Add support for NGCHW in grouped conv bwd wei (#1491)

* Add support for NGCHW in grouped conv bwd wei

* Comments fixes

* navi fixes

* Update function names

library/src/tensor_operation_instance/gpu/grouped_conv3d_bwd_weight/xdl/device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev5_instance.cpp

+// SPDX-License-Identifier: MIT
+// Copyright (c) 2024, Advanced Micro Devices, Inc. All rights reserved.
+
+#include "ck/library/tensor_operation_instance/add_device_operation_instance.hpp"
+#include "ck/library/tensor_operation_instance/gpu/grouped_conv_bwd_weight/device_grouped_conv_bwd_weight_two_stage_xdl_instance.hpp"
+
+namespace ck {
+namespace tensor_operation {
+namespace device {
+namespace instance {
+
+// Compilation parameters for in[n, hi, wi, g, c] * wei[g, k, y, x, c] = out[n, ho, wo, g, k]
+void add_device_grouped_conv3d_bwd_weight_two_stage_xdl_ngcdhw_gkzyxc_ngkdhw_f16_pipev5_instances(
+    std::vector<std::unique_ptr<DeviceGroupedConvBwdWeight<3,
+                                                           NGCDHW,
+                                                           GKZYXC,
+                                                           NGKDHW,
+                                                           F16,
+                                                           F16,
+                                                           F16,
+                                                           PassThrough,
+                                                           PassThrough,
+                                                           PassThrough>>>& instances)
+{
+    // 1. Default
+    add_device_operation_instances(
+        instances,
+        device_grouped_conv_bwd_weight_two_stage_ngchw_xdl_c_shuffle_f16_instances<
+            3,
+            NGCDHW,
+            GKZYXC,
+            NGKDHW,
+            ConvBwdWeightDefault,
+            BlockGemmPipelineScheduler::Intrawave,
+            BlockGemmPipelineVersion::v5>{});
+}
+
+} // namespace instance
+} // namespace device
+} // namespace tensor_operation
+} // namespace ck

profiler/src/profile_grouped_conv_bwd_weight.cpp

@@ -16,6 +16,7 @@ enum struct ConvLayout
     GNCHW_GKCYX_GNKHW, // 0
     GNHWC_GKYXC_GNHWK, // 1
     NHWGC_GKYXC_NHWGK, // 2
+    NGCHW_GKYXC_NGKHW, // 3
 };
 
 enum struct ConvDataType
@@ -178,6 +179,13 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
             return profile(I2, NHWGC{}, GKYXC{}, NHWGK{}, BF16{}, F32{}, BF16{}, BF16{}, BF16{});
         }
     }
+    else if(num_dim_spatial == 2 && layout == ConvLayout::NGCHW_GKYXC_NGKHW)
+    {
+        if(data_type == ConvDataType::F16_F16_F16)
+        {
+            return profile(I2, NGCHW{}, GKYXC{}, NGKHW{}, F16{}, F16{}, F16{}, F16{}, F16{});
+        }
+    }
     if(num_dim_spatial == 3 && layout == ConvLayout::GNHWC_GKYXC_GNHWK)
     {
         if(data_type == ConvDataType::F32_F32_F32)
@@ -224,6 +232,13 @@ int profile_grouped_conv_bwd_weight(int argc, char* argv[])
                 I3, NDHWGC{}, GKZYXC{}, NDHWGK{}, int8_t{}, int8_t{}, int8_t{}, int8_t{}, int8_t{});
         }
     }
+    else if(num_dim_spatial == 3 && layout == ConvLayout::NGCHW_GKYXC_NGKHW)
+    {
+        if(data_type == ConvDataType::F16_F16_F16)
+        {
+            return profile(I3, NGCDHW{}, GKZYXC{}, NGKDHW{}, F16{}, F16{}, F16{}, F16{}, F16{});
+        }
+    }
 
     std::cout << "this data_type & layout is not implemented" << std::endl;
 

script/convert_miopen_driver_to_profiler.py

@@ -23,6 +23,26 @@ def run_ck_profiler_cmd(cmd):
     subprocess.run(cmd)
 
 
+def parse_layouts(args):
+    if args.in_layout == "NCW" or args.in_layout == "NCHW" or \
+       args.in_layout == "NCDHW":
+        if args.ck_profier_op == "grouped_conv_bwd_weight":
+            args.layout = 3
+        else:
+            print('Not supported layout for this op')
+            exit(1)
+    elif args.in_layout == "NWC" or args.in_layout == "NHWC" or \
+       args.in_layout == "NDHWC":
+        if args.ck_profier_op == "grouped_conv_bwd_weight":
+            args.layout = 2
+        elif args.ck_profier_op == "grouped_conv_bwd_data" or \
+                args.ck_profier_op == "grouped_conv_fwd":
+            args.layout = 1
+    else:
+        print('Not supported layout for this op')
+        exit(1)
+
+
 def parse_data_type(args):
     if args.data_type == "fp32":
         if args.ck_profier_op == "grouped_conv_bwd_weight" or \
@@ -79,8 +99,7 @@ def add_conv_params_to_cmd(args, cmd):
 def run_ck_grouped_conv_fwd(args):
     args.ck_profier_op = "grouped_conv_fwd"
     parse_data_type(args)
-    # default for MIOpen NHWGC
-    args.layout = 1
+    parse_layouts(args)
     # use int32 by default
     args.index_type = 0
 
@@ -99,8 +118,7 @@ def run_ck_grouped_conv_fwd(args):
 def run_ck_grouped_conv_bwd_data(args):
     args.ck_profier_op = "grouped_conv_bwd_data"
     parse_data_type(args)
-    # default for MIOpen NHWGC
-    args.layout = 1
+    parse_layouts(args)
 
     cmd = [str(args.ck_profiler_cmd), str(args.ck_profier_op)]
     cmd += [str(args.data_type), str(args.layout)]
@@ -117,8 +135,7 @@ def run_ck_grouped_conv_bwd_data(args):
 def run_ck_grouped_conv_bwd_weight(args):
     args.ck_profier_op = "grouped_conv_bwd_weight"
     parse_data_type(args)
-    # default for MIOpen NHWGC
-    args.layout = 2
+    parse_layouts(args)
     # Test all split K value from the list {1, 2, 4, 8, 32, 64, 128}
     args.split_k_value = -1
 
@@ -181,8 +198,8 @@ if __name__ == "__main__":
     parser.add_argument(
         "-in_layout",
         "-I",
-        default=-1,
-        type=int,
+        default="NCHW",
+        type=str,
         required=False,
         help="Input Layout (Default=NCHW for 2d conv, NCDHW for 3d conv)"
     )

test/grouped_convnd_bwd_weight/test_grouped_convnd_bwd_weight.cpp

@@ -66,6 +66,12 @@ class TestGroupedConvndBwdWeight : public ::testing::Test
             {
                 return true;
             }
+            // Skip due to the lack of kernels for NGCDHW
+            if constexpr(std::is_same_v<InLayout, NGCW> || std::is_same_v<InLayout, NGCHW> ||
+                         std::is_same_v<InLayout, NGCDHW>)
+            {
+                return true;
+            }
         }
         else
         {
@@ -139,7 +145,8 @@ using KernelTypes2d = ::testing::Types<
     std::tuple<ck::bhalf_t, float, ck::bhalf_t, GNHWC, GKYXC, GNHWK, ck::Number<2>>,
     std::tuple<float, float, float, NHWGC, GKYXC, NHWGK, ck::Number<2>>,
     std::tuple<ck::half_t, ck::half_t, ck::half_t, NHWGC, GKYXC, NHWGK, ck::Number<2>>,
-    std::tuple<ck::bhalf_t, float, ck::bhalf_t, NHWGC, GKYXC, NHWGK, ck::Number<2>>>;
+    std::tuple<ck::bhalf_t, float, ck::bhalf_t, NHWGC, GKYXC, NHWGK, ck::Number<2>>,
+    std::tuple<ck::half_t, ck::half_t, ck::half_t, NGCHW, GKYXC, NGKHW, ck::Number<2>>>;
 using KernelTypes3d = ::testing::Types<
     std::tuple<float, float, float, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
     std::tuple<ck::half_t, ck::half_t, ck::half_t, GNDHWC, GKZYXC, GNDHWK, ck::Number<3>>,
@@ -148,7 +155,8 @@ using KernelTypes3d = ::testing::Types<
     std::tuple<float, float, float, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
     std::tuple<ck::half_t, ck::half_t, ck::half_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
     std::tuple<ck::bhalf_t, float, ck::bhalf_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
-    std::tuple<int8_t, int8_t, int8_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>>;
+    std::tuple<int8_t, int8_t, int8_t, NDHWGC, GKZYXC, NDHWGK, ck::Number<3>>,
+    std::tuple<ck::half_t, ck::half_t, ck::half_t, NGCDHW, GKZYXC, NGKDHW, ck::Number<3>>>;
 
 TYPED_TEST_SUITE(TestGroupedConvndBwdWeight1d, KernelTypes1d);
 TYPED_TEST_SUITE(TestGroupedConvndBwdWeight2d, KernelTypes2d);