Rushikesh main

backends/cadence/aot/functions_hifi.yaml

@@ -66,6 +66,56 @@
   kernels:
     - arg_meta: null
       kernel_name: torch::executor::embedding_out
+
+- op: eq.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::eq_scalar_out
+
+- op: eq.Tensor_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::eq_tensor_out
+
+- op: ge.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::ge_scalar_out
+
+- op: ge.Tensor_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::ge_tensor_out
+
+- op: gt.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::gt_scalar_out
+
+- op: gt.Tensor_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::gt_tensor_out   
+
+- op: le.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::le_scalar_out
+
+- op: le.Tensor_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::le_tensor_out  
+
+- op: lt.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::lt_scalar_out
+
+- op: lt.Tensor_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::lt_tensor_out
 
 - op: full.out
   kernels:
@@ -80,7 +130,17 @@
 - op: mean.out
   kernels:
     - arg_meta: null
-      kernel_name: impl::HiFi::mean_dim_out
+      kernel_name: impl::HiFi::mean_dim_out 
+
+- op: ne.Scalar_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::ne_scalar_out
+
+- op: ne.Tensor_out
+  kernels:
+    - arg_meta: null
+      kernel_name: impl::HiFi::ne_tensor_out      
 
 - op: minimum.out
   kernels:
@@ -90,7 +150,7 @@
 - op: mm.out
   kernels:
     - arg_meta: null
-      kernel_name: impl::HiFi::mm_out      
+      kernel_name: impl::HiFi::mm_out
 
 - op: mul.out
   kernels:
@@ -136,7 +196,7 @@
   kernels:
     - arg_meta: null
       kernel_name: impl::HiFi::where_out
-     
+
 # custom ops
 - func: cadence::quantize_per_tensor.out(Tensor input, float scale, int zero_point, int quant_min, int quant_max, ScalarType dtype, *, Tensor(a!) out) -> Tensor(a!)
   variants: function

backends/cadence/hifi/kernels/CMakeLists.txt

@@ -18,6 +18,7 @@ add_library(
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_elm_minimum_maximum_f32.c
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_elm_mul_f32_broadcast.c
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_elm_where_f32xf32_f32.c
+  ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_greater_lesser_equal_f32.c
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_reduce_32_32.c
   ${EXECUTORCH_ROOT}/backends/cadence/hifi/third-party/nnlib/xa_nn_transpose_32.c
 )

backends/cadence/hifi/kernels/kernels.h

@@ -32,10 +32,11 @@ extern "C" WORD32 xa_nn_elm_add_broadcast_4D_f32xf32_f32(
     const FLOAT32* __restrict__ p_inp2,
     const WORD32* const p_inp2_shape);
 
-extern "C" void xa_nn_elm_atan2_f32(FLOAT32 * z, 
-                                const FLOAT32 * y, 
-                                const FLOAT32 * x,  
-                                WORD32 N );   
+extern "C" void xa_nn_elm_atan2_f32(
+  FLOAT32 * z, 
+  const FLOAT32 * y, 
+  const FLOAT32 * x,  
+  WORD32 N );
 
 extern "C" WORD32 xa_nn_elm_clamp_f32xf32xf32_f32(
     FLOAT32* __restrict__ p_out,
@@ -70,13 +71,27 @@ extern "C" WORD32 xa_nn_elm_div_mode_f32xf32_f32(
     WORD32 mode);
 
 extern "C" WORD32 xa_nn_elm_div_mode_broadcast_4D_f32xf32_f32(
-    FLOAT32* __restrict__ p_out,
-    const WORD32* const p_out_shape,
-    const FLOAT32* __restrict__ p_inp1,
-    const WORD32* const p_inp1_shape,
-    const FLOAT32* __restrict__ p_inp2,
-    const WORD32* const p_inp2_shape,
+    FLOAT32 * __restrict__ p_out,
+    const WORD32 *const p_out_shape,
+    const FLOAT32 * __restrict__ p_inp1,
+    const WORD32 *const p_inp1_shape,
+    const FLOAT32 * __restrict__ p_inp2,
+    const WORD32 *const p_inp2_shape,
     WORD32 mode);
+
+extern "C" WORD32 xa_nn_elm_greater_lesser_equal_f32xf32_f32(WORD8 * __restrict__ p_out,
+    const FLOAT32 * __restrict__ p_inp1,
+    const FLOAT32 * __restrict__ p_inp2,
+    WORD32 num_elm,
+    WORD32 kernel_type);                                    
+
+extern "C" WORD32 xa_nn_elm_greater_lesser_equal_broadcast_4D_f32xf32_f32(WORD8 * __restrict__ p_out,
+    const WORD32 *const p_out_shape,
+    const FLOAT32 * __restrict__ p_inp1,
+    const WORD32 *const p_inp1_shape,
+    const FLOAT32 * __restrict__ p_inp2,
+    const WORD32 *const p_inp2_shape,
+    WORD32 kernel_type);
 
 extern "C" WORD32 xa_nn_elm_maximum_f32xf32_f32(
     FLOAT32* __restrict__ p_out,

backends/cadence/hifi/operators/CMakeLists.txt

@@ -25,12 +25,18 @@ set(_aten_ops__srcs
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_bmm.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_clamp.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_div.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_eq.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_full.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_ge.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_gt.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_le.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_lt.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_maximum.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_mean.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_minimum.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_mm.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_mul.cpp"
+    "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_ne.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_sigmoid.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_sub.cpp"
     "${EXECUTORCH_ROOT}/backends/cadence/hifi/operators/op_tanh.cpp"

backends/cadence/hifi/operators/op_eq.cpp

@@ -0,0 +1,180 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ * All rights reserved.
+ *
+ * This source code is licensed under the BSD-style license found in the
+ * LICENSE file in the root directory of this source tree.
+ */
+
+#include <executorch/kernels/portable/cpu/scalar_utils.h>
+#include <executorch/kernels/portable/cpu/util/broadcast_util.h>
+#include <executorch/kernels/portable/cpu/util/functional_util.h>
+#include <executorch/runtime/kernel/kernel_includes.h>
+#include <executorch/runtime/platform/assert.h>
+
+#include <executorch/backends/cadence/hifi/kernels/kernels.h>
+
+using exec_aten::Scalar;
+using exec_aten::ScalarType;
+using exec_aten::Tensor;
+using executorch::aten::RuntimeContext;
+using executorch::runtime::CppTypeToScalarType;
+using torch::executor::Error;
+
+namespace impl {
+namespace HiFi {
+namespace native {
+
+Tensor& eq_tensor_out(
+    RuntimeContext& ctx,
+    const Tensor& a,
+    const Tensor& b,
+    Tensor& out) {
+  ET_KERNEL_CHECK(
+      ctx,
+      resize_to_broadcast_target_size(a, b, out) == Error::Ok,
+      InvalidArgument,
+      out);
+
+  ScalarType a_type = a.scalar_type();
+  ScalarType b_type = b.scalar_type();
+  ScalarType out_type = out.scalar_type();
+
+  constexpr auto name = "eq.Tensor_out";
+  constexpr int kNnlibMaxDim = 4; /*fallback if broadcast and dim > 4 */
+
+  int a_dim = a.dim(), b_dim = b.dim(), out_dim = out.dim();
+  bool optimized = 1;
+  /*find broadcast*/
+  const bool a_is_broadcasted = !out.sizes().equals(a.sizes());
+  const bool b_is_broadcasted = !out.sizes().equals(b.sizes());
+  const bool broadcast = (a_is_broadcasted || b_is_broadcasted);
+  int max_dim = a.dim() > b.dim() ? a.dim() : b.dim();
+  max_dim = out.dim() > max_dim ? out.dim() : max_dim;
+
+  if (out_type != ScalarType::Float)
+    optimized = 0;
+
+  if ((a_dim == 0) || (b_dim == 0))
+    optimized = 0;
+
+  if ((broadcast == 1) && (max_dim > kNnlibMaxDim))
+    optimized = 0;
+
+  if (optimized) {
+    int8_t* __restrict__ p_out =
+        (int8_t* __restrict__)out.mutable_data_ptr<int8_t>();
+    const float* __restrict__ p_inp1 =
+        (const float* __restrict__)a.const_data_ptr<float>();
+    const float* __restrict__ p_inp2 =
+        (const float* __restrict__)b.const_data_ptr<float>();
+
+    if (broadcast) {
+      int out_shape[kNnlibMaxDim];
+      int inp1_shape[kNnlibMaxDim];
+      int inp2_shape[kNnlibMaxDim];
+
+      for (int i = 0; i < kNnlibMaxDim; i++) {
+        inp1_shape[i] = 1;
+        inp2_shape[i] = 1;
+        out_shape[i] = 1;
+      }
+
+      int off_o = kNnlibMaxDim - out.dim();
+      int off_a = kNnlibMaxDim - a.dim();
+      int off_b = kNnlibMaxDim - b.dim();
+
+      for (int i = 0; i < out.dim(); i++)
+        out_shape[i + off_o] = out.size(i);
+      for (int i = 0; i < a.dim(); i++)
+        inp1_shape[i + off_a] = a.size(i);
+      for (int i = 0; i < b.dim(); i++)
+        inp2_shape[i + off_b] = b.size(i);
+
+      xa_nn_elm_greater_lesser_equal_broadcast_4D_f32xf32_f32(
+          p_out, out_shape, p_inp1, inp1_shape, p_inp2, inp2_shape, 4);
+    } else {
+      int num_elm = out.numel();
+
+      xa_nn_elm_greater_lesser_equal_f32xf32_f32(
+          p_out, p_inp1, p_inp2, num_elm, 4);
+    }
+
+    return out;
+  }
+
+  ET_SWITCH_REAL_TYPES_AND(Bool, a_type, ctx, name, CTYPE_A, [&]() {
+    ET_SWITCH_REAL_TYPES_AND(Bool, b_type, ctx, name, CTYPE_B, [&]() {
+      using CTYPE_IN =
+          typename torch::executor::promote_types<CTYPE_A, CTYPE_B>::type;
+      ET_DCHECK(
+          CppTypeToScalarType<CTYPE_IN>::value == promoteTypes(a_type, b_type));
+      ET_SWITCH_REAL_TYPES_AND(Bool, out_type, ctx, name, CTYPE_OUT, [&]() {
+        torch::executor::
+            apply_binary_elementwise_fn<CTYPE_A, CTYPE_B, CTYPE_OUT>(
+                [](const CTYPE_A val_a, const CTYPE_B val_b) {
+                  CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a);
+                  CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b);
+                  bool value = a_casted == b_casted;
+                  return static_cast<CTYPE_OUT>(value);
+                },
+                a,
+                b,
+                out);
+      });
+    });
+  });
+
+  return out;
+}
+
+Tensor& eq_scalar_out(
+    RuntimeContext& ctx,
+    const Tensor& a,
+    const Scalar& b,
+    Tensor& out) {
+  (void)ctx;
+
+  // Resize for dynamic shape
+  ET_KERNEL_CHECK_MSG(
+      ctx,
+      resize_tensor(out, a.sizes()) == Error::Ok,
+      InvalidArgument,
+      out,
+      "Failed to resize output tensor.");
+
+  constexpr auto name = "eq.Scalar_out";
+
+  ScalarType a_type = a.scalar_type();
+  ScalarType b_type = torch::executor::native::utils::get_scalar_dtype(b);
+  ScalarType out_type = out.scalar_type();
+
+  ET_SWITCH_REAL_TYPES_AND(Bool, a_type, ctx, name, CTYPE_A, [&]() {
+    ET_SWITCH_SCALAR_OBJ_TYPES(b_type, ctx, name, CTYPE_B, [&]() {
+      using CTYPE_IN =
+          typename torch::executor::promote_types<CTYPE_A, CTYPE_B>::type;
+      ET_DCHECK(
+          CppTypeToScalarType<CTYPE_IN>::value == promoteTypes(a_type, b_type));
+      ET_SWITCH_REAL_TYPES_AND(Bool, out_type, ctx, name, CTYPE_OUT, [&]() {
+        CTYPE_B val_b = 0;
+        torch::executor::native::utils::extract_scalar(b, &val_b);
+        torch::executor::apply_unary_map_fn(
+            [val_b](const CTYPE_A val_a) {
+              CTYPE_IN a_casted = static_cast<CTYPE_IN>(val_a);
+              CTYPE_IN b_casted = static_cast<CTYPE_IN>(val_b);
+              bool value = a_casted == b_casted;
+              return static_cast<CTYPE_OUT>(value);
+            },
+            a.const_data_ptr<CTYPE_A>(),
+            out.mutable_data_ptr<CTYPE_OUT>(),
+            out.numel());
+      });
+    });
+  });
+
+  return out;
+}
+
+} // namespace native
+} // namespace HiFi
+} // namespace impl