Fixes dynamic shaped benchmark size usage (#80)

Fixes dynamic shaped benchmark size usage

This change sets the symbolic dimension values using the shapes
provided by the dynamic shape callback function.

It also fixes incorrectly setting the strides in ArgInfo.

hatlib/arg_info.py

@@ -81,7 +81,8 @@ class ArgInfo:
                 self.total_element_count = self.shape[major_dim] * self.element_strides[major_dim]
 
             else:
-                self.element_strides = self.numpy_strides = self.shape = [1]
+                self.shape = [1]
+                self.element_strides = self.numpy_strides = [self.element_num_bytes]
                 self.total_element_count = 1
             self.total_byte_size = self.element_num_bytes * self.total_element_count
 
@@ -97,7 +98,8 @@ class ArgInfo:
                 self.ctypes_type = ctypes_type
             else:
                 self.ctypes_type = ctypes.POINTER(ctypes_type)
-            self.element_strides = self.numpy_strides = self.shape = [1]
+            self.shape = [1]
+            self.element_strides = self.numpy_strides = [self.element_num_bytes]
             self.total_element_count = 1
             self.total_byte_size = self.element_num_bytes * self.total_element_count
 

hatlib/arg_value.py

@@ -82,8 +82,12 @@ class ArgValue:
                         raise ValueError(
                             f"expected argument to have strides={desc_numpy_strides} but received strides={self.value.strides}"
                         )
-                elif self.value.size != desc.total_element_count:
+                else:
+                    # Will raise ValueError if total_element_count can't be converted to int
+                    desc.total_element_count = int(desc.total_element_count)
+                    
                     # special casing for size=1 arrays
+                    if self.value.size != desc.total_element_count:
                         raise ValueError(
                             f"expected argument to have size={desc.total_element_count} but received shape={self.value.size}"
                         )
@@ -130,7 +134,7 @@ def get_dimension_arg_indices(array_arg: ArgInfo, all_arguments: List[ArgInfo])
     return indices
 
 
-def generate_arg_values(arguments: List[ArgInfo]) -> List[ArgValue]:
+def generate_arg_values(arguments: List[ArgInfo], dim_names_to_values = {}) -> List[ArgValue]:
     """Generate argument values from argument descriptions
     Input and input/output affine_arrays: initialized with random inputs
     Input and input/output runtime_arrays: initialized with arbitrary dimensions and random inputs
@@ -140,7 +144,6 @@ def generate_arg_values(arguments: List[ArgInfo]) -> List[ArgValue]:
     def generate_dim_value():
         return random.choice([2, 3, 4])    # example dimension values
 
-    dim_names_to_values = {}
     values = []
 
     for arg in arguments:
@@ -165,7 +168,8 @@ def generate_arg_values(arguments: List[ArgInfo]) -> List[ArgValue]:
                             shape.append(generate_dim_value())
                             dim_names_to_values[d] = ArgValue(dim_args[d], shape[-1])
                         else:
-                            shape.append(dim_names_to_values[d].value)
+                            v = dim_names_to_values[d].value
+                            shape.append(v if type(v) == int else v[0])
 
             # materialize an array input using the generated shape
             runtime_array_inputs = np.random.random(tuple(shape)).astype(arg.numpy_dtype)

hatlib/benchmark_hat_package.py

@@ -105,6 +105,7 @@ class Benchmark:
 
             set_size = 0
             for i in input_sets[0]:
+                if not i.dim_values:
                     set_size += i.value.size * i.value.dtype.itemsize
 
             if verbose:

hatlib/hat.py

@@ -24,38 +24,76 @@ For example:
     # call a package function named 'my_func_698b5e5c'
     package.my_func_698b5e5c(A, B, D, E)
 """
+import numpy as np
+
 from typing import Callable, List, Tuple, Union
 from functools import reduce
 
 from . import hat_file
 from . import hat_package
-from .arg_value import generate_arg_values
+from .arg_value import generate_arg_values, ArgValue
 from .arg_info import integer_like
 from .function_info import FunctionInfo
 
-
 PLACEHOLDER_SIZE = 128    # arbitrary, to be replaced with a better way to estimate size for runtime arrays
 
-def generate_arg_sets_for_func(func: hat_file.Function, input_sets_minimum_size_MB: int = 0, num_additional: int = 0, dyn_func_shape_fn: Callable[[FunctionInfo], List[List[int]]]=None):
-    def default_dyn_func_shape_fn(func: hat_file.Function) -> List[List[int]]:
-        return [[int(d) if integer_like(d) else PLACEHOLDER_SIZE for d in p.shape] for p in func.arguments]
+
+def generate_arg_sets_for_func(
+    func: hat_file.Function,
+    input_sets_minimum_size_MB: int = 0,
+    num_additional: int = 0,
+    dyn_func_shape_fn: Callable[[FunctionInfo], List[List[int]]] = None
+):
+
+    def default_dyn_func_shape_fn(func_info: FunctionInfo) -> List[List[int]]:
+        return [[int(d) if integer_like(d) else PLACEHOLDER_SIZE for d in p.shape] for p in func_info.arguments
+                if p.pointer_level == 1 and p.usage != hat_file.UsageType.Output]
 
     func_info = FunctionInfo(func)
+    parameters = func_info.arguments
 
     # plug in values for non-constant dimensions in an attempt to estimate the minimum set size
+    dim_names_to_values = {}
+    if any(map(lambda p: not p.is_constant_shaped, func_info.arguments)):
         if dyn_func_shape_fn is None:
             dyn_func_shape_fn = default_dyn_func_shape_fn
         numerical_shapes = dyn_func_shape_fn(func_info)
+        # TODO: We really need to be able to distinguish between args that are dimensions vs. just scalars
+        shape_idx = 0
+        param_idx = 0
+        while param_idx < len(parameters):
+            p = parameters[param_idx]
+            if p.is_constant_shaped:
+                if p.pointer_level and p.usage != hat_file.UsageType.Output:
+                    shape_idx += 1
 
-    parameters = func_info.arguments
+                param_idx += 1
+                continue
 
-    shapes_to_sizes = [reduce(lambda x, y: x * y, shape) for shape in numerical_shapes]
-    set_size = reduce(
-        lambda x, y: x + y, map(lambda size, p: size * p.element_num_bytes, shapes_to_sizes, parameters)
-    )
+            if p.usage == hat_file.UsageType.Output:
+                param_idx += 1
+                continue
+
+            numerical_shape = numerical_shapes[shape_idx]
+
+            # parameter is NOT constant shaped
+            dyn_dimensions = filter(lambda idx_d: not integer_like(idx_d[1]), enumerate(p.shape))
+            for dyn_dim_idx, dyn_dim in dyn_dimensions:
+                if dyn_dim not in dim_names_to_values:
+                    param_idx += 1
+                    dim_names_to_values[dyn_dim] = ArgValue(parameters[param_idx], numerical_shape[dyn_dim_idx])
+
+            param_idx += 1
+            shape_idx += 1
+
+    else:
+        numerical_shapes = [p.shape if p.shape else [int(p.size)] for p in func.arguments]
+
+    shapes_to_sizes = [reduce(lambda x, y: x * y, shape, 1) for shape in numerical_shapes]
+    set_size = reduce(lambda x, y: x + y, map(lambda size, p: size * p.element_num_bytes, shapes_to_sizes, parameters))
     num_input_sets = (input_sets_minimum_size_MB * 1024 * 1024 // set_size) + 1 + num_additional
 
-    arg_sets = [generate_arg_values(parameters) for _ in range(num_input_sets)]
+    arg_sets = [generate_arg_values(parameters, dim_names_to_values) for _ in range(num_input_sets)]
 
     return arg_sets[0] if len(arg_sets) == 1 else arg_sets