massive change

django/aiservice/optimizer/optimizer.py

@@ -298,8 +298,8 @@ async def optimize_python(
     """Optimize Python code for performance using LLMs."""
     system_prompt = ASYNC_SYSTEM_PROMPT if data.is_async else SYSTEM_PROMPT
     user_prompt = ASYNC_USER_PROMPT if data.is_async else USER_PROMPT
-    if data.is_numerical_code:
-        system_prompt += f"\n{JIT_INSTRUCTIONS}\n"
+    # if data.is_numerical_code:
+    #     system_prompt += f"\n{JIT_INSTRUCTIONS}\n"
     ctx: BaseOptimizerContext = BaseOptimizerContext.get_dynamic_context(
         system_prompt, user_prompt, data.source_code, DiffMethod.NO_DIFF
     )

django/aiservice/optimizer/optimizer_line_profiler.py

@@ -67,8 +67,8 @@ async def optimize_python_code_line_profiler_single(
     #  function doing and then ask it to describe how to speed it up and then generate optimization
     system_prompt = ctx.get_system_prompt(python_version_str=python_version_str)
     user_prompt = ctx.get_user_prompt(dependency_code or "", line_profiler_results)
-    if is_numerical_code:
-        system_prompt += f"\n{JIT_INSTRUCTIONS}\n"
+    # if is_numerical_code:
+    #     system_prompt += f"\n{JIT_INSTRUCTIONS}\n"
     system_message = ChatCompletionSystemMessageParam(role="system", content=system_prompt)
     user_message = ChatCompletionUserMessageParam(role="user", content=user_prompt)
     messages: list[ChatCompletionMessageParam] = [system_message, user_message]

django/aiservice/pyproject.toml

@@ -29,6 +29,8 @@ dependencies = [
     "tree-sitter>=0.25.2",
     "tree-sitter-javascript>=0.25.0",
     "tree-sitter-typescript>=0.23.2",
+    "torch>=2.10.0",
+    "torchvision>=0.25.0",
 ]
 
 [project.urls]

django/aiservice/testgen/execute_system_prompt.md

@@ -1,75 +1,6 @@
-**Role**: You are Codeflash, a world-class Python developer with an eagle eye for unintended bugs and edge cases. You write careful, accurate unit tests. When asked to reply only with code, you write all of your code in a single markdown code block.
 
-**Task** Your task is to create comprehensive, high quality test cases for the {function_name} function. These test cases should encompass Basic, Edge, and Large Scale scenarios to ensure the code's robustness, reliability, and scalability. These test cases should *define* the {function_name} function, meaning that the function should pass all the tests, and a function with different external functional behavior should fail them. In other words, the test suite should fail under mutation testing of the source code.
+** CALL MODEL.FORWARD() INSTEAD OF MODEL IN THE TEST **
 
-**CRITICAL: PRESERVE ORIGINAL FUNCTION**
-- You MUST use the EXACT original function signature and implementation provided
-- DO NOT modify, enhance, or add parameters to the original function
-- DO NOT change the function's internal logic or behavior
-- The function code provided is the ONLY version you should test
-- Write tests for the function AS-IS, not for an improved version
+**In the case of Pytorch, TensorFlow and Jax -> Write CUDA-only test functions with fp32 precision with a SINGLE INPUT SHAPE for all tests as our test machine is CUDA compatible. call model.forward() instead of model() in the test.**
 
-**CRITICAL: USE REAL CLASSES - NO STUBS OR FAKES**
-- NEVER define your own classes in the test file to replace real classes from the codebase
-- When the context shows `from X import Y`, use that EXACT same import in your tests
-- DO NOT create classes named `FakeX`, `StubX`, `MockX`, `DummyX`, `TestX`, `AsyncX`, or ANY class that mimics a real class
-- DO NOT create "minimal", "helper", "placeholder", or "stub" versions of classes
-- DO NOT add comments like "Minimal stub" or "Simple container" - these indicate you're doing it wrong
-- If a class requires dependencies, look at the context to see how to construct it properly
-- Tests that define their own classes WILL FAIL because isinstance() checks will fail
-
-**CRITICAL: HANDLING INSTANCE METHODS**
-- If the function has `self` as its first parameter, it is an **instance method** of a class
-- DO NOT copy the method body into your test file and call it as a standalone function like `method_name(some_obj, ...)`
-- Instead, you MUST:
-  1. Import the class from its real module (e.g., `from mypackage.module import MyClass`)
-  2. Create a real instance of the class (check conftest.py for fixtures that provide instances)
-  3. Call the method ON the instance: `instance.method_name(...)`
-- Python passes `self` automatically when you call methods on instances - do NOT pass it manually
-- Example: If testing `MyClass.process(self, data)`, write `instance = MyClass(...); result = instance.process(data)`
-
-**CRITICAL: USE CONFTEST.PY FIXTURES WHEN PROVIDED**
-- If the context includes conftest.py files, ALWAYS check for fixtures that provide the objects you need
-- Pytest fixtures are defined with `@pytest.fixture` decorator and can be used simply by adding them as test function parameters
-- Fixtures automatically handle setup/teardown and provide properly configured objects
-- Example: If conftest.py has `@pytest.fixture def embedding_model(): ...`, use it as `def test_something(embedding_model): ...`
-- PREFER fixtures over manual instantiation - they're pre-configured to work correctly
-- Fixtures may provide Fake/Mock implementations that are designed to work with the codebase - USE THESE instead of creating your own
-- Look for fixtures that return objects of the types you need (check return type hints and fixture names)
-
-**1. Basic Test Cases**:
-- **Objective**: To verify the fundamental functionality of the {function_name} function under normal conditions.
-  **2. Edge Test Cases**:
-- **Objective**: To evaluate the function's behavior under extreme or unusual conditions.
-  **3. Large Scale Test Cases**:
-- **Objective**: To assess the function's performance and scalability with large data samples.
-
-  **Instructions**:
-- Implement a comprehensive set of test cases following the guidelines above.
-- Ensure each test case is well-documented with comments explaining the scenario it covers.
-- Pay special attention to edge cases as they often reveal hidden bugs.
-- For large-scale tests, focus on the function's efficiency and performance under heavy loads. Avoid loops exceeding 1000 steps, and keep data structures under 1000 elements.
-- **CRITICAL: DO NOT MOCK THE FUNCTION UNDER TEST** - Never mock, stub, or patch the {function_name} function itself or any internal functions/methods it calls. You may mock external dependencies (APIs, databases, network calls, file I/O, etc.) if necessary, but the function being tested must execute with its real implementation.
-- **CRITICAL: IMPORT CLASSES FROM THEIR REAL MODULES** - When the context shows class definitions with file paths (e.g., ```python:path/to/module.py), you MUST import those classes from their actual modules instead of redefining them. For example, if you see `class Foo` in `mypackage/utils.py`, import it as `from mypackage.utils import Foo`. This is essential because the function under test uses `isinstance()` checks against the real classes, not mock versions. Never redefine classes that exist in the codebase - always import them.
-- **CRITICAL: IMPORT EVERYTHING YOU USE** - Every symbol (class, function, constant) you reference in your test code MUST have a corresponding import statement. If you use `Mock`, `MagicMock`, `AsyncMock`, `PropertyMock`, `patch`, or `call`, import each one explicitly from `unittest.mock`. If you use `pytest.raises`, `pytest.mark`, or `pytest.fixture`, import `pytest`. Double-check that every name in your code is either imported, defined locally, or is a Python builtin.
-- **CRITICAL: ONLY IMPORT WHAT YOU USE** - Do not add imports for classes or functions that you don't actually use in your test code. If you see a class mentioned in type hints or dependency code but don't need it for your tests, don't import it.
-- **CRITICAL: USE CORRECT IMPORT SOURCES** - When you see `from X import Y` in the dependency context, and you need to import `Y` in your tests, use the SAME source module `X` that the dependency uses. Do NOT guess or assume a different module. For example, if you see `from mypackage.utils import Helper` in the dependencies, import it as `from mypackage.utils import Helper`, not from any other module.
-- **CRITICAL: DO NOT USE MOCK OBJECTS FOR DOMAIN CLASSES** - Never use `Mock(spec=SomeClass)` to create instances of domain classes like Element, PreChunk, ChunkingOptions, etc. Mock objects cannot be serialized/pickled and will cause test failures. Instead, always create real instances by importing and instantiating the actual classes. This applies to ALL objects that will be passed to or used by the function being tested, including objects nested inside other objects.
-- **CRITICAL: USE CORRECT CONSTRUCTOR SIGNATURES** - When instantiating classes, use only the constructor arguments shown in the provided context. Do not assume or invent parameter names. If a class appears to be abstract (marked with `abc.ABC`) or its constructor doesn't accept the arguments you need, look for and use concrete subclasses instead. For example, if `Element` is abstract and doesn't accept text, use `Text("content")` from the same module instead.
-
-**CRITICAL: VALID PYTHON STRING LITERALS**
-- Python strings MUST be delimited with ASCII quotes: ' (U+0027) or " (U+0022)
-- Unicode curly quotes (' ' " ") are NOT valid Python string delimiters
-- To include special quote characters IN a string, put them inside ASCII-quoted strings:
-  - CORRECT: `text = "'test'"` (curly quotes as content inside ASCII double quotes)
-  - CORRECT: `text = '\u2018test\u2019'` (using Unicode escapes)
-  - WRONG: `text = ''test''` (curly quotes cannot be string delimiters)
-- Always verify your string literals are properly delimited and terminated
-
-**Output Format Requirements**:
-- Your response MUST be a single markdown code block containing valid Python code.
-- Do NOT nest code blocks inside each other.
-- Do NOT include markdown code fences (```) anywhere inside your code, including in string literals, comments, or docstrings.
-- Do NOT include "reference code" or "module code" as string variables - just import what you need from the real modules.
-- The code block MUST contain at least one test function (e.g., `def test_...`).
-- Follow the exact template structure provided in the user message.
+** CALL MODEL.FORWARD() INSTEAD OF MODEL IN THE TEST **

django/aiservice/testgen/execute_user_prompt.md

@@ -1,10 +1,11 @@
-Using Python and the `{unit_test_package}` package, write a suite of unit tests for the function '{function_name}', following the cases above. ONLY include assert/raise statements present in the Python library and NOT in libraries such as numpy, pandas etc. Include helpful comments to explain each line.
+
+**In the case of Pytorch, TensorFlow and Jax -> Write CUDA-only test functions with fp32 precision with a single input shape for all tests as our test machine is CUDA compatible. call model() instead of model.forward() in the test.**
 
 Reply with a single Python code block in this exact format:
 
 ```python
 # imports
-import {unit_test_package}  # used for our unit tests
+import pytest  # used for our unit tests
 # add other imports as needed
 
 # function to test
@@ -18,3 +19,4 @@ import {unit_test_package}  # used for our unit tests
 ```
 
 IMPORTANT: Replace the comment placeholder above with actual test functions. Your code MUST include `def test_` functions.
+

django/aiservice/testgen/instrumentation/instrument_new_tests.py

@@ -40,11 +40,11 @@ RNG_MODULES_SEEDS = {
 
 
 def detect_frameworks_from_code(code: str) -> dict[str, str]:
-    """Detect GPU/device frameworks (torch, tensorflow, jax) used in the code by analyzing imports.
+    """Detect PyTorch used in the code by analyzing imports.
 
     Returns:
         A dictionary mapping framework names to their import aliases.
-        For example: {"torch": "th", "tensorflow": "tf", "jax": "jax"}
+        For example: {"torch": "th"} or {"torch": "torch"}
 
     """
     frameworks: dict[str, str] = {}
@@ -60,294 +60,126 @@ def detect_frameworks_from_code(code: str) -> dict[str, str]:
                 if module_name == "torch":
                     # Use asname if available, otherwise use the module name
                     frameworks["torch"] = alias.asname if alias.asname else module_name
-                elif module_name == "tensorflow":
-                    frameworks["tensorflow"] = alias.asname if alias.asname else module_name
-                elif module_name == "jax":
-                    frameworks["jax"] = alias.asname if alias.asname else module_name
         elif isinstance(node, ast.ImportFrom):  # noqa: SIM102
             if node.module:
                 module_name = node.module.split(".")[0]
                 if module_name == "torch" and "torch" not in frameworks:
                     frameworks["torch"] = module_name
-                elif module_name == "tensorflow" and "tensorflow" not in frameworks:
-                    frameworks["tensorflow"] = module_name
-                elif module_name == "jax" and "jax" not in frameworks:
-                    frameworks["jax"] = module_name
 
     return frameworks
 
 
-def _create_device_sync_precompute_statements(used_frameworks: dict[str, str] | None) -> list[ast.stmt]:
-    """Create AST statements to pre-compute device sync conditions before profiling.
-
-    This moves the conditional checks (like is_available(), hasattr(), etc.) outside
-    the timing block to avoid their overhead affecting the measurements.
+def _create_cuda_event_setup_statements(used_frameworks: dict[str, str] | None) -> list[ast.stmt]:
+    """Create AST statements to set up CUDA events for GPU timing.
 
     Args:
         used_frameworks: Dict mapping framework names to their import aliases
 
     Returns:
-        List of AST statements that pre-compute sync conditions into boolean variables
+        List of AST statements that create CUDA events for timing
 
     """
-    if not used_frameworks:
+    if not used_frameworks or "torch" not in used_frameworks:
         return []
 
-    precompute_statements: list[ast.stmt] = []
+    torch_alias = used_frameworks["torch"]
 
-    # PyTorch: pre-compute whether to sync CUDA or MPS
-    if "torch" in used_frameworks:
-        torch_alias = used_frameworks["torch"]
-        # _codeflash_should_sync_cuda = torch.cuda.is_available() and torch.cuda.is_initialized()
-        precompute_statements.append(
-            ast.Assign(
-                targets=[ast.Name(id="_codeflash_should_sync_cuda", ctx=ast.Store())],
-                value=ast.BoolOp(
-                    op=ast.And(),
-                    values=[
-                        ast.Call(
-                            func=ast.Attribute(
-                                value=ast.Attribute(
-                                    value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="cuda", ctx=ast.Load()
-                                ),
-                                attr="is_available",
-                                ctx=ast.Load(),
-                            ),
-                            args=[],
-                            keywords=[],
-                        ),
-                        ast.Call(
-                            func=ast.Attribute(
-                                value=ast.Attribute(
-                                    value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="cuda", ctx=ast.Load()
-                                ),
-                                attr="is_initialized",
-                                ctx=ast.Load(),
-                            ),
-                            args=[],
-                            keywords=[],
-                        ),
-                    ],
+    # Create CUDA events: start_event = torch.cuda.Event(enable_timing=True)
+    return [
+        ast.Assign(
+            targets=[ast.Name(id="start_event", ctx=ast.Store())],
+            value=ast.Call(
+                func=ast.Attribute(
+                    value=ast.Attribute(
+                        value=ast.Name(id=torch_alias, ctx=ast.Load()),
+                        attr="cuda",
+                        ctx=ast.Load()
+                    ),
+                    attr="Event",
+                    ctx=ast.Load(),
                 ),
-                lineno=1,
-            )
-        )
-        # _codeflash_should_sync_mps = (not _codeflash_should_sync_cuda and
-        #     hasattr(torch.backends, 'mps') and torch.backends.mps.is_available() and
-        #     hasattr(torch.mps, 'synchronize'))
-        precompute_statements.append(
-            ast.Assign(
-                targets=[ast.Name(id="_codeflash_should_sync_mps", ctx=ast.Store())],
-                value=ast.BoolOp(
-                    op=ast.And(),
-                    values=[
-                        ast.UnaryOp(op=ast.Not(), operand=ast.Name(id="_codeflash_should_sync_cuda", ctx=ast.Load())),
-                        ast.Call(
-                            func=ast.Name(id="hasattr", ctx=ast.Load()),
-                            args=[
-                                ast.Attribute(
-                                    value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="backends", ctx=ast.Load()
-                                ),
-                                ast.Constant(value="mps"),
-                            ],
-                            keywords=[],
-                        ),
-                        ast.Call(
-                            func=ast.Attribute(
-                                value=ast.Attribute(
-                                    value=ast.Attribute(
-                                        value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="backends", ctx=ast.Load()
-                                    ),
-                                    attr="mps",
-                                    ctx=ast.Load(),
-                                ),
-                                attr="is_available",
-                                ctx=ast.Load(),
-                            ),
-                            args=[],
-                            keywords=[],
-                        ),
-                        ast.Call(
-                            func=ast.Name(id="hasattr", ctx=ast.Load()),
-                            args=[
-                                ast.Attribute(
-                                    value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="mps", ctx=ast.Load()
-                                ),
-                                ast.Constant(value="synchronize"),
-                            ],
-                            keywords=[],
-                        ),
-                    ],
+                args=[],
+                keywords=[ast.keyword(arg="enable_timing", value=ast.Constant(value=True))],
+            ),
+            lineno=1,
+        ),
+        ast.Assign(
+            targets=[ast.Name(id="end_event", ctx=ast.Store())],
+            value=ast.Call(
+                func=ast.Attribute(
+                    value=ast.Attribute(
+                        value=ast.Name(id=torch_alias, ctx=ast.Load()),
+                        attr="cuda",
+                        ctx=ast.Load()
+                    ),
+                    attr="Event",
+                    ctx=ast.Load(),
                 ),
-                lineno=1,
-            )
+                args=[],
+                keywords=[ast.keyword(arg="enable_timing", value=ast.Constant(value=True))],
+            ),
+            lineno=1,
+        ),
+    ]
+
+
+def _create_cuda_event_record_statement(event_name: str) -> ast.stmt:
+    """Create AST statement to record a CUDA event.
+
+    Args:
+        event_name: Name of the event variable to record
+
+    Returns:
+        AST statement that records the event
+
+    """
+    # event_name.record()
+    return ast.Expr(
+        value=ast.Call(
+            func=ast.Attribute(
+                value=ast.Name(id=event_name, ctx=ast.Load()),
+                attr="record",
+                ctx=ast.Load(),
+            ),
+            args=[],
+            keywords=[],
         )
-
-    # JAX: pre-compute whether jax.block_until_ready exists
-    if "jax" in used_frameworks:
-        jax_alias = used_frameworks["jax"]
-        # _codeflash_should_sync_jax = hasattr(jax, 'block_until_ready')
-        precompute_statements.append(
-            ast.Assign(
-                targets=[ast.Name(id="_codeflash_should_sync_jax", ctx=ast.Store())],
-                value=ast.Call(
-                    func=ast.Name(id="hasattr", ctx=ast.Load()),
-                    args=[ast.Name(id=jax_alias, ctx=ast.Load()), ast.Constant(value="block_until_ready")],
-                    keywords=[],
-                ),
-                lineno=1,
-            )
-        )
-
-    # TensorFlow: pre-compute whether tf.test.experimental.sync_devices exists
-    if "tensorflow" in used_frameworks:
-        tf_alias = used_frameworks["tensorflow"]
-        # _codeflash_should_sync_tf = hasattr(tf.test.experimental, 'sync_devices')
-        precompute_statements.append(
-            ast.Assign(
-                targets=[ast.Name(id="_codeflash_should_sync_tf", ctx=ast.Store())],
-                value=ast.Call(
-                    func=ast.Name(id="hasattr", ctx=ast.Load()),
-                    args=[
-                        ast.Attribute(
-                            value=ast.Attribute(
-                                value=ast.Name(id=tf_alias, ctx=ast.Load()), attr="test", ctx=ast.Load()
-                            ),
-                            attr="experimental",
-                            ctx=ast.Load(),
-                        ),
-                        ast.Constant(value="sync_devices"),
-                    ],
-                    keywords=[],
-                ),
-                lineno=1,
-            )
-        )
-
-    return precompute_statements
+    )
 
 
-def _create_device_sync_statements(
-    used_frameworks: dict[str, str] | None,
-    for_return_value: bool = False,  # noqa: FBT001, FBT002
-) -> list[ast.stmt]:
-    """Create AST statements for device synchronization using pre-computed conditions.
+def _create_cuda_synchronize_statement(used_frameworks: dict[str, str] | None) -> list[ast.stmt]:
+    """Create AST statement for CUDA synchronization.
 
     Args:
         used_frameworks: Dict mapping framework names to their import aliases
-                        (e.g., {'torch': 'th', 'tensorflow': 'tf', 'jax': 'jax'})
-        for_return_value: If True, creates sync for after function call (includes JAX block_until_ready)
 
     Returns:
-        List of AST statements for device synchronization using pre-computed boolean variables
+        List containing synchronize statement
 
     """
-    if not used_frameworks:
+    if not used_frameworks or "torch" not in used_frameworks:
         return []
 
-    sync_statements: list[ast.stmt] = []
+    torch_alias = used_frameworks["torch"]
 
-    # PyTorch synchronization using pre-computed conditions
-    if "torch" in used_frameworks:
-        torch_alias = used_frameworks["torch"]
-        # if _codeflash_should_sync_cuda:
-        #     torch.cuda.synchronize()
-        # elif _codeflash_should_sync_mps:
-        #     torch.mps.synchronize()
-        cuda_sync = ast.If(
-            test=ast.Name(id="_codeflash_should_sync_cuda", ctx=ast.Load()),
-            body=[
-                ast.Expr(
-                    value=ast.Call(
-                        func=ast.Attribute(
-                            value=ast.Attribute(
-                                value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="cuda", ctx=ast.Load()
-                            ),
-                            attr="synchronize",
-                            ctx=ast.Load(),
-                        ),
-                        args=[],
-                        keywords=[],
-                    )
-                )
-            ],
-            orelse=[
-                ast.If(
-                    test=ast.Name(id="_codeflash_should_sync_mps", ctx=ast.Load()),
-                    body=[
-                        ast.Expr(
-                            value=ast.Call(
-                                func=ast.Attribute(
-                                    value=ast.Attribute(
-                                        value=ast.Name(id=torch_alias, ctx=ast.Load()), attr="mps", ctx=ast.Load()
-                                    ),
-                                    attr="synchronize",
-                                    ctx=ast.Load(),
-                                ),
-                                args=[],
-                                keywords=[],
-                            )
-                        )
-                    ],
-                    orelse=[],
-                )
-            ],
+    # torch.cuda.synchronize()
+    return [
+        ast.Expr(
+            value=ast.Call(
+                func=ast.Attribute(
+                    value=ast.Attribute(
+                        value=ast.Name(id=torch_alias, ctx=ast.Load()),
+                        attr="cuda",
+                        ctx=ast.Load()
+                    ),
+                    attr="synchronize",
+                    ctx=ast.Load(),
+                ),
+                args=[],
+                keywords=[],
+            )
         )
-        sync_statements.append(cuda_sync)
-
-    # JAX synchronization (only after function call, using block_until_ready on return value)
-    if "jax" in used_frameworks and for_return_value:
-        jax_alias = used_frameworks["jax"]
-        # if _codeflash_should_sync_jax:
-        #     jax.block_until_ready(return_value)
-        jax_sync = ast.If(
-            test=ast.Name(id="_codeflash_should_sync_jax", ctx=ast.Load()),
-            body=[
-                ast.Expr(
-                    value=ast.Call(
-                        func=ast.Attribute(
-                            value=ast.Name(id=jax_alias, ctx=ast.Load()), attr="block_until_ready", ctx=ast.Load()
-                        ),
-                        args=[ast.Name(id="return_value", ctx=ast.Load())],
-                        keywords=[],
-                    )
-                )
-            ],
-            orelse=[],
-        )
-        sync_statements.append(jax_sync)
-
-    # TensorFlow synchronization using pre-computed condition
-    if "tensorflow" in used_frameworks:
-        tf_alias = used_frameworks["tensorflow"]
-        # if _codeflash_should_sync_tf:
-        #     tf.test.experimental.sync_devices()
-        tf_sync = ast.If(
-            test=ast.Name(id="_codeflash_should_sync_tf", ctx=ast.Load()),
-            body=[
-                ast.Expr(
-                    value=ast.Call(
-                        func=ast.Attribute(
-                            value=ast.Attribute(
-                                value=ast.Attribute(
-                                    value=ast.Name(id=tf_alias, ctx=ast.Load()), attr="test", ctx=ast.Load()
-                                ),
-                                attr="experimental",
-                                ctx=ast.Load(),
-                            ),
-                            attr="sync_devices",
-                            ctx=ast.Load(),
-                        ),
-                        args=[],
-                        keywords=[],
-                    )
-                )
-            ],
-            orelse=[],
-        )
-        sync_statements.append(tf_sync)
-
-    return sync_statements
+    ]
 
 
 def format_and_float_to_top(code: str) -> str:
@@ -442,6 +274,9 @@ class InjectPerfAndLogging(ast.NodeTransformer):
         # Cache for parent mappings to avoid repeated ast.walk() calls
         self._parent_map: dict[ast.AST, ast.AST] | None = None
 
+        # Track instance variables for handling model() calls when optimizing forward()
+        self.instance_variables: set[str] = set()  # Variables that are instances of the class we're optimizing
+
     def visit_ImportFrom(self, node: ast.ImportFrom) -> ast.ImportFrom | None:
         if all(name.name in [self.only_function_name, self.class_name] for name in node.names):
             return None  # Remove the import of the function the test generation code
@@ -495,14 +330,38 @@ class InjectPerfAndLogging(ast.NodeTransformer):
     def is_target_function_node(self, node: ast.AST) -> bool:
         # Check for regular call nodes
         if isinstance(node, ast.Call):
-            return (isinstance(node.func, ast.Name) and node.func.id == self.only_function_name) or (
-                isinstance(node.func, ast.Attribute) and node.func.attr == self.only_function_name
-            )
+            # Match direct function calls: function_name()
+            if isinstance(node.func, ast.Name) and node.func.id == self.only_function_name:
+                return True
+            # Match attribute calls: obj.function_name()
+            if isinstance(node.func, ast.Attribute) and node.func.attr == self.only_function_name:
+                return True
+            # Special case for PyTorch-style forward: match instance() calls when optimizing forward()
+            # This handles cases like model() instead of model.forward()
+            if (
+                self.only_function_name == "forward"
+                and self.class_name
+                and isinstance(node.func, ast.Name)
+                and node.func.id in self.instance_variables
+            ):
+                return True
         # Check for await expressions (for async functions)
         if isinstance(node, ast.Await) and isinstance(node.value, ast.Call):
-            return (isinstance(node.value.func, ast.Name) and node.value.func.id == self.only_function_name) or (
-                isinstance(node.value.func, ast.Attribute) and node.value.func.attr == self.only_function_name
-            )
+            call = node.value
+            # Match direct function calls: await function_name()
+            if isinstance(call.func, ast.Name) and call.func.id == self.only_function_name:
+                return True
+            # Match attribute calls: await obj.function_name()
+            if isinstance(call.func, ast.Attribute) and call.func.attr == self.only_function_name:
+                return True
+            # Special case for PyTorch-style forward with async
+            if (
+                self.only_function_name == "forward"
+                and self.class_name
+                and isinstance(call.func, ast.Name)
+                and call.func.id in self.instance_variables
+            ):
+                return True
         return False
 
     def update_line_node(
@@ -547,6 +406,28 @@ class InjectPerfAndLogging(ast.NodeTransformer):
         if isinstance(call_node.func, ast.Attribute) and isinstance(call_node.func.value, ast.Name):
             function_id = call_node.func.value.id + "." + function_id
 
+        # Determine what to use for signature inspection vs what to actually call
+        # For PyTorch-style model() calls when optimizing forward(), we need to:
+        # - Get signature from model.forward (for correct parameter binding)
+        # - But call model itself (so __call__ with hooks is invoked)
+        is_instance_call = (
+            self.only_function_name == "forward"
+            and self.class_name
+            and isinstance(call_node.func, ast.Name)
+            and call_node.func.id in self.instance_variables
+        )
+
+        if is_instance_call:
+            # For model(), use model.forward for signature but model for calling
+            signature_source = ast.Attribute(
+                value=call_node.func,
+                attr="forward",
+                ctx=ast.Load()
+            )
+        else:
+            # For model.forward() or regular functions, use as-is
+            signature_source = call_node.func
+
         new_nodes = [
             *(
                 [
@@ -558,7 +439,7 @@ class InjectPerfAndLogging(ast.NodeTransformer):
                                     func=ast.Attribute(
                                         value=ast.Name(id="inspect", ctx=ast.Load()), attr="signature", ctx=ast.Load()
                                     ),
-                                    args=[call_node.func],
+                                    args=[signature_source],
                                     keywords=[],
                                 ),
                                 attr="bind",
@@ -815,6 +696,12 @@ class InjectPerfAndLogging(ast.NodeTransformer):
         if is_test_function_name(node.name):
             self.present_modules = set()  # Reset tracked modules
             self.present_aliases = set()  # Reset tracked aliases
+            self.instance_variables = set()  # Reset instance variables for each test function
+
+            # Track instance variables for PyTorch-style forward() calls
+            # This must be done before processing statements
+            self._track_instance_variables(node.body)
+
             new_body = []
             # Insert seed-setting statements
             for alias, full_module_name in self.random_modules.items():
@@ -911,6 +798,56 @@ class InjectPerfAndLogging(ast.NodeTransformer):
                 parent_map[child] = parent
         return parent_map
 
+    def _track_instance_variables(self, statements: list[ast.stmt]) -> None:
+        """Track variables that are assigned to instances of the class we're optimizing.
+
+        This is used to handle PyTorch-style calls like model() instead of model.forward().
+        """
+        if not self.class_name or self.only_function_name != "forward":
+            return
+
+        for stmt in statements:
+            # Check for simple assignments: var = ClassName(...)
+            if isinstance(stmt, ast.Assign):
+                for target in stmt.targets:
+                    if isinstance(target, ast.Name) and isinstance(stmt.value, ast.Call):
+                        # Check if the call is to the class constructor
+                        if isinstance(stmt.value.func, ast.Name) and stmt.value.func.id == self.class_name:
+                            self.instance_variables.add(target.id)
+                        # Also check for attribute access: module.ClassName()
+                        elif isinstance(stmt.value.func, ast.Attribute) and stmt.value.func.attr == self.class_name:
+                            self.instance_variables.add(target.id)
+
+            # Check for with statements: with ... as var:
+            elif isinstance(stmt, (ast.With, ast.AsyncWith)):
+                for item in stmt.items:
+                    if item.optional_vars and isinstance(item.optional_vars, ast.Name):
+                        # Check if context expr is creating an instance
+                        if isinstance(item.context_expr, ast.Call):
+                            if isinstance(item.context_expr.func, ast.Name) and item.context_expr.func.id == self.class_name:
+                                self.instance_variables.add(item.optional_vars.id)
+                            elif (
+                                isinstance(item.context_expr.func, ast.Attribute)
+                                and item.context_expr.func.attr == self.class_name
+                            ):
+                                self.instance_variables.add(item.optional_vars.id)
+                # Recursively check the body of with statements
+                self._track_instance_variables(stmt.body)
+
+            # Recursively check bodies of control flow statements
+            elif isinstance(stmt, (ast.If, ast.For, ast.While, ast.AsyncFor)):
+                self._track_instance_variables(stmt.body)
+                if hasattr(stmt, "orelse"):
+                    self._track_instance_variables(stmt.orelse)  # type: ignore[attr-defined]
+
+            # Check try-except-finally blocks
+            elif isinstance(stmt, ast.Try):
+                self._track_instance_variables(stmt.body)
+                self._track_instance_variables(stmt.orelse)
+                self._track_instance_variables(stmt.finalbody)
+                for handler in stmt.handlers:
+                    self._track_instance_variables(handler.body)
+
 
 def create_wrapper_function(
     mode: TestingMode = TestingMode.BEHAVIOR, used_frameworks: dict[str, str] | None = None
@@ -1063,8 +1000,8 @@ def create_wrapper_function(
         ast.Assign(
             targets=[ast.Name(id="exception", ctx=ast.Store())], value=ast.Constant(value=None), lineno=lineno + 10
         ),
-        # Pre-compute device sync conditions before profiling to avoid overhead during timing
-        *_create_device_sync_precompute_statements(used_frameworks),
+        # Setup CUDA events for GPU timing
+        *_create_cuda_event_setup_statements(used_frameworks),
         ast.Expr(
             value=ast.Call(
                 func=ast.Attribute(value=ast.Name(id="gc", ctx=ast.Load()), attr="disable", ctx=ast.Load()),
@@ -1075,19 +1012,8 @@ def create_wrapper_function(
         ),
         ast.Try(
             body=[
-                # Pre-sync: synchronize device before starting timer
-                *_create_device_sync_statements(used_frameworks, for_return_value=False),
-                ast.Assign(
-                    targets=[ast.Name(id="counter", ctx=ast.Store())],
-                    value=ast.Call(
-                        func=ast.Attribute(
-                            value=ast.Name(id="time", ctx=ast.Load()), attr="perf_counter_ns", ctx=ast.Load()
-                        ),
-                        args=[],
-                        keywords=[],
-                    ),
-                    lineno=lineno + 11,
-                ),
+                # Record start event
+                _create_cuda_event_record_statement("start_event"),
                 ast.Assign(
                     targets=[ast.Name(id="return_value", ctx=ast.Store())],
                     value=ast.Call(
@@ -1097,20 +1023,31 @@ def create_wrapper_function(
                     ),
                     lineno=lineno + 12,
                 ),
-                # Post-sync: synchronize device after function call to ensure all device work is complete
-                *_create_device_sync_statements(used_frameworks, for_return_value=True),
+                # Record end event
+                _create_cuda_event_record_statement("end_event"),
+                # Synchronize to ensure all GPU work is complete
+                *_create_cuda_synchronize_statement(used_frameworks),
+                # Calculate duration in nanoseconds (elapsed_time returns milliseconds)
                 ast.Assign(
                     targets=[ast.Name(id="codeflash_duration", ctx=ast.Store())],
-                    value=ast.BinOp(
-                        left=ast.Call(
-                            func=ast.Attribute(
-                                value=ast.Name(id="time", ctx=ast.Load()), attr="perf_counter_ns", ctx=ast.Load()
-                            ),
-                            args=[],
-                            keywords=[],
-                        ),
-                        op=ast.Sub(),
-                        right=ast.Name(id="counter", ctx=ast.Load()),
+                    value=ast.Call(
+                        func=ast.Name(id="int", ctx=ast.Load()),
+                        args=[
+                            ast.BinOp(
+                                left=ast.Call(
+                                    func=ast.Attribute(
+                                        value=ast.Name(id="start_event", ctx=ast.Load()),
+                                        attr="elapsed_time",
+                                        ctx=ast.Load(),
+                                    ),
+                                    args=[ast.Name(id="end_event", ctx=ast.Load())],
+                                    keywords=[],
+                                ),
+                                op=ast.Mult(),
+                                right=ast.Constant(value=1_000_000),
+                            )
+                        ],
+                        keywords=[],
                     ),
                     lineno=lineno + 13,
                 ),
@@ -1120,20 +1057,31 @@ def create_wrapper_function(
                     type=ast.Name(id="Exception", ctx=ast.Load()),
                     name="e",
                     body=[
+                        # Record end event even on exception
+                        _create_cuda_event_record_statement("end_event"),
+                        # Synchronize to ensure all GPU work is complete
+                        *_create_cuda_synchronize_statement(used_frameworks),
+                        # Calculate duration in nanoseconds
                         ast.Assign(
                             targets=[ast.Name(id="codeflash_duration", ctx=ast.Store())],
-                            value=ast.BinOp(
-                                left=ast.Call(
-                                    func=ast.Attribute(
-                                        value=ast.Name(id="time", ctx=ast.Load()),
-                                        attr="perf_counter_ns",
-                                        ctx=ast.Load(),
-                                    ),
-                                    args=[],
-                                    keywords=[],
-                                ),
-                                op=ast.Sub(),
-                                right=ast.Name(id="counter", ctx=ast.Load()),
+                            value=ast.Call(
+                                func=ast.Name(id="int", ctx=ast.Load()),
+                                args=[
+                                    ast.BinOp(
+                                        left=ast.Call(
+                                            func=ast.Attribute(
+                                                value=ast.Name(id="start_event", ctx=ast.Load()),
+                                                attr="elapsed_time",
+                                                ctx=ast.Load(),
+                                            ),
+                                            args=[ast.Name(id="end_event", ctx=ast.Load())],
+                                            keywords=[],
+                                        ),
+                                        op=ast.Mult(),
+                                        right=ast.Constant(value=1_000_000),
+                                    )
+                                ],
+                                keywords=[],
                             ),
                             lineno=lineno + 15,
                         ),
@@ -1499,7 +1447,7 @@ from __future__ import annotations
 import gc
 import inspect
 import os
-import time
+import torch
 import dill as pickle
 
 from pathlib import Path
@@ -1531,13 +1479,19 @@ def codeflash_wrap(
         f"!$######{test_stdout_tag}######$!"
     )
     exception = None
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
     gc.disable()
     try:
-        counter = time.perf_counter_ns()
+        start_event.record()
         return_value = codeflash_wrapped(*args, **kwargs)
-        codeflash_duration = time.perf_counter_ns() - counter
+        end_event.record()
+        torch.cuda.synchronize()
+        codeflash_duration = int(start_event.elapsed_time(end_event) * 1_000_000)
     except Exception as e:
-        codeflash_duration = time.perf_counter_ns() - counter
+        end_event.record()
+        torch.cuda.synchronize()
+        codeflash_duration = int(start_event.elapsed_time(end_event) * 1_000_000)
         exception = e
     gc.enable()
     print(f"!######{test_stdout_tag}######!")
@@ -1568,29 +1522,25 @@ def codeflash_wrap(
 
 
 def inject_behavior_logging_code(test_code: str, used_frameworks: dict[str, str] | None = None) -> str:
-    """Inject behavior logging code using AST-based wrapper with GPU sync support."""
-    # Create the wrapper function with device sync support
-    wrapper_func = create_wrapper_function(TestingMode.BEHAVIOR, used_frameworks)
+    """Inject behavior logging code using AST-based wrapper with PyTorch CUDA events for GPU timing."""
+    # Always use "torch" as the alias in wrapper code to avoid conflicts with user's imports
+    # (e.g., if user has "import torch.nn as tnn", we still need "torch" for torch.cuda.Event)
+    wrapper_frameworks = {"torch": "torch"} if used_frameworks and "torch" in used_frameworks else None
+
+    # Create the wrapper function with CUDA event timing
+    wrapper_func = create_wrapper_function(TestingMode.BEHAVIOR, wrapper_frameworks)
     # Create necessary imports
     imports = [
         ast.ImportFrom(module="__future__", names=[ast.alias(name="annotations")], level=0),
         ast.Import(names=[ast.alias(name="gc")]),
         ast.Import(names=[ast.alias(name="inspect")]),
         ast.Import(names=[ast.alias(name="os")]),
-        ast.Import(names=[ast.alias(name="time")]),
         ast.ImportFrom(module="pathlib", names=[ast.alias(name="Path")], level=0),
         ast.Import(names=[ast.alias(name="dill", asname="pickle")]),
     ]
-    # Add framework imports for GPU sync code (needed when framework is only imported via submodule)
-    if used_frameworks:
-        for framework_name, framework_alias in used_frameworks.items():
-            if framework_alias == framework_name:
-                # Only add import if we're using the framework name directly (not an alias)
-                # This handles cases like "from torch.nn import Module" where torch needs to be imported
-                imports.append(ast.Import(names=[ast.alias(name=framework_name)]))
-            else:
-                # If there's an alias, use it (e.g., "import torch as th")
-                imports.append(ast.Import(names=[ast.alias(name=framework_name, asname=framework_alias)]))
+    # Add PyTorch import for CUDA events (always use "torch" directly)
+    if wrapper_frameworks:
+        imports.append(ast.Import(names=[ast.alias(name="torch")]))
     # Create a module with imports and wrapper function
     wrapper_module = ast.Module(body=[*imports, wrapper_func], type_ignores=[])
     wrapper_code = ast.unparse(wrapper_module)
@@ -1603,7 +1553,7 @@ from __future__ import annotations
 
 import gc
 import os
-import time
+import torch
 from typing import Any, Callable, Optional
 
 
@@ -1630,13 +1580,19 @@ def codeflash_wrap(
     exception = None
     test_stdout_tag = f"{codeflash_test_module_name}:{(codeflash_test_class_name + '.' if codeflash_test_class_name else '')}{codeflash_test_name}:{codeflash_function_name}:{codeflash_loop_index}:{invocation_id}"
     print(f"!$######{test_stdout_tag}######$!")
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
     gc.disable()
     try:
-        counter = time.perf_counter_ns()
+        start_event.record()
         return_value = codeflash_wrapped(*args, **kwargs)
-        codeflash_duration = time.perf_counter_ns() - counter
+        end_event.record()
+        torch.cuda.synchronize()
+        codeflash_duration = int(start_event.elapsed_time(end_event) * 1_000_000)
     except Exception as e:
-        codeflash_duration = time.perf_counter_ns() - counter
+        end_event.record()
+        torch.cuda.synchronize()
+        codeflash_duration = int(start_event.elapsed_time(end_event) * 1_000_000)
         exception = e
     gc.enable()
     print(f"!######{test_stdout_tag}:{codeflash_duration}######!")
@@ -1647,27 +1603,23 @@ def codeflash_wrap(
 
 
 def inject_perf_logging_code(test_code: str, used_frameworks: dict[str, str] | None = None) -> str:
-    """Inject performance logging code using AST-based wrapper with GPU sync support."""
-    # Create the wrapper function with device sync support
-    wrapper_func = create_wrapper_function(TestingMode.PERFORMANCE, used_frameworks)
+    """Inject performance logging code using AST-based wrapper with PyTorch CUDA events for GPU timing."""
+    # Always use "torch" as the alias in wrapper code to avoid conflicts with user's imports
+    # (e.g., if user has "import torch.nn as tnn", we still need "torch" for torch.cuda.Event)
+    wrapper_frameworks = {"torch": "torch"} if used_frameworks and "torch" in used_frameworks else None
+
+    # Create the wrapper function with CUDA event timing
+    wrapper_func = create_wrapper_function(TestingMode.PERFORMANCE, wrapper_frameworks)
 
     # Create necessary imports
     imports = [
         ast.ImportFrom(module="__future__", names=[ast.alias(name="annotations")], level=0),
         ast.Import(names=[ast.alias(name="gc")]),
         ast.Import(names=[ast.alias(name="os")]),
-        ast.Import(names=[ast.alias(name="time")]),
     ]
-    # Add framework imports for GPU sync code (needed when framework is only imported via submodule)
-    if used_frameworks:
-        for framework_name, framework_alias in used_frameworks.items():
-            if framework_alias == framework_name:
-                # Only add import if we're using the framework name directly (not an alias)
-                # This handles cases like "from torch.nn import Module" where torch needs to be imported
-                imports.append(ast.Import(names=[ast.alias(name=framework_name)]))
-            else:
-                # If there's an alias, use it (e.g., "import torch as th")
-                imports.append(ast.Import(names=[ast.alias(name=framework_name, asname=framework_alias)]))
+    # Add PyTorch import for CUDA events (always use "torch" directly)
+    if wrapper_frameworks:
+        imports.append(ast.Import(names=[ast.alias(name="torch")]))
     # Create a module with imports and wrapper function
     wrapper_module = ast.Module(body=[*imports, wrapper_func], type_ignores=[])
     wrapper_code = ast.unparse(wrapper_module)

django/aiservice/testgen/jit_system_prompt.md

@@ -1,301 +1,2 @@
-# JIT Compilation Test Writing Guidelines
+**In the case of Pytorch, TensorFlow, Jax and MLX -> Write CUDA-only test functions with fp32 precision as our test machine is CUDA compatible.**
 
-When generating tests for functions that may be optimized with JIT compilation (numba `@njit`, `@torch.compile`, `@tf.function`, `@jax.jit`), follow these guidelines to ensure tests remain valid after optimization:
-
-## Key Principles
-
-### 1. Use Concrete, Typed Inputs
-JIT compilers require consistent types. Always use concrete values with consistent types across test cases:
-- Use numpy arrays instead of Python lists for numerical data
-- Specify dtypes explicitly (e.g., `np.array([1, 2, 3], dtype=np.float64)`)
-- Avoid mixing types in the same parameter across different test cases
-
-### 2. Avoid Python Object Features
-JIT-compiled functions have limited support for Python objects. In tests:
-- Do not test with custom Python class instances as inputs (unless the function is clearly designed for them)
-- Avoid dictionary inputs with dynamic keys
-- Prefer numpy arrays and primitive types (int, float, bool)
-
-### 3. Handle Numerical Precision
-JIT compilation may produce slightly different floating-point results. When testing numerical functions:
-- Use `np.allclose()`/`torch.allclose()`/`jax.numpy.allclose()`/`tf.experimental.numpy.allclose(a, b)`/`pytest.approx()` for floating-point comparisons instead of exact equality
-- Allow for small numerical tolerances (e.g., `rtol=1e-7, atol=1e-10`)
-
-### 4. Do Not Mock JIT-Decorated Functions
-Never mock or patch JIT-decorated functions directly. Instead:
-- Test the function's actual behavior
-- Mock only external I/O dependencies if absolutely necessary
-
-### 5. Large Scale Tests Should Use Appropriate Data Types
-For performance/scalability tests with large data:
-- Use contiguous arrays if possible.
-- Ensure array dtypes are JIT-compatible (float64, float32, int64, int32, etc.)
-- Avoid object dtypes in arrays
-
-## Example Test Patterns
-
-### Good: Typed numpy input
-```python
-def test_compute_sum():
-    arr = np.array([1.0, 2.0, 3.0], dtype=np.float64)
-    result = compute_sum(arr)
-    assert np.isclose(result, 6.0)
-```
-
-### Good: Tolerance-based comparison
-```python
-def test_matrix_operation():
-    a = np.random.rand(100, 100).astype(np.float64)
-    b = np.random.rand(100, 100).astype(np.float64)
-    result = matrix_multiply(a, b)
-    expected = np.dot(a, b)
-    assert np.allclose(result, expected, rtol=1e-7)
-```
-
-### Avoid: Python list input for numerical functions
-```python
-# Avoid this pattern for functions that may be JIT-compiled
-def test_compute_sum():
-    result = compute_sum([1, 2, 3])  # Python list - may cause JIT issues
-```
-
-### Avoid: Mixed types
-```python
-# Avoid mixing types across test cases
-def test_function():
-    result1 = func(np.array([1, 2, 3]))  # int array
-    result2 = func(np.array([1.0, 2.0]))  # float array - different type signature
-```
-
----
-
-## Framework-Specific Guidelines
-
-### Numba (`@njit`, `@jit`)
-
-**Supported Types:**
-- Primitive types: `int`, `float`, `bool`, `complex`
-- NumPy arrays with numeric dtypes (`float32`, `float64`, `int32`, `int64`, `complex64`, `complex128`)
-- NumPy scalars
-- Tuples of supported types (homogeneous preferred)
-- Named tuples (with type annotations)
-
-**Test Input Guidelines:**
-```python
-# Good: Explicit dtype, contiguous array
-arr = np.array([1.0, 2.0, 3.0], dtype=np.float64)
-arr = np.ascontiguousarray(data)
-
-# Good: Scalar inputs with consistent types
-result = numba_func(1.0, 2.0)  # Both floats
-result = numba_func(np.float64(1.0), np.float64(2.0))  # Explicit numpy scalars
-
-# Avoid: Python objects, strings, dicts, sets, or lists as inputs
-# Avoid: Object dtype arrays (dtype=object)
-# Avoid: Structured arrays with complex nested types
-```
-
-**Common Pitfalls to Avoid in Tests:**
-- Do not pass Python lists directly - convert to numpy arrays first
-- Do not use `None` as default arguments in test inputs (unless the function explicitly handles it in nopython mode)
-- Do not pass non-contiguous array views without checking if the function supports them
-- Do not mix `int32` and `int64` across test cases for the same parameter
-
-**Numba-Specific Assertions:**
-```python
-# Use numpy comparison functions
-assert np.allclose(result, expected, rtol=1e-7, atol=1e-14)
-assert np.array_equal(int_result, expected_int)  # For integer results
-```
-
----
-
-### PyTorch (`@torch.compile`)
-
-**Supported Types:**
-- `torch.Tensor` with any dtype (`float32`, `float64`, `int32`, `int64`, `bfloat16`, etc.)
-- Python primitives (`int`, `float`, `bool`) - but prefer tensors
-- Tuples and lists of tensors
-- Dictionaries with string keys and tensor values
-
-**Test Input Guidelines:**
-```python
-# Good: Explicit dtype and device
-x = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)
-x = torch.randn(100, 100, dtype=torch.float64)
-
-# Good: Consistent device placement
-device = torch.device('cpu')  # or 'cuda' if available
-x = torch.tensor([1.0, 2.0], device=device)
-
-# Good: Using torch generator for reproducibility
-g = torch.Generator().manual_seed(42)
-x = torch.randn(100, 100, generator=g)
-
-# Avoid: Mixing CPU and CUDA tensors in same operation
-# Avoid: In-place operations on leaf tensors that require grad (if testing gradients)
-# Avoid: Dynamic control flow that changes based on tensor values (causes graph breaks)
-```
-
-**Common Pitfalls to Avoid in Tests:**
-- Do not use `tensor.item()` inside compiled functions - extract values outside
-- Do not modify tensor shapes dynamically within compiled regions
-- Do not use Python `print()` or logging inside compiled functions
-- Do not rely on specific compilation behavior - test functional correctness only
-
-**PyTorch-Specific Assertions:**
-```python
-# Use torch comparison functions
-assert torch.allclose(result, expected, rtol=1e-5, atol=1e-8)
-assert torch.equal(int_result, expected_int)  # For exact integer comparison
-
-# For gradient testing
-assert torch.allclose(tensor.grad, expected_grad, rtol=1e-4)
-```
-
-**Graph Break Considerations:**
-When testing `@torch.compile` functions, avoid inputs that cause graph breaks:
-- Data-dependent control flow
-- Calls to non-compilable Python functions
-- Dynamic shapes (unless using `dynamic=True`)
-
----
-
-### TensorFlow (`@tf.function`)
-
-**Supported Types:**
-- `tf.Tensor` with any dtype
-- `tf.Variable` (be cautious with mutations)
-- Python primitives (traced as constants - be aware of retracing)
-- `tf.TensorSpec` for input signatures
-- Nested structures of tensors (lists, tuples, dicts)
-
-**Test Input Guidelines:**
-```python
-# Good: Explicit dtype specification
-x = tf.constant([1.0, 2.0, 3.0], dtype=tf.float32)
-x = tf.random.normal([100, 100], dtype=tf.float64)
-
-# Good: Using input_signature to prevent retracing
-@tf.function(input_signature=[tf.TensorSpec(shape=[None, 10], dtype=tf.float32)])
-def my_func(x):
-    ...
-
-# Good: Setting seeds for reproducibility
-tf.random.set_seed(42)
-x = tf.random.normal([100, 100])
-
-# Avoid: Python objects that can't be converted to tensors
-# Avoid: Changing Python argument values between calls (causes retracing)
-# Avoid: Using tensor.numpy() inside @tf.function
-```
-
-**Common Pitfalls to Avoid in Tests:**
-- Do not pass different Python primitive values across test cases (causes retracing)
-- Do not use `tensor.numpy()` inside tf.function - only use outside
-- Do not test with `tf.Variable` mutations unless the function is designed for it
-- Do not rely on Python side effects inside tf.function (they only execute during tracing)
-
-**TensorFlow-Specific Assertions:**
-```python
-# Use numpy conversion for assertions (outside tf.function)
-result_np = result.numpy()
-expected_np = expected.numpy()
-np.testing.assert_allclose(result_np, expected_np, rtol=1e-5, atol=1e-8)
-
-# Or use tf functions
-tf.debugging.assert_near(result, expected, rtol=1e-5, atol=1e-8)
-
-# For exact comparison
-tf.debugging.assert_equal(int_result, expected_int)
-```
-
-**AutoGraph Considerations:**
-- Python `if` statements are converted to `tf.cond` - ensure both branches have same output structure
-- Python `for`/`while` loops are converted to `tf.while_loop` - ensure loop variables have consistent types
-- Avoid `break`/`continue` in complex nested loops
-
----
-
-### JAX (`@jax.jit`)
-
-**Supported Types:**
-- JAX arrays (`jax.Array`, `jnp.ndarray`)
-- NumPy arrays (converted automatically)
-- Python primitives (traced as tracers - use `static_argnums` for compile-time constants)
-- PyTrees: nested structures of arrays (tuples, lists, dicts, namedtuples)
-
-**Test Input Guidelines:**
-```python
-# Good: Using jax.numpy for array creation
-x = jnp.array([1.0, 2.0, 3.0], dtype=jnp.float32)
-x = jax.random.normal(jax.random.PRNGKey(42), (100, 100))
-
-# Good: Explicit PRNG key handling (JAX requires explicit randomness)
-key = jax.random.PRNGKey(42)
-key, subkey = jax.random.split(key)
-x = jax.random.normal(subkey, (100,))
-
-# Good: Using static_argnums for non-traced arguments
-@jax.jit
-def func(x, axis):  # axis should be static if it's used in shape operations
-    return jnp.sum(x, axis=axis)
-# Mark 'axis' as static: jax.jit(func, static_argnums=(1,))
-
-# Avoid: In-place mutations (JAX arrays are immutable)
-# Avoid: Data-dependent shapes
-# Avoid: Side effects (prints, global variable modifications)
-```
-
-**Common Pitfalls to Avoid in Tests:**
-- Do not test with in-place array modifications - JAX arrays are immutable
-- Do not pass different static argument values without recompiling
-- Do not use Python random module - use `jax.random` with explicit keys
-- Do not rely on side effects - JAX functions must be pure
-
-**JAX-Specific Assertions:**
-```python
-# Use jax.numpy or numpy for comparisons
-assert jnp.allclose(result, expected, rtol=1e-5, atol=1e-8)
-assert jnp.array_equal(int_result, expected_int)
-
-# For testing gradients
-grad_fn = jax.grad(func)
-computed_grad = grad_fn(x)
-assert jnp.allclose(computed_grad, expected_grad, rtol=1e-4)
-```
-
-**Functional Purity Requirements:**
-JAX requires functions to be pure (no side effects). In tests:
-- Do not expect `print()` statements to execute during JIT-compiled calls
-- Do not test functions that modify global state
-- Ensure random operations use explicit `PRNGKey` arguments
-
-**PyTree Handling:**
-```python
-# Good: Testing with PyTree inputs
-inputs = {'a': jnp.array([1.0, 2.0]), 'b': jnp.array([3.0, 4.0])}
-result = jitted_func(inputs)
-
-# Good: Testing with nested structures
-inputs = (jnp.array([1.0]), {'x': jnp.array([2.0, 3.0])})
-result = jitted_func(inputs)
-```
-
----
-
-## Cross-Framework Compatibility Notes
-
-When a function might be optimized with multiple JIT frameworks:
-
-1. **Prefer numpy arrays as the common input format** - all frameworks can consume them
-2. **Use framework-agnostic assertions when possible:**
-   ```python
-   # Convert to numpy for comparison
-   result_np = np.asarray(result)  # Works for torch, tf, jax, numpy
-   np.testing.assert_allclose(result_np, expected, rtol=1e-5)
-   ```
-3. **Test with float64 by default** for maximum precision compatibility
-4. **Avoid framework-specific features in test inputs** unless testing that specific functionality
-
-In the case of Pytorch, TensorFlow, Jax and MLX -> Write CPU-only, CUDA-only and MPS-only test functions and skip if the device is not available.

django/aiservice/testgen/testgen.py

@@ -121,8 +121,8 @@ To help unit test the function above, list diverse scenarios that the function s
     # if unit_test_package == "pytest":
     #     package_comment = "# below, each test case is represented by a tuple passed to the @pytest.mark.parametrize decorator"
     system_prompt = execute_system_prompt.format(function_name=ctx.data.qualified_name)
-    if is_numerical_code:
-        system_prompt += f"\n{JIT_INSTRUCTIONS}\n"
+    # if is_numerical_code:
+    #     system_prompt += f"\n{JIT_INSTRUCTIONS}\n"
     execute_system_message = {"role": "system", "content": system_prompt}
 
     execute_messages = [execute_system_message, plan_user_message]
@@ -498,8 +498,8 @@ async def testgen_python(
         # Using different LLMs for different test_index values to get more diverse tests
         test_index = data.test_index if data.test_index is not None else 0
         if test_index % 2 == 0:
-            execute_model = OPENAI_MODEL
-            model_source = "OpenAI"
+            execute_model = HAIKU_MODEL
+            model_source = "Anthropic"
         else:
             execute_model = HAIKU_MODEL
             model_source = "Anthropic"

django/aiservice/uv.lock

@@ -136,6 +136,8 @@ dependencies = [
     { name = "ruff" },
     { name = "sentry-sdk", extra = ["django"] },
     { name = "stamina" },
+    { name = "torch" },
+    { name = "torchvision" },
     { name = "tree-sitter" },
     { name = "tree-sitter-javascript" },
     { name = "tree-sitter-typescript" },
@@ -179,6 +181,8 @@ requires-dist = [
     { name = "ruff", specifier = ">=0.7.0" },
     { name = "sentry-sdk", extras = ["django"], specifier = ">=2.35.0" },
     { name = "stamina", specifier = ">=25.1.0" },
+    { name = "torch", specifier = ">=2.10.0" },
+    { name = "torchvision", specifier = ">=0.25.0" },
     { name = "tree-sitter", specifier = ">=0.25.2" },
     { name = "tree-sitter-javascript", specifier = ">=0.25.0" },
     { name = "tree-sitter-typescript", specifier = ">=0.23.2" },
@@ -214,7 +218,7 @@ wheels = [
 
 [[package]]
 name = "anthropic"
-version = "0.76.0"
+version = "0.77.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "anyio" },
@@ -226,9 +230,9 @@ dependencies = [
     { name = "sniffio" },
     { name = "typing-extensions" },
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-sdist = { url = "https://files.pythonhosted.org/packages/6e/be/d11abafaa15d6304826438170f7574d750218f49a106c54424a40cef4494/anthropic-0.76.0.tar.gz", hash = "sha256:e0cae6a368986d5cf6df743dfbb1b9519e6a9eee9c6c942ad8121c0b34416ffe", size = 495483, upload-time = "2026-01-13T18:41:14.908Z" }
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+version = "12.9.4"
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+]
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+]
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+[[package]]
+name = "cuda-pathfinder"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/0b/02/4dbe7568a42e46582248942f54dc64ad094769532adbe21e525e4edf7bc4/cuda_pathfinder-1.3.3-py3-none-any.whl", hash = "sha256:9984b664e404f7c134954a771be8775dfd6180ea1e1aef4a5a37d4be05d9bbb1", size = 27154, upload-time = "2025-12-04T22:35:08.996Z" },
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 name = "decorator"
 version = "5.2.1"
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 ]
 
+[[package]]
+name = "filelock"
+version = "3.20.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/1d/65/ce7f1b70157833bf3cb851b556a37d4547ceafc158aa9b34b36782f23696/filelock-3.20.3.tar.gz", hash = "sha256:18c57ee915c7ec61cff0ecf7f0f869936c7c30191bb0cf406f1341778d0834e1", size = 19485, upload-time = "2026-01-09T17:55:05.421Z" }
+wheels = [
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+]
+
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 name = "frozenlist"
 version = "1.8.0"
@@ -606,6 +642,15 @@ wheels = [
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+[[package]]
+name = "fsspec"
+version = "2026.1.0"
+source = { registry = "https://pypi.org/simple" }
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 version = "4.0.12"
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 ]
 
+[[package]]
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+version = "3.1.6"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "markupsafe" },
+]
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 version = "0.12.0"
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