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Posted to commits@tvm.apache.org by GitBox <gi...@apache.org> on 2021/12/01 14:09:50 UTC
[GitHub] [tvm] mbaret opened a new pull request #9623: Refactor Ethos-U codegen tests
mbaret opened a new pull request #9623:
URL: https://github.com/apache/tvm/pull/9623
The Ethos-U codegen tests had a lot of replicated functionality due to them being developed concurrently. This refactor consolidates those commonalities.
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[GitHub] [tvm] mbaret commented on a change in pull request #9623: [microNPU] Refactor codegen tests
Posted by GitBox <gi...@apache.org>.
mbaret commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r761110161
##########
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
+):
Review comment:
Agreed, I think we should move it to the 'end_to_end' infra when we add that directory.
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[GitHub] [tvm] NicolaLancellotti commented on a change in pull request #9623: [microNPU] Refactor codegen tests
Posted by GitBox <gi...@apache.org>.
NicolaLancellotti commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r761130747
##########
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)
-@pytest.mark.parametrize("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
+@pytest.mark.parametrize("ifm_shape", [(1, 10, 10, 8), (1, 23, 32, 7)])
@pytest.mark.parametrize(
"kernel_shape, activation",
Review comment:
```suggestion
"kernel_shape, activation_function",
```
##########
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)
-@pytest.mark.parametrize("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
+@pytest.mark.parametrize("ifm_shape", [(1, 10, 10, 8), (1, 23, 32, 7)])
@pytest.mark.parametrize(
"kernel_shape, activation",
[((3, 3), "relu"), ((1, 2), None)],
Review comment:
```suggestion
[((3, 3), "RELU"), ((1, 2), "NONE")],
```
##########
File path: tests/python/contrib/test_ethosu/test_codegen.py
##########
@@ -183,83 +288,28 @@ def test_tflite_depthwise_conv2d(
dilation,
activation,
Review comment:
```suggestion
activation_function,
```
##########
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:
Review comment:
```suggestion
if activation_function == "RELU":
```
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[GitHub] [tvm] manupa-arm commented on pull request #9623: [microNPU] Refactor codegen tests
Posted by GitBox <gi...@apache.org>.
manupa-arm commented on pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#issuecomment-985985321
Thanks @mbaret @ekalda @lhutton1 @NicolaLancellotti @dchauhan-arm !. This is merged now!
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[GitHub] [tvm] lhutton1 commented on a change in pull request #9623: [microNPU] Refactor codegen tests
Posted by GitBox <gi...@apache.org>.
lhutton1 commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r761045045
##########
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
+):
Review comment:
Good point, I believe this would also be useful for the tests in #9561 :)
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[GitHub] [tvm] mbaret commented on pull request #9623: Refactor Ethos-U codegen tests
Posted by GitBox <gi...@apache.org>.
mbaret commented on pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#issuecomment-983681583
cc @ekalda @lhutton1 @NicolaLancellotti @dchauhan-arm - let me know what you think :)
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[GitHub] [tvm] ekalda commented on a change in pull request #9623: [microNPU] Refactor codegen tests
Posted by GitBox <gi...@apache.org>.
ekalda commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r761032476
##########
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
+):
Review comment:
I was wondering if maybe this and the other helper functions could go to some more common location where they would be accessible to other files as well, so that we can make use of them in other tests that follow similar pattern, namely the tests in test_lookup_table.py
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[GitHub] [tvm] dchauhan-arm commented on a change in pull request #9623: Refactor Ethos-U codegen tests
Posted by GitBox <gi...@apache.org>.
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)
-@pytest.mark.parametrize("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
+@pytest.mark.parametrize("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?
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[GitHub] [tvm] dchauhan-arm commented on a change in pull request #9623: Refactor Ethos-U codegen tests
Posted by GitBox <gi...@apache.org>.
dchauhan-arm commented on a change in pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#discussion_r760240256
##########
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"])
-@pytest.mark.parametrize("ifm_shape", [[1, 3, 4, 3], [1, 4, 5, 2]])
+@pytest.mark.parametrize("ifm_shape", [[1, 10, 10, 24], [1, 4, 5, 2]])
Review comment:
Same as above re: ifm_shape
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[GitHub] [tvm] manupa-arm merged pull request #9623: [microNPU] Refactor codegen tests
Posted by GitBox <gi...@apache.org>.
manupa-arm merged pull request #9623:
URL: https://github.com/apache/tvm/pull/9623
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[GitHub] [tvm] dchauhan-arm commented on pull request #9623: Refactor Ethos-U codegen tests
Posted by GitBox <gi...@apache.org>.
dchauhan-arm commented on pull request #9623:
URL: https://github.com/apache/tvm/pull/9623#issuecomment-983698733
broadly LGTM! Especially the change to making printing the command stream be optional so it doesn't print a novel in the logs. Just a few minor Q's inline
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[GitHub] [tvm] mbaret commented on a change in pull request #9623: Refactor Ethos-U codegen tests
Posted by GitBox <gi...@apache.org>.
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)
-@pytest.mark.parametrize("ifm_shape", [(1, 55, 55, 3), (1, 23, 32, 7)])
+@pytest.mark.parametrize("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"])
-@pytest.mark.parametrize("ifm_shape", [[1, 3, 4, 3], [1, 4, 5, 2]])
+@pytest.mark.parametrize("ifm_shape", [[1, 10, 10, 24], [1, 4, 5, 2]])
Review comment:
Ack
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