mbaret commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r760262949
##########
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:
Ah, was testing something else and forgot to change back... Good catch :)
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -183,83 +288,28 @@ def test_tflite_depthwise_conv2d(
dilation,
activation,
):
- dtype = "int8"
-
- def create_tflite_graph():
- class Model(tf.Module):
- @tf.function
- def depthwise_conv2d(self, x):
- weight_shape = [kernel_shape[0], kernel_shape[1],
ifm_shape[3], 1]
- weight = tf.constant(np.random.uniform(size=weight_shape),
dtype=tf.float32)
- # The input strides to the TensorFlow API needs to be of shape
1x4
- tf_strides = [1, strides[0], strides[1], 1]
- op = tf.nn.depthwise_conv2d(
- x, weight, strides=tf_strides, padding=padding,
dilations=dilation
- )
- if activation:
- op = tf.nn.relu(op)
- return op
-
- model = Model()
- concrete_func = model.depthwise_conv2d.get_concrete_function(
- tf.TensorSpec(ifm_shape, dtype=tf.float32)
+ @tf.function
+ def depthwise_conv2d(x):
+ weight_shape = [kernel_shape[0], kernel_shape[1], ifm_shape[3], 1]
+ weight = tf.constant(np.random.uniform(size=weight_shape),
dtype=tf.float32)
+ # The input strides to the TensorFlow API needs to be of shape 1x4
+ tf_strides = [1, strides[0], strides[1], 1]
+ op = tf.nn.depthwise_conv2d(
+ x, weight, strides=tf_strides, padding=padding, dilations=dilation
)
+ if activation:
+ op = tf.nn.relu(op)
+ return op
- # Convert the model
- def representative_dataset():
- for _ in range(100):
- data = np.random.rand(*tuple(ifm_shape))
- yield [data.astype(np.float32)]
-
- 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_model = converter.convert()
- return tflite_model
-
- tflite_graph = create_tflite_graph()
- tflite_model = tflite.Model.Model.GetRootAsModel(tflite_graph, 0)
-
- relay_module, params = relay.frontend.from_tflite(
- tflite_model,
- shape_dict={"input": ifm_shape},
- dtype_dict={"input": dtype},
- )
- mod = partition_for_ethosu(relay_module, params)
-
- # Generate reference data
- input_data, output_data = infra.generate_ref_data_tflite(tflite_graph)
-
- compiled_models = infra.build_source(
- mod,
- input_data,
- output_data,
- accel_type,
- )
-
- # 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
- 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)
+ _compare_tvm_with_tflite(depthwise_conv2d, [ifm_shape], accel_type)
@pytest.mark.parametrize(
"accel_type",
ACCEL_TYPES,
)
@pytest.mark.parametrize("pooling_type", ["MAX", "AVG"])
[email protected]("ifm_shape", [[1, 3, 4, 3], [1, 4, 5, 2]])
[email protected]("ifm_shape", [[1, 10, 10, 24], [1, 4, 5, 2]])
Review comment:
Ack
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