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Add supports for AMD GPU #6161

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Merged
wyli merged 7 commits into from
May 24, 2023
Merged

Add supports for AMD GPU #6161

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018609c
add Dockerfile.amd to build MONAI docker image for AMD GPU. The file …
yaomingamd Mar 15, 2023
a8cfcf6
add ROCm HIP support
yaomingamd Mar 15, 2023
e83e77f
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Mar 16, 2023
59f7827
DCO Remediation Commit for Yaoming Mu <yaoming.mu@amd.com>
yaomingamd May 24, 2023
c218cea
As suggested by ericspod( Eric Kerfoot), using macros functions to re…
yaomingamd May 24, 2023
8445097
[MONAI] code formatting
monai-bot May 24, 2023
ca4eafc
Merge branch 'dev' into amd_dev
wyli May 24, 2023
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94 changes: 46 additions & 48 deletions monai/_extensions/gmm/gmm_cuda.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -21,6 +21,13 @@ limitations under the License.
#define EPSILON 1e-5
#define BLOCK_SIZE 32
#define TILE(SIZE, STRIDE) ((((SIZE)-1) / (STRIDE)) + 1)
#ifdef __HIP_PLATFORM_AMD__
#define __SHFL_DOWN(a, b) __shfl_down(a, b)
#define __SHFL_XOR(a, b) __shfl_xor(a, b)
#else
#define __SHFL_DOWN(a, b) __shfl_down_sync(0xffffffff, a, b)
#define __SHFL_XOR(a, b) __shfl_xor_sync(0xffffffff, a, b)
#endif

template <int warp_count, int load_count>
__global__ void CovarianceReductionKernel(
Expand Down Expand Up @@ -82,13 +89,11 @@ __global__ void CovarianceReductionKernel(

for (int i = 0; i < MATRIX_COMPONENT_COUNT; i++) {
float matrix_component = matrix[i];

matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 16);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 8);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 4);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 2);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 1);

matrix_component += __SHFL_DOWN(matrix_component, 16);
matrix_component += __SHFL_DOWN(matrix_component, 8);
matrix_component += __SHFL_DOWN(matrix_component, 4);
matrix_component += __SHFL_DOWN(matrix_component, 2);
matrix_component += __SHFL_DOWN(matrix_component, 1);
if (lane_index == 0) {
s_matrix_component[warp_index] = matrix_component;
}
Expand All @@ -97,23 +102,21 @@ __global__ void CovarianceReductionKernel(

if (warp_index == 0) {
matrix_component = s_matrix_component[lane_index];

if (warp_count >= 32) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 16);
matrix_component += __SHFL_DOWN(matrix_component, 16);
}
if (warp_count >= 16) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 8);
matrix_component += __SHFL_DOWN(matrix_component, 8);
}
if (warp_count >= 8) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 4);
matrix_component += __SHFL_DOWN(matrix_component, 4);
}
if (warp_count >= 4) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 2);
matrix_component += __SHFL_DOWN(matrix_component, 2);
}
if (warp_count >= 2) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 1);
matrix_component += __SHFL_DOWN(matrix_component, 1);
}

if (lane_index == 0) {
g_batch_matrices[matrix_offset + i] = matrix_component;
}
Expand Down Expand Up @@ -156,13 +159,11 @@ __global__ void CovarianceFinalizationKernel(const float* g_matrices, float* g_g
matrix_component += g_batch_matrices[(matrix_offset + matrix_index) * GMM_COMPONENT_COUNT + index];
}
}

matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 16);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 8);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 4);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 2);
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 1);

matrix_component += __SHFL_DOWN(matrix_component, 16);
matrix_component += __SHFL_DOWN(matrix_component, 8);
matrix_component += __SHFL_DOWN(matrix_component, 4);
matrix_component += __SHFL_DOWN(matrix_component, 2);
matrix_component += __SHFL_DOWN(matrix_component, 1);
if (lane_index == 0) {
s_matrix_component[warp_index] = matrix_component;
}
Expand All @@ -171,23 +172,21 @@ __global__ void CovarianceFinalizationKernel(const float* g_matrices, float* g_g

if (warp_index == 0) {
matrix_component = s_matrix_component[lane_index];

if (warp_count >= 32) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 16);
matrix_component += __SHFL_DOWN(matrix_component, 16);
}
if (warp_count >= 16) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 8);
matrix_component += __SHFL_DOWN(matrix_component, 8);
}
if (warp_count >= 8) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 4);
matrix_component += __SHFL_DOWN(matrix_component, 4);
}
if (warp_count >= 4) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 2);
matrix_component += __SHFL_DOWN(matrix_component, 2);
}
if (warp_count >= 2) {
matrix_component += __shfl_down_sync(0xffffffff, matrix_component, 1);
matrix_component += __SHFL_DOWN(matrix_component, 1);
}

if (lane_index == 0) {
float constant = i == 0 ? 0.0f : s_gmm[i] * s_gmm[j];

Expand Down Expand Up @@ -261,13 +260,11 @@ __global__ void GMMFindSplit(GMMSplit_t* gmmSplit, int gmmK, float* gmm) {
}

float max_value = eigenvalue;

max_value = max(max_value, __shfl_xor_sync(0xffffffff, max_value, 16));
max_value = max(max_value, __shfl_xor_sync(0xffffffff, max_value, 8));
max_value = max(max_value, __shfl_xor_sync(0xffffffff, max_value, 4));
max_value = max(max_value, __shfl_xor_sync(0xffffffff, max_value, 2));
max_value = max(max_value, __shfl_xor_sync(0xffffffff, max_value, 1));

max_value = max(max_value, __SHFL_XOR(max_value, 16));
max_value = max(max_value, __SHFL_XOR(max_value, 8));
max_value = max(max_value, __SHFL_XOR(max_value, 4));
max_value = max(max_value, __SHFL_XOR(max_value, 2));
max_value = max(max_value, __SHFL_XOR(max_value, 1));
if (max_value == eigenvalue) {
GMMSplit_t split;

Expand Down Expand Up @@ -347,12 +344,11 @@ __global__ void GMMcommonTerm(float* g_gmm) {
float gmm_n = threadIdx.x < MIXTURE_SIZE ? g_batch_gmm[gmm_index * GMM_COMPONENT_COUNT] : 0.0f;

float sum = gmm_n;

sum += __shfl_xor_sync(0xffffffff, sum, 1);
sum += __shfl_xor_sync(0xffffffff, sum, 2);
sum += __shfl_xor_sync(0xffffffff, sum, 4);
sum += __shfl_xor_sync(0xffffffff, sum, 8);
sum += __shfl_xor_sync(0xffffffff, sum, 16);
sum += __SHFL_XOR(sum, 1);
sum += __SHFL_XOR(sum, 2);
sum += __SHFL_XOR(sum, 4);
sum += __SHFL_XOR(sum, 8);
sum += __SHFL_XOR(sum, 16);

if (threadIdx.x < MIXTURE_SIZE) {
float det = g_batch_gmm[gmm_index * GMM_COMPONENT_COUNT + MATRIX_COMPONENT_COUNT] + EPSILON;
Expand Down Expand Up @@ -446,13 +442,14 @@ void GMMInitialize(
for (unsigned int k = MIXTURE_COUNT; k < gmm_N; k += MIXTURE_COUNT) {
for (unsigned int i = 0; i < k; ++i) {
CovarianceReductionKernel<WARPS, LOAD>
<<<{block_count, 1, batch_count}, BLOCK>>>(i, image, alpha, block_gmm_scratch, element_count);
<<<dim3(block_count, 1, batch_count), BLOCK>>>(i, image, alpha, block_gmm_scratch, element_count);
}

CovarianceFinalizationKernel<WARPS, false><<<{k, 1, batch_count}, BLOCK>>>(block_gmm_scratch, gmm, block_count);
CovarianceFinalizationKernel<WARPS, false><<<dim3(k, 1, batch_count), BLOCK>>>(block_gmm_scratch, gmm, block_count);

GMMFindSplit<<<{1, 1, batch_count}, dim3(BLOCK_SIZE, MIXTURE_COUNT)>>>(gmm_split_scratch, k / MIXTURE_COUNT, gmm);
GMMDoSplit<<<{TILE(element_count, BLOCK_SIZE * DO_SPLIT_DEGENERACY), 1, batch_count}, BLOCK_SIZE>>>(
GMMFindSplit<<<dim3(1, 1, batch_count), dim3(BLOCK_SIZE, MIXTURE_COUNT)>>>(
gmm_split_scratch, k / MIXTURE_COUNT, gmm);
GMMDoSplit<<<dim3(TILE(element_count, BLOCK_SIZE * DO_SPLIT_DEGENERACY), 1, batch_count), BLOCK_SIZE>>>(
gmm_split_scratch, (k / MIXTURE_COUNT) << 4, image, alpha, element_count);
}
}
Expand All @@ -472,12 +469,13 @@ void GMMUpdate(

for (unsigned int i = 0; i < gmm_N; ++i) {
CovarianceReductionKernel<WARPS, LOAD>
<<<{block_count, 1, batch_count}, BLOCK>>>(i, image, alpha, block_gmm_scratch, element_count);
<<<dim3(block_count, 1, batch_count), BLOCK>>>(i, image, alpha, block_gmm_scratch, element_count);
}

CovarianceFinalizationKernel<WARPS, true><<<{gmm_N, 1, batch_count}, BLOCK>>>(block_gmm_scratch, gmm, block_count);
CovarianceFinalizationKernel<WARPS, true>
<<<dim3(gmm_N, 1, batch_count), BLOCK>>>(block_gmm_scratch, gmm, block_count);

GMMcommonTerm<<<{1, 1, batch_count}, dim3(BLOCK_SIZE, MIXTURE_COUNT)>>>(gmm);
GMMcommonTerm<<<dim3(1, 1, batch_count), dim3(BLOCK_SIZE, MIXTURE_COUNT)>>>(gmm);
}

void GMMDataTerm(
Expand Down