experiments/STT-tensorflow
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Removed unused argument.

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PiperOrigin-RevId: 327261070
Change-Id: Ie0ccf02d692b4af8616b92340dda8725fcdb0c43
This commit is contained in:
Raman Sarokin 2020-08-18 10:49:58 -07:00 committed by TensorFlower Gardener
parent 06fd3ef336
commit 77cf50d67b
8 changed files with 27 additions and 43 deletions

4
tensorflow/lite/delegates/gpu/cl/api.cc
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@ -196,8 +196,8 @@ class DefaultTensorTie : public TensorTie {
ToTensorStorageType(d.object_def.object_type,
d.object_def.data_layout),
Layout::BHWC};
RETURN_IF_ERROR(AllocateTensorMemory(env->context(), env->device(),
shape, desc, &cl_memory_));
RETURN_IF_ERROR(
AllocateTensorMemory(env->context(), shape, desc, &cl_memory_));
if (d.object_def.object_type == ObjectType::OPENCL_TEXTURE) {
external_obj_ = OpenClTexture{cl_memory_.memory()};
} else {

2
tensorflow/lite/delegates/gpu/cl/environment.cc
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@ -59,7 +59,7 @@ absl::Status CheckKernelSupportOfOneLayerTextureArray(Environment* env,
Tensor tensor;
const BHWC shape(1, 4, 4, 4);
RETURN_IF_ERROR(CreateTensor(
env->context(), env->device(), shape,
env->context(), shape,
{DataType::FLOAT32, TensorStorageType::TEXTURE_ARRAY, Layout::HWC},
&tensor));
RETURN_IF_ERROR(kernel.SetMemory(0, tensor.GetMemoryPtr()));

2
tensorflow/lite/delegates/gpu/cl/inference_context.cc
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@ -501,7 +501,7 @@ absl::Status InferenceContext::AllocateMemoryForStrongShapes(
graph_ids_to_strong_shape_tensors_[t.first] = id;
const auto& it = strong_shape_tensors_.find(id);
if (it == strong_shape_tensors_.end()) {
RETURN_IF_ERROR(CreateTensor(*context, device, shape, t.second,
RETURN_IF_ERROR(CreateTensor(*context, shape, t.second,
&strong_shape_tensors_[id]));
}
}

6
tensorflow/lite/delegates/gpu/cl/kernels/cl_test.cc
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@ -34,8 +34,7 @@ absl::Status ExecuteGPUOperation(const std::vector<TensorFloat32>& src_cpu,
return absl::InvalidArgumentError(
"Layout doesn't have Batch dimension, but shape.b != 1");
}
RETURN_IF_ERROR(CreateTensor(*creation_context.context,
*creation_context.device, src_shape,
RETURN_IF_ERROR(CreateTensor(*creation_context.context, src_shape,
op_def.src_tensors[0], &src[i]));
RETURN_IF_ERROR(src[i].WriteData(creation_context.queue, src_cpu[i]));
operation->SetSrc(&src[i], i);
@ -48,8 +47,7 @@ absl::Status ExecuteGPUOperation(const std::vector<TensorFloat32>& src_cpu,
return absl::InvalidArgumentError(
"Layout doesn't have Batch dimension, but shape.b != 1");
}
RETURN_IF_ERROR(CreateTensor(*creation_context.context,
*creation_context.device, dst_shape,
RETURN_IF_ERROR(CreateTensor(*creation_context.context, dst_shape,
op_def.dst_tensors[0], &dst[i]));
operation->SetDst(&dst[i], i);

10
tensorflow/lite/delegates/gpu/cl/kernels/elementwise.cc
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@ -171,9 +171,8 @@ absl::Status CreateElementwiseTwoInput(
definition.GetDataType(), Layout::HWC);
TensorDescriptor desc{definition.GetDataType(), storage_type, Layout::HWC};
Tensor gpu_tensor;
RETURN_IF_ERROR(CreateTensor(*creation_context.context,
*creation_context.device, shape, desc,
&gpu_tensor));
RETURN_IF_ERROR(
CreateTensor(*creation_context.context, shape, desc, &gpu_tensor));
RETURN_IF_ERROR(
gpu_tensor.WriteData(creation_context.queue, constant_tensor));
@ -209,9 +208,8 @@ absl::Status CreateElementwiseTwoInput(
definition.GetDataType(), Layout::HWC);
TensorDescriptor desc{definition.GetDataType(), storage_type, Layout::HWC};
Tensor gpu_tensor;
RETURN_IF_ERROR(CreateTensor(*creation_context.context,
*creation_context.device, shape, desc,
&gpu_tensor));
RETURN_IF_ERROR(
CreateTensor(*creation_context.context, shape, desc, &gpu_tensor));
RETURN_IF_ERROR(
gpu_tensor.WriteData(creation_context.queue, constant_tensor));

26
tensorflow/lite/delegates/gpu/cl/tensor.cc
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@ -53,15 +53,13 @@ absl::Status CreateImageBufferFromBuffer(const CLContext& context,
return absl::OkStatus();
}
absl::Status CreateTensor(const CLContext& context, const CLDevice& device,
const BHWDC& shape,
absl::Status CreateTensor(const CLContext& context, const BHWDC& shape,
const TensorDescriptor& descriptor, cl_mem memory,
Tensor* result) {
const bool memory_owner = memory == nullptr;
if (memory_owner) {
CLMemory mem;
RETURN_IF_ERROR(
AllocateTensorMemory(context, device, shape, descriptor, &mem));
RETURN_IF_ERROR(AllocateTensorMemory(context, shape, descriptor, &mem));
memory = mem.Release();
}
if (descriptor.storage_type == TensorStorageType::IMAGE_BUFFER) {
@ -434,17 +432,15 @@ absl::Status Tensor::ReadData(CLCommandQueue* queue,
return ReadDataBHWDC(absl::MakeSpan(dst->data), queue);
}
absl::Status CreateTensor(const CLContext& context, const CLDevice& device,
const BHWC& shape, const TensorDescriptor& descriptor,
Tensor* result) {
absl::Status CreateTensor(const CLContext& context, const BHWC& shape,
const TensorDescriptor& descriptor, Tensor* result) {
const BHWDC shape5D(shape.b, shape.h, shape.w, 1, shape.c);
return CreateTensor(context, device, shape5D, descriptor, nullptr, result);
return CreateTensor(context, shape5D, descriptor, nullptr, result);
}
absl::Status CreateTensor(const CLContext& context, const CLDevice& device,
const BHWDC& shape,
absl::Status CreateTensor(const CLContext& context, const BHWDC& shape,
const TensorDescriptor& descriptor, Tensor* result) {
return CreateTensor(context, device, shape, descriptor, nullptr, result);
return CreateTensor(context, shape, descriptor, nullptr, result);
}
absl::Status CreateSharedTensor(const CLContext& context, cl_mem memory,
@ -462,16 +458,14 @@ absl::Status CreateSharedTensor(const CLContext& context, cl_mem memory,
return CreateTensorShared(context, shape, descriptor, memory, result);
}
absl::Status AllocateTensorMemory(const CLContext& context,
const CLDevice& device, const BHWC& shape,
absl::Status AllocateTensorMemory(const CLContext& context, const BHWC& shape,
const TensorDescriptor& descriptor,
CLMemory* result) {
const BHWDC shape5D(shape.b, shape.h, shape.w, 1, shape.c);
return AllocateTensorMemory(context, device, shape5D, descriptor, result);
return AllocateTensorMemory(context, shape5D, descriptor, result);
}
absl::Status AllocateTensorMemory(const CLContext& context,
const CLDevice& device, const BHWDC& shape,
absl::Status AllocateTensorMemory(const CLContext& context, const BHWDC& shape,
const TensorDescriptor& descriptor,
CLMemory* result) {
const int slices = DivideRoundUp(shape.c, 4);

14
tensorflow/lite/delegates/gpu/cl/tensor.h
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@ -147,22 +147,18 @@ class Tensor : public GPUObject {
using TensorPtr = std::shared_ptr<Tensor>;
absl::Status AllocateTensorMemory(const CLContext& context,
const CLDevice& device, const BHWC& shape,
absl::Status AllocateTensorMemory(const CLContext& context, const BHWC& shape,
const TensorDescriptor& descriptor,
CLMemory* result);
absl::Status AllocateTensorMemory(const CLContext& context,
const CLDevice& device, const BHWDC& shape,
absl::Status AllocateTensorMemory(const CLContext& context, const BHWDC& shape,
const TensorDescriptor& descriptor,
CLMemory* result);
absl::Status CreateTensor(const CLContext& context, const CLDevice& device,
const BHWC& shape, const TensorDescriptor& descriptor,
Tensor* result);
absl::Status CreateTensor(const CLContext& context, const BHWC& shape,
const TensorDescriptor& descriptor, Tensor* result);
absl::Status CreateTensor(const CLContext& context, const CLDevice& device,
const BHWDC& shape,
absl::Status CreateTensor(const CLContext& context, const BHWDC& shape,
const TensorDescriptor& descriptor, Tensor* result);
absl::Status CreateSharedTensor(const CLContext& context, cl_mem memory,

6
tensorflow/lite/delegates/gpu/cl/tensor_test.cc
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@ -47,8 +47,7 @@ absl::Status TensorGenericTest(const BHWC& shape,
}
Tensor tensor;
RETURN_IF_ERROR(
CreateTensor(env->context(), env->device(), shape, descriptor, &tensor));
RETURN_IF_ERROR(CreateTensor(env->context(), shape, descriptor, &tensor));
RETURN_IF_ERROR(tensor.WriteData(env->queue(), tensor_cpu));
RETURN_IF_ERROR(tensor.ReadData(env->queue(), &tensor_gpu));
@ -77,8 +76,7 @@ absl::Status Tensor5DGenericTest(const BHWDC& shape,
}
Tensor tensor;
RETURN_IF_ERROR(
CreateTensor(env->context(), env->device(), shape, descriptor, &tensor));
RETURN_IF_ERROR(CreateTensor(env->context(), shape, descriptor, &tensor));
RETURN_IF_ERROR(tensor.WriteData(env->queue(), tensor_cpu));
RETURN_IF_ERROR(tensor.ReadData(env->queue(), &tensor_gpu));