ekalda commented on a change in pull request #9508:
URL: https://github.com/apache/tvm/pull/9508#discussion_r749385068
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -48,122 +46,147 @@ def get_shape_expr(in_expr, out_expr):
return shape
[email protected](
- "accel_type",
- ACCEL_TYPES,
-)
-def test_ethosu_conv2d(accel_type):
- def create_graph_single(input_tensor_name, input_tensor_shape,
input_tensor_dtype):
- c1_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
- c1_params.ifm.shape = input_tensor_shape
- c1_params.kernel.shape = (3, 3, c1_params.ifm.shape[3], 32)
- c1_params.kernel.sc = relay.const(np.random.rand(32) * 2, "float32")
- c1_params.strides = (1, 1)
- c1_params.pad = "VALID"
- c1_params.update_output_qnn_params(
- input_tensor_dtype, input_tensor_dtype, input_tensor_dtype
- )
- input0 = relay.var(input_tensor_name, shape=c1_params.ifm.shape,
dtype=c1_params.ifm.dtype)
- c1, new_params = relay_ir_builder.create_qnn_conv2d(c1_params, input0)
- c1_params.ofm.shape = get_shape_expr(input0, c1)
[email protected]("ifm_shape", [(1, 299, 299, 3), (1, 55, 55, 3)])
[email protected]("kernel_shape", [(3, 2, 3, 3), (1, 3, 3, 3)])
[email protected]("padding", ["SAME", "VALID"])
[email protected]("accel_type", ACCEL_TYPES)
+def test_ethosu_conv2d(ifm_shape, kernel_shape, padding, accel_type):
+ dtype = "int8"
- f = relay.Function([input0], c1)
- mod = tvm.IRModule()
- mod["main"] = f
- return mod, [c1_params]
-
- def create_graph_double(input_tensor_name, input_tensor_shape,
input_tensor_dtype):
- c1_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
- c1_params.ifm.shape = input_tensor_shape
- c1_params.kernel.shape = (7, 7, c1_params.ifm.shape[3], 8)
- c1_params.strides = (2, 2)
- c1_params.pad = "VALID"
- c1_params.update_output_qnn_params(
- input_tensor_dtype, input_tensor_dtype, input_tensor_dtype
- )
- input0 = relay.var(input_tensor_name, shape=c1_params.ifm.shape,
dtype=c1_params.ifm.dtype)
- c1, new_params = relay_ir_builder.create_qnn_conv2d(c1_params, input0)
- c1_params.ofm.shape = get_shape_expr(input0, c1)
-
- c2_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
- c2_params.ifm.shape = c1_params.ofm.shape
- c2_params.kernel.shape = (5, 5, c2_params.ifm.shape[3], 16)
- c2_params.strides = (1, 1)
- c2_params.pad = "SAME"
- c2_params.update_output_qnn_params()
- c2, new_params = relay_ir_builder.create_qnn_conv2d(c2_params, c1)
- c2_params.ofm.shape = get_shape_expr(input0, c2)
-
- f = relay.Function([input0], c2)
- mod = tvm.IRModule()
- mod["main"] = f
- return mod, [c2_params, c1_params]
-
- def create_graph_activation(input_tensor_name, input_tensor_shape,
input_tensor_dtype):
- c1_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
- c1_params.ifm.shape = input_tensor_shape
- c1_params.kernel.shape = (7, 7, c1_params.ifm.shape[3], 8)
- c1_params.strides = (2, 2)
- c1_params.pad = "VALID"
- c1_params.activation = "CLIP"
- c1_params.clip_min = 90
- c1_params.clip_max = 110
- c1_params.update_output_qnn_params(
- input_tensor_dtype, input_tensor_dtype, input_tensor_dtype
+ def create_tflite_graph_single():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function(self, x):
+ # Use tf.nn API to create the model
+ op = tf.nn.conv2d(
+ x,
+ filters=tf.constant(np.random.uniform(size=kernel_shape),
dtype=tf.float32),
+ strides=(1, 1),
+ padding=padding,
+ data_format="NHWC",
+ dilations=1,
+ )
+ return op
+
+ model = Model()
+ concrete_func = model.tf_function.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32)
)
- input0 = relay.var(input_tensor_name, shape=c1_params.ifm.shape,
dtype=c1_params.ifm.dtype)
- c1, new_params = relay_ir_builder.create_qnn_conv2d(c1_params, input0)
- c1_params.ofm.shape = get_shape_expr(input0, c1)
-
- c2_params = relay_ir_builder.QnnConv2DParams(input_tensor_dtype)
- c2_params.ifm.shape = c1_params.ofm.shape
- c2_params.kernel.shape = (5, 5, c2_params.ifm.shape[3], 16)
- c2_params.strides = (1, 1)
- c2_params.pad = "SAME"
- c2_params.update_output_qnn_params()
- c2, new_params = relay_ir_builder.create_qnn_conv2d(c2_params, c1)
- c2_params.ofm.shape = get_shape_expr(input0, c2)
-
- f = relay.Function([input0], c2)
- mod = tvm.IRModule()
- mod["main"] = f
- return mod, [c2_params, c1_params]
-
- test_cases = [
- (create_graph_single, ["input", (1, 300, 300, 3), "int8"]),
- (create_graph_double, ["input", (1, 128, 256, 4), "int8"]),
- (create_graph_activation, ["input", (1, 64, 100, 4), "int8"]),
- ]
- np.random.seed(42)
- for test_case in test_cases:
- relay_module, conv_params = test_case[0](*test_case[1])
- input_tensor, input_shape, input_dtype = test_case[1]
- mod = partition_for_ethosu(relay_module)
-
- # Generate reference data
- in_min, in_max = util.get_range_for_dtype_str(input_dtype)
- input_data = {
- input_tensor: np.random.randint(
- in_min, high=in_max, size=input_shape, dtype=input_dtype
- )
- }
- output_data = generate_ref_data(relay_module, input_data)
-
- compiled_models = infra.build_source(
- mod, input_data, output_data, accel_type, output_tolerance=1
+
+ # 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
+
+ def create_tflite_graph_double():
+ class Model(tf.Module):
+ @tf.function
+ def tf_function_double(self, x):
+ # Use tf.nn API to create the model with two convolutions
+ op = tf.nn.conv2d(
+ x,
+ filters=tf.constant(np.random.uniform(size=kernel_shape),
dtype=tf.float32),
+ strides=(1, 1),
+ padding=padding,
+ data_format="NHWC",
+ dilations=1,
+ )
+ # Second convolution
+ op2 = tf.nn.conv2d(
+ op,
+ filters=tf.constant(np.random.uniform(size=kernel_shape),
dtype=tf.float32),
+ strides=(1, 1),
+ padding=padding,
+ data_format="NHWC",
+ dilations=2,
+ )
+ return op2
+
+ model = Model()
+ concrete_func = model.tf_function_double.get_concrete_function(
+ tf.TensorSpec(ifm_shape, dtype=tf.float32)
)
- # 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.ethosu.getcs")
- cmms = get_cs(ethosu_module)
- cmms = bytes.fromhex(cmms)
- infra.print_payload(cmms)
- infra.verify_source(compiled_models, accel_type)
+ # 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_single = create_tflite_graph_single()
+ tflite_model_single =
tflite.Model.Model.GetRootAsModel(tflite_graph_single, 0)
+
+ tflite_graph_double = create_tflite_graph_double()
+ tflite_model_double =
tflite.Model.Model.GetRootAsModel(tflite_graph_double, 0)
Review comment:
Wouldn't it make more sense to have single and double graphs as separate
tests with their own parameters? Currently, if that test fails, it is not
immediately obvious whether it was the single or double graph test that failed
cc: @manupa-arm
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