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[MLU] transpose avg_pool2d to NHWC for better performance. #44475

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Jul 21, 2022
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[MLU] transpose avg_pool2d to NHWC for better performance. #44475

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66 changes: 34 additions & 32 deletions paddle/fluid/operators/pool_op_mlu.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -100,6 +100,25 @@ class MLUPoolOpKernel : public framework::OpKernel<T> {
cnnlPoolingMode_t pool_mode =
ToCnnlPoolingMode(pooling_type, exclusive, adaptive);

// transpose NCHW to NHWC since cnnl pool2d has worse performance in that
// layout.
framework::Tensor trans_in_x;
framework::Tensor trans_out;
if (channel_last) {
trans_in_x = *in_x;
trans_out = *out;
} else {
std::vector<int> perm{0, 2, 3, 1};
TransposeFromMLUTensor<T>(
ctx, perm, in_x, &trans_in_x, true /*need_reshape_or_alloc*/);
trans_out = ctx.AllocateTmpTensor<T, MLUDeviceContext>(
{out_dims[0], out_dims[2], out_dims[3], out_dims[1]}, dev_ctx);
}
MLUCnnlTensorDesc trans_in_x_desc(
trans_in_x, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>());
MLUCnnlTensorDesc trans_out_desc(
trans_out, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>());

if (!adaptive) {
MLUCnnlPoolingDesc pool_desc(pool_mode,
CNNL_NOT_PROPAGATE_NAN,
Expand Down Expand Up @@ -128,8 +147,8 @@ class MLUPoolOpKernel : public framework::OpKernel<T> {
{static_cast<int64_t>(extra_input_size)}, cpu_ctx);
cnnlInitPoolingExtraInput(handle,
pool_desc.get(),
in_x_desc.get(),
out_desc.get(),
trans_in_x_desc.get(),
trans_out_desc.get(),
GetBasePtr(&extra_host_tensor));
framework::Tensor extra_device_tensor =
ctx.AllocateTmpTensor<int8_t, MLUDeviceContext>(
Expand All @@ -151,44 +170,27 @@ class MLUPoolOpKernel : public framework::OpKernel<T> {
out_w,
pool_desc.get(),
nullptr /*alpha*/,
in_x_desc.get(),
GetBasePtr(in_x),
trans_in_x_desc.get(),
GetBasePtr(&trans_in_x),
nullptr /*beta*/,
GetBasePtr(&extra_device_tensor) /*params_shape_ptr*/,
out_desc.get(),
GetBasePtr(out));
trans_out_desc.get(),
GetBasePtr(&trans_out));
} else {
MLUCnnl::PoolingForward(ctx,
pool_mode,
out_h,
out_w,
pool_desc.get(),
nullptr /*alpha*/,
in_x_desc.get(),
GetBasePtr(in_x),
trans_in_x_desc.get(),
GetBasePtr(&trans_in_x),
nullptr /*beta*/,
nullptr /*params_shape_ptr*/,
out_desc.get(),
GetBasePtr(out));
trans_out_desc.get(),
GetBasePtr(&trans_out));
}
} else {
// cnnl Adaptive pooling only support NHWC layout
framework::Tensor trans_in_x;
framework::Tensor trans_out;
if (channel_last) {
trans_in_x = *in_x;
trans_out = *out;
} else {
std::vector<int> perm{0, 2, 3, 1};
TransposeFromMLUTensor<T>(
ctx, perm, in_x, &trans_in_x, true /*need_reshape_or_alloc*/);
trans_out = ctx.AllocateTmpTensor<T, MLUDeviceContext>(
{out_dims[0], out_dims[2], out_dims[3], out_dims[1]}, dev_ctx);
}
MLUCnnlTensorDesc trans_in_x_desc(
trans_in_x, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>());
MLUCnnlTensorDesc trans_out_desc(
trans_out, CNNL_LAYOUT_NHWC, ToCnnlDataType<T>());
MLUCnnl::AdaptivePoolingForward(ctx,
pool_mode,
trans_in_x_desc.get(),
Expand All @@ -197,11 +199,11 @@ class MLUPoolOpKernel : public framework::OpKernel<T> {
GetBasePtr(&trans_out),
nullptr,
nullptr);
if (!channel_last) {
std::vector<int> perm{0, 3, 1, 2};
TransposeFromMLUTensor<T>(
ctx, perm, &trans_out, out, false /*need_reshape_or_alloc*/);
}
}
if (!channel_last) {
std::vector<int> perm{0, 3, 1, 2};
TransposeFromMLUTensor<T>(
ctx, perm, &trans_out, out, false /*need_reshape_or_alloc*/);
}
}
};
Expand Down