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fix gpt2 train loss Nan problem by add a line __syncthreads in BlockR… #33658

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Jun 22, 2021
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fix gpt2 train loss Nan problem by add a line __syncthreads in BlockR… #33658

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1 change: 1 addition & 0 deletions paddle/fluid/operators/correlation_op.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,7 @@ __forceinline__ __device__ T blockReduceSum(T val) {
int wid = threadIdx.x / warpSize;

val = warpReduceSum(val);
__syncthreads();
if (lane == 0) shared[wid] = val;

__syncthreads();
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19 changes: 12 additions & 7 deletions paddle/fluid/operators/layer_norm_op.cu
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Expand Up @@ -64,17 +64,16 @@ static __forceinline__ __device__ U WarpReduceSum(U val) {
}

template <typename U>
__forceinline__ __device__ U BlockReduceSum(U val) {
static __shared__ U shared[32];
__forceinline__ __device__ U BlockReduceSum(U val, U *shared) {
int lane = threadIdx.x % warpSize;
int wid = threadIdx.x / warpSize;

val = WarpReduceSum(val); // Each warp performs partial reduction

__syncthreads();
if (lane == 0) shared[wid] = val; // Write reduced value to shared memory

__syncthreads(); // Wait for all partial reductions

// read from shared memory only if that warp existed
val =
(threadIdx.x < blockDim.x / warpSize) ? shared[lane] : static_cast<U>(0);
Expand Down Expand Up @@ -183,6 +182,9 @@ __global__ void LayerNormForward(const T *x, const U *scale, const U *bias,
int64_t feature_size) {
__shared__ U mean_share;
__shared__ U var_share;
__shared__ U shared_mean[32]; // threadIdx.x / warpSize <= kMaxBlockDim /
// warpSize <= 1024/32 = 32;
__shared__ U shared_var[32];

int64_t beg_idx = blockIdx.x * feature_size + threadIdx.x;
int64_t end_idx = (blockIdx.x + 1) * feature_size;
Expand All @@ -196,8 +198,8 @@ __global__ void LayerNormForward(const T *x, const U *scale, const U *bias,
var_val += (tmp * tmp);
}

mean_val = BlockReduceSum<U>(mean_val);
var_val = BlockReduceSum<U>(var_val);
mean_val = BlockReduceSum<U>(mean_val, shared_mean);
var_val = BlockReduceSum<U>(var_val, shared_var);

if (threadIdx.x == 0) {
auto scale = static_cast<float>(1.) / static_cast<float>(feature_size);
Expand Down Expand Up @@ -541,8 +543,11 @@ __global__ void LayerNormBackwardGradientAll(
}
}

d_scale_partial = BlockReduceSum<U>(d_scale_partial);
d_bias_partial = BlockReduceSum<U>(d_bias_partial);
__shared__ U shared_scale[32]; // threadIdx.x / warpSize <= kMaxBlockDim /
// warpSize <= 1024/32 = 32;
__shared__ U shared_bias[32];
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用 kMaxBlockDim/lwarpSize 来代替32是否会更节省share memory

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已线下讨论，share memory整体空间足够大，可以保持32。

d_scale_partial = BlockReduceSum<U>(d_scale_partial, shared_scale);
d_bias_partial = BlockReduceSum<U>(d_bias_partial, shared_bias);

if (threadIdx.x == 0) {
d_scale[blockIdx.x + col_offset] = d_scale_partial;
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1 change: 1 addition & 0 deletions paddle/fluid/operators/math/math_cuda_utils.h
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Expand Up @@ -188,6 +188,7 @@ __inline__ __device__ T blockReduceSum(T val, unsigned mask) {

val = warpReduceSum<T>(val, mask);

__syncthreads();
if (lane == 0) shared[wid] = val;

__syncthreads();
Expand Down