Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Add supports for AMD GPU #6161

Merged
merged 7 commits into from
May 24, 2023
Merged

Add supports for AMD GPU #6161

Changes from all commits
Commits
Show all changes
7 commits
Select commit Hold shift + click to select a range
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
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
94 changes: 46 additions & 48 deletions monai/_extensions/gmm/gmm_cuda.cu
View file
Open in desktop
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