Merge pull request #69 from iotamudelta/master

Merge from upstream

Author: iotamudelta

.jenkins/pytorch/test.sh

@@ -44,13 +44,10 @@ if [[ "$BUILD_ENVIRONMENT" == *asan* ]]; then
     (cd test && ! get_exit_code python -c "import torch; torch._C._crash_if_aten_asan(3)")
 fi
 
-export ATEN_DISABLE_AVX=
-export ATEN_DISABLE_AVX2=
 if [[ "${JOB_BASE_NAME}" == *-NO_AVX-* ]]; then
-  export ATEN_DISABLE_AVX=1
-fi
-if [[ "${JOB_BASE_NAME}" == *-NO_AVX2-* ]]; then
-  export ATEN_DISABLE_AVX2=1
+  export ATEN_CPU_CAPABILITY=default
+elif [[ "${JOB_BASE_NAME}" == *-NO_AVX2-* ]]; then
+  export ATEN_CPU_CAPABILITY=avx
 fi
 
 test_python_nn() {

aten/src/ATen/Layout.h

@@ -1,6 +1,9 @@
 #pragma once
 
 #include <ATen/ScalarType.h>
+#include <ATen/Error.h>
+
+#include <iostream>
 
 namespace at {
 enum class Layout { Strided, Sparse };
@@ -18,3 +21,14 @@ inline Layout layout_from_backend(Backend backend) {
   }
 }
 } // namespace at
+
+inline std::ostream& operator<<(std::ostream& stream, at::Layout layout) {
+  switch (layout) {
+    case at::kStrided:
+      return stream << "Strided";
+    case at::kSparse:
+      return stream << "Sparse";
+    default:
+      AT_ERROR("Unknown layout");
+  }
+}

aten/src/ATen/ScalarType.h

@@ -1,11 +1,12 @@
 #pragma once
 
-#include <stdint.h>
-
 #include "ATen/ArrayRef.h"
 #include "ATen/ATenGeneral.h"
 #include "ATen/Half.h"
 
+#include <cstdint>
+#include <iostream>
+
 namespace at {
 
 // NB: Order matters for this macro; it is relied upon in
@@ -168,3 +169,9 @@ typedef ArrayRef<int64_t> IntList;
 typedef ArrayRef<Tensor> TensorList;
 
 } // namespace at
+
+inline std::ostream& operator<<(
+    std::ostream& stream,
+    at::ScalarType scalar_type) {
+  return stream << at::toString(scalar_type);
+}

aten/src/ATen/SparseTensorImpl.cpp

@@ -19,7 +19,7 @@ namespace at {
 // we don't currently support zero-size dimensions, so we can't actually
 // do this; so we just allocate zero-size tensors for everything.
 SparseTensorImpl::SparseTensorImpl(Type * type)
-    : TensorImpl(type)
+    : TensorImpl(type, nullptr)
     , size_{0}
     , sparseDims_(1)
     , denseDims_(0)

aten/src/ATen/TensorImpl.cpp

@@ -3,6 +3,8 @@
 #include <ATen/Tensor.h>
 #include <ATen/optional.h>
 
+#include <TH/THTensor.hpp>
+
 namespace at {
 Tensor& TensorImpl::grad() {
   AT_ERROR("grad is not implemented for Tensor");
@@ -33,4 +35,42 @@ void Tensor::backward(
     bool create_graph) {
   pImpl->backward(std::move(gradient), keep_graph, create_graph);
 }
+
+TensorImpl::~TensorImpl() {
+  if (tensor) tensor->release();
+}
+
+IntList TensorImpl::sizes() const {
+  // NB: dim in tensor is not synchronized with THTensor, so it's
+  // important to apply dim here
+  return IntList(THTensor_getSizePtr(tensor), dim());
+}
+
+IntList TensorImpl::strides() const {
+  // NB: dim in tensor is not synchronized with THTensor, so it's
+  // important to apply dim here
+  return IntList(THTensor_getStridePtr(tensor), dim());
+}
+
+void TensorImpl::release_resources() {
+  if (tensor) {
+      tensor->release();
+      tensor = nullptr;
+  }
+}
+
+int64_t TensorImpl::dim() const {
+  if (isScalar()) {
+    return 0;
+  }
+  return tensor->dim();
+}
+
+void * TensorImpl::unsafeGetTH(bool retain) {
+  if (retain) {
+    tensor->retain();
+  }
+  return tensor;
+}
+
 } // namespace at

aten/src/ATen/TensorImpl.h

@@ -7,6 +7,8 @@
 #include "ATen/ScalarType.h"
 #include "ATen/optional.h"
 
+struct THTensor;
+
 namespace at {
 class Scalar;
 struct Type;
@@ -15,23 +17,27 @@ struct Tensor;
 } // namespace at
 
 namespace at {
-struct TensorImpl : public Retainable {
-  explicit TensorImpl(Type * type)
-  : is_scalar(false), type_(type) {}
+struct AT_API TensorImpl : public Retainable {
+  explicit TensorImpl(Type * type, THTensor * tensor)
+  : is_scalar(false), type_(type), tensor(tensor) {}
+
+  virtual ~TensorImpl();
+
+  virtual void release_resources() override;
 
   Type & type() const {
     return *type_;
   }
   virtual const char * toString() const = 0;
-  virtual IntList sizes() const = 0;
-  virtual IntList strides() const = 0;
-  virtual int64_t dim() const = 0;
+  virtual IntList sizes() const;
+  virtual IntList strides() const;
+  virtual int64_t dim() const;
   /**
    * Perform a conversion of this tensor to a scalar, if numel() == 1.
    * Otherwise, raise an error.
    */
   virtual Scalar localScalar() = 0;
-  virtual void * unsafeGetTH(bool retain) = 0;
+  virtual void * unsafeGetTH(bool retain);
   virtual std::unique_ptr<Storage> storage() = 0;
   friend struct Type;
 
@@ -69,30 +75,32 @@ struct TensorImpl : public Retainable {
   // Some methods below are defined in TensorImpl.cpp because Tensor is an
   // incomplete type.
 
-  AT_API virtual void set_requires_grad(bool requires_grad) {
+  virtual void set_requires_grad(bool requires_grad) {
     AT_ERROR("set_requires_grad is not implemented for Tensor");
   }
-  AT_API virtual bool requires_grad() const {
+  virtual bool requires_grad() const {
     AT_ERROR("requires_grad is not implemented for Tensor");
   }
 
-  AT_API virtual Tensor& grad();
-  AT_API virtual const Tensor& grad() const;
+  virtual Tensor& grad();
+  virtual const Tensor& grad() const;
 
-  AT_API virtual Tensor detach() const;
-  AT_API virtual void detach_() {
+  virtual Tensor detach() const;
+  virtual void detach_() {
     AT_ERROR("detach_ is not implemented for Tensor");
   }
 
-  AT_API virtual void backward(
+  virtual void backward(
       at::optional<Tensor> gradient,
       bool keep_graph,
       bool create_graph);
 
-  AT_API virtual void set_data(Tensor new_data);
+  virtual void set_data(Tensor new_data);
 
 protected:
   bool is_scalar;
   Type * type_;
+public:
+  THTensor * tensor;
 };
 } // namespace at

aten/src/ATen/TensorOptions.cpp

@@ -6,6 +6,8 @@
 #include <ATen/ScalarType.h>
 #include <ATen/optional.h>
 
+#include <iostream>
+
 namespace at {
 
 TensorOptions::TensorOptions(bool use_thread_local_default_options) {
@@ -17,3 +19,13 @@ TensorOptions::TensorOptions(bool use_thread_local_default_options) {
   }
 }
 } // namespace at
+
+std::ostream& operator<<(
+    std::ostream& stream,
+    const at::TensorOptions& options) {
+  return stream << "TensorOptions(dtype=" << options.dtype()
+                << ", device=" << options.device()
+                << ", layout=" << options.layout()
+                << ", requires_grad=" << std::boolalpha
+                << options.requires_grad() << ")";
+}

aten/src/ATen/TensorOptions.h

@@ -9,6 +9,7 @@
 #include <ATen/Type.h>
 
 #include <cstddef>
+#include <iosfwd>
 #include <utility>
 
 namespace at {
@@ -277,3 +278,7 @@ inline Tensor Tensor::to(Device device, bool non_blocking) const {
   return detail::to(*this, options().device(device), non_blocking);
 }
 } // namespace at
+
+std::ostream& operator<<(
+    std::ostream& stream,
+    const at::TensorOptions& options);

aten/src/ATen/UndefinedTensor.cpp

@@ -6,7 +6,7 @@ namespace at {
 
 // should this use the globalContext?  Can it get a context passed in somehow?
 UndefinedTensor::UndefinedTensor()
-: TensorImpl(&(globalContext().getType(Backend::Undefined,ScalarType::Undefined))) {
+: TensorImpl(&(globalContext().getType(Backend::Undefined,ScalarType::Undefined)), nullptr) {
 }
 
 const char * UndefinedTensor::toString() const {

aten/src/ATen/code_template.py

@@ -50,15 +50,17 @@ def replace(match):
                     comma_after = ', '
                     key = key[:-1]
             v = lookup(key)
-            if indent is not None and isinstance(v, list):
+            if indent is not None:
+                if not isinstance(v, list):
+                    v = [v]
                 return indent_lines(indent, v)
             elif isinstance(v, list):
                 middle = ', '.join([str(x) for x in v])
                 if len(v) == 0:
                     return middle
                 return comma_before + middle + comma_after
             else:
-                return (indent or '') + str(v)
+                return str(v)
         return self.subtitution.sub(replace, self.pattern)
 
 

aten/src/ATen/copy_wrapper.py

@@ -116,7 +116,7 @@ def create_one_copy(dst_type, all_types):
         cuda = ''
         state = []
         if src_type['Backend'] == 'CUDA' or dst_type['Backend'] == 'CUDA':
-            state.append('context->getTHCState()')
+            state.append('globalContext().getTHCState()')
         if src_type['Backend'] == 'CUDA':
             if dst_type['Backend'] == 'CUDA':
                 cuda = 'Cuda'
@@ -183,7 +183,7 @@ def create_one_copy_from(src_type, all_types):
         if src_type['Backend'] == 'CUDA':
             cuda = 'Cuda'
         if dst_type['Backend'] == 'CUDA' or src_type['Backend'] == 'CUDA':
-            state.append('context->getTHCState()')
+            state.append('globalContext().getTHCState()')
 
         body_env = nested_dict({
             'src_scalar_name': src_type['ScalarName'],

aten/src/ATen/function_wrapper.py

@@ -180,7 +180,7 @@ def TypedDict(name, attrs, total=True):  # type: ignore
 }""")
 
 BUFFER_DEFINITION = CodeTemplate("""\
-auto ${name}_ = new ${Tensor}(context);
+auto ${name}_ = new ${Tensor}(${THTensor}_new());
 auto ${name} = Tensor(${name}_, false);""")
 
 CONDITIONAL_INITIALIZER = CodeTemplate("""\
@@ -277,7 +277,7 @@ def __init__(self, reason):
     'THStorage*': CodeTemplate('checked_cast_storage<${Storage}>(&${arg_name},"${arg_name}",${arg_pos})'),
     'THGenerator*':
         CodeTemplate(
-            'check_generator<${Backend}Generator>(${arg_name}, &context->defaultGenerator(backend()))'),
+            'check_generator<${Backend}Generator>(${arg_name}, &globalContext().defaultGenerator(backend()))'),
     # This is a cast done via direct-construction
     'THSize*': CodeTemplate('THLongStorageView ${result_name}(${arg_name}, THLongStorageViewKind::SIZE);'),
     # This is a cast done via direct-construction
@@ -306,14 +306,24 @@ def __init__(self, reason):
 
 CHECKED_USE_NULLABLE = CodeTemplate('${arg_name}_ ? ${usage} : NULL')
 
+ALLOC_NOARGS_WRAP = {
+    'THTensor*': 'detail::new_${Tensor}()',
+    'THBoolTensor*': 'detail::new_${Backend}ByteTensor()',
+    'THIndexTensor*': 'detail::new_${Backend}LongTensor()',
+    'THIntegerTensor*': 'detail::new_${Backend}IntTensor()',
+    'THSTensor*': 'detail::new_Sparse${Tensor}()',
+    'THDenseTensor*': 'detail::new_${DenseTensor}()',
+    'THDenseIndexTensor*': 'detail::new_${DenseBackend}LongTensor()',
+}
+
 ALLOC_WRAP = {
-    'THTensor*': 'new ${Tensor}(context${,arguments})',
-    'THBoolTensor*': 'new ${Backend}ByteTensor(context${,arguments})',
-    'THIndexTensor*': 'new ${Backend}LongTensor(context${,arguments})',
-    'THIntegerTensor*': 'new ${Backend}IntTensor(context${,arguments})',
-    'THSTensor*': 'new Sparse${Tensor}(context${,arguments})',
-    'THDenseTensor*': 'new ${DenseTensor}(context${,arguments})',
-    'THDenseIndexTensor*': 'new ${DenseBackend}LongTensor(context${,arguments})',
+    'THTensor*': 'new ${Tensor}(${arguments})',
+    'THBoolTensor*': 'new ${Backend}ByteTensor(${arguments})',
+    'THIndexTensor*': 'new ${Backend}LongTensor(${arguments})',
+    'THIntegerTensor*': 'new ${Backend}IntTensor(${arguments})',
+    'THSTensor*': 'new Sparse${Tensor}(${arguments})',
+    'THDenseTensor*': 'new ${DenseTensor}(${arguments})',
+    'THDenseIndexTensor*': 'new ${DenseBackend}LongTensor(${arguments})',
 }
 
 # Replacements for constants when calling into TH
@@ -1228,7 +1238,10 @@ def handle_sparse(env, option):
     def allocate_arg(env, arg, output_count):
         # type: (Environment, THFormal, int) -> List[str]
         name = arg['name']
-        allocation = CodeTemplate(ALLOC_WRAP[arg['type']]).substitute(env, arguments=[])
+        state = ''
+        if is_cuda:
+            state = 'globalContext().getTHCState()'
+        allocation = CodeTemplate(ALLOC_NOARGS_WRAP[arg['type']]).substitute(env)
         tensor_arg = '{}_'.format(name)
         if arg.get('mask', False):
             allocation = 'output_mask[{}] ? {} : nullptr'.format(output_count, allocation)
@@ -1257,7 +1270,7 @@ def handle_call(env, option, cimpl):
         is_nn = option['mode'] == 'NN'
         actuals = get_arguments(cimpl['arguments'], option)
         if is_cuda or is_nn:
-            actuals = ['context->getTHCState()'] + actuals
+            actuals = ['globalContext().getTHCState()'] + actuals
 
         cname = cimpl['cname']
         if option.get('sparse', False):

aten/src/ATen/gen.py

@@ -273,7 +273,7 @@ def generate_storage_type_and_tensor(backend, density, scalar_type, declarations
         env['THStorage'] = 'THCuda{}Storage'.format(sname)
         env['THTensor'] = 'THCuda{}Tensor'.format(sname)
         env['THIndexTensor'] = 'THCudaLongTensor'
-        env['state'] = ['context->getTHCState()']
+        env['state'] = ['globalContext().getTHCState()']
         env['isCUDA'] = 'true'
         env['storage_device'] = 'return storage->device;'
         env['Generator'] = 'CUDAGenerator'