Sync from upstream TF.

Author: TFLM-bot

tensorflow/lite/core/api/tensor_utils.cc

@@ -33,8 +33,8 @@ TfLiteStatus ResetVariableTensor(TfLiteTensor* tensor) {
   }
   // TODO(b/139446230): Provide a platform header to better handle these
   // specific scenarios.
-#if __ANDROID__ || defined(__x86_64__) || defined(__i386__) || \
-    defined(__i386) || defined(__x86__) || defined(__X86__) || \
+#if defined(__ANDROID__) || defined(__x86_64__) || defined(__i386__) || \
+    defined(__i386) || defined(__x86__) || defined(__X86__) ||          \
     defined(_X86_) || defined(_M_IX86) || defined(_M_X64)
   memset(tensor->data.raw, value, tensor->bytes);
 #else

tensorflow/lite/core/c/common.cc

@@ -113,14 +113,25 @@ TfLiteQuantization TfLiteQuantizationClone(const TfLiteQuantization& src) {
     case kTfLiteAffineQuantization: {
       dst.params = calloc(1, sizeof(TfLiteAffineQuantization));
       const TfLiteAffineQuantization* const src_params =
-          (TfLiteAffineQuantization*)(src.params);
+          reinterpret_cast<TfLiteAffineQuantization*>(src.params);
       TfLiteAffineQuantization* const dst_params =
-          (TfLiteAffineQuantization*)(dst.params);
+          reinterpret_cast<TfLiteAffineQuantization*>(dst.params);
       dst_params->quantized_dimension = src_params->quantized_dimension;
       dst_params->scale = TfLiteFloatArrayCopy(src_params->scale);
       dst_params->zero_point = TfLiteIntArrayCopy(src_params->zero_point);
       break;
     }
+    case kTfLiteBlockwiseQuantization: {
+      dst.params = calloc(1, sizeof(TfLiteBlockwiseQuantization));
+      const TfLiteBlockwiseQuantization* const src_params =
+          (TfLiteBlockwiseQuantization*)(src.params);
+      TfLiteBlockwiseQuantization* const dst_params =
+          (TfLiteBlockwiseQuantization*)(dst.params);
+      dst_params->blocksize = src_params->blocksize;
+      dst_params->scale = src_params->scale;
+      dst_params->zero_point = src_params->zero_point;
+      break;
+    }
   }
   return dst;
 }
@@ -225,7 +236,7 @@ void TfLiteTensorDataFree(TfLiteTensor* t) {
 void TfLiteQuantizationFree(TfLiteQuantization* quantization) {
   if (quantization->type == kTfLiteAffineQuantization) {
     TfLiteAffineQuantization* q_params =
-        (TfLiteAffineQuantization*)(quantization->params);
+        reinterpret_cast<TfLiteAffineQuantization*>(quantization->params);
     if (q_params->scale) {
       TfLiteFloatArrayFree(q_params->scale);
       q_params->scale = nullptr;

tensorflow/lite/core/c/common.h

@@ -328,6 +328,8 @@ typedef enum TfLiteQuantizationType : int {
   /// Affine quantization (with support for per-channel quantization).
   /// Corresponds to TfLiteAffineQuantization.
   kTfLiteAffineQuantization = 1,
+  /// Blockwise quantization.
+  kTfLiteBlockwiseQuantization = 2,
 } TfLiteQuantizationType;
 
 /// Structure specifying the quantization used by the tensor, if-any.
@@ -353,6 +355,20 @@ typedef struct TfLiteAffineQuantization {
   int32_t quantized_dimension;
 } TfLiteAffineQuantization;
 
+/// Parameters for blockwise quantization across the output channels dimension.
+/// For a particular value in quantized_dimension, quantized values can be
+/// converted back to float using:
+///     `real_value = scale * (quantized_value - zero_point)`
+typedef struct TfLiteBlockwiseQuantization {
+  // Index of the tensor containing the scales.
+  int32_t scale;
+  // Index of the tensor containing the zero points.
+  int32_t zero_point;
+  // Quantization blocksize.
+  int32_t blocksize;
+  int32_t quantized_dimension;
+} TfLiteBlockwiseQuantization;
+
 /// A union of pointers that points to memory for a given tensor.
 ///
 /// Do not access these members directly, if possible, use

tensorflow/lite/tools/flatbuffer_utils.py

@@ -21,7 +21,6 @@
 """
 
 import copy
-import functools
 import random
 import re
 import struct
@@ -490,12 +489,7 @@ def get_options_as(
   ):
     raise err
 
-  @functools.singledispatch
-  def _get_opts(unused_op):
-    return None
-
-  @_get_opts.register
-  def _(op: schema_fb.Operator):
+  if isinstance(op, schema_fb.Operator):
     if not is_opt_1_type:
       enum_val = getattr(schema_fb.BuiltinOptions2, base_type_name)
       opts_creator = schema_fb.BuiltinOptions2Creator
@@ -510,8 +504,7 @@ def _(op: schema_fb.Operator):
       return None
     return opts_creator(enum_val, raw_ops)
 
-  @_get_opts.register
-  def _(op: schema_fb.OperatorT):
+  elif isinstance(op, schema_fb.OperatorT):
     if is_opt_1_type:
       raw_ops_t = op.builtinOptions
     else:
@@ -520,4 +513,5 @@ def _(op: schema_fb.OperatorT):
       return None
     return raw_ops_t
 
-  return _get_opts(op)
+  else:
+    return None