⚡️ Speed up method `LoRALayer.calc_size` by 38% #139

codeflash-ai · 2025-11-12T05:41:38Z

📄 38% (0.38x) speedup for `LoRALayer.calc_size` in `invokeai/backend/patches/layers/lora_layer.py`

⏱️ Runtime : 97.8 microseconds → 70.9 microseconds (best of 44 runs)

📝 Explanation and details

The optimization achieves a 37% speedup by inlining tensor size calculations and eliminating function call overhead in the calc_tensors_size utility function.

Key optimization: The original code used a generator expression with sum() that called calc_tensor_size() for each tensor:

return sum(calc_tensor_size(t) for t in tensors if t is not None)

The optimized version replaces this with a direct loop that inlines the tensor size calculation:

total = 0
for t in tensors:
    if t is not None:
        total += t.nelement() * t.element_size()
return total

Why this is faster:

Eliminates function call overhead: Instead of calling calc_tensor_size() for each tensor (which involves Python function call stack operations), the calculation is performed directly inline
Reduces generator expression overhead: Direct loops are generally faster than generator expressions with sum() in Python
Fewer Python bytecode operations: The inlined approach requires fewer bytecode instructions per tensor

Performance characteristics from tests:

Most test cases show 30-45% speedup, with some edge cases reaching 45.7% improvement
The optimization is particularly effective for LoRA layers with multiple tensors (up, mid, down, bias)
Benefits scale well with the number of tensors being processed, as each tensor avoids the function call overhead

This optimization is especially valuable because calc_size() methods are likely called frequently during model loading, memory management, and optimization passes in ML frameworks like InvokeAI, where LoRA layers are commonly used for efficient model adaptation.

✅ Correctness verification report:

Test	Status
⚙️ Existing Unit Tests	✅ 9 Passed
🌀 Generated Regression Tests	✅ 101 Passed
⏪ Replay Tests	🔘 None Found
🔎 Concolic Coverage Tests	🔘 None Found
📊 Tests Coverage	100.0%

⚙️ Existing Unit Tests and Runtime

Test File::Test Function	Original ⏱️	Optimized ⏱️	Speedup
`backend/patches/layers/test_lora_layer.py::test_lora_layer_calc_size`	6.11μs	4.71μs	29.6%✅

🌀 Generated Regression Tests and Runtime

from typing import Optional

imports

import pytest
import torch
from invokeai.backend.patches.layers.lora_layer import LoRALayer

def calc_tensor_size(t: torch.Tensor) -> int:
"""Calculate the size of a tensor in bytes."""
return t.nelement() * t.element_size()
from invokeai.backend.patches.layers.lora_layer import LoRALayer

------------------------ UNIT TESTS ------------------------

----------- BASIC TEST CASES -----------

def test_calc_size_basic_all_float32():
"""Test with small float32 tensors for up, mid, down, and bias."""
up = torch.ones((2, 3), dtype=torch.float32)
mid = torch.ones((3, 4), dtype=torch.float32)
down = torch.ones((4, 2), dtype=torch.float32)
bias = torch.ones((2,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
# Each float32 element is 4 bytes
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 4.42μs -> 3.20μs (38.1% faster)

def test_calc_size_basic_none_mid():
"""Test with mid=None, up/down/bias present."""
up = torch.ones((2, 2), dtype=torch.float32)
mid = None
down = torch.ones((2, 2), dtype=torch.float32)
bias = torch.ones((2,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=0.5, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 3.78μs -> 2.72μs (39.0% faster)

def test_calc_size_basic_none_bias():
"""Test with bias=None, up/mid/down present."""
up = torch.ones((2, 2), dtype=torch.float32)
mid = torch.ones((2, 2), dtype=torch.float32)
down = torch.ones((2, 2), dtype=torch.float32)
bias = None
layer = LoRALayer(up, mid, down, alpha=2.0, bias=bias)
expected = (
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 3.52μs -> 2.48μs (41.9% faster)

def test_calc_size_basic_none_mid_and_bias():
"""Test with both mid and bias as None."""
up = torch.ones((2, 2), dtype=torch.float32)
mid = None
down = torch.ones((2, 2), dtype=torch.float32)
bias = None
layer = LoRALayer(up, mid, down, alpha=None, bias=bias)
expected = (
calc_tensor_size(up) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 3.50μs -> 2.42μs (44.7% faster)

def test_calc_size_basic_different_dtypes():
"""Test with tensors of different dtypes."""
up = torch.ones((2, 2), dtype=torch.float64) # 8 bytes per element
mid = torch.ones((2, 2), dtype=torch.int32) # 4 bytes per element
down = torch.ones((2, 2), dtype=torch.int8) # 1 byte per element
bias = torch.ones((2,), dtype=torch.float16) # 2 bytes per element
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 3.95μs -> 2.75μs (43.3% faster)

----------- EDGE TEST CASES -----------

def test_calc_size_edge_empty_tensors():
"""Test with tensors that have zero elements (empty shape)."""
up = torch.empty((0, 2), dtype=torch.float32)
mid = torch.empty((2, 0), dtype=torch.float32)
down = torch.empty((0, 0), dtype=torch.float32)
bias = torch.empty((0,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = 0
codeflash_output = layer.calc_size() # 4.53μs -> 3.33μs (35.8% faster)

def test_calc_size_edge_single_element_tensors():
"""Test with tensors containing a single element."""
up = torch.ones((1,), dtype=torch.float32)
mid = torch.ones((1,), dtype=torch.float32)
down = torch.ones((1,), dtype=torch.float32)
bias = torch.ones((1,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = 4 * 4 # 4 tensors, each with 1 float32 element (4 bytes)
codeflash_output = layer.calc_size()

def test_calc_size_edge_large_dtype_difference():
"""Test with tensors of maximum and minimum element sizes."""
up = torch.ones((2,), dtype=torch.int8) # 1 byte per element
mid = torch.ones((2,), dtype=torch.float64) # 8 bytes per element
down = torch.ones((2,), dtype=torch.float16) # 2 bytes per element
bias = torch.ones((2,), dtype=torch.int64) # 8 bytes per element
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size()

def test_calc_size_edge_bias_only():
"""Test with only bias present, up/mid/down are None or zero-size."""
up = torch.empty((0,), dtype=torch.float32)
mid = None
down = torch.empty((0,), dtype=torch.float32)
bias = torch.ones((3,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=0.0, bias=bias)
expected = calc_tensor_size(bias)
codeflash_output = layer.calc_size()

def test_calc_size_edge_all_none():
"""Test with all tensors as None (should raise TypeError on up/down)."""
with pytest.raises(TypeError):
# up/down cannot be None, LoRALayer expects torch.Tensor
LoRALayer(None, None, None, alpha=None, bias=None)

def test_calc_size_edge_bias_is_zero_tensor():
"""Test with bias as a tensor containing all zeros."""
up = torch.ones((2, 2), dtype=torch.float32)
mid = torch.ones((2, 2), dtype=torch.float32)
down = torch.ones((2, 2), dtype=torch.float32)
bias = torch.zeros((2,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 4.51μs -> 3.10μs (45.5% faster)

----------- LARGE SCALE TEST CASES -----------

def test_calc_size_large_scale_100x100_float32():
"""Test with large 100x100 float32 tensors, all present."""
up = torch.ones((100, 100), dtype=torch.float32)
mid = torch.ones((100, 100), dtype=torch.float32)
down = torch.ones((100, 100), dtype=torch.float32)
bias = torch.ones((100,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 4.01μs -> 3.08μs (30.0% faster)

def test_calc_size_large_scale_500x2_float64():
"""Test with large float64 tensors, ensuring <100MB total."""
up = torch.ones((500, 2), dtype=torch.float64) # 8000 bytes
mid = torch.ones((2, 500), dtype=torch.float64) # 8000 bytes
down = torch.ones((500, 2), dtype=torch.float64) # 8000 bytes
bias = torch.ones((1000,), dtype=torch.float64) # 8000 bytes
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 4.04μs -> 2.91μs (38.9% faster)

def test_calc_size_large_scale_sparse_mid():
"""Test with large up/down, mid=None, bias present."""
up = torch.ones((1000, 1), dtype=torch.float32)
mid = None
down = torch.ones((1, 1000), dtype=torch.float32)
bias = torch.ones((100,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 3.82μs -> 2.71μs (40.7% faster)

def test_calc_size_large_scale_bias_none():
"""Test with large up/mid/down, bias=None."""
up = torch.ones((500, 500), dtype=torch.float32)
mid = torch.ones((500, 500), dtype=torch.float32)
down = torch.ones((500, 500), dtype=torch.float32)
bias = None
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 4.96μs -> 3.41μs (45.4% faster)

def test_calc_size_large_scale_varied_shapes():
"""Test with up/mid/down/bias of different shapes and dtypes."""
up = torch.ones((100, 10), dtype=torch.float32)
mid = torch.ones((10, 100), dtype=torch.float16)
down = torch.ones((50, 20), dtype=torch.int32)
bias = torch.ones((200,), dtype=torch.float64)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = layer.calc_size() # 4.02μs -> 3.09μs (29.9% faster)

----------- DETERMINISM TEST -----------

def test_calc_size_determinism():
"""Test that repeated calls with the same tensors yield the same result."""
up = torch.ones((10, 10), dtype=torch.float32)
mid = torch.ones((10, 10), dtype=torch.float32)
down = torch.ones((10, 10), dtype=torch.float32)
bias = torch.ones((10,), dtype=torch.float32)
layer = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
codeflash_output = layer.calc_size(); result1 = codeflash_output # 4.65μs -> 3.44μs (35.0% faster)
codeflash_output = layer.calc_size(); result2 = codeflash_output # 2.30μs -> 1.69μs (36.4% faster)

----------- ERROR HANDLING TESTS -----------

def test_calc_size_error_non_tensor_up():
"""Test that passing a non-tensor for up raises TypeError."""
with pytest.raises(AttributeError):
LoRALayer("not_a_tensor", None, torch.ones((2,2)), alpha=1.0, bias=None)

def test_calc_size_error_non_tensor_down():
"""Test that passing a non-tensor for down raises TypeError."""
with pytest.raises(AttributeError):
LoRALayer(torch.ones((2,2)), None, "not_a_tensor", alpha=1.0, bias=None)

def test_calc_size_error_non_tensor_mid():
"""Test that passing a non-tensor for mid raises TypeError."""
with pytest.raises(AttributeError):
LoRALayer(torch.ones((2,2)), "not_a_tensor", torch.ones((2,2)), alpha=1.0, bias=None)

def test_calc_size_error_non_tensor_bias():
"""Test that passing a non-tensor for bias raises AttributeError (when not None)."""
up = torch.ones((2,2))
mid = torch.ones((2,2))
down = torch.ones((2,2))
with pytest.raises(AttributeError):
LoRALayer(up, mid, down, alpha=1.0, bias="not_a_tensor")

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

#------------------------------------------------
import pytest
import torch
from invokeai.backend.patches.layers.lora_layer import LoRALayer

Function to test (LoRALayer.calc_size and dependencies)

def calc_tensor_size(t: torch.Tensor) -> int:
"""Calculate the size of a tensor in bytes."""
return t.nelement() * t.element_size()
from invokeai.backend.patches.layers.lora_layer import LoRALayer

=======================

Unit Test Suite for LoRALayer.calc_size

=======================

----------- Basic Test Cases ------------

def test_basic_all_tensors_present():
"""Test with all tensors present and small sizes."""
up = torch.ones((2, 3), dtype=torch.float32)
mid = torch.ones((3, 3), dtype=torch.float32)
down = torch.ones((3, 2), dtype=torch.float32)
bias = torch.ones((2,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 3.96μs -> 2.99μs (32.8% faster)

def test_basic_mid_is_none():
"""Test with mid tensor as None."""
up = torch.ones((2, 3), dtype=torch.float32)
mid = None
down = torch.ones((3, 2), dtype=torch.float32)
bias = torch.ones((2,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 3.65μs -> 2.56μs (42.6% faster)

def test_basic_bias_is_none():
"""Test with bias tensor as None."""
up = torch.ones((2, 3), dtype=torch.float32)
mid = torch.ones((3, 3), dtype=torch.float32)
down = torch.ones((3, 2), dtype=torch.float32)
bias = None
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 3.64μs -> 2.53μs (44.2% faster)

def test_basic_all_none_except_up_down():
"""Test with mid and bias as None."""
up = torch.ones((2, 3), dtype=torch.float32)
mid = None
down = torch.ones((3, 2), dtype=torch.float32)
bias = None
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(up) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 3.35μs -> 2.47μs (35.9% faster)

----------- Edge Test Cases ------------

def test_edge_empty_tensors():
"""Test with tensors of zero elements (shape (0,))."""
up = torch.empty((0,), dtype=torch.float32)
mid = torch.empty((0,), dtype=torch.float32)
down = torch.empty((0,), dtype=torch.float32)
bias = torch.empty((0,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = 0
codeflash_output = lora.calc_size()

def test_edge_scalar_tensors():
"""Test with tensors of shape () (scalar)."""
up = torch.tensor(1.0, dtype=torch.float32)
mid = torch.tensor(2.0, dtype=torch.float32)
down = torch.tensor(3.0, dtype=torch.float32)
bias = torch.tensor(4.0, dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size()

def test_edge_different_dtypes():
"""Test with tensors of different dtypes."""
up = torch.ones((2, 3), dtype=torch.float64)
mid = torch.ones((3, 3), dtype=torch.int32)
down = torch.ones((3, 2), dtype=torch.float16)
bias = torch.ones((2,), dtype=torch.uint8)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 4.42μs -> 3.13μs (41.3% faster)

def test_edge_non_contiguous_tensor():
"""Test with non-contiguous tensors (e.g., transposed)."""
up = torch.ones((2, 3), dtype=torch.float32).t() # Now shape (3,2), non-contiguous
mid = torch.ones((3, 3), dtype=torch.float32)
down = torch.ones((3, 2), dtype=torch.float32)
bias = torch.ones((2,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 3.96μs -> 2.91μs (36.3% faster)

def test_edge_large_tensor_but_under_limit():
"""Test with a single large tensor, but total size < 100MB."""
# float32 = 4 bytes, so 25_000_000 elements = 100MB
# We'll use 10_000_000 elements for safety
up = torch.ones((10_000_000,), dtype=torch.float32)
mid = None
down = None
bias = None
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = calc_tensor_size(up)
codeflash_output = lora.calc_size()

def test_edge_up_down_shape_mismatch():
"""Test that are_ranks_equal is set correctly for shape mismatch, but calc_size is unaffected."""
up = torch.ones((2, 4), dtype=torch.float32)
mid = torch.ones((4, 4), dtype=torch.float32)
down = torch.ones((3, 2), dtype=torch.float32) # up.shape[1] != down.shape[0]
bias = torch.ones((2,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 4.32μs -> 3.16μs (36.6% faster)

def test_edge_up_down_shape_equal():
"""Test that are_ranks_equal is True for matching shapes."""
up = torch.ones((2, 3), dtype=torch.float32)
mid = torch.ones((3, 3), dtype=torch.float32)
down = torch.ones((3, 2), dtype=torch.float32) # up.shape[1] == down.shape[0]
bias = torch.ones((2,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 4.09μs -> 2.80μs (45.7% faster)

----------- Large Scale Test Cases ------------

def test_large_scale_many_elements():
"""Test with large tensors, total size under 100MB."""
# float32 = 4 bytes, so 5_000_000 elements = 20MB
up = torch.ones((5_000_000,), dtype=torch.float32)
mid = torch.ones((5_000_000,), dtype=torch.float32)
down = torch.ones((5_000_000,), dtype=torch.float32)
bias = torch.ones((5_000_000,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size()

def test_large_scale_many_small_tensors():
"""Test with many small tensors (simulate batch)."""
# Each tensor is small, but there are many
tensors = [torch.ones((10,), dtype=torch.float32) for _ in range(999)]
# We'll sum up their sizes for expected_size
up = tensors[0]
mid = tensors[1]
down = tensors[2]
bias = tensors[3]
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size()

def test_large_scale_high_dimensional_tensors():
"""Test with high-dimensional tensors, but total size < 100MB."""
up = torch.ones((5, 5, 5, 5, 5, 5), dtype=torch.float32) # 5^6 = 15625 elements
mid = torch.ones((5, 5, 5, 5, 5, 5), dtype=torch.float32)
down = torch.ones((5, 5, 5, 5, 5, 5), dtype=torch.float32)
bias = torch.ones((5, 5, 5, 5, 5, 5), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(bias) +
calc_tensor_size(up) +
calc_tensor_size(mid) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size() # 4.30μs -> 3.29μs (30.7% faster)

def test_large_scale_mixed_none_and_large():
"""Test with some tensors None and some large."""
up = torch.ones((10_000_000,), dtype=torch.float32)
mid = None
down = torch.ones((10_000_000,), dtype=torch.float32)
bias = None
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
expected_size = (
calc_tensor_size(up) +
calc_tensor_size(down)
)
codeflash_output = lora.calc_size()

----------- Determinism Test ------------

def test_determinism_repeatability():
"""Test that repeated calls to calc_size give the same result."""
up = torch.ones((100,), dtype=torch.float32)
mid = torch.ones((100,), dtype=torch.float32)
down = torch.ones((100,), dtype=torch.float32)
bias = torch.ones((100,), dtype=torch.float32)
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
sizes = [lora.calc_size() for _ in range(10)]

----------- Type Robustness Test ------------

def test_type_error_on_non_tensor():
"""Test that passing a non-tensor raises an AttributeError (since .nelement() would fail)."""
up = "not a tensor"
mid = None
down = torch.ones((2, 2), dtype=torch.float32)
bias = None
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
with pytest.raises(AttributeError):
lora.calc_size()

def test_type_error_on_list_instead_of_tensor():
"""Test that passing a list instead of tensor raises an AttributeError."""
up = [1, 2, 3]
mid = None
down = torch.ones((2, 2), dtype=torch.float32)
bias = None
lora = LoRALayer(up, mid, down, alpha=1.0, bias=bias)
with pytest.raises(AttributeError):
lora.calc_size()

----------- LoRALayerBase Coverage ------------

To edit these changes git checkout codeflash/optimize-LoRALayer.calc_size-mhvkodo7 and push.

The optimization achieves a 37% speedup by **inlining tensor size calculations** and **eliminating function call overhead** in the `calc_tensors_size` utility function. **Key optimization**: The original code used a generator expression with `sum()` that called `calc_tensor_size()` for each tensor: ```python return sum(calc_tensor_size(t) for t in tensors if t is not None) ``` The optimized version replaces this with a direct loop that inlines the tensor size calculation: ```python total = 0 for t in tensors: if t is not None: total += t.nelement() * t.element_size() return total ``` **Why this is faster**: 1. **Eliminates function call overhead**: Instead of calling `calc_tensor_size()` for each tensor (which involves Python function call stack operations), the calculation is performed directly inline 2. **Reduces generator expression overhead**: Direct loops are generally faster than generator expressions with `sum()` in Python 3. **Fewer Python bytecode operations**: The inlined approach requires fewer bytecode instructions per tensor **Performance characteristics from tests**: - Most test cases show 30-45% speedup, with some edge cases reaching 45.7% improvement - The optimization is particularly effective for LoRA layers with multiple tensors (up, mid, down, bias) - Benefits scale well with the number of tensors being processed, as each tensor avoids the function call overhead This optimization is especially valuable because `calc_size()` methods are likely called frequently during model loading, memory management, and optimization passes in ML frameworks like InvokeAI, where LoRA layers are commonly used for efficient model adaptation.

codeflash-ai bot requested a review from mashraf-222 November 12, 2025 05:41

codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Nov 12, 2025

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⚡️ Speed up method `LoRALayer.calc_size` by 38% #139

⚡️ Speed up method `LoRALayer.calc_size` by 38% #139

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⚡️ Speed up method LoRALayer.calc_size by 38% #139

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⚡️ Speed up method LoRALayer.calc_size by 38% #139

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codeflash-ai bot commented Nov 12, 2025

📄 38% (0.38x) speedup for LoRALayer.calc_size in invokeai/backend/patches/layers/lora_layer.py

📝 Explanation and details

imports

------------------------ UNIT TESTS ------------------------

----------- BASIC TEST CASES -----------

----------- EDGE TEST CASES -----------

----------- LARGE SCALE TEST CASES -----------

----------- DETERMINISM TEST -----------

----------- ERROR HANDLING TESTS -----------

codeflash_output is used to check that the output of the original code is the same as that of the optimized code.

Function to test (LoRALayer.calc_size and dependencies)

=======================

Unit Test Suite for LoRALayer.calc_size

=======================

----------- Basic Test Cases ------------

----------- Edge Test Cases ------------

----------- Large Scale Test Cases ------------

----------- Determinism Test ------------

----------- Type Robustness Test ------------

----------- LoRALayerBase Coverage ------------

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⚡️ Speed up method `LoRALayer.calc_size` by 38% #139

⚡️ Speed up method `LoRALayer.calc_size` by 38% #139

📄 38% (0.38x) speedup for `LoRALayer.calc_size` in `invokeai/backend/patches/layers/lora_layer.py`