add skip and bias on groupnorm triton implementation

onnxruntime/contrib_ops/rocm/diffusion/group_norm_triton.cuh

@@ -46,8 +46,6 @@ auto GetTritonGroupNormNHWCTypeStringAndOps() {
     auto block_size = metadata->constants.at("BLOCK_SIZE");
     auto hw_size = metadata->constants.at("HW_SIZE");
     auto impl = [i, block_size, hw_size](const GroupNormNHWCTunableParams<T>* params) -> Status {
-      TUNABLE_OP_RETURN_UNSUPPORTED_ARGUMENT_IF((params->skip != nullptr || params->bias != nullptr),
-                                                "Skip is not supported");
       TUNABLE_OP_RETURN_UNSUPPORTED_ARGUMENT_IF(
           params->channels_per_group > block_size || params->channels_per_group * 2 <= block_size,
           "Arg block_size (", block_size, ") is not the next power of 2 of channels_per_group (",
@@ -61,23 +59,36 @@ auto GetTritonGroupNormNHWCTypeStringAndOps() {
       }
       // Construct args for launch kernel
       struct {
-        void* X;
-        void* Y;
+        const void* src;
+        const void* skip;
+        const void* bias;
+        void* out;
+        void* add_out;
         const void* gamma;
         const void* beta;
         int hw;
         int c;
         int c_per_group;
         float eps;
+        bool has_skip;
+        bool has_bias;
+        bool broadcast_skip;
       } args = {
-          (void*)params->src,
+          (const void*)params->src,
+          (const void*)params->skip,
+          (const void*)params->bias,
           (void*)params->dst,
+          (void*)params->add_out,
           (const void*)params->gamma,
           (const void*)params->beta,
           params->hw,
           params->c,
           params->channels_per_group,
-          params->epsilon};
+          params->epsilon,
+          params->skip != nullptr,
+          params->bias != nullptr,
+          params->broadcast_skip,
+      };
 
       // Grid dim is (batch_count, groups, 1)
       return LaunchTritonKernel(params->StreamHandle(), i, params->n, params->groups, 1, &args, sizeof(args));

onnxruntime/contrib_ops/rocm/diffusion/group_norm_triton.py

@@ -12,13 +12,19 @@
 @triton.jit
 def group_norm_kernel(
     input_ptr,
+    skip_ptr,
+    bias_ptr,
     output_ptr,
+    add_out_ptr,
     gamma_ptr,
     beta_ptr,
     img_size,
     c,
     c_per_group,
     eps,
+    has_skip,
+    has_bias,
+    broadcast_skip,
     BLOCK_SIZE: tl.constexpr,
     HW_SIZE: tl.constexpr,
     ACTIVATION_SWISH: tl.constexpr,
@@ -31,19 +37,44 @@ def group_norm_kernel(
     gamma_ptr += row_y * c_per_group
     beta_ptr += row_y * c_per_group
 
+
+    add_out_ptr += row_x * stride + row_y * c_per_group
+
+
+
     cols = tl.arange(0, BLOCK_SIZE)
     hw = tl.arange(0, HW_SIZE)
     offsets = hw[:, None] * c + cols[None, :]
     mask = (cols < c_per_group)[None, :]
 
+    bias = tl.zeros([BLOCK_SIZE], dtype=tl.float32)
+    if has_skip:
+        if broadcast_skip:
+            broadcast_skip_ptr = skip_ptr + row_x * c + row_y * c_per_group
+            bias += tl.load(broadcast_skip_ptr + cols, mask=cols < c_per_group, other=0.0).to(tl.float32)
+        else:
+            skip_ptr += row_x * stride + row_y * c_per_group
+    if has_bias:
+        bias_ptr += row_y * c_per_group
+        bias += tl.load(bias_ptr + cols, mask=cols < c_per_group, other=0.0).to(tl.float32)
+
     # Calculate mean and variance
     _sum = tl.zeros([HW_SIZE, BLOCK_SIZE], dtype=tl.float32)
     _square_sum = tl.zeros([HW_SIZE, BLOCK_SIZE], dtype=tl.float32)
     for i in range(tl.cdiv(img_size, HW_SIZE)):
         x_ptr = input_ptr + i * HW_SIZE * c
         a = tl.load(x_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
+        if has_skip and not broadcast_skip:
+            s_ptr = skip_ptr + i * HW_SIZE * c
+            s = tl.load(s_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
+            a += s
+        if has_bias or broadcast_skip:
+            a += bias
         _sum += a
         _square_sum += a * a
+        if has_skip:
+            add_y_ptr = add_out_ptr + i * HW_SIZE * c
+            tl.store(add_y_ptr + offsets, a, mask=mask)
 
     # Set axis=None (or leave it unspecified) to reduce all axes.
     # TODO: In older Triton we have to reduce an axis at a time, but in our case
@@ -57,9 +88,13 @@ def group_norm_kernel(
     gamma = tl.load(gamma_ptr + cols, mask=cols < c_per_group).to(tl.float32)
     beta = tl.load(beta_ptr + cols, mask=cols < c_per_group).to(tl.float32)
     for i in range(tl.cdiv(img_size, HW_SIZE)):
-        x_ptr = input_ptr + i * HW_SIZE * c
         y_ptr = output_ptr + i * HW_SIZE * c
-        x = tl.load(x_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
+        if has_skip:
+            add_y_ptr = add_out_ptr + i * HW_SIZE * c
+            x = tl.load(add_y_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
+        else:
+            x_ptr = input_ptr + i * HW_SIZE * c
+            x = tl.load(x_ptr + offsets, mask=mask, other=0.0).to(tl.float32)
         x_hat = (x - group_mean) * rstd
         y = x_hat * gamma + beta
         if ACTIVATION_SWISH:
@@ -77,7 +112,7 @@ def group_norm_kernel(
 hw_sizes = [8, 16, 32, 64, 128, 256]
 warps = [1, 2, 4, 8, 16]
 name_pattern = "GroupNormTriton_{}_{}_b{}_hw{}_w{}"
-sig_pattern = "*{},*{},*fp32,*fp32,i32,i32,i32,fp32"
+sig_pattern = "*{},*{},*{},*{},*{},*fp32,*fp32,i32,i32,i32,fp32,i1,i1,i1"
 group_pattern = "GroupNormTriton_{}_{}"
 
 
@@ -88,7 +123,7 @@ def get_function_table():
         swish_suffix = "Swish" if swish else "Pass"
         name = name_pattern.format(swish_suffix, dtype, b, hw_size, warp)
         group = group_pattern.format(swish_suffix, dtype)
-        sig = sig_pattern.format(dtype, dtype)
+        sig = sig_pattern.format(dtype, dtype, dtype, dtype, dtype)
         kwargs = {
             "num_warps": warp,
             "constants": {"BLOCK_SIZE": b, "HW_SIZE": hw_size, "ACTIVATION_SWISH": int(swish)},

onnxruntime/python/tools/kernel_explorer/kernels/groupnorm_test.py

@@ -80,7 +80,13 @@ def run_group_norm(
     )
     use_swish = swish
     broadcast_skip = False
-    channels_per_block = 0  # Compute in params initialization
+    if(has_skip):
+        skip_x_shape = skip_x.shape
+        b2 = len(skip_x_shape) == 2 and skip_x_shape[0] == batch_size and skip_x_shape[1] == num_channels
+        b4 = len(skip_x_shape) == 4 and skip_x_shape[0] == batch_size and skip_x_shape[1] == 1 and skip_x_shape[2] == 1 and skip_x_shape[3] == num_channels
+        if b2 or b4:
+            broadcast_skip = True
+    channels_per_block = 0 # Compute in params initialization
 
     input_d = ke.DeviceArray(input_x.astype(dtype))
     skip_d = ke.DeviceArray(skip_x.astype(dtype))