Revert "[FRONTEND] Semantic analysis refactor (#473)" (#483)

This reverts commit 539961072c.

python/test/unit/language/test_core.py

@@ -1,4 +1,5 @@
 # flake8: noqa: F821,F841
+import copy
 import itertools
 import re
 from typing import Optional, Union
@@ -584,6 +585,7 @@ def test_f8_f16_roundtrip():
 
     f8_output_tensor = torch.empty_like(f16, dtype=torch.int8)
     f8_output = triton.reinterpret(f8_output_tensor, tl.float8)
+    print(f16.dtype, f8_output.dtype)
     copy_kernel[grid](f16, f8_output, n_elements, BLOCK_SIZE=1024)
 
     assert torch.all(f8_tensor == f8_output_tensor)
@@ -991,6 +993,27 @@ def test_noop(device='cuda'):
     kernel[(1, )](x)
 
 
+@pytest.mark.parametrize("value, value_type", [
+    (-1, 'i32'), (0, 'i32'), (1, None), (-2**31, 'i32'), (2**31 - 1, 'i32'),
+    (2**31, 'u32'), (2**32 - 1, 'u32'), (2**32, 'i64'), (2**63 - 1, 'i64'),
+    (-2**63, 'i64'), (2**63, 'u64'), (2**64 - 1, 'u64')
+])
+def test_value_specialization(value: int, value_type: str, device='cuda') -> None:
+
+    @triton.jit
+    def kernel(VALUE, X):
+        pass
+
+    x = torch.tensor([3.14159], device='cuda')
+    pgm = kernel[(1, )](value, x)
+
+    # Parse out the type of the 'VALUE' parameter from the Triton IR.
+    triton_ir = pgm.asm['ttir']
+    ir_value_match = re.match(r'\s*def void kernel\((\w+) VALUE ', triton_ir)
+    ir_value_type = None if ir_value_match is None else ir_value_match.group(1)
+    assert ir_value_type == value_type
+
+
 @pytest.mark.parametrize(
     "value, overflow",
     [(2**64 - 1, False), (2**64, True), (-2**63, False), (-2**63 - 1, True)]

python/test/unit/runtime/test_cache.py

@@ -1,5 +1,4 @@
 import os
-import re
 import shutil
 
 import pytest
@@ -103,30 +102,3 @@ def test_specialize(mode):
     for i in [1, 2, 4, 8, 16, 32]:
         function[(1,)](x, i, BLOCK=512)
     assert counter == target
-
-
-@pytest.mark.parametrize("value, value_type", [
-    (-1, 'int32'), (0, 'int32'), (1, None), (-2**31, 'int32'), (2**31 - 1, 'int32'),
-    (2**32, 'int64'), (2**63 - 1, 'int64'), (-2**63, 'int64'),
-    (2**31, 'uint32'), (2**32 - 1, 'uint32'), (2**63, 'uint64'), (2**64 - 1, 'uint64')
-])
-def test_value_specialization(value: int, value_type: str, device='cuda') -> None:
-
-    @triton.jit
-    def kernel(VALUE, X):
-        pass
-
-    cache_str = None
-
-    def get_cache_str(*args, **kwargs):
-        nonlocal cache_str
-        cache_str = kwargs['key'].split('-')
-    triton.code_gen.JITFunction.cache_hook = get_cache_str
-    reset_tmp_dir()
-    x = torch.tensor([3.14159], device='cuda')
-    kernel[(1, )](value, x)
-    triton.code_gen.JITFunction.cache_hook = None
-
-    cache_str_match = re.match(r'_(\w+)\[multipleof\(\d+\)]_float32\*\[multipleof\(16\)\]', cache_str[-1])
-    spec_type = None if cache_str_match is None else cache_str_match.group(1)
-    assert spec_type == value_type