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Fix fused_attention_op and fused_feedforward_op bug when pre_layer_no…
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…rm is false. (#36793)

* Fix bug when pre_layer_norm is false.
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@limin2021
limin2021 committed Oct 28, 2021
1 parent 96edcea commit d344aeb
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Showing 5 changed files with 254 additions and 144 deletions.
101 changes: 64 additions & 37 deletions paddle/fluid/operators/fused/fused_attention_op.cc
View file
Original file line number Diff line number Diff line change
Expand Up @@ -37,12 +37,15 @@ class FusedAttentionOp : public framework::OperatorWithKernel {
OP_INOUT_CHECK(ctx->HasInput("OutLinearBias"), "Input", "OutLinearBias",
"FusedAttentionOp");

OP_INOUT_CHECK(ctx->HasOutput("LnMean"), "Output", "LnMean",
"FusedAttentionOp");
OP_INOUT_CHECK(ctx->HasOutput("LnVariance"), "Output", "LnVariance",
"FusedAttentionOp");
OP_INOUT_CHECK(ctx->HasOutput("LnOut"), "Output", "LnOut",
"FusedAttentionOp");
if (ctx->Attrs().Get<bool>("pre_layer_norm") == true) {
OP_INOUT_CHECK(ctx->HasOutput("LnMean"), "Output", "LnMean",
"FusedAttentionOp");
OP_INOUT_CHECK(ctx->HasOutput("LnVariance"), "Output", "LnVariance",
"FusedAttentionOp");
OP_INOUT_CHECK(ctx->HasOutput("LnOut"), "Output", "LnOut",
"FusedAttentionOp");
}

// qkv_out: [batch_size, seq_len, 3, num_head, dim_head]
OP_INOUT_CHECK(ctx->HasOutput("QKVOut"), "Output", "QKVOut",
"FusedAttentionOp");
Expand Down Expand Up @@ -101,9 +104,11 @@ class FusedAttentionOp : public framework::OperatorWithKernel {
"input qkv_weight = [%s]",
x_dim, y_dim));

ctx->SetOutputDim("LnMean", {x_dim[0] * x_dim[1]});
ctx->SetOutputDim("LnVariance", {x_dim[0] * x_dim[1]});
ctx->SetOutputDim("LnOut", ctx->GetInputDim("X"));
if (ctx->Attrs().Get<bool>("pre_layer_norm") == true) {
ctx->SetOutputDim("LnMean", {x_dim[0] * x_dim[1]});
ctx->SetOutputDim("LnVariance", {x_dim[0] * x_dim[1]});
ctx->SetOutputDim("LnOut", ctx->GetInputDim("X"));
}
// [batch_size, seq_len, 3, num_head, head_size]
ctx->SetOutputDim("QKVOut",
{x_dim[0], x_dim[1], y_dim[0], y_dim[1], y_dim[2]});
Expand Down Expand Up @@ -351,11 +356,11 @@ class FusedAttentionGradOp : public framework::OperatorWithKernel {
ctx->GetInputDim("Ln2Bias"));
}
OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "FusedAttentionGrad");
OP_INOUT_CHECK(ctx->HasInput("LnMean"), "Input", "LnMean",
"FusedAttentionGrad");
OP_INOUT_CHECK(ctx->HasInput("LnVariance"), "Input", "LnVariance",
"FusedAttentionGrad");
if (ctx->Attrs().Get<bool>("pre_layer_norm") == true) {
OP_INOUT_CHECK(ctx->HasInput("LnMean"), "Input", "LnMean",
"FusedAttentionGrad");
OP_INOUT_CHECK(ctx->HasInput("LnVariance"), "Input", "LnVariance",
"FusedAttentionGrad");
OP_INOUT_CHECK(ctx->HasInput("LnOut"), "Input", "LnOut",
"FusedAttentionGrad");
}
Expand All @@ -370,13 +375,15 @@ class FusedAttentionGradOp : public framework::OperatorWithKernel {
OP_INOUT_CHECK(ctx->HasInput("OutLinearBias"), "Input", "OutLinearBias",
"FusedAttentionGrad");

if (ctx->HasOutput(framework::GradVarName("LnScale"))) {
ctx->SetOutputDim(framework::GradVarName("LnScale"),
ctx->GetInputDim("LnScale"));
}
if (ctx->HasOutput(framework::GradVarName("LnBias"))) {
ctx->SetOutputDim(framework::GradVarName("LnBias"),
ctx->GetInputDim("LnBias"));
if (ctx->Attrs().Get<bool>("pre_layer_norm") == true) {
if (ctx->HasOutput(framework::GradVarName("LnScale"))) {
ctx->SetOutputDim(framework::GradVarName("LnScale"),
ctx->GetInputDim("LnScale"));
}
if (ctx->HasOutput(framework::GradVarName("LnBias"))) {
ctx->SetOutputDim(framework::GradVarName("LnBias"),
ctx->GetInputDim("LnBias"));
}
}
if (ctx->HasOutput(framework::GradVarName("X"))) {
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
Expand All @@ -390,8 +397,10 @@ class FusedAttentionGradOp : public framework::OperatorWithKernel {
ctx->SetOutputDim(framework::GradVarName("QKVBias"),
ctx->GetInputDim("QKVBias"));

ctx->SetOutputDim(framework::GradVarName("LnOut"),
ctx->GetInputDim("LnOut"));
if (ctx->Attrs().Get<bool>("pre_layer_norm") == true) {
ctx->SetOutputDim(framework::GradVarName("LnOut"),
ctx->GetInputDim("LnOut"));
}
ctx->SetOutputDim(framework::GradVarName("FMHAOut"),
ctx->GetInputDim("FMHAOut"));
ctx->SetOutputDim(framework::GradVarName("QKTVOut"),
Expand Down Expand Up @@ -442,16 +451,23 @@ class FusedAttentionGradOpMaker : public framework::SingleGradOpMaker<T> {
op->SetInput("SrcMask", this->Input("SrcMask"));
op->SetInput("OutLinearW", this->Input("OutLinearW"));
op->SetInput("OutLinearBias", this->Input("OutLinearBias"));
if (this->HasInput("LnScale")) {
op->SetInput("LnScale", this->Input("LnScale"));
op->SetOutput(framework::GradVarName("LnScale"),
this->InputGrad("LnScale"));
}
if (this->HasInput("LnBias")) {
op->SetInput("LnBias", this->Input("LnBias"));
op->SetOutput(framework::GradVarName("LnBias"),
this->InputGrad("LnBias"));

op->SetAttrMap(this->Attrs());
bool is_pre_layer_norm =
BOOST_GET_CONST(bool, op->GetAttr("pre_layer_norm"));
if (is_pre_layer_norm) {
if (this->HasInput("LnScale")) {
op->SetInput("LnScale", this->Input("LnScale"));
op->SetOutput(framework::GradVarName("LnScale"),
this->InputGrad("LnScale"));
}
if (this->HasInput("LnBias")) {
op->SetInput("LnBias", this->Input("LnBias"));
op->SetOutput(framework::GradVarName("LnBias"),
this->InputGrad("LnBias"));
}
}

if (this->HasInput("Ln2Scale")) {
op->SetInput("Ln2Scale", this->Input("Ln2Scale"));
op->SetOutput(framework::GradVarName("Ln2Scale"),
Expand All @@ -473,9 +489,17 @@ class FusedAttentionGradOpMaker : public framework::SingleGradOpMaker<T> {
this->InputGrad("OutLinearW"));

// use forward outputs as backward inputs.
op->SetInput("LnOut", this->Output("LnOut"));
op->SetInput("LnMean", this->Output("LnMean"));
op->SetInput("LnVariance", this->Output("LnVariance"));
if (is_pre_layer_norm) {
if (this->HasOutput("LnOut")) {
op->SetInput("LnOut", this->Output("LnOut"));
}
if (this->HasOutput("LnMean")) {
op->SetInput("LnMean", this->Output("LnMean"));
}
if (this->HasOutput("LnVariance")) {
op->SetInput("LnVariance", this->Output("LnVariance"));
}
}
op->SetInput("QKVOut", this->Output("QKVOut"));
op->SetInput("QKVBiasOut", this->Output("QKVBiasOut"));
op->SetInput("TransposeOut2", this->Output("TransposeOut2"));
Expand All @@ -496,7 +520,12 @@ class FusedAttentionGradOpMaker : public framework::SingleGradOpMaker<T> {
op->SetInput("QKVOut", this->Output("QKVOut"));

// backward outputs: dinput
op->SetOutput(framework::GradVarName("LnOut"), this->OutputGrad("LnOut"));
if (is_pre_layer_norm) {
if (this->HasOutput("LnOut")) {
op->SetOutput(framework::GradVarName("LnOut"),
this->OutputGrad("LnOut"));
}
}
op->SetOutput(framework::GradVarName("QKVOut"), this->OutputGrad("QKVOut"));
op->SetOutput(framework::GradVarName("QKVBiasOut"),
this->OutputGrad("QKVBiasOut"));
Expand All @@ -517,8 +546,6 @@ class FusedAttentionGradOpMaker : public framework::SingleGradOpMaker<T> {
this->OutputGrad("BiasDropoutResidualOut"));
op->SetOutput(framework::GradVarName("OutLinearOut"),
this->OutputGrad("OutLinearOut"));

op->SetAttrMap(this->Attrs());
}
};

Expand Down
44 changes: 25 additions & 19 deletions paddle/fluid/operators/fused/fused_attention_op.cu
View file
Original file line number Diff line number Diff line change
Expand Up @@ -97,9 +97,12 @@ class FusedAttentionOpKernel : public framework::OpKernel<T> {
auto *x_data = input_x->data<T>();
auto *ln_scale_data = (ln_scale == nullptr ? nullptr : ln_scale->data<U>());
auto *ln_bias_data = (ln_bias == nullptr ? nullptr : ln_bias->data<U>());
auto *ln_mean_data = ln_mean->mutable_data<U>(ctx.GetPlace());
auto *ln_var_data = ln_var->mutable_data<U>(ctx.GetPlace());
auto *ln_out_data = ln_out->mutable_data<T>(ctx.GetPlace());
auto *ln_mean_data =
pre_layer_norm ? ln_mean->mutable_data<U>(ctx.GetPlace()) : nullptr;
auto *ln_var_data =
pre_layer_norm ? ln_var->mutable_data<U>(ctx.GetPlace()) : nullptr;
auto *ln_out_data =
pre_layer_norm ? ln_out->mutable_data<T>(ctx.GetPlace()) : nullptr;

auto *qkv_weight_data = qkv_weight->data<T>();
auto *qkv_bias_data = qkv_bias->data<T>();
Expand Down Expand Up @@ -243,9 +246,6 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {
auto *out_linear_bias_data = out_linear_bias->data<T>();

// fw output
auto *ln_mean = ctx.Input<Tensor>("LnMean");
auto *ln_var = ctx.Input<Tensor>("LnVariance");
auto *ln_out = ctx.Input<Tensor>("LnOut");
auto *fmha_out = ctx.Input<Tensor>("FMHAOut");
auto *transpose_out_2 = ctx.Input<Tensor>("TransposeOut2");
auto *qk_out = ctx.Input<Tensor>("QKOut");
Expand All @@ -260,9 +260,6 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {
auto *dropout_mask_out = ctx.Input<Tensor>("DropoutMaskOut");
auto *bias_dropout_residual_out =
ctx.Input<Tensor>("BiasDropoutResidualOut");
auto *ln_mean_data = ln_mean->data<U>();
auto *ln_var_data = ln_var->data<U>();
auto *ln_out_data = ln_out->data<T>();
auto *fmha_out_data = fmha_out->data<T>();
auto *transpose_out_2_data = transpose_out_2->data<T>();
auto *qk_out_data = qk_out->data<T>();
Expand All @@ -277,7 +274,6 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {

// output's grad
auto *d_x = ctx.Output<Tensor>(framework::GradVarName("X"));
auto *d_ln_out = ctx.Output<Tensor>(framework::GradVarName("LnOut"));
auto *d_qkv_out = ctx.Output<Tensor>(framework::GradVarName("QKVOut"));
auto *d_qkv_bias_out =
ctx.Output<Tensor>(framework::GradVarName("QKVBiasOut"));
Expand All @@ -297,7 +293,6 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {
auto *d_bias_dropout_residual_out =
ctx.Output<Tensor>(framework::GradVarName("BiasDropoutResidualOut"));
auto *d_x_data = d_x->mutable_data<T>(ctx.GetPlace());
auto *d_ln_out_data = d_ln_out->mutable_data<T>(ctx.GetPlace());
auto *d_qkv_out_data = d_qkv_out->mutable_data<T>(ctx.GetPlace());
auto *d_qkv_bias_out_data = d_qkv_bias_out->mutable_data<T>(ctx.GetPlace());
auto *d_qktv_out_data = d_qktv_out->mutable_data<T>(ctx.GetPlace());
Expand All @@ -315,8 +310,6 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {
d_bias_dropout_residual_out->mutable_data<T>(ctx.GetPlace());

// parameter grad
auto *d_ln_scale = ctx.Output<Tensor>(framework::GradVarName("LnScale"));
auto *d_ln_bias = ctx.Output<Tensor>(framework::GradVarName("LnBias"));
auto *d_qkv_weight = ctx.Output<Tensor>(framework::GradVarName("QKVW"));
auto *d_qkv_bias = ctx.Output<Tensor>(framework::GradVarName("QKVBias"));
auto *d_out_linear_weight =
Expand All @@ -325,12 +318,7 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {
ctx.Output<Tensor>(framework::GradVarName("OutLinearBias"));
auto *d_ln_2_scale = ctx.Output<Tensor>(framework::GradVarName("Ln2Scale"));
auto *d_ln_2_bias = ctx.Output<Tensor>(framework::GradVarName("Ln2Bias"));
auto *d_ln_scale_data =
(d_ln_scale == nullptr ? nullptr
: d_ln_scale->mutable_data<U>(ctx.GetPlace()));
auto *d_ln_bias_data =
(d_ln_bias == nullptr ? nullptr
: d_ln_bias->mutable_data<U>(ctx.GetPlace()));

auto *d_qkv_weight_data = d_qkv_weight->mutable_data<T>(ctx.GetPlace());
auto *d_qkv_bias_data = d_qkv_bias->mutable_data<T>(ctx.GetPlace());
auto *d_out_linear_weight_data =
Expand Down Expand Up @@ -407,6 +395,24 @@ class FusedAttentionGradKernel : public framework::OpKernel<T> {
cudaMemcpyDeviceToDevice);

if (pre_layer_norm) {
auto *ln_mean = ctx.Input<Tensor>("LnMean");
auto *ln_var = ctx.Input<Tensor>("LnVariance");
auto *ln_out = ctx.Input<Tensor>("LnOut");
auto *ln_mean_data = ln_mean->data<U>();
auto *ln_var_data = ln_var->data<U>();
auto *ln_out_data = ln_out->data<T>();

auto *d_ln_out = ctx.Output<Tensor>(framework::GradVarName("LnOut"));
auto *d_ln_scale = ctx.Output<Tensor>(framework::GradVarName("LnScale"));
auto *d_ln_bias = ctx.Output<Tensor>(framework::GradVarName("LnBias"));
auto *d_ln_out_data = d_ln_out->mutable_data<T>(ctx.GetPlace());
auto *d_ln_scale_data =
(d_ln_scale == nullptr ? nullptr
: d_ln_scale->mutable_data<U>(ctx.GetPlace()));
auto *d_ln_bias_data =
(d_ln_bias == nullptr ? nullptr
: d_ln_bias->mutable_data<U>(ctx.GetPlace()));

qkv_compute.ComputeBackward(ln_out_data, qkv_weight_data,
d_qkv_bias_out_data, d_ln_out_data,
d_qkv_weight_data, d_qkv_bias_data);
Expand Down
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