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Flatten subprojects into a single base monorepo
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---
include: hipsparselt_common.yaml
include: known_bugs.yaml
include: spmm_common.yaml
Definitions:
- &alpha_beta_range
- { alpha: 1, beta: 0 }
- &transA_transB_range
- { transA: N, transB: N }
- { transA: T, transB: T }
Tests:
- name: prune_bad_arg
category: pre_checkin
function:
- prune_bad_arg: *hpa_half_precision
transA: N
transB: N
func_version: [1, 2]
- name: prune_check_bad_arg
category: smoke
function:
- prune_check_bad_arg: *hpa_half_precision
transA: N
transB: N
func_version: [1, 2]
# Split *real_precisions_2b into *int8 and *nonint8_real_precisions. Since int8 has flags 0,1
- name: prune_small
category: quick
function:
prune: *real_precisions_2b
matrix_size: *small_matrix_size_range
transA_transB: *transA_transB_range
alpha_beta: *alpha_beta_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_medium
category: pre_checkin
function:
prune: *real_precisions_2b
matrix_size: *medium_matrix_size_range
transA_transB: *transA_transB_range
alpha_beta: *alpha_beta_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
inEqualOut: [ true, false]
- name: prune_medium_alt
category: pre_checkin
function:
prune: *hpa_half_precision
matrix_size: *medium_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: 0
initialization: special
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_medium_HMM
category: HMM
function:
prune: *real_precisions_2b
matrix_size: *medium_matrix_size_range
transA: [ N ]
transB: [ N ]
alpha: 1
beta: 1
HMM: true
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_chunk
category: pre_checkin
function:
prune: *real_precisions_2b
matrix_size: *chunk_matrix_size_range
transA_transB: *transA_transB_range
alpha: 2
beta: 3
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_deepbench
category: nightly
function:
prune: *real_precisions_2b
matrix_size: *deepbench_sizes
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_deepbench_alt
category: nightly
function:
prune: *hpa_half_precision
matrix_size: *deepbench_sizes
alpha: 1
beta: 0
flags: 4
initialization: special
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: resnet50_fwd
category: nightly
function:
prune: *real_precisions_2b
transA: N
transB: N
matrix_size: *resnet50_fwd_sizes
alpha: 1
beta: 0
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: resnet50_fwd_alt
category: nightly
function:
prune: *hpa_half_precision
transA: N
transB: N
matrix_size: *resnet50_fwd_sizes
alpha: 1
beta: 0
initialization: special
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: resnet50_bwdwrw
category: nightly
function:
prune: *real_precisions_2b
transA: T
transB: N
matrix_size: *resnet50_bwdwrw_sizes
alpha: 1
beta: 1
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: resnet50_bwddata
category: nightly
function:
prune: *real_precisions_2b
transA: N
transB: T
matrix_size: *resnet50_bwddata_sizes
alpha: 1
beta: 0
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: inception4_fwd
category: nightly
function:
prune: *real_precisions_2b
transA: N
transB: N
matrix_size: *inception4_fwd_sizes
alpha: 1
beta: 0
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: inception4_bwdwrw
category: nightly
function:
prune: *real_precisions_2b
transA: T
transB: N
matrix_size: *inception4_bwdwrw_sizes
alpha: 1
beta: 1
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: inception4_bwddata
category: nightly
function:
prune: *real_precisions_2b
transA: N
transB: T
matrix_size: *inception4_bwddata_sizes
alpha: 1
beta: 0
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: ctest_bwdwrw
category: nightly
function:
prune: *real_precisions_2b
transA: T
transB: N
alpha: 1
beta: 1
matrix_size: *ctest_bwdwrw_sizes
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: ctest_fwd
category: nightly
function:
prune: *real_precisions_2b
transA: N
transB: N
alpha: 1
beta: 0
matrix_size: *ctest_fwd_sizes
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_8
category: quick
function:
prune: *real_precisions_2b
M: 8
N: 8
K: 8
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_16
category: pre_checkin
function:
prune: *real_precisions_2b
M: 16
N: 16
K: 16
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_24
category: pre_checkin
function:
prune: *real_precisions_2b
M: 24
N: 24
K: 24
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_32_8_128
category: nightly
function:
prune: *real_precisions_2b
M: [8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128]
N: 32
K: 32
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_48_8_128
category: nightly
function:
prune: *real_precisions_2b
M: 48
N: [8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128]
K: 48
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_64_8_128
category: nightly
function:
prune: *real_precisions_2b
M: 64
N: 64
K: [16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128]
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_64_8_8
category: quick
function:
prune: *real_precisions_2b
M: 64
N: 8
K: 8
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_8_8_64
category: quick
function:
prune: *real_precisions_2b
M: 8
N: 8
K: 64
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_96
category: pre_checkin
function:
prune: *real_precisions_2b
M: 96
N: 96
K: 96
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_128
category: pre_checkin
function:
prune: *real_precisions_2b
M: 128
N: 128
K: 128
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_256
category: pre_checkin
function:
prune: *real_precisions_2b
M: 256
N: 256
K: 256
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_256_8_16
category: pre_checkin
function:
prune: *real_precisions_2b
M: 256
N: 8
K: 16
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
func_version: [1, 2]
- name: prune_16_256_8
category: pre_checkin
function:
prune: *real_precisions_2b
M: 16
N: 256
K: 8
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
func_version: [1, 2]
- name: prune_8_16_256
category: pre_checkin
function:
prune: *real_precisions_2b
M: 8
N: 16
K: 256
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_512
category: pre_checkin
function:
prune: *real_precisions_2b
M: 512
N: 512
K: 512
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_1024
category: nightly
function:
prune: *real_precisions_2b
M: 1024
N: 1024
K: 1024
alpha_beta: *alpha_beta_range
transA_transB: *transA_transB_range
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
- name: prune_bf16
category: pre_checkin
function:
prune: *hpa_bf16_precision
transA: T
transB: N
alpha: 1
beta: 0
matrix_size:
- { M: 512, N: 512, K: 512 }
- { M: 1024, N: 1024, K: 1024 }
- { M: 2048, N: 2048, K: 2048 }
- { M: 4096, N: 4096, K: 4096 }
- { M: 960, N: 1024, K: 1024 }
- { M: 3840, N: 4096, K: 4096 }
prune_algo: [ 0, 1 ]
sparse_b: [ true, false]
...
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