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Merge pull request #31 from JiacongSun/master
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New Feature: Support User-provided Mix Spatial Mapping, And spatial_mapping_hint Definition Can Be Provided in the Mapping File for Mix Spatial Mapping Auto-generation.
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@asyms
asyms authored Nov 7, 2023
2 parents 54cf7c2 + c605f6b commit 198a43f
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Empty file added tests/main/test_with_mix_spatial_mapping/__init__.py
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63 changes: 63 additions & 0 deletions tests/main/test_with_mix_spatial_mapping/test_ascend_like.py
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import pytest

from zigzag.api import get_hardware_performance_zigzag_with_mix_spatial_mapping

# Test case for when spatial_mapping is provided, while spatial_mapping_hint is not provided.

workloads = (
"zigzag/inputs/examples/workload/alexnet.onnx",
"zigzag/inputs/examples/workload/mobilenetv2.onnx",
"zigzag/inputs/examples/workload/resnet18.onnx",
"zigzag.inputs.examples.workload.resnet18",
)

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5667407342.66, 8528846),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (921552096.0700004, 3828967),
"zigzag/inputs/examples/workload/resnet18.onnx": (1679218425.5100002, 3713386),
"zigzag.inputs.examples.workload.resnet18": (2290766279.31, 4442443),
}


@pytest.fixture
def mapping():
ascend_like_mapping = {
"default": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("K", 16),
"D2": (("C", 4), ("FX", 3)),
"D3": ("OX", 2),
"D4": ("OY", 2),
},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
"Add": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("G", 16),
"D2": ("C", 1),
"D3": ("OX", 1),
"D4": ("OY", 1),
},
"memory_operand_links": {"O": "O", "X": "I2", "Y": "I1"},
},
}

return ascend_like_mapping


@pytest.fixture
def accelerator():
return "zigzag.inputs.examples.hardware.Ascend_like"


@pytest.mark.parametrize("workload", workloads)
def test_api(workload, accelerator, mapping):
(energy, latency, cmes) = get_hardware_performance_zigzag_with_mix_spatial_mapping(
workload, accelerator, mapping
)
(expected_energy, expected_latency) = ens_lats[workload]
assert energy == pytest.approx(expected_energy)
assert latency == pytest.approx(expected_latency)
79 changes: 79 additions & 0 deletions tests/main/test_with_mix_spatial_mapping/test_edge_tpu_like.py
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import pytest

from zigzag.api import get_hardware_performance_zigzag_with_mix_spatial_mapping

# Test case for when both spatial_mapping and spatial_mapping_hint are provided.

workloads = (
"zigzag/inputs/examples/workload/alexnet.onnx",
"zigzag/inputs/examples/workload/mobilenetv2.onnx",
"zigzag/inputs/examples/workload/resnet18.onnx",
"zigzag.inputs.examples.workload.resnet18",
)

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5582430184.085, 8343378),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (762066732.5049998, 3003074),
"zigzag/inputs/examples/workload/resnet18.onnx": (1743190534.155, 5305825),
"zigzag.inputs.examples.workload.resnet18": (2087322696.315, 6155355),
}


@pytest.fixture
def mapping():
edge_tpu_like_mapping = {
"default": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("K", 8),
"D2": (("C", 2), ("FX", 2), ("FY", 2)),
"D3": (("OX", 2), ("OY", 2)),
"D4": (("OX", 2), ("OY", 2)),
},
# spatial_mapping_hint will not work if the mapping on every dimension is provided in spatial_mapping
"spatial_mapping_hint": {
"D1": ["K"],
"D2": ["C", "FX", "FY"],
"D3": ["OX", "OY"],
"D4": ["OX", "OY"],
},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
"Add": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("G", 8),
"D2": ("C", 1),
"D3": ("OX", 1),
"D4": ("OY", 1),
},
"memory_operand_links": {"O": "O", "X": "I2", "Y": "I1"},
},
"Pooling": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("G", 8),
"D2": ("C", 1),
"D3": ("OX", 1),
"D4": ("OY", 1),
},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
}
return edge_tpu_like_mapping


@pytest.fixture
def accelerator():
return "zigzag.inputs.examples.hardware.Edge_TPU_like"


@pytest.mark.parametrize("workload", workloads)
def test_api(workload, accelerator, mapping):
(energy, latency, cmes) = get_hardware_performance_zigzag_with_mix_spatial_mapping(
workload, accelerator, mapping
)
(expected_energy, expected_latency) = ens_lats[workload]
assert energy == pytest.approx(expected_energy)
assert latency == pytest.approx(expected_latency)
63 changes: 63 additions & 0 deletions tests/main/test_with_mix_spatial_mapping/test_meta_prototype_like.py
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import pytest

from zigzag.api import get_hardware_performance_zigzag_with_mix_spatial_mapping

# Test case for when an incomplete spatial_mapping is provided and spatial_mapping_hint is also provided.

workloads = (
"zigzag/inputs/examples/workload/alexnet.onnx",
"zigzag/inputs/examples/workload/mobilenetv2.onnx",
"zigzag/inputs/examples/workload/resnet18.onnx",
"zigzag.inputs.examples.workload.resnet18",
)

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5681909351.240001, 8299150),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (919452681.2249999, 2894129),
"zigzag/inputs/examples/workload/resnet18.onnx": (1789888904.4450002, 3472280),
"zigzag.inputs.examples.workload.resnet18": (2348207081.7949996, 4238517),
}


@pytest.fixture
def mapping():
meta_prototype_like_mapping = {
"default": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("K", 32),
# "D2": ("C", 2),
"D3": (("OX", 2), ("OY", 2)),
"D4": (("OX", 2), ("OY", 2)),
},
"spatial_mapping_hint": {"D2": ["C"]},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
"Add": {
"core_allocation": 1,
"spatial_mapping": {
"D1": ("G", 32),
"D2": ("C", 1),
"D3": ("OX", 1),
"D4": ("OY", 1),
},
"memory_operand_links": {"O": "O", "X": "I2", "Y": "I1"},
},
}
return meta_prototype_like_mapping


@pytest.fixture
def accelerator():
return "zigzag.inputs.examples.hardware.Meta_prototype"


@pytest.mark.parametrize("workload", workloads)
def test_api(workload, accelerator, mapping):
(energy, latency, cmes) = get_hardware_performance_zigzag_with_mix_spatial_mapping(
workload, accelerator, mapping
)
(expected_energy, expected_latency) = ens_lats[workload]
assert energy == pytest.approx(expected_energy)
assert latency == pytest.approx(expected_latency)
59 changes: 59 additions & 0 deletions tests/main/test_with_mix_spatial_mapping/test_tesla_npu_like.py
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import pytest

from zigzag.api import get_hardware_performance_zigzag_with_mix_spatial_mapping

# Test case for when only spatial_mapping_hint is provided.

workloads = (
"zigzag/inputs/examples/workload/alexnet.onnx",
"zigzag/inputs/examples/workload/mobilenetv2.onnx",
"zigzag/inputs/examples/workload/resnet18.onnx",
"zigzag.inputs.examples.workload.resnet18",
)

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (6040086796.366001, 8389669),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (958401881.3470002, 1964453),
"zigzag/inputs/examples/workload/resnet18.onnx": (1724869681.4799998, 3257898),
"zigzag.inputs.examples.workload.resnet18": (2220861655.6660004, 3934616),
}


@pytest.fixture
def mapping():
tesla_npu_like_mapping = {
"default": {
"core_allocation": 1,
# "spatial_mapping": {"D1": ("K", 32), "D2": ("OX", 8), "D3": ("OY", 4)},
"spatial_mapping_hint": {"D1": ["K"], "D2": ["OX", "OY"]},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
"Add": {
"core_allocation": 1,
"spatial_mapping": {"D1": ("G", 32), "D2": ("OX", 1), "D3": ("OY", 1)},
"memory_operand_links": {"O": "O", "X": "I2", "Y": "I1"},
},
"Pooling": {
"core_allocation": 1,
"spatial_mapping": {"D1": ("G", 32), "D2": ("OX", 1), "D3": ("OY", 1)},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
}

return tesla_npu_like_mapping


@pytest.fixture
def accelerator():
return "zigzag.inputs.examples.hardware.Tesla_NPU_like"


@pytest.mark.parametrize("workload", workloads)
def test_api(workload, accelerator, mapping):
(energy, latency, cmes) = get_hardware_performance_zigzag_with_mix_spatial_mapping(
workload, accelerator, mapping
)
(expected_energy, expected_latency) = ens_lats[workload]
assert energy == pytest.approx(expected_energy)
assert latency == pytest.approx(expected_latency)
68 changes: 68 additions & 0 deletions tests/main/test_with_mix_spatial_mapping/test_tpu_like.py
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import pytest

from zigzag.api import get_hardware_performance_zigzag_with_mix_spatial_mapping

# Test case for when more non-existent dimensions are provided in spatial_mapping_hint.

workloads = (
"zigzag/inputs/examples/workload/alexnet.onnx",
"zigzag/inputs/examples/workload/mobilenetv2.onnx",
"zigzag/inputs/examples/workload/resnet18.onnx",
"zigzag.inputs.examples.workload.resnet18",
)

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5468347515.336, 8210374),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (730691567.0230001, 3358406),
"zigzag/inputs/examples/workload/resnet18.onnx": (1648700710.272, 2946593),
"zigzag.inputs.examples.workload.resnet18": (1972279074.768, 3455539),
}


@pytest.fixture
def mapping():
tpu_like_mapping = {
"default": {
"core_allocation": 1,
# "spatial_mapping": {
# "D1": ("K", 32),
# "D2": (("C", 2), ("FX", 3), ("FY", 3)),
# },
# D3 and D4 in spatial_mapping_hint will not work, since they do not exist in the hardware dimensions.
"spatial_mapping_hint": {
"D1": ["K"],
"D2": ["C", "FX", "FY"],
"D3": ["K", "OX"],
"D4": ["OX", "OY"],
},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
"Add": {
"core_allocation": 1,
"spatial_mapping": {"D1": ("G", 32), "D2": ("C", 1)},
"memory_operand_links": {"O": "O", "X": "I2", "Y": "I1"},
},
"Pooling": {
"core_allocation": 1,
"spatial_mapping": {"D1": ("G", 32), "D2": ("C", 1)},
"memory_operand_links": {"O": "O", "W": "I2", "I": "I1"},
},
}

return tpu_like_mapping


@pytest.fixture
def accelerator():
return "zigzag.inputs.examples.hardware.TPU_like"


@pytest.mark.parametrize("workload", workloads)
def test_api(workload, accelerator, mapping):
(energy, latency, cmes) = get_hardware_performance_zigzag_with_mix_spatial_mapping(
workload, accelerator, mapping
)
(expected_energy, expected_latency) = ens_lats[workload]
assert energy == pytest.approx(expected_energy)
assert latency == pytest.approx(expected_latency)
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