dchauhan-arm commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r760239611
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -166,8 +169,110 @@ def create_graph_activation(input_tensor_name,
input_tensor_shape, input_tensor_
infra.verify_source(compiled_models, accel_type)
+def _compare_ethosu_with_reference(
+ mod, input_data, output_data, accel_type, output_tolerance=0,
print_cmm=False
+):
+ compiled_models = infra.build_source(
+ mod,
+ input_data,
+ output_data,
+ accel_type,
+ output_tolerance=output_tolerance,
+ )
+
+ # Assumes only two runtime.Modules are created -- i.e. single offload
module
+ imported_modules = compiled_models[0].executor_factory.lib.imported_modules
+ assert len(imported_modules) == 2
+ ethosu_module = imported_modules[0]
+
+ # Verify generated C source
+ if print_cmm:
+ get_cs = tvm._ffi.get_global_func("runtime.module.ethos-u.getcs")
+ cmms = get_cs(ethosu_module)
+ cmms = bytes.fromhex(cmms)
+ infra.print_payload(cmms)
+
+ infra.verify_source(compiled_models, accel_type)
+
+
+def _compare_tvm_with_tflite(tf_func, shapes, accel_type, ranges=None,
print_cmm=False):
+ tensor_specs = [tf.TensorSpec(shape, dtype=tf.float32) for shape in shapes]
+ if not ranges:
+ ranges = [(0, 1) for _ in shapes]
+ concrete_func = tf_func.get_concrete_function(*tensor_specs)
+
+ # Convert the model
+ def representative_dataset():
+ for _ in range(100):
+ inputs = []
+ for i, shape in enumerate(shapes):
+ data = np.random.uniform(
+ low=ranges[i][0], high=ranges[i][1], size=tuple(shape)
+ ).astype("float32")
+ inputs.append(data)
+
+ yield inputs
+
+ converter =
tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
+ converter.optimizations = [tf.lite.Optimize.DEFAULT]
+ converter.representative_dataset = representative_dataset
+ converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
+ converter.inference_input_type = tf.int8
+ converter.inference_output_type = tf.int8
+ tflite_graph = converter.convert()
+
+ tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
+
+ relay_module, params = relay.frontend.from_tflite(tflite_model)
+ mod = partition_for_ethosu(relay_module, params)
+
+ # Generate reference data
+ input_data, output_data = infra.generate_ref_data_tflite(tflite_graph)
+
+ _compare_ethosu_with_reference(mod, input_data, output_data, accel_type,
print_cmm=print_cmm)
+
+
+class EthosUAnnotator(ExprMutator):
+ """Annotate entire graph for Ethos-U offload"""
+
+ def __init__(self):
+ super(EthosUAnnotator, self).__init__()
+ self.compiler = "ethos-u"
+ self.last_call = True
+
+ def visit_call(self, call):
+ curr_last = self.last_call
+ self.last_call = False
+
+ params = []
+ for arg in call.args:
+ param = super().visit(arg)
+ if isinstance(param, relay.expr.Var):
+ param = compiler_begin(param, self.compiler)
+ params.append(param)
+
+ new_call = relay.Call(call.op, params, call.attrs)
+ if curr_last:
+ new_call = compiler_end(new_call, self.compiler)
+ return new_call
+
+ def visit_constant(self, constant):
+ new_constant = compiler_begin(constant, self.compiler)
+ return new_constant
+
+
+def _create_ethosu_partition(mod):
+ mod["main"] = EthosUAnnotator().visit(mod["main"])
+ mod = relay.transform.MergeCompilerRegions()(mod)
+ mod = relay.transform.InferType()(mod)
+ mod = relay.transform.PartitionGraph()(mod)
+ mod = relay.transform.InferType()(mod)
+ mod = preprocess.preprocess_ext_io()(mod)
+ return mod
+
+
@pytest.mark.parametrize("accel_type", ACCEL_TYPES)
[email protected]("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
[email protected]("ifm_shape", [(1, 10, 10, 8), (1, 23, 32, 7)])
Review comment:
Any reason for channel changes here? Or is it just to vary the tests a
bit?
--
This is an automated message from the Apache Git Service.
To respond to the message, please log on to GitHub and use the
URL above to go to the specific comment.
To unsubscribe, e-mail: [email protected]
For queries about this service, please contact Infrastructure at:
[email protected]
