未验证 提交 870402fd 编写于 作者: Y YuanRisheng 提交者: GitHub

move meshgrid yaml (#41411)

上级 b72a7ebb
...@@ -410,5 +410,153 @@ std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x, ...@@ -410,5 +410,153 @@ std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x,
return x_grad; return x_grad;
} }
std::vector<Tensor> meshgrid_impl(const std::vector<Tensor>& inputs) {
Backend kernel_backend = Backend::UNDEFINED;
DataLayout kernel_layout = DataLayout::UNDEFINED;
DataType kernel_data_type = DataType::UNDEFINED;
if (kernel_backend == Backend::UNDEFINED ||
kernel_layout == DataLayout::UNDEFINED ||
kernel_data_type == DataType::UNDEFINED) {
auto kernel_key_set = ParseKernelKeyByInputArgs(inputs);
auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
if (kernel_backend == Backend::UNDEFINED) {
kernel_backend = kernel_key.backend();
}
if (kernel_layout == DataLayout::UNDEFINED) {
kernel_layout = kernel_key.layout();
}
if (kernel_data_type == DataType::UNDEFINED) {
kernel_data_type = kernel_key.dtype();
}
}
const auto& kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
"meshgrid", {kernel_backend, kernel_layout, kernel_data_type});
VLOG(6) << "meshgrid API kernel key: [" << kernel_backend << ", "
<< kernel_layout << ", " << kernel_data_type << "]";
VLOG(6) << "meshgrid API kernel: " << kernel;
auto* dev_ctx = GetDeviceContextByBackend(kernel_backend);
auto input_inputs_vec = PrepareData(inputs, kernel.InputAt(0), {});
std::vector<const phi::DenseTensor*> input_inputs(input_inputs_vec->size());
for (size_t i = 0; i < input_inputs.size(); ++i) {
input_inputs[i] = &input_inputs_vec->at(i);
}
auto x_meta_vec = MakeMetaTensor(input_inputs);
std::vector<phi::MetaTensor*> inputs_metas(x_meta_vec.size());
for (size_t i = 0; i < x_meta_vec.size(); ++i) {
inputs_metas[i] = &x_meta_vec[i];
}
// Calculate the number of out tensors
size_t out_number = inputs.size();
std::vector<Tensor> out;
auto dense_outs = SetKernelOutput(out_number, kernel_backend, &out);
std::vector<phi::MetaTensor> meta_outs;
meta_outs.reserve(out_number);
std::vector<phi::MetaTensor*> meta_out_ptrs;
meta_out_ptrs.reserve(out_number);
for (size_t i = 0; i < out_number; ++i) {
meta_outs.push_back(dense_outs[i]);
meta_out_ptrs.push_back(&meta_outs.back());
}
phi::MeshgridInferMeta(inputs_metas, meta_out_ptrs);
using kernel_signature = void (*)(const platform::DeviceContext&,
const std::vector<const phi::DenseTensor*>&,
std::vector<phi::DenseTensor*>&);
auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
(*kernel_fn)(*dev_ctx, input_inputs, dense_outs);
return out;
}
std::vector<Tensor> meshgrid_grad_impl(
const std::vector<Tensor>& inputs,
const std::vector<Tensor>& outputs_grad) {
Backend kernel_backend = Backend::UNDEFINED;
DataLayout kernel_layout = DataLayout::UNDEFINED;
DataType kernel_data_type = DataType::UNDEFINED;
if (kernel_backend == Backend::UNDEFINED ||
kernel_layout == DataLayout::UNDEFINED ||
kernel_data_type == DataType::UNDEFINED) {
auto kernel_key_set = ParseKernelKeyByInputArgs(inputs, outputs_grad);
auto kernel_key = kernel_key_set.GetHighestPriorityKernelKey();
if (kernel_backend == Backend::UNDEFINED) {
kernel_backend = kernel_key.backend();
}
if (kernel_layout == DataLayout::UNDEFINED) {
kernel_layout = kernel_key.layout();
}
if (kernel_data_type == DataType::UNDEFINED) {
kernel_data_type = kernel_key.dtype();
}
}
const auto& kernel = phi::KernelFactory::Instance().SelectKernelOrThrowError(
"meshgrid_grad", {kernel_backend, kernel_layout, kernel_data_type});
VLOG(6) << "meshgrid_grad API kernel key: [" << kernel_backend << ", "
<< kernel_layout << ", " << kernel_data_type << "]";
VLOG(6) << "meshgrid_grad API kernel: " << kernel;
auto* dev_ctx = GetDeviceContextByBackend(kernel_backend);
auto input_inputs_vec = PrepareData(inputs, kernel.InputAt(0), {});
std::vector<const phi::DenseTensor*> input_inputs(input_inputs_vec->size());
for (size_t i = 0; i < input_inputs.size(); ++i) {
input_inputs[i] = &input_inputs_vec->at(i);
}
auto input_outputs_grad_vec =
PrepareData(outputs_grad, kernel.InputAt(1), {});
std::vector<const phi::DenseTensor*> input_outputs_grad(
input_outputs_grad_vec->size());
for (size_t i = 0; i < input_outputs_grad.size(); ++i) {
input_outputs_grad[i] = &input_outputs_grad_vec->at(i);
}
size_t out_number = inputs.size();
std::vector<Tensor> api_output;
auto kernel_out = SetKernelOutput(out_number, kernel_backend, &api_output);
auto inputs_meta_vec = MakeMetaTensor(input_inputs);
std::vector<phi::MetaTensor*> inputs_metas(inputs_meta_vec.size());
for (size_t i = 0; i < inputs_meta_vec.size(); ++i) {
inputs_metas[i] = &inputs_meta_vec[i];
}
auto outputs_grad_meta_vec = MakeMetaTensor(input_outputs_grad);
std::vector<phi::MetaTensor*> outputs_grad_metas(
outputs_grad_meta_vec.size());
for (size_t i = 0; i < outputs_grad_meta_vec.size(); ++i) {
outputs_grad_metas[i] = &outputs_grad_meta_vec[i];
}
std::vector<phi::MetaTensor> meta_outs;
meta_outs.reserve(out_number);
std::vector<phi::MetaTensor*> meta_out_ptrs;
meta_out_ptrs.reserve(out_number);
for (size_t i = 0; i < out_number; ++i) {
meta_outs.push_back(kernel_out[i]);
meta_out_ptrs.push_back(&meta_outs.back());
}
phi::MeshgridGradInferMeta(inputs_metas, outputs_grad_metas, meta_out_ptrs);
using kernel_signature = void (*)(const platform::DeviceContext&,
const std::vector<const phi::DenseTensor*>&,
const std::vector<const phi::DenseTensor*>&,
std::vector<phi::DenseTensor*>&);
auto* kernel_fn = kernel.GetVariadicKernelFn<kernel_signature>();
(*kernel_fn)(*dev_ctx, input_inputs, input_outputs_grad, kernel_out);
return api_output;
}
} // namespace experimental } // namespace experimental
} // namespace paddle } // namespace paddle
...@@ -59,6 +59,9 @@ std::vector<Tensor> concat_grad_impl(const std::vector<Tensor>& x, ...@@ -59,6 +59,9 @@ std::vector<Tensor> concat_grad_impl(const std::vector<Tensor>& x,
std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x, std::vector<Tensor> stack_grad_impl(const std::vector<Tensor>& x,
const Tensor& out_grad, const Tensor& out_grad,
int axis); int axis);
std::vector<Tensor> meshgrid_impl(const std::vector<Tensor>& inputs);
std::vector<Tensor> meshgrid_grad_impl(const std::vector<Tensor>& inputs,
const std::vector<Tensor>& outputs_grad);
} // namespace experimental } // namespace experimental
} // namespace paddle } // namespace paddle
...@@ -245,6 +245,20 @@ void MaxPoolWithIndexGradInferMeta(const MetaTensor& x, ...@@ -245,6 +245,20 @@ void MaxPoolWithIndexGradInferMeta(const MetaTensor& x,
dx->share_meta(x); dx->share_meta(x);
} }
void MeshgridGradInferMeta(const std::vector<MetaTensor*>& inputs,
const std::vector<MetaTensor*>& outputs_grad,
std::vector<MetaTensor*> inputs_grad) {
PADDLE_ENFORCE_GT(outputs_grad.size(),
1,
errors::InvalidArgument(
"Number of Inputs(Out@Grad) should be larger than 1."
"But received Inputs(Out@Grad)' size = %d .",
outputs_grad.size()));
for (size_t i = 0; i < inputs.size(); i++) {
inputs_grad[i]->share_meta(*inputs[i]);
}
}
void NllLossGradInferMeta(const MetaTensor& x, void NllLossGradInferMeta(const MetaTensor& x,
const MetaTensor& label, const MetaTensor& label,
paddle::optional<const MetaTensor&> weight, paddle::optional<const MetaTensor&> weight,
......
...@@ -115,6 +115,10 @@ void MaxPoolWithIndexGradInferMeta(const MetaTensor& x, ...@@ -115,6 +115,10 @@ void MaxPoolWithIndexGradInferMeta(const MetaTensor& x,
bool adaptive, bool adaptive,
MetaTensor* dx); MetaTensor* dx);
void MeshgridGradInferMeta(const std::vector<MetaTensor*>& inputs,
const std::vector<MetaTensor*>& outputs_grad,
std::vector<MetaTensor*> inputs_grad);
void NllLossGradInferMeta(const MetaTensor& input, void NllLossGradInferMeta(const MetaTensor& input,
const MetaTensor& label, const MetaTensor& label,
paddle::optional<const MetaTensor&> weight, paddle::optional<const MetaTensor&> weight,
......
...@@ -20,6 +20,7 @@ from op_test import OpTest, skip_check_grad_ci ...@@ -20,6 +20,7 @@ from op_test import OpTest, skip_check_grad_ci
import paddle.fluid as fluid import paddle.fluid as fluid
import paddle import paddle
from paddle.fluid import compiler, Program, program_guard, core from paddle.fluid import compiler, Program, program_guard, core
from paddle.fluid.framework import _test_eager_guard
class TestMeshgridOp(OpTest): class TestMeshgridOp(OpTest):
...@@ -149,6 +150,10 @@ class TestMeshgridOp6(unittest.TestCase): ...@@ -149,6 +150,10 @@ class TestMeshgridOp6(unittest.TestCase):
assert np.array_equal(res_3.shape, [100, 200]) assert np.array_equal(res_3.shape, [100, 200])
assert np.array_equal(res_4.shape, [100, 200]) assert np.array_equal(res_4.shape, [100, 200])
def test_api_eager_dygraph(self):
with _test_eager_guard():
self.test_api_with_dygraph()
class TestMeshgridOp7(unittest.TestCase): class TestMeshgridOp7(unittest.TestCase):
def test_api_with_dygraph_list_input(self): def test_api_with_dygraph_list_input(self):
...@@ -163,6 +168,10 @@ class TestMeshgridOp7(unittest.TestCase): ...@@ -163,6 +168,10 @@ class TestMeshgridOp7(unittest.TestCase):
assert np.array_equal(res_3.shape, [100, 200]) assert np.array_equal(res_3.shape, [100, 200])
assert np.array_equal(res_4.shape, [100, 200]) assert np.array_equal(res_4.shape, [100, 200])
def test_api_eager_dygraph(self):
with _test_eager_guard():
self.test_api_with_dygraph_list_input()
class TestMeshgridOp8(unittest.TestCase): class TestMeshgridOp8(unittest.TestCase):
def test_api_with_dygraph_tuple_input(self): def test_api_with_dygraph_tuple_input(self):
...@@ -177,6 +186,40 @@ class TestMeshgridOp8(unittest.TestCase): ...@@ -177,6 +186,40 @@ class TestMeshgridOp8(unittest.TestCase):
assert np.array_equal(res_3.shape, [100, 200]) assert np.array_equal(res_3.shape, [100, 200])
assert np.array_equal(res_4.shape, [100, 200]) assert np.array_equal(res_4.shape, [100, 200])
def test_api_eager_dygraph(self):
with _test_eager_guard():
self.test_api_with_dygraph_tuple_input()
class TestMeshgridEager(unittest.TestCase):
def test_dygraph_final_state_api(self):
input_1 = np.random.randint(0, 100, [100, ]).astype('int32')
input_2 = np.random.randint(0, 100, [200, ]).astype('int32')
with fluid.dygraph.guard():
tensor_1 = fluid.dygraph.to_variable(input_1)
tensor_2 = fluid.dygraph.to_variable(input_2)
tensor_1.stop_gradient = False
tensor_2.stop_gradient = False
res_1, res_2 = paddle.tensor.meshgrid((tensor_1, tensor_2))
sum = paddle.add_n([res_1, res_2])
sum.backward()
with _test_eager_guard():
tensor_eager_1 = fluid.dygraph.to_variable(input_1)
tensor_eager_2 = fluid.dygraph.to_variable(input_2)
tensor_eager_1.stop_gradient = False
tensor_eager_2.stop_gradient = False
res_eager_1, res_eager_2 = paddle.tensor.meshgrid(
(tensor_eager_1, tensor_eager_2))
sum_eager = paddle.add_n([res_eager_1, res_eager_2])
sum_eager.backward()
self.assertEqual((
tensor_1.grad.numpy() == tensor_eager_1.grad.numpy()).all(),
True)
self.assertEqual((
tensor_2.grad.numpy() == tensor_eager_2.grad.numpy()).all(),
True)
if __name__ == '__main__': if __name__ == '__main__':
paddle.enable_static() paddle.enable_static()
......
...@@ -776,10 +776,12 @@ def meshgrid(*args, **kwargs): ...@@ -776,10 +776,12 @@ def meshgrid(*args, **kwargs):
if len(args) == 1 and isinstance(args[0], (list, tuple)): if len(args) == 1 and isinstance(args[0], (list, tuple)):
args = args[0] args = args[0]
if paddle.in_dynamic_mode(): if _in_legacy_dygraph():
num = len(args) num = len(args)
out = _C_ops.meshgrid(list(args), num) out = _C_ops.meshgrid(list(args), num)
return out return out
if in_dygraph_mode():
return _C_ops.final_state_meshgrid(list(args))
name = kwargs.get("name", None) name = kwargs.get("name", None)
helper = LayerHelper('meshgrid', **locals()) helper = LayerHelper('meshgrid', **locals())
......
...@@ -1120,6 +1120,12 @@ ...@@ -1120,6 +1120,12 @@
func : mean func : mean
backward : mean_grad backward : mean_grad
- api : meshgrid
args : (Tensor[] inputs)
output : Tensor[]
invoke : meshgrid_impl(inputs)
backward : meshgrid_grad
- api : min - api : min
args : (Tensor x, int64_t[] dims={}, bool keep_dim=false) args : (Tensor x, int64_t[] dims={}, bool keep_dim=false)
output : Tensor(out) output : Tensor(out)
......
...@@ -777,6 +777,12 @@ ...@@ -777,6 +777,12 @@
kernel : kernel :
func : mean_grad func : mean_grad
- backward_api : meshgrid_grad
forward : meshgrid (Tensor[] inputs) -> Tensor[](outputs)
args : (Tensor[] inputs, Tensor[] outputs_grad)
output : Tensor[](inputs_grad)
invoke : meshgrid_grad_impl(inputs, outputs_grad)
- backward_api : min_grad - backward_api : min_grad
forward: min (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out) forward: min (Tensor x, int64_t[] dims={}, bool keep_dim=false) -> Tensor(out)
args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false) args : (Tensor x, Tensor out, Tensor out_grad, int64_t[] dims={}, bool keep_dim=false, bool reduce_all=false)
......
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