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db2524be2d
* add proper GEMM layout verification * Handle "auto" strides. CalculateStrides only called when tensor's strides are empty or all of them are <=0 (auto strides). CalculateStrides now supports GEMM::ColumnsMajor order. The assumption is still that it applies only to the inner two dims. ValidateStrides throws if any of the tensor's strides is <=0. profile_gemm_multiply_add updated to support "auto" strides for tensors. Manual tests for profile_gemm_multiply_add (matrix B in Row and Col modes) auto-strides bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0 bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0 bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 -1 -1 -1 -1 -1 Note, -1 should be deprecated (use 0 instead) explicit strides (same as auto) bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 128 bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 128 128 128 128 128 explicit strides (not the same as auto) bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138 bin/ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138 mix of explicit and auto strides bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 128 128 128 128 0 invalid stride bin/ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 64 terminate called after throwing an instance of 'std::runtime_error' what(): Invalid strides for RowMajor: mLens: 128 128 , mStrides: 64 1 Aborted (core dumped) * - add more names to ck::tensor_layout for easier namespace hierarchy checking - updated convolutional layouts to use explicit ones or BaseConvolutionalLayout where it is not clear which layout to use (TBD) - see include/ck/library/utility/convolution_host_tensor_descriptor_helper.hpp * added handling of partially initialized strides for GEMM. fixed more tests. * clang-format and more fixes * replace long dash by a simple hyphen - causes build failure in CK codegen. * increase sizeof input, otherwise output size becomes zero or negative with large filter size * select stride based on layout * specify layout explicitly to avoid errors in HostTensorDescriptor creation * add validation for higher GEMM tensor dimensions.; Add docstring to `HostTensorDescriptor` * Not clear why permute test in test/permute_scale/test_permute_scale.cpp uses a lot of invalid strides. Setting layout to BypassLayoutVerification to avoid a lot of errors * fix test (incl removing invalid config) * fix moe examples: - (in .cpp) add layout argument to non-2D tensors - (in .hpp) fix asserts/failures that show up in Debug mode, specifically addressing 2D tensor by a single index (and 3D tensor by 2d index) * fix moe_gemm2 example. * fix profile and wmma examples * clean-up early mods for ckprofile. verified with: ``` ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 0 0 0 0 0 ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 0 0 0 0 0 ckProfiler gemm_multiply_add 0 0 1 1 0 1 128 128 128 130 132 134 136 138 ckProfiler gemm_multiply_add 0 1 1 1 0 1 128 128 128 130 132 134 136 138 # ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 1 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 2 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 3 1 2 0 1 128 128 128 0 0 0 ckProfiler gemm_fastgelu 1 0 1 2 0 1 128 128 128 128 128 128 # ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 0 0 0 0 # ckProfiler gemm_add_relu 0 1 1 1 0 1 128 128 128 0 0 0 0 # not implemented # ckProfiler gemm_add_relu 0 2 1 1 0 1 128 128 128 0 0 0 0 # not implemented # ckProfiler gemm_add_relu 0 3 1 1 0 1 128 128 128 0 0 0 0 # not implemented ckProfiler gemm_add_relu 0 0 1 1 0 1 128 128 128 128 128 128 128 # ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 1 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 2 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 3 1 1 0 0 128 128 128 0 0 0 0 0 ckProfiler gemm_add_relu_add_layernorm 1 0 1 1 0 0 128 128 128 130 132 134 136 138 # example_gemm_add_multiply_dl_fp16 example_gemm_add_multiply_xdl_fp16 # ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 0 0 0 ckProfiler gemm_blockscale_wp 7 1 1 1 1 0 1 128 128 128 128 128 128 ``` * temporary skip first 8 test configs - they throw error * temporary skip first 8 test configs in wmma too - they throw error --------- Co-authored-by: Illia Silin <98187287+illsilin@users.noreply.github.com>
Composable Kernel profiler
Profiler GEMM UNIVERSAL kernels
# arg1: tensor operation (gemm_universal: Universal GEMM)
# arg2: data type (0: fp32; 1: fp16; 2: bf16; 3: int8; 4: f8@f16; 5: f16@f8; 6: f16->f8; 7: f8->bf16, comp f8; 8: f16@i4; 9: bf16@i4
# arg3: matrix layout (0: A[m, k] * B[k, n] = C[m, n];
# 1: A[m, k] * B[n, k] = C[m, n];
# 2: A[k, m] * B[k, n] = C[m, n];
# 3: A[k, m] * B[n, k] = C[m, n])
# arg4: verification (0: no; 1: yes)
# arg5: initialization (0: no init; 1: integer value; 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0=no, 1=yes)
# arg8 to 13: M, N, K, StrideA, StrideB, StrideC
# arg14: split k into mulitiple batch
# optional:
# arg15: number of warm-up cycles (default 1)
# arg16: number of iterations (default 10)
# arg17: memory for rotating buffer (default 0, size in MB)
################ op datatype layout verify init print time M N K StrideA StrideB StrideC SplitK WarmupCycles Iterations MemoryBuffer
./bin/ckProfiler gemm_universal 1 0 1 1 0 1 4096 4096 4096 4096 4096 4096 1 1 10 0
Profile GEMM kernels
#arg1: tensor operation (gemm=GEMM)
#arg2: data type (0=fp32, 1=fp16)
#arg3: matrix layout (0=NN, 1=NT, 2=TN, 3=TT)
#arg4: verification (0=no, 1=yes)
#arg5: initialization (0=no init, 1=integer value, 2=decimal value)
#arg6: print matrix value (0=no, 1=yes)
#arg7: run kernel # of times (>1)
#arg8 to 13: M, N, K, StrideA, StrideB, StrideC
################ op datatype layout verify init log repeat M___ N___ K___ StrideA StrideB StrideC
./bin/ckProfiler gemm 1 1 1 1 0 5 3840 4096 4096 4096 4096 4096
Profile 2D forward convolution kernels
#arg1: tensor operation (conv=Convolution)
#arg2: data type (0=fp32, 1=fp16)
#arg3: input tensor layout (0=NCHW, 1=NHWC)
#arg4: weight tensor layout (0=KCYX, 1=KYXC)
#arg5: output tensor layout (0=NKHW, 1=NHWK)
#arg6: verification (0=no, 1=yes)
#arg7: initialization (0=no init, 1=integer value, 2=decimal value)
#arg8: print matrix value (0=no, 1=yes)
#arg9: run kernel # of times (>1)
#arg10 to 24: N, K, C, Y, X, Hi, Wi, Sy, Sx, Dy, Dx, LeftPy, LeftPx, RightPy, RightPx
################ op datatype in_layout wei_layout out_layout verify init log repeat N__ K___ C___ Y X Hi__ Wi__ Strides Dilations LeftPads RightPads
./bin/ckProfiler conv2d_fwd 1 1 1 1 1 1 0 5 128 256 192 3 3 71 71 2 2 1 1 1 1 1 1
Profile contraction kernels
#arg1: tensor operation (contraction_bilinear=CONTRACTION+Bilinear)
#arg2: data type (0: fp32; 1: f64; 2: f16; 3: bf16)
#arg3: compute data type (0: fp32; 1: f64; 2: f16; 3: bf16)
#arg4: Number of dimension for M, N and K (one for all)
#arg5: matrix layout (0: A[m0, m1, k0, k1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 1: A[m0, m1, k0, k1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 2: A[k0, k1, m0, m1] * B[k0, k1, n0, n1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1];
# 3: A[k0, k1, m0, m1] * B[n0, n1, k0, k1] + D[m0, m1, n0, n1] = E[m0, m1, n0, n1])
#arg6: verification (0: no; 1: yes)
#arg7: initialization (0: no init; 1: integer value; 2: decimal
# value)
#arg8: print tensor value (0: no; 1: yes)
#arg9: time kernel (0: no, 1: yes)
#arg10: alpha
#arg11: beta
#arg12 to 17/29: M0, M1, N0, N1, K0, K1
#arg18/30 to 33/77: Strides for A, B, D and E (skip for default)
################ op datatype compute_datatype num_dim layout verify init log time alpha beta M0 M1 N0 N1 K0 K1
./bin/ckProfiler contraction_bilinear 0 0 2 1 0 0 0 1 1.0 1.0 128 128 128 128 128 128
Profile batched gemm multiple D kernels
#arg1: tensor operation (batched_gemm_multi_d=Batched GEMM multi D);
#arg2: data type (0: fp16; 1: int8)
#arg3: matrix layout (0: A[g, m, k] * B[g, k, n] = C[g, m, n];
# 1: A[g, m, k] * B[g, n, k] = C[g, m, n];
# 2: A[g, k, m] * B[g, k, n] = C[g, m, n];
# 3: A[g, k, m] * B[g, n, k] = C[g, m, n])
#arg4: verification (0: no; 1: yes)
#arg5: initialization (0: no init; 1: integer value; 2: decimal value)
#arg6: print tensor value (0: no; 1: yes)
#arg7: time kernel (0=n0, 1=yes)
#arg8 to 17: M, N, K, StrideA, StrideB, StrideC, BatchStrideA, BatchStrideB, BatchStrideC, BatchCount
################ op datatype layout verify init log time M N K StrideA StrideB StrideC BatchStrideA BatchStrideB BatchStrideC BatchCount
./bin/ckProfiler batched_gemm_multi_d 0 1 0 0 0 1 4096 4096 4096 4096 4096 4096 16777216 16777216 16777216 16
Profile grouped convolution backward data kernels
# arg1: tensor operation (grouped_conv_bwd_data: Grouped Convolution Backward Data)
# arg2: data type (0: Output fp32, Weight fp32, Input fp32
# 1: Output fp16, Weight fp16, Input fp16
# 2: Output bf16, Weight bf16, Input bf16
# arg3: tensor layout (0: Output[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Input[G, N, Ho, Wo, K]
# 1: Output[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Input[N, Ho, Wo, G, K])
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
# G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx
./bin/ckProfiler grouped_conv_bwd_data 1 0 1 1 0 1 2 32 4 192 192 3 3 28 28 1 1 1 1 1 1 1 1
Profile grouped convolution backward weight kernels
# arg1: tensor operation (grouped_conv_bwd_weight: Grouped Convolution Backward Weight)
# arg2: data type (0: Input fp32, Weight fp32, Output fp32
# 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight fp32, Output bf16
# 3: Input fp16, Weight fp16, Output fp16, Gemm bf8@fp8
# 4: Input int8, Weight int8, Output int8)
# arg3: tensor layout (0: Input[G, N, C, Hi, Wi], Weight[G, K, C, Y, X], Output[G, N, K, Ho, Wo]
# 1: Input[G, N, Hi, Wi, C], Weight[G, K, Y, X, C], Output[G, N, Ho, Wo, K]
# 2: Input[N, Hi, Wi, G, C], Weight[G, K, Y, X, C], Output[N, Ho, Wo, G, K]
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
# G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
# SplitK (-1 for internally computed split-K value, positive value to set k batches explicitly, or 'all' to test all internal split-K values)
################ op datatype layout verify init log time Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx SplitK
./bin/ckProfiler grouped_conv_bwd_weight 1 1 0 1 0 1 2 32 256 256 512 3 3 28 28 1 1 1 1 1 0 0 0 1
Note: This kernel use atomic add, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
Profile image to column/column to image kernels
# arg1: tensor operation ( conv_tensor_rearrange : Conv Tensor Rearrange )
# arg2: data type (0: Input fp32, Weight fp32, Output fp32
# 1: Input fp16, Weight fp16, Output fp16
# 2: Input bf16, Weight bf16, Output bf16
# 3: Input int8, Weight int8, Output int8)
# arg3: tensor layout (0: Input[G, N, Hi, Wi, C], Output[G * N * Ho * Wo, Y * X * C],
# 1: Input[N, Hi, Wi, G, C], Output[N * Ho * Wo * G, Y * X * C])
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# arg8: operation type (0: ImageToColumn, 1: ColumnToImage)
# Following arguments (depending on number of spatial dims):
# Number of spatial dimensions (1=Conv1D, 2=Conv2D, 3=Conv3D)
# G, N, K, C,
# <filter spatial dimensions>, (ie Y, X for 2D)
# <input image spatial dimensions>, (ie Hi, Wi for 2D)
# <strides>, (ie Sy, Sx for 2D)
# <dilations>, (ie Dy, Dx for 2D)
# <left padding>, (ie LeftPy, LeftPx for 2D)
# <right padding>, (ie RightPy, RightPx for 2D)
################ op datatype layout verify init log time opType Ndims G N K C Y X Hi Wi Sy Sx Dy Dx LeftPy LeftPx RightPy RightPx
./bin/ckProfiler conv_tensor_rearrange 0 0 0 1 0 1 0 2 1 256 1 512 3 3 28 28 1 1 1 1 0 0 0 0
Note: Column to image kernel adds to the output memory, this will cause output buffer to be accumulated multiple times, causing verification failure. To work around it, do not use CK's own timer and do verification at the same time.
Profile Permute scale kernels
# arg1: tensor operation ( permute_scale : Permute Scale )
# arg2: data type (0: Input fp32, Output fp32
# 1: Input fp16, Output fp16
# arg4: verification (0: no, 1: yes)
# arg5: initialization (0: no init, 1: integer value, 2: decimal value)
# arg6: print tensor value (0: no; 1: yes)
# arg7: time kernel (0: no, 1: yes)
# from arg8: tensor lengths
# input strides
# output strides
################ op datatype verify init log time dim0 dim1 dim2 in_stride0 in_stride1 in_stride2 out_stride0 out_stride1 out_stride2
./bin/ckProfiler permute_scale 0 1 1 0 1 64 64 64 4096 64 1 1 64 4096
Convert MIOpen driver command to CKProfiler
python3 ../script/convert_miopen_driver_to_profiler.py
/opt/rocm/bin/MIOpenDriver conv -n 32 -c 64 -H 28 -W 28 -k 64 -y 3 -x 3
-p 1 -q 1 -u 2 -v 2 -l 1 -j 1 -m conv -g 32 -F 1 -t 1
Only convolution driver is supported.