add sphinx doc

.gitignore

@@ -21,4 +21,5 @@ venv/
 !README.md
 !README.cn.md
 python/jittor.egg-info
-dist/
+dist/
+!doc/source/*

doc/Makefile

@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

doc/build_doc.sh

@@ -0,0 +1,18 @@
+# sudo python3.7 -m pip install \
+#     recommonmark \
+#     sphinx sphinx-autobuild sphinx_rtd_theme \
+#     sphinx-autobuild \
+#     --timeout 100
+
+
+bpath=$(readlink -f "${BASH_SOURCE[0]}")
+bpath=$(dirname "${bpath}")
+
+jittor_path=$(readlink -f "${bpath}/..")
+
+echo "[doc path] $bpath" 
+echo "[jittor path] $jittor_path" 
+
+export PYTHONPATH=$jittor_path/python
+cd $bpath
+sphinx-autobuild -b html source build 

doc/source/README.cn.md

@@ -0,0 +1 @@
+../../README.cn.md

doc/source/conf.py

@@ -0,0 +1,98 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import sys
+jittor_path = os.path.abspath('../../python')
+print(f"[jittor_path] {jittor_path}")
+sys.path.insert(0, jittor_path)
+
+import jittor
+
+
+# -- Project information -----------------------------------------------------
+
+project = 'Jittor'
+copyright = '2020, Jittor'
+author = 'Jittor'
+
+# The full version, including alpha/beta/rc tags
+release = '1.1.3.1'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs.
+# Usually you set "language" from the command line for these cases.
+language = 'zh_CN'
+
+
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'recommonmark',
+    'sphinx.ext.autodoc',
+    # Auto-generate section labels.
+    'sphinx.ext.autosectionlabel',
+    'sphinx.ext.viewcode',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = []
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'alabaster'
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+import sphinx_rtd_theme
+html_theme = "sphinx_rtd_theme"
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+source_suffix = {
+    '.rst': 'restructuredtext',
+    '.txt': 'markdown',
+    '.md': 'markdown',
+}
+
+import recommonmark
+from recommonmark.transform import AutoStructify
+
+# At the bottom of conf.py
+def setup(app):
+    app.add_config_value('recommonmark_config', {
+            # 'url_resolver': lambda url: github_doc_root + url,
+            'auto_toc_tree_section': 'Contents',
+            }, True)
+    app.add_transform(AutoStructify)
+
+
+# Prefix document path to section labels, otherwise autogenerated labels would look like 'heading'
+# rather than 'path/to/file:heading'
+autosectionlabel_prefix_document = True

doc/source/index.rst

@@ -0,0 +1,40 @@
+.. Jittor documentation master file, created by
+   sphinx-quickstart on Mon May 18 23:05:53 2020.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+欢迎查阅计图文档
+==================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 内容一览:
+
+   README.cn.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 模块API:
+
+   jittor
+   jittor.nn
+   jittor.models
+   jittor.optim
+   jittor.init
+   jittor.contrib
+   jittor.dataset
+   jittor.transform
+
+
+.. toctree::
+   :maxdepth: 1
+   :caption: 其他:
+   
+   todo
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

doc/source/jittor.contrib.md

@@ -0,0 +1,10 @@
+jittor.contrib
+=====================
+
+这里是Jittor的贡献代码模块模块的API文档，此模块的代码可能还没有完全成熟，我们将在后续迭代开发中继续完善，您可以通过`from jittor import contrib`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor.contrib
+   :members:
+   :undoc-members:
+```

doc/source/jittor.dataset.md

@@ -0,0 +1,11 @@
+jittor.dataset
+=====================
+
+这里是Jittor的数据集模块的API文档，您可以通过`from jittor import dataset`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor.dataset
+   :imported-members:
+   :members:
+   :undoc-members:
+```

doc/source/jittor.init.md

@@ -0,0 +1,10 @@
+jittor.init
+=====================
+
+这里是Jittor的参数初始化模块的API文档，您可以通过`from jittor import init`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor.init
+   :members:
+   :undoc-members:
+```

doc/source/jittor.md

@@ -0,0 +1,44 @@
+jittor
+=====================
+
+## jittor
+
+这里是Jittor主模块的API文档，您可以通过`import jittor`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor
+   :members:
+   :undoc-members:
+```
+
+## jittor.core
+
+以下为Jittor的内核API，内核API可以通过`jittor.core.XXX`或者`jittor.XXX`直接访问。
+
+
+```eval_rst
+.. automodule:: jittor_core
+   :imported-members:
+   :members:
+   :undoc-members:
+```
+
+## jittor.ops
+
+这里是Jittor的基础算子模块的API文档，该API可以通过`jittor.ops.XXX`或者`jittor.XXX`直接访问。
+
+```eval_rst
+.. automodule:: jittor_core.ops
+   :members:
+   :undoc-members:
+```
+
+## jittor.Var
+
+这里是Jittor的基础变量类的API文档。该API可以通过`my_jittor_var.XXX`直接访问。
+
+```eval_rst
+.. automodule:: jittor_core.Var
+   :members:
+   :undoc-members:
+```

doc/source/jittor.models.md

@@ -0,0 +1,13 @@
+jittor.models
+=====================
+
+这里是Jittor的骨干网络模块的API文档，您可以通过`from jittor import models`来获取该模块。
+
+```eval_rst
+
+.. automodule:: jittor.models
+   :members:
+   :imported-members:
+   :undoc-members:
+```
+

doc/source/jittor.nn.md

@@ -0,0 +1,24 @@
+jittor.nn
+=====================
+
+这里是Jittor的神经网络模块的API文档，您可以通过`from jittor import nn`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor.nn
+   :members:
+   :undoc-members:
+
+.. automodule:: jittor.nn
+   :imported-members:
+   :members: Pool, pool, AdaptiveAvgPool2d
+   :undoc-members: 
+
+.. autoclass:: jittor.nn.ReLU
+   :members:
+.. autoclass:: jittor.nn.ReLU6
+   :members:
+.. autoclass:: jittor.nn.LeakyReLU
+   :members:
+.. autoclass:: jittor.nn.Softmax
+   :members:
+```

doc/source/jittor.optim.md

@@ -0,0 +1,10 @@
+jittor.optim
+=====================
+
+这里是Jittor的优化器模块的API文档，您可以通过`from jittor import optim`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor.optim
+   :members:
+   :undoc-members:
+```

doc/source/jittor.transform.md

@@ -0,0 +1,10 @@
+jittor.transform
+=====================
+
+这里是Jittor的 数据变换 模块的API文档，您可以通过`from jittor import transform`来获取该模块。
+
+```eval_rst
+.. automodule:: jittor.transform
+   :members:
+   :undoc-members:
+```

doc/source/todo.md

@@ -0,0 +1,12 @@
+TODO
+=====================
+
+## 文档相关
+
+*   文档语法规范
+*   文档加上教程链接
+*   MPI接口文档
+*   文档自动更新
+*   首页到文档的链接
+*   模型库的文档（GAN，segmentation，detection...）
+*   文档补全，重要的类加上使用example

extern/cuda/nccl/inc/nccl_warper.h

@@ -17,5 +17,6 @@ namespace jittor {
 
 extern ncclComm_t comm;
 extern ncclUniqueId id;
+extern int nccl_device_id;
 
 } // jittor

extern/cuda/nccl/src/nccl_warper.cc

@@ -6,6 +6,7 @@
 // This file is subject to the terms and conditions defined in
 // file 'LICENSE.txt', which is part of this source code package.
 // ***************************************************************
+#include "misc/cuda_flags.h"
 #include "nccl_warper.h"
 #include "event_queue.h"
 
@@ -17,25 +18,33 @@ namespace jittor {
 
 ncclComm_t comm;
 ncclUniqueId id;
+int nccl_device_id = 0;
 
 
 struct nccl_initer {
 
 nccl_initer() {
-    if (!get_device_count()) return;
+    int device_count = get_device_count();
+    if (!device_count) return;
+    if (!inside_mpi) return;
     if (mpi_world_rank == 0)
         checkCudaErrors(ncclGetUniqueId(&id));
     MPI_CHECK(MPI_Bcast((void *)&id, sizeof(id), MPI_BYTE, 0, MPI_COMM_WORLD));
-    LOGv << "NCCL init in device" << mpi_local_rank;
+    if (mpi_local_rank >= device_count)
-    checkCudaErrors(cudaSetDevice(mpi_local_rank));
+        LOGf << "mpi_local_rank(">>mpi_local_rank>>") is larger than device_count("
+            >>device_count>>")";
+    nccl_device_id = mpi_local_rank; // % device_count;
+    LOGv << "NCCL init in device" << nccl_device_id << "local_rank" << mpi_local_rank;
+    checkCudaErrors(cudaSetDevice(nccl_device_id));
     event_queue.run_sync([]() {
-        checkCudaErrors(cudaSetDevice(mpi_local_rank));
+        checkCudaErrors(cudaSetDevice(nccl_device_id));
     });
     checkCudaErrors(ncclCommInitRank(&comm, mpi_world_size, id, mpi_world_rank));
 }
 
 ~nccl_initer() {
     if (!get_device_count()) return;
+    if (!inside_mpi) return;
     checkCudaErrors(ncclCommDestroy(comm));
 }
 

extern/mpi/inc/mpi_warper.h

@@ -27,6 +27,7 @@ namespace jittor {
 extern int mpi_world_size;
 extern int mpi_world_rank;
 extern int mpi_local_rank;
+extern bool inside_mpi;
 
 // @pyjt(world_size)
 int _mpi_world_size();

extern/mpi/src/mpi_warper.cc

@@ -30,6 +30,7 @@ namespace jittor {
 int mpi_world_size = 1;
 int mpi_world_rank = 0;
 int mpi_local_rank = 0;
+bool inside_mpi = false;
 
 int _mpi_world_size() {
     return mpi_world_size;
@@ -73,6 +74,8 @@ static void getHostName(char* hostname, int maxlen) {
 struct mpi_initer {
 
 mpi_initer() {
+    inside_mpi = !!getenv("OMPI_COMM_WORLD_SIZE");
+    if (!inside_mpi) return;
     LOGvv << "MPI init...";
     MPI_CHECK(MPI_Init(NULL, NULL));
     MPI_CHECK(MPI_Comm_size(MPI_COMM_WORLD, &mpi_world_size));
@@ -95,6 +98,7 @@ mpi_initer() {
 }
 
 ~mpi_initer() {
+    if (!inside_mpi) return;
     MPI_CHECK(MPI_Finalize());
 }
 

python/jittor/__init__.py

@@ -17,6 +17,8 @@ with lock.lock_scope():
     from jittor_core.ops import *
     from . import compile_extern
     from .compile_extern import mkl_ops, mpi, mpi_ops
+    if core.get_device_count() == 0:
+        has_cuda = compile_extern.has_cuda = compiler.has_cuda = False
     if has_cuda:
         from .compile_extern import cudnn, curand, cublas
 
@@ -29,92 +31,6 @@ import pickle
 import sys
 import traceback
 
-def dfs(scope, vars):
-    for v in scope.children.values():
-        if type(v) == Scope:
-            dfs(v, vars)
-        else:
-            vars.append(v)
-
-def dfs_records(scope, records):
-    for v in scope.children.values():
-        if type(v) == Scope:
-            dfs_records(v, records)
-    for v in scope.records.values():
-        records.append(v)
-
-class Scope:
-    def __init__(self, parent=None, name=None):
-        self.children = OrderedDict()
-        self.index = {}
-        self.records = OrderedDict()
-        if name == None:
-            self.name = self.full_name = ""
-        else:
-            self.name = name
-            self.full_name = parent.full_name + name + "/"
-
-    def get_scope(self, name, unique=True):
-        if not unique:
-            index = self.index.get(name, 0)
-            self.index[name] = index+1
-            name = name + f'_{index}'
-        if name not in self.children:
-            sub_scope = Scope(self, name)
-            self.children[name] = sub_scope
-        else:
-            sub_scope = self.children[name]
-            assert type(sub_scope) == Scope, f"Name {name} is a Var: {sub_scope}"
-        return sub_scope
-
-    def make_var(self, shape, dtype, init, name, unique):
-        if not unique:
-            index = self.index.get(name, 0)
-            self.index[name] = index+1
-            name = name + f'_{index}'
-        if name in self.children:
-            var = self.children[name]
-            assert type(var) == core.Var, f"Name {name} exist: {var}"
-            assert (shape is None or var.shape == shape) and var.dtype == dtype, f"Shape or dtype not match {var} != {dtype}{shape}"
-            return var
-        else:
-            full_name = self.full_name + name
-            if type(init) != core.Var:
-                if callable(init):
-                    var = init(shape, dtype)
-                    if type(var) != core.Var:
-                        var = array(var)
-                else:
-                    assert init != None
-                    var = array(init)
-            else:
-                var = init
-            var.stop_fuse()
-            self.children[name] = var
-            var.name(full_name)
-            return var
-
-    def clean_index(self): self.index.clear()
-
-    def clean(self):
-        self.children.clear()
-        self.records.clear()
-        self.index.clear()
-
-current_scope = Scope()
-root_scope = current_scope
-
-class _call_record_scope:
-    def __enter__(self): pass
-    def __exit__(self, *exc): pass
-    def __call__(self, func):
-        def inner(*args, **kw):
-            with self:
-                ret = func(*args, **kw)
-                record_in_scope(ret, "output")
-            return ret
-        return inner
-
 class _call_no_record_scope:
     def __enter__(self): pass
     def __exit__(self, *exc): pass
@@ -143,30 +59,6 @@ class flag_scope(_call_no_record_scope):
         for k,v in self.flags_bk.items():
             setattr(flags, k, v)
 
-class var_scope(_call_record_scope):
-    def __init__(self, name="scope", unique=False, **jt_flags):
-        self.fs = flag_scope(**jt_flags)
-        self.name = name
-        self.unique = unique
-
-    def __enter__(self):
-        global current_scope
-        self.prev = current_scope
-        try:
-            current_scope = current_scope.get_scope(self.name, self.unique)
-            current_scope.clean_index()
-            self.fs.__enter__()
-        except:
-            current_scope = self.prev
-            del self.prev
-            raise
-
-    def __exit__(self, *exc):
-        self.fs.__exit__(*exc)
-        global current_scope
-        current_scope = self.prev
-        del self.prev
-
 single_log_capture = None
 
 class log_capture_scope(_call_no_record_scope):
@@ -229,75 +121,7 @@ class profile_scope(_call_no_record_scope):
         profiler.stop()
         self.report.extend(profiler.report())
 
-def make_var(shape=None, dtype="float32", init=None, name='var', unique=False):
-    return current_scope.make_var(shape, dtype, init, name, unique)
-
-def find_vars(path=None):
-    scope = current_scope
-    if path is not None:
-        assert isinstance(path, str)
-        ns = path.split("/")
-        if ns[-1] == "":
-            ns.pop()
-        for n in ns: scope = scope.children[n]
-    if not isinstance(scope, Scope):
-        return [scope]
-    vars = []
-    dfs(scope, vars)
-    return vars
-
-def find_var(path):
-    scope = current_scope
-    if path is not None:
-        assert isinstance(path, str)
-        ns = path.split("/")
-        for n in ns: scope = scope.children[n]
-    assert not isinstance(scope, Scope)
-    return scope
-
-def find_records(path=None):
-    scope = current_scope
-    if path is not None:
-        assert isinstance(path, str)
-        ns = path.split("/")
-        if ns[-1] == "":
-            ns.pop()
-        for n in ns: scope = scope.children[n]
-    assert isinstance(scope, Scope)
-    records = []
-    dfs_records(scope, records)
-    return records
-
-def find_record(path):
-    scope = current_scope
-    assert isinstance(path, str)
-    ns = path.split("/")
-    for n in ns[:-1]: scope = scope.children[n]
-    assert isinstance(scope, Scope)
-    return scope.records[ns[-1]]
-
-def find_scope(path):
-    scope = current_scope
-    if path is not None:
-        assert isinstance(path, str)
-        ns = path.split("/")
-        if ns[-1] == "":
-            ns.pop()
-        for n in ns: scope = scope.children[n]
-    assert isinstance(scope, Scope)
-    return scope
-
-def record_in_scope(self, name):
-    current_scope.records[name] = self
-    if isinstance(self, Var):
-        full_name = current_scope.full_name + name
-        self.name(full_name)
-    return self
-
-Var.record_in_scope = record_in_scope
-
 def clean():
-    current_scope.clean()
     import gc
     # make sure python do a full collection
     gc.collect()
@@ -411,11 +235,8 @@ def squeeze(x, dim):
 Var.squeeze = squeeze
 
 def clamp(x, min_v, max_v):
-    # TODO: change to x.maximum(min_v).minimum(max_v)
     assert min_v <= max_v
-    min_b = (x < min_v).int()
+    return x.maximum(min_v).minimum(max_v)
-    max_b = (x > max_v).int()
-    return x * (1 - min_b - max_b) + min_v * min_b + max_v * max_b
 Var.clamp = clamp
 
 def type_as(a, b):
@@ -456,32 +277,6 @@ def display_memory_info():
     fileline = f"{os.path.basename(fileline.filename)}:{fileline.lineno}"
     core.display_memory_info(fileline)
 
-def import_vars(data):
-    ''' Load variables into current scopes
-    example:
-        import_vars({"w":[1.0,2.0,3.0]})
-        jt.get_var([3], "float64", name="w", gen_index=False)
-    '''
-    for k in data:
-        v = data[k]
-        if type(v) != core.Var:
-            v = array(v).stop_fuse()
-        scopes = k.split("/")
-        scope = current_scope
-        for i in range(len(scopes)-1):
-            scope = scope.get_scope(scopes[i])
-        vname = scopes[-1]
-        assert vname not in scope.children, f"Var {k} exists. Please load_vars at the beginning"
-        v.name(k)
-        scope.children[vname] = v
-
-def export_vars():
-    ''' Export all vars into a dictionary
-    return: a dictionary, key is var name, value is numpy array
-    '''
-    data = { v.name():v.fetch_sync() for v in find_vars() }
-    return data
-
 def load(path):
     pkl_file = open(path, 'rb')
     model_dict = pickle.load(pkl_file)
@@ -489,7 +284,7 @@ def load(path):
 
 class Module:
     def __init__(self, *args, **kw):
-        __doc__ == 'doc'
+        pass
     def execute(self, *args, **kw):
         pass
     def __call__(self, *args, **kw):
@@ -498,8 +293,6 @@ class Module:
         return self.__str__()
     def _get_name(self):
         return self.__class__.__name__
-    def __doc__(self):
-        pass
     def __name__(self):
         pass
 
@@ -670,16 +463,12 @@ def make_module(func, exec_n_args=1):
         def __init__(self, *args, **kw):
             self.args = args
             self.kw = kw
-            self.__doc__ == 'doc'
         def execute(self, *args):
             return func(*args, *self.args, **self.kw)
         def __str__(self):
-            return 'str'
+            return f"{func.__name__}({self.extra_repr()})"
-        def __repr__(self):
-            return self.__str__()
         def extra_repr(self):
-            return ''
+            return ",".join(map(str, self.args))
-
     return MakeModule
 
 

python/jittor/compile_extern.py

@@ -6,7 +6,7 @@
 import os, sys, shutil
 from .compiler import *
 from jittor_utils import run_cmd, get_version
-from jittor.dataset.utils import download_url_to_local
+from jittor.utils.misc import download_url_to_local
 
 def search_file(dirs, name):
     for d in dirs:
@@ -256,6 +256,11 @@ def install_nccl(root_folder):
         LOG.i("Downloading nccl...")
         download_url_to_local(url, filename, root_folder, true_md5)
 
+        if core.get_device_count() == 0:
+            return
+        if not inside_mpi():
+            return
+
         import tarfile
         with tarfile.open(fullname, "r") as tar:
             tar.extractall(root_folder)
@@ -269,7 +274,7 @@ def setup_nccl():
     global nccl_ops, use_nccl
     use_nccl = os.environ.get("use_nccl", "1")=="1"
     nccl_ops = None
-    if not has_cuda or mpi is None:
+    if not has_cuda or not has_mpi:
         use_nccl = False
         return
     if not use_nccl: return
@@ -284,6 +289,7 @@ def setup_nccl():
         
         make_cache_dir(nccl_path)
         nccl_home = install_nccl(nccl_path)
+        if nccl_home is None: return
         nccl_include_path = os.path.join(nccl_home, "build", "include")
         nccl_lib_path = os.path.join(nccl_home, "build", "lib")
 
@@ -343,8 +349,6 @@ def setup_mpi():
     else:
         use_mpi = True
         has_mpi = True
-    if not inside_mpi():
-        use_mpi = False
     if not use_mpi:
         return
 
@@ -388,6 +392,8 @@ def setup_mpi():
         setattr(core.Var, k, warper(mpi_ops.__dict__[k]))
 
 setup_mpi()
+if not inside_mpi():
+    mpi = None
 setup_nccl()
 
 setup_cutt()

python/jittor/contrib.py

@@ -187,19 +187,3 @@ def setitem(x, slices, value):
 
 jt.Var.__getitem__ = jt.Var.slice_var = slice_var
 jt.Var.__setitem__ = setitem
-
-def adam(model, loss, lr=3e-4, betas=[0.9, 0.999], eps=1e-8):
-    ps = jt.find_vars(model)
-    gs = jt.grad(loss, ps)
-    with jt.var_scope('_'.join([model, 'adam']), unique=True):
-        adam_step = jt.make_var([1], init=jt.zeros)
-        adam_step += 1
-        for p,g in zip(ps,gs):
-            m = jt.make_var(p.shape, init=jt.zeros)
-            v = jt.make_var(p.shape, init=jt.zeros)
-            
-            m.assign(betas[0] * m + (1-betas[0]) * g)
-            v.assign(betas[1] * v + (1-betas[1]) * g * g)
-            step_size = lr * jt.sqrt(1-betas[1]**adam_step) / (1-betas[0] ** adam_step)
-            p -= m * step_size / (jt.sqrt(v) + eps)
-

python/jittor/dataset/__init__.py

@@ -0,0 +1,4 @@
+
+from .dataset import Dataset, ImageFolder
+from .mnist import MNIST
+from .voc import VOC

python/jittor/dataset/dataset.py

@@ -23,7 +23,7 @@ import jittor as jt
 
 dataset_root = os.path.join(pathlib.Path.home(), ".cache", "jittor", "dataset")
 mp_log_v = os.environ.get("mp_log_v", 0) 
-mpi = jt.compile_extern.mpi
+mpi = jt.mpi
 
 class Worker:
     def __init__(self, target, args, buffer_size):

python/jittor/dataset/mnist.py

@@ -12,7 +12,7 @@ import gzip
 from PIL import Image
 # our lib jittor import
 from jittor.dataset.dataset import Dataset, dataset_root
-from jittor.dataset.utils import ensure_dir, download_url_to_local
+from jittor.utils.misc import ensure_dir, download_url_to_local
 import jittor as jt 
 import jittor.transform as trans
 

python/jittor/dataset/utils.py

@@ -8,74 +8,9 @@
 # ***************************************************************
 
 import jittor as jt
-import os
-from six.moves import urllib
-import hashlib
-from tqdm import tqdm
 import numpy as np
 from collections.abc import Sequence, Mapping
 from PIL import Image
-from .. import lock
-
-def ensure_dir(dir_path):
-    if not os.path.isdir(dir_path):
-        os.makedirs(dir_path)
-
-def _progress():
-    pbar = tqdm(total=None)
-
-    def bar_update(block_num, block_size, total_size):
-        """ reporthook
-        @block_num: the num of downloaded data block
-        @block_size: the size of data block
-        @total_size: the total size of remote file
-        """
-        if pbar.total is None and total_size:
-            pbar.total = total_size
-        progress_bytes = block_num * block_size
-        pbar.update(progress_bytes - pbar.n)
-
-    return bar_update
-
-@lock.lock_scope()
-def download_url_to_local(url, filename, root_folder, md5):
-    ensure_dir(root_folder)
-    file_path = os.path.join(root_folder, filename)
-    if check_file_exist(file_path, md5):
-        print("Data file has been downloaded and verified")
-    else:
-        try:
-            print('Downloading ' + url + ' to ' + file_path)
-            urllib.request.urlretrieve(
-                url, file_path,
-                reporthook=_progress()
-            )
-        except(urllib.error.URLError, IOError) as e:
-            raise e
-    if not check_file_exist(file_path, md5):
-        raise RuntimeError("File downloads failed.")
-
-
-
-def check_file_exist(file_path, md5):
-    if not os.path.isfile(file_path):
-        return False
-    if md5 is None:
-        return True
-    return check_md5(file_path, md5)
-
-
-def calculate_md5(file_path, chunk_size=1024 * 1024):
-    md5 = hashlib.md5()
-    with open(file_path, 'rb') as f:
-        for chunk in iter(lambda: f.read(chunk_size), b''):
-            md5.update(chunk)
-    return md5.hexdigest()
-
-
-def check_md5(file_path, md5, **kwargs):
-    return md5 == calculate_md5(file_path, **kwargs)
-
 
 def get_random_list(n):
     return list(np.random.permutation(range(n)))

python/jittor/dataset/voc.py

@@ -12,7 +12,7 @@ import os
 from PIL import Image
 import matplotlib.pyplot as plt
 import cv2
-from dataset import Dataset, dataset_root
+from .dataset import Dataset, dataset_root
 
 class VOC(Dataset):
     NUM_CLASSES = 21

python/jittor/example.py

@@ -1,52 +0,0 @@
-import jittor as jt
-import numpy as np
-import sys, os
-f32 = jt.float32
-
-@jt.var_scope('linear')
-def linear(x, n):
-    w = jt.make_var([x.shape[-1], n], init=lambda *a:
-            (jt.random(*a)-f32(0.5)) / f32(x.shape[-1])**f32(0.5))
-    b = jt.make_var([n], init=lambda *a: jt.random(*a)-f32(0.5))
-    return jt.matmul(x, w) + b
-    
-def relu(x): return jt.maximum(x, f32(0))
-
-@jt.var_scope('model', unique=True)
-def model(x):
-    x = linear(x, 10)
-    x = relu(x)
-    x = linear(x, 1)
-    return x
-
-np.random.seed(0)
-jt.set_seed(3)
-n = 1000
-batch_size = 50
-base_lr = 0.05
-# we need to stop grad of global value to prevent memory leak
-lr = f32(base_lr).name("lr").stop_grad()
-
-def get_data(n):
-    for i in range(n):
-        x = np.random.rand(batch_size, 1)
-        y = x*x
-        yield np.float32(x), np.float32(y)
-
-for i,(x,y) in enumerate(get_data(n)):
-    pred_y = model(x).name("pred_y")
-    loss = ((pred_y - y)**f32(2)).name("loss")
-    loss_mean = loss.mean()
-    
-    ps = jt.find_vars('model')
-    gs = jt.grad(loss_mean, ps)
-    for p,g in zip(ps, gs):
-        p -= g * lr
-    if i>2:
-        assert prev == jt.liveness_info(), f"memory leak {prev} {jt.liveness_info()}"
-    prev = jt.liveness_info()
-    print(f"step {i}, loss = {loss_mean().sum()}")
-
-# result is 0.0009948202641680837
-result = 0.0009948202641680837
-assert abs(loss_mean.data - result) < 1e-6

python/jittor/nn.py

@@ -43,38 +43,6 @@ jt.Var.__imatmul__ = lambda a,b: a.assign(matmul(a,b))
 def get_init_var_rand(shape, dtype):
     return jt.array(np.random.normal(0.0, 1.0, shape).astype(np.float32))
 
-@jt.var_scope('conv')
-def conv(x, in_planes, out_planes, kernel_size, padding, stride = 1, init_method=None):
-    Kw = kernel_size
-    Kh = kernel_size
-    _C = in_planes
-    Kc = out_planes
-    N,C,H,W = x.shape
-
-    assert C==_C
-    if init_method==None:
-        w = jt.make_var([Kc, _C, Kh, Kw], init=lambda *a: init.relu_invariant_gauss(*a, mode="fan_out"))
-    else:
-        w = jt.make_var([Kc, _C, Kh, Kw], init=init_method)
-    xx = x.reindex([N,Kc,C,(H+padding*2-kernel_size)//stride+1,(W+padding*2-kernel_size)//stride+1,Kh,Kw], [
-        'i0', # Nid
-        'i2', # Cid
-        f'i3*{stride}-{padding}+i5', # Hid+Khid
-        f'i4*{stride}-{padding}+i6', # Wid+KWid
-    ])
-    ww = w.broadcast(xx.shape, [0,3,4])
-    yy = xx*ww
-    y = yy.sum([2,5,6]) # C, Kh, Kw
-    return y
-
-@jt.var_scope('linear')
-def linear(x, n):
-    w = jt.make_var([n, x.shape[-1]], init=lambda *a: init.invariant_uniform(*a))
-    w = w.reindex([w.shape[1], w.shape[0]],["i1","i0"])
-    bound = 1.0/math.sqrt(w.shape[0])
-    b = jt.make_var([n], init=lambda *a: init.uniform(*a,-bound,bound))
-    return jt.matmul(x, w) + b
-
 def relu(x): return jt.maximum(x, 0)
 def leaky_relu(x, scale=0.01): return jt.ternary(x>0, x, x*scale)
 def relu6(x): return jt.minimum(jt.maximum(x, 0), 6)

python/jittor/test/test_arg_pool_op.py

@@ -30,6 +30,7 @@ def check(jt_model, torch_model, shape, near_data):
         data = list(range(8)) * int((shape[0] * shape[1] * shape[2] * shape[3]) / 8)
         random.shuffle(data)
         x = jt.array(data).float32().reshape(shape)
+        x.data
     else:
         x = jt.random(shape)
     y = jt_model(x)

python/jittor/test/test_arg_reduce_op.py

@@ -12,7 +12,7 @@ import numpy as np
 from jittor import compile_extern
 from .test_log import find_log_with_re
 import copy
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cublas_ops, cudnn_ops, cub_ops
 else:
     cublas_ops = cudnn_ops = cub_ops = None

python/jittor/test/test_argsort_op.py

@@ -11,7 +11,7 @@ import jittor as jt
 import numpy as np
 from jittor import compile_extern
 from .test_log import find_log_with_re
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cublas_ops, cudnn_ops, cub_ops
 else:
     cublas_ops = cudnn_ops = cub_ops = None

python/jittor/test/test_array.py

@@ -30,7 +30,7 @@ class TestArray(unittest.TestCase):
         a.data = jt.array([7,8,9])
         assert (a.fetch_sync()==[7,8,9]).all()
 
-    @unittest.skipIf(not compile_extern.has_cuda, "Cuda not found")
+    @unittest.skipIf(not jt.has_cuda, "Cuda not found")
     @jt.flag_scope(use_cuda=1)
     def test_memcopy_overlap(self):
         import time
@@ -95,13 +95,13 @@ class TestArray(unittest.TestCase):
             with jt.flag_scope(use_cuda=1):
                 assert (jt.array([1,2,3]).reshape((1,3)).data==[1,2,3]).all()
     
-    @unittest.skipIf(not compile_extern.has_cuda, "Cuda not found")
+    @unittest.skipIf(not jt.has_cuda, "Cuda not found")
     def test_array_dual(self):
         with jt.flag_scope(use_cuda=1):
             a = jt.array(np.float32([1,2,3]))
             assert (a.data==[1,2,3]).all()
         
-    @unittest.skipIf(not compile_extern.has_cuda, "Cuda not found")
+    @unittest.skipIf(not jt.has_cuda, "Cuda not found")
     def test_array_migrate(self):
         with jt.flag_scope(use_cuda=1):
             a = jt.array(np.float32([1,2,3]))

python/jittor/test/test_compile_options.py

@@ -18,7 +18,7 @@ class TestCompileOptions(unittest.TestCase):
         assert a.compile_options=={"compile_shapes":1}
         b = a+a
         assert b.compile_options=={}
-        with jt.var_scope(compile_options={"compile_shapes":1}):
+        with jt.flag_scope(compile_options={"compile_shapes":1}):
             c = a+b
         assert c.compile_options=={"compile_shapes":1}
         with jt.profile_scope() as report:

python/jittor/test/test_conv_tuner.py

@@ -14,7 +14,7 @@ from jittor import compile_extern
 # TODO: compare with pytorch
 
 from jittor.test.test_log import find_log_with_re
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cublas_ops, cudnn_ops
 else:
     cublas_ops = cudnn_ops = None
@@ -28,10 +28,7 @@ def conv_nchw(x, in_planes, out_planes, kernel_size, padding, stride = 1, dilati
     
     assert C==_C
     if w_ is None:
-        if init_method==None:
+        assert 0
-            w = jt.make_var([Kc, _C, Kh, Kw], init=lambda *a: init.relu_invariant_gauss(*a, mode="fan_out"))
-        else:
-            w = jt.make_var([Kc, _C, Kh, Kw], init=init_method)
     else:
         w = w_
     oh = (H-Kh*dilation+dilation-1+padding*2)//stride+1
@@ -56,10 +53,7 @@ def conv_nhwc(x, in_planes, out_planes, kernel_size, padding, stride = 1, dilati
     
     assert C==_C
     if w_ is None:
-        if init_method==None:
+        assert 0
-            w = jt.make_var([Kc, _C, Kh, Kw], init=lambda *a: init.relu_invariant_gauss(*a, mode="fan_out"))
-        else:
-            w = jt.make_var([Kc, _C, Kh, Kw], init=init_method)
     else:
         w = w_
     oh = (H-Kh*dilation+dilation-1+padding*2)//stride+1

python/jittor/test/test_cublas_test_op.py

@@ -7,7 +7,7 @@ import unittest
 import jittor as jt
 import os
 from jittor import compile_extern
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cublas_ops, cudnn_ops, cub_ops
 else:
     cublas_ops = cudnn_ops = cub_ops = None

python/jittor/test/test_cudnn_op.py

@@ -10,7 +10,7 @@ import numpy as np
 from jittor import compile_extern
 
 from jittor.test.test_log import find_log_with_re
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cublas_ops, cudnn_ops
 else:
     cublas_ops = cudnn_ops = None

python/jittor/test/test_cutt.py

@@ -12,7 +12,7 @@ import numpy as np
 from jittor import compile_extern
 from .test_log import find_log_with_re
 import copy
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cutt_ops
 else:
     cutt_ops = None

python/jittor/test/test_cutt_transpose_op.py

@@ -11,7 +11,7 @@ from .test_grad import ngrad
 from itertools import permutations
 from jittor import compile_extern
 from .test_log import find_log_with_re
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cutt_ops
 else:
     cutt_ops = None

python/jittor/test/test_flags.py

@@ -21,7 +21,7 @@ class TestFlags(unittest.TestCase):
     
     def test_scope(self):
         prev = jt.flags.log_v
-        with jt.var_scope(log_v=1):
+        with jt.flag_scope(log_v=1):
             assert jt.flags.log_v == 1
         assert jt.flags.log_v == prev
 

python/jittor/test/test_fused_op.py

@@ -32,7 +32,7 @@ def performance_test_scope(warmup=0, rerun=0, **args):
         jt.profiler.start(warmup, rerun)
     report = []
     try:
-        with jt.var_scope(**args):
+        with jt.flag_scope(**args):
             yield report
     finally:
         jt.profiler.stop()

python/jittor/test/test_group_conv_tuner.py

@@ -14,7 +14,7 @@ from jittor import compile_extern
 # TODO: compare with pytorch
 
 from jittor.test.test_log import find_log_with_re
-if compile_extern.has_cuda:
+if jt.has_cuda:
     from jittor.compile_extern import cublas_ops, cudnn_ops
 else:
     cublas_ops = cudnn_ops = None
@@ -34,10 +34,7 @@ def conv_nchw(x, in_planes, out_planes, kernel_size, padding, stride=1, dilation
     ow = (W+padding[1]*2-Kw*dilation[1]+dilation[1]-1)//stride[1]+1
 
     if w_ is None:
-        if init_method==None:
+        assert 0
-            w = jt.make_var([oc, C // G, Kh, Kw], init=lambda *a: init.relu_invariant_gauss(*a, mode="fan_out"))
-        else:
-            w = jt.make_var([oc, C // G, Kh, Kw], init=init_method)
     else:
         w = w_
 

python/jittor/test/test_log.py

@@ -29,7 +29,7 @@ def find_log_with_re(logs, pattern=None, **args):
 class TestLog(unittest.TestCase):
     def test_log_capture(self):
         LOG.log_capture_start()
-        with jt.var_scope(log_v=1000, log_vprefix=""):
+        with jt.flag_scope(log_v=1000, log_vprefix=""):
             LOG.v("1")
             LOG.vv("2")
             LOG.i("3")

python/jittor/test/test_longest_dis_fuse.py

@@ -16,16 +16,6 @@ import numpy as np
 def get_init_var(shape, dtype):
     return jt.random(shape, dtype)
 
-def batch_norm(x):
-    xmean = jt.mean(x, dims=[0,2,3], keepdims=1)
-    x2mean = jt.mean(x*x, dims=[0,2,3], keepdims=1)
-    norm_x = (x-xmean.broadcast_var(x))/(jt.sqrt(x2mean-xmean*xmean+jt.float32(1e-5)).broadcast_var(x))
-    w = jt.make_var([x.shape[1]], init=get_init_var)
-    b = jt.make_var([x.shape[1]], init=get_init_var)
-    w = w.broadcast([1, w.shape[0],1,1], [0,2,3])
-    b = b.broadcast([1, b.shape[0],1,1], [0,2,3])
-    return norm_x * w + b
-
 def pool(x, size, op, padding, stride = 1): # TODO: stride, padding
     N,C,H,W = x.shape
     h = (H+padding*2-size)//stride+1
@@ -43,41 +33,25 @@ def pool(x, size, op, padding, stride = 1): # TODO: stride, padding
         "i3", # Wid
     ])
 
-def conv(x, in_planes, out_planes, kernel_size, padding, stride = 1):
-    Kw = kernel_size
-    Kh = kernel_size
-    _C = in_planes
-    Kc = out_planes
-    N,C,H,W = x.shape
-    assert C==_C
-    w = jt.make_var([Kc, _C, Kh, Kw], init=get_init_var)
-    xx = x.reindex([N,Kc,C,(H+padding*2-kernel_size)//stride+1,(W+padding*2-kernel_size)//stride+1,Kh,Kw], [
-        'i0', # Nid
-        'i2', # Cid
-        f'i3*{stride}-{padding}+i5', # Hid+Khid
-        f'i4*{stride}-{padding}+i6', # Wid+KWid
-    ])
-    ww = w.broadcast(xx.shape, [0,3,4])
-    yy = xx*ww
-    y = yy.sum([2,5,6]) # Kc, Kh, Kw
-    return y
-
 def relu(x): return jt.maximum(x, jt.float32(0))
 
-@jt.var_scope('resnet_fake', unique=True)
+def resnet_fake():
-def resnet_fake(x):
+    from jittor import nn
-    x = conv(x, 3, 64, 7, 3, 2)
+    net = nn.Sequential(
-    x = batch_norm(x)
+        nn.Conv(3, 64, 7, 2, 3),
-    x = relu(x)
+        nn.BatchNorm(64),
-    x = pool(x, 3, "maximum", 1, 2)
+        nn.ReLU(),
-    return x
+        nn.Pool(3, 2, 1)
+    )
+    return net
 
 class TestLongestDisFuse(unittest.TestCase):
         
     def test_longest_dis_fuse(self):
         x = jt.array(np.random.rand(1,3,224,224).astype(np.float32))
-        loss = jt.sum(resnet_fake(x))
+        net = resnet_fake()
-        ps = jt.find_vars('resnet_fake') 
+        loss = jt.sum(net(x))
+        ps = net.parameters()
         gs = jt.grad(loss, ps)
         jt.sync(gs)
         # assert not alloc big tensor

python/jittor/test/test_matmul.py

@@ -12,13 +12,6 @@ import numpy as np
 from .test_log import find_log_with_re
 f32 = jt.float32
 from jittor import nn, Module
-
-@jt.var_scope('linear')
-def linear(x, n):
-    w = jt.make_var([x.shape[-1], n], init=lambda *a:
-            (jt.random(*a)-f32(0.5)) / f32(x.shape[-1])**f32(0.5))
-    b = jt.make_var([n], init=lambda *a: jt.random(*a)-f32(0.5))
-    return jt.nn.matmul(x, w) + b
     
 def relu(x): return jt.maximum(x, f32(0))
 

python/jittor/test/test_merge_single_array_op.py

@@ -87,8 +87,7 @@ class TestSingleArray(unittest.TestCase):
             jt.set_seed(3)
 
             x = f32(np.random.rand(1, 1))
-            w = jt.make_var([x.shape[-1], 10], init=lambda *a:
+            w = (jt.random([x.shape[-1], 10])-f32(0.5)) / f32(x.shape[-1])**f32(0.5)
-                    (jt.random(*a)-f32(0.5)) / f32(x.shape[-1])**f32(0.5))
             jt.nn.matmul(x, w).data
 
     def test4(self):

python/jittor/test/test_mkl_conv_op.py

@@ -59,7 +59,7 @@ class TestMklConvOp(unittest.TestCase):
 
         a_jt = jt.array(a)
         b_jt = jt.array(b)
-        with jt.var_scope(enable_tuner=0,compile_options={"test_mkl_conv":1}):
+        with jt.flag_scope(enable_tuner=0,compile_options={"test_mkl_conv":1}):
             c_jt = conv(a_jt, b_jt, 3, 2).data
         with jt.log_capture_scope(
             enable_tuner=1,
@@ -84,7 +84,7 @@ class TestMklConvOp(unittest.TestCase):
 
             a_jt = jt.array(a)
             b_jt = jt.array(b)
-            with jt.var_scope(enable_tuner=0,
+            with jt.flag_scope(enable_tuner=0,
                 compile_options={"test_mkl_conv":uid[0]}):
                 c_jt = conv_nhwc_hwio(a_jt, b_jt, stride, pad).data
             with jt.log_capture_scope(
@@ -118,7 +118,7 @@ class TestMklConvOp(unittest.TestCase):
         a_jt = jt.array(a)
         b_jt = jt.array(b)
 
-        with jt.var_scope(
+        with jt.flag_scope(
             enable_tuner=0,
             compile_options={"test_mkl_conv":1}
         ):
@@ -164,7 +164,7 @@ class TestMklConvOp(unittest.TestCase):
         a_jt = jt.array(a)
         b_jt = jt.array(b)
 
-        with jt.var_scope(
+        with jt.flag_scope(
             enable_tuner=0,
             compile_options={"test_mkl_conv":1}
         ):

python/jittor/test/test_mpi_batchnorm.py

@@ -15,6 +15,8 @@ import numpy as np
 from jittor.test.test_mpi import run_mpi_test
 
 mpi = jt.compile_extern.mpi
+if mpi:
+    n = mpi.world_size()
 
 class FakeMpiBatchNorm(nn.Module):
     def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=None, is_train=True):
@@ -57,7 +59,8 @@ class TestMpiBatchnorm(unittest.TestCase):
         mpi = jt.compile_extern.mpi
         data = np.random.rand(30,3,10,10).astype("float32")
         x1 = jt.array(data)
-        x2 = jt.array(data[mpi.world_rank()*10:(mpi.world_rank()+1)*10,...])
+        stride = 30//n
+        x2 = jt.array(data[mpi.world_rank()*stride:(mpi.world_rank()+1)*stride,...])
         
         bn1 = nn.BatchNorm(3, sync=False)
         bn2 = nn.BatchNorm(3, sync=True)
@@ -75,7 +78,8 @@ class TestMpiBatchnorm(unittest.TestCase):
         mpi = jt.compile_extern.mpi
         data = np.random.rand(30,3,10,10).astype("float32")
         global_x = jt.array(data)
-        x = jt.array(data[mpi.world_rank()*10:(mpi.world_rank()+1)*10,...])
+        stride = 30//n
+        x = jt.array(data[mpi.world_rank()*stride:(mpi.world_rank()+1)*stride,...])
         
         bn1 = nn.BatchNorm(3, sync=True)
         bn2 = FakeMpiBatchNorm(3)
@@ -98,7 +102,7 @@ class TestMpiBatchnorm(unittest.TestCase):
 @unittest.skipIf(not jt.compile_extern.has_mpi, "no mpi found")
 class TestMpiBatchnormEntry(unittest.TestCase):
     def test(self):
-        run_mpi_test(3, "test_mpi_batchnorm")
+        run_mpi_test(2, "test_mpi_batchnorm")
 
 if __name__ == "__main__":
     unittest.main()

python/jittor/test/test_mpi_op.py

@@ -13,6 +13,8 @@ import numpy as np
 from jittor.test.test_mpi import run_mpi_test
 
 mpi = jt.compile_extern.mpi
+if mpi:
+    n = mpi.world_size()
 
 @unittest.skipIf(mpi is None, "no inside mpirun")
 class TestMpiOps(unittest.TestCase):
@@ -24,9 +26,9 @@ class TestMpiOps(unittest.TestCase):
     def test_all_reduce(self):
         x = jt.random([5, 5])
         y = x.mpi_all_reduce()
-        assert np.allclose(y.data, (x*3).data)
+        assert np.allclose(y.data, (x*n).data)
         g = jt.grad(y,x)
-        assert np.allclose(g.data, np.ones([5,5])*3)
+        assert np.allclose(g.data, np.ones([5,5])*n)
 
     def test_all_reduce_mean(self):
         x = jt.random([5, 5])
@@ -45,7 +47,7 @@ class TestMpiOps(unittest.TestCase):
         assert np.allclose(y.data, data.data)
         g = jt.grad(y,x)
         if mpi.world_rank() == 0:
-            assert np.allclose(g.data, np.ones([5,5])*3)
+            assert np.allclose(g.data, np.ones([5,5])*n)
         else:
             assert np.allclose(g.data, np.zeros([5,5]))
 
@@ -54,7 +56,7 @@ class TestMpiOps(unittest.TestCase):
         y = x.mpi_reduce(root=0)
         y.sync()
         if mpi.world_rank() == 0:
-            assert np.allclose(y.data, (x*3).data)
+            assert np.allclose(y.data, (x*n).data)
         else:
             assert np.allclose(y.data, np.zeros([5,5]))
         g = jt.grad(y,x)
@@ -64,7 +66,7 @@ class TestMpiOps(unittest.TestCase):
 @unittest.skipIf(not jt.compile_extern.has_mpi, "no mpi found")
 class TestMpiOpsEntry(unittest.TestCase):
     def test(self):
-        run_mpi_test(3, "test_mpi_op")
+        run_mpi_test(2, "test_mpi_op")
 
 if __name__ == "__main__":
     unittest.main()

python/jittor/test/test_scope.py

@@ -1,105 +0,0 @@
-# ***************************************************************
-# Copyright (c) 2020 Jittor. Authors: Dun Liang <randonlang@gmail.com>. All Rights Reserved.
-# This file is subject to the terms and conditions defined in
-# file 'LICENSE.txt', which is part of this source code package.
-# ***************************************************************
-import unittest
-import jittor as jt
-import numpy as np
-from .test_core import expect_error
-ops = jt.ops
-
-@jt.var_scope('linear')
-def linear(x, n):
-    w = jt.make_var([x.shape[-1], n], init=ops.random)
-    return jt.matmul(x, w)
-
-@jt.var_scope('model', unique=True)
-def model(x):
-    x = linear(x, 10)
-    # x = relu(x)
-    x = linear(x, 10)
-    # x = relu(x)
-    x = linear(x, 1)
-    return x
-
-class TestScope(unittest.TestCase):
-    def test_name(self):
-        jt.clean()
-        @jt.var_scope('model', unique=True)
-        def model():
-            with jt.var_scope('a'):
-                assert jt.current_scope.full_name == "model/a_0/"
-            with jt.var_scope('b'):
-                with jt.var_scope('b'):
-                    assert jt.current_scope.full_name == "model/b_0/b_0/"
-            with jt.var_scope('c'):
-                assert jt.current_scope.full_name == "model/c_0/"
-        model()
-        model()
-        model()
-        jt.clean()
-        
-    def test_var(self):
-        jt.clean()
-        for i in range(2):
-            x = jt.array([[1]])
-            y = model(x)
-            params = jt.find_vars()
-            assert len(params) == 3
-            names = [ p.name() for p in params ]
-            assert names == [
-                "model/linear_0/var_0",
-                "model/linear_1/var_0",
-                "model/linear_2/var_0",
-            ], str(names)
-            jt.find_var("model/linear_0/var_0")
-            expect_error(lambda: jt.find_var("model/linear_0"))
-            expect_error(lambda: jt.find_var("model/linear"))
-            assert len(jt.find_vars("model/linear_0/var_0")) == 1
-            assert len(jt.find_vars("model/linear_0/")) == 1
-            assert len(jt.find_vars("model/")) == 3
-        jt.clean()
-        
-    def test_get_var_unique(self):
-        jt.clean()
-        x = jt.make_var([1], init=ops.random)
-        y = jt.make_var([1], init=ops.random)
-        z = jt.make_var([1], init=ops.random)
-        assert x.name() == "var_0"
-        assert y.name() == "var_1", y.name()
-        assert z.name() == "var_2"
-        x = jt.make_var([1], name="x", unique=True, init=ops.random)
-        y = jt.make_var([1], name="y", unique=True, init=ops.random)
-        z = jt.make_var([1], name="z", unique=True, init=ops.random)
-        assert x.name() == "x"
-        assert y.name() == "y"
-        assert z.name() == "z"
-        expect_error(lambda: jt.make_var([2], name="x", unique=True, init=ops.random))
-        jt.clean()
-
-    def test_record_scope(self):
-        jt.clean()
-        @jt.var_scope("func")
-        def func(a):
-            b = a+1
-            jt.record_in_scope(b, "b")
-            c = b*2
-            return c
-        a = jt.array([1,2,3])
-        func(a)
-        assert np.allclose(jt.find_record("func_0/output").data, (a.data+1)*2)
-        assert np.allclose(jt.find_scope("func_0").records["output"].data, (a.data+1)*2)
-        recs = jt.find_records()
-        rec_names = [ r.name() for r in recs ]
-        assert len(recs)==2 and rec_names==["func_0/b","func_0/output"]
-        
-    def test_get_var_init(self):
-        jt.clean()
-        assert (jt.make_var(init=[1,2,3]).data == [1,2,3]).all()
-        assert (jt.make_var(shape=[3], init=np.zeros).data == [0,0,0]).all()
-        assert (jt.make_var(init=jt.array([1,2,3]) == [1,2,3]).data).all()
-        jt.clean()
-
-if __name__ == "__main__":
-    unittest.main()

python/jittor/test/test_tracer.py

@@ -12,7 +12,7 @@ class TestTracer(unittest.TestCase):
 
         # force use addr2line
         jt.flags.gdb_path = ""
-        with jt.var_scope(gdb_path=""):
+        with jt.flag_scope(gdb_path=""):
             jt.print_trace()
 
 

python/jittor/utils/misc.py

@@ -0,0 +1,73 @@
+# ***************************************************************
+# Copyright (c) 2020 Jittor. Authors:
+#     Meng-Hao Guo <guomenghao1997@gmail.com>
+#     Dun Liang <randonlang@gmail.com>.
+# All Rights Reserved.
+# This file is subject to the terms and conditions defined in
+# file 'LICENSE.txt', which is part of this source code package.
+# ***************************************************************
+import jittor as jt
+import os
+from six.moves import urllib
+import hashlib
+from tqdm import tqdm
+from .. import lock
+
+def ensure_dir(dir_path):
+    if not os.path.isdir(dir_path):
+        os.makedirs(dir_path)
+
+def _progress():
+    pbar = tqdm(total=None)
+
+    def bar_update(block_num, block_size, total_size):
+        """ reporthook
+        @block_num: the num of downloaded data block
+        @block_size: the size of data block
+        @total_size: the total size of remote file
+        """
+        if pbar.total is None and total_size:
+            pbar.total = total_size
+        progress_bytes = block_num * block_size
+        pbar.update(progress_bytes - pbar.n)
+
+    return bar_update
+
+@lock.lock_scope()
+def download_url_to_local(url, filename, root_folder, md5):
+    ensure_dir(root_folder)
+    file_path = os.path.join(root_folder, filename)
+    if check_file_exist(file_path, md5):
+        print("Data file has been downloaded and verified")
+    else:
+        try:
+            print('Downloading ' + url + ' to ' + file_path)
+            urllib.request.urlretrieve(
+                url, file_path,
+                reporthook=_progress()
+            )
+        except(urllib.error.URLError, IOError) as e:
+            raise e
+    if not check_file_exist(file_path, md5):
+        raise RuntimeError("File downloads failed.")
+
+
+
+def check_file_exist(file_path, md5):
+    if not os.path.isfile(file_path):
+        return False
+    if md5 is None:
+        return True
+    return check_md5(file_path, md5)
+
+
+def calculate_md5(file_path, chunk_size=1024 * 1024):
+    md5 = hashlib.md5()
+    with open(file_path, 'rb') as f:
+        for chunk in iter(lambda: f.read(chunk_size), b''):
+            md5.update(chunk)
+    return md5.hexdigest()
+
+
+def check_md5(file_path, md5, **kwargs):
+    return md5 == calculate_md5(file_path, **kwargs)

src/init.cc

@@ -23,7 +23,7 @@ int current_seed;
 
 static void init_cuda_devices() {
 #ifdef HAS_CUDA
-    int count;
+    int count=0;
     cudaGetDeviceCount(&count);
     for (int i=0; i<count; i++) {
         cudaDeviceProp devProp;

src/op.cc

@@ -91,7 +91,14 @@ void Op::do_jit_prepare() {
     memcheck_all_exist();
     jk << name();
     jit_prepare();
-    if (!jk.empty()) {
+    if (jk.empty()) {
+        // not a jit op
+        bool has_cuda = flags.get(NodeFlags::_cuda);
+        bool has_cpu = flags.get(NodeFlags::_cpu);
+        CHECK(has_cuda || has_cpu);
+        if (has_cuda && has_cpu && !use_cuda)
+            flags.set(NodeFlags::_cuda, 0);
+    } else {
         // check use int64_t as index_t if array is too big
         int in_id=0, out_id=0;
         bool use_int64_t = false;