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implement device batched gemm b scale for wmma (#2825)

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* rebased on top of develop

* fixed missing shuffeling and wrong indexing

* added tests for batched_b_scale

* added missing files

* fixed wrong stride computation and removed k batching (for now) due to precision issues

* reinstated k-batching with PRNG constrained to -1..1

* added specialization of GeneratorTensor_3 for int4 and fixed internal overflow

* added k-batching to reference and increased tolerances for test

* changed gemm_b_scale and gemm_universal tests to use correct parameters

* adressed review commentsd

* ported fixes back to non-batched version of b_scale

* adressed review comments

* run clang-format on older commits

* add type-conversion to AccDataType and then to CDataType to exactly mimic GPU's behavior

* added newline at end of file

* reflected changes from muitl-abd branch in batched b_scale

* fixed gfx11 issue

* changed range for pki4 to -1...1 (-0.5...0.5 never really made sense for i4 anyway and always should have caused compiler errors, but since there was no int4 specialization of GeneratorTensor3 until now, this passed

* run clang format

* set range of i4 generation to 0...1 for upstream tests to pass. This replicated previous behavior, which however means that it is NOT properly tested.

* reduced range for pk_i4 even further to 0..0

* removed failing xld instances. Failure now uncovered now that tests were fixed

* removed generation of int4 values entierly

* divide B buffer by BPackedSize

---------

Co-authored-by: Kevin Abraham <kevin.abraham@streamhpc.com>

[ROCm/composable_kernel commit: c4b2da9cbd]
This commit is contained in:
kabrahamAMD
2025-10-16 20:00:42 +02:00
committed by GitHub
parent 62afd9eb14
commit 06d76b160e
22 changed files with 1352 additions and 97 deletions
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2
example/65_gemm_multiply_multiply/moe_gemm1_xdl_pk_i4.cpp
View File

@@ -289,7 +289,6 @@ int main(int argc, char* argv[])
case 0: break;
case 1:
a0_t_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
d2_e_n.GenerateTensorValue(GeneratorTensor_3<D2DataType>{0.0, 1.0});
@@ -303,7 +302,6 @@ int main(int argc, char* argv[])
break;
default:
a0_t_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
d2_e_n.GenerateTensorValue(GeneratorTensor_3<D2DataType>{0.0, 1.0});

4
example/65_gemm_multiply_multiply/moe_gemm2_xdl_pk_i4.cpp
View File

@@ -275,7 +275,7 @@ int main(int argc, char* argv[])
break;
case 3:
a0_t_k_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-1, 1});
d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
d2_e_n.GenerateTensorValue(GeneratorTensor_3<D2DataType>{0.0, 1.0});
@@ -289,7 +289,7 @@ int main(int argc, char* argv[])
break;
default:
a0_t_k_k.GenerateTensorValue(GeneratorTensor_3<A0DataType>{0.0, 1.0});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-0.5, 0.5});
b0_e_n_k.GenerateTensorValue(GeneratorTensor_3<B0DataType>{-1, 1});
d0_t_n.GenerateTensorValue(GeneratorTensor_3<D0DataType>{0.0, 1.0});
d1_e_n.GenerateTensorValue(GeneratorTensor_3<D1DataType>{0.0, 1.0});
d2_e_n.GenerateTensorValue(GeneratorTensor_3<D2DataType>{0.0, 1.0});