put restrict back

extra/thneed.py

@@ -4,7 +4,7 @@ import struct
 import json
 import traceback
 import numpy as np
-from tinygrad.runtime.ops_gpu import CLProgram, CLImage, CLBuffer
+from tinygrad.runtime.ops_gpu import CLProgram
 from tinygrad.helpers import prod, getenv
 from collections import defaultdict
 import pyopencl as cl

test/test_speed_v_torch.py

@@ -37,7 +37,7 @@ def helper_test_speed(f1, *args):
     args = [(x+1).realize() if isinstance(x, Tensor) else (None if x is None else (x+1)) for x in args]  # cache defeats
 
     # force syncing
-    [x.numpy() if isinstance(x, Tensor) else x.cpu().numpy() for x in args if x is not None]
+    [x.numpy() if isinstance(x, Tensor) or str(torch_device) == "cpu" else x.cpu().numpy() for x in args if x is not None]
 
     GlobalCounters.global_ops = 0
     GlobalCounters.global_mem = 0
@@ -45,7 +45,7 @@ def helper_test_speed(f1, *args):
     st = time.monotonic()
     ret = f1(*args)
     # not ideal, it's copying (sometimes). why is this so slow in tinygrad?
-    if isinstance(ret, Tensor): ret.numpy()
+    if isinstance(ret, Tensor) or str(torch_device) == "cpu": ret.numpy()
     else: ret.cpu().numpy()
     et = (time.monotonic() - st) * 1000
     ets.append(et)

tinygrad/codegen/gpu.py

@@ -17,6 +17,7 @@ NATIVE_EXPLOG = getenv("NATIVE_EXPLOG", 0)  # this is needed as a switch for the
 class GPULanguage(NamedTuple):
   kernel_prefix : str = ""
   buffer_prefix : str = ""
+  buffer_suffix : str = ""
   smem_prefix : str = ""
   barrier : str = ""
   gid : List[str] = []
@@ -321,7 +322,7 @@ class GPUCodegen(ASTKernel):
     if GPUCodegen.kernel_cnt[function_name]:
       function_name = f"{function_name}{'_N'+str(GPUCodegen.kernel_cnt[function_name])}"
 
-    buftypes = [f"{'read_only' if i > 0 else 'write_only'} image2d_t" if hasattr(x._buf, "IMAGE") else self.lang.buffer_prefix+self.buftokens[i].decltype() for i,x in enumerate(self.bufs)]
+    buftypes = [f"{'read_only' if i > 0 else 'write_only'} image2d_t" if hasattr(x._buf, "IMAGE") else self.lang.buffer_prefix+self.buftokens[i].decltype()+self.lang.buffer_suffix for i,x in enumerate(self.bufs)]
     self.kernel = list(self.prekernel) + [f"{self.lang.kernel_prefix} void {function_name}(",] + \
       [', '.join([f'{t} data{i}' for i,t in enumerate(buftypes) if i not in self.bufs_to_delete] + self.lang.extra_args)] + \
       [") {\n"] + self.kernel

tinygrad/jit.py

@@ -1,5 +1,6 @@
 from typing import Callable, List, Tuple, Any, Dict, cast
 import itertools
+from tinygrad.helpers import DEBUG
 from tinygrad.lazy import Device
 from tinygrad.tensor import Tensor
 from tinygrad.ops import GlobalCounters, DeviceBuffer
@@ -26,6 +27,7 @@ class TinyJit:
       self.jit_cache = GlobalCounters.cache
       GlobalCounters.cache = None
       assert len(self.jit_cache) != 0, "didn't JIT anything!"
+      if DEBUG >= 1: print(f"JIT captured {len(self.jit_cache)} kernels with {len(input_tensors)} inputs")
 
       # get the inputs for replacement
       for prg, args in self.jit_cache:  # pylint: disable=E1133

tinygrad/ops.py

@@ -120,8 +120,9 @@ class CompiledBuffer(DeviceBuffer):  # pylint: disable=abstract-method
     assert backing is None or GlobalCounters.cache is None, f"can't copy in {backing.shape} while caching"
     return cls.raw_buffer_type(4*prod(shape)) if backing is None else cls.raw_buffer_type.fromCPU(backing)
   def raw(self) -> RawBuffer:
-    if self._buf is None: self._buf = self.create_raw_buffer(self._base_shape, self._backing)
+    if self._buf is None:
-    self._backing = None
+      self._buf = self.create_raw_buffer(self._base_shape, self._backing)
+      self._backing = None
     return self._buf
 
   @classmethod

tinygrad/runtime/ops_clang.py

@@ -6,7 +6,7 @@ import subprocess
 from collections import defaultdict
 from typing import Final, Dict
 from tinygrad.ops import CompiledBuffer, RawBufferCopyIn
-from tinygrad.codegen.gpu import GPUCodegen
+from tinygrad.codegen.gpu import GPUCodegen, GPULanguage
 import platform
 OSX = platform.system() == "Darwin"
 
@@ -31,7 +31,10 @@ class ClangProgram:
     self.fxn(*[x._buf for x in args[2:]])
     if wait: return time.monotonic()-st
 
+class ClangCodegen(GPUCodegen):
+  lang = GPULanguage(buffer_suffix="restrict")
+
 class ClangBuffer(CompiledBuffer):
   raw_buffer_type = RawMallocBuffer
-  codegen_type = GPUCodegen   # clang is the default
+  codegen_type = ClangCodegen
   runtime_type = ClangProgram