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Feb 16, 2023
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[CLML] Changes corresponding to OpenCL workspace refactorization #13972

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[CLML] Changes corresponding to OpenCL workspace refactorization
srkreddy1238 Feb 13, 2023
5d978f5
* review comments.
srkreddy1238 Feb 14, 2023
fddabb1
* review
srkreddy1238 Feb 16, 2023
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127 changes: 68 additions & 59 deletions src/runtime/contrib/clml/clml_runtime.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -140,15 +140,17 @@ class CLMLRuntime : public JSONRuntimeBase {
void InitCLML() {
// Setup CLML Context
cl_int result = 0;

workspace = cl::OpenCLWorkspace::Global();
workspace->Init();
tentry = workspace->GetThreadEntry();

if (!ExtensionStringPresent()) {
LOG(WARNING) << "CLML Runtime Init: Qualcomm extn not present.\n";
LOG(FATAL) << "CLML Runtime Init: Qualcomm extn not present.\n";
return;
}
device_id = workspace->GetCLDeviceID(tentry->device.device_id);
platform_id = workspace->device_to_platform[device_id];

// Query and Get CLML Interface
static const cl_uint MAX_VERSIONS = 256;
cl_int majorVersions[MAX_VERSIONS];
Expand Down Expand Up @@ -492,7 +494,7 @@ class CLMLRuntime : public JSONRuntimeBase {
JSONGraphNode node = it->second.second;
void* node_data = nullptr;

allocateTensorMemory(h_ClmlIntf, workspace->context, tensor_desc);
allocateTensorMemory(h_ClmlIntf, workspace->contexts[platform_id], tensor_desc);

if (node.GetOpType() == "const") {
node_data = data_entry_[EntryID(it->first, 0)]->data;
Expand Down Expand Up @@ -581,11 +583,9 @@ class CLMLRuntime : public JSONRuntimeBase {

bool ExtensionStringPresent(void) {
cl_int result = 0;
if (workspace->platform_id == nullptr) {
return 0;
}
size_t reqd_size = 0;
cl_device_id device_id = workspace->devices[workspace->GetThreadEntry()->device.device_id];
cl_device_id device_id =
workspace->GetCLDeviceID(workspace->GetThreadEntry()->device.device_id);
result = clGetDeviceInfo(device_id, CL_DEVICE_EXTENSIONS, 0, NULL, &reqd_size);
ICHECK(reqd_size > 0u && result == CL_SUCCESS) << "clGetDeviceInfo:" << result;

Expand All @@ -607,7 +607,8 @@ class CLMLRuntime : public JSONRuntimeBase {

cl_ml_tensor_desc_qcom desc = {
dtype, layout, dims.n, dims.c, dims.h, dims.w, 0, CL_TENSOR_DIMENSIONS_4D_QCOM, { 0 }};
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->context, NULL, &desc, &tensor);
result =
h_ClmlIntf->clCreateMLTensorQCOM(workspace->contexts[platform_id], NULL, &desc, &tensor);
ICHECK(tensor && result == CL_SUCCESS) << "clCreateMLTensorQCOM:" << result;
(void)result;
return tensor;
Expand All @@ -619,11 +620,12 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_int result = CL_OUT_OF_HOST_MEMORY;
cl_mem buffer = NULL;

result =
h_ClmlIntf->clGetMLTensorMemorySizeQCOM(workspace->context, pTensorMemDesc->tensor, &size);
result = h_ClmlIntf->clGetMLTensorMemorySizeQCOM(workspace->contexts[platform_id],
pTensorMemDesc->tensor, &size);
ICHECK(result == CL_SUCCESS) << "clGetMLTensorMemorySizeQCOM:" << result;

buffer = clCreateBuffer(workspace->context, CL_MEM_READ_WRITE, size, NULL, &result);
buffer =
clCreateBuffer(workspace->contexts[platform_id], CL_MEM_READ_WRITE, size, NULL, &result);
ICHECK(result == CL_SUCCESS) << "clCreateBuffer:" << result;

pTensorMemDesc->memory = buffer;
Expand Down Expand Up @@ -686,7 +688,8 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_channel_type cl_dtype = MakeCLDataType(tvm_dtype);

auto tensor_dsc = std::make_shared<cl_ml_tensor_memory_desc_qcom>();
tensor_dsc->tensor = DeviceMakeCLMLTensor(workspace->context, dims, layout, cl_dtype);
tensor_dsc->tensor =
DeviceMakeCLMLTensor(workspace->contexts[platform_id], dims, layout, cl_dtype);
return tensor_dsc;
}

Expand Down Expand Up @@ -800,8 +803,8 @@ class CLMLRuntime : public JSONRuntimeBase {
} else {
cl_ml_tensor_desc_qcom desc = {};
desc.num_dimensions = CL_TENSOR_UNUSED_QCOM;
result =
h_ClmlIntf->clCreateMLTensorQCOM(workspace->context, NULL, &desc, &layer_.unusedTensor);
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->contexts[platform_id], NULL, &desc,
&layer_.unusedTensor);
ICHECK(layer_.unusedTensor && result == CL_SUCCESS) << "clCreateMLTensorQCOM:" << result;
bias->tensor = layer_.unusedTensor;
}
Expand All @@ -821,13 +824,13 @@ class CLMLRuntime : public JSONRuntimeBase {
if (!has_bn) {
if (!has_act) {
result = h_ClmlIntf->clCreateMLOpConvolutionForwardQCOM(
workspace->context, 0, &conv_desc, input->tensor, weight->tensor, bias->tensor,
output->tensor, &op, NULL);
workspace->contexts[platform_id], 0, &conv_desc, input->tensor, weight->tensor,
bias->tensor, output->tensor, &op, NULL);
ICHECK(op && result == CL_SUCCESS) << "Convolution Error:" << result;
} else {
result = h_ClmlIntf->clCreateMLOpFusedConvolutionActivationForwardQCOM(
workspace->context, 0, &conv_desc, &act_desc, input->tensor, weight->tensor,
bias->tensor, NULL, output->tensor, &op, tuning_cache);
workspace->contexts[platform_id], 0, &conv_desc, &act_desc, input->tensor,
weight->tensor, bias->tensor, NULL, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Convolution Error:" << result;
}
layer_.func_ins.push_back(input);
Expand All @@ -854,15 +857,15 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_ml_op_batchnorm_desc_qcom bn_desc = {CL_BATCHNORM_MODE_SPATIAL_QCOM, cl_arithmetic_mode};
if (!has_act) {
result = h_ClmlIntf->clCreateMLOpFusedConvolutionBatchNormForwardQCOM(
workspace->context, 0, &conv_desc, &bn_desc, input->tensor, weight->tensor,
bias->tensor, output->tensor, bn_mean->tensor, bn_var->tensor, bn_scale->tensor,
bn_bias->tensor, &op, tuning_cache);
workspace->contexts[platform_id], 0, &conv_desc, &bn_desc, input->tensor,
weight->tensor, bias->tensor, output->tensor, bn_mean->tensor, bn_var->tensor,
bn_scale->tensor, bn_bias->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Convolution Error:" << result;
} else {
result = h_ClmlIntf->clCreateMLOpFusedConvolutionBatchNormActivationForwardQCOM(
workspace->context, 0, &conv_desc, &bn_desc, &act_desc, input->tensor, weight->tensor,
bias->tensor, output->tensor, NULL, bn_mean->tensor, bn_var->tensor, bn_scale->tensor,
bn_bias->tensor, &op, tuning_cache);
workspace->contexts[platform_id], 0, &conv_desc, &bn_desc, &act_desc, input->tensor,
weight->tensor, bias->tensor, output->tensor, NULL, bn_mean->tensor, bn_var->tensor,
bn_scale->tensor, bn_bias->tensor, &op, tuning_cache);

ICHECK(op && result == CL_SUCCESS) << "Convolution Error:" << result;
}
Expand Down Expand Up @@ -895,13 +898,13 @@ class CLMLRuntime : public JSONRuntimeBase {

cl_ml_tensor_desc_qcom desc = {};
desc.num_dimensions = CL_TENSOR_UNUSED_QCOM;
result =
h_ClmlIntf->clCreateMLTensorQCOM(workspace->context, NULL, &desc, &layer_.unusedTensor);
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->contexts[platform_id], NULL, &desc,
&layer_.unusedTensor);
ICHECK(layer_.unusedTensor && result == CL_SUCCESS) << ":" << result;

result = h_ClmlIntf->clCreateMLOpActivationForwardQCOM(workspace->context, 0, &act_desc,
input->tensor, layer_.unusedTensor,
output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpActivationForwardQCOM(
workspace->contexts[platform_id], 0, &act_desc, input->tensor, layer_.unusedTensor,
output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Activation Error:" << result;

layer_.func_ins.push_back(input);
Expand Down Expand Up @@ -947,8 +950,8 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_ml_op_batchnorm_desc_qcom bn_desc = {CL_BATCHNORM_MODE_SPATIAL_QCOM, cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpBatchNormForwardQCOM(
workspace->context, 0, &bn_desc, input->tensor, bn_mean->tensor, bn_var->tensor,
bn_scale->tensor, bn_bias->tensor, output->tensor, &op, tuning_cache);
workspace->contexts[platform_id], 0, &bn_desc, input->tensor, bn_mean->tensor,
bn_var->tensor, bn_scale->tensor, bn_bias->tensor, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Batchnorm Error:" << result;

layer->function.push_back(op);
Expand Down Expand Up @@ -997,12 +1000,13 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_ml_tensor_desc_qcom desc = {};
cl_ml_tensor_qcom unusedTensor = NULL;
desc.num_dimensions = CL_TENSOR_UNUSED_QCOM;
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->context, NULL, &desc, &unusedTensor);
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->contexts[platform_id], NULL, &desc,
&unusedTensor);
ICHECK(unusedTensor && result == CL_SUCCESS) << ":" << result;

result =
h_ClmlIntf->clCreateMLOpPoolingForwardQCOM(workspace->context, 0, &pool_desc, input->tensor,
unusedTensor, output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpPoolingForwardQCOM(workspace->contexts[platform_id], 0,
&pool_desc, input->tensor, unusedTensor,
output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Pooling Error:" << result;

layer_.func_ins.push_back(input);
Expand Down Expand Up @@ -1043,13 +1047,13 @@ class CLMLRuntime : public JSONRuntimeBase {

cl_ml_tensor_desc_qcom desc = {};
desc.num_dimensions = CL_TENSOR_UNUSED_QCOM;
result =
h_ClmlIntf->clCreateMLTensorQCOM(workspace->context, NULL, &desc, &layer_.unusedTensor);
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->contexts[platform_id], NULL, &desc,
&layer_.unusedTensor);
ICHECK(layer_.unusedTensor && result == CL_SUCCESS) << ":" << result;

result = h_ClmlIntf->clCreateMLOpPoolingForwardQCOM(workspace->context, 0, &pool_desc,
input->tensor, layer_.unusedTensor,
output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpPoolingForwardQCOM(
workspace->contexts[platform_id], 0, &pool_desc, input->tensor, layer_.unusedTensor,
output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Pooling Error:" << result;

layer_.func_ins.push_back(input);
Expand Down Expand Up @@ -1079,7 +1083,7 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_ml_op_softmax_desc_qcom softmax_desc = {CL_SOFTMAX_ALGORITHM_ACCURATE_QCOM,
CL_SOFTMAX_MODE_INSTANCE_QCOM, cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpSoftmaxQCOM(workspace->context, 0, &softmax_desc,
result = h_ClmlIntf->clCreateMLOpSoftmaxQCOM(workspace->contexts[platform_id], 0, &softmax_desc,
input->tensor, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "SoftMax Error:" << result;

Expand Down Expand Up @@ -1125,8 +1129,8 @@ class CLMLRuntime : public JSONRuntimeBase {
{clml_padding[0], clml_padding[1], clml_padding[2], clml_padding[3], 0, 0, 0, 0},
cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpPadQCOM(workspace->context, 0, &pad_desc, input->tensor,
output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpPadQCOM(workspace->contexts[platform_id], 0, &pad_desc,
input->tensor, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Pad Error:" << result;

layer_.func_ins.push_back(input);
Expand All @@ -1150,7 +1154,7 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_dtype);
auto output = MakeCLMLTensorFromJSONNode(node, CL_TENSOR_LAYOUT_OPTIMAL_QCOM, cl_dtype);

result = h_ClmlIntf->clCreateMLOpReshapeQCOM(workspace->context, 0, input->tensor,
result = h_ClmlIntf->clCreateMLOpReshapeQCOM(workspace->contexts[platform_id], 0, input->tensor,
output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Reshape Error:" << result;

Expand All @@ -1175,7 +1179,7 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_dtype);
auto output = MakeCLMLTensorFromJSONNode(node, CL_TENSOR_LAYOUT_OPTIMAL_QCOM, cl_dtype);

result = h_ClmlIntf->clCreateMLOpReshapeQCOM(workspace->context, 0, input->tensor,
result = h_ClmlIntf->clCreateMLOpReshapeQCOM(workspace->contexts[platform_id], 0, input->tensor,
output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Reshape Error:" << result;

Expand Down Expand Up @@ -1210,8 +1214,8 @@ class CLMLRuntime : public JSONRuntimeBase {
}
cl_ml_op_concat_desc_qcom concatDesc = {axis, (cl_uint)inputSize, cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpConcatQCOM(workspace->context, 0, &concatDesc, concatInputs,
output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpConcatQCOM(workspace->contexts[platform_id], 0, &concatDesc,
concatInputs, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Concat Error:" << result;

layer->function.push_back(op);
Expand Down Expand Up @@ -1250,8 +1254,8 @@ class CLMLRuntime : public JSONRuntimeBase {
} else {
cl_ml_tensor_desc_qcom desc = {};
desc.num_dimensions = CL_TENSOR_UNUSED_QCOM;
result =
h_ClmlIntf->clCreateMLTensorQCOM(workspace->context, NULL, &desc, &layer_.unusedTensor);
result = h_ClmlIntf->clCreateMLTensorQCOM(workspace->contexts[platform_id], NULL, &desc,
&layer_.unusedTensor);
ICHECK(layer_.unusedTensor && result == CL_SUCCESS) << "clCreateMLTensorQCOM:" << result;
bias->tensor = layer_.unusedTensor;
}
Expand All @@ -1269,8 +1273,8 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpConvolutionForwardQCOM(
workspace->context, 0, &conv_desc, input->tensor, weight->tensor, bias->tensor,
output->tensor, &op, NULL);
workspace->contexts[platform_id], 0, &conv_desc, input->tensor, weight->tensor,
bias->tensor, output->tensor, &op, NULL);
ICHECK(op && result == CL_SUCCESS) << "Fully Connected Error:" << result;

layer->function.push_back(op);
Expand Down Expand Up @@ -1300,8 +1304,8 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_ml_op_clip_desc_qcom clip_desc = {
CL_CLIP_BY_VALUE_QCOM, {{a_max}, CL_FLOAT}, {{a_min}, CL_FLOAT}, cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpClipQCOM(workspace->context, 0, &clip_desc, input->tensor,
output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpClipQCOM(workspace->contexts[platform_id], 0, &clip_desc,
input->tensor, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Clip Error:" << result;

layer_.func_ins.push_back(input);
Expand Down Expand Up @@ -1342,8 +1346,9 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_ml_op_binary_desc_qcom add_desc = {
binary_op, {{1.0}, CL_FLOAT}, {{1.0}, CL_FLOAT}, {{0.0}, CL_FLOAT}, cl_arithmetic_mode};

result = h_ClmlIntf->clCreateMLOpBinaryQCOM(workspace->context, 0, &add_desc, input_a->tensor,
input_b->tensor, output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpBinaryQCOM(workspace->contexts[platform_id], 0, &add_desc,
input_a->tensor, input_b->tensor, output->tensor,
&op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << op_name << " Node Error:" << result;

layer_.func_ins.push_back(input_a);
Expand Down Expand Up @@ -1371,8 +1376,9 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_uint block_size = std::stoi(node.GetAttr<std::vector<std::string>>("block_size")[0]);

cl_ml_op_depthtospace_desc_qcom dtos_desc = {block_size, cl_arithmetic_mode};
result = h_ClmlIntf->clCreateMLOpDepthToSpaceQCOM(
workspace->context, 0, &dtos_desc, input->tensor, output->tensor, &op, tuning_cache);
result =
h_ClmlIntf->clCreateMLOpDepthToSpaceQCOM(workspace->contexts[platform_id], 0, &dtos_desc,
input->tensor, output->tensor, &op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "DepthToSpace Layer Error:" << result;

layer_.func_ins.push_back(input);
Expand All @@ -1399,8 +1405,9 @@ class CLMLRuntime : public JSONRuntimeBase {
cl_bool align_corners = std::stoi(node.GetAttr<std::vector<std::string>>("align_corners")[0]);

cl_ml_op_resize_bilinear_desc_qcom resize_desc = {align_corners, false, cl_arithmetic_mode};
result = h_ClmlIntf->clCreateMLOpResizeBilinearQCOM(
workspace->context, 0, &resize_desc, input->tensor, output->tensor, &op, tuning_cache);
result = h_ClmlIntf->clCreateMLOpResizeBilinearQCOM(workspace->contexts[platform_id], 0,
&resize_desc, input->tensor, output->tensor,
&op, tuning_cache);
ICHECK(op && result == CL_SUCCESS) << "Resize Layer Error:" << result;

layer_.func_ins.push_back(input);
Expand All @@ -1418,6 +1425,8 @@ class CLMLRuntime : public JSONRuntimeBase {
GET_ML_API_INTERFACE* h_ClmlIntf = NULL;
cl::OpenCLWorkspace* workspace = NULL;
cl::OpenCLThreadEntry* tentry = NULL;
cl_device_id device_id;
cl_platform_id platform_id;
cl_ml_tuningcache_qcom tuning_cache = NULL;
bool is_tuning_run;
char* tuning_file;
Expand Down
2 changes: 2 additions & 0 deletions src/runtime/opencl/opencl_common.h
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -282,6 +282,8 @@ class OpenCLWorkspace : public DeviceAPI {

return prop & CL_QUEUE_PROFILING_ENABLE;
}
// Check if the device is present or not
bool IsDeviceExists(unsigned int device_id) { return device_id < devices.size(); }
// Enable queue profiling, recreate if required
void EnableQueueProfiling(Device dev, bool enable) {
bool is_enabled = cl::OpenCLWorkspace::Global()->IsProfiling(dev);
Expand Down