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Merge pull request fastmachinelearning#90 from fastmachinelearning/ma…
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…tmul_mac_update

update in the matmul mac calculation
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maltanar authored Feb 13, 2024
2 parents 99841c1 + 6f8efa2 commit 39442cb
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2 changes: 1 addition & 1 deletion src/qonnx/analysis/inference_cost.py
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Expand Up @@ -134,7 +134,7 @@ def inference_cost_matmul(model, node, discount_sparsity):
if tB is not None and tB.i == 1:
w_shape = w_shape[::-1]
# exclude common dim (last axis) from one side to avoid duplication
n_macs = np.prod(i_shape[:-1]) * np.prod(w_shape)
n_macs = i_shape[-1] * np.prod(o_shape)
# deal with both dyn,param and dyn,dyn cases for weight memory
inp0_is_const = model.get_initializer(node.input[0]) is not None
inp1_is_const = model.get_initializer(node.input[1]) is not None
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Binary file added src/qonnx/data/onnx/matmul_update/sdp.onnx
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45 changes: 45 additions & 0 deletions tests/analysis/test_matmul_mac_cost.py
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import pytest
import qonnx
from pkgutil import get_data
import qonnx.util.inference_cost as infc
from qonnx.util.cleanup import cleanup_model
from qonnx.core.modelwrapper import ModelWrapper


def test_matmul_mac_cost():
raw_model = get_data("qonnx","data/onnx/matmul_update/sdp.onnx")
model = ModelWrapper(raw_model)
cleaned_model = cleanup_model(model)
# Two Matmul layers with shape (i_shape, w_shape, o_shape), L1: ([4, 64, 32], [4, 32, 64], [4, 64, 64]) and L2: ([4, 64, 64], [4, 64, 32], [4, 64, 32])
inf_cost_dict = infc.inference_cost(cleaned_model, discount_sparsity=False)
mac_cost = inf_cost_dict['op_mac_FLOAT32_FLOAT32'] # Expected mac cost 4*32*64*64 + 4*64*64*32 = 1048576
assert mac_cost == 1048576.0, "Error: discrepancy in mac cost."

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