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Refactor Target Platform Capabilities - Phase 4 #1301

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merged 12 commits into from
Dec 31, 2024
Merged

Refactor Target Platform Capabilities - Phase 4 #1301

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ca7c104
Refactor Target Platform Capabilities - Phase 4
Dec 25, 2024
20fa29f
Add pydantic to requirements file
Dec 25, 2024
5502435
Replace max tensor with max cut (#1295)
elad-c Dec 25, 2024
a5f1899
Fix bug (#1303)
elad-c Dec 29, 2024
8863eec
Move splitting ops from default to qpreserving configs in TPCv4. (#1304)
elad-c Dec 30, 2024
6dfd044
Downgrade to pydantic v1.10 (since pydantic v2 is not compatible with…
Dec 31, 2024
2eba10e
Downgrade to pydantic v1.10 (since pydantic v2 is not compatible with…
Dec 31, 2024
e89e1e8
merge to main
Dec 31, 2024
cd0de86
Remove imports from test
Dec 31, 2024
9715e6f
Changed pydantic version
Dec 31, 2024
eb60dc2
Changed pydantic version
Dec 31, 2024
bc26fc5
Changed pydantic version
Dec 31, 2024
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481 changes: 308 additions & 173 deletions model_compression_toolkit/target_platform_capabilities/schema/v1.py
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44 changes: 22 additions & 22 deletions model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v1/tp_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -153,10 +153,10 @@ def generate_tp_model(default_config: OpQuantizationConfig,
# of possible configurations to consider when quantizing a set of operations (in mixed-precision, for example).
# If the QuantizationConfigOptions contains only one configuration,
# this configuration will be used for the operation quantization:
default_configuration_options = schema.QuantizationConfigOptions(tuple([default_config]))
default_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple([default_config]))

# Create Mixed-Precision quantization configuration options from the given list of OpQuantizationConfig objects
mixed_precision_configuration_options = schema.QuantizationConfigOptions(tuple(mixed_precision_cfg_list),
mixed_precision_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple(mixed_precision_cfg_list),
base_config=base_config)

# Create an OperatorsSet to represent a set of operations.
Expand All @@ -167,46 +167,46 @@ def generate_tp_model(default_config: OpQuantizationConfig,
operator_set = []
fusing_patterns = []

operator_set.append(schema.OperatorsSet("NoQuantization",
default_configuration_options.clone_and_edit(enable_activation_quantization=False)
operator_set.append(schema.OperatorsSet(name="NoQuantization",
qc_options=default_configuration_options.clone_and_edit(enable_activation_quantization=False)
.clone_and_edit_weight_attribute(enable_weights_quantization=False)))

# Define operator sets that use mixed_precision_configuration_options:
conv = schema.OperatorsSet("Conv", mixed_precision_configuration_options)
fc = schema.OperatorsSet("FullyConnected", mixed_precision_configuration_options)
conv = schema.OperatorsSet(name="Conv", qc_options=mixed_precision_configuration_options)
fc = schema.OperatorsSet(name="FullyConnected", qc_options=mixed_precision_configuration_options)

# Define operations sets without quantization configuration
# options (useful for creating fusing patterns, for example):
any_relu = schema.OperatorsSet("AnyReLU")
add = schema.OperatorsSet("Add")
sub = schema.OperatorsSet("Sub")
mul = schema.OperatorsSet("Mul")
div = schema.OperatorsSet("Div")
prelu = schema.OperatorsSet("PReLU")
swish = schema.OperatorsSet("Swish")
sigmoid = schema.OperatorsSet("Sigmoid")
tanh = schema.OperatorsSet("Tanh")
any_relu = schema.OperatorsSet(name="AnyReLU")
add = schema.OperatorsSet(name="Add")
sub = schema.OperatorsSet(name="Sub")
mul = schema.OperatorsSet(name="Mul")
div = schema.OperatorsSet(name="Div")
prelu = schema.OperatorsSet(name="PReLU")
swish = schema.OperatorsSet(name="Swish")
sigmoid = schema.OperatorsSet(name="Sigmoid")
tanh = schema.OperatorsSet(name="Tanh")

operator_set.extend([conv, fc, any_relu, add, sub, mul, div, prelu, swish, sigmoid, tanh])
# Combine multiple operators into a single operator to avoid quantization between
# them. To do this we define fusing patterns using the OperatorsSets that were created.
# To group multiple sets with regard to fusing, an OperatorSetConcat can be created
activations_after_conv_to_fuse = schema.OperatorSetConcat([any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat([any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat([add, sub, mul, div])
activations_after_conv_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat(operators_set=[add, sub, mul, div])

# ------------------- #
# Fusions
# ------------------- #
fusing_patterns.append(schema.Fusing((conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing((fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing((any_binary, any_relu)))
fusing_patterns.append(schema.Fusing(operator_groups=(conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(any_binary, any_relu)))

# Create a TargetPlatformModel and set its default quantization config.
# This default configuration will be used for all operations
# unless specified otherwise (see OperatorsSet, for example):
generated_tpc = schema.TargetPlatformModel(
default_configuration_options,
default_qco=default_configuration_options,
tpc_minor_version=1,
tpc_patch_version=0,
tpc_platform_type=IMX500_TP_MODEL,
Expand Down
44 changes: 22 additions & 22 deletions ...compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v1_lut/tp_model.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -151,10 +151,10 @@ def generate_tp_model(default_config: OpQuantizationConfig,
# of possible configurations to consider when quantizing a set of operations (in mixed-precision, for example).
# If the QuantizationConfigOptions contains only one configuration,
# this configuration will be used for the operation quantization:
default_configuration_options = schema.QuantizationConfigOptions(tuple([default_config]))
default_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple([default_config]))

# Create Mixed-Precision quantization configuration options from the given list of OpQuantizationConfig objects
mixed_precision_configuration_options = schema.QuantizationConfigOptions(tuple(mixed_precision_cfg_list),
mixed_precision_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple(mixed_precision_cfg_list),
base_config=base_config)

# Create an OperatorsSet to represent a set of operations.
Expand All @@ -166,47 +166,47 @@ def generate_tp_model(default_config: OpQuantizationConfig,
fusing_patterns = []

# May suit for operations like: Dropout, Reshape, etc.
operator_set.append(schema.OperatorsSet("NoQuantization",
default_configuration_options.clone_and_edit(
operator_set.append(schema.OperatorsSet(name="NoQuantization",
qc_options=default_configuration_options.clone_and_edit(
enable_activation_quantization=False)
.clone_and_edit_weight_attribute(enable_weights_quantization=False)))

# Define operator sets that use mixed_precision_configuration_options:
conv = schema.OperatorsSet("Conv", mixed_precision_configuration_options)
fc = schema.OperatorsSet("FullyConnected", mixed_precision_configuration_options)
conv = schema.OperatorsSet(name="Conv", qc_options=mixed_precision_configuration_options)
fc = schema.OperatorsSet(name="FullyConnected", qc_options=mixed_precision_configuration_options)

# Define operations sets without quantization configuration
# options (useful for creating fusing patterns, for example):
any_relu = schema.OperatorsSet("AnyReLU")
add = schema.OperatorsSet("Add")
sub = schema.OperatorsSet("Sub")
mul = schema.OperatorsSet("Mul")
div = schema.OperatorsSet("Div")
prelu = schema.OperatorsSet("PReLU")
swish = schema.OperatorsSet("Swish")
sigmoid = schema.OperatorsSet("Sigmoid")
tanh = schema.OperatorsSet("Tanh")
any_relu = schema.OperatorsSet(name="AnyReLU")
add = schema.OperatorsSet(name="Add")
sub = schema.OperatorsSet(name="Sub")
mul = schema.OperatorsSet(name="Mul")
div = schema.OperatorsSet(name="Div")
prelu = schema.OperatorsSet(name="PReLU")
swish = schema.OperatorsSet(name="Swish")
sigmoid = schema.OperatorsSet(name="Sigmoid")
tanh = schema.OperatorsSet(name="Tanh")

operator_set.extend([conv, fc, any_relu, add, sub, mul, div, prelu, swish, sigmoid, tanh])
# Combine multiple operators into a single operator to avoid quantization between
# them. To do this we define fusing patterns using the OperatorsSets that were created.
# To group multiple sets with regard to fusing, an OperatorSetConcat can be created
activations_after_conv_to_fuse = schema.OperatorSetConcat([any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat([any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat([add, sub, mul, div])
activations_after_conv_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat(operators_set=[add, sub, mul, div])

# ------------------- #
# Fusions
# ------------------- #
fusing_patterns.append(schema.Fusing((conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing((fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing((any_binary, any_relu)))
fusing_patterns.append(schema.Fusing(operator_groups=(conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(any_binary, any_relu)))

# Create a TargetPlatformModel and set its default quantization config.
# This default configuration will be used for all operations
# unless specified otherwise (see OperatorsSet, for example):
generated_tpc = schema.TargetPlatformModel(
default_configuration_options,
default_qco=default_configuration_options,
tpc_minor_version=1,
tpc_patch_version=0,
tpc_platform_type=IMX500_TP_MODEL,
Expand Down
44 changes: 22 additions & 22 deletions ...compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v1_pot/tp_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -147,10 +147,10 @@ def generate_tp_model(default_config: OpQuantizationConfig,
# of possible configurations to consider when quantizing a set of operations (in mixed-precision, for example).
# If the QuantizationConfigOptions contains only one configuration,
# this configuration will be used for the operation quantization:
default_configuration_options = schema.QuantizationConfigOptions(tuple([default_config]))
default_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple([default_config]))

# Create Mixed-Precision quantization configuration options from the given list of OpQuantizationConfig objects
mixed_precision_configuration_options = schema.QuantizationConfigOptions(tuple(mixed_precision_cfg_list),
mixed_precision_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple(mixed_precision_cfg_list),
base_config=base_config)

# Create an OperatorsSet to represent a set of operations.
Expand All @@ -162,48 +162,48 @@ def generate_tp_model(default_config: OpQuantizationConfig,
fusing_patterns = []

# May suit for operations like: Dropout, Reshape, etc.
operator_set.append(schema.OperatorsSet("NoQuantization",
default_configuration_options.clone_and_edit(
operator_set.append(schema.OperatorsSet(name="NoQuantization",
qc_options=default_configuration_options.clone_and_edit(
enable_activation_quantization=False)
.clone_and_edit_weight_attribute(enable_weights_quantization=False)))

# Define operator sets that use mixed_precision_configuration_options:
conv = schema.OperatorsSet("Conv", mixed_precision_configuration_options)
fc = schema.OperatorsSet("FullyConnected", mixed_precision_configuration_options)
conv = schema.OperatorsSet(name="Conv", qc_options=mixed_precision_configuration_options)
fc = schema.OperatorsSet(name="FullyConnected", qc_options=mixed_precision_configuration_options)

# Define operations sets without quantization configuration
# options (useful for creating fusing patterns, for example):
any_relu = schema.OperatorsSet("AnyReLU")
add = schema.OperatorsSet("Add")
sub = schema.OperatorsSet("Sub")
mul = schema.OperatorsSet("Mul")
div = schema.OperatorsSet("Div")
prelu = schema.OperatorsSet("PReLU")
swish = schema.OperatorsSet("Swish")
sigmoid = schema.OperatorsSet("Sigmoid")
tanh = schema.OperatorsSet("Tanh")
any_relu = schema.OperatorsSet(name="AnyReLU")
add = schema.OperatorsSet(name="Add")
sub = schema.OperatorsSet(name="Sub")
mul = schema.OperatorsSet(name="Mul")
div = schema.OperatorsSet(name="Div")
prelu = schema.OperatorsSet(name="PReLU")
swish = schema.OperatorsSet(name="Swish")
sigmoid = schema.OperatorsSet(name="Sigmoid")
tanh = schema.OperatorsSet(name="Tanh")

operator_set.extend([conv, fc, any_relu, add, sub, mul, div, prelu, swish, sigmoid, tanh])

# Combine multiple operators into a single operator to avoid quantization between
# them. To do this we define fusing patterns using the OperatorsSets that were created.
# To group multiple sets with regard to fusing, an OperatorSetConcat can be created
activations_after_conv_to_fuse = schema.OperatorSetConcat([any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat([any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat([add, sub, mul, div])
activations_after_conv_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat(operators_set=[add, sub, mul, div])

# ------------------- #
# Fusions
# ------------------- #
fusing_patterns.append(schema.Fusing((conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing((fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing((any_binary, any_relu)))
fusing_patterns.append(schema.Fusing(operator_groups=(conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(any_binary, any_relu)))

# Create a TargetPlatformModel and set its default quantization config.
# This default configuration will be used for all operations
# unless specified otherwise (see OperatorsSet, for example):
generated_tpc = schema.TargetPlatformModel(
default_configuration_options,
default_qco=default_configuration_options,
tpc_minor_version=1,
tpc_patch_version=0,
tpc_platform_type=IMX500_TP_MODEL,
Expand Down
42 changes: 21 additions & 21 deletions model_compression_toolkit/target_platform_capabilities/tpc_models/imx500_tpc/v2/tp_model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -156,10 +156,10 @@ def generate_tp_model(default_config: OpQuantizationConfig,
# of possible configurations to consider when quantizing a set of operations (in mixed-precision, for example).
# If the QuantizationConfigOptions contains only one configuration,
# this configuration will be used for the operation quantization:
default_configuration_options = schema.QuantizationConfigOptions(tuple([default_config]))
default_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple([default_config]))

# Create Mixed-Precision quantization configuration options from the given list of OpQuantizationConfig objects
mixed_precision_configuration_options = schema.QuantizationConfigOptions(tuple(mixed_precision_cfg_list),
mixed_precision_configuration_options = schema.QuantizationConfigOptions(quantization_configurations=tuple(mixed_precision_cfg_list),
base_config=base_config)

# Create an OperatorsSet to represent a set of operations.
Expand All @@ -170,46 +170,46 @@ def generate_tp_model(default_config: OpQuantizationConfig,
operator_set = []
fusing_patterns = []
# May suit for operations like: Dropout, Reshape, etc.
operator_set.append(schema.OperatorsSet("NoQuantization", default_configuration_options.clone_and_edit(
operator_set.append(schema.OperatorsSet(name="NoQuantization", qc_options=default_configuration_options.clone_and_edit(
enable_activation_quantization=False).clone_and_edit_weight_attribute(enable_weights_quantization=False)))

# Define operator sets that use mixed_precision_configuration_options:
conv = schema.OperatorsSet("Conv", mixed_precision_configuration_options)
fc = schema.OperatorsSet("FullyConnected", mixed_precision_configuration_options)
conv = schema.OperatorsSet(name="Conv", qc_options=mixed_precision_configuration_options)
fc = schema.OperatorsSet(name="FullyConnected", qc_options=mixed_precision_configuration_options)

# Define operations sets without quantization configuration
# options (useful for creating fusing patterns, for example):
any_relu = schema.OperatorsSet("AnyReLU")
add = schema.OperatorsSet("Add")
sub = schema.OperatorsSet("Sub")
mul = schema.OperatorsSet("Mul")
div = schema.OperatorsSet("Div")
prelu = schema.OperatorsSet("PReLU")
swish = schema.OperatorsSet("Swish")
sigmoid = schema.OperatorsSet("Sigmoid")
tanh = schema.OperatorsSet("Tanh")
any_relu = schema.OperatorsSet(name="AnyReLU")
add = schema.OperatorsSet(name="Add")
sub = schema.OperatorsSet(name="Sub")
mul = schema.OperatorsSet(name="Mul")
div = schema.OperatorsSet(name="Div")
prelu = schema.OperatorsSet(name="PReLU")
swish = schema.OperatorsSet(name="Swish")
sigmoid = schema.OperatorsSet(name="Sigmoid")
tanh = schema.OperatorsSet(name="Tanh")

operator_set.extend([conv, fc, any_relu, add, sub, mul, div, prelu, swish, sigmoid, tanh])

# Combine multiple operators into a single operator to avoid quantization between
# them. To do this we define fusing patterns using the OperatorsSets that were created.
# To group multiple sets with regard to fusing, an OperatorSetConcat can be created
activations_after_conv_to_fuse = schema.OperatorSetConcat([any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat([any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat([add, sub, mul, div])
activations_after_conv_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, prelu, sigmoid, tanh])
activations_after_fc_to_fuse = schema.OperatorSetConcat(operators_set=[any_relu, swish, sigmoid])
any_binary = schema.OperatorSetConcat(operators_set=[add, sub, mul, div])

# ------------------- #
# Fusions
# ------------------- #
fusing_patterns.append(schema.Fusing((conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing((fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing((any_binary, any_relu)))
fusing_patterns.append(schema.Fusing(operator_groups=(conv, activations_after_conv_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(fc, activations_after_fc_to_fuse)))
fusing_patterns.append(schema.Fusing(operator_groups=(any_binary, any_relu)))

# Create a TargetPlatformModel and set its default quantization config.
# This default configuration will be used for all operations
# unless specified otherwise (see OperatorsSet, for example):
generated_tpm = schema.TargetPlatformModel(
default_configuration_options,
default_qco=default_configuration_options,
tpc_minor_version=2,
tpc_patch_version=0,
tpc_platform_type=IMX500_TP_MODEL,
Expand Down
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