[BACKEND] Vulkan Runtime and SPIRV Codegen (#861)

* [BACKEND] Vulkan Runtime and SPIRV Codegen

* fix doc

CMakeLists.txt

-cmake_minimum_required(VERSION 3.5)
+cmake_minimum_required(VERSION 3.7)
 project(tvm C CXX)
 
 if(EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/build/private/local_config.cmake)
@@ -22,6 +22,7 @@ endif()
 
 tvm_option(USE_CUDA "Build with CUDA" OFF)
 tvm_option(USE_OPENCL "Build with OpenCL" OFF)
+tvm_option(USE_VULKAN "Build with Vulkan" OFF)
 tvm_option(USE_OPENGL "Build with OpenGL" OFF)
 tvm_option(USE_METAL "Build with Metal" OFF)
 tvm_option(USE_RPC "Build with RPC" ON)
@@ -88,9 +89,11 @@ file(GLOB_RECURSE HALIDEIR_SRCS HalideIR/src/*.cpp)
 list(APPEND COMPILER_SRCS ${HALIDEIR_SRCS})
 file(GLOB RUNTIME_SRCS src/runtime/*.cc)
 file(GLOB COMPILER_LLVM_SRCS src/codegen/llvm/*.cc)
+file(GLOB COMPILER_VULKAN_SRCS src/codegen/spirv/*.cc)
 file(GLOB RUNTIME_CUDA_SRCS src/runtime/cuda/*.cc)
 file(GLOB RUNTIME_OPENCL_SRCS src/runtime/opencl/*.cc)
 file(GLOB RUNTIME_OPENGL_SRCS src/runtime/opengl/*.cc)
+file(GLOB RUNTIME_VULKAN_SRCS src/runtime/vulkan/*.cc)
 file(GLOB RUNTIME_METAL_SRCS src/runtime/metal/*.mm)
 file(GLOB RUNTIME_RPC_SRCS src/runtime/rpc/*.cc)
 file(GLOB RUNTIME_GRAPH_SRCS src/runtime/graph/*.cc)
@@ -151,6 +154,22 @@ else(USE_OPENGL)
   add_definitions(-DTVM_OPENGL_RUNTIME=0)
 endif(USE_OPENGL)
 
+if(USE_VULKAN)
+  find_package(Vulkan REQUIRED)
+  message(STATUS "Build with VULKAN support")
+  include_directories(${Vulkan_INCLUDE_DIRS})
+  list(APPEND TVM_RUNTIME_LINKER_LIBS ${Vulkan_LIBRARIES})
+  list(APPEND RUNTIME_SRCS ${RUNTIME_VULKAN_SRCS})
+  list(APPEND COMPILER_SRCS ${COMPILER_VULKAN_SRCS})
+  get_filename_component(VULKAN_LIB_PATH ${Vulkan_LIBRARY} DIRECTORY)
+  find_library(SPIRV_TOOLS_LIB SPIRV-Tools
+               ${VULKAN_LIB_PATH}/spirv-tools)
+  list(APPEND TVM_LINKER_LIBS ${SPIRV_TOOLS_LIB})
+  add_definitions(-DTVM_VULKAN_RUNTIME=1)
+else(USE_VULKAN)
+  add_definitions(-DTVM_VULKAN_RUNTIME=0)
+endif(USE_VULKAN)
+
 if(USE_METAL)
   find_package(OpenCL QUIET REQUIRED)
   message(STATUS "Build with Metal support")
@@ -174,7 +193,7 @@ if(USE_GRAPH_RUNTIME)
 endif(USE_GRAPH_RUNTIME)
 
 if(USE_LLVM)
-  find_package(LLVM CONFIG REQUIRED)
+  find_spackage(LLVM CONFIG REQUIRED)
   include_directories(${LLVM_INCLUDE_DIRS})
   add_definitions(${LLVM_DEFINITIONS})
   set(TVM_LLVM_VERSION ${LLVM_VERSION_MAJOR}${LLVM_VERSION_MINOR})
@@ -252,4 +271,4 @@ if(MSVC)
   target_compile_definitions(tvm_runtime PRIVATE -DHalide_EXPORTS)
   target_compile_definitions(tvm PRIVATE -DTVM_EXPORTS)
   target_compile_definitions(tvm_runtime PRIVATE -DTVM_EXPORTS)
-endif()
+endif()
\ No newline at end of file

HalideIR @ aadbf02d

-Subproject commit 87b089a0ba20f2e8257038ee9211d6816088ce95
+Subproject commit aadbf02d6bd7a545edbf6652494a7b07a97a06c1

Makefile

@@ -56,6 +56,7 @@ CUDA_SRC = $(wildcard src/runtime/cuda/*.cc)
 ROCM_SRC = $(wildcard src/runtime/rocm/*.cc)
 OPENCL_SRC = $(wildcard src/runtime/opencl/*.cc)
 OPENGL_SRC = $(wildcard src/runtime/opengl/*.cc)
+VULKAN_SRC = $(wildcard src/runtime/vulkan/*.cc)
 RPC_SRC = $(wildcard src/runtime/rpc/*.cc)
 GRAPH_SRC = $(wildcard src/runtime/graph/*.cc)
 RUNTIME_SRC = $(wildcard src/runtime/*.cc)
@@ -69,6 +70,7 @@ CUDA_OBJ = $(patsubst src/%.cc, build/%.o, $(CUDA_SRC))
 ROCM_OBJ = $(patsubst src/%.cc, build/%.o, $(ROCM_SRC))
 OPENCL_OBJ = $(patsubst src/%.cc, build/%.o, $(OPENCL_SRC))
 OPENGL_OBJ = $(patsubst src/%.cc, build/%.o, $(OPENGL_SRC))
+VULKAN_OBJ = $(patsubst src/%.cc, build/%.o, $(VULKAN_SRC))
 RPC_OBJ = $(patsubst src/%.cc, build/%.o, $(RPC_SRC))
 GRAPH_OBJ = $(patsubst src/%.cc, build/%.o, $(GRAPH_SRC))
 CC_OBJ = $(patsubst src/%.cc, build/%.o, $(CC_SRC)) $(LLVM_OBJ)
@@ -129,6 +131,20 @@ else
 	CFLAGS += -DTVM_OPENCL_RUNTIME=0
 endif
 
+ifdef VULKAN_SDK
+	CFLAGS += -I$(VULKAN_SDK)/include
+	LDFLAGS += -L$(VULKAN_SDK)/lib
+	LDFLAGS += -L$(VULKAN_SDK)/lib/spirv-tools
+endif
+
+ifeq ($(USE_VULKAN), 1)
+	CFLAGS += -DTVM_VULKAN_RUNTIME=1
+	LDFLAGS += -lvulkan -lSPIRV-Tools
+	RUNTIME_DEP += $(VULKAN_OBJ)
+else
+	CFLAGS += -DTVM_VULKAN_RUNTIME=0
+endif
+
 ifeq ($(USE_OPENGL), 1)
 	CFLAGS += -DTVM_OPENGL_RUNTIME=1
 	EMCC_FLAGS += -DTVM_OPENGL_RUNTIME=1

include/tvm/ir_pass.h

@@ -422,6 +422,18 @@ LoweredFunc LowerTVMBuiltin(LoweredFunc f);
 LoweredFunc CombineContextCall(LoweredFunc f);
 
 /*!
+ * \brief Rewrite the pointer content type of arguments,
+ *  as well as Alloc internal to the function to use
+ *  the most frequently accessed type for load/store
+ *  to avoid pointer casting in backend when possible.
+ *
+ * \note implemeneted in storage_rewrite.cc
+ * \param f The function to be trasnformed
+ * \return Transformed function.
+ */
+LoweredFunc PointerValueTypeRewrite(LoweredFunc f);
+
+/*!
  * \brief Lower intrinsic function calls.
  * \param f The device function to be lowered.
  * \param target The target device.

include/tvm/runtime/c_runtime_api.h

@@ -55,8 +55,8 @@ typedef int64_t tvm_index_t;
 
 /*! \brief Extension device types in TVM */
 typedef enum {
+  kDLVulkan = 7,
   kOpenGL = 11,
-
   // Extension DRAM type, used for quickly test extension device
   // The device api can differ depending on the xpu driver registered.
   kExtDev = 12,

python/tvm/__init__.py

@@ -17,7 +17,8 @@ from . import ir_builder
 from . import target
 
 from . import ndarray as nd
-from .ndarray import context, cpu, gpu, opencl, cl, metal, mtl, vpi, rocm, opengl, ext_dev
+from .ndarray import context, cpu, gpu, opencl, cl, vulkan, metal, mtl
+from .ndarray import vpi, rocm, opengl, ext_dev
 
 from ._ffi.runtime_ctypes import TypeCode
 from ._ffi.function import Function

python/tvm/_ffi/runtime_ctypes.py

@@ -94,6 +94,7 @@ class TVMContext(ctypes.Structure):
         1 : 'cpu',
         2 : 'gpu',
         4 : 'opencl',
+        7 : 'vulkan',
         8 : 'metal',
         9 : 'vpi',
         10: 'rocm',
@@ -109,6 +110,7 @@ class TVMContext(ctypes.Structure):
         'nvptx': 2,
         'cl': 4,
         'opencl': 4,
+        'vulkan': 7,
         'metal': 8,
         'vpi': 9,
         'rocm': 10,

python/tvm/contrib/spirv.py

+"""Utility for Interacting with SPIRV Tools"""
+import subprocess
+import os
+from . import util
+
+
+def optimize(spv_bin):
+    """Optimize SPIRV using spirv-opt via CLI
+
+    Note that the spirv-opt is still experimental.
+
+    Parameters
+    ----------
+    spv_bin : bytearray
+        The spirv file
+
+    Return
+    ------
+    cobj_bin : bytearray
+        The HSA Code Object
+    """
+
+    tmp_dir = util.tempdir()
+    tmp_in = tmp_dir.relpath("input.spv")
+    tmp_out = tmp_dir.relpath("output.spv")
+    with open(tmp_in, "wb") as out_file:
+        out_file.write(bytes(spv_bin))
+
+    sdk = os.environ.get("VULKAN_SDK", None)
+    cmd = os.path.join(sdk, "bin/spirv-opt") if sdk else "spirv-opt"
+    args = [cmd, "-O", tmp_in, "-o", tmp_out]
+    proc = subprocess.Popen(
+        args,
+        stdout=subprocess.PIPE,
+        stderr=subprocess.STDOUT)
+    (out, _) = proc.communicate()
+
+    if proc.returncode != 0:
+        msg = "Opitmizationerror using spirv-opt:\n"
+        msg += str(out)
+        raise RuntimeError(msg)
+
+    return bytearray(open(tmp_out, "rb").read())

python/tvm/ndarray.py

@@ -120,6 +120,23 @@ def vpi(dev_id=0):
     """
     return TVMContext(9, dev_id)
 
+
+def vulkan(dev_id=0):
+    """Construct a Vulkan device
+
+    Parameters
+    ----------
+    dev_id : int, optional
+        The integer device id
+
+    Returns
+    -------
+    ctx : TVMContext
+        The created context
+    """
+    return TVMContext(7, dev_id)
+
+
 def opengl(dev_id=0):
     """Construct a OpenGL device
 
@@ -135,6 +152,7 @@ def opengl(dev_id=0):
     """
     return TVMContext(11, dev_id)
 
+
 def ext_dev(dev_id=0):
     """Construct a extension device
 

python/tvm/target.py

@@ -116,7 +116,7 @@ class Target(object):
             # For now assume rocm schedule for opencl
             self.keys += ("rocm", "gpu")
             self.max_num_threads = 256
-        elif target_name in ("metal",):
+        elif target_name in ("metal", "vulkan"):
             self.keys += ("gpu",)
             self.max_num_threads = 256
         elif target_name in ("opengl",):

src/codegen/codegen_c.cc

@@ -666,6 +666,8 @@ void CodeGenC::VisitExpr_(const Let* op, std::ostream& os) {  // NOLINT(*)
 }
 
 void CodeGenC::VisitExpr_(const Ramp* op, std::ostream& os) {  // NOLINT(*)
+  // constraint of current logic
+  CHECK_EQ(op->base.type(), Int(32));
   os << "((int" << op->lanes << ")(";
   for (int i = 0; i < op->lanes; i++) {
     os << "(" << PrintExpr(op->base) << ")" << "+(" << PrintExpr(op->stride) << "*" << i <<")";

src/codegen/codegen_common.h

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file codegen_common.h
+ * \brief Common utility for codegen.
+ */
+#ifndef TVM_CODEGEN_CODEGEN_COMMON_H_
+#define TVM_CODEGEN_CODEGEN_COMMON_H_
+
+#include <tvm/arithmetic.h>
+#include "../arithmetic/compute_expr.h"
+
+namespace tvm {
+namespace codegen {
+
+/*!
+ * \brief Visit AssertStmt recursively, update align_map from condition.
+ * \param op The AssertStmt
+ * \param align_map The alignmap
+ * \param fvisit The recursive visitor
+ * \tparam FVisit the recursive visitor
+ */
+template<typename FVisit>
+inline void VisitAssert(
+    const ir::AssertStmt* op,
+    std::unordered_map<const Variable*, arith::ModularEntry>* align_map,
+    FVisit fvisit) {
+  using namespace ir;
+  auto& align_map_ = *align_map;
+  // Detect useful invariant pattern and use them to visit child.
+  // Pattern: Var % const  == 0
+  // TODO(tqchen) merge these pattern to a generic scope info visitor.
+  if (const EQ* eq = op->condition.as<EQ>()) {
+    const Mod* mod = eq->a.as<Mod>();
+    int64_t factor = 0, offset = 0;
+    if (mod && arith::GetConst(eq->b, &offset)) {
+      const Variable *var = mod->a.as<Variable>();
+      if (var && arith::GetConst(mod->b, &factor)) {
+        arith::ModularEntry old = align_map_[var];
+        if (factor > old.coeff) {
+          arith::ModularEntry e;
+          e.coeff = static_cast<int>(factor);
+          e.base = static_cast<int>(offset);
+          // new alignment info,
+          align_map_[var] = e;
+          fvisit(op->body);
+          // restore old info
+          align_map_[var] = old;
+          return;
+        }
+      }
+    }
+  }
+  fvisit(op->body);
+}
+
+}  // namespace codegen
+}  // namespace tvm
+
+#endif  // TVM_CODEGEN_CODEGEN_COMMON_H_

src/codegen/llvm/codegen_llvm.cc

@@ -9,6 +9,7 @@
 #include <tvm/runtime/c_runtime_api.h>
 #include "./codegen_llvm.h"
 #include "./codegen_cpu.h"
+#include "../codegen_common.h"
 #include "../../pass/ir_util.h"
 #include "../../arithmetic/compute_expr.h"
 
@@ -341,7 +342,7 @@ void CodeGenLLVM::GetAlignment(Type t,
   int align_bits = t.bits();
   while (align_bits < max_align_bits &&
          me.base % 2  == 0 &&
-         me.coeff %2 == 0) {
+         me.coeff % 2 == 0) {
     me.base =  me.base / 2;
     me.coeff =  me.coeff / 2;
     align_bits *= 2;
@@ -1026,31 +1027,9 @@ void CodeGenLLVM::VisitStmt_(const AttrStmt* op) {
 }
 
 void CodeGenLLVM::VisitStmt_(const AssertStmt* op) {
-  // Detect useful invariant pattern and use them to visit child.
-  // Pattern: Var % const  == 0
-  // TODO(tqchen) move these pattern to a generic scope info visitor.
-  if (const EQ* eq = op->condition.as<EQ>()) {
-    const Mod* mod = eq->a.as<Mod>();
-    int64_t factor = 0, offset = 0;
-    if (mod && arith::GetConst(eq->b, &offset)) {
-      const Variable *var = mod->a.as<Variable>();
-      if (var && arith::GetConst(mod->b, &factor)) {
-        arith::ModularEntry old = align_map_[var];
-        if (factor > old.coeff) {
-          arith::ModularEntry e;
-          e.coeff = static_cast<int>(factor);
-          e.base = static_cast<int>(offset);
-          // new alignment info,
-          align_map_[var] = e;
-          this->VisitStmt(op->body);
-          // restore old info
-          align_map_[var] = old;
-          return;
-        }
-      }
-    }
-  }
-  this->VisitStmt(op->body);
+  VisitAssert(op, &align_map_, [this](const Stmt& body) {
+      this->VisitStmt(body);
+    });
 }
 
 void CodeGenLLVM::VisitStmt_(const LetStmt* op) {

src/codegen/spirv/build_vulkan.cc

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file build_vulkan.cc
+ * \brief Build SPIRV block
+ */
+#if TVM_VULKAN_RUNTIME
+
+// Use libspirv for parsing and validating code.
+#include <vulkan/libspirv.h>
+#include <dmlc/memory_io.h>
+#include <tvm/ir_pass.h>
+
+#include "./codegen_spirv.h"
+#include "../build_common.h"
+#include "../../runtime/vulkan/vulkan_module.h"
+
+namespace tvm {
+namespace codegen {
+
+class SPIRVTools {
+ public:
+  SPIRVTools() {
+    ctx_ = spvContextCreate(SPV_ENV_VULKAN_1_0);
+  }
+  ~SPIRVTools() {
+    spvContextDestroy(ctx_);
+  }
+  std::string BinaryToText(const std::vector<uint32_t>& bin) {
+    spv_text text = nullptr;
+    spv_diagnostic diagnostic;
+    spv_const_binary_t spv_bin{bin.data(), bin.size()};
+    spv_result_t res;
+
+    res = spvBinaryToText(
+       ctx_, spv_bin.code, spv_bin.wordCount,
+      SPV_BINARY_TO_TEXT_OPTION_FRIENDLY_NAMES |
+           SPV_BINARY_TO_TEXT_OPTION_INDENT,
+        &text, &diagnostic);
+
+    CHECK_EQ(res, SPV_SUCCESS)
+        << " line=" << diagnostic->position.line
+        << " column=" << diagnostic->position.column
+        << " index=" << diagnostic->position.index
+        << " error:" << diagnostic->error;
+
+    std::string ret(text->str);
+    spvTextDestroy(text);
+    return ret;
+  }
+
+ private:
+  spv_context ctx_;
+};
+
+runtime::Module BuildSPIRV(Array<LoweredFunc> funcs) {
+  using tvm::runtime::Registry;
+  using tvm::runtime::VulkanShader;
+
+  std::ostringstream code_data;
+  static SPIRVTools spirv_tools;
+  std::unordered_map<std::string, VulkanShader> smap;
+
+  const auto* postproc = Registry::Get("tvm_callback_vulkan_postproc");
+
+  CodeGenSPIRV cg;
+  for (LoweredFunc f : funcs) {
+    f = PointerValueTypeRewrite(f);
+    VulkanShader shader;
+    shader.data = cg.BuildFunction(f);
+
+    if (postproc != nullptr) {
+      TVMByteArray arr;
+      arr.data = reinterpret_cast<const char*>(dmlc::BeginPtr(shader.data));
+      arr.size = shader.data.size() * sizeof(uint32_t);
+      std::string transformed = (*postproc)(arr);
+      CHECK_EQ(transformed.length() % 4U, 0U);
+      shader.data.resize(transformed.size() / 4U);
+      std::copy(transformed.begin(), transformed.end(),
+                reinterpret_cast<char*>(dmlc::BeginPtr(shader.data)));
+    }
+    code_data << spirv_tools.BinaryToText(shader.data);
+    smap[f->name] = std::move(shader);
+  }
+  return runtime::VulkanModuleCreate(
+     smap, ExtractFuncInfo(funcs), code_data.str());
+}
+
+TVM_REGISTER_API("codegen.build_vulkan")
+.set_body([](TVMArgs args, TVMRetValue* rv) {
+    *rv = BuildSPIRV(args[0]);
+  });
+
+}  // namespace codegen
+}  // namespace tvm
+#endif  // TVM_VULKAN_RUNTIME

src/codegen/spirv/codegen_spirv.h

+/*!
+ *  Copyright (c) 2018 by Contributors
+ * \file ir_builder.h
+ * \brief Utility for building SPIRV code block
+ */
+#ifndef TVM_CODEGEN_SPIRV_CODEGEN_SPIRV_H_
+#define TVM_CODEGEN_SPIRV_CODEGEN_SPIRV_H_
+
+#include <tvm/ir.h>
+#include <tvm/ir_functor_ext.h>
+#include <tvm/lowered_func.h>
+
+#include <vector>
+
+#include "./ir_builder.h"
+#include "../../runtime/thread_storage_scope.h"
+
+namespace tvm {
+namespace codegen {
+
+using namespace ir;
+
+/*!
+ * \brief Code generator into SPIRV
+ */
+class CodeGenSPIRV:
+      public ExprFunctor<spirv::Value(const Expr&)>,
+      public StmtFunctor<void(const Stmt&)> {
+ public:
+  /*!
+   * \brief Compile and add function f to the current module.
+   * \param f The function to be added.
+   * \return The final spirv module.
+   */
+  virtual std::vector<uint32_t> BuildFunction(const LoweredFunc& f);
+  /*!
+   * \brief Create Value for expression e
+   * \param e The expression to be created value for.
+   * \return created value.
+   */
+  spirv::Value MakeValue(const Expr& e) {
+    return VisitExpr(e);
+  }
+  // override codegen
+  spirv::Value VisitExpr_(const Variable* op) override;
+  spirv::Value VisitExpr_(const Cast* op) override;
+  spirv::Value VisitExpr_(const IntImm* op) override;
+  spirv::Value VisitExpr_(const UIntImm* op) override;
+  spirv::Value VisitExpr_(const FloatImm* op) override;
+  spirv::Value VisitExpr_(const StringImm* op) override;
+  spirv::Value VisitExpr_(const Add* op) override;
+  spirv::Value VisitExpr_(const Sub* op) override;
+  spirv::Value VisitExpr_(const Mul* op) override;
+  spirv::Value VisitExpr_(const Div* op) override;
+  spirv::Value VisitExpr_(const Mod* op) override;
+  spirv::Value VisitExpr_(const Min* op) override;
+  spirv::Value VisitExpr_(const Max* op) override;
+  spirv::Value VisitExpr_(const LT* op) override;
+  spirv::Value VisitExpr_(const LE* op) override;
+  spirv::Value VisitExpr_(const GT* op) override;
+  spirv::Value VisitExpr_(const GE* op) override;
+  spirv::Value VisitExpr_(const EQ* op) override;
+  spirv::Value VisitExpr_(const NE* op) override;
+  spirv::Value VisitExpr_(const And* op) override;
+  spirv::Value VisitExpr_(const Or* op) override;
+  spirv::Value VisitExpr_(const Not* op) override;
+  spirv::Value VisitExpr_(const Select* op) override;
+  spirv::Value VisitExpr_(const Let* op) override;
+  spirv::Value VisitExpr_(const Call* op) override;
+  spirv::Value VisitExpr_(const Ramp* op) override;
+  spirv::Value VisitExpr_(const Broadcast* op) override;
+  spirv::Value VisitExpr_(const Load* op) override;
+  // stmt
+  void VisitStmt_(const Store* op) override;
+  void VisitStmt_(const For* op) override;
+  void VisitStmt_(const IfThenElse* op) override;
+  void VisitStmt_(const Allocate* op) override;
+  void VisitStmt_(const AttrStmt* op) override;
+  void VisitStmt_(const AssertStmt* op) override;
+  void VisitStmt_(const LetStmt* op) override;
+  void VisitStmt_(const Block* op) override;
+  void VisitStmt_(const Evaluate* op) override;
+  void VisitStmt_(const ProducerConsumer* op) override;
+
+ protected:
+  /*! \brief The storage information */
+  struct StorageInfo {
+    /*! \brief The storage scope */
+    runtime::StorageScope scope;
+    /*! \brief Whether it is volatile */
+    bool is_volatile{false};
+    /*! \brief Whether it is volatile */
+    bool content_fixed{false};
+    /*! \brief Current content type */
+    Type content_type{Handle()};
+
+    // Update content type if it hasn't beenupdated.
+    void UpdateContentType(Type type) {
+      if (content_fixed) {
+        CHECK_EQ(type, content_type)
+            << "Cannot use two different content type in GLSL model";
+      } else {
+        this->content_type = type;
+        content_fixed = true;
+      }
+    }
+  };
+  // Reset the state so it works for a new function.
+  void InitFuncState();
+  // Get the thread index
+  spirv::Value GetThreadIndex(const IterVar& iv, const Expr& extent);
+  spirv::Value CreateStorageSync(const Call* op);
+  void Scalarize(const Expr& e,
+                 std::function<void(int i, spirv::Value v)> f);
+  // The builder
+  std::unique_ptr<spirv::IRBuilder> builder_;
+  // Work group size of three
+  uint32_t workgroup_size_[3];
+  // Likely branch
+  uint32_t weight_likely_branch_{128};
+  // the storage scope of allocation
+  std::unordered_map<const Variable*, StorageInfo> storage_info_;
+  // The definition of local variable.
+  std::unordered_map<const Variable*, spirv::Value> var_map_;
+  // The alignment information
+  std::unordered_map<const Variable*, arith::ModularEntry> align_map_;
+};
+
+}  // namespace codegen
+}  // namespace tvm
+
+
+#endif  // TVM_CODEGEN_SPIRV_CODEGEN_SPIRV_H_

src/codegen/spirv/intrin_rule_spirv.cc

+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file intrin_rule_spirv.cc
+ */
+#if TVM_VULKAN_RUNTIME
+
+#include <tvm/packed_func_ext.h>
+#include <tvm/ir.h>
+#include <vulkan/GLSL.std.450.h>
+
+namespace tvm {
+namespace codegen {
+namespace spirv {
+
+using namespace runtime;
+
+// num_signature means number of arguments used to query signature
+template<unsigned id>
+inline void DispatchGLSLPureIntrin(const TVMArgs& targs, TVMRetValue* rv) {
+  Expr e = targs[0];
+  const ir::Call* call = e.as<ir::Call>();
+  CHECK(call != nullptr);
+  Array<Expr> cargs;
+  // intrin id.
+  cargs.push_back(ir::UIntImm::make(UInt(32), id));
+
+  for (Expr arg : call->args) {
+    cargs.push_back(arg);
+  }
+  *rv = ir::Call::make(
+      call->type, "spirv_glsl450", cargs, ir::Call::PureIntrinsic);
+}
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.exp")
+.set_body(DispatchGLSLPureIntrin<GLSLstd450Exp>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.log")
+.set_body(DispatchGLSLPureIntrin<GLSLstd450Log>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.sqrt")
+.set_body(DispatchGLSLPureIntrin<GLSLstd450Sqrt>);
+
+TVM_REGISTER_GLOBAL("tvm.intrin.rule.vulkan.pow")
+.set_body(DispatchGLSLPureIntrin<GLSLstd450Pow>);
+
+}  // namespace spirv
+}  // namespace codegen
+}  // namespace tvm
+
+#endif  // TVM_VULKAN_RUNTIME

src/pass/rewrite_unsafe_select.cc

@@ -59,7 +59,7 @@ class UnsafeExprDetector : public ExprFunctor<bool(const Expr& n)> {
     return VisitExpr(op->a);
   }
   bool VisitExpr_(const Let* op) final {
-    return VisitExpr(op->body) && VisitExpr(op->value);
+    return VisitExpr(op->body) || VisitExpr(op->value);
   }
   bool VisitExpr_(const Cast* op) final {
     return VisitExpr(op->value);
@@ -84,7 +84,7 @@ class UnsafeExprDetector : public ExprFunctor<bool(const Expr& n)> {
  private:
   template<typename T>
   bool BinaryOp(const T* op) {
-    return VisitExpr(op->a) && VisitExpr(op->b);
+    return VisitExpr(op->a) || VisitExpr(op->b);
   }
 };
 

src/pass/storage_rewrite.cc

@@ -903,20 +903,42 @@ class VectorAllocRewriter : public IRMutator {
     return stmt;
   }
 
-
- private:
   void UpdateTypeMap(const Variable* buffer, Type t) {
     auto& tvec = acc_map_[buffer];
     if (std::find(tvec.begin(), tvec.end(), t) == tvec.end()) {
       tvec.push_back(t);
     }
   }
+
   // Internal access map
-  std::unordered_map<const Variable*,
-                     std::vector<Type> > acc_map_;
+  std::unordered_map<const Variable*, std::vector<Type> > acc_map_;
 };
 
 
+LoweredFunc PointerValueTypeRewrite(LoweredFunc f) {
+  std::shared_ptr<LoweredFuncNode> n =
+      std::make_shared<LoweredFuncNode>(*f.operator->());
+  VectorAllocRewriter rewriter;
+  n->body = rewriter.Mutate(n->body);
+  for (Var arg : f->args) {
+    if (arg.type().is_handle()) {
+      const auto& tvec = rewriter.acc_map_[arg.get()];
+      if (tvec.size() == 1) {
+        Expr dtype = make_const(tvec[0], 0);
+        n->handle_data_type.Set(arg, dtype);
+      } else {
+        // always set data type to be non vectorized so
+        // load/store can still work via scalarization
+        if (tvec.size() != 0 && !n->handle_data_type.count(arg)) {
+          Expr dtype = make_const(tvec[0].with_lanes(1), 0);
+          n->handle_data_type.Set(arg, dtype);
+        }
+      }
+    }
+  }
+  return LoweredFunc(n);
+}
+
 Stmt StorageRewrite(Stmt stmt) {
   stmt = StoragePlanRewriter().Rewrite(stmt, true);
   return VectorAllocRewriter().Mutate(stmt);

src/runtime/c_runtime_api.cc

@@ -28,6 +28,7 @@ inline std::string DeviceName(int type) {
     case kDLCPU: return "cpu";
     case kDLGPU: return "gpu";
     case kDLOpenCL: return "opencl";
+    case kDLVulkan: return "vulkan";
     case kDLMetal: return "metal";
     case kDLVPI: return "vpi";
     case kDLROCM: return "rocm";

src/runtime/module.cc

@@ -119,6 +119,8 @@ bool RuntimeEnabled(const std::string& target) {
     f_name = "device_api.opengl";
   } else if (target == "mtl" || target == "metal") {
     f_name = "device_api.metal";
+  } else if (target == "vulkan") {
+    f_name = "device_api.vulkan";
   } else if (target == "stackvm") {
     f_name = "codegen.build_stackvm";
   } else if (target == "rpc") {

src/runtime/rocm/rocm_device_api.cc

@@ -44,11 +44,12 @@ class ROCMDeviceAPI final : public DeviceAPI {
         value = 64;
         break;
       }
-      case kComputeVersion:
+      case kComputeVersion: {
         hipDeviceProp_t prop;
         ROCM_CALL(hipGetDeviceProperties(&prop, ctx.device_id));
         *rv = prop.gcnArch;
         return;
+      }
     }
     *rv = value;
   }

src/runtime/vulkan/vulkan_common.h

+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file vulkan_common.h
+ * \brief Vulkan common header
+ */
+#ifndef TVM_RUNTIME_VULKAN_VULKAN_COMMON_H_
+#define TVM_RUNTIME_VULKAN_VULKAN_COMMON_H_
+
+#include <tvm/runtime/config.h>
+#include <tvm/runtime/c_runtime_api.h>
+#include <tvm/runtime/packed_func.h>
+#include <tvm/runtime/device_api.h>
+#include <dmlc/logging.h>
+
+#if TVM_VULKAN_RUNTIME
+
+#include <vulkan/vulkan.h>
+#include <mutex>
+#include <string>
+#include <vector>
+#include "../workspace_pool.h"
+
+namespace tvm {
+namespace runtime {
+namespace vulkan {
+
+inline const char* VKGetErrorString(VkResult error) {
+  switch (error) {
+    case VK_SUCCESS: return "VK_SUCCESS";
+    case VK_NOT_READY: return "VK_NOT_READY";
+    case VK_TIMEOUT: return "VK_TIMEOUT";
+    case VK_EVENT_SET: return "VK_EVENT_SET";
+    case VK_EVENT_RESET: return "VK_EVENT_RESET";
+    case VK_INCOMPLETE: return "VK_INCOMPLETE";
+    case VK_ERROR_OUT_OF_HOST_MEMORY: return "VK_ERROR_OUT_OF_HOST_MEMORY";
+    case VK_ERROR_OUT_OF_DEVICE_MEMORY: return "VK_ERROR_OUT_OF_DEVICE_MEMORY";
+    case VK_ERROR_INITIALIZATION_FAILED: return "VK_ERROR_INITIALIZATION_FAILED";
+    case VK_ERROR_DEVICE_LOST: return "VK_ERROR_DEVICE_LOST";
+    case VK_ERROR_MEMORY_MAP_FAILED: return "VK_ERROR_MEMORY_MAP_FAILED";
+    case VK_ERROR_LAYER_NOT_PRESENT: return "VK_ERROR_LAYER_NOT_PRESENT";
+    case VK_ERROR_EXTENSION_NOT_PRESENT: return "VK_ERROR_EXTENSION_NOT_PRESENT";
+    case VK_ERROR_FEATURE_NOT_PRESENT: return "VK_ERROR_FEATURE_NOT_PRESENT";
+    case VK_ERROR_INCOMPATIBLE_DRIVER: return "VK_ERROR_INCOMPATIBLE_DRIVER";
+    case VK_ERROR_TOO_MANY_OBJECTS: return "VK_ERROR_TOO_MANY_OBJECTS";
+    case VK_ERROR_FORMAT_NOT_SUPPORTED: return "VK_ERROR_FORMAT_NOT_SUPPORTED";
+    case VK_ERROR_FRAGMENTED_POOL: return "VK_ERROR_FRAGMENTED_POOL";
+    default: return "Unknown Vulkan error code";
+  }
+}
+
+/*!
+ * \brief Protected Vulkan call
+ * \param func Expression to call.
+ */
+#define VULKAN_CHECK_ERROR(__e)                                         \
+  {                                                                     \
+    CHECK(__e == VK_SUCCESS)                                            \
+        << "Vulan Error, code=" << __e << ": " << vulkan::VKGetErrorString(__e); \
+  }
+
+#define VULKAN_CALL(func)                                             \
+  {                                                                   \
+    VkResult __e = (func);                                            \
+    VULKAN_CHECK_ERROR(__e);                                          \
+  }
+
+/*! \brief Auxiliary context structure for vulkan */
+struct VulkanContext {
+  // phyiscal device
+  VkPhysicalDevice phy_device{nullptr};
+  // Phyiscal device property
+  VkPhysicalDeviceProperties phy_device_prop;
+  // Memory type index for staging.
+  uint32_t staging_mtype_index{0};
+  // whether staging is coherent
+  bool coherent_staging{false};
+  // Memory type index for compute
+  uint32_t compute_mtype_index{0};
+  // The logical device
+  VkDevice device{nullptr};
+  // command queue
+  VkQueue queue{nullptr};
+  // queue family_index;
+  uint32_t queue_family_index{0};
+  // Queue family index.
+  VkQueueFamilyProperties queue_prop;
+};
+
+/*! \brief The buffer object */
+struct VulkanBuffer {
+  /*! \brief underlying buffer */
+  VkBuffer buffer{nullptr};
+  /*! \brief underlying buffer */
+  VkDeviceMemory memory{nullptr};
+};
+
+/*! \brief Buffer only used for stagging */
+struct VulkanStagingBuffer {
+  /*! \brief the corresponding device */
+  VkDevice device{nullptr};
+  /*! \brief underlying buffer */
+  VkBuffer buffer{nullptr};
+  /*! \brief underlying buffer */
+  VkDeviceMemory memory{nullptr};
+  /*! \brief host address */
+  void* host_addr{nullptr};
+  /*! \brief size of the memory */
+  size_t size{0};
+};
+
+/*!
+ * \brief Process global Vulkan workspace.
+ */
+class VulkanWorkspace final : public DeviceAPI {
+ public:
+  // global mutex
+  std::mutex mu;
+  // whether the workspace it initialized.
+  bool initialized_{false};
+  // vulkan instance
+  VkInstance instance_{nullptr};
+  // The physical devices, have 1 to 1 mapping to devices
+  std::vector<VulkanContext> context_;
+  // Destructor
+  ~VulkanWorkspace();
+  // Initialize workspace
+  // Return false if already initialized, otherwise return true.
+  void Init();
+  // override device API
+  void SetDevice(TVMContext ctx) final;
+  void GetAttr(TVMContext ctx, DeviceAttrKind kind, TVMRetValue* rv) final;
+  void* AllocDataSpace(TVMContext ctx,
+                       size_t nbytes,
+                       size_t alignment,
+                       TVMType type_hint) final;
+  void FreeDataSpace(TVMContext ctx, void* ptr) final;
+  void CopyDataFromTo(const void* from,
+                      size_t from_size,
+                      void* to,
+                      size_t to_size,
+                      size_t size,
+                      TVMContext ctx_from,
+                      TVMContext ctx_to,
+                      TVMStreamHandle stream) final;
+  void StreamSync(TVMContext ctx, TVMStreamHandle stream) final;
+  void* AllocWorkspace(TVMContext ctx, size_t size, TVMType type_hint) final;
+  void FreeWorkspace(TVMContext ctx, void* data) final;
+  // get the global workspace
+  static const std::shared_ptr<VulkanWorkspace>& Global();
+};
+
+/*! \brief Helper command buffer resource */
+struct VulkanCommandBuffer {
+  /*! \brief fence to signal the resource is ready to use */
+  VkFence fence{nullptr};
+  /*! \brief The internal command buffer */
+  VkCommandBuffer cmd_buffer{nullptr};
+  /*! \brief Descriptor set used to bind arguments */
+  VkDescriptorSet descriptor_set{nullptr};
+  /*! \brief Internal utilities for write command */
+  VkWriteDescriptorSet write_descriptor_set;
+
+  VulkanCommandBuffer() {
+    write_descriptor_set.sType = VK_STRUCTURE_TYPE_WRITE_DESCRIPTOR_SET;
+    write_descriptor_set.pNext = nullptr;
+    write_descriptor_set.dstSet = nullptr;
+    write_descriptor_set.dstBinding = 0;
+    write_descriptor_set.dstArrayElement = 0;
+    write_descriptor_set.descriptorCount = 1;
+    write_descriptor_set.descriptorType = VK_DESCRIPTOR_TYPE_STORAGE_BUFFER;
+    write_descriptor_set.pImageInfo = nullptr;
+    write_descriptor_set.pBufferInfo = nullptr;
+    write_descriptor_set.pTexelBufferView = nullptr;
+  }
+};
+
+/*!
+ * \brief Command pool backed by a fixed size ring buffer.
+ *
+ *  Vulkan requires us not to reuse command buffer until
+ *  All its corresponding jobs have finished.
+ *
+ *  This class to faciliate automatic management
+ *  of the command buffers. A fence is created
+ *  for each launch of command buffer jobs
+ *  and when we try to reuse the same entry
+ *  in the ring, we need to make sure that
+ *  the previous pending job already finishes.
+ *
+ */
+class VulkanCommandPool {
+ public:
+  /*! \brief Maximum number of pending jobs in the pool */
+  static constexpr const int kMaxPending = 4;
+  /*! \brief Maximum number of pending jobs in the pool */
+  static constexpr const int kMaxNumArgs = 16;
+  /*!
+   * \brief constructor
+   * \param vctx The corresponding vulkan context.
+   */
+  explicit VulkanCommandPool(const VulkanContext& vctx);
+  /*! \brief destructor */
+  ~VulkanCommandPool();
+  /*!
+   * \brief Allocate a new command buffer entry
+   *
+   *  The caller must only submit the entry once
+   *  with the given fence in the entry,
+   *  before calling next Alloc.
+   *
+   *  This function may block to wait for a
+   *  previously unfinished command when
+   *  there is more than kMaxPending jobs.
+   *
+   * \returns The allocated entry.
+   */
+  VulkanCommandBuffer* Alloc();
+
+  /*!
+   * \brief Allocate a new command buffer entry
+   * \param dlayout the descriptor layout.
+   *
+   * \returns The allocated entry.
+   */
+  VulkanCommandBuffer* Alloc(const VkDescriptorSetLayout* dlayout);
+
+ private:
+  /*! \brief Local ring buffer */
+  std::vector<VulkanCommandBuffer> ring_;
+  /*! \brief clock pointer */
+  size_t clock_ptr_{0};
+  /*! \brief the corresponding device*/
+  VkDevice device_{nullptr};
+  /*! \brief internal command buffer pool */
+  VkCommandPool cmd_pool_{nullptr};
+  /*! \brief Descriptor pool */
+  VkDescriptorPool descriptor_pool_{nullptr};
+};
+
+/*! \brief Thread local workspace */
+class VulkanThreadEntry {
+ public:
+  /*! \brief The current context */
+  TVMContext context;
+  /*! \brief workspace pool */
+  WorkspacePool pool;
+  /*! \brief The staging buffers */
+  std::vector<VulkanStagingBuffer> staging_buffer_;
+  /*!
+   * \brief Get the command pool of corresponding device;
+   * \param device_id The device id
+   * \return The corresponding command buffer.
+   */
+  VulkanCommandPool* CommandPool(int device_id);
+  /*!
+   * \brief Get the stagging buffer.
+   * \param device_id The device id
+   * \return The corresponding stagging buffer.
+   */
+  VulkanStagingBuffer* StagingBuffer(int device_id, size_t size);
+
+  // constructor
+  VulkanThreadEntry()
+      : pool(static_cast<DLDeviceType>(kDLVulkan), VulkanWorkspace::Global()) {
+    context.device_id = 0;
+    context.device_type = static_cast<DLDeviceType>(kDLVulkan);
+  }
+  ~VulkanThreadEntry();
+  // get the global workspace
+  static VulkanThreadEntry* ThreadLocal();
+
+ private:
+  /*! \brief the command pools */
+  std::vector<std::unique_ptr<VulkanCommandPool> > pool_;
+};
+
+// inline implementation
+
+
+}  // namespace vulkan
+}  // namespace runtime
+}  // namespace tvm
+#endif  // TVM_VULKAN_RUNTIME
+#endif  // TVM_RUNTIME_VULKAN_VULKAN_COMMON_H_

src/runtime/vulkan/vulkan_module.h

+/*!
+ *  Copyright (c) 2017 by Contributors
+ * \file metal_module.h
+ * \brief Execution handling of Metal kernels
+ */
+#ifndef TVM_RUNTIME_VULKAN_VULKAN_MODULE_H_
+#define TVM_RUNTIME_VULKAN_VULKAN_MODULE_H_
+
+#include <tvm/runtime/config.h>
+#include <tvm/runtime/packed_func.h>
+#include <dmlc/type_traits.h>
+#include <memory>
+#include <vector>
+#include <string>
+#include <unordered_map>
+#include "../meta_data.h"
+
+namespace tvm {
+namespace runtime {
+/*! \brief Maximum number of GPU supported in VulkanModule. */
+static constexpr const int kVulkanMaxNumDevice = 8;
+
+/*! \brief TVM Vulkan binary pack magic number */
+static constexpr const int kVulkanModuleMagic = 0x02700027;
+
+/*!
+ * \brief A single VK shader program
+ *
+ *  Due to the global resource declaration.
+ *  Current SPIRV only allows one entry program per shader,
+ *  making it less useful for a Module like system.
+ *
+ *  Instead we pass in map of str->VulkanShader until
+ *  there is a native solution available.
+ */
+struct VulkanShader {
+  /*! \brief header flag */
+  uint32_t flag{0};
+  /*! \brief Data segment */
+  std::vector<uint32_t> data;
+
+  void Save(dmlc::Stream *writer) const;
+  bool Load(dmlc::Stream *reader);
+};
+
+/*!
+ * \brief create a metal module from data.
+ *
+ * \param pmap The program map.
+ * \param fmap The function information map.
+ * \param source Optional, source code.
+ */
+Module VulkanModuleCreate(
+    std::unordered_map<std::string, VulkanShader> smap,
+    std::unordered_map<std::string, FunctionInfo> fmap,
+    std::string source);
+}  // namespace runtime
+}  // namespace tvm
+
+namespace dmlc {
+DMLC_DECLARE_TRAITS(has_saveload, ::tvm::runtime::VulkanShader, true);
+}  // namespace dmlc
+
+#endif  // TVM_RUNTIME_VULKAN_VULKAN_MODULE_H_

tests/python/integration/test_ewise.py

@@ -18,7 +18,8 @@ def test_exp():
     def check_device(device, host="stackvm"):
         if not tvm.module.enabled(host):
             return
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             return
         fexp = tvm.build(s, [A, B],
                          device, host,
@@ -33,6 +34,7 @@ def test_exp():
             b.asnumpy(), np.exp(a.asnumpy()), rtol=1e-5)
 
     check_device("cuda", "llvm")
+    check_device("vulkan")
     check_device("opencl")
 
 
@@ -75,11 +77,12 @@ def test_popcount():
         bx, tx = s[B].split(B.op.axis[0], factor=num_thread)
 
         def check_device(device):
-            if not tvm.module.enabled(device):
+            ctx = tvm.context(device, 0)
+            if not ctx.exist:
                 print("skip because %s is not enabled.." % device)
                 return
-            ctx = tvm.context(device, 0)
-            if str(ctx).startswith('gpu'):
+            target = tvm.target.create(device)
+            if "cpu" not in target.keys:
                 s[B].bind(bx, tvm.thread_axis("blockIdx.x"))
                 s[B].bind(tx, tvm.thread_axis("threadIdx.x"))
             func = tvm.build(s, [A, B], device)
@@ -95,6 +98,8 @@ def test_popcount():
         check_device("cuda")
         check_device("opencl")
         check_device("metal")
+        if dtype == "uint32":
+            check_device("vulkan")
     run('uint32')
     run('uint64')
 
@@ -121,14 +126,14 @@ def test_add():
 
         # one line to build the function.
         def check_device(device):
-            if not tvm.module.enabled(device):
+            ctx = tvm.context(device, 0)
+            if not ctx.exist:
                 print("skip because %s is not enabled.." % device)
                 return
             fadd = tvm.build(s, [A, B, C],
                              device,
                              name="myadd")
-            print(fadd.imported_modules[0].get_source())
-            ctx = tvm.context(device, 0)
+
             # launch the kernel.
             n = 1024
             a = tvm.nd.array((np.random.uniform(size=n) * 256).astype(A.dtype), ctx)
@@ -142,6 +147,8 @@ def test_add():
         check_device("opencl")
         check_device("metal")
         check_device("cuda")
+        check_device("vulkan")
+
     run("float32")
     run("int32")
     run("int64")
@@ -149,7 +156,7 @@ def test_add():
 
 
 if __name__ == "__main__":
+    test_add()
     test_log_pow_llvm()
     test_exp()
-    test_add()
     test_popcount()

tests/python/integration/test_gemm.py

@@ -2,6 +2,7 @@ import tvm
 import numpy as np
 import time
 
+
 def test_gemm():
     # graph
     nn = 1024
@@ -64,13 +65,14 @@ def test_gemm():
 
     # one line to build the function.
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("skip because %s is not enabled.." % device)
             return
 
         with tvm.target.create(device):
             f = tvm.build(s, [A, B, C])
-        ctx = tvm.context(device, 0)
+
         # launch the kernel.
         n = nn
         m = n
@@ -86,12 +88,12 @@ def test_gemm():
         np.testing.assert_allclose(
             c.asnumpy(), np.dot(a_np, b_np.T), rtol=1e-5)
 
+    check_device("vulkan")
     check_device("nvptx -mcpu=sm_20")
     check_device("rocm")
     check_device("metal")
     check_device("opencl")
     check_device("cuda")
-    #check_device("nvptx -mcpu=sm_20")
 
 if __name__ == "__main__":
     test_gemm()

tests/python/integration/test_reduce.py

 import tvm
 import numpy as np
 
+
 def test_reduce_prims():
     def test_prim(reducer, np_reducer):
         # graph
@@ -21,12 +22,12 @@ def test_reduce_prims():
 
         # one line to build the function.
         def check_device(device, host="stackvm"):
+            ctx = tvm.context(device, 0)
             if not tvm.module.enabled(host):
                 return
-            if not tvm.module.enabled(device):
+            if not ctx.exist:
                 print("skip because %s is not enabled.." % device)
                 return
-            ctx = tvm.context(device, 0)
             freduce = tvm.build(s,
                              args=[A, B],
                              target=device, target_host=host,
@@ -44,6 +45,7 @@ def test_reduce_prims():
             np.testing.assert_allclose(npy, res, rtol=1e-4)
 
         check_device("metal")
+        check_device("vulkan")
         check_device("cuda")
         check_device("opencl")
     test_prim(tvm.sum, np.sum)
@@ -106,10 +108,11 @@ def test_rfactor_threads():
 
     # one line to build the function.
     def check_target(device, host="stackvm"):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("skip because %s is not enabled.." % device)
             return
-        ctx = tvm.context(device, 0)
+
         fapi = tvm.lower(s, args=[A, B])
         fsum = tvm.build(fapi,
                          target=device,
@@ -125,6 +128,7 @@ def test_rfactor_threads():
         np.testing.assert_allclose(
             b.asnumpy(), res, rtol=1e-4)
 
+    check_target("vulkan")
     check_target("cuda")
     check_target("metal")
     check_target("opencl")
@@ -159,15 +163,14 @@ def test_rfactor_elemwise_threads():
 
     # one line to build the function.
     def check_target(device, host="stackvm"):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("skip because %s is not enabled.." % device)
             return
-        ctx = tvm.context(device, 0)
         fapi = tvm.lower(s, args=[A, C])
         fsum = tvm.build(fapi,
                          target=device,
                          name="mysum")
-        print(fsum.imported_modules[0].get_source())
         # launch the kernel.
         a = tvm.nd.array(np.random.uniform(size=(m, n)).astype(A.dtype), ctx)
         b  = tvm.nd.array(np.zeros(m, dtype=B.dtype), ctx)
@@ -176,6 +179,7 @@ def test_rfactor_elemwise_threads():
         np.testing.assert_allclose(
             b.asnumpy(), res, rtol=1e-4)
 
+    check_target("vulkan")
     check_target("cuda")
     check_target("metal")
     check_target("opencl")
@@ -264,10 +268,10 @@ def test_rfactor_argmax():
     s[B0].set_store_predicate(thread_x.var.equal(0))
 
     def check_target(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("skip because %s is not enabled.." % device)
             return
-        ctx = tvm.context(device, 0)
         fapi = tvm.lower(s, args=[A0, A1, B0, B1])
         fargmax = tvm.build(fapi,
                             target=device,
@@ -285,6 +289,7 @@ def test_rfactor_argmax():
         np.testing.assert_allclose(np_res, nd_res0.asnumpy())
 
     check_target("cuda")
+    check_target("vulkan")
 
 if __name__ == "__main__":
     test_rfactor_elemwise_threads()

tests/python/integration/test_scan.py

@@ -24,13 +24,13 @@ def test_scan():
 
     # one line to build the function.
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("skip because %s is not enabled.." % device)
             return
         fscan = tvm.build(s, [X, res],
                           device,
                           name="myscan")
-        ctx = tvm.context(device, 0)
         # launch the kernel.
         n = 1024
         m = 10
@@ -41,6 +41,7 @@ def test_scan():
         np.testing.assert_allclose(
             b.asnumpy(), np.cumsum(a_np, axis=0))
 
+    check_device("vulkan")
     check_device("cuda")
     check_device("metal")
     check_device("opencl")

tests/python/unittest/test_codegen_device.py

@@ -13,12 +13,12 @@ def test_add_pipeline():
     # GPU schedule have to split by gridIdx and threadIdx
     num_thread = 256
     xo, xi = s[C].split(C.op.axis[0], factor=num_thread)
-    s[C].bind(xo, tvm.thread_axis("threadIdx.x"))
-    s[C].bind(xi, tvm.thread_axis("blockIdx.x"))
+    s[C].bind(xi, tvm.thread_axis("threadIdx.x"))
+    s[C].bind(xo, tvm.thread_axis("blockIdx.x"))
 
     xo, xi = s[D].split(D.op.axis[0], factor=num_thread)
-    s[D].bind(xo, tvm.thread_axis("threadIdx.x"))
-    s[D].bind(xi, tvm.thread_axis("blockIdx.x"))
+    s[D].bind(xi, tvm.thread_axis("threadIdx.x"))
+    s[D].bind(xo, tvm.thread_axis("blockIdx.x"))
 
     # compile to IR
     s = s.normalize()
@@ -35,11 +35,11 @@ def test_add_pipeline():
     fsplits[0] = tvm.ir_pass.LowerTVMBuiltin(fsplits[0])
 
     def check_target(device, host="stackvm"):
-        if not tvm.module.enabled(host):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             return
-        if not tvm.module.enabled(device):
+        if not tvm.module.enabled(host):
             return
-        ctx = tvm.context(device, 0)
         mhost = tvm.codegen.build_module(fsplits[0], host)
         mdev = tvm.codegen.build_module(fsplits[1:], device)
         mhost.import_module(mdev)
@@ -55,12 +55,12 @@ def test_add_pipeline():
             d.asnumpy(), a.asnumpy() + b.asnumpy() + 1)
 
     def check_module_save(device, host="stackvm"):
-        if not tvm.module.enabled(host):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             return
-        if not tvm.module.enabled(device):
+        if not tvm.module.enabled(host):
             return
-        ctx = tvm.context(device, 0)
-        fmt = "ptx" if device == "cuda" else "cl"
+        fmt = "ptx" if device == "cuda" else device
         mhost = tvm.codegen.build_module(fsplits[0], host)
         mdev = tvm.codegen.build_module(fsplits[1:], device)
         temp = util.tempdir()
@@ -82,7 +82,9 @@ def test_add_pipeline():
     check_target("cuda", host="llvm")
     check_module_save("cuda", host="stackvm")
     check_target("nvptx", host="llvm")
+    check_target("vulkan", host="llvm")
     check_target("rocm", host="llvm")
+    check_module_save("vulkan", host="stackvm")
 
 
 if __name__ == "__main__":

tests/python/unittest/test_module_load.py

@@ -95,7 +95,7 @@ def test_device_module_dump():
             f = tvm.build(s, [A, B], device, "llvm", name=name)
         else:
             raise ValueError("Unsupported platform")
-       
+
         path_dso = temp.relpath("dev_lib.so")
         f.export_library(path_dso)
 
@@ -110,6 +110,7 @@ def test_device_module_dump():
             np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
 
     check_device("cuda")
+    check_device("vulkan")
     check_device("opencl")
     check_device("metal")
 
@@ -172,7 +173,7 @@ def test_combine_module_llvm():
         np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
         mm['myadd2'](a, b)
         np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
-    
+
     if sys.platform != "win32":
         check_system_lib()
     check_llvm()

tests/python/unittest/test_runtime_ndarray.py

@@ -7,6 +7,7 @@ def enabled_ctx_list():
                 ('cl', tvm.opencl(0)),
                 ('metal', tvm.metal(0)),
                 ('rocm', tvm.rocm(0)),
+                ('vulkan', tvm.vulkan(0)),
                 ('vpi', tvm.vpi(0))]
     for k, v  in ctx_list:
         assert tvm.context(k, 0) == v

topi/recipe/gemm/cuda_gemm_square.py

@@ -2,6 +2,7 @@
 import tvm
 import os
 from tvm.contrib import nvcc
+from tvm.contrib import spirv
 import numpy as np
 
 TASK="gemm"
@@ -25,6 +26,7 @@ def tvm_callback_cuda_postproc(code):
         code = open("perf/%s_manual.cu" % TASK).read()
     return code
 
+
 def test_gemm():
     # graph
     nn = 2048
@@ -101,12 +103,12 @@ def test_gemm():
     s[BB].double_buffer()
     # correctness
     def check_device(device):
-        print("Device %s" % device)
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
+        print("Device %s" % device)
         f = tvm.build(s, [A, B, C], device)
-        ctx = tvm.context(device, 0)
         # launch the kernel.
         n, m, l = nn, nn, nn
         a_np = np.random.uniform(size=(n, l)).astype(A.dtype)
@@ -126,7 +128,7 @@ def test_gemm():
         GFLOPS = num_flops / (t * 1e3) / 1e6
         print("average time cost of %d runs = %g ms, %g GFLOPS." % (num_runs, t * 1e3, GFLOPS))
 
-    for device in ["cuda", "opencl", "rocm", "nvptx"]:
+    for device in ["cuda", "opencl", "rocm", "nvptx", "vulkan"]:
         with tvm.build_config(auto_unroll_max_step=128,
                               unroll_explicit=(device != "cuda")):
             check_device(device)

topi/tests/python/test_topi_broadcast.py

@@ -9,13 +9,13 @@ def verify_broadcast_to_ele(in_shape, out_shape):
     A = tvm.placeholder(shape=in_shape, name="A")
     B = topi.broadcast_to(A, out_shape)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_broadcast(B)
-        ctx = tvm.context(device, 0)
         foo = tvm.build(s, [A, B], device, name="broadcast_to")
         data_npy = np.random.uniform(size=in_shape).astype(A.dtype)
         out_npy = np.broadcast_to(data_npy, out_shape)
@@ -25,6 +25,7 @@ def verify_broadcast_to_ele(in_shape, out_shape):
             foo(data_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
+    check_device("vulkan")
     check_device("opencl")
     check_device("cuda")
     check_device("metal")
@@ -50,13 +51,13 @@ def verify_broadcast_binary_ele(lhs_shape, rhs_shape, typ="add"):
     else:
         raise NotImplementedError
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_broadcast(C)
-        ctx = tvm.context(device, 0)
         foo = tvm.build(s, [A, B, C], device, name="broadcast_binary" + "_" + typ)
         lhs_npy = np.random.uniform(size=lhs_shape).astype(A.dtype)
         rhs_npy = np.random.uniform(size=rhs_shape).astype(A.dtype)
@@ -82,6 +83,7 @@ def verify_broadcast_binary_ele(lhs_shape, rhs_shape, typ="add"):
             foo(lhs_nd, rhs_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy, rtol=1E-4, atol=1E-4)
 
+    check_device("vulkan")
     check_device("opencl")
     check_device("cuda")
     check_device("metal")
@@ -105,5 +107,5 @@ def test_broadcast_binary():
 
 
 if __name__ == "__main__":
-    test_broadcast_to()
     test_broadcast_binary()
+    test_broadcast_to()

topi/tests/python/test_topi_conv2d_hwcn.py

@@ -31,11 +31,11 @@ def verify_conv2d_hwcn(batch, in_channel, in_size, num_filter, kernel, stride, p
     a_np, w_np, b_np, c_np = get_ref_data()
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         w = tvm.nd.array(w_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
@@ -49,7 +49,7 @@ def verify_conv2d_hwcn(batch, in_channel, in_size, num_filter, kernel, stride, p
             np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
             np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 

topi/tests/python/test_topi_conv2d_nchw.py

@@ -29,14 +29,14 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p
     a_np, w_np, b_np, c_np = get_ref_data()
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s1 = topi.generic.schedule_conv2d_nchw([B])
             s2 = topi.generic.schedule_conv2d_nchw([C])
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         w = tvm.nd.array(w_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
@@ -50,7 +50,7 @@ def verify_conv2d_nchw(batch, in_channel, in_size, num_filter, kernel, stride, p
             np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
             np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 

topi/tests/python/test_topi_conv2d_transpose_nchw.py

@@ -29,14 +29,14 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
     a_np, w_np, b_np, c_np = get_ref_data()
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s1 = topi.generic.schedule_conv2d_transpose_nchw([B])
             s2 = topi.generic.schedule_conv2d_transpose_nchw([C])
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         w = tvm.nd.array(w_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
@@ -50,7 +50,7 @@ def verify_conv2d_transpose_nchw(batch, in_channel, in_size, num_filter, kernel,
             np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
             np.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 

topi/tests/python/test_topi_dense.py

@@ -29,13 +29,13 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True):
     a_np, b_np, c_np, d_np = get_ref_data()
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_dense(D)
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(b_np, ctx)
         c = tvm.nd.array(c_np, ctx)
@@ -44,7 +44,7 @@ def verify_dense(batch, in_dim, out_dim, use_bias=True):
         f(a, b, c, d)
         np.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 def test_dense():

topi/tests/python/test_topi_depthwise_conv2d.py

@@ -23,7 +23,8 @@ def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_mu
 
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
@@ -32,7 +33,6 @@ def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_mu
             s1 = topi.generic.schedule_depthwise_conv2d_nchw(DepthwiseConv2d)
             s2 = topi.generic.schedule_depthwise_conv2d_nchw(ScaleShift)
             s3 = topi.generic.schedule_depthwise_conv2d_nchw(Relu)
-        ctx = tvm.context(device, 0)
         # build the kernels
         f1 = tvm.build(s1, [Input, Filter, DepthwiseConv2d], device)
         f2 = tvm.build(s2, [Input, Filter, Scale, Shift, ScaleShift], device)
@@ -90,6 +90,8 @@ def depthwise_conv2d_with_workload_nchw(batch, in_channel, in_height, channel_mu
     check_device("cuda")
     check_device("metal")
     check_device("rocm")
+    check_device("vulkan")
+
 
 def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_multiplier, filter_height, stride_h, padding):
     in_width = in_height
@@ -108,7 +110,8 @@ def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_mu
     # schedule
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
@@ -117,7 +120,6 @@ def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_mu
             s1 = topi.generic.schedule_depthwise_conv2d_nhwc(DepthwiseConv2d)
             s2 = topi.generic.schedule_depthwise_conv2d_nhwc(ScaleShift)
             s3 = topi.generic.schedule_depthwise_conv2d_nhwc(Relu)
-        ctx = tvm.context(device, 0)
         # build the kernels
         f1 = tvm.build(s1, [Input, Filter, DepthwiseConv2d], device)
         f2 = tvm.build(s2, [Input, Filter, Scale, Shift, ScaleShift], device)
@@ -177,6 +179,7 @@ def depthwise_conv2d_with_workload_nhwc(batch, in_channel, in_height, channel_mu
     check_device("cuda")
     check_device("metal")
     check_device("rocm")
+    check_device("vulkan")
 
 def test_depthwise_conv2d():
     print("testing nchw")

topi/tests/python/test_topi_depthwise_conv2d_back_input.py

@@ -32,11 +32,11 @@ def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multipli
     schedule = schedule_depthwise_conv2d_backward_input_nhwc(In_grad)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
-        ctx = tvm.context(device, 0)
         # build the kernel
         f = tvm.build(schedule, [Filter, Out_grad, In_grad], device)
         # prepare pod type for test data closure
@@ -85,6 +85,7 @@ def verify_depthwise_conv2d_back_input(batch, in_channel, in_h, channel_multipli
     check_device("cuda")
     check_device("metal")
     check_device("rocm")
+    check_device("vulkan")
 
 def test_topi_depthwise_conv2d_backward_input_nhwc():
     verify_depthwise_conv2d_back_input(16, 256, 56, 1, 3, 1, 1)

topi/tests/python/test_topi_depthwise_conv2d_back_weight.py

@@ -32,11 +32,11 @@ def verify_depthwise_conv2d_back_weight(batch, in_channel, in_h, channel_multipl
     schedule = schedule_depthwise_conv2d_backward_weight_nhwc(Weight_grad)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
-        ctx = tvm.context(device, 0)
         # build the kernel
         f = tvm.build(schedule, [Input, Out_grad, Weight_grad], device)
         # prepare pod type for test data closure
@@ -78,6 +78,7 @@ def verify_depthwise_conv2d_back_weight(batch, in_channel, in_h, channel_multipl
     check_device("cuda")
     check_device("metal")
     check_device("rocm")
+    check_device("vulkan")
 
 def test_topi_depthwise_conv2d_backward_weight_nhwc():
     verify_depthwise_conv2d_back_weight(16, 256, 56, 1, 3, 1, 1)

topi/tests/python/test_topi_pooling.py

@@ -44,20 +44,21 @@ def verify_pool(n, ic, ih, kh, sh, padding, pool_type, ceil_mode):
     b_np = np.maximum(b_np, 0.0)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_pool(B)
-        ctx = tvm.context(device, 0)
+
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=dtype), ctx)
         f = tvm.build(s, [A, B], device)
         f(a, b)
         np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 def test_pool():
@@ -82,20 +83,20 @@ def verify_global_pool(n, c, h, w, pool_type):
     b_np = np.maximum(b_np, 0.0)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_global_pool(B)
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
         f = tvm.build(s, [A, B], device)
         f(a, b)
         np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 def test_global_pool():

topi/tests/python/test_topi_reduce.py

@@ -47,13 +47,14 @@ def verify_reduce_map_ele(in_shape, axis, keepdims, type="sum"):
         raise NotImplementedError
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_reduce(B)
-        ctx = tvm.context(device, 0)
+
         foo = tvm.build(s, [A, B], device, name=type)
         # Test
         in_npy = np.random.uniform(size=in_shape).astype(np.float32)
@@ -90,7 +91,7 @@ def verify_reduce_map_ele(in_shape, axis, keepdims, type="sum"):
                 np.testing.assert_allclose(out_tvm_val, in_npy_map.min(axis=axis), 1E-3, 1E-3)
         else:
             np.testing.assert_allclose(out_tvm.asnumpy(), out_npy, 1E-3, 1E-3)
-    for device in ["cuda", "opencl", "metal", "llvm", "rocm"]:
+    for device in ["cuda", "opencl", "metal", "llvm", "rocm", "vulkan"]:
         check_device(device)
 
 

topi/tests/python/test_topi_relu.py

@@ -13,20 +13,21 @@ def verify_relu(m, n):
     b_np = a_np * (a_np > 0)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_elemwise(B)
-        ctx = tvm.context(device, 0)
+
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
         foo = tvm.build(s, [A, B], device, name="relu")
         foo(a, b)
         np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 

topi/tests/python/test_topi_softmax.py

@@ -17,20 +17,21 @@ def verify_softmax(m, n):
     b_np = topi.testing.softmax_python(a_np)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_softmax(B)
-        ctx = tvm.context(device, 0)
+
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
         foo = tvm.build(s, [A, B], device, name="softmax")
         foo(a, b)
         np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-    for device in ['cuda', 'opencl', 'metal', 'rocm']:
+    for device in ['cuda', 'opencl', 'metal', 'rocm', 'vulkan']:
         check_device(device)
 
 def test_softmax():
@@ -48,20 +49,20 @@ def verify_log_softmax(m, n):
     b_np = topi.testing.log_softmax_python(a_np)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_softmax(B)
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(get_const_tuple(B.shape), dtype=B.dtype), ctx)
         foo = tvm.build(s, [A, B], device, name="log_softmax")
         foo(a, b)
         np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-    for device in ["cuda", "opencl", "metal", "rocm"]:
+    for device in ["cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 

topi/tests/python/test_topi_transform.py

@@ -7,13 +7,13 @@ def verify_expand_dims(in_shape, out_shape, axis, num_newaxis):
     A = tvm.placeholder(shape=in_shape, name="A")
     B = topi.expand_dims(A, axis, num_newaxis)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_broadcast(B)
-        ctx = tvm.context(device, 0)
         foo = tvm.build(s, [A, B], device, name="expand_dims")
         data_npy = np.random.uniform(size=in_shape).astype(A.dtype)
         out_npy = data_npy.reshape(out_shape)
@@ -22,7 +22,7 @@ def verify_expand_dims(in_shape, out_shape, axis, num_newaxis):
         foo(data_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
-    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm"]:
+    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 
@@ -30,13 +30,13 @@ def verify_tranpose(in_shape, axes):
     A = tvm.placeholder(shape=in_shape, name="A")
     B = topi.transpose(A, axes)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_injective(B)
-        ctx = tvm.context(device, 0)
         foo = tvm.build(s, [A, B], device, name="tranpose")
         data_npy = np.arange(np.prod(in_shape)).reshape(in_shape).astype(A.dtype)
         out_npy = data_npy.transpose(axes)
@@ -45,7 +45,7 @@ def verify_tranpose(in_shape, axes):
         foo(data_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
-    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm"]:
+    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 
@@ -53,13 +53,13 @@ def verify_reshape(src_shape, dst_shape):
     A = tvm.placeholder(shape=src_shape, name="A")
     B = topi.reshape(A, dst_shape)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_injective(B)
-        ctx = tvm.context(device, 0)
         foo = tvm.build(s, [A, B], device, name="reshape")
         data_npy = np.random.normal(size=src_shape).astype(A.dtype)
         out_npy = np.reshape(data_npy, newshape=dst_shape)
@@ -68,7 +68,7 @@ def verify_reshape(src_shape, dst_shape):
         foo(data_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
-    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm"]:
+    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 
@@ -76,13 +76,14 @@ def verify_squeeze(src_shape, axis):
     A = tvm.placeholder(shape=src_shape, name="A")
     B = topi.squeeze(A, axis=axis)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_injective(B)
-        ctx = tvm.context(device, 0)
+
         foo = tvm.build(s, [A, B], device, name="squeeze")
         data_npy = np.random.normal(size=src_shape).astype(A.dtype)
         out_npy = np.squeeze(data_npy, axis=axis)
@@ -95,7 +96,7 @@ def verify_squeeze(src_shape, axis):
         foo(data_nd, out_nd)
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
-    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm"]:
+    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 def verify_concatenate(shapes, axis):
@@ -104,13 +105,14 @@ def verify_concatenate(shapes, axis):
         tensor_l.append(tvm.placeholder(shape, name="A" + str(i)))
     out_tensor = topi.concatenate(a_tuple=tensor_l, axis=axis)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_injective(out_tensor)
-        ctx = tvm.context(device, 0)
+
         foo = tvm.build(s, tensor_l + [out_tensor], device, name="concatenate")
         data_npys = [np.random.normal(size=shape).astype(tensor_l[0].dtype) for shape in shapes]
         out_npy = np.concatenate(data_npys, axis=axis)
@@ -119,7 +121,7 @@ def verify_concatenate(shapes, axis):
         foo(*(data_nds + [out_nd]))
         np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
-    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm"]:
+    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 
@@ -127,13 +129,14 @@ def verify_split(src_shape, indices_or_sections, axis):
     A = tvm.placeholder(shape=src_shape, name="A")
     tensor_l = topi.split(A, indices_or_sections, axis=axis)
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_injective(tensor_l)
-        ctx = tvm.context(device, 0)
+
         foo = tvm.build(s, [A] + tensor_l, device, name="split")
         data_npy = np.random.normal(size=src_shape).astype(A.dtype)
         out_npys = np.split(data_npy, indices_or_sections, axis=axis)
@@ -143,7 +146,7 @@ def verify_split(src_shape, indices_or_sections, axis):
         for out_nd, out_npy in zip(out_nds, out_npys):
             np.testing.assert_allclose(out_nd.asnumpy(), out_npy)
 
-    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm"]:
+    for device in ["llvm", "nvptx", "cuda", "opencl", "metal", "rocm", "vulkan"]:
         check_device(device)
 
 

topi/tests/python/test_topi_upsampling.py

@@ -14,13 +14,13 @@ def verify_upsampling(batch, in_channel, in_height, in_width, scale):
     b_np = topi.testing.upsampling_python(a_np, scale)
 
     def check_device(device):
-        if not tvm.module.enabled(device):
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
             print("Skip because %s is not enabled" % device)
             return
         print("Running on target: %s" % device)
         with tvm.target.create(device):
             s = topi.generic.schedule_injective(B)
-        ctx = tvm.context(device, 0)
         a = tvm.nd.array(a_np, ctx)
         b = tvm.nd.array(np.zeros(out_shape, dtype=dtype), ctx)
         f = tvm.build(s, [A, B], device)
@@ -28,7 +28,7 @@ def verify_upsampling(batch, in_channel, in_height, in_width, scale):
 
         np.testing.assert_allclose(b.asnumpy(), b_np, rtol=1e-5)
 
-    for device in ['llvm', 'cuda']:
+    for device in ['llvm', 'cuda', 'vulkan']:
         check_device(device)
 
 def test_upsampling():