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Commit 2afe0248 by Dayananda V Committed by Tianqi Chen
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Vulkan TVM Android Support (#1571)

parent 764516a6
Showing with 125 additions and 46 deletions
apps/android_rpc/README.md
......@@ -123,18 +123,25 @@ export TVM_NDK_CC=/opt/android-toolchain-arm64/bin/aarch64-linux-android-g++
python android_rpc_test.py
```
This will compile TVM IR to shared libraries (CPU and OpenCL) and run vector addition on your Android device. On my test device, it gives following results.
This will compile TVM IR to shared libraries (CPU, OpenCL and Vulkan) and run vector addition on your Android device. To verify compiled TVM IR shared libraries on OpenCL target set [`'test_opencl = True'`](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py#L25) and on Vulkan target set [`'test_vulkan = False'`](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py#L27) in [tests/android_rpc_test.py](https://github.com/dmlc/tvm/blob/master/apps/android_rpc/tests/android_rpc_test.py), by default on CPU target will execute.
On my test device, it gives following results.
```bash
TVM: Initializing cython mode...
[01:21:43] src/codegen/llvm/codegen_llvm.cc:75: set native vector to be 32 for target aarch64
[01:21:43] src/runtime/opencl/opencl_device_api.cc:194: Initialize OpenCL platform 'Apple'
[01:21:43] src/runtime/opencl/opencl_device_api.cc:214: opencl(0)='Iris' cl_device_id=0x1024500
[01:21:44] src/codegen/llvm/codegen_llvm.cc:75: set native vector to be 32 for target aarch64
Run GPU test ...
0.000155807 secs/op
Run CPU test ...
0.00139824 secs/op
0.000962932 secs/op
Run GPU(OpenCL Flavor) test ...
0.000155807 secs/op
[23:29:34] /home/tvm/src/runtime/vulkan/vulkan_device_api.cc:674: Cannot initialize vulkan: [23:29:34] /home/tvm/src/runtime/vulkan/vulkan_device_api.cc:512: Check failed: __e == VK_SUCCESS Vulan Error, code=-9: VK_ERROR_INCOMPATIBLE_DRIVER
Stack trace returned 10 entries:
[bt] (0) /home/user/.local/lib/python3.6/site-packages/tvm-0.4.0-py3.6-linux-x86_64.egg/tvm/libtvm.so(dmlc::StackTrace[abi:cxx11]()+0x53) [0x7f477f5399f3]
.........
You can still compile vulkan module but cannot run locally
Run GPU(Vulkan Flavor) test ...
0.000225198 secs/op
```
You can define your own TVM operators and test via this RPC app on your Android device to find the most optimized TVM schedule.
apps/android_rpc/app/src/main/jni/Application.mk
ifndef config
ifneq ("$(wildcard ./config.mk)","")
config ?= config.mk
else
config ?= make/config.mk
endif
ifneq ("$(wildcard ./config.mk)","")
config ?= config.mk
else
config ?= make/config.mk
endif
endif
include $(config)
......@@ -16,10 +16,10 @@ APP_STL := c++_static
APP_CPPFLAGS += -DDMLC_LOG_STACK_TRACE=0 -DTVM4J_ANDROID=1 -std=c++11 -Oz -frtti
ifeq ($(USE_OPENCL), 1)
APP_CPPFLAGS += -DTVM_OPENCL_RUNTIME=1
APP_CPPFLAGS += -DTVM_OPENCL_RUNTIME=1
endif
ifeq ($(USE_VULKAN), 1)
APP_CPPFLAGS += -DTVM_VULKAN_RUNTIME=1
APP_LDFLAGS += -lvulkan
APP_CPPFLAGS += -DTVM_VULKAN_RUNTIME=1
APP_LDFLAGS += -lvulkan
endif
apps/android_rpc/tests/android_rpc_test.py
......@@ -21,59 +21,92 @@ key = "android"
arch = "arm64"
target = "llvm -target=%s-linux-android" % arch
# whether enable to execute test on OpenCL target
test_opencl = False
# whether enable to execute test on Vulkan target
test_vulkan = False
def test_rpc_module():
# graph
n = tvm.convert(1024)
A = tvm.placeholder((n,), name='A')
B = tvm.compute(A.shape, lambda *i: A(*i) + 1.0, name='B')
a_np = np.random.uniform(size=1024).astype(A.dtype)
temp = util.tempdir()
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
# Build the dynamic lib.
# If we don't want to do metal and only use cpu, just set target to be target
f = tvm.build(s, [A, B], "opencl", target_host=target, name="myadd")
path_dso1 = temp.relpath("dev_lib2.so")
f.export_library(path_dso1, ndk.create_shared)
# Establish remote connection with target hardware
tracker = rpc.connect_tracker(tracker_host, tracker_port)
remote = tracker.request(key, priority=0,
session_timeout=60)
# Compile the Graph for CPU target
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].parallel(xi)
s[B].pragma(xo, "parallel_launch_point")
s[B].pragma(xi, "parallel_barrier_when_finish")
f = tvm.build(s, [A, B], target, name="myadd_cpu")
path_dso2 = temp.relpath("cpu_lib.so")
f.export_library(path_dso2, ndk.create_shared)
tracker = rpc.connect_tracker(tracker_host, tracker_port)
remote = tracker.request(key, priority=0,
session_timeout=60)
path_dso_cpu = temp.relpath("cpu_lib.so")
f.export_library(path_dso_cpu, ndk.create_shared)
# Execute the portable graph on cpu target
print('Run CPU test ...')
ctx = remote.cpu(0)
remote.upload(path_dso2)
remote.upload(path_dso_cpu)
f2 = remote.load_module("cpu_lib.so")
a_np = np.random.uniform(size=1024).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f2.time_evaluator(f2.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
print('%g secs/op\n' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
# Compile the Graph for OpenCL target
if test_opencl:
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
# Build the dynamic lib.
# If we don't want to do metal and only use cpu, just set target to be target
f = tvm.build(s, [A, B], "opencl", target_host=target, name="myadd")
path_dso_cl = temp.relpath("dev_lib_cl.so")
f.export_library(path_dso_cl, ndk.create_shared)
print('Run GPU(OpenCL Flavor) test ...')
ctx = remote.cl(0)
remote.upload(path_dso_cl)
f1 = remote.load_module("dev_lib_cl.so")
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op\n' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
# Compile the Graph for Vulkan target
if test_vulkan:
s = tvm.create_schedule(B.op)
xo, xi = s[B].split(B.op.axis[0], factor=64)
s[B].bind(xi, tvm.thread_axis("threadIdx.x"))
s[B].bind(xo, tvm.thread_axis("blockIdx.x"))
# Build the dynamic lib.
# If we don't want to do metal and only use cpu, just set target to be target
f = tvm.build(s, [A, B], "vulkan", target_host=target, name="myadd")
path_dso_vulkan = temp.relpath("dev_lib_vulkan.so")
f.export_library(path_dso_vulkan, ndk.create_shared)
print('Run GPU(Vulkan Flavor) test ...')
ctx = remote.vulkan(0)
remote.upload(path_dso_vulkan)
f1 = remote.load_module("dev_lib_vulkan.so")
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op\n' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
print('Run GPU test ...')
ctx = remote.cl(0)
remote.upload(path_dso1)
f1 = remote.load_module("dev_lib2.so")
a_np = np.random.uniform(size=1024).astype(A.dtype)
a = tvm.nd.array(a_np, ctx)
b = tvm.nd.array(np.zeros(1024, dtype=A.dtype), ctx)
time_f = f1.time_evaluator(f1.entry_name, ctx, number=10)
cost = time_f(a, b).mean
print('%g secs/op' % cost)
np.testing.assert_equal(b.asnumpy(), a.asnumpy() + 1)
if __name__ == "__main__":
test_rpc_module()
jvm/core/src/main/java/ml/dmlc/tvm/TVMContext.java
......@@ -30,6 +30,7 @@ public class TVMContext {
MASK2STR.put(1, "cpu");
MASK2STR.put(2, "gpu");
MASK2STR.put(4, "opencl");
MASK2STR.put(7, "vulkan");
MASK2STR.put(8, "metal");
MASK2STR.put(9, "vpi");
......@@ -38,6 +39,7 @@ public class TVMContext {
STR2MASK.put("cuda", 2);
STR2MASK.put("cl", 4);
STR2MASK.put("opencl", 4);
STR2MASK.put("vulkan", 7);
STR2MASK.put("metal", 8);
STR2MASK.put("vpi", 9);
}
......@@ -82,6 +84,19 @@ public class TVMContext {
}
/**
* Construct a Vulkan device.
* @param devId The device id
* @return The created context
*/
public static TVMContext vulkan(int devId) {
return new TVMContext(7, devId);
}
public static TVMContext vulkan() {
return vulkan(0);
}
/**
* Construct a metal device.
* @param devId The device id
* @return The created context
......
jvm/core/src/main/java/ml/dmlc/tvm/rpc/RPCSession.java
......@@ -144,6 +144,24 @@ public class RPCSession {
}
/**
* Construct remote OpenCL device.
* @param devId device id.
* @return Remote OpenCL context.
*/
public TVMContext vulkan(int devId) {
return context(7, devId);
}
/**
* Construct remote OpenCL device.
* @return Remote OpenCL context.
*/
public TVMContext vulkan() {
return vulkan(0);
}
/**
* Construct remote Metal device.
* @param devId device id.
* @return Remote metal context.
......
python/tvm/rpc/client.py
......@@ -130,6 +130,10 @@ class RPCSession(object):
"""Construct OpenCL device."""
return self.context(4, dev_id)
def vulkan(self, dev_id=0):
"""Construct Vulkan device."""
return self.context(7, dev_id)
def metal(self, dev_id=0):
"""Construct Metal device."""
return self.context(8, dev_id)
......
web/tvm_runtime.js
......@@ -696,6 +696,7 @@ var tvm_runtime = tvm_runtime || {};
1 : "cpu",
2 : "gpu",
4 : "opencl",
7 : "vulkan",
8 : "metal",
9 : "vpi",
11 : "opengl",
......@@ -706,6 +707,7 @@ var tvm_runtime = tvm_runtime || {};
"cuda": 2,
"cl": 4,
"opencl": 4,
"vulkan": 7,
"metal": 8,
"vpi": 9,
"opengl": 11,
......
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