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Reform the user-provided sm loop if it exceeds the layer size. Update…
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… pytest results.
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@JiacongSun
JiacongSun committed Nov 13, 2023
1 parent 20f541c commit c684a49
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Showing 13 changed files with 82 additions and 48 deletions.
2 changes: 1 addition & 1 deletion tests/main/test_origin/test_ascend_like.py
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Expand Up @@ -12,7 +12,7 @@
# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5738192980.375, 8728331),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1913797698.5250015, 7439255),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1913797698.5250015, 7426499),
"zigzag/inputs/examples/workload/resnet18.onnx": (1858697886.165, 3720129),
"zigzag.inputs.examples.workload.resnet18": (2408671233.7250004, 4804196),
}
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2 changes: 1 addition & 1 deletion tests/main/test_origin/test_meta_prototype_like.py
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Expand Up @@ -12,7 +12,7 @@
# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5771558839.89, 8400651),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1731935837.864999, 3606391),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1731935837.864999, 3594631),
"zigzag/inputs/examples/workload/resnet18.onnx": (1869519792.3449998, 3408373),
"zigzag.inputs.examples.workload.resnet18": (2419893343.4549994, 4176163),
}
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8 changes: 4 additions & 4 deletions tests/main/test_origin/test_tesla_npu_like.py
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Expand Up @@ -11,10 +11,10 @@

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (6131950030.816001, 8496179),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1671933042.2130003, 2964784),
"zigzag/inputs/examples/workload/resnet18.onnx": (1863717063.505, 3410738),
"zigzag.inputs.examples.workload.resnet18": (2375316568.8910007, 4096544),
"zigzag/inputs/examples/workload/alexnet.onnx": (6131950030.816001, 8486444),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1671933042.2130003, 2909436),
"zigzag/inputs/examples/workload/resnet18.onnx": (1863717063.505, 3395752),
"zigzag.inputs.examples.workload.resnet18": (2375316568.8910007, 4082454),
}


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4 changes: 2 additions & 2 deletions tests/main/test_origin/test_tpu_like.py
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Expand Up @@ -11,8 +11,8 @@

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5567502618.941999, 9080913),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1904494517.552001, 23131716),
"zigzag/inputs/examples/workload/alexnet.onnx": (5567502618.941999, 9078209),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1904494517.552001, 23112606),
"zigzag/inputs/examples/workload/resnet18.onnx": (1795904779.6570003, 4160591),
"zigzag.inputs.examples.workload.resnet18": (2296491401.491, 4909027),
}
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2 changes: 1 addition & 1 deletion tests/main/test_with_mix_spatial_mapping/test_ascend_like.py
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Expand Up @@ -14,7 +14,7 @@
# 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, 3835435),
"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),
}
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8 changes: 4 additions & 4 deletions tests/main/test_with_mix_spatial_mapping/test_edge_tpu_like.py
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Expand Up @@ -13,10 +13,10 @@

# 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),
"zigzag/inputs/examples/workload/alexnet.onnx": (5582059481.445, 8343378),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (819971935.77, 2430583),
"zigzag/inputs/examples/workload/resnet18.onnx": (1763135800.67, 5001291),
"zigzag.inputs.examples.workload.resnet18": (2090252961.0700002, 5858437),
}


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8 changes: 4 additions & 4 deletions tests/main/test_with_mix_spatial_mapping/test_meta_prototype_like.py
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Expand Up @@ -13,10 +13,10 @@

# 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),
"zigzag/inputs/examples/workload/alexnet.onnx": (5679695605, 8299150),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (901092009, 2610609),
"zigzag/inputs/examples/workload/resnet18.onnx": (1730672410, 3262009),
"zigzag.inputs.examples.workload.resnet18": (2265438430, 4017227),
}


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8 changes: 4 additions & 4 deletions tests/main/test_with_mix_spatial_mapping/test_tesla_npu_like.py
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Expand Up @@ -13,10 +13,10 @@

# 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),
"zigzag/inputs/examples/workload/alexnet.onnx": (6044768678, 8370470),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (930702060, 1965457),
"zigzag/inputs/examples/workload/resnet18.onnx": (1724869681, 3257898),
"zigzag.inputs.examples.workload.resnet18": (2220861655, 3934616),
}


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2 changes: 1 addition & 1 deletion tests/main/test_without_unused_memory/test_ascend_like.py
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Expand Up @@ -12,7 +12,7 @@
# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5649555894.9, 8637780),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1881386179.71, 6499441),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (1881386179.71, 6486685),
"zigzag/inputs/examples/workload/resnet18.onnx": (1709089377.83, 3583047),
"zigzag.inputs.examples.workload.resnet18": (2243493483.15, 4657130),
}
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6 changes: 3 additions & 3 deletions tests/main/test_without_unused_memory/test_tesla_npu_like.py
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Expand Up @@ -12,9 +12,9 @@
# 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": (930702060.6110002, 1969009),
"zigzag/inputs/examples/workload/resnet18.onnx": (1724869681.4799998, 3267252),
"zigzag.inputs.examples.workload.resnet18": (2220861655.6660004, 3943074),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (930702060.6110002, 1965457),
"zigzag/inputs/examples/workload/resnet18.onnx": (1724869681.4799998, 3257898),
"zigzag.inputs.examples.workload.resnet18": (2220861655.6660004, 3934616),
}


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4 changes: 2 additions & 2 deletions tests/main/test_without_unused_memory/test_tpu_like.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,8 @@

# Expected energy and latency for each workload defined above
ens_lats = {
"zigzag/inputs/examples/workload/alexnet.onnx": (5475639384.492001, 8981556),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (952688145.0069999, 21873319),
"zigzag/inputs/examples/workload/alexnet.onnx": (5475639384.492001, 8979956),
"zigzag/inputs/examples/workload/mobilenetv2.onnx": (952688145.0069999, 21873214),
"zigzag/inputs/examples/workload/resnet18.onnx": (1659252422.016, 4000289),
"zigzag.inputs.examples.workload.resnet18": (1982830786.5119998, 4509235),
}
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23 changes: 22 additions & 1 deletion zigzag/classes/opt/spatial/generator.py
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Expand Up @@ -134,7 +134,28 @@ def generate_user_spatial_mappings(
defined_mapping is not None
and defined_mapping.get(oa_dim.name) is not None
):
oa_dim_unrollings = [defined_mapping.get(oa_dim.name)]
# scale down the defined_mapping size if it exceeds the layer dim size
ori_loop = defined_mapping.get(oa_dim.name)
loop_to_reform = []
if self.is_nested_tuple(ori_loop): # mix sm loop
for sub_loop in ori_loop:
sub_loop_dim = sub_loop[0]
sub_loop_size = sub_loop[1]
if sub_loop_dim in self.layer.loop_dim_size.keys():
if sub_loop_size > self.layer.loop_dim_size[sub_loop_dim]:
sub_loop_size = self.layer.loop_dim_size[sub_loop_dim]
loop_to_reform.append((sub_loop_dim, sub_loop_size))
else: # single layer sm loop
loop_dim = ori_loop[0]
loop_size = ori_loop[1]
if loop_dim in self.layer.loop_dim_size.keys():
if loop_size > self.layer.loop_dim_size[loop_dim]:
loop_size = self.layer.loop_dim_size[loop_dim]
loop_to_reform.append((loop_dim, loop_size))
loop_to_reform = tuple(loop_to_reform)
if len(loop_to_reform) == 0:
loop_to_reform = None
oa_dim_unrollings = [loop_to_reform]
else:
oa_dim_unrollings = []
oa_dim_unrolling_hints = user_spatial_mapping_hint[oa_dim.name]
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53 changes: 33 additions & 20 deletions zigzag/classes/stages/SpatialMappingConversionStage.py
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Expand Up @@ -120,6 +120,7 @@ def convert_user_spatial_mapping(self, user_spatial_mapping):
oa_dim_name,
spatial_loop_element,
user_spatial_mapping,
limited_user_spatial_mapping,
)
)
limited_user_spatial_mapping_int_to_check = (
Expand All @@ -129,6 +130,7 @@ def convert_user_spatial_mapping(self, user_spatial_mapping):
oa_dim_name,
spatial_loop_element,
user_spatial_mapping,
limited_user_spatial_mapping,
False,
)
)
Expand Down Expand Up @@ -164,6 +166,7 @@ def convert_user_spatial_mapping(self, user_spatial_mapping):
oa_dim_name,
spatial_loop,
user_spatial_mapping,
limited_user_spatial_mapping,
)
)
limited_user_spatial_mapping_int_to_check = (
Expand All @@ -173,6 +176,7 @@ def convert_user_spatial_mapping(self, user_spatial_mapping):
oa_dim_name,
spatial_loop,
user_spatial_mapping,
limited_user_spatial_mapping,
False,
)
)
Expand Down Expand Up @@ -230,7 +234,8 @@ def generate_limited_user_spatial_mapping(
oa_dim_name,
spatial_loop,
user_spatial_mapping,
check_3=True,
limited_user_spatial_mapping,
allow_decimal_sm_loop_size=True,
):
## Do check on spatial mapping, and convert the mapping to a tuple
(loop_dim_unrolled, loop_size_unrolled) = spatial_loop
Expand All @@ -245,35 +250,43 @@ def generate_limited_user_spatial_mapping(
# Check 2: Limit unrolling if layer dimension is smaller than provided unrolling or if the loop dim doesn't exist
layer_dim_size = layer_dim_sizes.get(loop_dim_unrolled, 1)
loop_size_unrolled = min(layer_dim_size, loop_size_unrolled)
if check_3:
# Check 3: Adjust unrolling if it is not a multiple of the layer dimension size
# and if there is no more mapping for this layer dimension
no_more_mapping_for_current_layer_dim = (
self.check_if_there_is_further_oa_mapping_for_current_layer_dim(
# Check 3: Adjust unrolling if it is not a multiple of the layer dimension size
# and if there is no more mapping for this layer dimension
no_more_mapping_for_current_layer_dim = (
self.check_if_there_is_further_oa_mapping_for_current_layer_dim(
oa_dim_name=oa_dim_name,
loop_dim_unrolled=loop_dim_unrolled,
user_spatial_mapping=user_spatial_mapping,
)
)
if no_more_mapping_for_current_layer_dim:
loop_size_unrolled_on_early_oa_dims = (
self.calc_unrolled_loop_size_on_early_oa_dims(
oa_dim_name=oa_dim_name,
loop_dim_unrolled=loop_dim_unrolled,
user_spatial_mapping=user_spatial_mapping,
user_spatial_mapping=limited_user_spatial_mapping,
)
)
if no_more_mapping_for_current_layer_dim:
loop_size_unrolled_on_early_oa_dims = (
self.calc_unrolled_loop_size_on_early_oa_dims(
oa_dim_name=oa_dim_name,
loop_dim_unrolled=loop_dim_unrolled,
user_spatial_mapping=user_spatial_mapping,
)
)
temporal_remainder = int(
np.ceil(
layer_dim_size
/ (loop_size_unrolled * loop_size_unrolled_on_early_oa_dims)
)
temporal_remainder = int(
np.ceil(
layer_dim_size
/ (loop_size_unrolled * loop_size_unrolled_on_early_oa_dims)
)
)
if allow_decimal_sm_loop_size:
loop_size_unrolled = (
layer_dim_size
/ temporal_remainder
/ loop_size_unrolled_on_early_oa_dims
)
else:
loop_size_unrolled = int(
np.ceil(
layer_dim_size
/ temporal_remainder
/ loop_size_unrolled_on_early_oa_dims
)
)
return (
loop_dim_unrolled,
loop_size_unrolled,
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