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layer_norm_grad for npu #10560

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layer_norm_grad for npu #10560

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df98f12
layer_norm_grad npu
fpzh2011 Nov 7, 2024
dfe78fb
fix layernorm grad sbp (#10561)
ShawnXuan Nov 11, 2024
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15 changes: 3 additions & 12 deletions oneflow/core/autograd/gradient_funcs/layer_norm.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -107,22 +107,13 @@ Maybe<void> LayerNorm::Apply(const LayerNormCaptureState* ctx, const TensorTuple
std::shared_ptr<Tensor> mean = saved_tensors.at(ctx->mean_index);
std::shared_ptr<Tensor> inv_variance = saved_tensors.at(ctx->inv_variance_index);

if (ctx->has_affine) {
// Use LayerNormParamGrad(Tensor dy, Tensor x, Tensor mean, Tensor inv_variance,
// Int64 begin_params_axis)
const auto& results =
JUST(functional::LayerNormParamGrad(dy, x, mean, inv_variance, begin_params_axis));
in_grads->at(1) = results->at(0); // For gamma.
in_grads->at(2) = results->at(1); // For beta.
}
CHECK(ctx->has_affine) << "LayerNorm::Apply must has_affine for NPU GPT2 test";
if (ctx->x_requires_grad) {
if (ctx->scale) {
std::shared_ptr<Tensor> gamma = saved_tensors.at(ctx->gamma_index);
in_grads->at(0) = JUST(functional::LayerNormAffineGrad(dy, x, mean, inv_variance, gamma,
begin_norm_axis, ctx->epsilon));
*in_grads = *JUST(functional::LayerNormAffineGrad(dy, x, mean, inv_variance, gamma, begin_norm_axis, begin_params_axis, ctx->epsilon));
} else {
in_grads->at(0) =
JUST(functional::LayerNormGrad(dy, x, mean, inv_variance, begin_norm_axis, ctx->epsilon));
*in_grads = *JUST(functional::LayerNormGrad(dy, x, mean, inv_variance, begin_norm_axis, begin_params_axis, ctx->epsilon));
}
}
return Maybe<void>::Ok();
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4 changes: 2 additions & 2 deletions oneflow/core/functional/functional_api.yaml
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Expand Up @@ -1551,11 +1551,11 @@
bind_python: True

- name: "layer_norm_grad"
signature: "Tensor (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Int64 begin_norm_axis, Double epsilon) => LayerNormGrad"
signature: "TensorTuple (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Int64 begin_norm_axis, Int64 begin_params_axis, Double epsilon) => LayerNormGrad"
bind_python: False

- name: "layer_norm_affine_grad"
signature: "Tensor (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Tensor gamma, Int64 begin_norm_axis, Double epsilon) => LayerNormAffineGrad"
signature: "TensorTuple (Tensor dy, Tensor x, Tensor mean, Tensor inv_variance, Tensor gamma, Int64 begin_norm_axis, Int64 begin_params_axis, Double epsilon) => LayerNormAffineGrad"
bind_python: False

- name: "layer_norm_param_grad"
Expand Down
24 changes: 14 additions & 10 deletions oneflow/core/functional/impl/nn_grad_functor.cpp
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Expand Up @@ -940,16 +940,18 @@ class LayerNormGradFunctor {
.Input("mean")
.Input("inv_variance")
.Output("dx")
.Output("gamma_diff")
.Output("beta_diff")
.Build());
}
Maybe<Tensor> operator()(const std::shared_ptr<one::Tensor>& dy,
Maybe<TensorTuple> operator()(const std::shared_ptr<one::Tensor>& dy,
const std::shared_ptr<one::Tensor>& x,
const std::shared_ptr<one::Tensor>& mean,
const std::shared_ptr<one::Tensor>& inv_variance,
const int64_t& begin_norm_axis, const double& epsilon) const {
auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "epsilon");
attrs.SetAllAttrs(begin_norm_axis, epsilon);
return OpInterpUtil::Dispatch<Tensor>(*op_, {dy, x, mean, inv_variance}, attrs);
const int64_t& begin_norm_axis, const int64_t& begin_params_axis, const double& epsilon) const {
auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "begin_params_axis", "epsilon");
attrs.SetAllAttrs(begin_norm_axis, begin_params_axis, epsilon);
return OpInterpUtil::Dispatch<TensorTuple>(*op_, {dy, x, mean, inv_variance}, attrs);
}

private:
Expand All @@ -966,17 +968,19 @@ class LayerNormAffineGradFunctor {
.Input("inv_variance")
.Input("gamma")
.Output("dx")
.Output("gamma_diff")
.Output("beta_diff")
.Build());
}
Maybe<Tensor> operator()(const std::shared_ptr<one::Tensor>& dy,
Maybe<TensorTuple> operator()(const std::shared_ptr<one::Tensor>& dy,
const std::shared_ptr<one::Tensor>& x,
const std::shared_ptr<one::Tensor>& mean,
const std::shared_ptr<one::Tensor>& inv_variance,
const std::shared_ptr<one::Tensor>& gamma,
const int64_t& begin_norm_axis, const double& epsilon) const {
auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "epsilon");
attrs.SetAllAttrs(begin_norm_axis, epsilon);
return OpInterpUtil::Dispatch<Tensor>(*op_, {dy, x, mean, inv_variance, gamma}, attrs);
const int64_t& begin_norm_axis, const int64_t& begin_params_axis, const double& epsilon) const {
auto& attrs = THREAD_CACHED_MUTABLE_ATTR_MAP("begin_norm_axis", "begin_params_axis", "epsilon");
attrs.SetAllAttrs(begin_norm_axis, begin_params_axis, epsilon);
return OpInterpUtil::Dispatch<TensorTuple>(*op_, {dy, x, mean, inv_variance, gamma}, attrs);
}

private:
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8 changes: 6 additions & 2 deletions oneflow/ir/include/OneFlow/OneFlowUserOps.td
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Expand Up @@ -7056,14 +7056,18 @@ def OneFlow_LayerNormGradOp : OneFlow_BaseOp<"layer_norm_grad", [NoMemoryEffect,
Optional<OneFlow_Tensor>:$_add_to_output
);
let output = (outs
OneFlow_Tensor:$dx
OneFlow_Tensor:$dx,
OneFlow_Tensor:$gamma_diff,
OneFlow_Tensor:$beta_diff
);
let attrs = (ins
DefaultValuedAttr<SI64Attr, "0">:$begin_norm_axis,
DefaultValuedAttr<SI64Attr, "0">:$begin_params_axis,
DefaultValuedAttr<F64Attr, "0.">:$epsilon
);
let trait_attrs = (ins
DenseI32ArrayAttr:$operand_segment_sizes
DenseI32ArrayAttr:$operand_segment_sizes,
DenseI32ArrayAttr:$result_segment_sizes
);
let has_logical_tensor_desc_infer_fn = 1;
let has_physical_tensor_desc_infer_fn = 1;
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58 changes: 57 additions & 1 deletion oneflow/user/ops/layer_norm_op.cpp
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Expand Up @@ -141,6 +141,35 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) {
const auto& add_to_output = ctx->InputTensorDesc("_add_to_output", 0);
CHECK_EQ_OR_RETURN(add_to_output.shape(), dx->shape());
}

auto has_tensor = [ctx](const std::string& bn) -> bool {
bool ret = false;
for (const auto& t : ctx->inputs()) {
if (bn == t.first) { return true; }
}
for (const auto& t : ctx->outputs()) {
if (bn == t.first) { return true; }
}
return ret;
};
const int64_t begin_params_axis = ctx->Attr<int64_t>("begin_params_axis");
const bool has_beta_diff = has_tensor("beta_diff");
const bool has_gamma_diff = has_tensor("gamma_diff");
CHECK_GE_OR_RETURN(begin_params_axis, 1);
CHECK_LT_OR_RETURN(begin_params_axis, dy.shape().NumAxes());
DimVector param_shape_dim_vec;
param_shape_dim_vec.insert(param_shape_dim_vec.end(),
dy.shape().dim_vec().cbegin() + begin_params_axis,
dy.shape().dim_vec().cend());
const Shape param_shape(param_shape_dim_vec);
if (has_beta_diff) {
user_op::TensorDesc* beta_diff = ctx->MutOutputTensorDesc("beta_diff", 0);
beta_diff->set_shape(param_shape);
}
if (has_gamma_diff) {
user_op::TensorDesc* gamma_diff = ctx->MutOutputTensorDesc("gamma_diff", 0);
gamma_diff->set_shape(param_shape);
}
return Maybe<void>::Ok();
}

Expand All @@ -154,10 +183,16 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) {
broadcast_args.emplace_back(user_op::OpArg("gamma", 0));
}
int64_t begin_norm_axis = ctx->Attr<int64_t>("begin_norm_axis");
int64_t begin_params_axis = ctx->Attr<int64_t>("begin_params_axis");
CHECK_EQ(begin_norm_axis, begin_params_axis)
<< "begin_norm_axis and begin_params_axis must be equal, but got "
<< begin_norm_axis << " and " << begin_params_axis;
for (int i = 0; i < begin_norm_axis; ++i) {
ctx->NewBuilder()
.Split(ctx->inputs(), i)
.Split(ctx->outputs(), i)
.Split(user_op::OpArg("dx", 0), i)
.PartialSum(user_op::OpArg("gamma_diff", 0))
.PartialSum(user_op::OpArg("beta_diff", 0))
.Broadcast(broadcast_args)
.Build();
}
Expand Down Expand Up @@ -187,6 +222,27 @@ oneflow::DataType InferBnParamDataType(const DataType x_data_type) {
<< "InferDataType Failed. Expected " << DataType_Name(dx->data_type()) << ", but got "
<< DataType_Name(add_to_output.data_type());
}

auto has_tensor = [ctx](const std::string& bn) -> bool {
bool ret = false;
for (auto& t : ctx->inputs()) {
if (bn == t.first) { return true; }
}
for (auto& t : ctx->outputs()) {
if (bn == t.first) { return true; }
}
return ret;
};
const bool has_beta_diff = has_tensor("beta_diff");
const bool has_gamma_diff = has_tensor("gamma_diff");
if (has_beta_diff) {
user_op::TensorDesc* beta_diff = ctx->MutOutputTensorDesc("beta_diff", 0);
beta_diff->set_data_type(dy.data_type());
}
if (has_gamma_diff) {
user_op::TensorDesc* gamma_diff = ctx->MutOutputTensorDesc("gamma_diff", 0);
gamma_diff->set_data_type(dy.data_type());
}
return Maybe<void>::Ok();
}

Expand Down