diff --git a/shark_turbine/kernel/wave/codegen.py b/shark_turbine/kernel/wave/codegen.py
index 3587e0f3..adcd69b8 100644
--- a/shark_turbine/kernel/wave/codegen.py
+++ b/shark_turbine/kernel/wave/codegen.py
@@ -12,6 +12,7 @@
 from dataclasses import dataclass
 import torch.fx as fx
 import torch.utils._pytree as pytree
+from collections import namedtuple
 
 from ..compiler.ir import (
     Attribute,
@@ -200,76 +201,139 @@ def add_emitter_subs(emitter: WaveEmitter) -> dict[IndexSymbol, Any]:
     return dynamics
 
 
+_Rational = namedtuple("_Rational", ["numerator", "denominator"])
+
+
 def gen_sympy_index(dynamics: dict[IndexSymbol, Any], expr: sympy.Expr) -> OpResult:
     stack: list[OpResult] = []
 
-    def _broadcast(a, b):
-        if not isinstance(a, (Value, OpResult)):
-            a = a.result
+    def _get_ir_value(arg):
+        if not isinstance(arg, (Value, OpResult)):
+            arg = arg.result
+
+        return arg
 
-        if not isinstance(b, (Value, OpResult)):
-            b = b.result
+    def _check_vec_scalar(a, b):
+        return isinstance(a.type, VectorType) and a.type.element_type == b.type
+
+    def _broadcast(a, b):
+        a = _get_ir_value(a)
+        b = _get_ir_value(b)
 
         if a.type == b.type:
             return a, b
 
-        if isinstance(a.type, VectorType) and isinstance(
-            b.type, (IndexType, IntegerType)
-        ):
-            assert a.type.element_type == b.type
+        if _check_vec_scalar(a, b):
             b = vector_d.splat(a.type, b)
             return a, b
 
-        if isinstance(a.type, (IndexType, IntegerType)) and isinstance(
-            b.type, VectorType
-        ):
-            assert b.type.element_type == a.type
+        if _check_vec_scalar(b, a):
             a = vector_d.splat(b.type, a)
             return a, b
 
         raise CodegenError(f"Cannot broadcast {a.type} and {b.type}")
 
-    def _process_mul_add_ops(term, is_mul):
-        args = []
-        callables = []
-        for _ in range(len(term.args)):
-            val = stack.pop()
-            if callable(val):
-                callables.append(val)
-            else:
-                args.append(val)
-        operation = None
-        for arg in args:
-            if operation is None:
-                operation = arg
-                continue
+    def get_const_val(arg):
+        if isinstance(arg, OpResult):
+            arg = arg.owner.opview
 
-            if is_mul:
-                operation = arith_d.MulIOp(*_broadcast(operation, arg))
-            else:
-                operation = arith_d.AddIOp(*_broadcast(operation, arg))
+        if isinstance(arg, arith_d.ConstantOp):
+            value = arg.attributes["value"]
+            if isinstance(value, IntegerAttr):
+                return int(value)
 
-        for arg in callables:
-            operation = arg(operation, is_mul)
+        return None
 
-        stack.append(operation)
+    def muli_fold(lhs, rhs):
+        if get_const_val(lhs) == 1:
+            return rhs
+
+        if get_const_val(rhs) == 1:
+            return lhs
+
+        return arith_d.muli(lhs, rhs)
+
+    # `x + (a/b)` transformed into `(x*b + a) / b`
+    def _add(lhs, rhs):
+        is_rational_lhs = isinstance(lhs, _Rational)
+        is_rational_rhs = isinstance(rhs, _Rational)
+        if is_rational_lhs and not is_rational_rhs:
+            numerator = muli_fold(*_broadcast(lhs.denominator, rhs))
+            numerator = arith_d.addi(*_broadcast(numerator, lhs.numerator))
+            return _Rational(numerator, lhs.denominator)
+        elif not is_rational_lhs and is_rational_rhs:
+            numerator = muli_fold(*_broadcast(lhs, rhs.denominator))
+            numerator = arith_d.addi(*_broadcast(numerator, rhs.numerator))
+            return _Rational(numerator, rhs.denominator)
+        elif is_rational_lhs and is_rational_rhs:
+            lhs_numerator = muli_fold(*_broadcast(lhs.numerator, rhs.denominator))
+            rhs_numerator = muli_fold(*_broadcast(rhs.numerator, lhs.denominator))
+            numerator = arith_d.addi(*_broadcast(lhs_numerator, rhs_numerator))
+            denominator = muli_fold(*_broadcast(lhs.denominator, rhs.denominator))
+            return _Rational(numerator, denominator)
+        else:
+            return arith_d.addi(*_broadcast(lhs, rhs))
+
+    # `x * (a/b)` transformed into `(x * a) / b`
+    def _mul(lhs, rhs):
+        is_rational_lhs = isinstance(lhs, _Rational)
+        is_rational_rhs = isinstance(rhs, _Rational)
+        if is_rational_lhs and not is_rational_rhs:
+            numerator = muli_fold(*_broadcast(lhs.numerator, rhs))
+            return _Rational(numerator, lhs.denominator)
+        elif not is_rational_lhs and is_rational_rhs:
+            numerator = muli_fold(*_broadcast(lhs, rhs.numerator))
+            return _Rational(numerator, rhs.denominator)
+        elif is_rational_lhs and is_rational_rhs:
+            numerator = muli_fold(*_broadcast(lhs.numerator, rhs.numerator))
+            denominator = muli_fold(*_broadcast(lhs.denominator, rhs.denominator))
+            return _Rational(numerator, denominator)
+        else:
+            return muli_fold(*_broadcast(lhs, rhs))
 
-    def _get_mul(numerator):
-        return lambda x: arith_d.MulIOp(*_broadcast(x, numerator))
+    def _floor(value):
+        if isinstance(value, _Rational):
+            value = arith_d.divsi(*_broadcast(value.numerator, value.denominator))
 
-    def _get_add(numerator, denominator):
-        return lambda x: arith_d.AddIOp(
-            *_broadcast(arith_d.MulIOp(*_broadcast(x, denominator)), numerator)
-        )
+        return value
 
-    def _get_div(mul, add, denominator):
-        return lambda x, is_mul: arith_d.DivSIOp(
-            *_broadcast(mul(x) if is_mul else add(x), denominator)
-        )
+    def _ceiling(value):
+        if isinstance(value, _Rational):
+            value = arith_d.ceildivsi(*_broadcast(value.numerator, value.denominator))
+
+        return value
+
+    def _group_rationals(stack, count):
+        """Group rationals and non-rationals args into 2 contiguous sets.
+
+        This allows to mul/add all non-rationals first, reducing total number of ops.
+        """
+        rationals = []
+        non_rationals = []
+        for _ in range(count):
+            val = stack.pop()
+            if isinstance(val, _Rational):
+                rationals.append(val)
+            else:
+                non_rationals.append(val)
+
+        return non_rationals + rationals
+
+    def _apply(args, func):
+        assert len(args) > 0
+        value = args[0]
+        for val in args[1:]:
+            value = func(value, val)
+
+        return value
+
+    def _enforce_non_rational(val, term):
+        if isinstance(val, _Rational):
+            raise CodegenError(f"Rational is not supported yet in '{type(term)}'")
 
     def _get_const(val):
         if isinstance(val, int):
-            return arith_d.constant(IndexType.get(), res)
+            return arith_d.constant(IndexType.get(), val)
 
         if isinstance(val, (tuple, list)):
             vec_type = VectorType.get([len(val)], IndexType.get())
@@ -296,56 +360,50 @@ def _get_const(val):
                 else:
                     raise CodegenError(f"Unknown symbol {term}")
             case sympy.Integer():
-                stack.append(arith_d.constant(IndexType.get(), int(term)))
+                stack.append(_get_const(int(term)))
             case sympy.Mul():
-                _process_mul_add_ops(term, is_mul=True)
+                args = _group_rationals(stack, len(term.args))
+                stack.append(_apply(args, _mul))
             case sympy.Add():
-                _process_mul_add_ops(term, is_mul=False)
+                args = _group_rationals(stack, len(term.args))
+                stack.append(_apply(args, _add))
             case sympy.Mod():
                 rhs = stack.pop()
                 lhs = stack.pop()
-                mod = arith_d.RemSIOp(*_broadcast(lhs, rhs))
+                _enforce_non_rational(rhs, term)
+                _enforce_non_rational(lhs, term)
+                mod = arith_d.remsi(*_broadcast(lhs, rhs))
                 stack.append(mod)
             case sympy.floor():
-                # TODO: Since divsi rounds to zero, this seems to work.
-                # But check whether floordivsi is needed.
-                stack.append(stack.pop())
+                stack.append(_floor(stack.pop()))
+            case sympy.ceiling():
+                stack.append(_ceiling(stack.pop()))
             case sympy.Rational():
-                # `x * (a/b)` transformed into `(x * a) / b`
-                # `x + (a/b)` transformed into `(x*b + a) / b`
-                numerator = arith_d.constant(IndexType.get(), abs(term.p))
-                denominator = arith_d.constant(IndexType.get(), abs(term.q))
-                # Assumes that the negative term is always carried on the numerator
-                if abs(term.p) > term.p:
-                    zero = arith_d.constant(IndexType.get(), int(0))
-                    numerator = arith_d.SubIOp(*_broadcast(zero, numerator))
-                mul = lambda x: x
-                if abs(term.p) != 1:
-                    mul = _get_mul(numerator)
-                add = _get_add(numerator, denominator)
-                operation = _get_div(mul, add, denominator)
-                stack.append(operation)
+                numerator = _get_const(term.p)
+                denominator = _get_const(term.q)
+                stack.append(_Rational(numerator, denominator))
             case sympy.StrictLessThan():
                 rhs = stack.pop()
                 lhs = stack.pop()
+                _enforce_non_rational(rhs, term)
+                _enforce_non_rational(lhs, term)
                 res = arith_d.cmpi(arith_d.CmpIPredicate.slt, *_broadcast(lhs, rhs))
                 stack.append(res)
             case sympy.And():
                 rhs = stack.pop()
                 lhs = stack.pop()
+                _enforce_non_rational(rhs, term)
+                _enforce_non_rational(lhs, term)
                 res = arith_d.andi(*_broadcast(lhs, rhs))
                 stack.append(res)
-            case sympy.ceiling():
-                value = stack.pop()
-                if not isinstance(value, arith_d.DivSIOp):
-                    raise CodegenError(f"Cannot handle ceil({value}) yet")
-                stack.append(arith_d.CeilDivSIOp(value.lhs, value.rhs))
             case sympy.UnevaluatedExpr():
                 continue
             case _:
                 raise CodegenError(f"Can not handle {type(term)} : {term}")
-    if len(stack) != 1:
+
+    if len(stack) != 1 or isinstance(stack[0], _Rational):
         raise CodegenError(f"Expected single result, got {len(stack)}")
+
     return stack[0]
 
 
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 00000000..93bd8d6f
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,31 @@
+# Copyright 2024 The IREE Authors
+#
+# Licensed under the Apache License v2.0 with LLVM Exceptions.
+# See https://llvm.org/LICENSE.txt for license information.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import pytest
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--runperf", action="store_true", default=False, help="run performance tests"
+    )
+
+
+def pytest_configure(config):
+    config.addinivalue_line(
+        "markers", "perf_only: performace test, runs only with '--runperf'"
+    )
+
+
+def pytest_collection_modifyitems(config, items):
+    run_perf = config.getoption("--runperf")
+    for item in items:
+        is_perf_only = next(item.iter_markers("perf_only"), None) is not None
+        if run_perf:
+            if not is_perf_only:
+                item.add_marker(pytest.mark.skip("skip non-perf test"))
+        else:
+            if is_perf_only:
+                item.add_marker(pytest.mark.skip("skip perf test"))
diff --git a/tests/kernel/wave/wave_e2e_test.py b/tests/kernel/wave/wave_e2e_test.py
index 5b6aa640..69ba718a 100644
--- a/tests/kernel/wave/wave_e2e_test.py
+++ b/tests/kernel/wave/wave_e2e_test.py
@@ -641,15 +641,193 @@ def repeat(acc: tkl.Register[M, NF, tkl.f32]) -> tkl.Register[M, NF, tkl.f32]:
         assert_allclose(out, out_ref, rtol=1e-05, atol=1e-05)
 
 
+_igemm_cases = [
+    (4, 5, 5, 10, 2, 2, 16, 3),
+    (2, 5, 5, 10, 2, 2, 16, 3),
+    (1, 5, 5, 10, 2, 2, 16, 3),
+    (4, 5, 5, 4, 2, 2, 16, 3),
+    (1, 5, 5, 4, 2, 2, 16, 3),
+    (1, 5, 5, 3, 2, 2, 16, 3),
+    (2, 5, 5, 1, 2, 2, 16, 3),
+    (4, 5, 5, 10, 2, 2, 2, 3),
+    (2, 5, 5, 10, 2, 2, 2, 3),
+    (1, 5, 5, 10, 2, 2, 2, 3),
+    (4, 5, 5, 4, 2, 2, 2, 3),
+    (2, 5, 5, 4, 2, 2, 2, 3),
+    (1, 5, 5, 3, 2, 2, 2, 3),
+    (2, 5, 5, 1, 2, 2, 2, 3),
+    (1, 5, 5, 1, 2, 2, 2, 3),
+    (4, 5, 5, 10, 2, 2, 1, 3),
+    (2, 5, 5, 10, 2, 2, 1, 3),
+    (1, 5, 5, 10, 2, 2, 1, 3),
+    (4, 5, 5, 4, 2, 2, 1, 3),
+    (2, 5, 5, 4, 2, 2, 1, 3),
+    (1, 5, 5, 4, 2, 2, 1, 3),
+    (2, 5, 5, 3, 2, 2, 1, 3),
+    (4, 5, 5, 1, 2, 2, 1, 3),
+    (2, 5, 5, 1, 2, 2, 1, 3),
+    (1, 5, 5, 1, 2, 2, 1, 3),
+    (4, 5, 5, 10, 2, 2, 16, 2),
+    (2, 5, 5, 10, 2, 2, 16, 2),
+    (1, 5, 5, 10, 2, 2, 16, 2),
+    (4, 5, 5, 4, 2, 2, 16, 2),
+    (1, 5, 5, 4, 2, 2, 16, 2),
+    (4, 5, 5, 3, 2, 2, 16, 2),
+    (4, 5, 5, 1, 2, 2, 16, 2),
+    (1, 5, 5, 1, 2, 2, 16, 2),
+    (4, 5, 5, 10, 2, 2, 2, 2),
+    (2, 5, 5, 10, 2, 2, 2, 2),
+    (1, 5, 5, 10, 2, 2, 2, 2),
+    (4, 5, 5, 4, 2, 2, 2, 2),
+    (2, 5, 5, 4, 2, 2, 2, 2),
+    (2, 5, 5, 3, 2, 2, 2, 2),
+    (2, 5, 5, 1, 2, 2, 2, 2),
+    (1, 5, 5, 1, 2, 2, 2, 2),
+    (4, 5, 5, 10, 2, 2, 1, 2),
+    (2, 5, 5, 10, 2, 2, 1, 2),
+    (1, 5, 5, 10, 2, 2, 1, 2),
+    (4, 5, 5, 4, 2, 2, 1, 2),
+    (2, 5, 5, 4, 2, 2, 1, 2),
+    (1, 5, 5, 4, 2, 2, 1, 2),
+    (4, 5, 5, 1, 2, 2, 1, 2),
+    (1, 5, 5, 1, 2, 2, 1, 2),
+    (4, 5, 5, 10, 2, 2, 16, 1),
+    (2, 5, 5, 10, 2, 2, 16, 1),
+    (4, 5, 5, 4, 2, 2, 16, 1),
+    (2, 5, 5, 4, 2, 2, 16, 1),
+    (1, 5, 5, 4, 2, 2, 16, 1),
+    (4, 5, 5, 3, 2, 2, 16, 1),
+    (1, 5, 5, 3, 2, 2, 16, 1),
+    (2, 5, 5, 1, 2, 2, 16, 1),
+    (1, 5, 5, 1, 2, 2, 16, 1),
+    (4, 5, 5, 10, 2, 2, 2, 1),
+    (2, 5, 5, 10, 2, 2, 2, 1),
+    (1, 5, 5, 10, 2, 2, 2, 1),
+    (4, 5, 5, 4, 2, 2, 2, 1),
+    (2, 5, 5, 4, 2, 2, 2, 1),
+    (1, 5, 5, 4, 2, 2, 2, 1),
+    (1, 5, 5, 3, 2, 2, 2, 1),
+    (2, 5, 5, 1, 2, 2, 2, 1),
+    (1, 5, 5, 1, 2, 2, 2, 1),
+    (4, 5, 5, 10, 2, 2, 1, 1),
+    (2, 5, 5, 10, 2, 2, 1, 1),
+    (4, 5, 5, 4, 2, 2, 1, 1),
+    (2, 5, 5, 4, 2, 2, 1, 1),
+    (1, 5, 5, 4, 2, 2, 1, 1),
+    (2, 5, 5, 1, 2, 2, 1, 1),
+    (1, 5, 5, 1, 2, 2, 1, 1),
+    (4, 5, 5, 10, 2, 2, 16, 3),
+    (2, 5, 5, 10, 2, 2, 16, 3),
+    (1, 5, 5, 10, 2, 2, 16, 3),
+    (4, 5, 5, 4, 2, 2, 16, 3),
+    (2, 5, 5, 4, 2, 2, 16, 3),
+    (1, 5, 5, 4, 2, 2, 16, 3),
+    (4, 5, 5, 1, 2, 2, 16, 3),
+    (1, 5, 5, 1, 2, 2, 16, 3),
+    (4, 5, 5, 10, 2, 2, 2, 3),
+    (1, 5, 5, 10, 2, 2, 2, 3),
+    (2, 5, 5, 4, 2, 2, 2, 3),
+    (1, 5, 5, 4, 2, 2, 2, 3),
+    (2, 5, 5, 3, 2, 2, 2, 3),
+    (4, 5, 5, 1, 2, 2, 2, 3),
+    (2, 5, 5, 1, 2, 2, 2, 3),
+    (1, 5, 5, 1, 2, 2, 2, 3),
+    (4, 5, 5, 10, 2, 2, 1, 3),
+    (2, 5, 5, 10, 2, 2, 1, 3),
+    (1, 5, 5, 10, 2, 2, 1, 3),
+    (4, 5, 5, 4, 2, 2, 1, 3),
+    (2, 5, 5, 4, 2, 2, 1, 3),
+    (1, 5, 5, 4, 2, 2, 1, 3),
+    (4, 5, 5, 1, 2, 2, 1, 3),
+    (2, 5, 5, 1, 2, 2, 1, 3),
+    (1, 5, 5, 1, 2, 2, 1, 3),
+    (4, 5, 5, 10, 2, 2, 16, 2),
+    (2, 5, 5, 10, 2, 2, 16, 2),
+    (1, 5, 5, 10, 2, 2, 16, 2),
+    (4, 5, 5, 4, 2, 2, 16, 2),
+    (1, 5, 5, 4, 2, 2, 16, 2),
+    (4, 5, 5, 1, 2, 2, 16, 2),
+    (2, 5, 5, 1, 2, 2, 16, 2),
+    (4, 5, 5, 10, 2, 2, 2, 2),
+    (2, 5, 5, 10, 2, 2, 2, 2),
+    (1, 5, 5, 10, 2, 2, 2, 2),
+    (4, 5, 5, 4, 2, 2, 2, 2),
+    (2, 5, 5, 4, 2, 2, 2, 2),
+    (1, 5, 5, 4, 2, 2, 2, 2),
+    (1, 5, 5, 3, 2, 2, 2, 2),
+    (2, 5, 5, 1, 2, 2, 2, 2),
+    (1, 5, 5, 1, 2, 2, 2, 2),
+    (2, 5, 5, 10, 2, 2, 1, 2),
+    (1, 5, 5, 10, 2, 2, 1, 2),
+    (4, 5, 5, 4, 2, 2, 1, 2),
+    (2, 5, 5, 4, 2, 2, 1, 2),
+    (1, 5, 5, 4, 2, 2, 1, 2),
+    (1, 5, 5, 3, 2, 2, 1, 2),
+    (2, 5, 5, 1, 2, 2, 1, 2),
+    (1, 5, 5, 1, 2, 2, 1, 2),
+    (4, 5, 5, 10, 2, 2, 16, 1),
+    (2, 5, 5, 10, 2, 2, 16, 1),
+    (1, 5, 5, 10, 2, 2, 16, 1),
+    (4, 5, 5, 4, 2, 2, 16, 1),
+    (2, 5, 5, 4, 2, 2, 16, 1),
+    (1, 5, 5, 4, 2, 2, 16, 1),
+    (2, 5, 5, 3, 2, 2, 16, 1),
+    (1, 5, 5, 3, 2, 2, 16, 1),
+    (4, 5, 5, 1, 2, 2, 16, 1),
+    (1, 5, 5, 1, 2, 2, 16, 1),
+    (4, 5, 5, 10, 2, 2, 2, 1),
+    (1, 5, 5, 10, 2, 2, 2, 1),
+    (4, 5, 5, 4, 2, 2, 2, 1),
+    (2, 5, 5, 4, 2, 2, 2, 1),
+    (1, 5, 5, 4, 2, 2, 2, 1),
+    (4, 5, 5, 3, 2, 2, 2, 1),
+    (4, 5, 5, 1, 2, 2, 2, 1),
+    (2, 5, 5, 1, 2, 2, 2, 1),
+    (1, 5, 5, 1, 2, 2, 2, 1),
+    (4, 5, 5, 10, 2, 2, 1, 1),
+    (2, 5, 5, 10, 2, 2, 1, 1),
+    (4, 5, 5, 4, 2, 2, 1, 1),
+    (2, 5, 5, 4, 2, 2, 1, 1),
+    (1, 5, 5, 4, 2, 2, 1, 1),
+    (4, 5, 5, 3, 2, 2, 1, 1),
+    (2, 5, 5, 3, 2, 2, 1, 1),
+    (1, 5, 5, 3, 2, 2, 1, 1),
+    (2, 5, 5, 1, 2, 2, 1, 1),
+    (1, 5, 5, 1, 2, 2, 1, 1),
+    (1, 5, 5, 1, 3, 3, 1, 1),
+]
+
+perf_test = lambda *a: pytest.param(*a, marks=pytest.mark.perf_only)
+
+_igemm_cases += [
+    perf_test(2, 128, 128, 16, 3, 3, 320, 1),
+    perf_test(2, 128, 128, 320, 1, 1, 640, 1),
+    perf_test(2, 128, 128, 320, 1, 1, 960, 1),
+    perf_test(2, 128, 128, 320, 3, 3, 16, 1),
+    perf_test(2, 128, 128, 320, 3, 3, 320, 1),
+    perf_test(2, 32, 32, 1280, 1, 1, 1920, 1),
+    perf_test(2, 32, 32, 1280, 1, 1, 2560, 1),
+    perf_test(2, 32, 32, 1280, 1, 1, 640, 1),
+    perf_test(2, 32, 32, 1280, 3, 3, 1280, 1),
+    perf_test(2, 32, 32, 1280, 3, 3, 1920, 1),
+    perf_test(2, 32, 32, 1280, 3, 3, 2560, 1),
+    perf_test(2, 32, 32, 1280, 3, 3, 640, 1),
+    perf_test(2, 32, 32, 640, 3, 3, 640, 1),
+    perf_test(2, 64, 64, 320, 3, 3, 320, 1),
+    perf_test(2, 64, 64, 640, 1, 1, 1280, 1),
+    perf_test(2, 64, 64, 640, 1, 1, 1920, 1),
+    perf_test(2, 64, 64, 640, 1, 1, 320, 1),
+    perf_test(2, 64, 64, 640, 1, 1, 960, 1),
+    perf_test(2, 64, 64, 640, 3, 3, 320, 1),
+    perf_test(2, 64, 64, 640, 3, 3, 640, 1),
+]
+
+
 @require_e2e
-@pytest.mark.parametrize("n", [1, 2, 4])
-@pytest.mark.parametrize("c", [1, 3, 4, 10])
-@pytest.mark.parametrize("nf", [1, 2, 16])
-@pytest.mark.parametrize("stride", [1, 2, 3])
+@pytest.mark.parametrize("n, h, w, c, hf, wf, nf, stride", _igemm_cases)
 @pytest.mark.parametrize("mem_space", [GLOBAL_ADDRESS_SPACE, SHARED_ADDRESS_SPACE])
-def test_igemm_conv(n, c, nf, stride, mem_space):
-    h, w = 5, 5  # Image.
-    cf, hf, wf = c, 2, 2  # Filters.
+def test_igemm_conv(n, h, w, c, hf, wf, nf, stride, mem_space):
+    cf = c
     padding = 0  # TODO: only pad=0 is supported for now
 
     torch.manual_seed(1)