
diff --git a/torchtext/csrc/register_pybindings.cpp b/torchtext/csrc/register_pybindings.cpp
index 80f3591..afa4708 100644
--- a/torchtext/csrc/register_pybindings.cpp
+++ b/torchtext/csrc/register_pybindings.cpp
@@ -179,6 +179,27 @@ PYBIND11_MODULE(_torchtext, m) {
.def_property_readonly("byte_encoder_", &GPT2BPEEncoder::GetByteEncoder)
.def("encode", &GPT2BPEEncoder::Encode)
.def("tokenize", &GPT2BPEEncoder::Tokenize)
+ .def(
+ "decode",
+ [](const c10::intrusive_ptr<GPT2BPEEncoder>& self,
+ const std::vector<int64_t>& tokens) {
+ std::string s = self->Decode(tokens);
+ PyObject* py_obj =
+ PyUnicode_DecodeUTF8(s.data(), s.length(), "ignore");
+ py::str py_s = py::reinterpret_steal<py::str>(py_obj);
+ return py_s;
+ })
+ .def(
+ "add_special_tokens",
+ [](const c10::intrusive_ptr<GPT2BPEEncoder>& self,
+ const std::unordered_map<std::string, std::string>& items,
+ const std::vector<std::string>& additional) {
+ c10::Dict<std::string, std::string> d;
+ for (const auto& item : items) {
+ d.insert(item.first, item.second);
+ }
+ return (self->AddSpecialTokens(d, additional));
+ })
.def(py::pickle(
// __getstate__
[](const c10::intrusive_ptr<GPT2BPEEncoder>& self)
diff --git a/torchtext/csrc/register_torchbindings.cpp b/torchtext/csrc/register_torchbindings.cpp
index 64427f1..25b23a0 100644
--- a/torchtext/csrc/register_torchbindings.cpp
+++ b/torchtext/csrc/register_torchbindings.cpp
@@ -139,7 +139,9 @@ TORCH_LIBRARY_FRAGMENT(torchtext, m) {
c10::Dict<int64_t, std::string>,
bool>())
.def("encode", &GPT2BPEEncoder::Encode)
+ .def("decode", &GPT2BPEEncoder::Decode)
.def("tokenize", &GPT2BPEEncoder::Tokenize)
+ .def("add_special_tokens", &GPT2BPEEncoder::AddSpecialTokens)
.def_pickle(
// __getstate__
[](const c10::intrusive_ptr<GPT2BPEEncoder>& self)
diff --git a/torchtext/datasets/ag_news.py b/torchtext/datasets/ag_news.py
index f07b3ae..5f1c774 100644
--- a/torchtext/datasets/ag_news.py
+++ b/torchtext/datasets/ag_news.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = {
"train": "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/train.csv",
"test": "https://raw.githubusercontent.com/mhjabreel/CharCnn_Keras/master/data/ag_news_csv/test.csv",
diff --git a/torchtext/datasets/amazonreviewfull.py b/torchtext/datasets/amazonreviewfull.py
index eb527a8..06e6882 100644
--- a/torchtext/datasets/amazonreviewfull.py
+++ b/torchtext/datasets/amazonreviewfull.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZVhsUnRWRDhETzA"
MD5 = "57d28bd5d930e772930baddf36641c7c"
diff --git a/torchtext/datasets/amazonreviewpolarity.py b/torchtext/datasets/amazonreviewpolarity.py
index f4a47da..9616dc1 100644
--- a/torchtext/datasets/amazonreviewpolarity.py
+++ b/torchtext/datasets/amazonreviewpolarity.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbaW12WVVZS2drcnM"
MD5 = "fe39f8b653cada45afd5792e0f0e8f9b"
diff --git a/torchtext/datasets/cc100.py b/torchtext/datasets/cc100.py
index 755f277..4ce2e92 100644
--- a/torchtext/datasets/cc100.py
+++ b/torchtext/datasets/cc100.py
@@ -1,15 +1,12 @@
import os.path
from functools import partial
-from torchtext._internal.module_utils import is_module_available
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext.data.datasets_utils import (
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "http://data.statmt.org/cc-100/%s.txt.xz"
VALID_CODES = {
diff --git a/torchtext/datasets/cnndm.py b/torchtext/datasets/cnndm.py
index db65680..2adba04 100644
--- a/torchtext/datasets/cnndm.py
+++ b/torchtext/datasets/cnndm.py
@@ -3,20 +3,18 @@ import os
from functools import partial
from typing import Union, Set, Tuple
+from torchdata.datapipes.iter import (
+ FileOpener,
+ IterableWrapper,
+ OnlineReader,
+ GDriveReader,
+)
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import (
- FileOpener,
- IterableWrapper,
- OnlineReader,
- GDriveReader,
- )
-
DATASET_NAME = "CNNDM"
SPLIT_LIST = {
@@ -77,7 +75,7 @@ def _hash_urls(s: tuple):
Code is inspired from https://github.com/abisee/cnn-dailymail/blob/master/make_datafiles.py
"""
url = s[1]
- h = hashlib.sha1()
+ h = hashlib.new("sha1", usedforsecurity=False)
h.update(url)
url_hash = h.hexdigest()
story_fname = url_hash + ".story"
diff --git a/torchtext/datasets/cola.py b/torchtext/datasets/cola.py
index a56c615..214c435 100644
--- a/torchtext/datasets/cola.py
+++ b/torchtext/datasets/cola.py
@@ -3,13 +3,11 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import _create_dataset_directory, _wrap_split_argument
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "https://nyu-mll.github.io/CoLA/cola_public_1.1.zip"
MD5 = "9f6d88c3558ec424cd9d66ea03589aba"
diff --git a/torchtext/datasets/conll2000chunking.py b/torchtext/datasets/conll2000chunking.py
index 25b60e4..acbd9cb 100644
--- a/torchtext/datasets/conll2000chunking.py
+++ b/torchtext/datasets/conll2000chunking.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = {
"train": "https://www.clips.uantwerpen.be/conll2000/chunking/train.txt.gz",
"test": "https://www.clips.uantwerpen.be/conll2000/chunking/test.txt.gz",
diff --git a/torchtext/datasets/dbpedia.py b/torchtext/datasets/dbpedia.py
index 3afc414..be86f1a 100644
--- a/torchtext/datasets/dbpedia.py
+++ b/torchtext/datasets/dbpedia.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k"
MD5 = "dca7b1ae12b1091090db52aa7ec5ca64"
diff --git a/torchtext/datasets/enwik9.py b/torchtext/datasets/enwik9.py
index 744cf22..cbd5e64 100644
--- a/torchtext/datasets/enwik9.py
+++ b/torchtext/datasets/enwik9.py
@@ -1,13 +1,11 @@
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import _create_dataset_directory
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "http://mattmahoney.net/dc/enwik9.zip"
MD5 = "3e773f8a1577fda2e27f871ca17f31fd"
diff --git a/torchtext/datasets/imdb.py b/torchtext/datasets/imdb.py
index d996234..09fba57 100644
--- a/torchtext/datasets/imdb.py
+++ b/torchtext/datasets/imdb.py
@@ -3,14 +3,12 @@ from functools import partial
from pathlib import Path
from typing import Tuple, Union
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import _create_dataset_directory
from torchtext.data.datasets_utils import _wrap_split_argument
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz"
MD5 = "7c2ac02c03563afcf9b574c7e56c153a"
diff --git a/torchtext/datasets/iwslt2016.py b/torchtext/datasets/iwslt2016.py
index 1bc1386..dd4b806 100644
--- a/torchtext/datasets/iwslt2016.py
+++ b/torchtext/datasets/iwslt2016.py
@@ -1,6 +1,8 @@
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_clean_files,
@@ -9,10 +11,6 @@ from torchtext.data.datasets_utils import (
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?id=1l5y6Giag9aRPwGtuZHswh3w5v3qEz8D8"
_PATH = "2016-01.tgz"
diff --git a/torchtext/datasets/iwslt2017.py b/torchtext/datasets/iwslt2017.py
index 1691e0c..4767218 100644
--- a/torchtext/datasets/iwslt2017.py
+++ b/torchtext/datasets/iwslt2017.py
@@ -1,6 +1,8 @@
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_clean_files,
@@ -9,10 +11,6 @@ from torchtext.data.datasets_utils import (
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/u/0/uc?id=12ycYSzLIG253AFN35Y6qoyf9wtkOjakp"
_PATH = "2017-01-trnmted.tgz"
MD5 = "aca701032b1c4411afc4d9fa367796ba"
diff --git a/torchtext/datasets/mnli.py b/torchtext/datasets/mnli.py
index ff27b18..f4335c5 100644
--- a/torchtext/datasets/mnli.py
+++ b/torchtext/datasets/mnli.py
@@ -3,19 +3,17 @@ import csv
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+# we import HttpReader from _download_hooks so we can swap out public URLs
+# with interal URLs when the dataset is used within Facebook
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_create_dataset_directory,
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
-
- # we import HttpReader from _download_hooks so we can swap out public URLs
- # with interal URLs when the dataset is used within Facebook
- from torchtext._download_hooks import HttpReader
-
URL = "https://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip"
diff --git a/torchtext/datasets/mrpc.py b/torchtext/datasets/mrpc.py
index d958865..e9abea1 100644
--- a/torchtext/datasets/mrpc.py
+++ b/torchtext/datasets/mrpc.py
@@ -3,15 +3,13 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, HttpReader, IterableWrapper
-
URL = {
"train": "https://dl.fbaipublicfiles.com/senteval/senteval_data/msr_paraphrase_train.txt",
diff --git a/torchtext/datasets/multi30k.py b/torchtext/datasets/multi30k.py
index ce974e9..1f60831 100644
--- a/torchtext/datasets/multi30k.py
+++ b/torchtext/datasets/multi30k.py
@@ -2,16 +2,15 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader # noqa
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = {
"train": "http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/training.tar.gz",
"valid": "http://www.quest.dcs.shef.ac.uk/wmt16_files_mmt/validation.tar.gz",
@@ -21,7 +20,7 @@ URL = {
MD5 = {
"train": "20140d013d05dd9a72dfde46478663ba05737ce983f478f960c1123c6671be5e",
"valid": "a7aa20e9ebd5ba5adce7909498b94410996040857154dab029851af3a866da8c",
- "test": "6d1ca1dba99e2c5dd54cae1226ff11c2551e6ce63527ebb072a1f70f72a5cd36",
+ "test": "0681be16a532912288a91ddd573594fbdd57c0fbb81486eff7c55247e35326c2",
}
_PREFIX = {
diff --git a/torchtext/datasets/penntreebank.py b/torchtext/datasets/penntreebank.py
index f6f8cc7..1e0d9f2 100644
--- a/torchtext/datasets/penntreebank.py
+++ b/torchtext/datasets/penntreebank.py
@@ -2,16 +2,15 @@ import os
from functools import partial
from typing import Tuple, Union
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader # noqa
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = {
"train": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.train.txt",
"test": "https://raw.githubusercontent.com/wojzaremba/lstm/master/data/ptb.test.txt",
diff --git a/torchtext/datasets/qnli.py b/torchtext/datasets/qnli.py
index 47fd14f..aa71eeb 100644
--- a/torchtext/datasets/qnli.py
+++ b/torchtext/datasets/qnli.py
@@ -3,19 +3,17 @@ import csv
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+# we import HttpReader from _download_hooks so we can swap out public URLs
+# with interal URLs when the dataset is used within Facebook
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_create_dataset_directory,
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
-
- # we import HttpReader from _download_hooks so we can swap out public URLs
- # with interal URLs when the dataset is used within Facebook
- from torchtext._download_hooks import HttpReader
-
URL = "https://dl.fbaipublicfiles.com/glue/data/QNLIv2.zip"
diff --git a/torchtext/datasets/qqp.py b/torchtext/datasets/qqp.py
index 6ef8e18..013a6a8 100644
--- a/torchtext/datasets/qqp.py
+++ b/torchtext/datasets/qqp.py
@@ -1,13 +1,11 @@
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import _create_dataset_directory
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "http://qim.fs.quoracdn.net/quora_duplicate_questions.tsv"
MD5 = "b6d5672bd9dc1e66ab2bb020ebeafb8d"
diff --git a/torchtext/datasets/rte.py b/torchtext/datasets/rte.py
index 88cd29d..0635546 100644
--- a/torchtext/datasets/rte.py
+++ b/torchtext/datasets/rte.py
@@ -3,28 +3,26 @@ import csv
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+# we import HttpReader from _download_hooks so we can swap out public URLs
+# with interal URLs when the dataset is used within Facebook
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_create_dataset_directory,
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
-
- # we import HttpReader from _download_hooks so we can swap out public URLs
- # with interal URLs when the dataset is used within Facebook
- from torchtext._download_hooks import HttpReader
-
URL = "https://dl.fbaipublicfiles.com/glue/data/RTE.zip"
MD5 = "bef554d0cafd4ab6743488101c638539"
NUM_LINES = {
- "train": 67349,
- "dev": 872,
- "test": 1821,
+ "train": 2490,
+ "dev": 277,
+ "test": 3000,
}
_PATH = "RTE.zip"
@@ -67,9 +65,9 @@ def RTE(root, split):
For additional details refer to https://aclweb.org/aclwiki/Recognizing_Textual_Entailment
Number of lines per split:
- - train: 67349
- - dev: 872
- - test: 1821
+ - train: 2490
+ - dev: 277
+ - test: 3000
Args:
root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
diff --git a/torchtext/datasets/sogounews.py b/torchtext/datasets/sogounews.py
index a160c5b..80c7c9a 100644
--- a/torchtext/datasets/sogounews.py
+++ b/torchtext/datasets/sogounews.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbUkVqNEszd0pHaFE"
MD5 = "0c1700ba70b73f964dd8de569d3fd03e"
diff --git a/torchtext/datasets/squad1.py b/torchtext/datasets/squad1.py
index ef110da..5c83bcd 100644
--- a/torchtext/datasets/squad1.py
+++ b/torchtext/datasets/squad1.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = {
"train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json",
"dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v1.1.json",
diff --git a/torchtext/datasets/squad2.py b/torchtext/datasets/squad2.py
index 11b3fdd..48ef865 100644
--- a/torchtext/datasets/squad2.py
+++ b/torchtext/datasets/squad2.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = {
"train": "https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v2.0.json",
"dev": "https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json",
diff --git a/torchtext/datasets/sst2.py b/torchtext/datasets/sst2.py
index 40c269a..132b22d 100644
--- a/torchtext/datasets/sst2.py
+++ b/torchtext/datasets/sst2.py
@@ -2,19 +2,17 @@
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+# we import HttpReader from _download_hooks so we can swap out public URLs
+# with interal URLs when the dataset is used within Facebook
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_create_dataset_directory,
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
-
- # we import HttpReader from _download_hooks so we can swap out public URLs
- # with interal URLs when the dataset is used within Facebook
- from torchtext._download_hooks import HttpReader
-
URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
diff --git a/torchtext/datasets/stsb.py b/torchtext/datasets/stsb.py
index 153391d..324ed77 100644
--- a/torchtext/datasets/stsb.py
+++ b/torchtext/datasets/stsb.py
@@ -2,19 +2,17 @@ import csv
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+# we import HttpReader from _download_hooks so we can swap out public URLs
+# with interal URLs when the dataset is used within Facebook
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_create_dataset_directory,
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
-
- # we import HttpReader from _download_hooks so we can swap out public URLs
- # with interal URLs when the dataset is used within Facebook
- from torchtext._download_hooks import HttpReader
-
URL = "http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz"
@@ -95,7 +93,7 @@ def STSB(root, split):
cache_decompressed_dp = cache_compressed_dp.on_disk_cache(filepath_fn=partial(_extracted_filepath_fn, root, split))
cache_decompressed_dp = (
- FileOpener(cache_decompressed_dp, mode="b").read_from_tar().filter(partial(_filter_fn, split))
+ FileOpener(cache_decompressed_dp, mode="b").load_from_tar().filter(partial(_filter_fn, split))
)
cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
diff --git a/torchtext/datasets/udpos.py b/torchtext/datasets/udpos.py
index a7bf1b2..3c7b76b 100644
--- a/torchtext/datasets/udpos.py
+++ b/torchtext/datasets/udpos.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "https://bitbucket.org/sivareddyg/public/downloads/en-ud-v2.zip"
MD5 = "bdcac7c52d934656bae1699541424545"
diff --git a/torchtext/datasets/wikitext103.py b/torchtext/datasets/wikitext103.py
index fad94bf..0914d70 100644
--- a/torchtext/datasets/wikitext103.py
+++ b/torchtext/datasets/wikitext103.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-103-v1.zip"
MD5 = "9ddaacaf6af0710eda8c456decff7832"
diff --git a/torchtext/datasets/wikitext2.py b/torchtext/datasets/wikitext2.py
index e7ad4a8..ec686b9 100644
--- a/torchtext/datasets/wikitext2.py
+++ b/torchtext/datasets/wikitext2.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import HttpReader
-
URL = "https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-v1.zip"
MD5 = "542ccefacc6c27f945fb54453812b3cd"
diff --git a/torchtext/datasets/wnli.py b/torchtext/datasets/wnli.py
index 5c0226e..c864275 100644
--- a/torchtext/datasets/wnli.py
+++ b/torchtext/datasets/wnli.py
@@ -2,19 +2,17 @@
import os
from functools import partial
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+# we import HttpReader from _download_hooks so we can swap out public URLs
+# with interal URLs when the dataset is used within Facebook
+from torchtext._download_hooks import HttpReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_create_dataset_directory,
_wrap_split_argument,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
-
- # we import HttpReader from _download_hooks so we can swap out public URLs
- # with interal URLs when the dataset is used within Facebook
- from torchtext._download_hooks import HttpReader
-
URL = "https://dl.fbaipublicfiles.com/glue/data/WNLI.zip"
diff --git a/torchtext/datasets/yahooanswers.py b/torchtext/datasets/yahooanswers.py
index 1721adb..9fad10f 100644
--- a/torchtext/datasets/yahooanswers.py
+++ b/torchtext/datasets/yahooanswers.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9Qhbd2JNdDBsQUdocVU"
MD5 = "f3f9899b997a42beb24157e62e3eea8d"
diff --git a/torchtext/datasets/yelpreviewfull.py b/torchtext/datasets/yelpreviewfull.py
index a6e355d..1272dae 100644
--- a/torchtext/datasets/yelpreviewfull.py
+++ b/torchtext/datasets/yelpreviewfull.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbZlU4dXhHTFhZQU0"
MD5 = "f7ddfafed1033f68ec72b9267863af6c"
diff --git a/torchtext/datasets/yelpreviewpolarity.py b/torchtext/datasets/yelpreviewpolarity.py
index 82c04e6..90e1e31 100644
--- a/torchtext/datasets/yelpreviewpolarity.py
+++ b/torchtext/datasets/yelpreviewpolarity.py
@@ -2,16 +2,14 @@ import os
from functools import partial
from typing import Union, Tuple
+from torchdata.datapipes.iter import FileOpener, IterableWrapper
+from torchtext._download_hooks import GDriveReader
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
_wrap_split_argument,
_create_dataset_directory,
)
-if is_module_available("torchdata"):
- from torchdata.datapipes.iter import FileOpener, IterableWrapper
- from torchtext._download_hooks import GDriveReader
-
URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbNUpYQ2N3SGlFaDg"
MD5 = "620c8ae4bd5a150b730f1ba9a7c6a4d3"
diff --git a/torchtext/models/__init__.py b/torchtext/models/__init__.py
index a7cbc0c..7b80407 100644
--- a/torchtext/models/__init__.py
+++ b/torchtext/models/__init__.py
@@ -1 +1,2 @@
from .roberta import * # noqa: F401, F403
+from .t5 import * # noqa: F401, F403
diff --git a/torchtext/models/roberta/__init__.py b/torchtext/models/roberta/__init__.py
index 57265ae..ee1bdd2 100644
--- a/torchtext/models/roberta/__init__.py
+++ b/torchtext/models/roberta/__init__.py
@@ -1,6 +1,7 @@
from .bundler import (
ROBERTA_BASE_ENCODER,
ROBERTA_LARGE_ENCODER,
+ ROBERTA_DISTILLED_ENCODER,
RobertaBundle,
XLMR_BASE_ENCODER,
XLMR_LARGE_ENCODER,
@@ -16,4 +17,5 @@ __all__ = [
"XLMR_LARGE_ENCODER",
"ROBERTA_BASE_ENCODER",
"ROBERTA_LARGE_ENCODER",
+ "ROBERTA_DISTILLED_ENCODER",
]
diff --git a/torchtext/models/roberta/bundler.py b/torchtext/models/roberta/bundler.py
index 1fe243f..df9a522 100644
--- a/torchtext/models/roberta/bundler.py
+++ b/torchtext/models/roberta/bundler.py
@@ -294,3 +294,40 @@ ROBERTA_LARGE_ENCODER.__doc__ = """
Please refer to :func:`torchtext.models.RobertaBundle` for the usage.
"""
+
+
+ROBERTA_DISTILLED_ENCODER = RobertaBundle(
+ _path=urljoin(_TEXT_BUCKET, "roberta.distilled.encoder.pt"),
+ _encoder_conf=RobertaEncoderConf(
+ num_encoder_layers=6,
+ padding_idx=1,
+ ),
+ transform=lambda: T.Sequential(
+ T.GPT2BPETokenizer(
+ encoder_json_path=urljoin(_TEXT_BUCKET, "gpt2_bpe_encoder.json"),
+ vocab_bpe_path=urljoin(_TEXT_BUCKET, "gpt2_bpe_vocab.bpe"),
+ ),
+ T.VocabTransform(load_state_dict_from_url(urljoin(_TEXT_BUCKET, "roberta.vocab.pt"))),
+ T.Truncate(510),
+ T.AddToken(token=0, begin=True),
+ T.AddToken(token=2, begin=False),
+ ),
+)
+
+ROBERTA_DISTILLED_ENCODER.__doc__ = """
+ Roberta Encoder with Distilled Weights
+
+ DistilRoBERTa is trained using knowledge distillation, a technique to compress a large
+ model called the teacher into a smaller model called the student. By distillating RoBERTa,
+ a smaller and faster Transformer model is obtained while maintaining most of the performance.
+
+ DistilRoBERTa was pretrained solely on OpenWebTextCorpus, a reproduction of OpenAI's WebText dataset.
+ On average DistilRoBERTa is twice as fast as RoBERTa Base.
+
+ Originally published by Hugging Face under the Apache 2.0 License
+ and redistributed with the same license.
+ [`License <https://www.apache.org/licenses/LICENSE-2.0>`__,
+ `Source <https://github.com/huggingface/transformers/tree/main/examples/research_projects/distillation>`__]
+
+ Please refer to :func:`torchtext.models.RobertaBundle` for the usage.
+ """
diff --git a/torchtext/prototype/models/t5/__init__.py b/torchtext/models/t5/__init__.py
similarity index 57%
rename from torchtext/prototype/models/t5/__init__.py
rename to torchtext/models/t5/__init__.py
index d23b5b8..9340649 100644
--- a/torchtext/prototype/models/t5/__init__.py
+++ b/torchtext/models/t5/__init__.py
@@ -1,19 +1,31 @@
from .bundler import (
- T5_BASE_ENCODER,
+ FLAN_T5_BASE_ENCODER,
+ FLAN_T5_BASE,
+ FLAN_T5_BASE_GENERATION,
+ FLAN_T5_LARGE_ENCODER,
+ FLAN_T5_LARGE,
+ FLAN_T5_LARGE_GENERATION,
+ FLAN_T5_XL_ENCODER,
+ FLAN_T5_XL,
+ FLAN_T5_XL_GENERATION,
+ FLAN_T5_XXL_ENCODER,
+ FLAN_T5_XXL,
+ FLAN_T5_XXL_GENERATION,
+ T5_11B,
+ T5_11B_ENCODER,
+ T5_11B_GENERATION,
+ T5_3B,
+ T5_3B_ENCODER,
+ T5_3B_GENERATION,
T5_BASE,
+ T5_BASE_ENCODER,
T5_BASE_GENERATION,
- T5_SMALL_ENCODER,
- T5_SMALL,
- T5_SMALL_GENERATION,
- T5_LARGE_ENCODER,
T5_LARGE,
+ T5_LARGE_ENCODER,
T5_LARGE_GENERATION,
- T5_3B_ENCODER,
- T5_3B,
- T5_3B_GENERATION,
- T5_11B_ENCODER,
- T5_11B,
- T5_11B_GENERATION,
+ T5_SMALL,
+ T5_SMALL_ENCODER,
+ T5_SMALL_GENERATION,
T5Bundle,
)
from .model import T5Conf, T5Model
@@ -38,5 +50,17 @@ __all__ = [
"T5_11B_ENCODER",
"T5_11B",
"T5_11B_GENERATION",
+ "FLAN_T5_BASE_ENCODER",
+ "FLAN_T5_BASE",
+ "FLAN_T5_BASE_GENERATION",
+ "FLAN_T5_LARGE_ENCODER",
+ "FLAN_T5_LARGE",
+ "FLAN_T5_LARGE_GENERATION",
+ "FLAN_T5_XL_ENCODER",
+ "FLAN_T5_XL",
+ "FLAN_T5_XL_GENERATION",
+ "FLAN_T5_XXL_ENCODER",
+ "FLAN_T5_XXL",
+ "FLAN_T5_XXL_GENERATION",
"T5Transform",
]
diff --git a/torchtext/models/t5/bundler.py b/torchtext/models/t5/bundler.py
new file mode 100644
index 0000000..f20c13d
--- /dev/null
+++ b/torchtext/models/t5/bundler.py
@@ -0,0 +1,834 @@
+# /* Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# 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. */
+import json
+import logging
+import os
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, Optional, Union
+from urllib.parse import urljoin
+
+import torch
+from torchtext import _TEXT_BUCKET
+from torchtext._download_hooks import load_state_dict_from_url
+
+from .model import T5Conf, T5Model
+from .t5_transform import T5Transform
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class T5Bundle:
+ """T5Bundle(_config: torchtext.prototype.models.T5Conf, _path: Optional[str] = None, transform: Optional[Callable] = None)
+
+ Example - Pretrained base t5 encoder
+ >>> import torch, torchtext
+ >>> t5_encoder_base = torchtext.prototype.models.T5_BASE_ENCODER
+ >>> transform = t5_encoder_base.transform()
+ >>> input_seq = ["Hello world", "Attention rocks!"]
+ >>> model = t5_encoder_base.get_model()
+ >>> model_input = transform(input_seq)
+ >>> output = model(model_input)['encoder_output']
+ >>> output.shape
+ torch.Size([2, 4, 768])
+
+ Example - Pretrained base t5 model
+ >>> import torch, torchtext
+ >>> t5_base = torchtext.prototype.models.T5_BASE
+ >>> transform = t5_base.transform()
+ >>> input_seq = ["Hello world", "Attention rocks!"]
+ >>> model = t5_base.get_model()
+ >>> model_input = transform(input_seq)
+ >>> output = model(model_input)['decoder_output']
+ >>> output.shape
+ torch.Size([2, 1, 768])
+
+ Example - Pretrained base t5 model for generation
+ >>> import torch, torchtext
+ >>> import torch.nn.functional as F
+ >>> t5_base_generation = torchtext.prototype.models.T5_BASE_GENERATION
+ >>> transform = t5_base_generation.transform()
+ >>> input_seq = ["Hello world", "Attention rocks!"]
+ >>> model = t5_base_generation.get_model()
+ >>> model_input = transform(input_seq)
+ >>> output = model(model_input)['decoder_output']
+ >>> logits = F.log_softmax(output[:,-1], dim=-1)
+ >>> logits.shape
+ torch.Size([2, 1, 32128])
+
+ Example - User-specified configuration and checkpoint
+ >>> from torchtext.prototype.models import T5Conf, T5Bundle
+ >>> model_weights_path = "https://download.pytorch.org/models/text/t5.base.encoder.pt"
+ >>> encoder_conf = T5Conf(encoder_only=True)
+ >>> model = T5Bundle.build_model(config=encoder_conf, checkpoint=model_weights_path)
+ """
+
+ _config: T5Conf
+ _path: Optional[str] = None
+ transform: Optional[Callable] = None
+
+ def get_model(
+ self,
+ *,
+ load_weights: bool = True,
+ freeze_model: bool = False,
+ dl_kwargs: Optional[Dict[str, Any]] = None,
+ ) -> T5Model:
+ r"""get_model(load_weights: bool = True, freeze_model: bool = False, *, dl_kwargs=None) -> torctext.prototype.models.T5Model
+
+ Args:
+ load_weights (bool): Indicates whether or not to load weights if available. (Default: `True`)
+ freeze_model (bool): Indicates whether or not to freeze the model weights. (Default: `False`)
+ dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. (Default: `None`)
+ """
+
+ if load_weights:
+ assert (
+ self._path is not None
+ ), "load_weights cannot be True. The pre-trained model weights are not available for the current object"
+
+ if freeze_model:
+ if not load_weights or not self._path:
+ logger.warning(
+ "The model is not loaded with pre-trained weights. Setting freeze_model to True will hinder model from learning appropriate weights."
+ )
+
+ return T5Bundle.build_model(
+ config=self._config,
+ freeze_model=freeze_model,
+ checkpoint=self._path if load_weights else None,
+ strict=True,
+ dl_kwargs=dl_kwargs,
+ )
+
+ @classmethod
+ def build_model(
+ cls,
+ config: T5Conf,
+ *,
+ freeze_model: bool = False,
+ checkpoint: Optional[Union[str, Dict[str, torch.Tensor]]] = None,
+ strict: bool = False,
+ dl_kwargs: Optional[Dict[str, Any]] = None,
+ ) -> T5Model:
+ """Class builder method
+
+ Args:
+ config (T5Conf): An instance of classT5Conf that defined the model configuration
+ freeze_model (bool): Indicates whether to freeze the model weights. (Default: `False`)
+ checkpoint (str or Dict[str, torch.Tensor]): Path to or actual model state_dict. state_dict can have partial weights i.e only for encoder. (Default: ``None``)
+ strict (bool): Passed to :func: `torch.nn.Module.load_state_dict` method. (Default: `False`)
+ dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. (Default: `None`)
+ """
+ model = T5Model(config, freeze_model)
+ if checkpoint is not None:
+ if torch.jit.isinstance(checkpoint, Dict[str, torch.Tensor]):
+ state_dict = checkpoint
+ elif isinstance(checkpoint, str):
+ dl_kwargs = {} if dl_kwargs is None else dl_kwargs
+ state_dict = load_state_dict_from_url(checkpoint, **dl_kwargs)
+ else:
+ raise TypeError(
+ "checkpoint must be of type `str` or `Dict[str, torch.Tensor]` but got {}".format(type(checkpoint))
+ )
+
+ model.load_state_dict(state_dict, strict=strict)
+
+ return model
+
+ @staticmethod
+ def build_model_from_huggingface_ckpt(
+ ckpt_path: Union[str, os.PathLike],
+ encoder_only: bool = False,
+ *,
+ freeze_model: bool = False,
+ strict: bool = True,
+ ) -> T5Model:
+ """Build T5Model model from a HuggingFace checkpoint.
+
+ Note: Only works with Huggingface models saved in the PyTorch format. Will not work with TensorFlow or JAX.
+ This also requires a fully saved model, sharded checkpoints are not supported.
+
+ Args:
+ ckpt_path (str, Path): Path to the HF checkpoint file. Assumes that the file is local.
+ freeze_model (bool): Freeze the model upon loading. (Default: `False`)
+ strict (bool): Load model in strict mode. (Default: `True`)
+
+ Returns:
+ T5Model loaded with the weights of the HuggingFace checkpoint provided
+ """
+ config_path = f"{ckpt_path}/config.json"
+ model_path = f"{ckpt_path}/pytorch_model.bin"
+
+ with open(config_path, "r") as handle:
+ config_json = json.load(handle)
+ hf_weights = torch.load(model_path)
+
+ config = T5Conf(
+ encoder_only=encoder_only,
+ linear_head="lm_head.weight" in hf_weights.keys(),
+ embedding_dim=config_json["d_model"],
+ num_attention_heads=config_json["num_heads"],
+ num_encoder_layers=config_json["num_layers"],
+ num_decoder_layers=config_json["num_decoder_layers"],
+ ffn_dimension=config_json["d_ff"],
+ feed_forward_proj=config_json.get("feed_forward_proj"),
+ )
+
+ t5_model = T5Model(config, freeze_model)
+
+ t5_model_state_dict = {
+ "token_embeddings.weight": hf_weights["shared.weight"],
+ "encoder.token_embeddings.weight": hf_weights["shared.weight"],
+ "encoder.norm.weight": hf_weights["encoder.final_layer_norm.weight"],
+ "encoder.layers.0.self_attn.relative_attention_bias.weight": hf_weights[
+ "encoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
+ ],
+ }
+ # Convert encoder layers
+ for i in range(config.num_encoder_layers):
+ if config.is_gated_act:
+ t5_model_state_dict[f"encoder.layers.{i}.linear1_0.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.1.DenseReluDense.wi_0.weight"
+ ]
+
+ t5_model_state_dict[f"encoder.layers.{i}.linear1_1.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.1.DenseReluDense.wi_1.weight"
+ ]
+ else:
+ t5_model_state_dict[f"encoder.layers.{i}.linear1.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.1.DenseReluDense.wi.weight"
+ ]
+
+ t5_model_state_dict[f"encoder.layers.{i}.linear2.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.1.DenseReluDense.wo.weight"
+ ]
+ t5_model_state_dict[f"encoder.layers.{i}.norm1.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.0.layer_norm.weight"
+ ]
+ t5_model_state_dict[f"encoder.layers.{i}.norm2.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.1.layer_norm.weight"
+ ]
+ t5_model_state_dict[f"encoder.layers.{i}.self_attn.out_proj.weight"] = hf_weights[
+ f"encoder.block.{i}.layer.0.SelfAttention.o.weight"
+ ]
+ t5_model_state_dict[f"encoder.layers.{i}.self_attn.q_proj_weight"] = hf_weights[
+ f"encoder.block.{i}.layer.0.SelfAttention.q.weight"
+ ]
+ t5_model_state_dict[f"encoder.layers.{i}.self_attn.k_proj_weight"] = hf_weights[
+ f"encoder.block.{i}.layer.0.SelfAttention.k.weight"
+ ]
+ t5_model_state_dict[f"encoder.layers.{i}.self_attn.v_proj_weight"] = hf_weights[
+ f"encoder.block.{i}.layer.0.SelfAttention.v.weight"
+ ]
+
+ # Convert decoder layers if model is encoder-decoder
+ if not config.encoder_only:
+ t5_model_state_dict["decoder.norm.weight"] = hf_weights["decoder.final_layer_norm.weight"]
+ t5_model_state_dict["decoder.layers.0.self_attn.relative_attention_bias.weight"] = hf_weights[
+ "decoder.block.0.layer.0.SelfAttention.relative_attention_bias.weight"
+ ]
+
+ for i in range(config.num_decoder_layers):
+ if config.is_gated_act:
+ t5_model_state_dict[f"decoder.layers.{i}.linear1_0.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.2.DenseReluDense.wi_0.weight"
+ ]
+
+ t5_model_state_dict[f"decoder.layers.{i}.linear1_1.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.2.DenseReluDense.wi_1.weight"
+ ]
+ else:
+ t5_model_state_dict[f"decoder.layers.{i}.linear1.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.2.DenseReluDense.wi.weight"
+ ]
+
+ t5_model_state_dict[f"decoder.layers.{i}.linear2.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.2.DenseReluDense.wo.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.norm1.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.0.layer_norm.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.norm2.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.2.layer_norm.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.norm3.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.1.layer_norm.weight"
+ ]
+
+ t5_model_state_dict[f"decoder.layers.{i}.self_attn.out_proj.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.0.SelfAttention.o.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.self_attn.q_proj_weight"] = hf_weights[
+ f"decoder.block.{i}.layer.0.SelfAttention.q.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.self_attn.k_proj_weight"] = hf_weights[
+ f"decoder.block.{i}.layer.0.SelfAttention.k.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.self_attn.v_proj_weight"] = hf_weights[
+ f"decoder.block.{i}.layer.0.SelfAttention.v.weight"
+ ]
+
+ t5_model_state_dict[f"decoder.layers.{i}.cross_attn.out_proj.weight"] = hf_weights[
+ f"decoder.block.{i}.layer.1.EncDecAttention.o.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.cross_attn.q_proj_weight"] = hf_weights[
+ f"decoder.block.{i}.layer.1.EncDecAttention.q.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.cross_attn.k_proj_weight"] = hf_weights[
+ f"decoder.block.{i}.layer.1.EncDecAttention.k.weight"
+ ]
+ t5_model_state_dict[f"decoder.layers.{i}.cross_attn.v_proj_weight"] = hf_weights[
+ f"decoder.block.{i}.layer.1.EncDecAttention.v.weight"
+ ]
+
+ # Convert language modeling head if there is one
+ if config.linear_head:
+ t5_model_state_dict["lm_head.weight"] = hf_weights["lm_head.weight"]
+
+ # Load state dict into our model
+ t5_model.load_state_dict(t5_model_state_dict, strict=False)
+
+ return t5_model
+
+ @property
+ def config(self) -> T5Conf:
+ return self._config
+
+
+ENCODER_DOC = """
+ T5_{}_ENCODER is an encoder-only model from a pre-trained T5 model with the {} configuration.
+ It returns the normalized output from the final layer of the encoder.
+
+ The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
+ <http://jmlr.org/papers/v21/20-074.html>`. It introduces a unified framework that converts text-based
+ language problems, such as translation, question-answering, and summarization, into a text-to-text format. The
+ Colossal Clean Crawled Corpus (C4) dataset is used to pre-train the model on a masked language modeling task,
+ and various datasets are used to fine-tune the model on each downstream task. The model's architecture is a modified version
+ of the canonical Transformer architecture.
+
+ Originally published by the authors of T5 under Apache License, Version 2.0
+ and redistributed with the same license.
+ [`License <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/LICENSE>`__,
+ `Source <https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints>`__]
+
+ Please refer to :func:`torchtext.prototype.models.T5Bundle` for the usage.
+"""
+
+MODEL_DOC = """
+ T5_{} is an encoder-decoder model from a pre-trained T5 model with the {} configuration.
+ It returns the normalized output from the final layer of the decoder.
+
+ The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
+ <http://jmlr.org/papers/v21/20-074.html>`. It introduces a unified framework that converts text-based
+ language problems, such as translation, question-answering, and summarization, into a text-to-text format. The
+ Colossal Clean Crawled Corpus (C4) dataset is used to pre-train the model on a masked language modeling task,
+ and various datasets are used to fine-tune the model on each downstream task. The model's architecture is a modified version
+ of the canonical Transformer architecture.
+
+ Originally published by the authors of T5 under Apache License, Version 2.0
+ and redistributed with the same license.
+ [`License <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/LICENSE>`__,
+ `Source <https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints>`__]
+
+ Please refer to :func:`torchtext.prototype.models.T5Bundle` for the usage.
+ """
+
+GENERATION_DOC = """
+ T5_{}_GENERATION is an encoder-decoder model from a pre-trained T5 model with the {} configuration.
+ It returns the output of the final layer of the decoder after passing through a linear layer to project the hidden states to
+ the model vocabulary. This output can then be used for language generation.
+
+ The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
+ <http://jmlr.org/papers/v21/20-074.html>`. It introduces a unified framework that converts text-based
+ language problems, such as translation, question-answering, and summarization, into a text-to-text format. The
+ Colossal Clean Crawled Corpus (C4) dataset is used to pre-train the model on a masked language modeling task,
+ and various datasets are used to fine-tune the model on each downstream task. The model's architecture is a modified version
+ of the canonical Transformer architecture.
+
+ Originally published by the authors of T5 under Apache License, Version 2.0
+ and redistributed with the same license.
+ [`License <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/LICENSE>`__,
+ `Source <https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints>`__]
+
+ Please refer to :func:`torchtext.prototype.models.T5Bundle` for the usage.
+ """
+
+FLAN_ENCODER_DOC = """
+ FLAN_T5_{}_ENCODER is an encoder model from a pre-trained Flan-T5 model with the {} configuration.
+
+ From the Flan-T5 paper entitled `Scaling Instruction-Finetuned Language Models <https://arxiv.org/pdf/2210.11416.pdf>`:
+ > Finetuning language models on a collection of datasets phrased as instructions has been shown to improve
+ model performance and generalization to unseen tasks. In this paper we explore instruction finetuning
+ with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on
+ chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves
+ performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT),
+ and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation, RealToxicityPrompts).
+ For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PaLM 540B by a large margin
+ (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as
+ 75.2% on five-shot MMLU.
+
+ Originally published by Google under Apache License, Version 2.0 and redistributed with the same license.
+ [`License <https://github.com/google-research/t5x/blob/main/LICENSE>`__,
+ `Source <https://console.cloud.google.com/storage/browser/t5-data/pretrained_models/t5x;tab=objects?pli=1&prefix=&forceOnObjectsSortingFiltering=false>`__]
+
+ Please refer to :func:`torchtext.models.T5Bundle` for the usage.
+"""
+
+FLAN_DOC = """
+ FLAN_T5_{} is an encoder-decoder model from a pre-trained Flan-T5 model with the {} configuration.
+ It returns the normalized output from the final layer of the decoder.
+
+ From the Flan-T5 paper entitled `Scaling Instruction-Finetuned Language Models <https://arxiv.org/pdf/2210.11416.pdf>`:
+ > Finetuning language models on a collection of datasets phrased as instructions has been shown to improve
+ model performance and generalization to unseen tasks. In this paper we explore instruction finetuning
+ with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on
+ chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves
+ performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT),
+ and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation, RealToxicityPrompts).
+ For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PaLM 540B by a large margin
+ (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as
+ 75.2% on five-shot MMLU.
+
+ Originally published by Google under Apache License, Version 2.0 and redistributed with the same license.
+ [`License <https://github.com/google-research/t5x/blob/main/LICENSE>`__,
+ `Source <https://console.cloud.google.com/storage/browser/t5-data/pretrained_models/t5x;tab=objects?pli=1&prefix=&forceOnObjectsSortingFiltering=false>`__]
+
+ Please refer to :func:`torchtext.models.T5Bundle` for the usage.
+"""
+
+FLAN_GENERATION_DOC = """
+ FLAN_T5_{}_GENERATION is an encoder-decoder model with a language modeling head from a pre-trained Flan-T5 model with the {} configuration.
+ It returns the output of the final layer of the decoder after passing through a linear layer to project the hidden states to
+ the model vocabulary. This output can then be used for language generation.
+
+ From the Flan-T5 paper entitled `Scaling Instruction-Finetuned Language Models <https://arxiv.org/pdf/2210.11416.pdf>`:
+ > Finetuning language models on a collection of datasets phrased as instructions has been shown to improve
+ model performance and generalization to unseen tasks. In this paper we explore instruction finetuning
+ with a particular focus on (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on
+ chain-of-thought data. We find that instruction finetuning with the above aspects dramatically improves
+ performance on a variety of model classes (PaLM, T5, U-PaLM), prompting setups (zero-shot, few-shot, CoT),
+ and evaluation benchmarks (MMLU, BBH, TyDiQA, MGSM, open-ended generation, RealToxicityPrompts).
+ For instance, Flan-PaLM 540B instruction-finetuned on 1.8K tasks outperforms PaLM 540B by a large margin
+ (+9.4% on average). Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as
+ 75.2% on five-shot MMLU.
+
+ Originally published by Google under Apache License, Version 2.0 and redistributed with the same license.
+ [`License <https://github.com/google-research/t5x/blob/main/LICENSE>`__,
+ `Source <https://console.cloud.google.com/storage/browser/t5-data/pretrained_models/t5x;tab=objects?pli=1&prefix=&forceOnObjectsSortingFiltering=false>`__]
+
+ Please refer to :func:`torchtext.models.T5Bundle` for the usage.
+"""
+
+
+def t5_transform() -> T5Transform:
+ return T5Transform(
+ urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
+ max_seq_len=512,
+ eos_idx=1,
+ padding_idx=0,
+ )
+
+
+T5_BASE_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.base.encoder.v2.pt"),
+ _config=T5Conf(encoder_only=True),
+ transform=t5_transform,
+)
+
+T5_BASE_ENCODER.__doc__ = ENCODER_DOC.format("BASE", "base")
+
+T5_BASE = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.base.v2.pt"), _config=T5Conf(encoder_only=False), transform=t5_transform
+)
+
+T5_BASE.__doc__ = MODEL_DOC.format("BASE", "base")
+
+T5_BASE_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.base.generation.v2.pt"),
+ _config=T5Conf(encoder_only=False, linear_head=True),
+ transform=t5_transform,
+)
+
+T5_BASE_GENERATION.__doc__ = GENERATION_DOC.format("BASE", "base")
+
+T5_SMALL_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.small.encoder.v2.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=512,
+ num_attention_heads=8,
+ num_encoder_layers=6,
+ num_decoder_layers=6,
+ ffn_dimension=2048,
+ ),
+ transform=t5_transform,
+)
+
+T5_SMALL_ENCODER.__doc__ = ENCODER_DOC.format("SMALL", "small")
+
+
+T5_SMALL = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.small.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=512,
+ num_attention_heads=8,
+ num_encoder_layers=6,
+ num_decoder_layers=6,
+ ffn_dimension=2048,
+ ),
+ transform=t5_transform,
+)
+
+T5_SMALL.__doc__ = MODEL_DOC.format("SMALL", "small")
+
+T5_SMALL_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.small.generation.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=512,
+ num_attention_heads=8,
+ num_encoder_layers=6,
+ num_decoder_layers=6,
+ ffn_dimension=2048,
+ ),
+ transform=t5_transform,
+)
+
+T5_SMALL_GENERATION.__doc__ = GENERATION_DOC.format("SMALL", "small")
+
+T5_LARGE_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.large.encoder.v2.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=1024,
+ num_attention_heads=16,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=4096,
+ ),
+ transform=t5_transform,
+)
+
+T5_LARGE_ENCODER.__doc__ = ENCODER_DOC.format("LARGE", "large")
+
+T5_LARGE = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.large.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=1024,
+ num_attention_heads=16,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=4096,
+ ),
+ transform=t5_transform,
+)
+
+T5_LARGE.__doc__ = MODEL_DOC.format("LARGE", "large")
+
+T5_LARGE_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.large.generation.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=1024,
+ num_attention_heads=16,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=4096,
+ ),
+ transform=t5_transform,
+)
+
+T5_LARGE_GENERATION.__doc__ = GENERATION_DOC.format("LARGE", "large")
+
+T5_3B_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.3b.encoder.v2.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=1024,
+ qkv_dim=128,
+ num_attention_heads=32,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=16384,
+ ),
+ transform=t5_transform,
+)
+
+T5_3B_ENCODER.__doc__ = ENCODER_DOC.format("3B", "3B")
+
+T5_3B = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.3b.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=1024,
+ qkv_dim=128,
+ num_attention_heads=32,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=16384,
+ ),
+ transform=t5_transform,
+)
+
+T5_3B.__doc__ = MODEL_DOC.format("3B", "3B")
+
+T5_3B_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.3b.generation.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=1024,
+ qkv_dim=128,
+ num_attention_heads=32,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=16384,
+ ),
+ transform=t5_transform,
+)
+
+T5_3B_GENERATION.__doc__ = GENERATION_DOC.format("3B", "3B")
+
+T5_11B_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.11b.encoder.v2.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=1024,
+ qkv_dim=128,
+ num_attention_heads=128,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=65536,
+ ),
+ transform=t5_transform,
+)
+
+T5_11B_ENCODER.__doc__ = ENCODER_DOC.format("11B", "11B")
+
+T5_11B = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.11b.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=1024,
+ qkv_dim=128,
+ num_attention_heads=128,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=65536,
+ ),
+ transform=t5_transform,
+)
+
+T5_11B.__doc__ = MODEL_DOC.format("11B", "11B")
+
+T5_11B_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.11b.generation.v2.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=1024,
+ qkv_dim=128,
+ num_attention_heads=128,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=65536,
+ ),
+ transform=t5_transform,
+)
+
+T5_11B_GENERATION.__doc__ = GENERATION_DOC.format("11B", "11B")
+
+FLAN_T5_BASE_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.base.encoder.pt"),
+ _config=T5Conf(encoder_only=True, ffn_dimension=2048, feed_forward_proj="gated-gelu"),
+ transform=t5_transform,
+)
+
+FLAN_T5_BASE_ENCODER.__doc__ = FLAN_ENCODER_DOC.format("BASE", "BASE")
+
+
+FLAN_T5_BASE = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.base.pt"),
+ _config=T5Conf(encoder_only=False, ffn_dimension=2048, feed_forward_proj="gated-gelu"),
+ transform=t5_transform,
+)
+
+FLAN_T5_BASE.__doc__ = FLAN_DOC.format("BASE", "BASE")
+
+
+FLAN_T5_BASE_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.base.generation.pt"),
+ _config=T5Conf(encoder_only=False, linear_head=True, ffn_dimension=2048, feed_forward_proj="gated-gelu"),
+ transform=t5_transform,
+)
+
+FLAN_T5_BASE_GENERATION.__doc__ = FLAN_GENERATION_DOC.format("BASE", "BASE")
+
+
+FLAN_T5_LARGE_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.large.encoder.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=1024,
+ num_attention_heads=16,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=2816,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_LARGE_ENCODER.__doc__ = FLAN_ENCODER_DOC.format("LARGE", "LARGE")
+
+
+FLAN_T5_LARGE = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.large.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=1024,
+ num_attention_heads=16,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=2816,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_BASE.__doc__ = FLAN_DOC.format("LARGE", "LARGE")
+
+
+FLAN_T5_LARGE_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.large.generation.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=1024,
+ num_attention_heads=16,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=2816,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_BASE_GENERATION.__doc__ = FLAN_GENERATION_DOC.format("LARGE", "LARGE")
+
+
+FLAN_T5_XL_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.xl.encoder.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=2048,
+ num_attention_heads=32,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=5120,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_XL_ENCODER.__doc__ = FLAN_ENCODER_DOC.format("XL", "XL")
+
+
+FLAN_T5_XL = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.xl.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=2048,
+ num_attention_heads=32,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=5120,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_XL.__doc__ = FLAN_DOC.format("XL", "XL")
+
+
+FLAN_T5_XL_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.xl.generation.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=2048,
+ num_attention_heads=32,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=5120,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_XL_GENERATION.__doc__ = FLAN_GENERATION_DOC.format("XL", "XL")
+
+
+FLAN_T5_XXL_ENCODER = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.xxl.encoder.pt"),
+ _config=T5Conf(
+ encoder_only=True,
+ embedding_dim=4096,
+ num_attention_heads=64,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=10240,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_XXL_ENCODER.__doc__ = FLAN_ENCODER_DOC.format("XXL", "XXL")
+
+
+FLAN_T5_XXL = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.xxl.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ embedding_dim=4096,
+ num_attention_heads=64,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=10240,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_XXL.__doc__ = FLAN_DOC.format("XXL", "XXL")
+
+
+FLAN_T5_XXL_GENERATION = T5Bundle(
+ _path=urljoin(_TEXT_BUCKET, "t5.flan.xxl.generation.pt"),
+ _config=T5Conf(
+ encoder_only=False,
+ linear_head=True,
+ embedding_dim=4096,
+ num_attention_heads=64,
+ num_encoder_layers=24,
+ num_decoder_layers=24,
+ ffn_dimension=10240,
+ feed_forward_proj="gated-gelu",
+ ),
+ transform=t5_transform,
+)
+
+FLAN_T5_XXL_GENERATION.__doc__ = FLAN_GENERATION_DOC.format("XXL", "XXL")
diff --git a/torchtext/prototype/models/t5/model.py b/torchtext/models/t5/model.py
similarity index 54%
rename from torchtext/prototype/models/t5/model.py
rename to torchtext/models/t5/model.py
index 2812af3..eebc9ce 100644
--- a/torchtext/prototype/models/t5/model.py
+++ b/torchtext/models/t5/model.py
@@ -1,11 +1,22 @@
+# /* Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# 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. */
+import warnings
from dataclasses import dataclass
-from typing import Dict, List, Optional, Union, Callable
+from typing import Callable, Dict, List, Optional, Union
import torch
import torch.nn as nn
from torch import Tensor
-from .modules import T5Encoder, T5Decoder, T5LayerNorm
+from .modules import DECODER_OUTPUTS_TYPE, ENCODER_OUTPUTS_TYPE, PAST_KEY_VALUES_TYPE, T5Decoder, T5Encoder
@dataclass
@@ -27,15 +38,39 @@ class T5Conf:
max_seq_len: int = 512
vocab_size: int = 32128
training: bool = False
+ feed_forward_proj: str = None
+ is_gated_act: bool = False
+
+ def __post_init__(self):
+ """The following is modified from:
+ https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/configuration_t5.py
+
+ Supports T5 1.1 and FLAN-T5.
+ """
+ if self.feed_forward_proj:
+ act_info = self.feed_forward_proj.split("-")
+ self.activation = act_info[-1]
+ self.is_gated_act = act_info[0] == "gated"
+
+ if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2:
+ raise ValueError(
+ f"`feed_forward_proj`: {self.feed_forward_proj} is not a valid activation function of the dense layer."
+ "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
+ "'gated-gelu' or 'relu'"
+ )
+
+ # for backwards compatibility
+ if self.feed_forward_proj == "gated-gelu":
+ self.activation = "gelu_new"
-# NOTE: Comparable HuggingFace implentation can be found at https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L1269
class T5Model(nn.Module):
r"""A T5 model. User is able to modify the attributes as needed. The architecture
is based on the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer".
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena,
Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Journal of Machine Learning Research.
Volume 21 Issue 140 pages 1-67. http://jmlr.org/papers/v21/20-074.html
+
Args:
config.encoder_only: Whether or not model should consist of only the encoder as opposed to encoder-decoder (default=False).
config.linear_head: Whether or not a linear layer should be used to project the output of the decoder's last layer to the vocab (default=False).
@@ -57,8 +92,9 @@ class T5Model(nn.Module):
config.vocab_size: Size of vocabulary (default: 32128)
config.training: Whether or not to apply dropout (default: False)
freeze: Indicates whether or not to freeze the model weights. (default: False)
+
Examples:
- >>> from torchtext.prototype.models import T5Conf, T5Model
+ >>> from torchtext.models import T5Conf, T5Model
>>> t5_config = T5Conf(encoder_only=False, linear_head=True)
>>> t5_model = T5Model(t5_config)
>>> encoder_input = torch.randint(0, t5_config.vocab_size, (32, 512))
@@ -85,7 +121,6 @@ class T5Model(nn.Module):
self.padding_idx = config.padding_idx
self.training = config.training
self.dropout = config.dropout if config.training else 0.0
- self.device = device
self.dtype = dtype
self.token_embeddings = nn.Embedding(config.vocab_size, config.embedding_dim, config.padding_idx)
@@ -100,12 +135,11 @@ class T5Model(nn.Module):
layer_norm_eps=config.layer_norm_eps,
relative_attention_num_buckets=config.relative_attention_num_buckets,
relative_attention_max_distance=config.relative_attention_max_distance,
+ token_embeddings=self.token_embeddings,
+ is_gated_act=config.is_gated_act,
device=device,
dtype=dtype,
)
- self.norm1 = T5LayerNorm(config.embedding_dim)
- self.dropout1 = nn.Dropout(self.dropout)
- self.dropout2 = nn.Dropout(self.dropout)
if not config.encoder_only:
self.decoder = T5Decoder(
@@ -119,12 +153,10 @@ class T5Model(nn.Module):
layer_norm_eps=config.layer_norm_eps,
relative_attention_num_buckets=config.relative_attention_num_buckets,
relative_attention_max_distance=config.relative_attention_max_distance,
+ is_gated_act=config.is_gated_act,
device=device,
dtype=dtype,
)
- self.norm2 = T5LayerNorm(config.embedding_dim)
- self.dropout3 = nn.Dropout(self.dropout)
- self.dropout4 = nn.Dropout(self.dropout)
else:
self.decoder = None
@@ -137,18 +169,75 @@ class T5Model(nn.Module):
for p in self.parameters():
p.requires_grad = False
+ @torch.jit.export
+ def _reorder_cache(self, past: List[PAST_KEY_VALUES_TYPE], beam_idx: Tensor) -> List[PAST_KEY_VALUES_TYPE]:
+ """Reorder past key value pairs in cache. Only relevant in incremental decoding with beam search generation."""
+ # if decoder past is not included in output
+ # speedy decoding is disabled and no need to reorder
+ if past is None:
+ return past
+
+ reordered_decoder_past: List[PAST_KEY_VALUES_TYPE] = []
+ for layer_past_states in past:
+ # get the correct batch idx from layer past batch dim
+ # batch dim of `past` is at 2nd position
+ reordered_layer_past_states = ()
+ for layer_past_state in layer_past_states:
+ # need to set correct `past` for each of the four key / value states
+ reordered_layer_past_states = reordered_layer_past_states + (
+ layer_past_state.index_select(0, beam_idx.to(layer_past_state.device)),
+ )
+
+ assert len(reordered_layer_past_states) == len(layer_past_states)
+
+ reordered_decoder_past.append(reordered_layer_past_states)
+ return reordered_decoder_past
+
+ @torch.jit.export
+ def prepare_inputs_for_generation(
+ self,
+ input_ids: Tensor,
+ encoder_outputs: ENCODER_OUTPUTS_TYPE,
+ past: Optional[List[PAST_KEY_VALUES_TYPE]] = None,
+ return_past_key_values: bool = True,
+ ) -> Dict[str, Union[Tensor, ENCODER_OUTPUTS_TYPE, Optional[List[PAST_KEY_VALUES_TYPE]], bool]]:
+ # Incremental decoding if past key values are provided
+ if past is not None:
+ input_ids = input_ids[:, -1:]
+
+ return {
+ "decoder_tokens": input_ids,
+ "encoder_outputs": encoder_outputs,
+ "past_key_values": past,
+ "return_past_key_values": return_past_key_values,
+ }
+
+ def get_encoder(self) -> T5Encoder:
+ return self.encoder
+
+ def get_decoder(self) -> Optional[T5Decoder]:
+ if self.decoder is None:
+ warnings.warn("Decoder is not set on this model.")
+ return self.decoder
+
def forward(
self,
- encoder_tokens: Tensor,
+ encoder_tokens: Optional[Tensor] = None,
decoder_tokens: Optional[Tensor] = None,
encoder_mask: Optional[Tensor] = None,
decoder_mask: Optional[Tensor] = None,
- ) -> Dict[str, Union[Tensor, List[Tensor], Optional[Tensor], List[Optional[Tensor]]]]:
- r"""Pass the inputs (and mask) through the decoder layer in turn.
+ encoder_padding_mask: Optional[Tensor] = None,
+ decoder_padding_mask: Optional[Tensor] = None,
+ encoder_outputs: Optional[ENCODER_OUTPUTS_TYPE] = None,
+ past_key_values: Optional[List[PAST_KEY_VALUES_TYPE]] = None,
+ return_past_key_values: bool = False,
+ ) -> Union[DECODER_OUTPUTS_TYPE, ENCODER_OUTPUTS_TYPE]:
+ r"""Pass the inputs (and mask) through the T5Encoder/T5Decoder in turn.
+
Args:
encoder_tokens: Tokenized input sequence to the encoder.
Must be batch first with shape (B, Ne) where B is the batch size and Ne is the
- encoder input sequence length. (required).
+ encoder input sequence length. (optional if `encoder_outputs` is provided)
decoder_tokens: Tokenized input sequence to the decoder.
Must be batch first with shape (B, Nd) where B is the batch size and Nd is the
decoder input sequence length. If None and model is encoder-decoder, will initialize decoder
@@ -157,6 +246,14 @@ class T5Model(nn.Module):
Must have shape (Ne, Ne) (optional).
decoder_mask: Self-attention mask for the decoder input sequence.
Must have shape (Nd, Nd) (optional).
+ encoder_padding_mask: Padding mask for encoder input sequence.
+ Must have shape (B, Ne) (optional).
+ decoder_padding_mask: Padding mask for decoder input sequence.
+ Must have shape (B, Nd) (optional).
+ encoder_outputs: Outputs from previous run of T5Encoder. (optional)
+ past_key_values: Previously calculated key values, used in incremental decoding. (optional)
+ return_past_key_values: Boolean indicating whether to return key values to user. (default: False)
+
Returns:
encoder_output: Output Tensor from the final layer of the encoder
encoder_hidden_states: Tuple of output Tensors from each layer of the encoder
@@ -167,48 +264,53 @@ class T5Model(nn.Module):
decoder_position_bias: Tensor of relative attention bias computed for input sequence to decoder
encoder_sa_scores: Tuple of self-attention scores computed at each layer of the decoder
encoder_ca_scores: Tuple of cross-attention scores computed at each layer of the decoder
+ past_key_values: List of Tuples of key values calculated during this run, or None.
"""
- encoder_padding_mask = encoder_tokens.eq(self.padding_idx)
- encoder_embeddings = self.dropout1(self.token_embeddings(encoder_tokens))
- encoder_output, encoder_hidden_states, encoder_position_bias, encoder_sa = self.encoder(
- encoder_embeddings, tgt_mask=encoder_mask, tgt_key_padding_mask=encoder_padding_mask
- )
+ if encoder_outputs is None:
+ assert encoder_tokens is not None, "If `encoder_outputs` is not specified, must provide `encoder_tokens`"
- encoder_output = self.norm1(encoder_output)
- encoder_output = self.dropout2(encoder_output)
- encoder_hidden_states.append(encoder_output)
+ if encoder_padding_mask is None:
+ encoder_padding_mask = encoder_tokens.eq(self.padding_idx).to(device=encoder_tokens.device)
- if not self.encoder_only:
+ encoder_outputs = self.encoder(
+ src=encoder_tokens, mask=encoder_mask, src_key_padding_mask=encoder_padding_mask
+ )
+ if not self.encoder_only:
assert self.decoder is not None
+ assert encoder_outputs is not None
+
+ encoder_output = encoder_outputs.get("encoder_output")
+ assert torch.jit.isinstance(encoder_output, Tensor)
+
+ batch_size = encoder_output.size(0)
+ encoder_output_device = encoder_output.device
# decoder_tokens is None means at start of inference, in which case decoder sequence should begin with padding idx.
if decoder_tokens is None:
- decoder_tokens = torch.ones((encoder_tokens.size(0), 1), dtype=torch.long) * self.padding_idx
-
- if decoder_mask is None:
- assert decoder_tokens is not None and decoder_tokens.dim() == 2
- tgt_len = decoder_tokens.shape[1]
- decoder_mask = torch.triu(torch.ones((tgt_len, tgt_len), dtype=torch.float64), diagonal=1)
- decoder_mask = decoder_mask.to(torch.bool)
+ decoder_tokens = (
+ torch.ones((batch_size, 1), device=encoder_output_device, dtype=torch.long) * self.padding_idx
+ )
- decoder_padding_mask = decoder_tokens.eq(self.padding_idx)
- # T5 implemention uses padding idx to start sequence. Want to ignore this when masking
- decoder_padding_mask[:, 0] = False
+ if decoder_padding_mask is None:
+ decoder_padding_mask = decoder_tokens.eq(self.padding_idx)
+ # T5 implemention uses padding idx to start sequence. Want to ignore this when masking
+ decoder_padding_mask[:, 0] = False
- decoder_embeddings = self.dropout3(self.token_embeddings(decoder_tokens))
- decoder_output, decoder_hidden_states, decoder_position_bias, decoder_sa, decoder_ca = self.decoder(
+ decoder_embeddings = self.token_embeddings(decoder_tokens)
+ decoder_outputs = self.decoder(
decoder_embeddings,
memory=encoder_output,
tgt_mask=decoder_mask,
memory_mask=encoder_mask,
tgt_key_padding_mask=decoder_padding_mask,
memory_key_padding_mask=encoder_padding_mask,
+ past_key_values=past_key_values,
+ return_past_key_values=return_past_key_values,
)
- decoder_output = self.norm2(decoder_output)
- decoder_output = self.dropout4(decoder_output)
- decoder_hidden_states.append(decoder_output)
+ decoder_output = decoder_outputs.get("decoder_output")
+ assert torch.jit.isinstance(decoder_output, Tensor)
if self.linear_head:
assert self.lm_head is not None
@@ -217,28 +319,15 @@ class T5Model(nn.Module):
# See https://github.com/huggingface/transformers/blob/d0acc9537829e7d067edbb791473bbceb2ecf056/src/transformers/models/t5/modeling_t5.py#L1661
decoder_output = decoder_output * (self.embedding_dim ** -0.5)
decoder_output = self.lm_head(decoder_output)
+ decoder_outputs["decoder_output"] = decoder_output
- t5_output = {
- "encoder_output": encoder_output,
- "encoder_hidden_states": encoder_hidden_states,
- "encoder_position_bias": encoder_position_bias,
- "encoder_sa_scores": encoder_sa,
- "decoder_output": decoder_output,
- "decoder_hidden_states": decoder_hidden_states,
- "decoder_position_bias": decoder_position_bias,
- "decoder_sa_scores": decoder_sa,
- "decoder_ca_scores": decoder_ca,
- }
- else:
- t5_output = {
- "encoder_output": encoder_output,
- "encoder_hidden_states": encoder_hidden_states,
- "encoder_position_bias": encoder_position_bias,
- "encoder_sa_scores": encoder_sa,
- }
-
- assert torch.jit.isinstance(
- t5_output, Dict[str, Union[Tensor, List[Tensor], Optional[Tensor], List[Optional[Tensor]]]]
- )
+ # Make TorchScript pick up the correct types
+ encoder_decoder_outputs: DECODER_OUTPUTS_TYPE = {}
+ for key, val in encoder_outputs.items():
+ encoder_decoder_outputs[key] = val
+ for key, val in decoder_outputs.items():
+ encoder_decoder_outputs[key] = val
+
+ return encoder_decoder_outputs
- return t5_output
+ return encoder_outputs
diff --git a/torchtext/prototype/models/t5/modules.py b/torchtext/models/t5/modules.py
similarity index 59%
rename from torchtext/prototype/models/t5/modules.py
rename to torchtext/models/t5/modules.py
index f66036b..2a7ee79 100644
--- a/torchtext/prototype/models/t5/modules.py
+++ b/torchtext/models/t5/modules.py
@@ -9,13 +9,13 @@
# See the License for the specific language governing permissions and
# limitations under the License.
-# Parts of code are originally from
+# Parts of code are originally from the HuggingFace team and can be found here
# https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py
# */
import math
import warnings
-from typing import List, Optional, Tuple, Union, Callable
+from typing import Callable, Dict, List, Optional, Tuple, Union
import torch
import torch.nn as nn
@@ -23,6 +23,18 @@ import torch.nn.functional as F
from torch import Tensor
from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
+# Define reusable types for past_key_values
+PAST_KEY_VALUES_TYPE = Tuple[Tensor, Tensor, Tensor, Tensor]
+PAST_KEY_VALUE_TYPE = Tuple[Tensor, Tensor]
+# If running forward pass in encoder only, there won't be KVs from cross-attention therefore we need a version with optional tensors
+PAST_KEY_VALUES_UNFILLED_TYPE = Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]
+
+# Define reusable types for encoder/decoder outputs
+ENCODER_OUTPUTS_TYPE = Dict[str, Union[Optional[Tensor], List[Tensor], List[Optional[Tensor]]]]
+DECODER_OUTPUTS_TYPE = Dict[
+ str, Union[Optional[Tensor], List[Tensor], List[Optional[Tensor]], List[PAST_KEY_VALUES_UNFILLED_TYPE]]
+]
+
class T5MultiheadAttention(nn.MultiheadAttention):
def __init__(
@@ -39,7 +51,8 @@ class T5MultiheadAttention(nn.MultiheadAttention):
device: Optional[torch.device] = None,
dtype=None,
) -> None:
- r"""
+ r"""T5MultiheadAttention based on `nn.MultiheadAttention`.
+
Args:
embed_dim: Total dimension of the model.
num_heads: Parallel attention heads.
@@ -79,18 +92,18 @@ class T5MultiheadAttention(nn.MultiheadAttention):
query: Tensor,
key: Tensor,
value: Tensor,
+ query_length: Optional[int] = None,
key_padding_mask: Optional[Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[Tensor] = None,
average_attn_weights: bool = False,
position_bias: Optional[Tensor] = None,
- ) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
- r"""
- Allows the model to jointly attend to information from different representation subspaces
- as described in the paper:
- `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.
+ past_key_value: Optional[PAST_KEY_VALUE_TYPE] = None,
+ ) -> Tuple[Tensor, Tensor, Optional[Tensor], PAST_KEY_VALUE_TYPE]:
+ r"""Allows the model to jointly attend to information from different representation subspaces
+ as described in the paper: `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`.
Also incorporates relative attention bias when computing attention scores as descripted in the paper:
- `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/pdf/1910.10683.pdf>`_.
+ `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer <https://arxiv.org/pdf/1910.10683.pdf>`.
Args:
query: Query embeddings of shape :math:`(N, L, E_q)`, where :math:`N` is the batch size, :math:`L` is the target sequence length,
@@ -118,41 +131,48 @@ class T5MultiheadAttention(nn.MultiheadAttention):
heads. Otherwise, `attn_weights` are provided separately per head. Note that this flag only has an
effect when `need_weights=True`. Default: `False` (i.e. average weights across heads)
position_bias: Position bias tensor used if to add relative attention bias to attention scores. Default: `None`
- Outputs:
- - **attn_output** - Attention outputs of shape :math:`(N, L, E)`, where :math:`N` is the batch size,
+
+ Returns:
+ attn_output: Attention outputs of shape :math:`(N, L, E)`, where :math:`N` is the batch size,
:math:`L` is the target sequence length, and :math:`E` is the embedding dimension `embed_dim`.
- - **attn_output_weights** - Only returned when `need_weights=True`. If `average_attn_weights=True`,
+ attn_output_weights: Only returned when `need_weights=True`. If `average_attn_weights=True`,
returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
:math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
:math:`S` is the source sequence length. If `average_weights=False`, returns attention weights per
head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.
- - **position_bias** - Used in attention scoring. Only computed when `compute_relative_attention_bias=True`
+ position_bias: Used in attention scoring. Only computed when `compute_relative_attention_bias=True`
and `position_bias=None`. Has shape :math:`(1, num_heads, L, S)`.
+ key_value: Calculated weights for keys and values. Used for incremental decoding.
"""
- attn_output, position_bias, attn_output_weights = self._t5_multi_head_attention_forward(
+ attn_output, position_bias, attn_output_weights, key_value = self._t5_multi_head_attention_forward(
query,
key,
value,
+ query_length=query_length,
position_bias=position_bias,
key_padding_mask=key_padding_mask,
need_weights=need_weights,
attn_mask=attn_mask,
average_attn_weights=average_attn_weights,
+ past_key_value=past_key_value,
)
- return attn_output, position_bias, attn_output_weights
+ return attn_output, position_bias, attn_output_weights, key_value
- # NOTE: Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4909
def _t5_multi_head_attention_forward(
self,
query: Tensor,
key: Tensor,
value: Tensor,
+ query_length: Optional[int],
position_bias: Optional[Tensor],
key_padding_mask: Optional[Tensor] = None,
need_weights: bool = True,
attn_mask: Optional[Tensor] = None,
average_attn_weights: bool = False,
- ) -> Tuple[Tensor, Tensor, Optional[Tensor]]:
+ past_key_value: Optional[PAST_KEY_VALUE_TYPE] = None,
+ ) -> Tuple[Tensor, Tensor, Optional[Tensor], PAST_KEY_VALUE_TYPE]:
+ """Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4909."""
+ is_self_attention = torch.equal(query, key)
is_batched = F._mha_shape_check(query, key, value, key_padding_mask, attn_mask, self.num_heads)
# For unbatched input, we unsqueeze at the expected batch-dim to pretend that the input
@@ -168,7 +188,15 @@ class T5MultiheadAttention(nn.MultiheadAttention):
# Set up shape vars
bsz, tgt_len, embed_dim = query.shape
- _, src_len, _ = key.shape
+ real_seq_length = tgt_len
+
+ if past_key_value is not None:
+ assert (
+ len(past_key_value) == 2
+ ), f"past_key_value should have 2 past states: keys and values. Got { len(past_key_value)} past states"
+ real_seq_length += past_key_value[0].shape[2] if query_length is None else query_length
+
+ src_len = real_seq_length if is_self_attention else key.shape[1]
assert (
embed_dim == self.embed_dim
@@ -187,10 +215,34 @@ class T5MultiheadAttention(nn.MultiheadAttention):
b_q = b_k = b_v = None
else:
b_q, b_k, b_v = self.in_proj_bias.chunk(3)
+
q, k, v = self._t5_in_projection(
- query, key, value, self.q_proj_weight, self.k_proj_weight, self.v_proj_weight, b_q, b_k, b_v
+ query,
+ key,
+ value,
+ bsz,
+ head_dim,
+ self.q_proj_weight,
+ self.k_proj_weight,
+ self.v_proj_weight,
+ b_q,
+ b_k,
+ b_v,
+ is_self_attention,
+ past_key_value,
)
+ if attn_mask is None:
+ if self.is_decoder:
+ if is_self_attention:
+ attn_mask = torch.triu(
+ torch.ones((tgt_len, tgt_len), dtype=torch.bool, device=query.device), diagonal=1
+ )
+ else:
+ attn_mask = torch.zeros((tgt_len, src_len), device=query.device)
+ else:
+ attn_mask = torch.zeros((src_len, src_len), device=query.device, dtype=torch.bool)
+
# Prep attention mask
if attn_mask is not None:
if attn_mask.dtype == torch.uint8:
@@ -200,14 +252,9 @@ class T5MultiheadAttention(nn.MultiheadAttention):
assert (
attn_mask.is_floating_point() or attn_mask.dtype == torch.bool
), f"Only float and bool types are supported for attn_mask, not {attn_mask.dtype}"
- # Ensure attn_mask's dim is 3
if attn_mask.dim() == 2:
- correct_2d_size = (tgt_len, src_len)
- if attn_mask.shape != correct_2d_size:
- raise RuntimeError(
- f"The shape of the 2D attn_mask is {attn_mask.shape}, but should be {correct_2d_size}."
- )
- attn_mask = attn_mask.view(1, 1, tgt_len, tgt_len).expand(bsz, self.num_heads, -1, -1)
+ x, y = attn_mask.shape
+ attn_mask = attn_mask.view(1, 1, x, y).expand(bsz, self.num_heads, -1, -1)
else:
raise RuntimeError(f"attn_mask's dimension {attn_mask.dim()} is not supported")
@@ -218,11 +265,21 @@ class T5MultiheadAttention(nn.MultiheadAttention):
# Reshape q, k, v for multihead attention and make them batch first
q = q.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
- k = k.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
- v = v.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
+ if past_key_value is None:
+ k = k.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
+ v = v.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
+
+ # Have to check this after resize
src_len = k.size(2)
if key_padding_mask is not None:
+ if key_padding_mask.shape != (bsz, src_len):
+ # It's possible that padding mask only takes into acct curr tgt_length instead of past_key_value
+ assert (
+ past_key_value is not None
+ ), "Must provide past_key_value if key_padding_mask needs to be expanded."
+ key_padding_mask = key_padding_mask.expand(bsz, src_len)
+
assert key_padding_mask.shape == (
bsz,
src_len,
@@ -237,9 +294,8 @@ class T5MultiheadAttention(nn.MultiheadAttention):
# Convert mask to float
if attn_mask is not None and attn_mask.dtype == torch.bool:
- new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
- new_attn_mask.masked_fill_(attn_mask, float("-inf"))
- attn_mask = new_attn_mask
+ tmp_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
+ attn_mask = tmp_attn_mask.masked_fill(attn_mask, float("-inf"))
# Adjust dropout probability
if not self.training:
@@ -247,22 +303,26 @@ class T5MultiheadAttention(nn.MultiheadAttention):
else:
dropout_p = self.dropout
- # NOTE: Modification to torch.nn.functional._multi_head_attention_forward to incorporate relative attention bias
+ # Modification to torch.nn.functional._multi_head_attention_forward to incorporate relative attention bias
if position_bias is None:
if not self.compute_relative_attention_bias:
position_bias = torch.zeros(
- (self.num_heads, tgt_len, src_len), device=k.device, dtype=k.dtype
- ).unsqueeze(0)
+ (1, self.num_heads, real_seq_length, src_len), device=k.device, dtype=k.dtype
+ )
else:
position_bias = self._compute_bias(
- tgt_len,
- src_len,
- bidirectional=(not self.is_decoder),
- device=k.device,
+ real_seq_length, src_len, bidirectional=(not self.is_decoder), device=k.device
)
+ if past_key_value is not None:
+ position_bias = position_bias[:, :, -query.size(1) :, :]
+
+ # Always return KV pair; let user discard if they don't want it
+ new_key_val = (k, v)
+
# Calculate attention and out projection
attn_output, attn_output_weights = self._t5_dot_product_attention(q, k, v, position_bias, attn_mask, dropout_p)
+
attn_output = F.linear(attn_output, self.out_proj.weight, self.out_proj.bias)
if need_weights:
@@ -275,37 +335,43 @@ class T5MultiheadAttention(nn.MultiheadAttention):
attn_output = attn_output.squeeze(1)
attn_output_weights = attn_output_weights.squeeze(0)
- return attn_output, position_bias, attn_output_weights
+ return attn_output, position_bias, attn_output_weights, new_key_val
else:
if not is_batched:
# Squeeze the output if input was unbatched
attn_output = attn_output.squeeze(1)
- return attn_output, position_bias, None
+ return attn_output, position_bias, None, new_key_val
- # NOTE: Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4761
def _t5_in_projection(
self,
- q: Tensor,
- k: Tensor,
- v: Tensor,
+ query: Tensor,
+ key: Tensor,
+ value: Tensor,
+ bsz: int,
+ head_dim: int,
w_q: Tensor,
w_k: Tensor,
w_v: Tensor,
b_q: Optional[Tensor] = None,
b_k: Optional[Tensor] = None,
b_v: Optional[Tensor] = None,
+ is_self_attention: bool = True,
+ past_key_value: Optional[PAST_KEY_VALUE_TYPE] = None,
) -> Tuple[Tensor, Tensor, Tensor]:
- r"""
- Performs the in-projection step of the attention operation. This is simply
+ r"""Performs the in-projection step of the attention operation. This is simply
a triple of linear projections, with shape constraints on the weights which
ensure embedding dimension uniformity in the projected outputs.
Output is a triple containing projection tensors for query, key and value.
+
+ Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4761.
+
Args:
q, k, v: query, key and value tensors to be projected.
w_q, w_k, w_v: weights for q, k and v, respectively.
b_q, b_k, b_v: optional biases for q, k and v, respectively.
+
Shape:
Inputs:
- q: :math:`(Qdims..., Eq)` where Eq is the query embedding dimension and Qdims are any
@@ -326,7 +392,7 @@ class T5MultiheadAttention(nn.MultiheadAttention):
- k': :math:`[Kdims..., Ei]`
- v': :math:`[Vdims..., Ei]`
"""
- Eq, Ek, Ev = q.size(-1), k.size(-1), v.size(-1)
+ Eq, Ek, Ev = query.size(-1), key.size(-1), value.size(-1)
assert w_q.shape == (
self.inner_dim,
Eq,
@@ -348,9 +414,39 @@ class T5MultiheadAttention(nn.MultiheadAttention):
assert b_v is None or b_v.shape == (
self.inner_dim,
), f"expecting value bias shape of {(self.inner_dim,)}, but got {b_v.shape}"
- return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
+ query_proj = F.linear(query, w_q, b_q)
+
+ if is_self_attention:
+ # Self-attention over query (hidden states)
+ key_proj = F.linear(query, w_k, b_k)
+ value_proj = F.linear(query, w_v, b_v)
+ else:
+ if past_key_value is None:
+ # Cross-attention (over current key/val states)
+ key_proj = F.linear(key, w_k, b_k)
+ value_proj = F.linear(value, w_v, b_v)
+ else:
+ # Should never reach this branch
+ key_proj = key
+ value_proj = value
+
+ if past_key_value is not None:
+ if is_self_attention:
+ # Concat old key vals w/ new calculated ones for speed in decoding
+ key_proj = key_proj.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
+ value_proj = value_proj.view(bsz, -1, self.num_heads, head_dim).transpose(1, 2)
+ key_proj = torch.cat([past_key_value[0], key_proj], dim=2)
+ value_proj = torch.cat([past_key_value[1], value_proj], dim=2)
+ else:
+ # Cross-attention context
+ key_proj = past_key_value[0]
+ value_proj = past_key_value[1]
+
+ assert key_proj is not None
+ assert value_proj is not None
+
+ return query_proj, key_proj, value_proj
- # NOTE: Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4814
def _t5_dot_product_attention(
self,
q: Tensor,
@@ -360,17 +456,19 @@ class T5MultiheadAttention(nn.MultiheadAttention):
attn_mask: Optional[Tensor] = None,
dropout_p: float = 0.0,
) -> Tuple[Tensor, Tensor]:
- r"""
- Computes scaled dot product attention on query, key and value tensors, using
+ r"""Computes scaled dot product attention on query, key and value tensors, using
an optional attention mask if passed, and applying dropout if a probability
greater than 0.0 is specified.
- Returns a tensor pair containing attended values and attention weights.
+
+ Modified from https://github.com/pytorch/pytorch/blob/5953fd9133c0bdcc0158acf1472fac403bc5f636/torch/nn/functional.py#L4814.
+
Args:
q, k, v: Query, key and value tensors. See Shape section for shape details.
attn_mask: Optional tensor containing mask values to be added to calculated
attention. May be 2D or 3D; see Shape section for details.
dropout_p: Dropout probability. If greater than 0.0, dropout is applied.
position_bias: Position bias used to incorporate realtive attention bias in attention scors
+
Shape:
- q: :math:`(B, H, Nt, E)` where B is the batch size, H is the number of heads, Nt is the target sequence length,
and E is the head dimension.
@@ -382,9 +480,12 @@ class T5MultiheadAttention(nn.MultiheadAttention):
- position_bias: :math:`(1, H, Nt, Ns)`
- Output: attention values have shape :math:`(B, Nt, H*E)`; attention weights
have shape :math:`(B, H, Nt, Ns)`
+
+ Returns:
+ Tensor pair containing attended values and attention weights.
"""
B, H, _, E = q.shape
- # NOTE: HF implementation does not perform this normalization. For the sake of matching test results, we have commented it out
+ # HF implementation does not perform this normalization. For the sake of matching test results, we have commented it out
# q = q / math.sqrt(E)
attn = torch.matmul(q, k.transpose(3, 2))
@@ -393,27 +494,25 @@ class T5MultiheadAttention(nn.MultiheadAttention):
position_bias = position_bias.repeat(B, 1, 1, 1)
if attn_mask is not None:
position_bias += attn_mask
- attn += position_bias
+ attn += position_bias
attn = F.softmax(attn, dim=-1)
+
if dropout_p > 0.0:
attn = F.dropout(attn, p=dropout_p)
+
output = torch.matmul(attn, v)
output = output.transpose(1, 2).contiguous().view(B, -1, H * E)
return output, attn
- # NOTE: modified from https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L421
def _compute_bias(
- self,
- query_length: int,
- key_length: int,
- bidirectional: bool = True,
- device: Optional[torch.device] = None,
+ self, query_length: int, key_length: int, bidirectional: bool = True, device: Optional[torch.device] = None
) -> Tensor:
- """Compute binned relative position bias"""
+ """Compute binned relative position bias.
+
+ Modified from https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L421.
+ """
assert self.relative_attention_bias is not None
- if device is None:
- device = self.relative_attention_bias.weight.device
context_position = torch.arange(query_length, dtype=torch.long, device=device)[:, None]
memory_position = torch.arange(key_length, dtype=torch.long, device=device)[None, :]
relative_position = memory_position - context_position # shape (query_length, key_length)
@@ -427,28 +526,30 @@ class T5MultiheadAttention(nn.MultiheadAttention):
values = values.permute([2, 0, 1]).unsqueeze(0) # shape (1, num_heads, query_length, key_length)
return values
- # NOTE: Taken from https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L374
def _relative_position_bucket(
self, relative_position: Tensor, bidirectional: bool = True, num_buckets: int = 32, max_distance: int = 128
) -> Tensor:
- """
- Adapted from Mesh Tensorflow:
+ r"""Adapted from Mesh Tensorflow:
https://github.com/tensorflow/mesh/blob/0cb87fe07da627bf0b7e60475d59f95ed6b5be3d/mesh_tensorflow/transformer/transformer_layers.py#L593
- Translate relative position to a bucket number for relative attention. The relative position is defined as
+ and https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L374.
+
+ Translates relative position to a bucket number for relative attention. The relative position is defined as
memory_position - query_position, i.e. the distance in tokens from the attending position to the attended-to
position. If bidirectional=False, then positive relative positions are invalid. We use smaller buckets for
small absolute relative_position and larger buckets for larger absolute relative_positions. All relative
positions >=max_distance map to the same bucket. All relative positions <=-max_distance map to the same bucket.
- This should allow for more graceful generalization to longer sequences than the model has been trained on
+ This should allow for more graceful generalization to longer sequences than the model has been trained on.
+
Args:
- relative_position: an int32 Tensor
- bidirectional: a boolean - whether the attention is bidirectional
- num_buckets: an integer
- max_distance: an integer
+ relative_position: Tensor w/ initially constructed relative positions.
+ bidirectional: If attention is bidirectional; when in decoder, this should be False.
+ num_buckets: Number of buckets to utilize.
+ max_distance: Maximum distance between positions.
+
Returns:
- a Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets)
+ Tensor with the same shape as relative_position, containing int32 values in the range [0, num_buckets).
"""
- relative_buckets = torch.zeros(relative_position.shape, dtype=torch.long)
+ relative_buckets = torch.zeros(relative_position.shape, dtype=torch.long, device=relative_position.device)
if bidirectional:
num_buckets = num_buckets // 2
relative_buckets += (relative_position > 0).to(torch.long) * num_buckets
@@ -475,26 +576,27 @@ class T5MultiheadAttention(nn.MultiheadAttention):
return relative_buckets
-# NOTE: Taken from https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L239
class T5LayerNorm(nn.Module):
def __init__(self, d_model: int, eps: float = 1e-6) -> None:
- """
- Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
+ r"""Construct a layernorm module in the T5 style. No bias and no subtraction of mean.
+
+ Based on https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L239.
"""
super().__init__()
self.weight = nn.Parameter(torch.ones(d_model))
self.variance_epsilon = eps
def forward(self, hidden_states: Tensor) -> Tensor:
- r"""
- T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
+ r"""T5 uses a layer_norm which only scales and doesn't shift, which is also known as Root Mean
Square Layer Normalization https://arxiv.org/abs/1910.07467 thus varience is calculated
w/o mean and there is no bias. Additionally we want to make sure that the accumulation for
half-precision inputs is done in fp32.
+
Args:
- hidden_states: Tensor to be normalized. Final dimension must be model dimension (i.e. number of expected features in the input)
+ hidden_states: Tensor to be normalized. Final dimension must be model dimension (i.e. number of expected features in the input).
+
Returns:
- a Tensor with the same shape as hidden_states after having been normalized
+ Tensor with the same shape as hidden_states after having been normalized.
"""
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
@@ -507,35 +609,46 @@ class T5LayerNorm(nn.Module):
return self.weight * hidden_states
-# NOTE: Comparable HuggingFace implentation can be found at https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L622
-class T5EncoderLayer(nn.Module):
- r"""T5EncoderLayer is made up of a self-attn block and feedforward network.
+class T5Layer(nn.Module):
+ r"""T5Layer is made up of a self-attn block, optional cross-attn block, and feed-forward network.
+
This T5 layer is based on the paper:
"Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer".
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena,
Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Journal of Machine Learning Research.
Volume 21 Issue 140 pages 1-67. http://jmlr.org/papers/v21/20-074.html
Users may modify or implement in a different way during application.
+
Args:
d_model: Number of expected features in the input (required).
nhead: Number of heads in the multihead attention models (required).
- dim_feedforward: Dimension of the feedforward network model (default=3072).
- qkv_dim: Projection dimension (per head) for query, keys, and values. (defualt=64).
- dropout: Dropout value (default=0.1).
+ dim_feedforward: Dimension of the feedforward network model (default: 3072).
+ qkv_dim: Projection dimension (per head) for query, keys, and values. (default: 64).
+ dropout: Dropout value (default: 0.1).
activation: Activation function of the intermediate layer, can be a string
- ("relu" or "gelu") or a unary callable. (default: relu)
- layer_norm_eps: The eps value in layer normalization components (default=1e-6).
- relative_attention_num_buckets: Number of relative position buckets (default: 32)
+ ("relu", "gelu", or "gelu_new") or a unary callable. (default: F.relu)
+ is_gated_act: Option to include gated activated as done in FLAN-T5, see
+ https://huggingface.co/google/flan-t5-xxl. (default: False)
+ layer_norm_eps: The eps value in layer normalization components. (default=1e-6)
+ relative_attention_num_buckets: Number of relative position buckets. (default: 32)
relative_attention_max_distance: Maximum threshold on the relative distance used to
- allocate buckets. Anything larger gets placed in the same bucket (default: 128)
+ allocate buckets. Anything larger gets placed in the same bucket. (default: 128)
compute_relative_attention_bias: Whether or not the relative position embeddings
need to be computed. Typically occurs in the first layer of the encoder
and resulting position embeddings are returned to be passed up to higher layers. (default: False)
+ is_decoder: Whether the T5Layer will be instantiated as a decoder layer or encoder layer. (default: False)
+ device: Device to use any newly constructed Tensors. (optional)
+ dtype: Datatype to use on any newly constructed Tensors. (optional)
Examples::
- >>> encoder_layer = T5EncoderLayer(d_model=768, nhead=12)
- >>> tgt = torch.rand(32, 20, 768)
- >>> out = encoder_layer(tgt)
+ >>> single_encoder_layer = T5Layer(d_model=768, nhead=12)
+ >>> src = torch.rand(32, 20, 768)
+ >>> single_encoder_layer(src)
+
+ >>> single_decoder_layer = T5Layer(d_model=768, nhead=12, is_decoder=True)
+ >>> src = torch.rand(32, 20, 768)
+ >>> tgt = torch.rand(32, 1, 768)
+ >>> single_decoder_layer(tgt, src)
"""
def __init__(
@@ -546,23 +659,26 @@ class T5EncoderLayer(nn.Module):
qkv_dim: int = 64,
dropout: float = 0.1,
activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+ is_gated_act: bool = False,
layer_norm_eps: float = 1e-6,
relative_attention_num_buckets: int = 32,
relative_attention_max_distance: int = 128,
compute_relative_attention_bias: bool = False,
+ is_decoder: bool = False,
device: Optional[torch.device] = None,
dtype=None,
) -> None:
super().__init__()
-
+ self.is_gated_act = is_gated_act
self.compute_relative_attention_bias = compute_relative_attention_bias
self.relative_attention_num_buckets = relative_attention_num_buckets
self.relative_attention_max_distance = relative_attention_max_distance
+ self.is_decoder = is_decoder
self.self_attn = T5MultiheadAttention(
d_model,
nhead,
- is_decoder=False,
+ is_decoder=is_decoder,
dropout=dropout,
qkv_dim=qkv_dim,
compute_relative_attention_bias=compute_relative_attention_bias,
@@ -571,7 +687,36 @@ class T5EncoderLayer(nn.Module):
device=device,
dtype=dtype,
)
- self.linear1 = nn.Linear(d_model, dim_feedforward, bias=False)
+
+ if self.is_decoder:
+ self.cross_attn = T5MultiheadAttention(
+ d_model,
+ nhead,
+ is_decoder=True,
+ dropout=dropout,
+ qkv_dim=qkv_dim,
+ compute_relative_attention_bias=False,
+ relative_attention_num_buckets=relative_attention_num_buckets,
+ relative_attention_max_distance=relative_attention_max_distance,
+ device=device,
+ dtype=dtype,
+ )
+ self.norm3 = T5LayerNorm(d_model, eps=layer_norm_eps)
+ self.dropout4 = nn.Dropout(dropout)
+ else:
+ self.cross_attn = None
+ self.norm3 = None
+ self.dropout4 = None
+
+ if self.is_gated_act:
+ self.linear1 = None
+ self.linear1_0 = nn.Linear(d_model, dim_feedforward, bias=False)
+ self.linear1_1 = nn.Linear(d_model, dim_feedforward, bias=False)
+ else:
+ self.linear1 = nn.Linear(d_model, dim_feedforward, bias=False)
+ self.linear1_0 = None
+ self.linear1_1 = None
+
self.linear2 = nn.Linear(dim_feedforward, d_model, bias=False)
self.norm1 = T5LayerNorm(d_model, eps=layer_norm_eps)
self.norm2 = T5LayerNorm(d_model, eps=layer_norm_eps)
@@ -583,51 +728,104 @@ class T5EncoderLayer(nn.Module):
assert activation in (
"relu",
"gelu",
- ), f"Do not support '{activation}' activation. Use either 'relu' or 'gelu'"
+ "gelu_new",
+ ), f"Do not support '{activation}' activation. Use 'relu' or 'gelu' or 'gelu_new'"
if activation == "relu":
self.activation = F.relu
elif activation == "gelu":
self.activation = F.gelu
+ elif activation == "gelu_new":
+ # The following should match the math of https://github.com/huggingface/transformers/blob/main/src/transformers/activations.py
+ self.activation = nn.GELU(approximate="tanh")
else:
self.activation = activation
def forward(
self,
- tgt: Tensor,
- tgt_mask: Optional[Tensor] = None,
- tgt_key_padding_mask: Optional[Tensor] = None,
+ seq: Tensor,
+ memory: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ seq_key_padding_mask: Optional[Tensor] = None,
+ memory_mask: Optional[Tensor] = None,
+ memory_key_padding_mask: Optional[Tensor] = None,
position_bias: Optional[Tensor] = None,
- ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
+ past_key_values: Optional[PAST_KEY_VALUES_TYPE] = None,
+ ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor], PAST_KEY_VALUES_UNFILLED_TYPE]:
r"""Pass the inputs (and mask) through the encoder layer.
+
Args:
- tgt: Input sequence to the encoder layer. (required).
- Must have shape (B, Nt, E) where B is the batch size, Nt is the target sequence
- length, and E is the model dimension.
- tgt_mask: Attention mask for self-attention. (optional).
- Must have shape (Nt, Nt).
- tgt_key_padding_mask: Mask for the tgt keys per batch (optional).
- Must have shape (B, Nt).
+ seq: Input sequence (required).
+ Must have shape (B, Ns, E) where B is the batch size, Nt is the sequence length,
+ and E is the model dimension. This will be the src sequence if `self.is_decoder = False`
+ and tgt sequence if `self.is_decoder = True`.
+ memory: Encoder sequence (optional).
+ Output from encoder layer, only needs to be included when in decoding context.
+ mask: Attention mask for self-attention. (optional).
+ Must have shape (Ns, Ns).
+ seq_key_padding_mask: Mask for the seq keys per batch (optional).
+ Must have shape (B, Ns).
+ memory_mask: Attention mask for attention in decoding context. (optional)
+ Must have shape (Nm, Nm).
+ memory_key_padding_mask: Mask for the memory keys per batch (optional).
+ Must have shape (B, Ns).
position_bias: Relative attention bias to be used when computing self-attention scores (optional)
- Must have shape (B, H, Nt, Nt) where H is the number of heads.
- """
+ Must have shape (B, H, Ns, Ns) where H is the number of heads.
+ past_key_values: Past key values used for incremental decoding (optional).
+ Tuple with Tensors of shape (B, H, N)>>>>> Check this????
+ Returns:
+ Tuple of Tensors being hidden states, position bias, self-attention scores, cross-attention scores,
+ and key-value pairs.
+ """
# See Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
- x = tgt
- sa_out, position_bias, sa_scores = self._sa_block(self.norm1(x), tgt_mask, tgt_key_padding_mask, position_bias)
+ if past_key_values is not None:
+ self_attn_past_key_value = past_key_values[:2]
+ cross_attn_past_key_value = past_key_values[2:]
+ else:
+ self_attn_past_key_value, cross_attn_past_key_value = None, None
+
+ x = seq
+ sa_out, position_bias, sa_scores, sa_kv = self._sa_block(
+ self.norm1(x), mask, seq_key_padding_mask, position_bias, self_attn_past_key_value
+ )
x = x + sa_out
+
+ if self.is_decoder:
+ assert memory is not None, "Must provide memory (encoder hidden states)."
+ assert self.norm3 is not None
+ query_length = sa_kv[0].shape[2]
+ ca_out, ca_scores, ca_kv = self._ca_block(
+ self.norm3(x), memory, query_length, memory_mask, memory_key_padding_mask, cross_attn_past_key_value
+ )
+ x = x + ca_out
+ else:
+ ca_scores, ca_kv = None, None
+
x = x + self._ff_block(self.norm2(x))
- return x, position_bias, sa_scores
+ new_key_value = sa_kv + (
+ ca_kv
+ if ca_kv is not None
+ else (
+ None,
+ None,
+ )
+ )
+
+ assert torch.jit.isinstance(new_key_value, PAST_KEY_VALUES_UNFILLED_TYPE)
+
+ return x, position_bias, sa_scores, ca_scores, new_key_value
- # Self-attention block
def _sa_block(
self,
x: Tensor,
attn_mask: Optional[Tensor],
key_padding_mask: Optional[Tensor],
position_bias: Optional[Tensor],
- ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor]]:
- attn = self.self_attn(
+ past_key_value: Optional[PAST_KEY_VALUE_TYPE] = None,
+ ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor], PAST_KEY_VALUE_TYPE]:
+ """Self-attention block."""
+ attn, curr_position_bias, scores, curr_key_value = self.self_attn(
x,
x,
x,
@@ -635,154 +833,57 @@ class T5EncoderLayer(nn.Module):
key_padding_mask=key_padding_mask,
need_weights=True,
position_bias=position_bias,
+ past_key_value=past_key_value,
)
- x = attn[0]
- scores = attn[2]
- if self.compute_relative_attention_bias and position_bias is None:
- position_bias = attn[1]
+ if self.compute_relative_attention_bias:
+ position_bias = curr_position_bias
- return self.dropout1(x), position_bias, scores
+ return self.dropout1(attn), position_bias, scores, curr_key_value
- # Feed forward block
- def _ff_block(self, x: Tensor) -> Tensor:
- x = self.linear2(self.dropout2(self.activation(self.linear1(x))))
- return self.dropout3(x)
-
-
-# NOTE: Comparable HuggingFace implentation can be found at https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L622
-class T5DecoderLayer(T5EncoderLayer):
- r"""T5DecoderLayer is made up of a self-attn block, cross-attn block, and feedforward network.
- This T5 layer is based on the paper:
- "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer".
- Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena,
- Yanqi Zhou, Wei Li, and Peter J. Liu. 2020. Journal of Machine Learning Research.
- Volume 21 Issue 140 pages 1-67. http://jmlr.org/papers/v21/20-074.html
- Users may modify or implement in a different way during application.
- Args:
- d_model: Number of expected features in the input (required).
- nhead: Number of heads in the multihead attention models (required).
- dim_feedforward: Dimension of the feedforward network model (default=3072).
- qkv_dim: Projection dimension (per head) for query, keys, and values. (defualt=64).
- dropout: Dropout value (default=0.1).
- activation: Activation function of the intermediate layer, can be a string
- ("relu" or "gelu") or a unary callable. (default: relu)
- layer_norm_eps: The eps value in layer normalization components (default=1e-6).
- relative_attention_num_buckets: Number of relative position buckets (default: 32)
- relative_attention_max_distance: Maximum threshold on the relative distance used to
- allocate buckets. Anything larger gets placed in the same bucket (default: 128)
- compute_relative_attention_bias: Whether or not the relative position embeddings
- need to be computed. Typically occurs in the first layer of the decoder
- and resulting position embeddings are returned to be passed up to higher layers. (default: False)
-
- Examples::
- >>> decoder_layer = T5DecoderLayer(d_model=768, nhead=12)
- >>> memory = torch.rand(32, 10, 768)
- >>> tgt = torch.rand(32, 20, 768)
- >>> out = decoder_layer(tgt, memory)
- """
-
- def __init__(
+ def _ca_block(
self,
- d_model: int,
- nhead: int,
- dim_feedforward: int = 3072,
- qkv_dim: int = 64,
- dropout: float = 0.1,
- activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
- layer_norm_eps: float = 1e-6,
- relative_attention_num_buckets: int = 32,
- relative_attention_max_distance: int = 128,
- compute_relative_attention_bias: bool = False,
- device: Optional[torch.device] = None,
- dtype=None,
- ) -> None:
- super().__init__(
- d_model,
- nhead,
- dim_feedforward,
- qkv_dim,
- dropout,
- activation,
- layer_norm_eps,
- relative_attention_num_buckets,
- relative_attention_max_distance,
- compute_relative_attention_bias,
- device,
- dtype,
- )
-
- self.cross_attn = T5MultiheadAttention(
- d_model, nhead, is_decoder=True, dropout=dropout, qkv_dim=qkv_dim, device=device, dtype=dtype
+ x: Tensor,
+ mem: Tensor,
+ query_length: Optional[int],
+ attn_mask: Optional[Tensor],
+ key_padding_mask: Optional[Tensor],
+ past_key_value: Optional[PAST_KEY_VALUE_TYPE] = None,
+ ) -> Tuple[Tensor, Optional[Tensor], PAST_KEY_VALUE_TYPE]:
+ """Cross-attention block."""
+ assert self.cross_attn is not None
+ assert self.dropout4 is not None
+ attn, _, scores, curr_key_value = self.cross_attn(
+ x,
+ mem, # Pass in memory (enc) states as keys
+ mem, # Pass in memory (enc) states as values
+ query_length=query_length,
+ attn_mask=attn_mask,
+ key_padding_mask=key_padding_mask,
+ need_weights=True,
+ past_key_value=past_key_value,
)
- self.norm3 = T5LayerNorm(d_model, eps=layer_norm_eps)
- self.dropout4 = nn.Dropout(dropout)
+ return self.dropout4(attn), scores, curr_key_value
- if isinstance(activation, str):
- assert activation in (
- "relu",
- "gelu",
- ), f"Do not support '{activation}' activation. Use either 'relu' or 'gelu'"
- if activation == "relu":
- self.activation = F.relu
- elif activation == "gelu":
- self.activation = F.gelu
+ def _ff_block(self, x: Tensor) -> Tensor:
+ """Feed-forward block."""
+ if self.is_gated_act:
+ assert self.linear1_0 is not None
+ assert self.linear1_1 is not None
+ wi_0 = self.activation(self.linear1_0(x))
+ wi_1 = self.linear1_1(x)
+ hidden_states = wi_0 * wi_1
+ hidden_states = self.dropout2(hidden_states)
+ hidden_states = self.linear2(hidden_states)
else:
- self.activation = activation
+ assert self.linear1 is not None
+ hidden_states = self.linear2(self.dropout2(self.activation(self.linear1(x))))
+ return self.dropout3(hidden_states)
- def forward(
- self,
- tgt: Tensor,
- memory: Tensor,
- tgt_mask: Optional[Tensor] = None,
- memory_mask: Optional[Tensor] = None,
- tgt_key_padding_mask: Optional[Tensor] = None,
- memory_key_padding_mask: Optional[Tensor] = None,
- position_bias: Optional[Tensor] = None,
- ) -> Tuple[Tensor, Optional[Tensor], Optional[Tensor], Optional[Tensor]]:
- r"""Pass the inputs (and mask) through the encoder/decoder layer.
- Args:
- tgt: Input sequence to the decoder layer. (required).
- Must have shape (B, Nt, E) where B is the batch size, Nt is the target sequence
- length, and E is the model dimension.
- memory: Sequence from the last layer of the encoder. (required).
- Must have shape (B, Nts, E) where B is the batch size, Ns is the source sequence
- length, and E is the model dimension.
- tgt_mask: Attention mask for self-attention. (optional).
- Must have shape (Nt, Nt).
- memory_mask: Attention mask for cross-attention (optional).
- Must have shape (Nt, Ns).
- tgt_key_padding_mask: Mask for the tgt keys per batch (optional).
- Must have shape (B, Nt).
- memory_key_padding_mask: Mask for the memory keys per batch (optional).
- Must have shape (B, Ns).
- position_bias: Relative attention bias to be used when computing self-attention scores (optional)
- Must have shape (B, H, Nt, Nt) where H is the number of heads.
- """
-
- # See Fig. 1 of https://arxiv.org/pdf/2002.04745v1.pdf
- x = tgt
- sa_out, position_bias, sa_scores = self._sa_block(self.norm1(x), tgt_mask, tgt_key_padding_mask, position_bias)
- x = x + sa_out
- ca_out, ca_scores = self._ca_block(self.norm3(x), memory, memory_mask, memory_key_padding_mask)
- x = x + ca_out
- x = x + self._ff_block(self.norm2(x))
-
- return x, position_bias, sa_scores, ca_scores
-
- # Cross attention block
- def _ca_block(
- self, x: Tensor, mem: Tensor, attn_mask: Optional[Tensor], key_padding_mask: Optional[Tensor]
- ) -> Tuple[Tensor, Optional[Tensor]]:
- attn = self.cross_attn(x, mem, mem, attn_mask=attn_mask, key_padding_mask=key_padding_mask, need_weights=True)
- x = attn[0]
- scores = attn[2]
- return self.dropout4(x), scores
-
-# NOTE: Comparable HuggingFace implentation can be found at https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L835
class T5Encoder(nn.Module):
- r"""T5Encoder is a stack of N encoder layers
+ """T5Encoder is a stack of N encoder layers.
+
Args:
d_model: Number of expected features in the input (required).
nhead: Number of heads in the multihead attention models (required).
@@ -791,15 +892,22 @@ class T5Encoder(nn.Module):
qkv_dim: Projection dimension (per head) for query, keys, and values. (defualt=64).
dropout: Dropout value (default=0.1).
activation: Activation function of the intermediate layer, can be a string
- ("relu" or "gelu") or a unary callable. (default: relu)
+ ("relu", "gelu", or "gelu_new") or a unary callable. (default: F.relu)
+ is_gated_act: Option to include gated activated as done in FLAN-T5, see
+ https://huggingface.co/google/flan-t5-xxl. (default: False)
layer_norm_eps: The eps value in layer normalization components (default=1e-6).
relative_attention_num_buckets: Number of relative position buckets (default: 32)
relative_attention_max_distance: Maximum threshold on the relative distance used to
allocate buckets. Anything larger gets placed in the same bucket (defulat: 128)
+ token_embeddings (nn.Module): Embedding layer to be passed in the case that the input to `forward`
+ is not already embedded.
+ device: Device to use any newly constructed Tensors. (optional)
+ dtype: Datatype to use on any newly constructed Tensors. (optional)
+
Examples::
>>> encoder = T5Encoder(d_model=768, nhead=12, num_layers=12)
>>> tgt = torch.rand(32, 10, 512)
- >>> out = encoder(tgt)
+ >>> encoder(tgt)
"""
def __init__(
@@ -811,27 +919,31 @@ class T5Encoder(nn.Module):
qkv_dim: int = 64,
dropout: float = 0.1,
activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+ is_gated_act: bool = False,
layer_norm_eps: float = 1e-6,
relative_attention_num_buckets: int = 32,
relative_attention_max_distance: int = 128,
+ token_embeddings: Optional[nn.Module] = None,
device: Optional[torch.device] = None,
dtype=None,
) -> None:
super().__init__()
-
+ self.token_embeddings = token_embeddings
self.layers = nn.ModuleList(
[
- T5EncoderLayer(
+ T5Layer(
d_model,
nhead,
- dim_feedforward,
- qkv_dim,
- dropout,
- activation,
- layer_norm_eps,
- relative_attention_num_buckets,
- relative_attention_max_distance,
+ dim_feedforward=dim_feedforward,
+ qkv_dim=qkv_dim,
+ dropout=dropout,
+ activation=activation,
+ is_gated_act=is_gated_act,
+ layer_norm_eps=layer_norm_eps,
+ relative_attention_num_buckets=relative_attention_num_buckets,
+ relative_attention_max_distance=relative_attention_max_distance,
compute_relative_attention_bias=True if i == 0 else False,
+ is_decoder=False,
device=device,
dtype=dtype,
)
@@ -839,43 +951,73 @@ class T5Encoder(nn.Module):
]
)
self.num_layers = num_layers
+ self.norm = T5LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
def forward(
self,
- tgt: Tensor,
- tgt_mask: Optional[Tensor] = None,
- tgt_key_padding_mask: Optional[Tensor] = None,
- ) -> Tuple[Tensor, List[Tensor], Optional[Tensor], List[Optional[Tensor]]]:
+ src: Optional[Tensor] = None,
+ mask: Optional[Tensor] = None,
+ src_key_padding_mask: Optional[Tensor] = None,
+ embedded_src: Optional[Tensor] = None,
+ ) -> ENCODER_OUTPUTS_TYPE:
r"""Pass the input (and masks) through the stack of encoder layers.
+
Args:
- tgt: Input sequence to the encoder layer. (required).
- Must have shape (B, Nt, E) where B is the batch size, Nt is the target sequence
- length, and E is the model dimension.
- tgt_mask: Attention mask for self-attention. (optional).
+ src (Optional[Tensor]): Tokenized input sequence to the encoder.
+ Must be batch first with shape (B, Ne) where B is the batch size and Ne is the
+ encoder input sequence length.
+ mask (Optional[Tensor]): Attention mask for self-attention.
Must have shape (Nt, Nt).
- tgt_key_padding_mask: Mask for the tgt keys per batch (optional).
+ src_key_padding_mask (Optional[Tensor]): Mask for the tgt keys per batch.
Must have shape (B, Nt).
+ embedded_src (Optional[Tensor]): Embedded input sequence to the encoder layer.
+ Must have shape (B, Nt, E) where B is the batch size, Nt is the target sequence
+ length, and E is the model dimension.
+ *Note*: If you do not provide this `embedded_tgt`, you must have provided a `token_embedding` layer \
+ in the initialization of the T5Encoder.
+
+ Returns:
+ Dictionary of last hidden layer, all hidden layers, position bias, and self-attention scores.
"""
- output = tgt
+ # This keeps the encoder self-contained and easy to use individually
+ if embedded_src is None:
+ assert (
+ self.token_embeddings is not None and src is not None
+ ), "Must provide `token_embeddings` and `tgt` if not providing already embedded tokens."
+ embedded_src = self.token_embeddings(src)
+
+ output = self.dropout1(embedded_src)
position_bias = None
all_outputs = torch.jit.annotate(List[Tensor], [])
all_sa_scores = torch.jit.annotate(List[Optional[Tensor]], [])
for mod in self.layers:
all_outputs.append(output)
- output, position_bias, sa_score = mod(
+ output, position_bias, sa_score, _, _ = mod(
output,
- tgt_mask=tgt_mask,
- tgt_key_padding_mask=tgt_key_padding_mask,
+ mask=mask,
+ seq_key_padding_mask=src_key_padding_mask,
position_bias=position_bias,
)
all_sa_scores.append(sa_score)
- return output, all_outputs, position_bias, all_sa_scores
+ output = self.norm(output)
+ output = self.dropout2(output)
+
+ all_outputs.append(output)
+
+ return {
+ "encoder_output": output,
+ "encoder_hidden_states": all_outputs,
+ "encoder_position_bias": position_bias,
+ "encoder_sa_scores": all_sa_scores,
+ }
-# NOTE: Comparable HuggingFace implentation can be found at https://github.com/huggingface/transformers/blob/8581a798c0a48fca07b29ce2ca2ef55adcae8c7e/src/transformers/models/t5/modeling_t5.py#L835
class T5Decoder(nn.Module):
- r"""T5Decoder is a stack of N decoder layers
+ r"""T5Decoder is a stack of N decoder layers.
+
Args:
d_model: Number of expected features in the input (required).
nhead: Number of heads in the multihead attention models (required).
@@ -884,16 +1026,22 @@ class T5Decoder(nn.Module):
qkv_dim: Projection dimension (per head) for query, keys, and values. (defualt=64).
dropout: Dropout value (default=0.1).
activation: Activation function of the intermediate layer, can be a string
- ("relu" or "gelu") or a unary callable. (default: relu)
+ ("relu", "gelu", or "gelu_new") or a unary callable. (default: F.relu)
+ is_gated_act: Option to include gated activated as done in FLAN-T5, see
+ https://huggingface.co/google/flan-t5-xxl. (default: False)
layer_norm_eps: The eps value in layer normalization components (default=1e-6).
relative_attention_num_buckets: Number of relative position buckets (default: 32)
relative_attention_max_distance: Maximum threshold on the relative distance used to
allocate buckets. Anything larger gets placed in the same bucket (defulat: 128)
+ device: Device to use any newly constructed Tensors. (optional)
+ dtype: Datatype to use on any newly constructed Tensors. (optional)
+
+
Examples::
>>> decoder = T5Decoder(d_model=768, nhead=12, num_layers=12)
>>> memory = torch.rand(32, 10, 512)
- >>> tgt = torch.rand(32, 10, 512)
- >>> out = decoder(tgt, memory)
+ >>> tgt = torch.rand(32, 1, 512)
+ >>> decoder(tgt, memory)
"""
def __init__(
@@ -905,6 +1053,7 @@ class T5Decoder(nn.Module):
qkv_dim: int = 64,
dropout: float = 0.1,
activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+ is_gated_act: bool = False,
layer_norm_eps: float = 1e-6,
relative_attention_num_buckets: int = 32,
relative_attention_max_distance: int = 128,
@@ -915,37 +1064,45 @@ class T5Decoder(nn.Module):
self.layers = nn.ModuleList(
[
- T5DecoderLayer(
+ T5Layer(
d_model,
nhead,
- dim_feedforward,
- qkv_dim,
- dropout,
- activation,
- layer_norm_eps,
- relative_attention_num_buckets,
- relative_attention_max_distance,
+ dim_feedforward=dim_feedforward,
+ qkv_dim=qkv_dim,
+ dropout=dropout,
+ activation=activation,
+ is_gated_act=is_gated_act,
+ layer_norm_eps=layer_norm_eps,
+ relative_attention_num_buckets=relative_attention_num_buckets,
+ relative_attention_max_distance=relative_attention_max_distance,
compute_relative_attention_bias=True if i == 0 else False,
+ is_decoder=True,
device=device,
dtype=dtype,
)
for i in range(num_layers)
]
)
+ self.norm = T5LayerNorm(d_model)
+ self.dropout1 = nn.Dropout(dropout)
+ self.dropout2 = nn.Dropout(dropout)
self.num_layers = num_layers
def forward(
self,
- tgt: Tensor,
+ embedded_tgt: Tensor,
memory: Tensor,
tgt_mask: Optional[Tensor] = None,
memory_mask: Optional[Tensor] = None,
tgt_key_padding_mask: Optional[Tensor] = None,
memory_key_padding_mask: Optional[Tensor] = None,
- ) -> Tuple[Tensor, List[Tensor], Optional[Tensor], List[Optional[Tensor]], List[Optional[Tensor]]]:
+ past_key_values: Optional[List[PAST_KEY_VALUES_TYPE]] = None,
+ return_past_key_values: bool = False,
+ ) -> DECODER_OUTPUTS_TYPE:
r"""Pass the inputs (and masks) through the stack of decoder layers.
+
Args:
- tgt: Input sequence to the decoder layer. (required).
+ embedded_tgt: Input sequence to the decoder layer. (required).
Must have shape (B, Nt, E) where B is the batch size, Nt is the target sequence
length, and E is the model dimension.
memory: Sequence from the last layer of the encoder. (required).
@@ -959,24 +1116,47 @@ class T5Decoder(nn.Module):
Must have shape (B, Nt).
memory_key_padding_mask: Mask for the memory keys per batch (optional).
Must have shape (B, Ns).
+ past_key_values: Past key values used for incremental decoding (optional).
+ List of Tuple with Tensors of shape (B, H, N)>>>>> Check this????
+ return_past_key_values: Boolean stating whether to return past_key_values from model. (default: False)
+
+ Returns:
+ Dictionary of last hidden state, all hidden states, position bias, self-attention scores, cross-attention scores
+ and past key values (if requested).
"""
- output = tgt
+ output = self.dropout1(embedded_tgt)
position_bias = None
all_outputs = torch.jit.annotate(List[Tensor], [])
all_sa_scores = torch.jit.annotate(List[Optional[Tensor]], [])
all_ca_scores = torch.jit.annotate(List[Optional[Tensor]], [])
- for mod in self.layers:
+ all_key_values = torch.jit.annotate(List[Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]], [])
+ for i, mod in enumerate(self.layers):
all_outputs.append(output)
- output, position_bias, sa_score, ca_score = mod(
+ output, position_bias, sa_score, ca_score, past_key_value = mod(
output,
memory,
- tgt_mask=tgt_mask,
+ mask=tgt_mask,
memory_mask=memory_mask,
- tgt_key_padding_mask=tgt_key_padding_mask,
+ seq_key_padding_mask=tgt_key_padding_mask,
memory_key_padding_mask=memory_key_padding_mask,
position_bias=position_bias,
+ past_key_values=past_key_values[i] if past_key_values is not None else None,
)
all_sa_scores.append(sa_score)
all_ca_scores.append(ca_score)
-
- return output, all_outputs, position_bias, all_sa_scores, all_ca_scores
+ # TODO: Can pass in enc-dec position_bias to avoid recalculating in cross-attn
+ if past_key_value is not None and return_past_key_values:
+ all_key_values.append(past_key_value)
+
+ output = self.norm(output)
+ output = self.dropout2(output)
+ all_outputs.append(output)
+
+ return {
+ "decoder_output": output,
+ "decoder_hidden_states": all_outputs,
+ "decoder_position_bias": position_bias,
+ "decoder_sa_scores": all_sa_scores,
+ "decoder_ca_scores": all_ca_scores,
+ "past_key_values": all_key_values,
+ }
diff --git a/torchtext/prototype/models/t5/t5_transform.py b/torchtext/models/t5/t5_transform.py
similarity index 85%
rename from torchtext/prototype/models/t5/t5_transform.py
rename to torchtext/models/t5/t5_transform.py
index 7154e31..380778e 100644
--- a/torchtext/prototype/models/t5/t5_transform.py
+++ b/torchtext/models/t5/t5_transform.py
@@ -1,3 +1,13 @@
+# /* Portions Copyright (c) Meta Platforms, Inc. and affiliates.
+# 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. */
from typing import List, Union
import torch
diff --git a/torchtext/prototype/generate.py b/torchtext/prototype/generate.py
new file mode 100644
index 0000000..dd74948
--- /dev/null
+++ b/torchtext/prototype/generate.py
@@ -0,0 +1,144 @@
+import logging
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MAX_SEQ_LEN = 256
+
+
+class GenerationUtils:
+ """Wrapper to provide generation utils for encoder/decoder models and decoder models.
+
+ Example:
+ >>> model = T5_BASE_GENERATION.get_model()
+ >>> generative_model = GenerationUtils(model=model)
+ >>> generative_model.generate(input_ids, num_beams=1, max_length=100)
+
+ The wrapper can work with *any* model as long as it meets the following requirements:
+ 1. Is an encoder/decoder or decoder based model.
+ 2. Includes a `get_encoder` method (if applicable) and a `prepare_inputs_for_generation` method.
+
+ This means that popular HuggingFace implementation of T5, Bart, and GPT-2 can all be used with these generation utils!
+ >>> from transformers import T5Model
+ >>> model = T5Model.from_pretrained("t5-base")
+ >>> generative_model = GenerationUtils(model=model, is_huggingface_model=True)
+ >>> generative_model.generate(input_ids, num_beams=1, max_length=100)
+
+ `Note`: We cannot make any claims about the stability of APIs from HuggingFace so all models used from the `transformers`
+ library are marked 'experimental.'
+
+ More examples can be found in the `notebooks` directory of this repository.
+ """
+
+ def __init__(self, model: nn.Module, **kwargs) -> None:
+ self.model = model
+ self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", True)
+ self.is_huggingface_model = kwargs.pop("is_huggingface_model", False)
+
+ def _prepare_decoder_ids_for_generation(
+ self, batch_size: int, pad_idx: int = 0, device: Optional[torch.device] = None, **model_kwargs
+ ):
+ if model_kwargs is not None and "decoder_input_ids" in model_kwargs:
+ return model_kwargs.pop("decoder_input_ids")
+ else:
+ return torch.ones((batch_size, 1), dtype=torch.long, device=device) * pad_idx
+
+ def greedy_search(
+ self, input_ids: torch.Tensor, max_length: int, eos_idx: int, pad_idx: Optional[int] = None, **model_kwargs
+ ) -> torch.Tensor:
+ """Greedy search decoding for text generation. Takes the most likely next token every time.
+
+ Inputs:
+ input_ids (Tensor): Text prompt(s) for greedy generation.
+ max_length (int): Max length to generate responses.
+ eos_idx (int): End of sequence index.
+ pad_idx (int): Padding index.
+ **model_kwargs
+
+ Returns:
+ Batch of sequences decoded by greedy search.
+ """
+ unfinished_sequences = torch.ones((input_ids.shape[0], 1), device=input_ids.device, dtype=torch.long)
+
+ while True:
+ model_inputs = self.model.prepare_inputs_for_generation(input_ids, **model_kwargs)
+ if self.is_huggingface_model:
+ model_inputs["return_dict"] = True
+ model_inputs["output_hidden_states"] = True
+
+ # Get model output
+ outputs = self.model(**model_inputs)
+ output_key = "logits" if self.is_huggingface_model else "decoder_output"
+ decoder_output = outputs[output_key]
+
+ # Calculate probabilities and take the most likely next token
+ probs = F.log_softmax(decoder_output[:, -1], dim=-1)
+ _, next_tokens = torch.topk(probs, 1)
+
+ # For any finished sequences, padding idx should be the last token
+ if eos_idx is not None:
+ if pad_idx is not None:
+ next_tokens = next_tokens * unfinished_sequences + pad_idx * (1 - unfinished_sequences)
+
+ # Append the next tokens to the previous tokens
+ input_ids = torch.cat([input_ids, next_tokens], dim=-1)
+
+ if eos_idx is not None:
+ unfinished_sequences = unfinished_sequences.mul((next_tokens != eos_idx).long())
+
+ # Stop iterating once all sequences are finished or exceed the max_length
+ if unfinished_sequences.max() == 0 or len(input_ids[0]) >= max_length:
+ break
+
+ return input_ids
+
+ def beam_search(self, input_ids: torch.Tensor, num_beams: int, max_length: Optional[int]) -> torch.Tensor:
+ raise NotImplementedError()
+
+ def generate(
+ self,
+ inputs: Optional[torch.Tensor] = None,
+ num_beams: Optional[int] = None,
+ max_length: Optional[int] = None,
+ pad_idx: int = 0,
+ eos_idx: int = 1,
+ ) -> torch.Tensor:
+ """Generation method.
+
+ `num_beams` == 1 or `num_beams` is None -> greedy search
+ `num_beams` > 1 -> beam search
+
+ Args:
+ input_ids (Tensor): Ids of tokenized input tokens. The 'seed' text for generation.
+ num_beams (int): If provided, specifies the number of beams to use in beam search generation.
+ max_length (int): Max length to generate responses.
+ pad_idx (int): Padding index. Defaults to 0.
+ eos_idx (int): End of sequence index. Defaults to 1.
+
+ Returns:
+ Tensor of Tensors containing output sequences as ids.
+
+ `Note`: If one beam is provided or no beams are specified, the generation method will default to greedy search.
+ """
+ model_kwargs = {}
+
+ if self.is_encoder_decoder:
+ encoder = self.model.get_encoder()
+ model_kwargs["encoder_outputs"] = encoder(inputs)
+ inputs = self._prepare_decoder_ids_for_generation(len(inputs), device=inputs.device, **model_kwargs)
+
+ if max_length is None:
+ # Too hard to try to figure out the exact max_seq_length for each model
+ logger.warning(f"`max_length` was not specified. Defaulting to {DEFAULT_MAX_SEQ_LEN} tokens.")
+ max_length = DEFAULT_MAX_SEQ_LEN
+
+ if num_beams == 1 or num_beams is None:
+ return self.greedy_search(inputs, max_length, eos_idx, pad_idx=pad_idx, **model_kwargs)
+ elif num_beams > 1:
+ return self.beam_search(inputs, num_beams, max_length)
+ else:
+ raise ValueError("`num_beams` must be >= 1.")
diff --git a/torchtext/prototype/models/__init__.py b/torchtext/prototype/models/__init__.py
deleted file mode 100644
index ab659dd..0000000
--- a/torchtext/prototype/models/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from .t5 import * # noqa: F401, F403
diff --git a/torchtext/prototype/models/t5/bundler.py b/torchtext/prototype/models/t5/bundler.py
deleted file mode 100644
index 76b0d9b..0000000
--- a/torchtext/prototype/models/t5/bundler.py
+++ /dev/null
@@ -1,487 +0,0 @@
-import logging
-from dataclasses import dataclass
-from typing import Any, Callable, Dict, Optional, Union
-from urllib.parse import urljoin
-
-import torch
-from torchtext import _TEXT_BUCKET
-from torchtext._download_hooks import load_state_dict_from_url
-
-from .model import T5Conf, T5Model
-from .t5_transform import T5Transform
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class T5Bundle:
- """T5Bundle(_config: torchtext.prototype.models.T5Conf, _path: Optional[str] = None, transform: Optional[Callable] = None)
-
- Example - Pretrained base t5 encoder
- >>> import torch, torchtext
- >>> t5_encoder_base = torchtext.prototype.models.T5_BASE_ENCODER
- >>> transform = t5_encoder_base.transform()
- >>> input_seq = ["Hello world", "Attention rocks!"]
- >>> model = t5_encoder_base.get_model()
- >>> model_input = transform(input_seq)
- >>> output = model(model_input)['encoder_output']
- >>> output.shape
- torch.Size([2, 4, 768])
-
- Example - Pretrained base t5 model
- >>> import torch, torchtext
- >>> t5_base = torchtext.prototype.models.T5_BASE
- >>> transform = t5_base.transform()
- >>> input_seq = ["Hello world", "Attention rocks!"]
- >>> model = t5_base.get_model()
- >>> model_input = transform(input_seq)
- >>> output = model(model_input)['decoder_output']
- >>> output.shape
- torch.Size([2, 1, 768])
-
- Example - Pretrained base t5 model for generation
- >>> import torch, torchtext
- >>> import torch.nn.functional as F
- >>> t5_base_generation = torchtext.prototype.models.T5_BASE_GENERATION
- >>> transform = t5_base_generation.transform()
- >>> input_seq = ["Hello world", "Attention rocks!"]
- >>> model = t5_base_generation.get_model()
- >>> model_input = transform(input_seq)
- >>> output = model(model_input)['decoder_output']
- >>> logits = F.log_softmax(output[:,-1], dim=-1)
- >>> logits.shape
- torch.Size([2, 1, 32128])
-
- Example - User-specified configuration and checkpoint
- >>> from torchtext.prototype.models import T5Conf, T5Bundle
- >>> model_weights_path = "https://download.pytorch.org/models/text/t5.base.encoder.pt"
- >>> encoder_conf = T5Conf(encoder_only=True)
- >>> model = T5Bundle.build_model(config=encoder_conf, checkpoint=model_weights_path)
- """
-
- _config: T5Conf
- _path: Optional[str] = None
- transform: Optional[Callable] = None
-
- def get_model(
- self,
- *,
- load_weights: bool = True,
- freeze_model: bool = False,
- dl_kwargs: Optional[Dict[str, Any]] = None,
- ) -> T5Model:
- r"""get_model(load_weights: bool = True, freeze_model: bool = False, *, dl_kwargs=None) -> torctext.prototype.models.T5Model
-
- Args:
- load_weights (bool): Indicates whether or not to load weights if available. (Default: `True`)
- freeze_model (bool): Indicates whether or not to freeze the model weights. (Default: `False`)
- dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. (Default: `None`)
- """
-
- if load_weights:
- assert (
- self._path is not None
- ), "load_weights cannot be True. The pre-trained model weights are not available for the current object"
-
- if freeze_model:
- if not load_weights or not self._path:
- logger.warning(
- "The model is not loaded with pre-trained weights. Setting freeze_model to True will hinder model from learning appropriate weights."
- )
-
- return T5Bundle.build_model(
- config=self._config,
- freeze_model=freeze_model,
- checkpoint=self._path if load_weights else None,
- strict=True,
- dl_kwargs=dl_kwargs,
- )
-
- @classmethod
- def build_model(
- cls,
- config: T5Conf,
- *,
- freeze_model: bool = False,
- checkpoint: Optional[Union[str, Dict[str, torch.Tensor]]] = None,
- strict: bool = False,
- dl_kwargs: Optional[Dict[str, Any]] = None,
- ) -> T5Model:
- """Class builder method
-
- Args:
- config (T5Conf): An instance of classT5Conf that defined the model configuration
- freeze_model (bool): Indicates whether to freeze the model weights. (Default: `False`)
- checkpoint (str or Dict[str, torch.Tensor]): Path to or actual model state_dict. state_dict can have partial weights i.e only for encoder. (Default: ``None``)
- strict (bool): Passed to :func: `torch.nn.Module.load_state_dict` method. (Default: `False`)
- dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. (Default: `None`)
- """
-
- model = T5Model(config, freeze_model)
- if checkpoint is not None:
- if torch.jit.isinstance(checkpoint, Dict[str, torch.Tensor]):
- state_dict = checkpoint
- elif isinstance(checkpoint, str):
- dl_kwargs = {} if dl_kwargs is None else dl_kwargs
- state_dict = load_state_dict_from_url(checkpoint, **dl_kwargs)
- else:
- raise TypeError(
- "checkpoint must be of type `str` or `Dict[str, torch.Tensor]` but got {}".format(type(checkpoint))
- )
-
- model.load_state_dict(state_dict, strict=strict)
-
- return model
-
- @property
- def config(self) -> T5Conf:
- return self._config
-
-
-ENCODER_DOC = """
- T5_{}_ENCODER is an encoder-only model from a pre-trained T5 model with the {} configuration.
- It returns the normalized output from the final layer of the encoder.
-
- The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- <http://jmlr.org/papers/v21/20-074.html>`. It introduces a unified framework that converts text-based
- language problems, such as translation, question-answering, and summarization, into a text-to-text format. The
- Colossal Clean Crawled Corpus (C4) dataset is used to pre-train the model on a masked language modeling task,
- and various datasets are used to fine-tune the model on each downstream task. The model's architecture is a modified version
- of the canonical Transformer architecture.
-
- Originally published by the authors of T5 under Apache License, Version 2.0
- and redistributed with the same license.
- [`License <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/LICENSE>`__,
- `Source <https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints>`__]
-
- Please refer to :func:`torchtext.prototype.models.T5Bundle` for the usage.
-"""
-
-MODEL_DOC = """
- T5_{} is an encoder-decoder model from a pre-trained T5 model with the {} configuration.
- It returns the normalized output from the final layer of the decoder.
-
- The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- <http://jmlr.org/papers/v21/20-074.html>`. It introduces a unified framework that converts text-based
- language problems, such as translation, question-answering, and summarization, into a text-to-text format. The
- Colossal Clean Crawled Corpus (C4) dataset is used to pre-train the model on a masked language modeling task,
- and various datasets are used to fine-tune the model on each downstream task. The model's architecture is a modified version
- of the canonical Transformer architecture.
-
- Originally published by the authors of T5 under Apache License, Version 2.0
- and redistributed with the same license.
- [`License <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/LICENSE>`__,
- `Source <https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints>`__]
-
- Please refer to :func:`torchtext.prototype.models.T5Bundle` for the usage.
- """
-
-GENERATION_DOC = """
- T5_{}_GENERATION is an encoder-decoder model from a pre-trained T5 model with the {} configuration.
- It returns the output of the final layer of the decoder after passing through a linear layer to project the hidden states to
- the model vocabulary. This output can then be used for language generation.
-
- The T5 model was proposed in `Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
- <http://jmlr.org/papers/v21/20-074.html>`. It introduces a unified framework that converts text-based
- language problems, such as translation, question-answering, and summarization, into a text-to-text format. The
- Colossal Clean Crawled Corpus (C4) dataset is used to pre-train the model on a masked language modeling task,
- and various datasets are used to fine-tune the model on each downstream task. The model's architecture is a modified version
- of the canonical Transformer architecture.
-
- Originally published by the authors of T5 under Apache License, Version 2.0
- and redistributed with the same license.
- [`License <https://github.com/google-research/text-to-text-transfer-transformer/blob/main/LICENSE>`__,
- `Source <https://github.com/google-research/text-to-text-transfer-transformer#released-model-checkpoints>`__]
-
- Please refer to :func:`torchtext.prototype.models.T5Bundle` for the usage.
- """
-
-T5_BASE_ENCODER = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.base.encoder.pt"),
- _config=T5Conf(encoder_only=True),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_BASE_ENCODER.__doc__ = ENCODER_DOC.format("BASE", "base")
-
-T5_BASE = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.base.pt"),
- _config=T5Conf(encoder_only=False),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_BASE.__doc__ = MODEL_DOC.format("BASE", "base")
-
-T5_BASE_GENERATION = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.base.generation.pt"),
- _config=T5Conf(encoder_only=False, linear_head=True),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_BASE_GENERATION.__doc__ = GENERATION_DOC.format("BASE", "base")
-
-T5_SMALL_ENCODER = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.small.encoder.pt"),
- _config=T5Conf(
- encoder_only=True,
- embedding_dim=512,
- num_attention_heads=8,
- num_encoder_layers=6,
- num_decoder_layers=6,
- ffn_dimension=2048,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_SMALL_ENCODER.__doc__ = ENCODER_DOC.format("SMALL", "small")
-
-
-T5_SMALL = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.small.pt"),
- _config=T5Conf(
- encoder_only=False,
- embedding_dim=512,
- num_attention_heads=8,
- num_encoder_layers=6,
- num_decoder_layers=6,
- ffn_dimension=2048,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_SMALL.__doc__ = MODEL_DOC.format("SMALL", "small")
-
-T5_SMALL_GENERATION = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.small.generation.pt"),
- _config=T5Conf(
- encoder_only=False,
- linear_head=True,
- embedding_dim=512,
- num_attention_heads=8,
- num_encoder_layers=6,
- num_decoder_layers=6,
- ffn_dimension=2048,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_SMALL_GENERATION.__doc__ = GENERATION_DOC.format("SMALL", "small")
-
-T5_LARGE_ENCODER = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.large.encoder.pt"),
- _config=T5Conf(
- encoder_only=True,
- embedding_dim=1024,
- num_attention_heads=16,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=4096,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_LARGE_ENCODER.__doc__ = ENCODER_DOC.format("LARGE", "large")
-
-T5_LARGE = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.large.pt"),
- _config=T5Conf(
- encoder_only=False,
- embedding_dim=1024,
- num_attention_heads=16,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=4096,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_LARGE.__doc__ = MODEL_DOC.format("LARGE", "large")
-
-T5_LARGE_GENERATION = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.large.generation.pt"),
- _config=T5Conf(
- encoder_only=False,
- linear_head=True,
- embedding_dim=1024,
- num_attention_heads=16,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=4096,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_LARGE_GENERATION.__doc__ = GENERATION_DOC.format("LARGE", "large")
-
-T5_3B_ENCODER = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.3b.encoder.pt"),
- _config=T5Conf(
- encoder_only=True,
- embedding_dim=1024,
- qkv_dim=128,
- num_attention_heads=32,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=16384,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_3B_ENCODER.__doc__ = ENCODER_DOC.format("3B", "3B")
-
-T5_3B = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.3b.pt"),
- _config=T5Conf(
- encoder_only=False,
- embedding_dim=1024,
- qkv_dim=128,
- num_attention_heads=32,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=16384,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_3B.__doc__ = MODEL_DOC.format("3B", "3B")
-
-T5_3B_GENERATION = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.3b.generation.pt"),
- _config=T5Conf(
- encoder_only=False,
- linear_head=True,
- embedding_dim=1024,
- qkv_dim=128,
- num_attention_heads=32,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=16384,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_3B_GENERATION.__doc__ = GENERATION_DOC.format("3B", "3B")
-
-T5_11B_ENCODER = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.11b.encoder.pt"),
- _config=T5Conf(
- encoder_only=True,
- embedding_dim=1024,
- qkv_dim=128,
- num_attention_heads=128,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=65536,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_11B_ENCODER.__doc__ = ENCODER_DOC.format("11B", "11B")
-
-T5_11B = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.11b.pt"),
- _config=T5Conf(
- encoder_only=False,
- embedding_dim=1024,
- qkv_dim=128,
- num_attention_heads=128,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=65536,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_11B.__doc__ = MODEL_DOC.format("11B", "11B")
-
-T5_11B_GENERATION = T5Bundle(
- _path=urljoin(_TEXT_BUCKET, "t5.11b.generation.pt"),
- _config=T5Conf(
- encoder_only=False,
- linear_head=True,
- embedding_dim=1024,
- qkv_dim=128,
- num_attention_heads=128,
- num_encoder_layers=24,
- num_decoder_layers=24,
- ffn_dimension=65536,
- ),
- transform=lambda: T5Transform(
- urljoin(_TEXT_BUCKET, "t5_tokenizer_base.model"),
- max_seq_len=512,
- eos_idx=1,
- padding_idx=0,
- ),
-)
-
-T5_11B_GENERATION.__doc__ = GENERATION_DOC.format("11B", "11B")
diff --git a/torchtext/prototype/models/t5/wrapper.py b/torchtext/prototype/models/t5/wrapper.py
deleted file mode 100644
index 8fa38a0..0000000
--- a/torchtext/prototype/models/t5/wrapper.py
+++ /dev/null
@@ -1,212 +0,0 @@
-from typing import Any, Dict, List, Optional, Tuple, Union
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from torch import Tensor
-from torchtext.prototype.models import (
- T5_BASE_GENERATION,
- T5_SMALL_GENERATION,
- T5_LARGE_GENERATION,
- T5_3B_GENERATION,
- T5_11B_GENERATION,
- T5Conf,
- T5Transform,
- T5Bundle,
-)
-
-
-BUNDLERS = {
- "base": T5_BASE_GENERATION,
- "small": T5_SMALL_GENERATION,
- "large": T5_LARGE_GENERATION,
- "3b": T5_3B_GENERATION,
- "11b": T5_11B_GENERATION,
-}
-
-
-class T5Wrapper(nn.Module):
- def __init__(
- self,
- configuration: Optional[str] = None,
- checkpoint: Optional[Union[str, Dict[str, torch.Tensor]]] = None,
- t5_config: Optional[T5Conf] = None,
- transform: Optional[T5Transform] = None,
- freeze_model: bool = False,
- strict: bool = False,
- dl_kwargs: Dict[str, Any] = None,
- device: Optional[torch.device] = None,
- ) -> None:
- """
- Args:
- configuration (str or None): The model configuration. Only support 'base', 'small', 'large', '3b', and '11b' . Must be `None` if checkpoint is not `None`. (Default: `None`)
- checkpoint (str, Dict[str, torch.Tensor], or None): Path to or actual model state_dict. state_dict can have partial weights i.e only for encoder. Must be `None` if configuration is not `None`.(Default: ``None``)
- t5_config (T5Conf or None): An instance of T5Conf that defined the model configuration (i.e. number of layer, attention heads, etc). Must be provided if configuration is `None`. (Default: `None`)
- transform (T5Transfrom or None): An instance of T5Transform that defines the text processing pipeline. Must be provided if configuration is `None`. (Default: `None`)
- freeze_model (bool): Indicates whether to freeze the model weights. (Default: `False`)
- strict (bool): Passed to :func: `torch.nn.Module.load_state_dict` method. (Default: `False`)
- dl_kwargs (dictionary of keyword arguments): Passed to :func:`torch.hub.load_state_dict_from_url`. (Default: `None`)
- """
- super().__init__()
-
- if configuration is None:
- assert checkpoint is not None, "Must provide a checkpoint if configuration is None"
- assert t5_config is not None, "Must provide t5_config if using checkpoint"
- assert isinstance(t5_config, T5Conf), f"t5_config must have type {T5Conf.__module__}"
- assert not t5_config.encoder_only, "t5_config.encoder_only must be False"
- assert t5_config.linear_head, "t5_config.linear_head must be True"
- assert transform is not None, "Must provide transform if using checkpoint"
- assert isinstance(transform, T5Transform), f"transform must have type {T5Transform.__module__}"
-
- else:
- assert checkpoint is None, "configuration and checkpoint were both provided. Can only provide one."
- assert (
- configuration in BUNDLERS
- ), f"Invalid configuration provided. Only support the following configurations: {[key for key in BUNDLERS.keys()]}"
-
- if configuration is None and checkpoint is not None:
- self.bundler = T5Bundle(_path=checkpoint, _config=t5_config, transform=lambda: transform)
- self.model = self.bundler.build_model(
- config=t5_config, freeze_model=freeze_model, checkpoint=checkpoint, strict=strict, dl_kwargs=dl_kwargs
- )
- else:
- self.bundler = BUNDLERS[configuration]
- self.model = self.bundler.get_model()
-
- self.transform = self.bundler.transform()
-
- def beam_search(
- self,
- beam_size: int,
- step: int,
- bsz: int,
- decoder_output: Tensor,
- decoder_tokens: Tensor,
- scores: Tensor,
- incomplete_sentences: Tensor,
- ) -> Tuple[Tensor, Tensor, Tensor]:
- probs = F.log_softmax(decoder_output[:, -1], dim=-1)
- top = torch.topk(probs, beam_size)
-
- # N is number of sequences, L is length of sequences, B is beam_size
- # decoder tokens has shape (N,L) -> (N,B,L)
- # top.indices has shape (N,B) - > (N,B,1)
- # x has shape (N,B,L+1)
- # note that when step == 1, N = batch_size, and when step > 1, N = batch_size * beam_size
- x = torch.cat([decoder_tokens.unsqueeze(1).repeat(1, beam_size, 1), top.indices.unsqueeze(-1)], dim=-1)
-
- # beams are first created for a given sequence
- if step == 1:
- # x has shape (batch_size, B, L+1) -> (batch_size * B,L+1)
- # new_scores has shape (batch_size,B)
- # incomplete_sentences has shape (batch_size * B) = (N)
- new_decoder_tokens = x.view(-1, step + 1)
- new_scores = top.values
- new_incomplete_sentences = incomplete_sentences
-
- # beams already exist, want to expand each beam into possible new tokens to add
- # and for all expanded beams belonging to the same sequences, choose the top k
- else:
- # scores has shape (batch_size,B) -> (N,1) -> (N,B)
- # top.values has shape (N,B)
- # new_scores has shape (N,B) -> (batch_size, B^2)
- new_scores = (scores.view(-1, 1).repeat(1, beam_size) + top.values).view(bsz, -1)
-
- # v, i have shapes (batch_size, B)
- v, i = torch.topk(new_scores, beam_size)
-
- # x has shape (N,B,L+1) -> (batch_size, B, L+1)
- # i has shape (batch_size, B) -> (batch_size, B, L+1)
- # new_decoder_tokens has shape (batch_size, B, L+1) -> (N, L)
- x = x.view(bsz, -1, step + 1)
- new_decoder_tokens = x.gather(index=i.unsqueeze(-1).repeat(1, 1, step + 1), dim=1).view(-1, step + 1)
-
- # need update incomplete sentences incase one of the beams was kicked out
- # y has shape (N) -> (N, 1) -> (N, B) -> (batch_size, B^2)
- y = incomplete_sentences.unsqueeze(-1).repeat(1, beam_size).view(bsz, -1)
-
- # now can use i to extract those beams that were selected
- # new_incomplete_sentences has shape (batch_size, B^2) -> (batch_size, B) -> (N, 1) -> N
- new_incomplete_sentences = y.gather(index=i, dim=1).view(bsz * beam_size, 1).squeeze(-1)
-
- # new_scores has shape (batch_size, B)
- new_scores = v
-
- return new_decoder_tokens, new_scores, new_incomplete_sentences
-
- def generate(self, encoder_tokens: Tensor, beam_size: int, eos_idx: int = 1, max_seq_len: int = 512) -> Tensor:
-
- # pass tokens through encoder
- bsz = encoder_tokens.size(0)
- encoder_padding_mask = encoder_tokens.eq(self.model.padding_idx)
- encoder_embeddings = self.model.dropout1(self.model.token_embeddings(encoder_tokens))
- encoder_output = self.model.encoder(encoder_embeddings, tgt_key_padding_mask=encoder_padding_mask)[0]
-
- encoder_output = self.model.norm1(encoder_output)
- encoder_output = self.model.dropout2(encoder_output)
-
- # initialize decoder input sequence; T5 uses padding index as starter index to decoder sequence
- decoder_tokens = torch.ones((bsz, 1), dtype=torch.long) * self.model.padding_idx
- scores = torch.zeros((bsz, beam_size))
-
- # mask to keep track of sequences for which the decoder has not produced an end-of-sequence token yet
- incomplete_sentences = torch.ones(bsz * beam_size, dtype=torch.long)
-
- # iteratively generate output sequence until all sequences in the batch have generated the end-of-sequence token
- for step in range(max_seq_len):
-
- if step == 1:
- # duplicate and order encoder output so that each beam is treated as its own independent sequence
- new_order = torch.arange(bsz).view(-1, 1).repeat(1, beam_size).view(-1)
- new_order = new_order.to(encoder_tokens.device).long()
- encoder_output = encoder_output.index_select(0, new_order)
- encoder_padding_mask = encoder_padding_mask.index_select(0, new_order)
-
- # causal mask and padding mask for decoder sequence
- tgt_len = decoder_tokens.shape[1]
- decoder_mask = torch.triu(torch.ones((tgt_len, tgt_len), dtype=torch.float64), diagonal=1)
- decoder_mask = decoder_mask.to(torch.bool)
- decoder_padding_mask = decoder_tokens.eq(self.model.padding_idx)
-
- # T5 implemention uses padding idx to start sequence. Want to ignore this when masking
- decoder_padding_mask[:, 0] = False
-
- # pass decoder sequence through decoder
- decoder_embeddings = self.model.dropout3(self.model.token_embeddings(decoder_tokens))
- decoder_output = self.model.decoder(
- decoder_embeddings,
- memory=encoder_output,
- tgt_mask=decoder_mask,
- tgt_key_padding_mask=decoder_padding_mask,
- memory_key_padding_mask=encoder_padding_mask,
- )[0]
-
- decoder_output = self.model.norm2(decoder_output)
- decoder_output = self.model.dropout4(decoder_output)
- decoder_output = decoder_output * (self.model.embedding_dim ** -0.5)
- decoder_output = self.model.lm_head(decoder_output)
-
- decoder_tokens, scores, incomplete_sentences = self.beam_search(
- beam_size, step + 1, bsz, decoder_output, decoder_tokens, scores, incomplete_sentences
- )
- # ignore newest tokens for sentences that are already complete
- decoder_tokens[:, -1] *= incomplete_sentences
-
- # update incomplete_sentences to remove those that were just ended
- incomplete_sentences = incomplete_sentences - (decoder_tokens[:, -1] == eos_idx).long()
-
- if (incomplete_sentences == 0).all():
- break
-
- # take most likely sequence
- decoder_tokens = decoder_tokens.view(bsz, beam_size, -1)[:, 0, :]
- return decoder_tokens
-
- def forward(self, input_text: List[str], beam_size: int, max_seq_len: int) -> Union[List[str], str]:
-
- model_input = self.transform(input_text)
- model_output_tensor = self.generate(encoder_tokens=model_input, beam_size=beam_size, max_seq_len=max_seq_len)
- model_output_list = torch.jit.annotate(List[List[int]], model_output_tensor.tolist())
- output_text = self.transform.decode(model_output_list)
-
- return output_text
diff --git a/torchtext/transforms.py b/torchtext/transforms.py
index b2d90c8..4684d58 100644
--- a/torchtext/transforms.py
+++ b/torchtext/transforms.py
@@ -1,7 +1,8 @@
import json
+import re
from copy import deepcopy
from functools import lru_cache
-from typing import Any, List, Optional, Tuple, Union
+from typing import Any, List, Mapping, Optional, Sequence, Tuple, Union
import torch
import torchtext # noqa: F401
@@ -29,6 +30,7 @@ __all__ = [
"PadTransform",
"StrToIntTransform",
"GPT2BPETokenizer",
+ "CharBPETokenizer",
"RegexTokenizer",
"Sequential",
]
@@ -288,6 +290,16 @@ class GPT2BPETokenizer(Module):
:type return_input: bool
"""
+ SPECIAL_TOKENS_ATTRIBUTES = [
+ "bos_token",
+ "eos_token",
+ "unk_token",
+ "sep_token",
+ "pad_token",
+ "cls_token",
+ "mask_token",
+ "additional_special_tokens",
+ ]
__jit_unused_properties__ = ["is_jitable"]
_seperator: torch.jit.Final[str]
@@ -349,6 +361,26 @@ class GPT2BPETokenizer(Module):
"""
return self.bpe.tokenize(text)
+ def add_special_tokens(self, special_tokens_dict: Mapping[str, Union[str, Sequence[str]]]) -> int:
+ """Add a dictionary of special tokens (eos, pad, cls…) to the encoder
+
+ :param special_tokens_dict: dict of string. Keys should be in the list of predefined special attributes:
+ [bos_token, eos_token, unk_token, sep_token, pad_token, cls_token, mask_token, additional_special_tokens].
+ Tokens are only added if they are not already in the vocabulary.
+ :type special_tokens_dict: Dict[str, Union[str, List[str]]]
+ :return: Number of tokens added to the vocabulary.
+ :rtype: int
+ """
+ for key in special_tokens_dict.keys():
+ assert (
+ key in self.SPECIAL_TOKENS_ATTRIBUTES
+ ), f"Key '{key}' is not in the special token list: {self.SPECIAL_TOKENS_ATTRIBUTES}"
+
+ return self.bpe.add_special_tokens(
+ {k: v for k, v in special_tokens_dict.items() if k != "additional_special_tokens"},
+ special_tokens_dict.get("additional_special_tokens", []),
+ )
+
def forward(self, input: Any) -> Any:
"""
:param input: Input sentence or list of sentences on which to apply tokenizer.
@@ -383,6 +415,222 @@ class GPT2BPETokenizer(Module):
return tokenizer_copy
return self
+ def decode(self, tokens: List[str]) -> str:
+ """Return a decoded string given a list of string token ids.
+
+ :param input: A list of strings, each string corresponds to token ids.
+ :type input: List[str]
+ :return: decoded text
+ :rtype: str
+ """
+ return self.bpe.decode([int(token) for token in tokens])
+
+
+def get_pairs(word):
+ """
+ Return set of symbol pairs in a word.
+ Word is represented as tuple of symbols (symbols being variable-length strings).
+ """
+ pairs = set()
+ prev_char = word[0]
+ for char in word[1:]:
+ pairs.add((prev_char, char))
+ prev_char = char
+ return pairs
+
+
+class CharBPETokenizer(Module):
+ """
+ Transform for a Character Byte-Pair-Encoding Tokenizer.
+
+ Args:
+ :param bpe_encoder_path: Path to the BPE encoder json file.
+ :type bpe_encoder_path: str
+ :param bpe_merges_path: Path to the BPE merges text file.
+ :type bpe_merges_path: str
+ :param return_tokens: Indicate whether to return split tokens. If False, it will return encoded token IDs (default: False).
+ :type return_tokens: bool
+ :param unk_token: The unknown token. If provided, it must exist in encoder.
+ :type unk_token: Optional[str]
+ :param suffix: The suffix to be used for every subword that is an end-of-word.
+ :type suffix: Optional[str]
+ :param special_tokens: Special tokens which should not be split into individual characters. If provided, these must exist in encoder.
+ :type special_tokens: Optional[List[str]]
+ """
+
+ def __init__(
+ self,
+ bpe_encoder_path: str,
+ bpe_merges_path: str,
+ return_tokens: bool = False,
+ unk_token: Optional[str] = None,
+ suffix: Optional[str] = None,
+ special_tokens: Optional[List[str]] = None,
+ ):
+ super().__init__()
+ with open(get_asset_local_path(bpe_encoder_path), "r") as f:
+ self._encoder = dict(json.load(f))
+ with open(get_asset_local_path(bpe_merges_path), "r", encoding="utf-8") as f:
+ bpe_data = f.read()
+
+ merges = [tuple(merge_str.split()) for merge_str in bpe_data.split("\n")[1:-1]]
+ self._decoder = {v: k for k, v in self._encoder.items()}
+ self._bpe_ranks = dict(zip(merges, range(len(merges))))
+ self._return_tokens = return_tokens
+ self._cache = {}
+ self._pat = r"\S+\n?"
+ if unk_token and unk_token not in self._encoder:
+ raise RuntimeError(
+ "Unknown token {} not found in encoder. Special tokens must be in encoder.".format(unk_token)
+ )
+ self._unk_token = unk_token
+ self._suffix = suffix
+ if special_tokens:
+ for token in special_tokens:
+ if token not in self._encoder:
+ raise RuntimeError(
+ "Special token {} not found in encoder. Special tokens must be in encoder.".format(token)
+ )
+ else:
+ self._cache[token] = token
+
+ @property
+ def vocab_size(self):
+ return len(self._encoder)
+
+ def _bpe(self, token):
+ """Splits the input token into bpe tokens. The output depends on the encoder and merge list specified in the class
+ constructor. For example, _bpe("pytorch") may return "p y t o r c h" or "py tor ch" or "pytorch" depending on which
+ merges exist.
+
+ Args:
+ text: An input text string.
+
+ Returns:
+ A string of space separated bpe tokens.
+ """
+ if token in self._cache:
+ return self._cache[token]
+
+ if self._suffix:
+ word = tuple(token[:-1]) + (token[-1] + self._suffix,)
+ else:
+ word = tuple(token)
+
+ pairs = get_pairs(word)
+
+ if not pairs:
+ if self._suffix:
+ return token + self._suffix
+ else:
+ return token
+
+ while True:
+ bigram = min(pairs, key=lambda pair: self._bpe_ranks.get(pair, float("inf")))
+ if bigram not in self._bpe_ranks:
+ break
+ first, second = bigram
+ new_word = []
+ i = 0
+ while i < len(word):
+ try:
+ j = word.index(first, i)
+ except ValueError:
+ new_word.extend(word[i:])
+ break
+ else:
+ new_word.extend(word[i:j])
+ i = j
+
+ if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+ new_word.append(first + second)
+ i += 2
+ else:
+ new_word.append(word[i])
+ i += 1
+ new_word = tuple(new_word)
+ word = new_word
+ if len(word) == 1:
+ break
+ else:
+ pairs = get_pairs(word)
+ word = " ".join(word)
+ self._cache[token] = word
+ return word
+
+ def encode(self, text: str) -> Union[List[int], List[str]]:
+ """Encode text into a list of tokens IDs
+
+ Args:
+ text: An input text string.
+
+ Returns:
+ A list of bpe token ids represents each bpe token. Return type depends on provided encoder file.
+ """
+ encoded_tokens = [
+ self._encoder.get(bpe_token, self._encoder.get(self._unk_token))
+ if self._unk_token
+ else self._encoder[bpe_token]
+ for bpe_token in self._tokenize(text)
+ ]
+ return encoded_tokens
+
+ def _tokenize(self, text: str) -> List[str]:
+ """Tokenize text into a list of tokens
+
+ Args:
+ text: An input text string.
+
+ Returns:
+ A list of bpe token strings
+ """
+ tokens = []
+ for token in re.findall(self._pat, text):
+ tokens.extend(bpe_token for bpe_token in self._bpe(token).split(" "))
+ return tokens
+
+ def decode(self, tokens: Union[List[int], List[str]]) -> str:
+ """Decode a list of token IDs into a string
+
+ Args:
+ token: A list of IDs (either str or int depending on encoder json)
+
+ Returns:
+ A decoded string
+ """
+ decoded_list = [
+ self._decoder.get(token, self._unk_token) if self._unk_token else self._decoder[token] for token in tokens
+ ]
+ if self._suffix:
+ return "".join(decoded_list).replace(self._suffix, " ")
+ else:
+ return " ".join(decoded_list)
+
+ def forward(self, input: Union[str, List[str]]) -> Union[List, List[List]]:
+ """Forward method of module encodes strings or list of strings into token ids
+
+ Args:
+ input: Input sentence or list of sentences on which to apply tokenizer.
+
+ Returns:
+ A list or list of lists of token IDs
+ """
+ if isinstance(input, List):
+ tokens: List[List[str]] = []
+ for text in input:
+ if self._return_tokens:
+ tokens.append(self._tokenize(text))
+ else:
+ tokens.append(self.encode(text))
+ return tokens
+ elif isinstance(input, str):
+ if self._return_tokens:
+ return self._tokenize(input)
+ else:
+ return self.encode(input)
+ else:
+ raise TypeError("Input type not supported")
+
class CLIPTokenizer(Module):
"""
@@ -582,7 +830,7 @@ class BERTTokenizer(Module):
if never_split is None:
never_split = []
self.bert_model = BERTEncoderPyBind(
- get_asset_local_path(vocab_path, overwite=True), do_lower_case, strip_accents, never_split
+ get_asset_local_path(vocab_path, overwrite=True), do_lower_case, strip_accents, never_split
)
self._return_tokens = return_tokens
self._vocab_path = vocab_path
@@ -681,7 +929,7 @@ class RegexTokenizer(Module):
Caveats
- The RE2 library does not support arbitrary lookahead or lookbehind assertions, nor does it support backreferences. Look at the `docs <https://swtch.com/~rsc/regexp/regexp3.html#caveats>`_ here for more info.
- - The final tokenization step always uses spaces as seperators. To split strings based on a specific regex pattern, similar to Python's `re.split <https://docs.python.org/3/library/re.html#re.split>`_, a tuple of ``('<regex_pattern>', ' ')`` can be provided.
+ - The final tokenization step always uses spaces as separators. To split strings based on a specific regex pattern, similar to Python's `re.split <https://docs.python.org/3/library/re.html#re.split>`_, a tuple of ``('<regex_pattern>', ' ')`` can be provided.
Example
Regex tokenization based on ``(patterns, replacements)`` list.
@@ -750,7 +998,7 @@ def bytes_to_unicode():
The reversible bpe codes work on unicode strings.
This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
- This is a signficant percentage of your normal, say, 32K bpe vocab.
+ This is a significant percentage of your normal, say, 32K bpe vocab.
To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
And avoids mapping to whitespace/control characters the bpe code barfs on.
"""
diff --git a/torchtext/utils.py b/torchtext/utils.py
index a7910b2..fb5dde7 100644
--- a/torchtext/utils.py
+++ b/torchtext/utils.py
@@ -47,10 +47,8 @@ def validate_file(file_obj, hash_value, hash_type="sha256"):
bool: return True if its a valid file, else False.
"""
- if hash_type == "sha256":
- hash_func = hashlib.sha256()
- elif hash_type == "md5":
- hash_func = hashlib.md5()
+ if hash_type in ("sha256", "md5"):
+ hash_func = hashlib.new(hash_type, usedforsecurity=False)
else:
raise ValueError
@@ -209,8 +207,8 @@ def _log_class_usage(klass):
torch._C._log_api_usage_once(identifier)
-def get_asset_local_path(asset_path: str, overwite=False) -> str:
- """Get local path for assets. Download if path does not exost locally
+def get_asset_local_path(asset_path: str, overwrite=False) -> str:
+ """Get local path for assets. Download if path does not exist locally
Args:
asset_path: Local path to asset or remote URL
overwrite: Indicate whether to overwrite the file when downloading from URL (default: False)
@@ -226,5 +224,5 @@ def get_asset_local_path(asset_path: str, overwite=False) -> str:
if os.path.exists(asset_path):
local_path = asset_path
else:
- local_path = download_from_url(url=asset_path, root=_CACHE_DIR, overwrite=overwite)
+ local_path = download_from_url(url=asset_path, root=_CACHE_DIR, overwrite=overwrite)
return local_path
diff --git a/version.txt b/version.txt
index 7cf6e10..bd25d3c 100644
--- a/version.txt
+++ b/version.txt
@@ -1 +1 @@
-0.14.1a0
+0.15.2a0
Debdiff
[The following lists of changes regard files as different if they have different names, permissions or owners.]
Files in second set of .debs but not in first
-rw-r--r-- root/root /usr/lib/debug/.build-id/5b/d59d7377200176807c24bbb15a896272abb68c.debug -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.15.2a0.egg-info/PKG-INFO -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.15.2a0.egg-info/dependency_links.txt -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.15.2a0.egg-info/not-zip-safe -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.15.2a0.egg-info/requires.txt -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.15.2a0.egg-info/top_level.txt -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/models/t5/__init__.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/models/t5/bundler.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/models/t5/model.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/models/t5/modules.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/models/t5/t5_transform.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/generate.py
Files in first set of .debs but not in second
-rw-r--r-- root/root /usr/lib/debug/.build-id/42/427f3ece7be794293fedf83ce5d94d6251a297.debug -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.14.1a0.egg-info/PKG-INFO -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.14.1a0.egg-info/dependency_links.txt -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.14.1a0.egg-info/not-zip-safe -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.14.1a0.egg-info/requires.txt -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext-0.14.1a0.egg-info/top_level.txt -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/__init__.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/t5/__init__.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/t5/bundler.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/t5/model.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/t5/modules.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/t5/t5_transform.py -rw-r--r-- root/root /usr/lib/python3/dist-packages/torchtext/prototype/models/t5/wrapper.py
No differences were encountered between the control files of package python3-torchtext
Control files of package python3-torchtext-dbgsym: lines which differ (wdiff format)
Build-Ids: 42427f3ece7be794293fedf83ce5d94d6251a297 5bd59d7377200176807c24bbb15a896272abb68c