LHS broadcasting addition for dynamic fusion

* Binary elementwise operator now can have broadcasting in either
  X dimension, Y+Z dimension, or both, in either LHS or RHS
  operand.
* Fix bug in CL code to support batching.

Resolves: COMPMID-5704
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: I51b04986d30861f255ca9f754adffa0e6c85a26b
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8898
Reviewed-by: SiCong Li <sicong.li@arm.com>
Reviewed-by: Ramy Elgammal <ramy.elgammal@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Dynamic-Fusion: Ramy Elgammal <ramy.elgammal@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>

2 files changed, 60 insertions, 94 deletions

diff --git a/src/dynamic_fusion/sketch/gpu/components/cl/ClComponentElementwiseBinary.cpp b/src/dynamic_fusion/sketch/gpu/components/cl/ClComponentElementwiseBinary.cpp
index a17d835ac6..9b006b13ce 100644
--- a/src/dynamic_fusion/sketch/gpu/components/cl/ClComponentElementwiseBinary.cpp
+++ b/src/dynamic_fusion/sketch/gpu/components/cl/ClComponentElementwiseBinary.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -66,8 +66,30 @@ Status ClComponentElementwiseBinary::validate(const ArgumentPack<ITensorInfo> &t
     const TensorShape out_shape = TensorShape::broadcast_shape(lhs->tensor_shape(), rhs->tensor_shape());
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0, "Inputs are not broadcast compatible");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(detail::have_different_dimensions(out_shape, dst->tensor_shape(), 0), "Wrong shape for dst.");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((!rhs_in_place && !lhs_in_place) && detail::have_different_dimensions(lhs->tensor_shape(), dst->tensor_shape(), 0),
-                                    "Only the rhs operand can be broadcast to match the accumulator's (lhs) shape");
+
+    const auto &lhs_shape = lhs->tensor_shape();
+    const auto &rhs_shape = rhs->tensor_shape();
+    const auto &dst_shape = dst->tensor_shape();
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+        detail::have_different_dimensions(lhs_shape, dst_shape, 0) && detail::have_different_dimensions(rhs_shape, dst_shape, 0),
+        "Only LHS or RHS can be broadcasting, not both.");
+
+    // Dimension Y and Z are collapsed together in the current kernel implementation,
+    // hence they cannot be independently broadcast or non-broadcast.
+    // See: ClTemplateElementwiseBinary::get_window
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+        (lhs_shape[1] != dst_shape[1] || rhs_shape[1] != dst_shape[1]) != (lhs_shape[2] != dst_shape[2] || rhs_shape[2] != dst_shape[2]),
+        "Dimension Y and Z must both be either broadcast or non-broadcast.");
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+        detail::have_different_dimensions(lhs_shape, dst_shape, 3),
+        "LHS broadcast in dimension 3 or higher is not supported.");
+
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+        detail::have_different_dimensions(rhs_shape, dst_shape, 3),
+        "RHS broadcast in dimension 3 or higher is not supported.");
+
     // Matching data type
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, rhs);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(lhs, dst);
@@ -76,10 +98,6 @@ Status ClComponentElementwiseBinary::validate(const ArgumentPack<ITensorInfo> &t
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, rhs);
     ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(lhs, dst);
 
-    // Batching case not supported yet
-    const size_t idx_batch = get_data_layout_dimension_index(lhs->data_layout(), DataLayoutDimension::BATCHES);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((lhs->tensor_shape()[idx_batch] != 1) || (rhs->tensor_shape()[idx_batch] != 1) || (dst->tensor_shape()[idx_batch] != 1), "Batching case not supported yet");
-
     // All tensor infos are initialized
     ARM_COMPUTE_RETURN_ERROR_ON(lhs->tensor_shape().total_size() == 0);
     ARM_COMPUTE_RETURN_ERROR_ON(rhs->tensor_shape().total_size() == 0);
diff --git a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp
index df8deee44f..01017ed909 100644
--- a/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp
+++ b/src/dynamic_fusion/sketch/gpu/template_writer/cl/ClTemplateElementwiseBinary.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2022 Arm Limited.
+ * Copyright (c) 2022-2023 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -61,9 +61,7 @@ std::string ClTemplateElementwiseBinary::get_name() const
 
 std::string ClTemplateElementwiseBinary::get_component_code(const ComponentGroup &comp_group) const
 {
-    ARM_COMPUTE_UNUSED(comp_group);
     std::string code;
-    const bool  is_broadcast = _lhs->tensor_shape() != _rhs->tensor_shape();
     const bool  is_root      = (comp_group.get_root_component()->id() == this->id());
     const bool  is_lhs_input = comp_group.is_input_tensor(_lhs);
     const bool  is_rhs_input = comp_group.is_input_tensor(_rhs);
@@ -85,7 +83,7 @@ R"_(
     {
         code +=
 R"_(
-    TILE({{DATA_TYPE}}, M0, N0, {{lhs}});
+    TILE({{DATA_TYPE}}, {{lhs_m0}}, N0, {{lhs}});
 )_";
     }
 
@@ -93,7 +91,7 @@ R"_(
     {
         code +=
 R"_(
-    TILE({{DATA_TYPE}}, M0, N0, {{rhs}});
+    TILE({{DATA_TYPE}}, {{rhs_m0}}, N0, {{rhs}});
 )_";
     }
 
@@ -106,7 +104,7 @@ R"_(
     {
         code +=
 R"_(
-        {{lhs}}_offset_first_element_in_bytes += g_ind_2 * {{lhs}}_stride_z;
+        {{lhs}}_offset_first_element_in_bytes += g_ind_2 * {{lhs}}_stride_w;
         T_LOAD({{DATA_TYPE}}, {{lhs_m0}}, {{lhs_n0}}, BUFFER, {{lhs}}, {{lhs_start_ind_0}}, {{lhs_start_ind_1}}, 1, {{lhs}}_stride_y, {{lhs}});
 )_";
     }
@@ -115,25 +113,15 @@ R"_(
     {
         code +=
 R"_(
-        {{rhs}}_offset_first_element_in_bytes += g_ind_2 * {{rhs}}_stride_z;
+        {{rhs}}_offset_first_element_in_bytes += g_ind_2 * {{rhs}}_stride_w;
         T_LOAD({{DATA_TYPE}}, {{rhs_m0}}, {{rhs_n0}}, BUFFER, {{rhs}}, {{rhs_start_ind_0}}, {{rhs_start_ind_1}}, 1, {{rhs}}_stride_y, {{rhs}});
 )_";
     }
 
-    if(is_broadcast)
-    {
-        code +=
-            R"_(
-        T_ELTWISE_BROADCAST_{{ELTWISE_OP}}_X({{DATA_TYPE}}, M0, N0, {{lhs}}, {{rhs}}, {{dst}});
-)_";
-    }
-    else
-    {
-        code +=
-            R"_(
-        T_ELTWISE_{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, {{lhs}}, {{rhs}}, {{dst}});
+    code +=
+R"_(
+        T_ELTWISE_{{BROADCAST_OP}}{{ELTWISE_OP}}({{DATA_TYPE}}, M0, N0, {{lhs}}, {{rhs}}, {{dst}});
 )_";
-    }
 
     if(is_root)
     {
@@ -210,73 +198,33 @@ TagLUT ClTemplateElementwiseBinary::get_tag_lut(const GpuKernelVariableTable &vt
 
     // Set broadcast parameters
     // PRE: All tensors are broadcast-compatible
-    if(_lhs->tensor_shape() != _dst->tensor_shape())
-    {
-        const auto is_broadcast_x = _lhs->dimension(0) == 1U && _dst->dimension(0) != 1U;
-        const auto is_broadcast_y = _lhs->dimension(1) == 1U && _dst->dimension(1) != 1U;
-        const auto is_broadcast_z = _lhs->dimension(2) == 1U && _dst->dimension(2) != 1U;
-
-        // Note that n0 maps to input tensor dimension 0, m0 maps to input dimensions 1 and 2 because of our collapse strategy
-        if(is_broadcast_x && is_broadcast_y && is_broadcast_z) // Broadcast in X, Y, Z: collapsed lhs win [M0xN0] = [1x1]
-        {
-            lut["lhs_m0"]          = "1";
-            lut["lhs_n0"]          = "1";
-            lut["lhs_start_ind_1"] = "0";
-            lut["lhs_start_ind_0"] = "0";
-        }
-        else if(is_broadcast_y && is_broadcast_z) // Broadcast in Y and Z: collapsed lhs win [M0xN0] = [1xN]
-        {
-            lut["lhs_m0"]          = "1";
-            lut["lhs_n0"]          = "N0";
-            lut["lhs_start_ind_1"] = "0";
-            lut["lhs_start_ind_0"] = "g_ind_0";
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR("Only support lhs broadcasting in all X, Y, Z dimensions, or just in Y and Z dimensions");
-        }
-    }
-    else
-    {
-        lut["lhs_m0"]          = "M0";
-        lut["lhs_n0"]          = "N0";
-        lut["lhs_start_ind_1"] = "g_ind_1";
-        lut["lhs_start_ind_0"] = "g_ind_0";
-    }
-
-    if(_rhs->tensor_shape() != _dst->tensor_shape())
-    {
-        const auto is_broadcast_x = _rhs->dimension(0) == 1U && _dst->dimension(0) != 1U;
-        const auto is_broadcast_y = _rhs->dimension(1) == 1U && _dst->dimension(1) != 1U;
-        const auto is_broadcast_z = _rhs->dimension(2) == 1U && _dst->dimension(2) != 1U;
-
-        // Note that n0 maps to input tensor dimension 0, m0 maps to input dimensions 1 and 2 because of our collapse strategy
-        if(is_broadcast_x && is_broadcast_y && is_broadcast_z) // Broadcast in X, Y, Z: collapsed rhs win [M0xN0] = [1x1]
-        {
-            lut["rhs_m0"]          = "1";
-            lut["rhs_n0"]          = "1";
-            lut["rhs_start_ind_1"] = "0";
-            lut["rhs_start_ind_0"] = "0";
-        }
-        else if(is_broadcast_y && is_broadcast_z) // Broadcast in Y and Z: collapsed rhs win [M0xN0] = [1xN]
-        {
-            lut["rhs_m0"]          = "1";
-            lut["rhs_n0"]          = "N0";
-            lut["rhs_start_ind_1"] = "0";
-            lut["rhs_start_ind_0"] = "g_ind_0";
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR("Only support rhs broadcasting in all X, Y, Z dimensions, or just in Y and Z dimensions");
-        }
-    }
-    else
-    {
-        lut["rhs_m0"]          = "M0";
-        lut["rhs_n0"]          = "N0";
-        lut["rhs_start_ind_1"] = "g_ind_1";
-        lut["rhs_start_ind_0"] = "g_ind_0";
-    }
+    const auto &lhs_dims = _lhs->tensor_shape();
+    const auto &rhs_dims = _rhs->tensor_shape();
+    const auto &dst_dims = _dst->tensor_shape();
+
+    const auto lhs_broadcast_x = dst_dims[0] != 1 && lhs_dims[0] == 1;
+    const auto rhs_broadcast_x = dst_dims[0] != 1 && rhs_dims[0] == 1;
+    const auto lhs_broadcast_y = dst_dims[1] != 1 && lhs_dims[1] == 1;
+    const auto rhs_broadcast_y = dst_dims[1] != 1 && rhs_dims[1] == 1;
+    const auto lhs_broadcast_z = dst_dims[2] != 1 && lhs_dims[2] == 1;
+    const auto rhs_broadcast_z = dst_dims[2] != 1 && rhs_dims[2] == 1;
+
+    const auto lhs_broadcast_yz = lhs_broadcast_y && lhs_broadcast_z;
+    const auto rhs_broadcast_yz = rhs_broadcast_y && rhs_broadcast_z;
+
+    lut["lhs_n0"]          = (lhs_broadcast_x) ? "1" : "N0";
+    lut["lhs_start_ind_0"] = (lhs_broadcast_x) ? "0" : "g_ind_0";
+    lut["rhs_n0"]          = (rhs_broadcast_x) ? "1" : "N0";
+    lut["rhs_start_ind_0"] = (rhs_broadcast_x) ? "0" : "g_ind_0";
+
+    lut["lhs_m0"]          = (lhs_broadcast_yz) ? "1" : "M0";
+    lut["lhs_start_ind_1"] = (lhs_broadcast_yz) ? "0" : "g_ind_1";
+    lut["rhs_m0"]          = (rhs_broadcast_yz) ? "1" : "M0";
+    lut["rhs_start_ind_1"] = (rhs_broadcast_yz) ? "0" : "g_ind_1";
+
+    lut["BROADCAST_OP"]    = (lhs_broadcast_yz) ? "BROADCAST_LHS_X_" :
+                             (rhs_broadcast_yz) ? "BROADCAST_RHS_X_" :
+                                                  "";
 
     return lut;
 }