Skip MLX bench for the bench runner. (#429)

bindings/python/benches/test_mlx.py

@@ -1,59 +1,58 @@
 import os
+import platform
 import tempfile
 
-import mlx.core as mx
-from safetensors.mlx import load_file, save_file
-
-
-def create_gpt2(n_layers: int):
-    tensors = {}
-    tensors["wte"] = mx.zeros((50257, 768))
-    tensors["wpe"] = mx.zeros((1024, 768))
-    for i in range(n_layers):
-        tensors[f"h.{i}.ln_1.weight"] = mx.zeros((768,))
-        tensors[f"h.{i}.ln_1.bias"] = mx.zeros((768,))
-        tensors[f"h.{i}.attn.bias"] = mx.zeros((1, 1, 1024, 1024))
-        tensors[f"h.{i}.attn.c_attn.weight"] = mx.zeros((768, 2304))
-        tensors[f"h.{i}.attn.c_attn.bias"] = mx.zeros((2304))
-        tensors[f"h.{i}.attn.c_proj.weight"] = mx.zeros((768, 768))
-        tensors[f"h.{i}.attn.c_proj.bias"] = mx.zeros((768))
-        tensors[f"h.{i}.ln_2.weight"] = mx.zeros((768))
-        tensors[f"h.{i}.ln_2.bias"] = mx.zeros((768))
-        tensors[f"h.{i}.mlp.c_fc.weight"] = mx.zeros((768, 3072))
-        tensors[f"h.{i}.mlp.c_fc.bias"] = mx.zeros((3072))
-        tensors[f"h.{i}.mlp.c_proj.weight"] = mx.zeros((3072, 768))
-        tensors[f"h.{i}.mlp.c_proj.bias"] = mx.zeros((768))
-    tensors["ln_f.weight"] = mx.zeros((768))
-    tensors["ln_f.bias"] = mx.zeros((768))
-    return tensors
-
-
-def load(filename):
-    return mx.load(filename)
-
-
-def test_mlx_mlx_load(benchmark):
-    # benchmark something
-    weights = create_gpt2(12)
-    with tempfile.NamedTemporaryFile(delete=False) as f:
-        filename = f"{f.name}.npz"
-        mx.savez(filename, **weights)
-        result = benchmark(load, filename)
-    os.unlink(f.name)
-
-    for k, v in weights.items():
-        tv = result[k]
-        assert mx.allclose(v, tv)
-
-
-def test_mlx_sf_load(benchmark):
-    # benchmark something
-    weights = create_gpt2(12)
-    with tempfile.NamedTemporaryFile(delete=False) as f:
-        save_file(weights, f.name)
-        result = benchmark(load_file, f.name)
-    os.unlink(f.name)
-
-    for k, v in weights.items():
-        tv = result[k]
-        assert mx.allclose(v, tv)
+
+if platform.system() == "Darwin":
+    import mlx.core as mx
+    from safetensors.mlx import load_file, save_file
+
+    def create_gpt2(n_layers: int):
+        tensors = {}
+        tensors["wte"] = mx.zeros((50257, 768))
+        tensors["wpe"] = mx.zeros((1024, 768))
+        for i in range(n_layers):
+            tensors[f"h.{i}.ln_1.weight"] = mx.zeros((768,))
+            tensors[f"h.{i}.ln_1.bias"] = mx.zeros((768,))
+            tensors[f"h.{i}.attn.bias"] = mx.zeros((1, 1, 1024, 1024))
+            tensors[f"h.{i}.attn.c_attn.weight"] = mx.zeros((768, 2304))
+            tensors[f"h.{i}.attn.c_attn.bias"] = mx.zeros((2304))
+            tensors[f"h.{i}.attn.c_proj.weight"] = mx.zeros((768, 768))
+            tensors[f"h.{i}.attn.c_proj.bias"] = mx.zeros((768))
+            tensors[f"h.{i}.ln_2.weight"] = mx.zeros((768))
+            tensors[f"h.{i}.ln_2.bias"] = mx.zeros((768))
+            tensors[f"h.{i}.mlp.c_fc.weight"] = mx.zeros((768, 3072))
+            tensors[f"h.{i}.mlp.c_fc.bias"] = mx.zeros((3072))
+            tensors[f"h.{i}.mlp.c_proj.weight"] = mx.zeros((3072, 768))
+            tensors[f"h.{i}.mlp.c_proj.bias"] = mx.zeros((768))
+        tensors["ln_f.weight"] = mx.zeros((768))
+        tensors["ln_f.bias"] = mx.zeros((768))
+        return tensors
+
+    def load(filename):
+        return mx.load(filename)
+
+    def test_mlx_mlx_load(benchmark):
+        # benchmark something
+        weights = create_gpt2(12)
+        with tempfile.NamedTemporaryFile(delete=False) as f:
+            filename = f"{f.name}.npz"
+            mx.savez(filename, **weights)
+            result = benchmark(load, filename)
+        os.unlink(f.name)
+
+        for k, v in weights.items():
+            tv = result[k]
+            assert mx.allclose(v, tv)
+
+    def test_mlx_sf_load(benchmark):
+        # benchmark something
+        weights = create_gpt2(12)
+        with tempfile.NamedTemporaryFile(delete=False) as f:
+            save_file(weights, f.name)
+            result = benchmark(load_file, f.name)
+        os.unlink(f.name)
+
+        for k, v in weights.items():
+            tv = result[k]
+            assert mx.allclose(v, tv)