133 lines
4.2 KiB
Python
133 lines
4.2 KiB
Python
#!/usr/bin/env python3
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"""Test script to verify fp16 and int16 support in bitwise operations."""
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import sys
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import os
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import torch
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# Add the custom_nodes/more_math directory to path
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sys.path.insert(0, os.path.dirname(__file__))
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from more_math.Parser.UnifiedMathVisitor import UnifiedMathVisitor
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def test_bitwise_fp16():
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"""Test bitwise NOT operation with fp16 tensors."""
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print("Testing bitwise operations with fp16...")
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# Create fp16 tensors
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a_fp16 = torch.tensor([1.5, 2.5, 3.5], dtype=torch.float16)
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variables = {"a": a_fp16}
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visitor = UnifiedMathVisitor(variables, shape=(3,))
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# Test bitwise NOT
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result = visitor._bitwise_not(a_fp16)
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print(f" fp16 tensor: {a_fp16}")
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print(f" bitwise_not result dtype: {result.dtype}")
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assert result.dtype == torch.float16, f"Expected float16, got {result.dtype}"
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print(" ✓ fp16 bitwise NOT test passed")
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def test_bitwise_int16():
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"""Test bitwise NOT operation with int16 tensors."""
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print("Testing bitwise operations with int16...")
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# Create int16 tensors
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a_int16 = torch.tensor([1, 2, 3], dtype=torch.int16)
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variables = {"a": a_int16}
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visitor = UnifiedMathVisitor(variables, shape=(3,))
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# Test bitwise NOT
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result = visitor._bitwise_not(a_int16)
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print(f" int16 tensor: {a_int16}")
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print(f" bitwise_not result dtype: {result.dtype}")
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assert result.dtype == torch.int16, f"Expected int16, got {result.dtype}"
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print(" ✓ int16 bitwise NOT test passed")
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def test_bitwise_and_int16():
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"""Test bitwise AND operation with int16 tensors."""
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print("Testing bitwise AND with int16...")
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# Create a dummy context for testing
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class DummyContext:
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class Start:
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line = 0
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column = 0
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start = Start()
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ctx = DummyContext()
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a = torch.tensor([7, 14, 21], dtype=torch.int16)
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b = torch.tensor([3, 5, 7], dtype=torch.int16)
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variables = {"a": a, "b": b}
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visitor = UnifiedMathVisitor(variables)
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# Test bitwise AND
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result = visitor._bitwise_op(a, b, torch.bitwise_and, lambda x, y: x & y, ctx)
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print(f" a: {a}, b: {b}")
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print(f" bitwise_and result: {result}")
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assert result.dtype in [torch.int16, torch.int32], f"Unexpected dtype {result.dtype}"
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print(" ✓ int16 bitwise AND test passed")
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def test_bitwise_int8():
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"""Test bitwise operations with int8 tensors."""
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print("Testing bitwise operations with int8...")
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a_int8 = torch.tensor([1, 2, 3], dtype=torch.int8)
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variables = {"a": a_int8}
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visitor = UnifiedMathVisitor(variables, shape=(3,))
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# Test bitwise NOT
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result = visitor._bitwise_not(a_int8)
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print(f" int8 tensor: {a_int8}")
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print(f" bitwise_not result dtype: {result.dtype}")
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assert result.dtype == torch.int8, f"Expected int8, got {result.dtype}"
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print(" ✓ int8 bitwise NOT test passed")
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def test_get_bitwise_view_dtype():
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"""Test the element size to dtype mapping function."""
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print("Testing _get_bitwise_view_dtype...")
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visitor = UnifiedMathVisitor({})
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# Test various element sizes
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test_cases = [
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(1, torch.int8),
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(2, torch.int16),
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(4, torch.int32),
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(8, torch.int64),
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]
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for elem_size, expected_dtype in test_cases:
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result_dtype = visitor._get_bitwise_view_dtype(elem_size)
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print(f" element_size={elem_size} -> {result_dtype}")
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assert result_dtype == expected_dtype, f"Expected {expected_dtype}, got {result_dtype}"
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print(" ✓ All element size mappings correct")
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if __name__ == "__main__":
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print("=" * 60)
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print("Testing fp16 and int16 support in bitwise operations")
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print("=" * 60)
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try:
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test_get_bitwise_view_dtype()
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print()
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test_bitwise_int8()
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print()
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test_bitwise_int16()
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print()
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test_bitwise_fp16()
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print()
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test_bitwise_and_int16()
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print()
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print("=" * 60)
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print("✓ All tests passed successfully!")
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print("=" * 60)
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except Exception as e:
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print(f"\n✗ Test failed with error: {e}")
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import traceback
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traceback.print_exc()
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sys.exit(1)
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