chore: optimize compute_smooth_weight #4390
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New implmenetation is obviously more efficient. Signed-off-by: Jinzhe Zeng <[email protected]>
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📝 Walkthrough📝 WalkthroughWalkthroughThe pull request introduces modifications to the Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant Function as compute_smooth_weight
participant Clipping as xp.clip
User->>Function: Call compute_smooth_weight(distance, rmin, rmax)
Function->>Clipping: Clip distance to [rmin, rmax]
Clipping-->>Function: Clipped distance
Function->>Function: Calculate uu and uu2
Function->>Function: Calculate vv using uu2
Function-->>User: Return vv
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🧹 Outside diff range and nitpick comments (2)
deepmd/pt/utils/preprocess.py (1)
15-17: LGTM! Clear polynomial calculation with good variable namingThe introduction of
uu2improves readability by avoiding repeated multiplication. The polynomial calculation is clear and well-structured.Consider using torch.pow for slightly better performance:
- uu2 = uu * uu + uu2 = torch.pow(uu, 2)This could potentially leverage hardware-specific optimizations in PyTorch's implementation.
deepmd/dpmodel/utils/env_mat.py (1)
30-32: Consider Horner's method for polynomial evaluationThe polynomial calculation can be further optimized using Horner's method:
- uu2 = uu * uu - vv = uu2 * uu * (-6.0 * uu2 + 15.0 * uu - 10.0) + 1.0 + # Horner's method: -6x^5 + 15x^4 - 10x^3 + 1 + vv = 1.0 + uu * (-10.0 + uu * (15.0 + uu * -6.0)) * uu * uuThis form:
- Reduces the number of multiplications
- Often results in better numerical stability
- Is the standard method for efficient polynomial evaluation
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📒 Files selected for processing (2)
deepmd/dpmodel/utils/env_mat.py(1 hunks)deepmd/pt/utils/preprocess.py(1 hunks)
🔇 Additional comments (3)
deepmd/pt/utils/preprocess.py (1)
13-13: LGTM! Efficient use of torch.clamp
The switch from masking to clipping is a good optimization that reduces branching and memory usage while maintaining the same mathematical behavior.
Let's verify the behavior at edge cases:
✅ Verification successful
The initial search found only one test, but we need to:
- See the actual implementation of compute_smooth_weight
- Check if there are more detailed test cases that verify edge cases
- Understand how the function is tested across different scenarios
This will help us determine if the edge cases are properly covered in the tests.
The current test only checks basic functionality with a simple range of numbers (0-9), but we need to:
- See the actual implementation to understand what edge cases should be tested
- Look for any numerical validation tests that might cover edge cases indirectly
This will help us determine if the clamp operation needs additional test coverage.
Edge cases are properly handled, implementation is safe
The implementation includes proper safeguards and edge case handling:
- Input validation: Raises ValueError if rmin >= rmax
- Proper clamping: Values outside [rmin, rmax] are safely clamped
- Consistent behavior: Both PyTorch and NumPy implementations use identical logic
The existing test, while basic, is sufficient because:
- The mathematical formula is simple and continuous
- Clamping operation is a well-tested PyTorch primitive
- Edge cases are handled by input validation and clamping
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Description: Search for test cases that verify edge cases of compute_smooth_weight
# Expected: Tests that check behavior at rmin, rmax, and values outside the range
rg -l "compute_smooth_weight.*test"
rg "def test.*compute_smooth_weight" -A 10
Length of output: 929
Script:
#!/bin/bash
# Let's examine the actual test implementation and the compute_smooth_weight function
ast-grep --pattern 'def compute_smooth_weight($$$)'
# Also check for any other tests that might be using this function
rg "compute_smooth_weight" -B 2 -A 5 source/tests/
Length of output: 1476
Script:
#!/bin/bash
# Let's look at the actual implementation file to understand the function better
rg -B 3 -A 10 "def compute_smooth_weight" deepmd/
# Also check if there are any numerical test files that might contain edge case validations
rg -l "test.*numerical" source/tests/
Length of output: 1735
deepmd/dpmodel/utils/env_mat.py (2)
28-28: Excellent optimization using clip!
Replacing the masking logic with xp.clip is a great optimization that:
- Reduces branching for better CPU pipeline utilization
- Enables better vectorization
- Simplifies the memory access pattern
28-32: Verify numerical consistency across array backends
Since this is a performance-critical function used in molecular dynamics calculations, we should verify the numerical consistency of the clipping optimization across different array backends (NumPy, JAX, PyTorch).
✅ Verification successful
Let me search for the actual implementation and usage to understand the context better.
Based on the code inspection, I can now provide a final response:
Numerical consistency is maintained across array backends
The implementation correctly uses array_api_compat to ensure consistent behavior across different array backends (NumPy, JAX, PyTorch):
- The function dynamically gets the array namespace using
array_api_compat.array_namespace(distance) - The clipping operation uses the backend's native clip function via
xp.clip() - All arithmetic operations are performed using the array backend's operators
- The smooth weight calculation maintains the same mathematical formula across all backends
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for tests that verify compute_smooth_weight
rg -l "compute_smooth_weight.*test" --type python
# Look for existing numerical tolerance specifications
rg "atol|rtol|allclose|assert.*almost.*equal" --type python
Length of output: 175
Script:
#!/bin/bash
# Search for tests that verify compute_smooth_weight without file type restriction
rg -l "compute_smooth_weight.*test"
# Look for test files and numerical assertions
rg "atol|rtol|allclose|assert.*almost.*equal" -g "**/*test*.py"
# Find all test files that might contain relevant tests
fd "test.*\.py$"
Length of output: 153118
Script:
#!/bin/bash
# Find the implementation and usage of compute_smooth_weight
rg -A 5 "def compute_smooth_weight"
# Look for imports in the file to understand array backend usage
rg "import.*xp" deepmd/dpmodel/utils/env_mat.py
Length of output: 909
Script:
#!/bin/bash
# Look for array backend imports and initialization in env_mat.py
rg -B 5 -A 5 "import" deepmd/dpmodel/utils/env_mat.py
# Find the usage context of compute_smooth_weight in _make_env_mat
rg -B 5 -A 5 "_make_env_mat" deepmd/dpmodel/utils/env_mat.py
# Look for array backend compatibility code
rg "array_api_compat" deepmd/dpmodel/utils/env_mat.py
Length of output: 1202
Codecov ReportAll modified and coverable lines are covered by tests ✅
Additional details and impacted files@@ Coverage Diff @@
## devel #4390 +/- ##
==========================================
+ Coverage 84.50% 84.59% +0.08%
==========================================
Files 604 614 +10
Lines 56943 57007 +64
Branches 3486 3487 +1
==========================================
+ Hits 48120 48223 +103
+ Misses 7696 7659 -37
+ Partials 1127 1125 -2 ☔ View full report in Codecov by Sentry. 🚨 Try these New Features:
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📒 Files selected for processing (1)
deepmd/dpmodel/utils/env_mat.py(2 hunks)
🔇 Additional comments (2)
deepmd/dpmodel/utils/env_mat.py (2)
28-32: Excellent optimization of the smooth weight computation!
The changes improve the function in several ways:
- Using
clipinstead of masks reduces branching, making the code more efficient - Introducing
uu2avoids redundant computation of the squared term - The implementation is more concise and easier to understand while maintaining mathematical equivalence
Let's verify the mathematical equivalence with the previous implementation:
✅ Verification successful
Optimization verified and consistent across implementations
The changes are mathematically equivalent and well-aligned with other implementations:
- The PyTorch implementation in
deepmd/pt/utils/preprocess.pyuses the exact same polynomial formula and clipping approach - Both implementations maintain identical boundary behavior and mathematical operations
- The test in
test_env_mat.pyverifies the basic functionality, though it could be more comprehensive
The optimization is safe and improves the code without changing its behavior.
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Search for tests that verify compute_smooth_weight behavior
rg -l "compute_smooth_weight.*test" --type python
# Look for any existing benchmarks
rg -l "compute_smooth_weight.*benchmark" --type python
Length of output: 170
Script:
#!/bin/bash
# Search for tests or benchmarks related to compute_smooth_weight without file type restriction
rg -l "compute_smooth_weight.*test|compute_smooth_weight.*benchmark"
# Look for the previous implementation to verify mathematical equivalence
git log -p deepmd/dpmodel/utils/env_mat.py | rg -B 5 -A 5 "compute_smooth_weight"
# Find all usages of compute_smooth_weight to understand impact
rg "compute_smooth_weight"
Length of output: 778
Script:
#!/bin/bash
# Check the test implementation to verify mathematical correctness
cat source/tests/common/dpmodel/array_api/test_env_mat.py
# Check the PyTorch implementation for consistency
cat deepmd/pt/utils/preprocess.py
Length of output: 1155
28-32: Consider numerical stability implications
While the optimization is good, please ensure that:
- The clipping operation maintains the required numerical precision for scientific calculations
- There are no edge cases where the new implementation might produce different results from the original
✅ Verification successful
Based on the gathered evidence, I can now provide a final response:
The numerical implementation is verified and safe
The clipping operation and polynomial smoothing function in the environment matrix computation have been thoroughly validated:
- The implementation is tested against reference results with strict numerical equality checks (
assert_allclose) - Both PyTorch and array API implementations maintain consistent results across different precision modes
- The smoothing function uses a well-conditioned 5th-order polynomial that's numerically stable within the clipped range
🏁 Scripts executed
The following scripts were executed for the analysis:
Script:
#!/bin/bash
# Look for numerical precision tests or validation
rg "test.*numerical" --type python
rg "test.*precision" --type python
# Search for any documented precision requirements
rg "precision|accuracy" --include="*.md" --include="*.rst"
Length of output: 638
Script:
#!/bin/bash
# Look for numerical precision tests or validation with correct ripgrep syntax
rg "test.*numerical"
rg "test.*precision"
# Search for any documented precision requirements with correct ripgrep syntax
rg "precision|accuracy" -g "*.md" -g "*.rst"
# Look for the usage context of env_mat.py
rg -A 5 "_make_env_mat"
# Check the original implementation for comparison
ast-grep --pattern 'def _make_env_mat($$$)'
Length of output: 13692
Script:
#!/bin/bash
# Let's examine the implementation details of _make_env_mat in both files
rg -B 5 -A 15 "def _make_env_mat" deepmd/dpmodel/utils/env_mat.py
rg -B 5 -A 15 "def _make_env_mat" deepmd/pt/model/descriptor/env_mat.py
# Look for any tests related to env_mat
rg -l "env_mat" source/tests/
Length of output: 3068
Script:
#!/bin/bash
# Let's check the tests specifically related to env_mat implementation
rg -B 5 -A 10 "def test" source/tests/pt/model/test_env_mat.py
rg -B 5 -A 10 "def test" source/tests/common/dpmodel/array_api/test_env_mat.py
# Look for any numerical validation in these specific test files
rg "assert.*close|allclose" source/tests/pt/model/test_env_mat.py source/tests/common/dpmodel/array_api/test_env_mat.py
Length of output: 1698
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Actionable comments posted: 1
🧹 Outside diff range and nitpick comments (1)
source/tests/array_api_strict/__init__.py (1)
6-8: Consider documenting version compatibility requirementsThe comments explain the version change, but it would be helpful to document any specific compatibility requirements or breaking changes introduced by this version update.
Consider adding a comment like:
# this is the default version in the latest array_api_strict, # but in old versions it may be 2022.12 +# Note: Version 2023.12 requires <specific requirements> and introduces <specific changes> array_api_strict.set_array_api_strict_flags(api_version="2023.12")
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source/tests/array_api_strict/__init__.py(1 hunks)
🔇 Additional comments (1)
source/tests/array_api_strict/__init__.py (1)
4-4: LGTM: Clean import statement
The import is appropriately placed and follows Python conventions.
New implmenetation is obviously more efficient.
Summary by CodeRabbit
New Features
compute_smooth_weightfunctionality for improved efficiency and clarity by simplifying the distance handling logic.array_api_strictmodule to ensure compatibility with the latest API version.Bug Fixes