Enable stable diffusion unet

src/targets/gpu/fuse_ops.cpp

@@ -878,7 +878,8 @@ void fuse_ops::apply(module& m) const
                         find_concat_pointwise{},
                         find_gemm_pointwise{},
                         find_contiguous_tranpose_gemm{},
-                        find_contiguous_tranpose_precompile{},
+                        // Commented out as workaround for reshape error when running Unet
+                        // find_contiguous_tranpose_precompile{},
                         find_commutative_broadcast{});
     match::find_matches(m, find_contiguous{});
 }

src/targets/gpu/jit/ck_gemm_softmax_gemm.cpp

@@ -155,13 +155,9 @@ struct ck_gemm_softmax_gemm_compiler : compiler<ck_gemm_softmax_gemm_compiler>
 {
     static std::string get_layout(const shape& s)
     {
-        if(not s.transposed())
-            return "ck::tensor_layout::gemm::RowMajor";
-
-        auto lens = s.lens();
-        return lens[lens.size() - 1] > lens[lens.size() - 2]
-                   ? "ck::tensor_layout::gemm::ColumnMajor"
-                   : "ck::tensor_layout::gemm::RowMajor";
+        return s.strides().back() == 1
+                   ? "ck::tensor_layout::gemm::RowMajor"
+                   : "ck::tensor_layout::gemm::ColumnMajor";
     }
 
     static std::string get_type(const shape& s)

src/targets/gpu/kernels/include/migraphx/kernels/index.hpp

@@ -182,7 +182,7 @@ struct index
             }
             else
             {
-                static_assert(max_stride_iterations(n, stride) < 64);
+                static_assert(max_stride_iterations(n, stride) < 128);
                 sequence(max_stride_iterations(n, stride), [&](auto... ks) {
                     fold([&](auto d, auto k) {
                         auto i = start + stride * k;

tools/tune_models.py

@@ -40,9 +40,12 @@ def tune_models(models, batch_sizes, seq_len, n, existing):
     json_file = "ck_tuning_{}.json".format(time_stamp)
     for model in models:
         for batch in batch_sizes:
+            params = "--input-dim @sample {} 4 64 64 @timestep 1 @encoder_hidden_states {} 64 1024 --fp16 ".format(batch, batch)
+            if "bert" in model:
+                params = "--fill1 input_ids --input-dim @input_ids {} {} ".format(batch, seq_len)
             out = subprocess.run(
-                'MIGRAPHX_LOG_CK_GEMM=1 ../build/bin/driver run {} -g --fill1 input_ids --input-dim @input_ids {} {}  | grep \'ck_gemm.*: \[{{\' | sort -u >> {}'
-                .format(model, batch, seq_len, log_file),
+                'MIGRAPHX_LOG_CK_GEMM=1 ../build/bin/driver run {} -g {} | grep \'ck_gemm.*: \[{{\' | sort -u >> {}'
+                .format(model, params, log_file),
                 capture_output=True,
                 check=True,
                 shell=True)