From 17879045f17c762592efaf29d09b565422d2b130 Mon Sep 17 00:00:00 2001
From: zhoutianzi666 <39978853+zhoutianzi666@users.noreply.github.com>
Date: Wed, 7 Dec 2022 13:09:50 +0800
Subject: [PATCH] optimize nchw<->nhwc kernel in fp16 model (#48692)

---
 paddle/phi/kernels/funcs/math_function.cu    | 81 ++++++++++++++++++--
 paddle/phi/kernels/funcs/math_function.h     |  3 +
 paddle/phi/kernels/transfer_layout_kernel.cc | 26 +++++++
 3 files changed, 105 insertions(+), 5 deletions(-)

diff --git a/paddle/phi/kernels/funcs/math_function.cu b/paddle/phi/kernels/funcs/math_function.cu
index a0e59f8f3f..e1ab8922fd 100644
--- a/paddle/phi/kernels/funcs/math_function.cu
+++ b/paddle/phi/kernels/funcs/math_function.cu
@@ -27,11 +27,83 @@ limitations under the License. */
 namespace phi {
 namespace funcs {
 
+// The following part of the code refers to NVIDIA-cutlass
+// https://github.com/NVIDIA/cutlass/blob/master/tools/util/include/cutlass/util/device_nchw_to_nhwc.h
+// Copyright (c) 2017 - 2022 NVIDIA CORPORATION & AFFILIATES. All rights
+// reserved. SPDX-License-Identifier: BSD-3-Clause
+template <typename T>
+__global__ void batch_transpose_kernel(
+    T* output, const T* input, const int batch, const int M, const int N) {
+  const int num = M * N;
+  // "+1" to avoid smem bank conflict
+  __shared__ T shbuf[32 * (32 + 1)];
+  const int32_t tid = threadIdx.y * blockDim.x + threadIdx.x;
+  const int32_t wid = tid / 32;
+  const int32_t lid = tid % 32;
+  const int32_t batch_i = blockIdx.z;
+  const int32_t mi0 = blockIdx.y * 32;
+  const int32_t ni0 = blockIdx.x * 32;
+
+  const size_t input_idx = batch_i * num + (mi0 + wid) * N + ni0;
+  const T* A = input + input_idx;
+  if (ni0 + lid < N) {
+    const int lid_x_33 = lid * 33;
+    if ((mi0 + 32) <= M) {
+      int mi = wid;  // between 0 and 7
+#pragma unroll
+      for (int mLoopIdx = 0; mLoopIdx < 4; mLoopIdx++) {
+        shbuf[lid_x_33 + mi] = A[lid];
+        A = &A[8 * N];
+        mi += 8;
+      }
+    } else {
+      for (int mi = wid; mi < 32; mi += 8) {
+        if ((mi + mi0) < M) {
+          shbuf[lid_x_33 + mi] = A[lid];
+        }
+        A = &A[8 * N];
+      }
+    }
+  }
+  __syncthreads();
+
+  const int32_t miOut = mi0 + lid;
+  output = &output[batch_i * num + miOut];
+  if (miOut < M) {
+    if (ni0 + 32 < N) {
+      int nI = wid;
+#pragma unroll
+      for (int nLoopIdx = 0; nLoopIdx < 4; ++nLoopIdx) {
+        output[(ni0 + nI) * M] = shbuf[(nI)*33 + lid];
+        nI += 8;
+      }
+    } else {
+      for (int nI = wid; nI < 32; nI += 8) {
+        if (ni0 + nI < N) {
+          output[(ni0 + nI) * M] = shbuf[(nI)*33 + lid];
+        }
+      }
+    }
+  }
+}
+
+template <typename T>
+void BatchTranspose(T* output, const T* input, int batch, int m, int n) {
+  dim3 grid((n + 31) / 32, (m + 31) / 32, batch);
+  dim3 block(32, 8);
+  batch_transpose_kernel<<<grid, block>>>(output, input, batch, m, n);
+}
+
 using float16 = phi::dtype::float16;
 using bfloat16 = phi::dtype::bfloat16;
 
-template struct SetConstant<phi::GPUContext, phi::dtype::float16>;
-template struct SetConstant<phi::GPUContext, phi::dtype::bfloat16>;
+template void BatchTranspose(
+    float16* output, const float16* input, int batch, int m, int n);
+template void BatchTranspose(
+    float* output, const float* input, int batch, int m, int n);
+
+template struct SetConstant<phi::GPUContext, float16>;
+template struct SetConstant<phi::GPUContext, bfloat16>;
 template struct SetConstant<phi::GPUContext, float>;
 template struct SetConstant<phi::GPUContext, double>;
 template struct SetConstant<phi::GPUContext, uint8_t>;
@@ -42,10 +114,9 @@ template struct SetConstant<phi::GPUContext, bool>;
 template struct SetConstant<phi::GPUContext, phi::dtype::complex<float>>;
 template struct SetConstant<phi::GPUContext, phi::dtype::complex<double>>;
 
+template struct SetConstant<paddle::platform::CUDAPinnedDeviceContext, float16>;
 template struct SetConstant<paddle::platform::CUDAPinnedDeviceContext,
-                            phi::dtype::float16>;
-template struct SetConstant<paddle::platform::CUDAPinnedDeviceContext,
-                            phi::dtype::bfloat16>;
+                            bfloat16>;
 template struct SetConstant<paddle::platform::CUDAPinnedDeviceContext, float>;
 template struct SetConstant<paddle::platform::CUDAPinnedDeviceContext, double>;
 template struct SetConstant<paddle::platform::CUDAPinnedDeviceContext, uint8_t>;
diff --git a/paddle/phi/kernels/funcs/math_function.h b/paddle/phi/kernels/funcs/math_function.h
index 48649a454a..6f1cac4935 100644
--- a/paddle/phi/kernels/funcs/math_function.h
+++ b/paddle/phi/kernels/funcs/math_function.h
@@ -29,6 +29,9 @@ limitations under the License. */
 namespace phi {
 namespace funcs {
 
+template <typename T>
+void BatchTranspose(T* output, const T* input, int batch, int m, int n);
+
 template <typename DeviceContext, typename T>
 struct TransposeNormal {
   // for dims >= 7 situation
diff --git a/paddle/phi/kernels/transfer_layout_kernel.cc b/paddle/phi/kernels/transfer_layout_kernel.cc
index d7b8d55707..f2c57150c6 100644
--- a/paddle/phi/kernels/transfer_layout_kernel.cc
+++ b/paddle/phi/kernels/transfer_layout_kernel.cc
@@ -70,6 +70,32 @@ void TransferLayoutGeneral(const Context& dev_ctx,
   out->Resize(phi::make_ddim(dst_dim));
   dev_ctx.Alloc(out, x.dtype());
 
+  // In GPU fp16 model, we will insert many transfer_layout ops in
+  // conv2d_fusion_layout_transfer_pass, so we optimize this kernel on GPU
+  if (std::is_same<Context, phi::GPUContext>::value) {
+    std::vector<int> axis_nchw_nhwc = {0, 2, 3, 1};
+    std::vector<int> axis_nhwc_nchw = {0, 3, 1, 2};
+    const int batch = src_dim[0];
+    int row_len = src_dim[1];
+    int col_len = src_dim[2] * src_dim[3];
+    if (axis == axis_nhwc_nchw) {
+      row_len = src_dim[1] * src_dim[2];
+      col_len = src_dim[3];
+    }
+    if (x.dtype() == phi::DataType::FLOAT16) {
+      funcs::BatchTranspose(out->data<phi::dtype::float16>(),
+                            x.data<phi::dtype::float16>(),
+                            batch,
+                            row_len,
+                            col_len);
+      return;
+    } else if (x.dtype() == phi::DataType::FLOAT32) {
+      funcs::BatchTranspose(
+          out->data<float>(), x.data<float>(), batch, row_len, col_len);
+      return;
+    }
+  }
+
   PD_VISIT_ALL_TYPES(x.dtype(), "CastDataLayout", ([&] {
                        CastDataLayout<data_t, Context>(dev_ctx, x, axis, out);
                      }));
-- 
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