[microNPU] Support different constant datatypes

Currently only uint8 datatype is supported for constants, as this is
all that was necessary until now. This PR allows different datatypes
to be used for constants, including different datatypes within the
same graph.

A workaround was previously added for Mean legalization, this has
also been removed and replaced with the expected datatype of the
constant.

Change-Id: I99e34fe17905b1bb7d916e346cebfc324e3a2a0c

python/tvm/relay/backend/contrib/ethosu/legalize.py

@@ -1040,7 +1040,7 @@ def callback(
             n = int(filter_height * filter_width)
             eps = 1 / (256 * (n + 1)) if n % 2 == 0 else 0
 
-            scalar_tensor = relay.const(np.ones([1, 1, 1, 1], dtype="uint8"), dtype="uint8")
+            scalar_tensor = relay.const(np.ones([1, 1, 1, 1], dtype="int16"), dtype="int16")
 
             reduced_op = ethosu_ops.ethosu_binary_elementwise(
                 ifm=reduced_op,
@@ -1156,6 +1156,7 @@ def transform_module(
         mod = LegalizeReshape()(mod)
         mod = LegalizeStridedSlice()(mod)
         mod = LegalizeNoOps()(mod)
+        print(mod)
         return mod
 
     def __call__(self, *args, **kwargs):

python/tvm/relay/backend/contrib/ethosu/tir_to_cs_translator.py

@@ -110,12 +110,11 @@ def translate(tir_module, params):
     _npu_ops = list()
     for call_extern in call_extern_list:
         _npu_ops.append(translate_ethosu_tir_call_extern(call_extern))
-    _npu_ops, constant_tensor, scratch_size = assign_addresses(buffer_info, _npu_ops)
+    _npu_ops, constant_data, scratch_size = assign_addresses(buffer_info, _npu_ops)
     target_accel_config = vela_api.get_accelerator_config()
     cmds = vapi.npu_generate_register_command_stream(_npu_ops, target_accel_config)
     payload = vapi.npu_create_driver_payload(cmds, target_accel_config)
-    hex_value = "" if constant_tensor is None else constant_tensor.tobytes().hex()
-    return payload.hex(), hex_value, scratch_size
+    return payload.hex(), constant_data, scratch_size
 
 
 def extract_call_extern_list(mod):
@@ -277,27 +276,24 @@ def classify_io(buffer):
         raise ValueError(f"Unused IO : {buffer} in tir module.")
 
     scratch_size = 0
-    constant_tensor = None
+    constant_hex_data = []
+    total_constant_len = 0
     buffer_addresses = dict()
     for _buffer, info in buffer_info.items():
+        dtype_bytes = np.iinfo(np.dtype(info.dtype)).bits // 8
         if info.values is not None:
-            assert np.dtype(info.dtype) == np.uint8
             assert info.btype == BufferType.constant
             assert len(info.shape) == 1
-            if constant_tensor is None:
-                buffer_addresses[_buffer] = (0, info.btype)
-                assert info.values.dtype == np.uint8
-                size_in_bytes = info.values.size
-                # Every memory address the NPU access have to be 16 byte aligned
-                size_in_bytes = util.round_up(size_in_bytes, 16)
-                constant_tensor = np.resize(info.values, size_in_bytes)
-            else:
-                buffer_addresses[_buffer] = (constant_tensor.size, info.btype)
-                assert info.values.dtype == np.uint8
-                size_in_bytes = info.values.size
-                # Every memory address the NPU access have to be 16 byte aligned
-                size_in_bytes = util.round_up(size_in_bytes, 16)
-                constant_tensor = np.append(constant_tensor, np.resize(info.values, size_in_bytes))
+            buffer_addresses[_buffer] = (
+                (total_constant_len, info.btype) if constant_hex_data else (0, info.btype)
+            )
+            size_in_bytes = dtype_bytes * np.prod(list(info.shape))
+            # Every memory address the NPU access have to be 16 byte aligned
+            size_in_bytes = util.round_up(size_in_bytes, 16)
+            constant_tensor = np.resize(info.values, size_in_bytes // dtype_bytes)
+            constant_tensor = constant_tensor.tobytes().hex()
+            constant_hex_data.append(constant_tensor)
+            total_constant_len += len(constant_tensor) // 2
         else:
             if info.btype == BufferType.input_or_output:
                 buffer_type = classify_io(_buffer)
@@ -310,9 +306,7 @@ def classify_io(buffer):
                 address = arch_config.lut_start_address
                 buffer_addresses[_buffer] = (address, info.btype)
             else:
-                size_in_bytes = int(
-                    (np.iinfo(np.dtype(info.dtype)).bits // 8) * np.prod(list(info.shape))
-                )
+                size_in_bytes = int(dtype_bytes * np.prod(list(info.shape)))
                 # Every memory address the NPU access have to be 16 byte aligned
                 size_in_bytes = util.round_up(size_in_bytes, 16)
                 assert info.btype == BufferType.scratch
@@ -330,7 +324,12 @@ def classify_io(buffer):
             else:
                 setattr(npu_op, attr_name, replace_tir_loads(attr))
 
-    return npu_ops, constant_tensor, scratch_size
+    constant_data = "".join(constant_hex_data)
+    return (
+        npu_ops,
+        constant_data,
+        scratch_size,
+    )
 
 
 def translate_ethosu_tir_call_extern(tir_call_extern):

src/relay/op/contrib/ethosu/binary_elementwise.cc

@@ -128,21 +128,6 @@ struct EthosuBinaryElementwiseAttrs : public tvm::AttrsNode<EthosuBinaryElementw
 
 TVM_REGISTER_NODE_TYPE(EthosuBinaryElementwiseAttrs);
 
-bool IsScalarTensor(const Array<PrimExpr>& ifm_shape, const DataType& ifm_dtype) {
-  if (ifm_dtype != DataType::UInt(8)) {
-    return false;
-  }
-
-  for (const auto& expr : ifm_shape) {
-    const auto& dim_int_node = expr.as<IntImmNode>();
-    CHECK(dim_int_node) << "Expected IntImmNode for shape dimensions.";
-    int dim = dim_int_node->value;
-    if (dim != 1) return false;
-  }
-
-  return true;
-}
-
 bool EthosuBinaryElementwiseRel(const Array<Type>& types, int num_inputs, const Attrs& attrs,
                                 const TypeReporter& reporter) {
   const int ifm_index = 0;
@@ -167,11 +152,13 @@ bool EthosuBinaryElementwiseRel(const Array<Type>& types, int num_inputs, const
     ofm_dtype = DataType::Int(8);
   } else if (param->ofm_dtype == "uint8") {
     ofm_dtype = DataType::UInt(8);
+  } else if (param->ofm_dtype == "int16") {
+    ofm_dtype = DataType::Int(16);
   } else if (param->ofm_dtype == "int32") {
     ofm_dtype = DataType::Int(32);
   }
 
-  if (ifm_dtype != ifm2_dtype && !IsScalarTensor(ifm2->shape, ifm2_dtype)) {
+  if (ifm_dtype != ifm2_dtype) {
     reporter->GetDiagCtx().EmitFatal(Diagnostic::Error(reporter->GetSpan())
                                      << "Invalid operator: expected ethosu_binary_elementwise "
                                      << "type for ifm2 be the same of ifm but was " << ifm2_dtype
@@ -189,11 +176,11 @@ bool EthosuBinaryElementwiseRel(const Array<Type>& types, int num_inputs, const
       return false;
     }
     if (ofm_dtype != DataType::UInt(8) && ofm_dtype != DataType::Int(8) &&
-        ofm_dtype != DataType::Int(32)) {
+        ofm_dtype != DataType::Int(16) && ofm_dtype != DataType::Int(32)) {
       reporter->GetDiagCtx().EmitFatal(
           Diagnostic::Error(reporter->GetSpan())
           << "Invalid operator: expected ethosu_binary_elementwise " << operator_type
-          << " type(uint8) or type(int8) or type(int32) for ofm but was " << ofm_dtype);
+          << " type(uint8), type(int8), type(int16) or type(int32) for ofm but was " << ofm_dtype);
       return false;
     }
   } else if (operator_type == "MIN" || operator_type == "MAX") {

tests/python/contrib/test_ethosu/test_codegen.py

@@ -607,8 +607,8 @@ def create_mod_from_relay():
 
 
 @pytest.mark.parametrize("accel_type", ACCEL_TYPES)
-def test_binary_add_from_constant_scalar(accel_type):
-    dtype = "uint8"
+@pytest.mark.parametrize("dtype", ["int8", "uint8"])
+def test_elementwise_add_from_constant_scalar(accel_type, dtype):
     ifm_shape = (1, 4, 4, 8)
 
     def create_relay_graph():
@@ -631,7 +631,11 @@ def create_relay_graph():
     partitioned_mod = partition_for_ethosu(mod)
 
     # Generate reference data
-    input_data = {"input": np.random.randint(low=0, high=255, size=ifm_shape, dtype=dtype)}
+    input_data = {
+        "input": np.random.randint(
+            low=np.iinfo(dtype).min, high=np.iinfo(dtype).max, size=ifm_shape, dtype=dtype
+        ),
+    }
     output_data = generate_ref_data(mod, input_data)
 
     compiled_models = infra.build_source(

tests/python/contrib/test_ethosu/test_tir_to_cs_translator.py

@@ -651,6 +651,31 @@ def populate_ethosu_copy_calls(stmt):
             assert npu_dma_op.dest.length == test_case["ref"][idx]["length"]
 
 
+# fmt: off
+@tvm.script.ir_module
+class MixedConstantDatatypes:
+    @T.prim_func
+    def main(placeholder: T.handle, placeholder_1: T.handle, placeholder_2: T.handle, ethosu_write: T.handle, placeholder_3: T.handle) -> None:
+        # function attr dict
+        T.func_attr({"from_legacy_te_schedule": True, "global_symbol": "main", "tir.noalias": True})
+        placeholder_4 = T.match_buffer(placeholder, [1, 8, 16, 16], dtype="int8")
+        buffer = T.match_buffer(placeholder_1, [160], dtype="uint8")
+        placeholder_5 = T.match_buffer(placeholder_2, [1, 1, 1, 1], dtype="int16")
+        ethosu_write_1 = T.match_buffer(ethosu_write, [1, 1, 1, 16], dtype="int8")
+        buffer_1 = T.match_buffer(placeholder_3, [272], dtype="uint8")
+        # body
+        placeholder_global = T.allocate([272], "uint8", "global")
+        placeholder_d_global = T.allocate([160], "uint8", "global")
+        ethosu_write_2 = T.allocate([16], "int16", "global")
+        placeholder_d_global_1 = T.allocate([1], "int16", "global")
+        T.evaluate(T.call_extern("ethosu_copy", T.load("uint8", buffer_1.data, 0), 272, T.load("uint8", placeholder_global, 0), dtype="uint8"))
+        T.evaluate(T.call_extern("ethosu_copy", T.load("uint8", buffer.data, 0), 160, T.load("uint8", placeholder_d_global, 0), dtype="uint8"))
+        T.evaluate(T.call_extern("ethosu_depthwise_conv2d", "int8", 8, 16, 16, 8, 0, 16, T.load("int8", placeholder_4.data, 0), 0, 0, 0, T.float32(0.0039215548895299435), -128, "NHWC", 256, 16, 1, "int16", 1, 1, 16, 1, 0, 1, T.load("int16", ethosu_write_2, 0), 0, 0, 0, T.float32(0.0023205536417663097), -128, "NHWC", 1, 1, 1, 16, 8, 1, 1, 1, 1, T.load("uint8", placeholder_global, 0), 272, 0, T.load("uint8", placeholder_d_global, 0), 160, 0, 0, 0, 0, "NONE", 0, 0, "TFL", "NONE", dtype="int16"))
+        T.evaluate(T.call_extern("ethosu_copy", T.load("int16", placeholder_5.data, 0), 1, T.load("int16", placeholder_d_global_1, 0), dtype="int16"))
+        T.evaluate(T.call_extern("ethosu_binary_elementwise", "int16", 1, 1, 16, 1, 0, 1, T.load("int16", ethosu_write_2, 0), 0, 0, 0, T.float32(0.0023205536417663097), -128, "NHWC", 1, 1, 1, "int16", 1, 1, 1, 1, 0, 1, T.load("int16", placeholder_d_global_1, 0), 0, 0, 0, T.float32(0.0078125018482064768), 0, "NHWC", 1, 1, 1, "int8", 1, 1, 16, 1, 0, 1, T.load("int8", ethosu_write_1.data, 0), 0, 0, 0, T.float32(0.0023205536417663097), -128, "NHWC", 1, 1, 1, "MUL", 0, "NONE", 0, 0, "NATURAL", dtype="int8"))
+# fmt: on
+
+
 def test_assign_addresses():
     test_cases = [
         {
@@ -683,6 +708,15 @@ def test_assign_addresses():
                 11: np.random.randint(np.iinfo("uint8").min, np.iinfo("uint8").max, [20], "uint8"),
             },
         },
+        {
+            # Stimulus
+            "tir_module": MixedConstantDatatypes,
+            "param_dict": {
+                1: np.random.randint(np.iinfo("uint8").min, np.iinfo("uint8").max, [160], "uint8"),
+                2: np.random.randint(np.iinfo("int16").min, np.iinfo("int16").max, [1], "int16"),
+                4: np.random.randint(np.iinfo("uint8").min, np.iinfo("uint8").max, [272], "uint8"),
+            },
+        },
     ]
 
     def extract_call_extern_list(mod):
@@ -747,24 +781,36 @@ def _check_buffer(address, region, length, buffer_var):
             4: tir_to_cs_translator.BufferType.output,
         }
         buffer_type = inverse_region_map[region]
+        buffer_dtype = buffer_var.type_annotation.element_type.dtype
+        dtype_bytes = np.iinfo(np.dtype(buffer_dtype)).bits // 8
         if buffer_type == tir_to_cs_translator.BufferType.constant:
             ref = buffer_info[buffer_var].values
-            assert (constant_tensor[address : address + length] == ref).all()
+            hex_from = address * dtype_bytes * 2
+            hex_to = hex_from + length * dtype_bytes * 2
+            constant_hex = constant_hex_string[hex_from:hex_to]
+            constant_tensor = np.frombuffer(bytearray.fromhex(constant_hex), dtype=buffer_dtype)
+            np.array_equal(constant_tensor, ref)
             # Every buffer is adjusted to align to 16 bytes
             length = util.round_up(length, 16)
             # Mark these constants are read at least once
-            constant_tensor_read_mask[address : address + length] = np.ones(length, dtype="uint8")
+            constant_tensor_read_mask[address : address + length] = np.ones(
+                length, dtype=buffer_dtype
+            )
         elif buffer_type == tir_to_cs_translator.BufferType.scratch:
             shape = list(buffer_info[buffer_var].shape)
             assert length == np.prod(shape)
             assert address < scratch_size
+
+            size_in_bytes = int(np.prod(shape)) * dtype_bytes
             # Every buffer is adjusted to align to 16 bytes
-            length = util.round_up(length, 16)
-            assert address + length <= scratch_size
+            size_in_bytes = util.round_up(size_in_bytes, 16)
+            assert address + size_in_bytes <= scratch_size
             # The scratch area should not be used by anyother buffer
-            assert not scratch_allocation_mask[address : address + length].any()
+            assert not scratch_allocation_mask[address : address + size_in_bytes].any()
             # The scratch area is marked as used
-            scratch_allocation_mask[address : address + length] = np.ones(length, dtype="uint8")
+            scratch_allocation_mask[address : address + size_in_bytes] = np.ones(
+                size_in_bytes, dtype="uint8"
+            )
         elif buffer_type == tir_to_cs_translator.BufferType.input:
             assert address == 0
         else:
@@ -841,11 +887,11 @@ def check_buffer(address, region, length, buffer_var):
         for extern_call in extern_calls:
             _npu_ops.append(tir_to_cs_translator.translate_ethosu_tir_call_extern(extern_call))
         npu_op_tir_buffers = collect_tir_buffer_info(_npu_ops)
-        _npu_ops, constant_tensor, scratch_size = tir_to_cs_translator.assign_addresses(
+        _npu_ops, constant_hex_string, scratch_size = tir_to_cs_translator.assign_addresses(
             buffer_info, _npu_ops
         )
         scratch_allocation_mask = np.zeros(scratch_size, dtype="uint8")
-        constant_tensor_read_mask = np.zeros(constant_tensor.size, dtype="uint8")
+        constant_tensor_read_mask = np.zeros(len(constant_hex_string) // 2, dtype="uint8")
         verify(_npu_ops)
         # This will be only 1 if all allocated scratch is used.
         assert np.prod(scratch_allocation_mask) == 1