diff --git a/python/test/unit/runtime/test_cache.py b/python/test/unit/runtime/test_cache.py
index cf4de3ce063d..2bca3f28379d 100644
--- a/python/test/unit/runtime/test_cache.py
+++ b/python/test/unit/runtime/test_cache.py
@@ -1,7 +1,5 @@
-import multiprocessing
 import os
 import shutil
-from collections import namedtuple
 
 import pytest
 import torch
@@ -198,39 +196,6 @@ def kernel_add_device(a, b, o, N: tl.constexpr):
     assert inline_ttir != noinline_ttir
 
 
-instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"])
-
-
-def compile_fn(config, cc):
-    @triton.jit
-    def kernel_sub(a, b, o, N: tl.constexpr):
-        idx = tl.arange(0, N)
-        tl.store(o + idx, tl.load(a + idx) - tl.load(b + idx) * 777)
-    triton.compile(
-        fn=kernel_sub,
-        signature={0: "*fp32", 1: "*fp32", 2: "*fp32"},
-        device=0,
-        constants={3: 32},
-        configs=[config],
-        warm_cache_only=True,
-        cc=cc,
-    )
-
-
-def test_compile_in_subproc() -> None:
-    major, minor = torch.cuda.get_device_capability(0)
-    cc = major * 10 + minor
-    config = instance_descriptor(tuple(range(4)), ())
-
-    multiprocessing.set_start_method('spawn')
-    proc = multiprocessing.Process(
-        target=compile_fn,
-        args=(config, cc))
-    proc.start()
-    proc.join()
-    assert proc.exitcode == 0
-
-
 def test_memory_leak() -> None:
     @triton.jit
     def kernel(in_ptr0, out_ptr0, xnumel, XBLOCK: tl.constexpr):
diff --git a/python/test/unit/runtime/test_subproc.py b/python/test/unit/runtime/test_subproc.py
new file mode 100644
index 000000000000..0e0d33c6fd21
--- /dev/null
+++ b/python/test/unit/runtime/test_subproc.py
@@ -0,0 +1,83 @@
+import multiprocessing
+import os
+import shutil
+from collections import namedtuple
+
+import torch
+
+import triton
+import triton.language as tl
+
+tmpdir = ".tmp"
+
+
+def reset_tmp_dir():
+    os.environ["TRITON_CACHE_DIR"] = tmpdir
+    if os.path.exists(tmpdir):
+        shutil.rmtree(tmpdir)
+
+
+instance_descriptor = namedtuple("instance_descriptor", ["divisible_by_16", "equal_to_1"])
+
+
+def compile_fn(config, cc):
+    @triton.jit
+    def kernel_sub(a, b, o, N: tl.constexpr):
+        idx = tl.arange(0, N)
+        tl.store(o + idx, tl.load(a + idx) - tl.load(b + idx) * 777)
+    triton.compile(
+        fn=kernel_sub,
+        signature={0: "*fp32", 1: "*fp32", 2: "*fp32"},
+        device=0,
+        constants={3: 32},
+        configs=[config],
+        warm_cache_only=True,
+        cc=cc,
+    )
+
+
+def test_compile_in_subproc() -> None:
+    major, minor = torch.cuda.get_device_capability(0)
+    cc = major * 10 + minor
+    config = instance_descriptor(tuple(range(4)), ())
+
+    multiprocessing.set_start_method('fork')
+    proc = multiprocessing.Process(
+        target=compile_fn,
+        args=(config, cc))
+    proc.start()
+    proc.join()
+    assert proc.exitcode == 0
+
+
+def compile_fn_dot(config, cc):
+    @triton.jit
+    def kernel_dot(Z):
+        offs = tl.arange(0, 16)[:, None] * 16 + tl.arange(0, 16)[None, :]
+        z = tl.load(Z + offs)
+        z = tl.dot(z, z)
+        tl.store(Z + offs, z)
+
+    triton.compile(
+        fn=kernel_dot,
+        signature={0: "*fp32"},
+        device=0,
+        configs=[config],
+        warm_cache_only=True,
+        cc=cc,
+    )
+
+
+def test_compile_in_forked_subproc() -> None:
+    reset_tmp_dir()
+    major, minor = torch.cuda.get_device_capability(0)
+    cc = major * 10 + minor
+    config = instance_descriptor(tuple(range(1)), ())
+
+    assert multiprocessing.get_start_method() == 'fork'
+    proc = multiprocessing.Process(
+        target=compile_fn_dot,
+        args=(config, cc))
+    proc.start()
+    proc.join()
+    assert proc.exitcode == 0
diff --git a/python/triton/language/semantic.py b/python/triton/language/semantic.py
index 678ee8cc780e..b4a55c0fa9a8 100644
--- a/python/triton/language/semantic.py
+++ b/python/triton/language/semantic.py
@@ -3,7 +3,6 @@
 from functools import wraps
 from typing import List, Optional, Sequence, Tuple, TypeVar
 
-import triton
 from . import core as tl
 from triton._C.libtriton.triton import ir
 
@@ -1181,18 +1180,6 @@ def dot(lhs: tl.tensor,
         allow_tf32: bool,
         out_dtype: tl.dtype,
         builder: ir.builder) -> tl.tensor:
-    try:
-        import torch
-    except ImportError:
-        raise ImportError("Triton requires PyTorch to be installed")
-    if torch.version.hip is None:
-        device = triton.runtime.jit.get_current_device()
-        capability = triton.runtime.jit.get_device_capability(device)
-        capability = capability[0] * 10 + capability[1]
-        if capability < 70:
-            assert (
-                not rhs.dtype.is_fp16() and not rhs.dtype.is_fp8()
-            ), "Float8 and Float16 types are not supported for compute capability < 70 (use Float32 or above)"
     assert lhs.type.is_block() and rhs.type.is_block()
     assert lhs.dtype == rhs.dtype, "lhs and rhs must have the same dtype!"
     assert len(lhs.shape) == 2 and len(rhs.shape) == 2