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Flatten subprojects into a single base monorepo
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---
include: hipblaslt_common.yaml
include: known_bugs.yaml
include: matmul_common.yaml
Definitions:
- &alpha_beta_range
- { alpha: 5, beta: 0 }
- { alpha: 0, beta: 3 }
- { alpha: 1, beta: 3 }
- { alpha: 1, beta: 1 }
- &alpha_beta_range_small
- { alpha: 2, alphai: 2, beta: -1.0, betai: 2.0 }
- &transA_transB_range
- { transA: N, transB: N }
- { transA: N, transB: T }
- { transA: T, transB: N }
- { transA: T, transB: T }
Tests:
- name: matmul_smoke
category: smoke
function:
matmul: *real_precisions
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha_beta: *alpha_beta_range
- name: matmul_bias_relu_SAV_smoke
category: smoke
function:
matmul: *real_precisions
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleAlpha_vector: [0, 1]
activation_type: relu
bias_vector: [0, 1]
unit_check: 1
- name: matmul_bias_sigmoid_smoke
category: smoke
function:
matmul: *real_precisions
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
activation_type: sigmoid
bias_vector: [0, 1]
unit_check: 1
- name: matmul_bias_gelu_smoke
category: smoke
function:
matmul: *real_precisions
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
activation_type: gelu
bias_vector: [0, 1]
unit_check: 0
norm_check: 1
- name: matmul_bias_only_smoke
category: smoke
function:
matmul: *real_precisions
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
bias_vector: 1
unit_check: 1
- name: matmul_bias_type_smoke
category: smoke
function:
matmul: *real_precisions_2b
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
bias_vector: 1
bias_type: [default, f32_r]
unit_check: 1
- name: matmul_f8_bf8_fnuz_dst_fp16_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_f16
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [ 0, 1]
scaleB: [ 0, 1]
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 1
gpu_arch: '942'
- name: matmul_f8_bf8_fnuz_dst_bf16_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_bf16
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [ 0, 1]
scaleB: [ 0, 1]
bias_vector: [0, 1]
bias_type: bf16_r
unit_check: 1
gpu_arch: '942'
### GFX12 FP8
- name: matmul_f8_bf8_dst_fp32_gfx12_smoke
category: smoke
function:
matmul: *real_precisions_1b_dst_f32
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [ 0, 1]
scaleB: [ 0, 1]
bias_vector: [0, 1]
bias_type: bf16_r
unit_check: 0
norm_check: 1
gpu_arch: '12(0[0-1]|50)'
- name: matmul_f8_bf8_dst_fp16_gfx12_smoke
category: smoke
function:
matmul: *real_precisions_1b_dst_f16
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [ 0, 1]
scaleB: [ 0, 1]
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 1
gpu_arch: '(120[0-1]|1250)'
- name: matmul_f8_bf8_dst_bf16_gfx12_smoke
category: smoke
function:
matmul: *real_precisions_1b_dst_bf16
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [ 0, 1]
scaleB: [ 0, 1]
bias_vector: [0, 1]
bias_type: bf16_r
unit_check: 1
gpu_arch: '(120[0-1]|1250)'
- name: matmul_real_1b_dst_f8_SCDInt1_gfx12_smoke
category: smoke
function:
matmul: *real_precisions_1b_dst_f8
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [0] # will use default value '1'
scaleD: [0] # will use default value '1'
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 1
gpu_arch: '(120[0-1]|1250)'
- name: matmul_real_1b_dst_f8_SCDNotInt_gfx12_smoke
category: smoke
function:
matmul: *real_precisions_1b_dst_f8
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [1] # will use random value from 0.0 to 1.0
scaleD: [1] # will use random value from 0.0 to 1.0
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 1
gpu_arch: '(120[0-1]|1250)'
- name: matmul_one_integer_precisions_i8_gfx12_smoke
category: smoke
function:
matmul: *integer_precisions_i8
matrix_size: *one_matrix_size_range
transA_transB: *transA_transB_range
alpha: [ 0.0, 1.0 ]
beta: [ 0.0, 2.0 ]
gpu_arch: '12(0[0-1]|50)'
- name: matmul_real_1b_fnuz_dst_f8_fnuz_SCDInt1_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_f8_fnuz
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [0] # will use default value '1'
scaleD: [0] # will use default value '1'
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 1
gpu_arch: '942'
- name: matmul_real_1b_fnuz_dst_1b_fnuz_smallsize_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_1b_fnuz
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [0]
scaleD: [0]
bias_vector: [0, 1]
bias_type: f32_r
unit_check: 1
gpu_arch: '942'
#TODO: extend to all f8 transpose and datatype if necessary
- name: matmul_real_f8_fnuz_dst_fp32_smoke
category: smoke
function:
matmul: *f8_fnuz_precision_dst_fp32
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [0]
scaleD: [0]
bias_vector: [0, 1]
bias_type: f32_r
unit_check: 1
gpu_arch: '942'
- name: matmul_real_1b_fnuz_dst_f8_fnuz_SCDNotInt_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_f8_fnuz
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [1] # will use random value from 0.0 to 1.0
scaleD: [1] # will use random value from 0.0 to 1.0
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 0
norm_check: 1
gpu_arch: '942'
- name: matmul_real_1b_fnuz_dst_bf8_fnuz_SCDInt1_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_bf8_fnuz
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [0] # will use default value '1'
scaleD: [0] # will use default value '1'
bias_vector: [0,1]
bias_type: f16_r
unit_check: 1
gpu_arch: '942'
- name: matmul_real_1b_fnuz_dst_bf8_fnuz_SCDNotInt_smoke
category: smoke
function:
matmul: *real_precisions_1b_fnuz_dst_bf8_fnuz
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
scaleA: [0, 1]
scaleB: [0, 1]
scaleC: [1] # will use random value from 0.0 to 1.0
scaleD: [1] # will use random value from 0.0 to 1.0
bias_vector: [0, 1]
bias_type: f16_r
unit_check: 0
norm_check: 1
gpu_arch: '942'
- name: matmul_gemm_xf32_smoke
category: smoke
function:
matmul: *xf32_precision
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
unit_check: 1
gpu_arch: '(942|1250)'
- name: matmul_gemm_double_smoke
category: smoke
function:
matmul: *double_precision
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0.0, 2.0 ]
unit_check: 1
gpu_arch: '9(0a|4[0-2])'
- name: matmul_gemm_i8_dst_i32_smoke
category: smoke
function:
matmul: *i8_precision_dst_i32
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0, 2 ]
unit_check: 1
gpu_arch: '90a'
- name: matmul_gemm_i8_dst_i32_94x_smoke
category: smoke
function:
matmul: *i8_precision_dst_i32
matrix_size: *smoke_matrix_size_range
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0, 2 ]
scaleAlpha_vector: [0, 1]
activation_type: [relu, sigmoid]
unit_check: 1
gpu_arch: '942'
- name: matmul_gemm_i8_dst_i8_1xxx_smoke
category: smoke
function:
matmul: *i8_precision_dst_i8
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0, 2 ]
scaleAlpha_vector: [0, 1]
activation_type: [relu, sigmoid]
unit_check: 1
gpu_arch: '1[1-2]\d{2}'
- name: matmul_gemm_i8_dst_i32_1xxx_smoke
category: smoke
function:
matmul: *i8_precision_dst_i32
matrix_size:
- { M: 128, N: 128, K: 128 }
transA_transB: *transA_transB_range
alpha: 1
beta: [ 0, 2 ]
scaleAlpha_vector: [0, 1]
activation_type: [relu, sigmoid]
unit_check: 1
gpu_arch: '1[1-2]\d{2}'
- name: matmul_gemm_tn_mxf4_dst_bf16_1250_smoke
category: smoke
function:
matmul:
- { a_type: f4_r, b_type: f4_r, c_type: bf16_r, d_type: bf16_r, compute_type: c_f32_r, scaleA: 3, scaleB: 3, scale_type: f32_r}
- { a_type: f4_r, b_type: f4_r, c_type: bf16_r, d_type: bf16_r, compute_type: c_f32_r, scaleA: 6, scaleB: 6, scale_type: f32_r}
matrix_size:
- { M: 128, N: 128, K: 512 }
- { M: 128, N: 128, K: 544 }
- { M: 130, N: 128, K: 512 }
- { M: 128, N: 130, K: 512 }
transA: T
transB: N
alpha: 1
beta: [ 0.0, 2.0 ]
initialization: uniform_low_precision
unit_check: 0
norm_check: 1
gpu_arch: '1250'
- name: matmul_gemm_tn_mxf4_dst_s_1250_smoke
category: smoke
function:
matmul:
- { a_type: f4_r, b_type: f4_r, c_type: f32_r, d_type: f32_r, compute_type: c_f32_r, scaleA: 3, scaleB: 3, scale_type: f32_r}
- { a_type: f4_r, b_type: f4_r, c_type: f32_r, d_type: f32_r, compute_type: c_f32_r, scaleA: 6, scaleB: 6, scale_type: f32_r}
matrix_size:
- { M: 128, N: 128, K: 512 }
- { M: 128, N: 128, K: 544 }
- { M: 130, N: 128, K: 512 }
- { M: 128, N: 130, K: 512 }
transA: T
transB: N
alpha: 1
beta: [ 0.0, 2.0 ]
initialization: uniform_low_precision
unit_check: 0
norm_check: 1
gpu_arch: '1250'
- name: matmul_gemm_tn_mxf8f4_dst_s_1250_bias_smoke
category: smoke
function:
matmul:
- { a_type: f8_r, b_type: f4_r, c_type: f32_r, d_type: f32_r, compute_type: c_f32_r, compute_input_typeA: f8_r, compute_input_typeB: f4_r, scaleA: 3, scaleB: 3, scale_type: f32_r }
matrix_size:
- { M: 128, N: 128, K: 512 }
- { M: 128, N: 128, K: 544 }
- { M: 130, N: 128, K: 512 }
- { M: 128, N: 130, K: 512 }
transA: T
transB: N
alpha: 1
beta: [ 0.0, 2.0 ]
bias_vector: [ 0, 1 ]
bias_type: f32_r
unit_check: 1
gpu_arch: '1250'
- name: matmul_gemm_tn_mxf8_dst_bf16_1250_smoke
category: smoke
function:
matmul:
- { a_type: f8_r, b_type: f8_r, c_type: bf16_r, d_type: bf16_r, compute_type: c_f32_r, scaleA: 3, scaleB: 3, scale_type: f32_r}
matrix_size:
- { M: 128, N: 128, K: 512 }
- { M: 128, N: 128, K: 544 }
- { M: 129, N: 128, K: 512 }
- { M: 128, N: 129, K: 512 }
transA: T
transB: N
alpha: 1
beta: [ 0.0, 2.0 ]
batch_count: [1, 16]
unit_check: 0
norm_check: 1
gpu_arch: '1250'
- name: matmul_gemm_tn_mxf8_dst_s_1250_smoke
category: smoke
function:
matmul:
- { a_type: f8_r, b_type: f8_r, c_type: f32_r, d_type: f32_r, compute_type: c_f32_r, scaleA: 3, scaleB: 3, scale_type: f32_r}
matrix_size:
- { M: 128, N: 128, K: 512 }
- { M: 128, N: 128, K: 544 }
- { M: 129, N: 128, K: 512 }
- { M: 128, N: 129, K: 512 }
transA: T
transB: N
alpha: 1
beta: [ 0.0, 2.0 ]
unit_check: 1
gpu_arch: '1250'
...
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