Merge c4884ca into f3f1150

compiler/noirc_evaluator/src/ssa/acir_gen/mod.rs

@@ -957,13 +957,39 @@ impl<'a> Context<'a> {
             return Ok(());
         }
 
-        let (new_index, new_value) =
-            self.convert_array_operation_inputs(array, dfg, index, store_value)?;
+        // Get an offset such that the type of the array at the offset is the same as the type at the 'index'
+        // If we find one, we will use it when computing the index under the enable_side_effect predicate
+        // If not, array_get(..) will use a fallback costing one multiplication in the worst case.
+        // cf. https://github.com/noir-lang/noir/pull/4971
+        let array_id = dfg.resolve(array);
+        let array_typ = dfg.type_of_value(array_id);
+        // For simplicity we compute the offset only for simple arrays
+        let is_simple_array = dfg.instruction_results(instruction).len() == 1
+            && can_omit_element_sizes_array(&array_typ);
+        let offset = if is_simple_array {
+            let result_type = dfg.type_of_value(dfg.instruction_results(instruction)[0]);
+            match array_typ {
+                Type::Array(item_type, _) | Type::Slice(item_type) => item_type
+                    .iter()
+                    .enumerate()
+                    .find_map(|(index, typ)| (result_type == *typ).then_some(index)),
+                _ => None,
+            }
+        } else {
+            None
+        };
+        let (new_index, new_value) = self.convert_array_operation_inputs(
+            array,
+            dfg,
+            index,
+            store_value,
+            offset.unwrap_or_default(),
+        )?;
 
         if let Some(new_value) = new_value {
             self.array_set(instruction, new_index, new_value, dfg, mutable_array_set)?;
         } else {
-            self.array_get(instruction, array, new_index, dfg)?;
+            self.array_get(instruction, array, new_index, dfg, offset.is_none())?;
         }
 
         Ok(())
@@ -1053,7 +1079,7 @@ impl<'a> Context<'a> {
     /// - new_index is the index of the array. ACIR memory operations work with a flat memory, so we fully flattened the specified index
     ///     in case we have a nested array. The index for SSA array operations only represents the flattened index of the current array.
     ///     Thus internal array element type sizes need to be computed to accurately transform the index.
-    /// - predicate_index is 0, or the index if the predicate is true
+    /// - predicate_index is offset, or the index if the predicate is true
     /// - new_value is the optional value when the operation is an array_set
     ///     When there is a predicate, it is predicate*value + (1-predicate)*dummy, where dummy is the value of the array at the requested index.
     ///     It is a dummy value because in the case of a false predicate, the value stored at the requested index will be itself.
@@ -1063,14 +1089,18 @@ impl<'a> Context<'a> {
         dfg: &DataFlowGraph,
         index: ValueId,
         store_value: Option<ValueId>,
+        offset: usize,
     ) -> Result<(AcirVar, Option<AcirValue>), RuntimeError> {
         let (array_id, array_typ, block_id) = self.check_array_is_initialized(array, dfg)?;
 
         let index_var = self.convert_numeric_value(index, dfg)?;
         let index_var = self.get_flattened_index(&array_typ, array_id, index_var, dfg)?;
 
-        let predicate_index =
-            self.acir_context.mul_var(index_var, self.current_side_effects_enabled_var)?;
+        // predicate_index = index*predicate + (1-predicate)*offset
+        let offset = self.acir_context.add_constant(offset);
+        let sub = self.acir_context.sub_var(index_var, offset)?;
+        let pred = self.acir_context.mul_var(sub, self.current_side_effects_enabled_var)?;
+        let predicate_index = self.acir_context.add_var(pred, offset)?;
 
         let new_value = if let Some(store) = store_value {
             let store_value = self.convert_value(store, dfg);
@@ -1171,12 +1201,14 @@ impl<'a> Context<'a> {
     }
 
     /// Generates a read opcode for the array
+    /// `index_side_effect == false` means that we ensured `var_index` will have a type matching the value in the array
     fn array_get(
         &mut self,
         instruction: InstructionId,
         array: ValueId,
         mut var_index: AcirVar,
         dfg: &DataFlowGraph,
+        mut index_side_effect: bool,
     ) -> Result<AcirValue, RuntimeError> {
         let (array_id, _, block_id) = self.check_array_is_initialized(array, dfg)?;
         let results = dfg.instruction_results(instruction);
@@ -1195,7 +1227,7 @@ impl<'a> Context<'a> {
                     self.data_bus.call_data_map[&array_id] as i128,
                 ));
                 let new_index = self.acir_context.add_var(offset, bus_index)?;
-                return self.array_get(instruction, call_data, new_index, dfg);
+                return self.array_get(instruction, call_data, new_index, dfg, index_side_effect);
             }
         }
 
@@ -1204,7 +1236,28 @@ impl<'a> Context<'a> {
             !res_typ.contains_slice_element(),
             "ICE: Nested slice result found during ACIR generation"
         );
-        let value = self.array_get_value(&res_typ, block_id, &mut var_index)?;
+        let mut value = self.array_get_value(&res_typ, block_id, &mut var_index)?;
+
+        if let AcirValue::Var(value_var, typ) = &value {
+            let array_id = dfg.resolve(array_id);
+            let array_typ = dfg.type_of_value(array_id);
+            if let (Type::Numeric(numeric_type), AcirType::NumericType(num)) =
+                (array_typ.first(), typ)
+            {
+                if numeric_type.bit_size() <= num.bit_size() {
+                    // first element is compatible
+                    index_side_effect = false;
+                }
+            }
+            // Fallback to multiplication if the index side_effects have not already been handled
+            if index_side_effect {
+                // Set the value to 0 if current_side_effects is 0, to ensure it fits in any value type
+                value = AcirValue::Var(
+                    self.acir_context.mul_var(*value_var, self.current_side_effects_enabled_var)?,
+                    typ.clone(),
+                );
+            }
+        }
 
         self.define_result(dfg, instruction, value.clone());
 

compiler/noirc_evaluator/src/ssa/ir/types.rs

@@ -166,6 +166,14 @@ impl Type {
             other => panic!("element_types: Expected array or slice, found {other}"),
         }
     }
+
+    pub(crate) fn first(&self) -> Type {
+        match self {
+            Type::Numeric(_) | Type::Function => self.clone(),
+            Type::Reference(typ) => typ.first(),
+            Type::Slice(element_types) | Type::Array(element_types, _) => element_types[0].first(),
+        }
+    }
 }
 
 /// Composite Types are essentially flattened struct or tuple types.

test_programs/execution_success/regression_struct_array_conditional/Nargo.toml

@@ -0,0 +1,7 @@
+[package]
+name = "regression_struct_array_conditional"
+version = "0.1.0"
+type = "bin"
+authors = [""]
+
+[dependencies]

test_programs/execution_success/regression_struct_array_conditional/Prover.toml

@@ -0,0 +1,18 @@
+y = 1
+z = 1
+
+[[x]]
+value =  "0x23de33be058ce5504e1ade738db8bdacfe268fa9dbde777092bf1d38519bdf59"
+counter = "10"
+dummy = "0"
+
+[[x]]
+value = "3"
+counter = "2"
+dummy = "0"
+
+[[x]]
+value = "2"
+counter = "0"
+dummy = "0"
+

test_programs/execution_success/regression_struct_array_conditional/src/main.nr

@@ -0,0 +1,38 @@
+struct foo { 
+    value: Field,
+    counter: u8,
+    dummy: u8,
+}
+struct bar {
+    dummy: [u8;3],
+    value: Field,
+    counter: u8,
+}
+struct bar_field {
+    dummy: [Field;3],
+    value: Field,
+    counter: u8,
+}
+fn main(x: [foo; 3], y: u32, z: u32) -> pub u8 {
+    let a = [y, z, x[y].counter as u32];
+    let mut b = [bar { value: 0, counter: 0, dummy: [0; 3] }; 3];
+    let mut c = [bar_field { value: 0, counter: 0, dummy: [0; 3] }; 3];
+    for i in 0..3 {
+        b[i].value = x[i].value;
+        b[i].counter = x[i].counter;
+        b[i].dummy[0] = x[i].dummy;
+        c[i].value = x[i].value;
+        c[i].counter = x[i].counter;
+        c[i].dummy[0] = x[i].dummy as Field;
+    }
+    if z == 0 {
+        // offset
+        assert(y as u8 < x[y].counter);
+        assert(y <= a[y]);
+        // first element is compatible
+        assert(y as u8 < b[y].counter);
+        // fallback
+        assert(y as u8 < c[y].counter);
+    }
+    x[0].counter
+}