Merge branch 'develop' into aosewski/ggemm_splitk

.github/dependabot.yml

+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "pip" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    open-pull-requests-limit: 10
+    schedule:
+      interval: "daily"

client_example/09_quantization/conv2d_fwd_bias_relu_perchannel_quantization.cpp

@@ -73,7 +73,7 @@ int main(int argc, char* argv[])
     SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
     SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
     SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C);
-    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C);
+    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K);
     SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
 
     using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<

client_example/09_quantization/conv2d_fwd_bias_tanh_perchannel_quantization.cpp

@@ -76,7 +76,7 @@ int main(int argc, char* argv[])
     SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
     SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
     SimpleDeviceMem bias(sizeof(BiasDataType) * K * Y * X * C);
-    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C);
+    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K);
     SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
 
     using DeviceOp = ck::tensor_operation::device::DeviceGroupedConvFwdMultipleD<

client_example/09_quantization/conv2d_fwd_perchannel_quantization.cpp

@@ -69,7 +69,7 @@ int main(int argc, char* argv[])
 
     SimpleDeviceMem in(sizeof(InDataType) * N * Hi * Wi * C);
     SimpleDeviceMem wei(sizeof(WeiDataType) * K * Y * X * C);
-    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K * Y * X * C);
+    SimpleDeviceMem requant_scale(sizeof(RequantScaleDataType) * K);
     SimpleDeviceMem out(sizeof(OutDataType) * N * Ho * Wo * K);
 
     using DeviceOp =

client_example/18_groupnorm/groupnorm_swish.cpp

@@ -13,8 +13,8 @@
 #include "ck/library/tensor_operation_instance/gpu/normalization_swish.hpp"
 
 using XDataType       = ck::half_t;
-using GammaDataType   = ck::half_t;
-using BetaDataType    = ck::half_t;
+using GammaDataType   = float;
+using BetaDataType    = float;
 using YDataType       = ck::half_t;
 using ComputeDataType = float;
 using Swish           = ck::tensor_operation::element_wise::Swish;

cmake/googletest.cmake

@@ -21,6 +21,7 @@ list(APPEND GTEST_CMAKE_CXX_FLAGS
      -Wno-comma
      -Wno-old-style-cast
      -Wno-deprecated
+     -Wno-unsafe-buffer-usage
 )
 message(STATUS "Suppressing googltest warnings with flags: ${GTEST_CMAKE_CXX_FLAGS}")
 

docs/.sphinx/requirements.in

-git+https://github.com/RadeonOpenCompute/rocm-docs-core.git
+rocm-docs-core==0.2.0
 sphinxcontrib-bibtex==2.5.0

docs/.sphinx/requirements.txt

@@ -2,9 +2,9 @@
 # This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
-#    pip-compile requirements.in
+#    pip-compile .sphinx/requirements.in
 #
-accessible-pygments==0.0.4
+accessible-pygments==0.0.3
     # via pydata-sphinx-theme
 alabaster==0.7.13
     # via sphinx
@@ -20,7 +20,7 @@ babel==2.12.1
     #   sphinx
 backcall==0.2.0
     # via ipython
-beautifulsoup4==4.12.0
+beautifulsoup4==4.11.2
     # via pydata-sphinx-theme
 breathe==4.34.0
     # via rocm-docs-core
@@ -34,7 +34,7 @@ click==8.1.3
     # via
     #   jupyter-cache
     #   sphinx-external-toc
-comm==0.1.3
+comm==0.1.2
     # via ipykernel
 debugpy==1.6.6
     # via ipykernel
@@ -65,13 +65,11 @@ idna==3.4
     # via requests
 imagesize==1.4.1
     # via sphinx
-importlib-metadata==6.1.0
+importlib-metadata==6.0.0
     # via
     #   jupyter-cache
     #   myst-nb
-importlib-resources==5.10.4
-    # via rocm-docs-core
-ipykernel==6.22.0
+ipykernel==6.21.3
     # via myst-nb
 ipython==8.11.0
     # via
@@ -87,7 +85,7 @@ jsonschema==4.17.3
     # via nbformat
 jupyter-cache==0.5.0
     # via myst-nb
-jupyter-client==8.1.0
+jupyter-client==8.0.3
     # via
     #   ipykernel
     #   nbclient
@@ -124,7 +122,7 @@ nbclient==0.5.13
     # via
     #   jupyter-cache
     #   myst-nb
-nbformat==5.8.0
+nbformat==5.7.3
     # via
     #   jupyter-cache
     #   myst-nb
@@ -187,7 +185,7 @@ pyyaml==6.0
     #   myst-parser
     #   pybtex
     #   sphinx-external-toc
-pyzmq==25.0.2
+pyzmq==25.0.1
     # via
     #   ipykernel
     #   jupyter-client
@@ -195,8 +193,8 @@ requests==2.28.2
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core @ git+https://github.com/RadeonOpenCompute/rocm-docs-core.git
-    # via -r requirements.in
+rocm-docs-core==0.2.0
+    # via -r .sphinx/requirements.in
 six==1.16.0
     # via
     #   asttokens
@@ -235,9 +233,7 @@ sphinx-notfound-page==0.8.3
 sphinxcontrib-applehelp==1.0.4
     # via sphinx
 sphinxcontrib-bibtex==2.5.0
-    # via
-    #   -r requirements.in
-    #   rocm-docs-core
+    # via -r .sphinx/requirements.in
 sphinxcontrib-devhelp==1.0.2
     # via sphinx
 sphinxcontrib-htmlhelp==2.0.1
@@ -248,7 +244,7 @@ sphinxcontrib-qthelp==1.0.3
     # via sphinx
 sphinxcontrib-serializinghtml==1.1.5
     # via sphinx
-sqlalchemy==1.4.47
+sqlalchemy==1.4.46
     # via jupyter-cache
 stack-data==0.6.2
     # via ipython

include/ck/ck.hpp

@@ -168,6 +168,11 @@
 // flag to enable (1) or disable (0) the debugging output in some kernels
 #define DEBUG_LOG 0
 
+// denorm test fix, required to work around dissue
+#ifndef CK_WORKAROUND_DENORM_FIX
+#define CK_WORKAROUND_DENORM_FIX 0
+#endif
+
 namespace ck {
 
 enum struct InMemoryDataOperationEnum

include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_wmma_cshuffle.hpp

@@ -505,6 +505,15 @@ struct GridwiseGemmMultipleD_k0mk1_k0nk1_mn_wmma_cshuffle
         }
 
         // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        constexpr long_index_t TwoGB = (long_index_t{1} << 31);
+
+        if(!(a_grid_desc_k0_m_k1.GetElementSpaceSize() * sizeof(ADataType) <= TwoGB &&
+             b_grid_desc_k0_n_k1.GetElementSpaceSize() * sizeof(BDataType) <= TwoGB &&
+             e_grid_desc_m_n.GetElementSpaceSize() * sizeof(EDataType) <= TwoGB))
+        {
+            return false;
+        }
+
         return true;
     }
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_multiple_d_xdl_cshuffle.hpp

@@ -96,7 +96,7 @@ struct GridwiseGemmMultipleD_xdl_cshuffle
     // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
     // when mfma if fixed, remove this section and update
     // ABDataTypeAdjusted -> ABDataType throughout this file
-#if defined(__gfx90a__)
+#if CK_WORKAROUND_DENORM_FIX && defined(__gfx90a__)
     using ABDataTypeAdjusted =
         conditional_t<is_same_v<ABDataType, ck::half_t>, ck::bhalf_t, ABDataType>;
 #else

include/ck/tensor_operation/gpu/grid/gridwise_gemm_wmma.hpp

@@ -264,6 +264,13 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_wmma
         }
 
         // TODO: also check validity of all components (blockwise-copy, threadwise-copy, etc)
+        constexpr long_index_t TwoGB = (long_index_t{1} << 31);
+
+        if(!(a_grid_desc_k0_m_k1.GetElementSpaceSize() * sizeof(FloatA) <= TwoGB &&
+             b_grid_desc_k0_n_k1.GetElementSpaceSize() * sizeof(FloatB) <= TwoGB))
+        {
+            return false;
+        }
         return true;
     }
 

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_bwd_weight.hpp

@@ -265,7 +265,7 @@ struct GridwiseGemm_bk0mk1_bk0nk1_mn_xdlops_bwd_weight
     // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
     // when mfma if fixed, remove this section and update
     // FloatABAdjusted -> FloatAB throughout this file
-#if defined(__gfx90a__)
+#if CK_WORKAROUND_DENORM_FIX && defined(__gfx90a__)
     using FloatABAdjusted = conditional_t<is_same_v<FloatAB, ck::half_t>, ck::bhalf_t, FloatAB>;
 #else
     using FloatABAdjusted = FloatAB;

include/ck/tensor_operation/gpu/grid/gridwise_gemm_xdlops_v2r3.hpp

@@ -135,7 +135,7 @@ struct GridwiseGemm_k0mk1_k0nk1_mn_xdlops_v2r3
     // we convert fp16->fp32->bf16 and execute bf16 mfma instruction
     // when mfma if fixed, remove this section and update
     // FloatABAdjusted -> FloatAB throughout this file
-#if defined(__gfx90a__)
+#if CK_WORKAROUND_DENORM_FIX && defined(__gfx90a__)
     using FloatABAdjusted = conditional_t<is_same_v<FloatAB, ck::half_t>, ck::bhalf_t, FloatAB>;
 #else
     using FloatABAdjusted = FloatAB;

library/include/ck/library/tensor_operation_instance/gpu/normalization_swish.hpp

@@ -25,6 +25,10 @@ void add_device_normalization_rank_5_3_swish_f16_instances(
 void add_device_normalization_rank_5_3_swish_f32_instances(
     std::vector<std::unique_ptr<DeviceNormalization<F32, F32, F32, F32, F32, Swish, 5, 3>>>&);
 
+// [x, gamma, beta, y] = [f16, f32, f32, f16]
+void add_device_normalization_rank_5_3_swish_f16_f32_f32_f16_instances(
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F32, F32, F32, F16, Swish, 5, 3>>>&);
+
 template <typename XDataType,
           typename GammaDataType,
           typename BetaDataType,
@@ -70,6 +74,14 @@ struct DeviceOperationInstanceFactory<
                 add_device_normalization_rank_5_3_swish_f32_instances(op_ptrs);
             }
         }
+        else if constexpr(is_same_v<XDataType, F16> && is_same_v<GammaDataType, F32> &&
+                          is_same_v<BetaDataType, F32> && is_same_v<YDataType, F16>)
+        {
+            if constexpr(Rank == 5 && NumReduceDim == 3)
+            {
+                add_device_normalization_rank_5_3_swish_f16_f32_f32_f16_instances(op_ptrs);
+            }
+        }
 
         return op_ptrs;
     }

library/src/tensor_operation_instance/gpu/normalization/CMakeLists.txt

@@ -7,4 +7,5 @@ add_instance_library(device_normalization_instance
     device_groupnorm_f32_instance.cpp
     device_groupnorm_swish_f16_instance.cpp
     device_groupnorm_swish_f32_instance.cpp
+    device_groupnorm_swish_f16_f32_f32_f16_instance.cpp
 )

library/src/tensor_operation_instance/gpu/normalization/device_groupnorm_swish_f16_f32_f32_f16_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2018-2022, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "normalization_instance_common.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+using Swish = ck::tensor_operation::element_wise::Swish;
+
+void add_device_normalization_rank_5_3_swish_f16_f32_f32_f16_instances(
+    std::vector<std::unique_ptr<DeviceNormalization<F16, F32, F32, F32, F16, Swish, 5, 3>>>&
+        instances)
+{
+    add_device_operation_instances(instances,
+                                   device_normalization_f16_f32_f32_f16_instances<Swish, 5, 3>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

library/src/tensor_operation_instance/gpu/normalization/normalization_instance_common.hpp

@@ -69,6 +69,32 @@ using device_normalization_f32_instances = std::tuple<
     // clang-format on
     >;
 
+template <typename OutElementwise, index_t Rank, index_t Reduce>
+using device_normalization_f16_f32_f32_f16_instances = std::tuple<
+    // clang-format off
+        // XDataType, GammaDataType, BetaDataType, ComputeDataType, YDataType, Rank, NumReduceDim, BlockSize, MThreadClusterSize, KThreadClusterSize, MThreadSliceSize, KThreadSliceSize, XYSrcVectorDim, XSrcVectorSize, GammaSrcVectorSize, BetaSrcVectorSize, YDstVectorSize>
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 1, 1, 1, 1, 1, 1, 1, 1>,   // irregular size
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 1, 1, 1, 1, 1, 1, 1, 1>,   // irregular size
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 1, 1, 1, 1, 1, 1, 1, 1>,   // irregular size
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 1, 1, 1, 1, 1, 1, 1, 1>, // irregular size
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 2, 1, 2, 1, 2, 1, 2, 2>,   // irregular size
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 4, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 16, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 128, 1, 128, 1, 32, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 4, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 16, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 2, 16, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 256, 1, 256, 1, 32, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 4, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 1, 8, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 512, 1, 512, 2, 8, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 4, 1, 4, 1, 4, 1, 4, 4>,
+        DeviceNormalizationImpl<F16, F32, F32, F32, F16, OutElementwise, Rank, Reduce, 1024, 1, 1024, 1, 8, 1, 4, 1, 4, 1, 4, 4>
+    // clang-format on
+    >;
+
 } // namespace instance
 } // namespace device
 } // namespace tensor_operation