fix st_vars

* cleanup fix_kernel

* early load buffer

* early meta ops

* move those to fix_kernel_ops

* fix tests

* remote metal was flaky

* Revert "fix tests"

This reverts commit a27019383d.

* that hack broke things

* fine for ptx

.github/workflows/test.yml

@@ -870,29 +870,29 @@ jobs:
     - name: Test ONNX Runner (WEBGPU)
       run: WEBGPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
 
-  osxremote:
-    name: MacOS (remote metal)
-    runs-on: macos-15
-    timeout-minutes: 10
-    env:
-      REMOTE: 1
-      REMOTEDEV: METAL
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: macos-remote
-          deps: testing_minimal
-      - name: Check Device.DEFAULT and print some source
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-          python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
-          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-      - name: Run REMOTE=1 Test
-        run: |
-          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
+  #osxremote:
+  #  name: MacOS (remote metal)
+  #  runs-on: macos-15
+  #  timeout-minutes: 10
+  #  env:
+  #    REMOTE: 1
+  #    REMOTEDEV: METAL
+  #  steps:
+  #    - name: Checkout Code
+  #      uses: actions/checkout@v4
+  #    - name: Setup Environment
+  #      uses: ./.github/actions/setup-tinygrad
+  #      with:
+  #        key: macos-remote
+  #        deps: testing_minimal
+  #    - name: Check Device.DEFAULT and print some source
+  #      run: |
+  #        python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
+  #        python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
+  #        DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
+  #    - name: Run REMOTE=1 Test
+  #      run: |
+  #        python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
 
   amdremote:
     name: Linux (remote)

docs/ramp.py

@@ -126,7 +126,7 @@ print(t_log_grad.uop)
 """
 void E_(float* restrict data0, float* restrict data1) {
   float val0 = *(data1+0);
-  *(data0+0) = (0.6931471805599453f*(1/(val0*0.6931471805599453f)));
+  *(data0+0) = (1/val0);
 }
 """
 # the derivative is close to 1/3

examples/mlperf/model_train.py

@@ -1318,18 +1318,47 @@ def train_llama3():
   if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
   model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
 
+  if (DP := getenv("DP", 1)) > 1:
+    device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
+    for v in get_parameters(model):
+      v.shard_(device, axis=None)
+
+  # TODO: MP
+  # if (GPUS := getenv("GPUS", 1)) > 1:
+  #   device = tuple(f"{Device.DEFAULT}:{i}" for i in range(GPUS))
+  #   for k,v in get_state_dict(model).items():
+  #     if 'scale' in k: v.shard_(device, axis=None)  # from quantized
+  #     # elif '.attention.wq' in k: v.shard_(device, axis=0)
+  #     # elif '.attention.wk' in k: v.shard_(device, axis=0)
+  #     # elif '.attention.wv' in k: v.shard_(device, axis=0)
+  #     # elif '.attention.wo' in k: v.shard_(device, axis=1)
+  #     # elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
+  #     # elif '.feed_forward.w2.' in k: v.shard_(device, axis=1)
+  #     # elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
+  #     # elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
+  #     elif 'output.weight' in k: v.shard_(device, axis=0)  # 243.32
+  #     else:
+  #       # print(k)
+  #       # attention_norm, ffn_norm, norm
+  #       v.shard_(device, axis=None)
+
   optim = AdamW(get_parameters(model), lr=0.0,
                 b1=opt_adamw_beta_1, b2=opt_adamw_beta_2, eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay)
   scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
 
   @TinyJit
   @Tensor.train()
-  def train_step(model, tokens):
+  def train_step(model, tokens:Tensor, grad_acc:int):
     optim.zero_grad()
-    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
-    loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
-    loss.backward()
-
+    # grad acc
+    for batch in tokens.split(tokens.shape[0]//grad_acc):
+      if (DP := getenv("DP", 1)) > 1:
+        device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
+        batch = batch.shard(device, 0)
+      logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan)
+      loss = logits.sparse_categorical_crossentropy(batch[:, 1:])
+      loss.backward()
+      Tensor.realize(*[p.grad for p in optim.params])
     # L2 norm grad clip
     # https://github.com/NVIDIA/NeMo/blob/3368c3fc0b4a186ab33a1d68a504315100c0b2a6/nemo/collections/nlp/modules/common/megatron/clip_grads.py#L57
     # https://docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html
@@ -1358,11 +1387,12 @@ def train_llama3():
     iter = batch_load_llama3(GBS, SAMPLES, SEQLEN, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=SEED, val=bool(TRAIN_ON_VAL))
 
   i = 0
-  for tokens in tqdm(iter, total=SAMPLES//BS):
+  for tokens in tqdm(iter, total=SAMPLES//GBS):
+    t = time.perf_counter()
     GlobalCounters.reset()
-    loss, lr = train_step(model, tokens)
+    loss, lr = train_step(model, tokens, grad_acc)
     # above as tqdm.write f-string
-    tqdm.write(f"{loss.item():.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used")
+    tqdm.write(f"{loss.item():.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
     if (fname:=getenv("LOSS_FILE", "")):
       with open(fname, "a") as f:
         f.write(f"{i} {loss.item():.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")

extra/hcq/hcq_smi.py

@@ -16,7 +16,7 @@ def _do_reset_device(pci_bus): System.pci_reset(pci_bus)
 def _is_module_loaded(name: str) -> bool: return os.path.isdir(f"/sys/module/{name}")
 
 def cmd_remove_module(args):
-  modules = ["nvidia_drm", "nvidia_modeset", "nvidia_uvm", "nvidia"] if args.backend == "nv" else ["amdgpu"]
+  modules = ["nvidia_drm", "nvidia_modeset", "nvidia_uvm", "nvidia", "ast"] if args.backend == "nv" else ["amdgpu"]
   to_unload = [m for m in modules if _is_module_loaded(m)]
   if not to_unload: print("Kernel modules are not loaded")
   else:

setup.py

@@ -9,7 +9,7 @@ with open(directory / 'README.md', encoding='utf-8') as f:
 
 testing_minimal = [
   "numpy",
-  "torch",
+  "torch==2.7.1",
   "pytest",
   "pytest-xdist",
   "hypothesis",

test/external/external_llm_eval.py

@@ -10,7 +10,7 @@ if __name__ == "__main__":
 
   model, kv = Transformer.from_gguf(Tensor.from_url(models["1B"]), max_context=4096)
 
-  tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
+  tok = SimpleTokenizer.from_gguf_kv(kv)
   bos_id: int = kv['tokenizer.ggml.bos_token_id']
   eos_id: int = kv['tokenizer.ggml.eos_token_id']
 

test/external/external_test_simple_tokenizer.py

@@ -1,17 +1,19 @@
 from transformers import AutoTokenizer
 from datasets import load_dataset
-from tinygrad.apps.llm import SimpleTokenizer
-from tinygrad.helpers import tqdm, getenv
+from tinygrad.apps.llm import SimpleTokenizer, gpt2_decode_vocab, get_llama_re
+from tinygrad.helpers import tqdm, getenv, partition
 
 # use ALLOW_FAILED=-1 to go over the entire dataset without printing.
 if __name__ == "__main__":
   base_tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
-  vocab_words = [ word for word, _ in sorted(base_tokenizer.get_vocab().items(), key=lambda t: t[1]) ]
+  special_tokens, normal_tokens = partition(((t, tid) for t, tid in base_tokenizer.vocab.items()),
+    lambda e: e[1] in base_tokenizer.all_special_ids)
   inv_vocab = { tid: word for word, tid in base_tokenizer.get_vocab().items() }
-  simple_tokenizer = SimpleTokenizer(vocab_words)
+  simple_tokenizer = SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
 
   color_codes = [ 91, 92, 94, 93, 95 ]
-  def color_tokens(tids): return "".join(f"\033[{color_codes[i%len(color_codes)]}m{inv_vocab[t]}" for i, t in enumerate(tids)) + "\033[0m"
+  def color_tokens(tids):
+    return "".join(f"\033[{color_codes[i%len(color_codes)]}m{base_tokenizer.decode([t])}" for i, t in enumerate(tids)) + "\033[0m"
 
   ds = load_dataset("OpenAssistant/oasst1")
   allow_failed = getenv("ALLOW_FAILED", 10)

test/external/process_replay/process_replay.py

@@ -74,6 +74,7 @@ def diff(offset:int, fxns:dict[str, Callable[..., tuple|None]]) -> None:
       warnings.warn(f"detected changes in over {MAX_DIFF_PCT}%. skipping further diff generation.", ProcessReplayWarning)
       early_stop.set()
       break
+    name, loc = "", ""
     try:
       name, args, kwargs, ctx_vals, loc, ret = pickle.loads(row[0])
       ctx_vars = {k:v.value for k,v in ctx_vals.items() if k != "DEBUG" and (var:=ContextVar._cache.get(k)) is not None and var.value != v.value}
@@ -90,7 +91,7 @@ def diff(offset:int, fxns:dict[str, Callable[..., tuple|None]]) -> None:
         warnings.warn("PROCESS REPLAY DETECTED CHANGE", ProcessReplayWarning)
     except Exception as e:
       changed += 1
-      warnings.warn(e, ProcessReplayWarning)
+      warnings.warn(f"{name=} {loc=} {e=}", ProcessReplayWarning)
   conn.commit()
   cur.close()
 
@@ -123,5 +124,5 @@ if __name__ == "__main__":
   logging.info(f"running process replay with {ASSERT_DIFF=}")
   try: _pmap(replayers)
   except Exception as e:
-    logging.info("process replay err", e)
+    logging.info(f"process replay err: {e}")
     exit(int(ASSERT_DIFF))

test/test_dtype.py

@@ -4,7 +4,7 @@ import torch
 from typing import Any, List
 from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv, DEBUG, CI
-from tinygrad.dtype import DType, DTYPES_DICT, ImageDType, PtrDType, least_upper_dtype, to_dtype, fp8_to_float, float_to_fp8
+from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8
 from tinygrad import Device, Tensor, dtypes
 from tinygrad.tensor import _to_np_dtype
 from hypothesis import assume, given, settings, strategies as strat
@@ -384,30 +384,6 @@ class TestPtrDType(unittest.TestCase):
     self.assertEqual(dt.v, 4)
     self.assertEqual(dt.count, 4)
 
-class TestImageDType(unittest.TestCase):
-  def test_image_scalar(self):
-    assert dtypes.imagef((10,10)).base.scalar() == dtypes.float32
-    assert dtypes.imageh((10,10)).base.scalar() == dtypes.float32
-  def test_image_vec(self):
-    assert dtypes.imagef((10,10)).base.vec(4) == dtypes.float32.vec(4)
-    assert dtypes.imageh((10,10)).base.vec(4) == dtypes.float32.vec(4)
-
-class TestEqStrDType(unittest.TestCase):
-  def test_image_ne(self):
-    if ImageDType is None: raise unittest.SkipTest("no ImageDType support")
-    assert dtypes.float == dtypes.float32, "float doesn't match?"
-    assert dtypes.imagef((1,2,4)) != dtypes.imageh((1,2,4)), "different image dtype doesn't match"
-    assert dtypes.imageh((1,2,4)) != dtypes.imageh((1,4,2)), "different shape doesn't match"
-    assert dtypes.imageh((1,2,4)) == dtypes.imageh((1,2,4)), "same shape matches"
-    assert isinstance(dtypes.imageh((1,2,4)), ImageDType)
-  def test_ptr_eq(self):
-    assert dtypes.float32.ptr() == dtypes.float32.ptr()
-    assert not (dtypes.float32.ptr() != dtypes.float32.ptr())
-  def test_strs(self):
-    if PtrDType is None: raise unittest.SkipTest("no PtrDType support")
-    self.assertEqual(str(dtypes.imagef((1,2,4))), "dtypes.imagef((1, 2, 4))")
-    self.assertEqual(str(dtypes.float32.ptr(16)), "dtypes.float.ptr(16)")
-
 class TestImplicitFunctionTypeChange(unittest.TestCase):
   def test_functions(self):
     result = []
@@ -438,19 +414,6 @@ class TestDtypeUsage(unittest.TestCase):
         t = Tensor([[1, 2], [3, 4]], dtype=d)
         (t*t).max().item()
 
-class TestToDtype(unittest.TestCase):
-  def test_dtype_to_dtype(self):
-    dtype = dtypes.int32
-    res = to_dtype(dtype)
-    self.assertIsInstance(res, DType)
-    self.assertEqual(res, dtypes.int32)
-
-  def test_str_to_dtype(self):
-    dtype = "int32"
-    res = to_dtype(dtype)
-    self.assertIsInstance(res, DType)
-    self.assertEqual(res, dtypes.int32)
-
 @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
 class TestOpsBFloat16(unittest.TestCase):
   def test_cast(self):

test/test_graph.py

@@ -107,8 +107,9 @@ class TestGraph(unittest.TestCase):
     helper_test_graphs(Device[d0].graph, graphs)
 
   def skip_if_not_multigraph(self):
-    graph = g.func if isinstance(g:=Device[Device.DEFAULT].graph, functools.partial) else g
+    graph = g.func if isinstance(g:=(d:=Device[Device.DEFAULT]).graph, functools.partial) else g
     if not issubclass(graph, MultiGraphRunner): self.skipTest("graph is not supported (not MultiGraphRunner)")
+    if not hasattr(d.allocator, '_transfer'): self.skipTest("device is not supported (no transfers)")
 
   def test_order_copy_writed(self):
     self.skip_if_not_multigraph()

test/test_jit.py

@@ -5,9 +5,10 @@ import numpy as np
 from hypothesis import given, settings, strategies as strat
 from test.helpers import assert_jit_cache_len, not_support_multi_device, REAL_DEV
 from tinygrad.tensor import Tensor
-from tinygrad.engine.jit import TinyJit
+from tinygrad.engine.jit import TinyJit, GraphRunner, MultiGraphRunner, graph_class
+from tinygrad.engine.realize import CompiledRunner, BufferCopy, BufferXfer
 from tinygrad.device import Device
-from tinygrad.helpers import Context, JIT, GlobalCounters
+from tinygrad.helpers import Context, JIT, GlobalCounters, getenv
 from tinygrad.dtype import dtypes
 from extra.models.unet import ResBlock
 
@@ -669,5 +670,169 @@ class TestJitFree(unittest.TestCase):
     out = fxn(Tensor([11,1,2,3,4]))
     self.assertEqual(out.item(), 13600)
 
+class TestJitGraphSplit(unittest.TestCase):
+  def compute(self, device, inp):
+    assert inp.device == device, f"Input device {inp.device} does not match expected {device}"
+    return (inp + 1.0).contiguous().realize()
+
+  def copy(self, device, to_device, inp):
+    assert inp.device == device, f"Input device {inp.device} does not match expected {device}"
+    return inp.to(to_device).realize()
+
+  def expect(self, f, *args, graph=None, multigraph=None, hcqgraph=None):
+    def _numpies(tpl): return tpl.numpy() if tpl.__class__ is Tensor else tuple([t.numpy() for t in tpl])
+
+    expected = _numpies(f(*args))
+    for i in range(4):
+      res = _numpies(f(*args))
+      np.testing.assert_allclose(res, expected, atol=1e-4, rtol=1e-5)
+
+    dev = Device[Device.DEFAULT]
+    graph_t = graph_class(dev)
+    if graph_t is None: return
+
+    got = f.jit_cache
+    from tinygrad.runtime.graph.hcq import HCQGraph
+    if graph_t is HCQGraph:
+      validate = hcqgraph
+    elif issubclass(graph_t, MultiGraphRunner):
+      validate = multigraph
+    else:
+      validate = graph
+
+    assert len(got) == len(validate), f"Expected {len(validate)} operations, got {len(got)}"
+    for expected, got in zip(validate, got):
+      if expected["type"] == "graph":
+        assert isinstance(got.prg, GraphRunner), f"Expected GraphRunner, got {type(got.prg)}"
+        assert len(got.prg.jit_cache) == expected["cnt"], f"Expected {expected['cnt']} operations in graph, got {len(got.prg.jit_cache)}"
+      elif expected["type"] == "comp":
+        assert isinstance(got.prg, CompiledRunner), f"Expected CompiledRunner, got {type(got.prg)}"
+      elif expected["type"] == "copy":
+        assert isinstance(got.prg, BufferCopy), f"Expected BufferCopy, got {type(got.prg)}"
+      elif expected["type"] == "xfer":
+        assert isinstance(got.prg, BufferXfer), f"Expected BufferXfer, got {type(got.prg)}"
+
+  def ji_graph(self, cnt): return {"type": "graph", "cnt": cnt}
+  def ji_comp(self): return {"type": "comp"}
+  def ji_copy(self): return {"type": "copy"}
+  def ji_xfer(self): return {"type": "xfer"}
+
+  def test_jit_split_simple(self):
+    if Device.DEFAULT == "REMOTE": raise unittest.SkipTest("REMOTE gpu is broken")
+
+    @TinyJit
+    def f(inp):
+      op0 = self.compute(Device.DEFAULT, inp)
+      op1 = self.compute(Device.DEFAULT, op0)
+      op2 = self.compute(Device.DEFAULT, op1)
+      return op2
+
+    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
+    self.expect(f, inp,
+      graph=[self.ji_graph(3)],
+      multigraph=[self.ji_graph(3)],
+      hcqgraph=[self.ji_graph(3)])
+
+  def test_jit_cpu_simple(self):
+    if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
+
+    @TinyJit
+    def f(inp, inp_cpu):
+      op0 = self.compute(Device.DEFAULT, inp)
+      op1 = self.compute(Device.DEFAULT, op0)
+      op2 = self.compute("CPU", inp_cpu)
+      op3 = self.compute(Device.DEFAULT, op1)
+      return op2, op3
+
+    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
+    inp_cpu = Tensor.randn(10, 10, device="CPU").realize()
+    self.expect(f, inp, inp_cpu,
+      graph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()],
+      multigraph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()],
+      hcqgraph=[self.ji_graph(4)])
+
+  def test_jit_cpu_several(self):
+    if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
+
+    @TinyJit
+    def f(inp, inp_cpu):
+      op0 = self.compute(Device.DEFAULT, inp)
+      op1 = self.compute(Device.DEFAULT, op0)
+      op2 = self.compute("CPU", inp_cpu)
+      op3 = self.compute("CPU", op2)
+      op4 = self.compute(Device.DEFAULT, op1)
+      return op3, op4
+
+    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
+    inp_cpu = Tensor.randn(10, 10, device="CPU").realize()
+    self.expect(f, inp, inp_cpu,
+      graph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
+      multigraph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
+      hcqgraph=[self.ji_graph(5)])
+
+  def test_jit_multidev(self):
+    if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
+
+    try: Device[f"{Device.DEFAULT}:1"]
+    except Exception: raise unittest.SkipTest("no multidevice")
+
+    @TinyJit
+    def f(inp, inp_d1):
+      op0 = self.compute(Device.DEFAULT, inp)
+      op1 = self.compute(Device.DEFAULT, op0)
+      op2 = self.compute(f"{Device.DEFAULT}:1", inp_d1)
+      op3 = self.compute(f"{Device.DEFAULT}:1", op2)
+      op4 = self.compute(Device.DEFAULT, op1)
+      return op3, op4
+
+    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
+    inp_d1 = Tensor.randn(10, 10, device=f"{Device.DEFAULT}:1").realize()
+    self.expect(f, inp, inp_d1,
+      graph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
+      multigraph=[self.ji_graph(5)],
+      hcqgraph=[self.ji_graph(5)])
+
+  def test_jit_multidev_xfer(self):
+    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
+
+    try: Device[f"{Device.DEFAULT}:1"]
+    except Exception: raise unittest.SkipTest("no multidevice")
+
+    @TinyJit
+    def f(inp, inp_d1):
+      op0 = self.compute(Device.DEFAULT, inp)
+      op1 = self.compute(Device.DEFAULT, op0)
+      op2 = self.compute(f"{Device.DEFAULT}:1", inp_d1)
+      op3 = self.copy(f"{Device.DEFAULT}:1", Device.DEFAULT, op2)
+      op4 = self.compute(f"{Device.DEFAULT}:1", op2)
+      op5 = self.compute(Device.DEFAULT, op3)
+      return op1, op4, op5
+
+    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
+    inp_d1 = Tensor.randn(10, 10, device=f"{Device.DEFAULT}:1").realize()
+    self.expect(f, inp, inp_d1,
+      graph=[self.ji_graph(2), self.ji_comp(), self.ji_xfer(), self.ji_comp(), self.ji_comp()],
+      multigraph=[self.ji_graph(6)],
+      hcqgraph=[self.ji_graph(6)])
+
+  @unittest.skipIf(getenv("MOCKGPU"), "MockGPU does not support parallel copies")
+  def test_jit_multidev_copy(self):
+    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
+    if Device.DEFAULT == "REMOTE": raise unittest.SkipTest("REMOTE gpu is broken")
+
+    @TinyJit
+    def f(inp):
+      op0 = self.compute(Device.DEFAULT, inp)
+      op1 = self.compute(Device.DEFAULT, op0)
+      op2 = self.copy(Device.DEFAULT, "CPU", op1)
+      op3 = self.compute("CPU", op2)
+      return op3
+
+    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
+    self.expect(f, inp,
+      graph=[self.ji_graph(2), self.ji_copy(), self.ji_comp()],
+      multigraph=[self.ji_graph(2), self.ji_copy(), self.ji_comp()],
+      hcqgraph=[self.ji_graph(4)])
+
 if __name__ == '__main__':
   unittest.main()

test/test_linalg.py

@@ -16,7 +16,7 @@ def reconstruction_helper(A:List[Tensor],B:Tensor, tolerance=1.0e-5):
 class TestLinAlg(unittest.TestCase):
 
   def test_svd_general(self):
-    sizes = [(2,2),(5,3),(3,5),(2,2,2,2,3)]
+    sizes = [(2,2),(5,3),(3,5),(3,4,4),(2,2,2,2,3)]
     for size in sizes:
       a = Tensor.randn(size).realize()
       U,S,V = Tensor.svd(a)

test/test_multitensor.py

@@ -2,7 +2,7 @@ import unittest, functools, random
 from tinygrad import Tensor, Device, nn, GlobalCounters, TinyJit, dtypes, Variable
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import Ops, UOp
-from tinygrad.helpers import CI, getenv, prod, Context, OSX
+from tinygrad.helpers import CI, getenv, prod, Context
 from tinygrad.nn.state import get_parameters, get_state_dict
 from tinygrad.engine.realize import lower_schedule, BufferCopy, CompiledRunner, run_schedule
 import numpy as np
@@ -374,7 +374,6 @@ class TestMultiTensor(unittest.TestCase):
 
   # NOTE: this is failing on LLVM CI, no idea why. Works locally.
   @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU"), "slow, and flaky on LLVM/CPU")
-  @unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_data_parallel_resnet(self):
     from extra.models.resnet import ResNet18
 
@@ -411,7 +410,6 @@ class TestMultiTensor(unittest.TestCase):
     np.testing.assert_allclose(grad, shard_grad, atol=1e-5, rtol=1e-5)
 
   @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU"), "slow, and flaky on LLVM/CPU")
-  @unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_data_parallel_resnet_train_step(self):
     from extra.models.resnet import ResNet18
     fake_image = Tensor.rand((2, 3, 224//8, 224//8))
@@ -938,7 +936,6 @@ class TestShrinkMultiTensorShardedAxis(unittest.TestCase):
     np.testing.assert_allclose(output.numpy(), expected)
 
 @unittest.skipIf(not_support_multi_device(), "no multi")
-@unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
 class TestBatchNorm(unittest.TestCase):
   def test_unsynced_backprop_conv_bn(self):
     with Tensor.train():
@@ -966,7 +963,6 @@ class TestBatchNorm(unittest.TestCase):
       optim.step()
       out.numpy()
 
-  @unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_unsynced_backprop_standalone_bn(self):
     from extra.lr_scheduler import OneCycleLR
     GPUS = (d1, d2)

test/test_nn.py

@@ -4,7 +4,7 @@ import numpy as np
 import torch
 from tinygrad import Tensor, Device, TinyJit
 from tinygrad.uop.ops import Ops
-from tinygrad.helpers import GlobalCounters, CI, Context, OSX
+from tinygrad.helpers import GlobalCounters, CI, Context
 from tinygrad.nn import Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, Embedding
 from tinygrad.nn import BatchNorm, LayerNorm, LayerNorm2d, GroupNorm, InstanceNorm, RMSNorm, LSTMCell
 from tinygrad.nn.state import load_state_dict
@@ -108,105 +108,39 @@ class TestNN(unittest.TestCase):
     _test_linear(Tensor.randn(BS, in_dim), in_dim, out_dim)
     _test_linear(Tensor.randn(BS, T, in_dim), in_dim, out_dim) # test with more dims
 
-  def test_conv1d(self):
-    BS, C1, W = 4, 16, 224//4
-    C2, K, S, P = 64, 7, 2, 1
-
+  def _test_conv(self, tiny_conv, torch_conv, BS, C1, DIMS, C2, K, S, P, D=1):
     # create in tinygrad
-    layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)
+    layer = tiny_conv(C1, C2, kernel_size=K, stride=S, padding=P, dilation=D)
 
     # create in torch
     with torch.no_grad():
-      torch_layer = torch.nn.Conv1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
+      torch_layer = torch_conv(C1, C2, kernel_size=K, stride=S, padding=P, dilation=D).eval()
       torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
       torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
 
     # test
-    x = Tensor.uniform(BS, C1, W)
+    x = Tensor.uniform(BS, C1, *DIMS)
     z = layer(x)
     torch_x = torch.tensor(x.numpy())
     torch_z = torch_layer(torch_x)
     np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
 
-  def test_conv2d(self):
-    BS, C1, H, W = 4, 16, 224//4, 224//4
-    C2, K, S, P = 64, 7, 2, 1
-
-    # create in tinygrad
-    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
-
-    # create in torch
-    with torch.no_grad():
-      torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
-      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
-      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
-
-    # test
-    x = Tensor.uniform(BS, C1, H, W)
-    z = layer(x)
-    torch_x = torch.tensor(x.numpy())
-    torch_z = torch_layer(torch_x)
-    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
+  def test_conv1d(self): self._test_conv(Conv1d, torch.nn.Conv1d, BS=4, C1=16, DIMS=[224//4], C2=64, K=7, S=2, P=1)
+  def test_conv2d(self): self._test_conv(Conv2d, torch.nn.Conv2d, BS=4, C1=16, DIMS=[224//4, 224//4], C2=64, K=7, S=2, P=1)
 
   def test_conv1d_same_padding(self):
-    BS, C1, W = 8, 3, 32
-    C2, K, S, P = 16, 3, 1, 'same'
-
-    # create in tinygrad
-    layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)
-
-    # create in torch
-    with torch.no_grad():
-      torch_layer = torch.nn.Conv1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
-      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
-      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
-
-    # test
-    x = Tensor.uniform(BS, C1, W)
-    z = layer(x)
-    torch_x = torch.tensor(x.numpy())
-    torch_z = torch_layer(torch_x)
-    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
-
-  def _run_conv2d_same_padding_test(self, BS, C1, C2, H, W, K, S, padding='same', D=1):
-    # create in tinygrad
-    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=padding, dilation=D)
-
-    # create in torch
-    with torch.no_grad():
-      torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=padding, dilation=D).eval()
-      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
-      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
-
-    # test
-    x = Tensor.uniform(BS, C1, H, W)
-    z = layer(x)
-    torch_x = torch.tensor(x.numpy())
-    torch_z = torch_layer(torch_x)
-    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
-
+    self._test_conv(Conv1d, torch.nn.Conv1d, BS=8, C1=3, DIMS=[32], C2=16, K=3, S=1, P='same')
   def test_conv2d_same_padding_odd_input(self):
-    BS, C1, H, W = 16, 16, 29, 31
-    C2, K, S, P = 32, 5, 1, 'same'
-    self._run_conv2d_same_padding_test(BS, C1, C2, H, W, K, S, P)
-
+    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=16, DIMS=[29, 31], C2=32, K=5, S=1, P='same')
   def test_conv2d_same_padding_large_kernel(self):
-    BS, C1, H, W = 16, 16, 28, 33
-    C2, K, S, P = 32, 9, 1, 'same'
-    self._run_conv2d_same_padding_test(BS, C1, C2, H, W, K, S, P)
-
+    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=16, DIMS=[28, 33], C2=32, K=9, S=1, P='same')
   def test_conv2d_same_padding_with_dilation(self):
-    BS, C1, H, W = 16, 3, 28, 28
-    C2, K, S, P, D = 32, 3, 1, 'same', 3
-    self._run_conv2d_same_padding_test(BS, C1, C2, H, W, K, S, P, D)
+    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=3, DIMS=[28, 28], C2=32, K=3, S=1, P='same', D=3)
 
   def test_conv2d_same_padding_invalid_stride(self):
-    C1, C2, K, S, P = 16, 32, 2, 2, 'same'
-    self.assertRaises(ValueError, Conv2d, C1, C2, kernel_size=K, stride=S, padding=P)
-
+    self.assertRaises(ValueError, Conv2d, in_channels=16, out_channels=32, kernel_size=2, stride=2, padding='same')
   def test_conv2d_same_padding_invalid_padding_str(self):
-    C1, C2, K, S, P = 16, 32, 2, 1, 'not_same'
-    self.assertRaises(ValueError, Conv2d, C1, C2, kernel_size=K, stride=S, padding=P)
+    self.assertRaises(ValueError, Conv2d, in_channels=16, out_channels=32, kernel_size=2, stride=1, padding='not_same')
 
   @unittest.skip("Takes too long to compile for Compiled backends")
   def test_conv2d_winograd(self):
@@ -229,12 +163,13 @@ class TestNN(unittest.TestCase):
     with Context(WINO=1):
       z = layer(x)
 
+    m = z.mean()
+    m.backward()
+
     torch_x = torch.tensor(x.numpy(), requires_grad=True)
     torch_z = torch_layer(torch_x)
     np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
 
-    m = z.mean()
-    m.backward()
     gw = layer.weight.grad.realize()
     gb = layer.bias.grad.realize()
     gx = x.grad.realize()
@@ -245,46 +180,10 @@ class TestNN(unittest.TestCase):
     np.testing.assert_allclose(gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5)
 
   def test_conv_transpose1d(self):
-    BS, C1, W = 4, 16, 224//4
-    C2, K, S, P = 64, 7, 2, 1
-
-    # create in tinygrad
-    layer = ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P)
-
-    # create in torch
-    with torch.no_grad():
-      torch_layer = torch.nn.ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
-      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
-      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
-
-    # test
-    x = Tensor.uniform(BS, C1, W)
-    z = layer(x)
-    torch_x = torch.tensor(x.numpy())
-    torch_z = torch_layer(torch_x)
-    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
-
+    self._test_conv(ConvTranspose1d, torch.nn.ConvTranspose1d, BS=4, C1=16, DIMS=[224//4], C2=64, K=7, S=2, P=1)
   def test_conv_transpose2d(self):
-    BS, C1, H, W = 4, 16, 224//4, 224//4
-    C2, K, S, P = 64, 7, 2, 1
+    self._test_conv(ConvTranspose2d, torch.nn.ConvTranspose2d, BS=4, C1=16, DIMS=[224//4, 224//4], C2=64, K=7, S=2, P=1)
 
-    # create in tinygrad
-    layer = ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P)
-
-    # create in torch
-    with torch.no_grad():
-      torch_layer = torch.nn.ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
-      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
-      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
-
-    # test
-    x = Tensor.uniform(BS, C1, H, W)
-    z = layer(x)
-    torch_x = torch.tensor(x.numpy())
-    torch_z = torch_layer(torch_x)
-    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
-
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_groupnorm(self):
     BS, H, W, C, G = 20, 10, 10, 6, 3
 
@@ -311,7 +210,6 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_layernorm(self):
     N, C, H, W = 20, 5, 10, 10
 
@@ -338,7 +236,6 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_layernorm_2d(self):
     N, C, H, W = 20, 5, 10, 10
 
@@ -365,7 +262,6 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_instancenorm_2d(self):
     N, C, H, W = 20, 10, 10, 10
 
@@ -392,7 +288,6 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_instancenorm_3d(self):
     N, C, D, H, W = 20, 10, 10, 10, 10
 
@@ -419,7 +314,6 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=2e-3, rtol=1e-3)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_rmsnorm(self):
     class TorchRMSNorm(torch.nn.Module):
       # https://github.com/meta-llama/llama/blob/be327c427cc5e89cc1d3ab3d3fec4484df771245/llama/model.py#L34C1-L77C36

test/test_ops.py

@@ -2,7 +2,7 @@ import time, math, unittest, functools, platform, warnings
 import numpy as np
 from typing import List, Callable
 import torch
-from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, OSX, AMD_LLVM
+from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, AMD_LLVM
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
@@ -2682,7 +2682,6 @@ class TestOps(unittest.TestCase):
     i, j, k, o, p = [Tensor(tor.detach().cpu().numpy().astype(np.int32), requires_grad=False) for tor in [a,b,c,d,e]]
     return a,b,c,d,e,i,j,k,o,p
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU can only run kernels with up to 10 buffers")
   def test_slice_fancy_indexing_no_dim_collapse(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # no dim collapse from int or dim injection from None
@@ -2734,7 +2733,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,3)], lambda x: x[torch.tensor([[0,1,-1],[-1,-2,0]]), torch.tensor([2,1,-1])],
                             lambda x: x[Tensor([[0,1,-1],[-1,-2,0]]), Tensor([2,1,-1])])
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU can only run kernels with up to 10 buffers")
   def test_slice_fancy_indexing_list_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]])
@@ -2754,7 +2752,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,((2,),(1,),(0,)),c,(2,1,0)], lambda x: x[i,((2,),(1,),(0,)),k,(2,1,0)])
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,(2,1,0),None,c,(2,1,0),e], lambda x: x[1,(2,1,0),None,k,(2,1,0),p])
 
-  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_slice_fancy_indexing_list_with_tensors(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a]], lambda x: x[[i]])

test/test_sample.py

@@ -3,6 +3,7 @@ import numpy as np
 from tinygrad import Tensor, Variable, Device
 from tinygrad.helpers import OSX
 
+# TODO: still fails with MAX_KERNEL_BUFFERS
 @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
 class TestSample(unittest.TestCase):
   def test_sample(self):

test/test_softmax_fusion.py

@@ -86,6 +86,18 @@ class TestFuse(unittest.TestCase):
       return (arange == idx).mul(vals).sum(-2, dtype=vals.dtype)
     self._test_fuse(embedding, a, atol=1e-5)
 
+  def test_attention_kernel_count(self):
+    wq = Tensor.empty(32, 32)
+    wk = Tensor.empty(32, 32)
+    wv = Tensor.empty(32, 32)
+    x = Tensor.empty(2, 100, 32)
+    q = (x @ wq).contiguous()
+    k = (x @ wk).contiguous()
+    v = (x @ wv).contiguous()
+    attn = q.scaled_dot_product_attention(k, v).fuse()
+    s = attn.schedule()
+    self.assertEqual(len(s), 4) # 3 matmul and 1 attention
+
   def test_flash_attention(self):
     BS = 4
     HEADS = 2
@@ -121,7 +133,7 @@ class TestSoftmaxFusion(unittest.TestCase):
       out = (inp / div).reshape(32, 10)
       out.realize()
 
-    np.testing.assert_allclose(sout.numpy(), out.numpy())
+    np.testing.assert_allclose(sout.numpy(), out.numpy(), atol=3e-7)
 
   def test_softmax(self):
     # this is the softmax from scaled_dot_product_attention

test/test_tensor.py

@@ -892,13 +892,13 @@ class TestIdxUpcast(unittest.TestCase):
 
   @unittest.skipUnless(is_dtype_supported(dtypes.long), "int64 is supported")
   def test_overflow_sym(self):
-    self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 0, 2048).bind(32))
+    self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 1, 2048).bind(32))
 
   def test_regular(self):
     self.do_op_then_assert(dtypes.int, 64, 64, 64)
 
   def test_regular_sym(self):
-    self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 0, 64).bind(32))
+    self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 1, 64).bind(32))
 
   @unittest.skipIf(PTX, "PTX always convert Ops.INDEX to int64")
   def test_symfold(self):
@@ -910,7 +910,7 @@ class TestIdxUpcast(unittest.TestCase):
   @unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
   def test_int64_unsupported_overflow_sym(self):
     with self.assertRaises(KeyError):
-      self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 0, 2048).bind(32))
+      self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 1, 2048).bind(32))
 
   @unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
   def test_int64_unsupported_overflow(self):

test/unit/test_dtype.py

@@ -0,0 +1,57 @@
+import unittest
+from tinygrad.tensor import Tensor
+from tinygrad.dtype import dtypes, DType, ImageDType, PtrDType, to_dtype
+
+class TestImageDType(unittest.TestCase):
+  def test_image_scalar(self):
+    assert dtypes.imagef((10,10)).base.scalar() == dtypes.float32
+    assert dtypes.imageh((10,10)).base.scalar() == dtypes.float32
+  def test_image_vec(self):
+    assert dtypes.imagef((10,10)).base.vec(4) == dtypes.float32.vec(4)
+    assert dtypes.imageh((10,10)).base.vec(4) == dtypes.float32.vec(4)
+
+class TestEqStrDType(unittest.TestCase):
+  def test_image_ne(self):
+    if ImageDType is None: raise unittest.SkipTest("no ImageDType support")
+    assert dtypes.float == dtypes.float32, "float doesn't match?"
+    assert dtypes.imagef((1,2,4)) != dtypes.imageh((1,2,4)), "different image dtype doesn't match"
+    assert dtypes.imageh((1,2,4)) != dtypes.imageh((1,4,2)), "different shape doesn't match"
+    assert dtypes.imageh((1,2,4)) == dtypes.imageh((1,2,4)), "same shape matches"
+    assert isinstance(dtypes.imageh((1,2,4)), ImageDType)
+  def test_ptr_eq(self):
+    assert dtypes.float32.ptr() == dtypes.float32.ptr()
+    assert not (dtypes.float32.ptr() != dtypes.float32.ptr())
+  def test_strs(self):
+    if PtrDType is None: raise unittest.SkipTest("no PtrDType support")
+    self.assertEqual(str(dtypes.imagef((1,2,4))), "dtypes.imagef((1, 2, 4))")
+    self.assertEqual(str(dtypes.float32.ptr(16)), "dtypes.float.ptr(16)")
+
+class TestToDtype(unittest.TestCase):
+  def test_dtype_to_dtype(self):
+    dtype = dtypes.int32
+    res = to_dtype(dtype)
+    self.assertIsInstance(res, DType)
+    self.assertEqual(res, dtypes.int32)
+
+  def test_str_to_dtype(self):
+    dtype = "int32"
+    res = to_dtype(dtype)
+    self.assertIsInstance(res, DType)
+    self.assertEqual(res, dtypes.int32)
+
+class TestCastConvenienceMethod(unittest.TestCase):
+  def test_method(self):
+    for input_dtype in (dtypes.float, dtypes.int):
+      t = Tensor([1, 2], dtype=input_dtype)
+      self.assertEqual(t.dtype, input_dtype)
+      self.assertEqual(t.bool().dtype, dtypes.bool)
+      self.assertEqual(t.short().dtype, dtypes.short)
+      self.assertEqual(t.int().dtype, dtypes.int)
+      self.assertEqual(t.long().dtype, dtypes.long)
+      self.assertEqual(t.half().dtype, dtypes.half)
+      self.assertEqual(t.bfloat16().dtype, dtypes.bfloat16)
+      self.assertEqual(t.float().dtype, dtypes.float)
+      self.assertEqual(t.double().dtype, dtypes.double)
+
+if __name__ == "__main__":
+  unittest.main()

test/unit/test_llm_tokenizer.py

@@ -0,0 +1,57 @@
+import unittest, base64, functools
+from tinygrad.apps.llm import SimpleTokenizer, get_llama_re
+from tinygrad.helpers import fetch
+
+class TestLLMTokenizer(unittest.TestCase):
+  @functools.cached_property
+  def basic_tok(self): return SimpleTokenizer(".*", { b"a": 0, b"b": 1, b"c": 2, b"ab": 3, b"bc": 4 }, { "<x>": 5, "<y>": 6, "<z>": 7 })
+
+  @functools.cached_property
+  def llama_tok(self):
+    # from https://github.com/tinygrad/tinygrad/blob/e0106b6b257ebc003eb3694144e3e198f7d8cc37/examples/llama3.py#L14
+    model_file = fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model")
+    with open(model_file, "rt") as fd:
+      str_vocab = [ line.split(maxsplit=1) for line in fd.read().splitlines() if line ]
+      normal_tokens = { base64.b64decode(stok): int(srank) for stok, srank in str_vocab }
+
+    special_tokens = [
+      "<|begin_of_text|>",
+      "<|end_of_text|>",
+      "<|reserved_special_token_0|>",
+      "<|reserved_special_token_1|>",
+      "<|reserved_special_token_2|>",
+      "<|reserved_special_token_3|>",
+      "<|start_header_id|>",
+      "<|end_header_id|>",
+      "<|reserved_special_token_4|>",
+      "<|eot_id|>",
+    ] + [ f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5) ]
+    return SimpleTokenizer(get_llama_re(), normal_tokens, { token: len(normal_tokens) + i for i, token in enumerate(special_tokens) })
+
+  def _test_coding(self, tok: SimpleTokenizer, text: str, expected_tokens: list[int]):
+    self.assertEqual(tok.encode(text), expected_tokens)
+    self.assertEqual(tok.decode(expected_tokens), text)
+
+  def test_abc(self): self._test_coding(self.basic_tok, "abc", [ 3, 2 ])
+  def test_abbc(self): self._test_coding(self.basic_tok, "abbc", [ 3, 4 ])
+  def test_aabbbcc(self): self._test_coding(self.basic_tok, "aabbbcc", [ 0, 3, 1, 4, 2 ])
+  def test_specials1(self): self._test_coding(self.basic_tok, "a<x>a<y>a<z>a", [ 0, 5, 0, 6, 0, 7, 0 ])
+  def test_specials2(self): self._test_coding(self.basic_tok, "<x>a<y>a<z>", [ 5, 0, 6, 0, 7 ])
+  def test_invalid_token(self):
+    with self.assertRaises(RuntimeError): self._test_coding(self.basic_tok, "L", [])
+
+  def test_no_specials(self): self._test_coding(SimpleTokenizer(".*", { bytes([i]): i for i in range(256) }, {}), "abc", [97, 98, 99])
+
+  # NOTE: the correct tokenization for this can only be found by looking up the text chunk in the vocab, not by applying merges
+  def test_llama_early_tokenize(self): self._test_coding(self.llama_tok, " например", [ 111797 ])
+
+  def test_llama_basic(self): self._test_coding(self.llama_tok, "hello world", [ 15339, 1917 ])
+  def test_llama_control_char(self): self._test_coding(self.llama_tok, " \x850", [ 220, 116360, 15 ])
+  def test_llama_bytes(self): self._test_coding(self.llama_tok, " \xec\x8b\xa4\xed", [ 1717, 105, 116174, 82638, 2483 ])
+  def test_llama_special1(self): self._test_coding(self.llama_tok, "hello <|end_of_text|>", [ 15339, 220, 128001 ])
+  def test_llama_special2(self): self._test_coding(self.llama_tok, "<|start_header_id|>user<|end_header_id|>\n\n", [ 128006, 882, 128007, 271 ])
+  def test_llama_repeat(self): self._test_coding(self.llama_tok, "00000000000000000", [ 931, 931, 931, 931, 931, 410 ])
+  def test_llama_pat(self): self._test_coding(self.llama_tok, "today\n  \n", [ 31213, 14211 ])
+
+if __name__ == '__main__':
+  unittest.main()

test/unit/test_uop_symbolic.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-import unittest, pickle, functools
+import unittest, pickle, functools, math
 import z3
 
 from tinygrad.dtype import dtypes, ConstType
@@ -29,16 +29,17 @@ class TestSymbolicPickle(unittest.TestCase):
   def test_pickle_variable_times_2(self): self._test_pickle_unpickle(Variable("a", 3, 8)*2)
 
 class TestSymbolic(unittest.TestCase):
-  def helper_test_variable(self, v, n, m, s):
+  def helper_test_variable(self, v, n, m, s, test_z3:bool=True):
     rendered, nmin, nmax = render(v)
     if isinstance(s, tuple): self.assertIn(rendered, s)
     else: self.assertEqual(rendered, s)
     self.assertEqual(nmin, n)
     self.assertEqual(nmax, m)
-    solver = z3.Solver()
-    z3_sink = graph_rewrite(v.sink(v.simplify()), z3_renderer, ctx=(solver, {}))
-    expr, epxr_simplified = z3_sink.src[0].arg, z3_sink.src[1].arg
-    self.assertEqual(solver.check(expr != epxr_simplified), z3.unsat, "simplified expression not equal to original")
+    if test_z3:
+      solver = z3.Solver()
+      z3_sink = graph_rewrite(v.sink(v.simplify()), z3_renderer, ctx=(solver, {}))
+      expr, epxr_simplified = z3_sink.src[0].arg, z3_sink.src[1].arg
+      self.assertEqual(solver.check(expr != epxr_simplified), z3.unsat, "simplified expression not equal to original")
 
   def test_cmp_simple(self):
     self.helper_test_variable(Variable("a", 3, 8) < 4, 0, 1, "(a<4)")
@@ -672,6 +673,12 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(numerator, 3, 390, "(a*((a*4)+-1))")
     self.helper_test_variable((numerator//denominator)<=0, 1, 1, "True")
 
+  def test_const_reciprocal(self):
+    a = Variable("a", 1, 10, dtypes.float)
+    # TODO: bounds for reciprocal
+    # TODO: should z3 work?
+    self.helper_test_variable(2*(2*a).reciprocal(), -math.inf, math.inf, "(1/a)", test_z3=False)
+
 class TestSymbolicNumeric(unittest.TestCase):
   def helper_test_numeric(self, f):
     MIN, MAX = 0, 10

test/unit/test_viz.py

@@ -106,13 +106,12 @@ class TestViz(BaseTestViz):
 
   # name can also come from a function that returns a TracingKey
   def test_tracing_key(self):
-    @track_rewrites(name=lambda inp,ret: TracingKey("custom_name", (inp,), fmt=f"input={inp.render()}"))
+    @track_rewrites(name=lambda inp,ret: TracingKey("custom_name", (inp,)))
     def test(s:UOp): return graph_rewrite(s, PatternMatcher([]))
     test(UOp.variable("a", 1, 10)+1)
     lst = get_viz_list()
     # NOTE: names from TracingKey do not get deduped
     self.assertEqual(lst[0]["name"], "custom_name")
-    self.assertEqual(lst[0]["fmt"], "input=(a+1)")
 
   def test_colored_label(self):
     # NOTE: dataclass repr prints literal escape codes instead of unicode chars

tinygrad/apps/llm.py

@@ -1,33 +1,57 @@
 from __future__ import annotations
-import sys, argparse
-from tinygrad import Tensor, nn, UOp, TinyJit, getenv
+import sys, argparse, typing, re, itertools, unicodedata
+from tinygrad import Tensor, nn, UOp, TinyJit, getenv, helpers
+
+def gpt2_decode_vocab(voc: dict[str, int]): # https://github.com/openai/gpt-2/blob/9b63575ef42771a015060c964af2c3da4cf7c8ab/src/encoder.py#L9
+  c2b = { chr(cp): cp for cp in itertools.chain(range(ord("!"), ord("~")+1), range(ord("¡"), ord("¬")+1), range(ord("®"), ord("ÿ")+1)) }
+  c2b.update({ chr(256+off): cp for off, cp in enumerate(cp for cp in range(256) if chr(cp) not in c2b) })
+  return { bytes(c2b[c] for c in tok): tid for tok, tid in voc.items() }
+
+def get_llama_re():
+  def ucat_range(pre: str): return "".join(re.escape(chr(cp)) for cp in range(sys.maxunicode + 1) if unicodedata.category(chr(cp)).startswith(pre))
+  r_ws, r_p_N, r_p_L = r"\t\n\x0b\x0c\r\x85" + ucat_range("Z"), ucat_range("N"), ucat_range("L")
+  # https://github.com/ggml-org/llama.cpp/blob/94933c8c2eeaa9a7983e3f6c08af76bd86724094/src/llama-vocab.cpp#L286
+  return "(?i:'s|'t|'re|'ve|'m|'ll|'d)|" + \
+    f"[^\\r\\n{r_p_N}{r_p_L}]?[{r_p_L}]+|[{r_p_N}]{{1,3}}| ?[^{r_ws}{r_p_N}{r_p_L}]+[\\r\\n]*|[{r_ws}]*[\\r\\n]+|[{r_ws}]+(?![^{r_ws}])|[{r_ws}]+"
 
 class SimpleTokenizer:
-  def __init__(self, vocab: list[str]):
-    self.vocab: list[str] = vocab
-    self.biggest_token: int = max(map(len, vocab))
-    self.token_to_id: dict[str, int] = {tok: i for i, tok in enumerate(vocab)}
-    self.replace_space = "Ġ"
-    self.replace_newline = "Ċ"
+  def __init__(self, pat: str, normal_tokens: dict[bytes, int], special_tokens: dict[str, int]):
+    self._normal_tokens, self._special_tokens, self._pat = normal_tokens, special_tokens, re.compile(pat)
+    self._tok2str = { tid: tok.encode() for tok, tid in special_tokens.items() } | { tid: tok for tok, tid in normal_tokens.items()  }
+    self._special_re = re.compile("|".join(re.escape(tok) for tok in self._special_tokens.keys()) if special_tokens else r"(?!)")
 
-  def encode(self, text:str) -> list[int]:
-    s = text.replace(" ", self.replace_space).replace("\n", self.replace_newline)
-    out: list[int] = []
-    i = 0
-    while i < len(s):
-      j = min(i+self.biggest_token, len(s))
-      while i < j and (tid:=self.token_to_id.get(s[i:j])) is None: j -= 1
-      if tid is None: raise RuntimeError(f"token not found in {s}")
-      assert tid is not None, f"token not found in {s}"
-      out.append(tid)
-      i = j
-    return out
+  @staticmethod
+  def from_gguf_kv(kv: dict):
+    # https://github.com/ggml-org/llama.cpp/blob/94933c8c2eeaa9a7983e3f6c08af76bd86724094/src/llama-vocab.cpp#L1818-L1820
+    if kv["tokenizer.ggml.pre"] not in ("llama3","llama-v3","llama-bpe"): raise ValueError(f"Invalid tokenizer preset '{kv['tokenizer.ggml.pre']}'")
+    vocab: typing.Iterable[tuple[str, int]] = ((tok, idx) for idx, tok in enumerate(kv["tokenizer.ggml.tokens"]))
+    normal_tokens, special_tokens = helpers.partition(vocab, lambda e: kv["tokenizer.ggml.token_type"][e[1]] == 1)
+    return SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
 
-  def decode(self, ids: list[int]) -> str:
-    return ''.join(self.vocab[tid] for tid in ids).replace(self.replace_space, " ").replace(self.replace_newline, "\n")
+  def encode(self, text: str):
+    tokens: list[int] = []
+    pos = 0
+    for match in self._special_re.finditer(text):
+      tokens.extend(self._encode_sentence(text[pos:match.start(0)]) + [self._special_tokens[text[match.start(0):match.end(0)]]])
+      pos = match.end(0)
+    return tokens + self._encode_sentence(text[pos:])
 
-  def role(self, role:str):
-    return [t for x in ["<|start_header_id|>", role, "<|end_header_id|>\n\n"] for t in self.encode(x)]  # llama style
+  def decode(self, ids: list[int]) -> str: return b''.join(self._tok2str[tid] for tid in ids).decode()
+  def role(self, role:str): return self.encode("<|start_header_id|>" + role + "<|end_header_id|>\n\n")
+
+  def _encode_sentence(self, chunk: str): return [ tok for word in self._pat.findall(chunk) for tok in self._encode_word(word.encode()) ]
+  def _encode_word(self, word: bytes):
+    if (early_token:=self._normal_tokens.get(word)) is not None: return [early_token]
+    parts = [word[i:i+1] for i in range(len(word))]
+    while True:
+      min_tid, min_idx = 2**32, -1
+      for idx, (p1, p2) in enumerate(zip(parts[:-1], parts[1:])):
+        tid = self._normal_tokens.get(p1 + p2, min_tid)
+        if tid < min_tid: min_tid, min_idx = tid, idx
+      if min_idx == -1: break
+      parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx+1]] + parts[min_idx+2:]
+    try: return [ self._normal_tokens[p] for p in parts ]
+    except KeyError: raise RuntimeError("token not found")
 
 def apply_rope(x:Tensor, start_pos:int|UOp, base:int=10000):
   B, H, T, Hd = x.shape
@@ -165,7 +189,7 @@ if __name__ == "__main__":
   model, kv = Transformer.from_gguf(Tensor.from_url(models[args.size]), args.max_context)
 
   # extract some metadata
-  tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
+  tok = SimpleTokenizer.from_gguf_kv(kv)
   bos_id: int = kv['tokenizer.ggml.bos_token_id']
   eos_id: int = kv['tokenizer.ggml.eos_token_id']
 

tinygrad/codegen/__init__.py

@@ -16,6 +16,8 @@ from tinygrad.codegen.devectorizer import load_store_folding, load_store_indexin
   ReduceContext, correct_load_store, pm_render
 from tinygrad.codegen.optional import get_late_rewrite_patterns
 from tinygrad.codegen.linearize import block_create, pm_blockend_merge, block_merge, pm_finalize, BlockContext
+from tinygrad.opt import pm_optimize
+from tinygrad.opt.swizzler import view_left, view_right, fix_kernel_ops
 
 @dataclass
 class RewriteStep:
@@ -42,6 +44,15 @@ def get_rewrites_for_renderer(opts:Renderer, linearizer:bool=True) -> list[Rewri
 def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVECTORIZE, _TRANSCENDENTAL) -> list[RewriteStep]:
   # ** lowerer (rewrite_shapetracker_with_index) **
   ret: list[RewriteStep] = []
+
+  # TODO: move these to codegen
+  ret.append(RewriteStep(view_left, name="Main View Left"))
+  ret.append(RewriteStep(view_right, name="Main View Right"))
+  ret.append(RewriteStep(view_left+fix_kernel_ops, bottom_up=True, name="replace buffer"))
+
+  # this is kernel.py
+  ret.append(RewriteStep(pm_optimize, ctx=lambda _: opts, name="optimize ast"))
+
   if _QUANTIZE and opts.device in {"CPU", "DSP"}: ret.append(RewriteStep(pm_quant, name="quantize"))
   ret.append(RewriteStep(pm_lowerer, get_index, name="lowerer", bottom_up=True))
 

tinygrad/dtype.py

@@ -193,6 +193,21 @@ def least_upper_float(dt:DType) -> DType: return dt if dtypes.is_float(dt) else
 DTYPES_DICT = {k: v for k, v in dtypes.__dict__.items() if isinstance(v, DType) and not k.startswith(("default", "void"))}
 INVERSE_DTYPES_DICT = {**{v.name:k for k,v in DTYPES_DICT.items()}, "void": "void"}
 
+@functools.cache
+def can_safe_cast(dt0:DType, dt1:DType) -> bool:
+  # return if dt1 preserves value of dt0
+  # https://numpy.org/doc/stable/reference/generated/numpy.can_cast.html
+  if dt0 == dt1 or dt0 == dtypes.bool: return True
+  match dt1:
+    case dtypes.double: return dt0 in (dtypes.float, dtypes.half, dtypes.bfloat16)
+    case dtypes.float: return dt0 in (dtypes.half, dtypes.bfloat16)
+    case dtypes.uint64: return dt0 in (dtypes.uint32, dtypes.uint16, dtypes.uint8)
+    case dtypes.uint32: return dt0 in (dtypes.uint16, dtypes.uint8)
+    case dtypes.int64: return dt0 in (dtypes.uint32, dtypes.uint16, dtypes.uint8, dtypes.int32, dtypes.int16, dtypes.int8)
+    case dtypes.int32: return dt0 in (dtypes.uint16, dtypes.uint8, dtypes.int16, dtypes.int8)
+    case dtypes.int16: return dt0 in (dtypes.uint8, dtypes.int8)
+    case _: return False
+
 def sum_acc_dtype(dt:DType):
   # default acc dtype for sum
   if dtypes.is_unsigned(dt): return least_upper_dtype(dt, dtypes.uint)

tinygrad/engine/jit.py

@@ -21,24 +21,24 @@ def apply_graph_to_jit(jit_cache: list[ExecItem], input_rawbuffers: list[Buffer]
   # This allows the accelerator to run some batches while subsequent graphs are still being updated.
   graphed_jit_cache: list[ExecItem] = []
   current_batch: list[ExecItem] = []
-  current_device: Compiled|None = None
+  current_batch_devs: list[Compiled] = []
 
   def flush_batch():
-    nonlocal current_batch, current_device, max_batch_size
+    nonlocal current_batch, current_batch_devs, max_batch_size
     try:
-      if current_device is None: raise GraphException("no device for graph")
+      if len(current_batch_devs) == 0: raise GraphException("no device for graph")
       if len(current_batch) <= 1 and not getenv("GRAPH_ONE_KERNEL"): raise GraphException("only one kernel doesn't graph")
-      graph_runner = current_device.graph(current_batch, input_rawbuffers, var_vals)
+      graph_runner = current_batch_devs[0].graph(current_batch, input_rawbuffers, var_vals)
       # clear jit inputs to allow their memory to be freed/reused
       for (j,i) in graph_runner.input_replace.keys(): graph_runner.jit_cache[j].bufs[i] = None
       graphed_jit_cache.append(ExecItem(graph_runner, cast(list[Buffer|None], input_rawbuffers)))
       max_batch_size *= 2
-      if DEBUG >= 2: print(f"JIT GRAPHing batch with {len(current_batch)} kernels on device {current_device}")
+      if DEBUG >= 2: print(f"JIT GRAPHing batch with {len(current_batch)} kernels on device {current_batch_devs[0]}")
     except GraphException as e:
       graphed_jit_cache.extend(current_batch)
-      if DEBUG >= 2: print(f"JIT GRAPHing failed batch with {len(current_batch)} kernels on device {current_device}: {e}")
+      if DEBUG >= 2: print(f"JIT GRAPHing failed batch with {len(current_batch)} kernels on device {current_batch_devs[0]}: {e}")
     current_batch = []
-    current_device = None
+    current_batch_devs = []
 
   for ji in jit_cache:
     match ji.prg:
@@ -48,13 +48,18 @@ def apply_graph_to_jit(jit_cache: list[ExecItem], input_rawbuffers: list[Buffer]
       case ViewOp(): continue # ViewOps are just ignored
       case _: ji_graph_dev = None # Everything else is not graphed and flushes existing graph if it's being constructed
 
-    can_be_graphed = ji_graph_dev is not None and ji_graph_dev.graph is not None and graph_class(ji_graph_dev).supports_exec_item(ji_graph_dev, ji)
-    is_multigraph = can_be_graphed and issubclass(graph_class(ji_graph_dev), MultiGraphRunner)
-    can_share_graph = can_be_graphed and (type(ji_graph_dev) is type(current_device) if is_multigraph else ji_graph_dev == current_device)
+    # Check if this jit item can be graphed at all, so check if a new graph supports the current item.
+    can_be_graphed = ji_graph_dev is not None and ji_graph_dev.graph is not None and graph_class(ji_graph_dev).supports_exec_item([ji_graph_dev], ji)
+
+    # Check if the current batch can be extended with this item.
+    can_share_graph = can_be_graphed and len(current_batch_devs) > 0 and \
+                      graph_class(current_batch_devs[0]).supports_exec_item(dedup(current_batch_devs + [ji_graph_dev]), ji)
     can_extend_graph_batch = can_share_graph and (max_batch_size == 0 or len(current_batch) < max_batch_size)
+
+    # Flush the current batch if any, since it can't be extended or is full.
     if not can_extend_graph_batch and len(current_batch) > 0: flush_batch()
     (current_batch if can_be_graphed else graphed_jit_cache).append(ji)
-    current_device = ji_graph_dev if can_be_graphed else None
+    current_batch_devs = dedup(current_batch_devs + [ji_graph_dev]) if can_be_graphed else []
 
   if len(current_batch) > 0: flush_batch()
   return graphed_jit_cache
@@ -127,12 +132,14 @@ class GraphRunner(Runner):
     return list({id(x):x for x in wait_nodes}.values())
 
   @staticmethod
-  def supports_exec_item(dev, ei:ExecItem) -> bool: return isinstance(ei.prg, CompiledRunner)
+  def supports_exec_item(devs:list[Compiled], ei:ExecItem) -> bool: return isinstance(ei.prg, CompiledRunner) and len(dedup(devs)) == 1
 
 # a marker for your graph supporting multiple devices of the same type
 class MultiGraphRunner(GraphRunner):
   @staticmethod
-  def supports_exec_item(dev, ei:ExecItem) -> bool: return isinstance(ei.prg, (CompiledRunner, BufferXfer))
+  def supports_exec_item(devs:list[Compiled], ei:ExecItem) -> bool:
+    # Devices must be the same type
+    return isinstance(ei.prg, (CompiledRunner, BufferXfer)) and len(dedup([type(Device[b.device]) for b in ei.bufs if b]+[type(d) for d in devs]))==1
 
 def get_out_buffers_for_ei(ei:ExecItem) -> list[Buffer]:
   if isinstance(ei.prg, CompiledRunner): return [cast(Buffer, ei.bufs[out]) for out in ei.prg.p.outs if out not in ei.prg.p.ins]

tinygrad/engine/realize.py

@@ -2,18 +2,16 @@ from typing import cast, Generator
 import time, pprint
 from dataclasses import dataclass, replace, field
 from tinygrad.helpers import all_same, colored, DEBUG, GlobalCounters, ansilen, BEAM, NOOPT, all_int, CAPTURING, Metadata, TRACEMETA, TracingKey
-from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv
+from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile
 from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites
 from tinygrad.device import Device, Buffer
 from tinygrad.renderer import Renderer, ProgramSpec, Estimates
 from tinygrad.engine.schedule import ScheduleItem
-from tinygrad.opt import get_optimized_ast
 from tinygrad.codegen import full_rewrite
-from tinygrad.uop.spec import type_verify
 
 # **************** Program Creation ****************
 
-@track_rewrites(name=lambda _ast,_renderer,ret: TracingKey(ret.name, (ret.function_name, ret.ast), ret.src, ret=ret))
+@track_rewrites(name=lambda _ast,_renderer,ret: TracingKey(ret.name, (ret.function_name, ret.ast), ret=ret))
 def get_program(ast:UOp, renderer:Renderer) -> ProgramSpec:
   """
   Transform an AST into a ProgramSpec. May trigger BEAM search.
@@ -27,16 +25,13 @@ def get_program(ast:UOp, renderer:Renderer) -> ProgramSpec:
   """
 
   if getenv("VIZ"): graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
-  modified_ast = get_optimized_ast(ast, renderer) if ast.arg is None or ast.arg.opts_to_apply is not None else ast
-  if __debug__: type_verify(list(modified_ast.toposort()))
 
   # linearize
   try:
-    uops = full_rewrite(modified_ast, renderer)
+    uops = full_rewrite(ast, renderer)
   except RuntimeError:
     print("***** LINEARIZE FAILURE *****")
     print(f"ast = {ast}")
-    print(f"opts = {modified_ast.arg.applied_opts}")
     raise
   assert uops[-1].op is Ops.SINK, "last uop must be sink"
 
@@ -63,7 +58,10 @@ class CompiledRunner(Runner):
   def __init__(self, p:ProgramSpec, precompiled:bytes|None=None, prg=None):
     if DEBUG >= 4: print(p.src)
     self.p:ProgramSpec = p
-    self.lib:bytes = precompiled if precompiled is not None else Device[p.device].compiler.compile_cached(p.src)
+    if precompiled is not None: self.lib = precompiled
+    else:
+      with cpu_profile(TracingKey(f"compile {p.name}", (p.function_name,), cat="compiler"), "TINY"):
+        self.lib = Device[p.device].compiler.compile_cached(p.src)
     if DEBUG >= 7: Device[p.device].compiler.disassemble(self.lib)
     self._prg = Device[p.device].runtime(p.function_name, self.lib) if prg is None else prg
     super().__init__(p.name, p.device, p.estimates)

tinygrad/gradient.py

@@ -1,6 +1,5 @@
 from typing import cast
 import math, dataclasses
-from tinygrad.dtype import dtypes, sum_acc_dtype
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, all_metadata
 from tinygrad.helpers import argsort
 
@@ -8,7 +7,7 @@ def reduce_gradient(ctx:UOp, ret:UOp):
   def to_inp_shape(x): return x.reshape(x.shape+(1,)*(len(ret.src[0].shape)-len(x.shape))).expand(ret.src[0].shape)
   if ret.arg[0] == Ops.ADD: return (to_inp_shape(ctx),)
   if ret.arg[0] == Ops.MAX:
-    max_is_1s = ret.src[0].ne(to_inp_shape(ret)).ne(ret.src[0].const_like(1).cast(dtypes.bool)).cast(ctx.dtype)
+    max_is_1s = ret.src[0].eq(to_inp_shape(ret)).cast(ctx.dtype)
     div = to_inp_shape(max_is_1s.r(Ops.ADD, ret.arg[1]))
     return ((max_is_1s/div) * to_inp_shape(ctx),)
   if ret.arg[0] == Ops.MUL: return (to_inp_shape(ctx * ret) / ret.src[0],)
@@ -38,9 +37,7 @@ pm_gradient = PatternMatcher([
   (UPat(Ops.PAD, name="ret"), lambda ctx, ret: (ctx.shrink(tuple([(p[0], s+p[0]) for s,p in zip(ret.src[0].shape, ret.arg)])),)),
   (UPat(Ops.SHRINK, name="ret"), lambda ctx, ret: (ctx.pad(tuple([(p[0], s-p[1]) for s,p in zip(ret.src[0].shape, ret.arg)])),)),
   (UPat(Ops.FLIP, name="ret"), lambda ctx, ret: (ctx.flip(ret.arg),)),
-  # TODO: this cast can be removed by putting the casts around the EXPAND
-  (UPat(Ops.EXPAND, name="ret"), lambda ctx, ret:
-    (ctx.cast(sum_acc_dtype(ctx.dtype)).r(Ops.ADD, tuple(i for i,(si,so) in enumerate(zip(ret.src[0].shape, ret.arg)) if si!=so)).cast(ctx.dtype),)),
+  (UPat(Ops.EXPAND, name="ret"), lambda ctx, ret: (ctx.r(Ops.ADD, tuple(i for i,(si,so) in enumerate(zip(ret.src[0].shape, ret.arg)) if si!=so)),)),
   (UPat(Ops.MULTI, name="ret"), lambda ctx, ret: ctx.shard(ret.device, ret.axis).src),
   # there's no gradient for bitcast
   (UPat(Ops.BITCAST), lambda ctx: (None,)),

tinygrad/helpers.py

@@ -196,7 +196,6 @@ class Profiling(contextlib.ContextDecorator):
 class TracingKey:
   display_name:str                       # display name of this trace event
   keys:tuple[str, ...]=()                # optional keys to search for related traces
-  fmt:str|None=None                      # optional detailed formatting
   cat:str|None=None                      # optional category to color this by
   ret:Any=None
 

tinygrad/nn/__init__.py

@@ -10,7 +10,6 @@ class BatchNorm:
   """
   Applies Batch Normalization over a 2D or 3D input.
 
-  - Described: https://paperswithcode.com/method/batch-normalization
   - Paper: https://arxiv.org/abs/1502.03167v3
 
   See: `Tensor.batchnorm`
@@ -182,7 +181,6 @@ class GroupNorm:
   """
   Applies Group Normalization over a mini-batch of inputs.
 
-  - Described: https://paperswithcode.com/method/group-normalization
   - Paper: https://arxiv.org/abs/1803.08494v3
 
   ```python exec="true" source="above" session="tensor" result="python"
@@ -213,7 +211,6 @@ class InstanceNorm:
   """
   Applies Instance Normalization over a mini-batch of inputs.
 
-  - Described: https://paperswithcode.com/method/instance-normalization
   - Paper: https://arxiv.org/abs/1607.08022v3
 
   ```python exec="true" source="above" session="tensor" result="python"
@@ -240,7 +237,6 @@ class LayerNorm:
   """
   Applies Layer Normalization over a mini-batch of inputs.
 
-  - Described: https://paperswithcode.com/method/layer-normalization
   - Paper: https://arxiv.org/abs/1607.06450v1
 
   ```python exec="true" source="above" session="tensor" result="python"
@@ -287,7 +283,6 @@ class RMSNorm:
   """
   Applies Root Mean Square Normalization to input.
 
-  - Described: https://paperswithcode.com/method/rmsnorm
   - Paper: https://arxiv.org/abs/1910.07467
 
   ```python exec="true" source="above" session="tensor" result="python"

tinygrad/nn/optim.py

@@ -76,8 +76,6 @@ def SGD(params: list[Tensor], lr=0.001, momentum=0.0, weight_decay=0.0, nesterov
   Stochastic Gradient Descent (SGD) optimizer with optional momentum and weight decay.
 
   `classic` is a boolean flag that determines whether to use the popular momentum update rule or the classic momentum update rule.
-
-  - Described: https://paperswithcode.com/method/sgd
   """
   return LARS(params, lr, momentum, weight_decay, nesterov, classic, tcoef=0.0, fused=fused)
 
@@ -85,7 +83,6 @@ class LARS(Optimizer):
   """
   Layer-wise Adaptive Rate Scaling (LARS) optimizer with optional momentum and weight decay.
 
-  - Described: https://paperswithcode.com/method/lars
   - Paper: https://arxiv.org/abs/1708.03888v3
   """
   def __init__(self, params:list[Tensor], lr=0.001, momentum=0.9, weight_decay=1e-4, nesterov=False, classic=True, tcoef=0.001, fused=FUSE_OPTIM):
@@ -119,7 +116,6 @@ def AdamW(params: list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-8, weight_dec
   """
   AdamW optimizer with optional weight decay.
 
-  - Described: https://paperswithcode.com/method/adamw
   - Paper: https://arxiv.org/abs/1711.05101v3
   """
   return LAMB(params, lr, b1, b2, eps, weight_decay, adam=True, fused=fused)
@@ -127,7 +123,6 @@ def Adam(params: list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-8, fused=FUSE_
   """
   Adam optimizer.
 
-  - Described: https://paperswithcode.com/method/adam
   - Paper: https://arxiv.org/abs/1412.6980
   """
   return LAMB(params, lr, b1, b2, eps, 0.0, adam=True, fused=fused)
@@ -136,7 +131,6 @@ class LAMB(Optimizer):
   """
   LAMB optimizer with optional weight decay.
 
-  - Described: https://paperswithcode.com/method/lamb
   - Paper: https://arxiv.org/abs/1904.00962
   """
   def __init__(self, params: list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-6, weight_decay=0.0, adam=False, fused=FUSE_OPTIM):

tinygrad/opt/__init__.py

@@ -2,9 +2,10 @@
 
 from tinygrad.opt.kernel import Kernel
 from tinygrad.opt.heuristic import hand_coded_optimizations
-from tinygrad.uop.ops import UOp
+from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops
 from tinygrad.helpers import NOOPT, BEAM, USE_TC, getenv
 from tinygrad.renderer import Renderer
+from tinygrad.uop.spec import type_verify
 
 def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp:
   """
@@ -27,4 +28,11 @@ def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp:
       kb = Kernel(ast, opts=renderer)
       rawbufs = bufs_from_lin(kb, allocate=False)
       k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
-  return k.get_optimized_ast()
+  ret = k.get_optimized_ast()
+  if __debug__: type_verify(list(ret.toposort()))
+  return ret
+
+pm_optimize = PatternMatcher([
+  (UPat(Ops.SINK, name="ast"), lambda ctx,ast:
+   get_optimized_ast(ast, ctx) if (ast.arg is None or ast.arg.opts_to_apply is not None) and ast.src[0].st is not None else None),
+])

tinygrad/opt/kernel.py

@@ -14,7 +14,7 @@ from tinygrad.dtype import ImageDType, AddrSpace
 from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, TC_SELECT, TC_OPT, AMX
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import strides_for_shape, get_contraction
-from tinygrad.schedule.kernelize import view_left
+from tinygrad.opt.swizzler import view_left
 
 class OptOps(Enum):
   TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
@@ -90,11 +90,10 @@ class Kernel:
 
     # axis types
     global_loops = AxisType.GLOBAL if self.opts.has_local else AxisType.LOOP
-    self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.sts[0].shape, self.sts[-1].shape)]
+    self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.output_shape, self.full_shape)]
 
     # confirm all reduce axes are at the end
-    final_reduces = [i for i,(s,n) in enumerate(zip(self.full_shape, self.output_shape)) if resolve(s != n)]
-    if final_reduces != list(range(len(self.full_shape)-len(final_reduces), len(self.full_shape))):
+    if (final_reduces := [x for x in self.axis_types if x == AxisType.REDUCE]) and final_reduces != self.axis_types[-len(final_reduces):]:
       raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}")
 
   def copy(self):
@@ -201,7 +200,7 @@ class Kernel:
     if self.shape_len == 0: return
     shapes, strides = [x.shape for x in self.sts], [x.real_strides() for x in self.sts]
     # NOTE: we can't use self.first_reduce yet
-    first_reduce = [resolve(x!=y) for x,y in zip(self.sts[0].shape+(0,), self.full_shape+(1,))].index(True)
+    first_reduce = [resolve(x!=y) for x,y in zip(self.output_shape+(0,), self.full_shape+(1,))].index(True)
 
     # if it's an image, insert fake strides such that this fusion doesn't happen across image axes
     # TODO: remove membufs

tinygrad/opt/swizzler.py

@@ -0,0 +1,133 @@
+from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, resolve, sint
+from tinygrad.helpers import all_same, prod, unwrap, colored
+from tinygrad.shape.shapetracker import ShapeTracker
+from tinygrad.shape.view import View, strides_for_shape, get_contraction_with_reduce
+from tinygrad.schedule.grouper import ALWAYS_CONTIGUOUS
+from tinygrad.dtype import ImageDType, dtypes
+
+merge_views = PatternMatcher([
+  # merge adjacent views
+  (UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
+  # replace MovementOps with VIEW
+  (UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
+  # remove NOOP views
+  (UPat.var("x").view(name="view"),
+   lambda x,view: x if x.st is not None and x.op not in GroupOp.Defines and view.st.contiguous and view.shape == x.shape else None),
+  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
+   lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
+  # only unmaksed VIEW on CONST replaces the ShapeTracker
+  (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
+   lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
+])
+
+def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
+  # contiguous, expand, and the same with ones removed
+  if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
+      tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
+    new_shape: list[sint] = []
+    new_reduce_axis = []
+    if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
+    for i,pairs in enumerate(contraction):
+      new_shape_chunk = [view.shape[p] for p in pairs]
+      if i in r.arg[1]:
+        # if this is a reduce axis, we need a 1 in the view here to put it
+        assert len(new_shape_chunk) > 0
+        new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
+        new_reduce_axis.append(len(new_shape)-1)
+      else:
+        # otherwise, pass through the new_shape_chunk
+        new_shape += new_shape_chunk
+    ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
+    assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
+    return ret
+  return None
+
+view_left = merge_views+PatternMatcher([
+  # view before elementwise and buffer ops
+  (UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID, Ops.SINK}, name="e"),), name="view"),
+   lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
+  # if there's ones added after reduce, put this before the reduce
+  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
+])
+
+def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
+
+# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
+def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
+  # contiguous and same size can push to children
+  # if there's a reduce child, shapes match with ones removed
+  if unwrap(view.st).contiguous and view.size == r.size and \
+      (not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
+       tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
+    return None
+  # swizzle the input
+  input_st = ShapeTracker.from_shape(src.shape)
+  tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
+  prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
+  strides = strides_for_shape(rshape)
+  nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
+                    v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
+  new_view = tmp + ShapeTracker(tuple(nv))
+  swizzled_input = apply_swizzle(src.view(new_view))
+  # create a new reduceop
+  new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
+  if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
+  else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
+  return red.reshape(view.shape)
+
+def reduceop_view_right(src:UOp, v:UOp, r:UOp):
+  assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
+  new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
+  return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
+
+def elementwise_view_right(root:UOp):
+  if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
+  assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
+  # place view after applying the elementwise op
+  new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
+  new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
+  # reshape to match downstream shapes
+  return root.replace(src=tuple(new_src)).reshape(root.shape)
+
+# push VIEW to children
+view_right = merge_views+PatternMatcher([
+  # push a non contiguous ShapeTracker through reduceop
+  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
+  # apply view after reduceops
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
+  # apply view after elementwise ops
+  (UPat(GroupOp.All-{Ops.SINK, Ops.REDUCE_AXIS, Ops.LOAD, Ops.STORE}, name="root"), elementwise_view_right),
+  # merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
+   lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
+])
+
+def check_load_st(glbl:UOp, view:UOp):
+  if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
+  # if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
+  if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
+  # if it has a single view and it's equal when you shrink a contig, it's fine
+  if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
+  # otherwise, it's not fine
+  raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
+                     +colored("   - a += a.T\n", "red")+colored("   + a += a.T.contiguous()", "green"))
+
+fix_kernel_ops = PatternMatcher([
+  # add the LOAD
+  (UPat(Ops.DEFINE_GLOBAL, name="x"), lambda x: x.replace(tag=None).view(x.st).load() if x.tag is not None else None),
+  # STORE (except for meta ops)
+  (UPat(Ops.SINK, src=UPat(GroupOp.All-{Ops.STORE}), name="sink"), lambda sink:
+   UOp.sink(*[UOp.store(UOp(Ops.DEFINE_GLOBAL, (s:=x.base).dtype.ptr(s.st.real_size()), (), i).view(s.st), s) for i,x in enumerate(sink.src)])),
+  # passthrough ASSIGN
+  (UPat(Ops.ASSIGN, name="x"), lambda x: x.src[1]),
+  # VALID
+  (UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"),
+   lambda self: UOp.where(UOp(Ops.VALID, dtypes.bool, (UOp(Ops.VIEW, arg=self.st),)), self.const_like(self.base.arg), 0)),
+  # remove CONTIGUOUS/DEVICE from kernel AST
+  (UPat((Ops.CONTIGUOUS, Ops.MSELECT), src=(UPat.var("x"),)), lambda x: x),
+  (UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),), name="view"), lambda view: view.replace(src=())),
+  # no ImageDType after index
+  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
+  # if this kernel also assigns to the loaded buffer, ensure we can index it correctly
+  (UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
+])

tinygrad/runtime/graph/hcq.py

@@ -1,7 +1,7 @@
 import collections, time
 from typing import Any, cast
-from tinygrad.helpers import round_up, PROFILE, merge_dicts, getenv
-from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQSignal, HCQBuffer, HWQueue, HCQArgsState, BumpAllocator
+from tinygrad.helpers import round_up, PROFILE, merge_dicts, getenv, dedup
+from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQSignal, HCQBuffer, HWQueue, HCQArgsState, BumpAllocator, MMIOInterface
 from tinygrad.device import Buffer, BufferSpec, Compiled, Device, ProfileGraphEntry, ProfileGraphEvent
 from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, Variable
@@ -29,7 +29,7 @@ class HCQGraph(MultiGraphRunner):
     for ji in jit_cache:
       if not isinstance(ji.prg, CompiledRunner): continue
       kernargs_size[ji.prg.dev] += round_up(ji.prg._prg.kernargs_alloc_size, 16)
-    self.kernargs_bufs: dict[Compiled, HCQBuffer] = {dev:dev.allocator._alloc(sz, BufferSpec(cpu_access=True)) for dev,sz in kernargs_size.items()}
+    self.kernargs_bufs: dict[Compiled, HCQBuffer] = {d:d.allocator._alloc(max(sz, 1), BufferSpec(cpu_access=True)) for d,sz in kernargs_size.items()}
 
     # Fill initial arguments.
     self.ji_args: dict[int, HCQArgsState] = {}
@@ -51,8 +51,8 @@ class HCQGraph(MultiGraphRunner):
     self.comp_queues: dict[HCQCompiled, HWQueue] = {dev: dev.hw_compute_queue_t() for dev in self.devices}
     self.copy_queues: dict[HCQCompiled, HWQueue] = {} # lazy allocation
 
-    self.signals: dict[Any, HCQSignal] = {**{dev: dev.new_signal(value=0) for dev in self.devices if dev.device != "CPU"},
-      **{"KICK": self.devices[0].new_signal(value=0)}, **{dev: self.devices[0].new_signal(value=0) for dev in self.devices if dev.device == "CPU"}}
+    self.signals: dict[Any, HCQSignal] = {**{dev: dev.new_signal(value=0) for dev in self.devices if not dev._is_cpu()},
+      **{"KICK": self.devices[0].new_signal(value=0)}, **{dev: self.devices[0].new_signal(value=0) for dev in self.devices if dev._is_cpu()}}
     self.kickoff_value: int = 0
     self.kickoff_var = UOp.variable("kickoff_var", 0, 0xffffffff, dtype=dtypes.uint32)
 
@@ -87,7 +87,7 @@ class HCQGraph(MultiGraphRunner):
         assert (enqueue_dev.hw_copy_queue_t is not None), "device must implement a copy queue"
         enqueue_queue = self.copy_queues.setdefault(enqueue_dev, enqueue_dev.hw_copy_queue_t())
 
-      out_signal = self.signals.setdefault(enqueue_queue, enqueue_dev.new_signal(value=0))
+      out_signal = self.signals.setdefault(enqueue_queue, self.devices[0].new_signal(value=0))
 
       # Get dependencies based on input and output buffers.
       rdeps = self._access_resources(ji.bufs, ji.prg.p.outs if is_exec_prg else [0], (enqueue_queue, j + 1)) #type:ignore
@@ -225,7 +225,17 @@ class HCQGraph(MultiGraphRunner):
     for fdev, buf in self.kernargs_bufs.items(): fdev.allocator._free(buf, BufferSpec(cpu_access=True))
 
   @staticmethod
-  def supports_exec_item(dev, ei:ExecItem) -> bool:
+  def supports_exec_item(devs:list[Compiled], ei:ExecItem) -> bool:
+    # Check if all devices are HCQ
+    all_devs = cast(list[HCQCompiled], dedup(devs + [Device[b.device] for b in ei.bufs if b]))
+    if not all(issubclass(type(d), HCQCompiled) for d in all_devs): return False
+
+    # If all of devices are mapped into CPU address space, can use CPU inside the peer group.
+    cpu_support = all(isinstance(d.timeline_signal.base_buf.view, MMIOInterface) for d in all_devs)
+
+    # Check if all devices are within the same peer group. If CPU is supported, don't count it as a separate peer group.
+    if len(set(d.peer_group for d in all_devs if cpu_support and not d._is_cpu())) > 1: return False
+
     # MOCKGPU is not supported, since it can't execute commands in parallel
-    copy = (isinstance(ei.prg, BufferCopy) and cast(HCQCompiled, dev).hw_copy_queue_t is not None) and not getenv("MOCKGPU")
-    return all(issubclass(type(Device[b.device]), HCQCompiled) for b in ei.bufs if b) and (isinstance(ei.prg, (CompiledRunner, BufferXfer)) or copy)
+    copy = (isinstance(ei.prg, BufferCopy) and cast(HCQCompiled, devs[0]).hw_copy_queue_t is not None) and not getenv("MOCKGPU")
+    return isinstance(ei.prg, (CompiledRunner, BufferXfer)) or copy

tinygrad/runtime/ops_amd.py

@@ -673,8 +673,8 @@ class PCIIface(PCIIfaceBase):
       self.dev_impl.gfx.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr, wptr_addr=gart.va_addr+0x10,
         eop_addr=eop_buffer.va_addr, eop_size=eop_buffer.size, doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_MEC_RING0), pipe=0, queue=0)
 
-    return AMDQueueDesc(ring=MMIOInterface(ring.va_addr, ring.size, fmt='I'), read_ptrs=[MMIOInterface(gart.va_addr, 8, fmt='Q')],
-      write_ptrs=[MMIOInterface(gart.va_addr+0x10, 8, fmt='Q')], doorbells=[MMIOInterface(self.doorbell_cpu_addr + doorbell_index * 8, 8, fmt='Q')])
+    return AMDQueueDesc(ring=ring.cpu_view().view(fmt='I'), doorbells=[self.dev_impl.doorbell64.view(doorbell_index * 8, 8, fmt='Q')],
+      read_ptrs=[gart.cpu_view().view(size=8, fmt='Q')], write_ptrs=[gart.cpu_view().view(offset=0x10, size=8, fmt='Q')])
 
   def sleep(self, timeout):
     if self.pci_dev.irq_poller is not None and (events_cnt:=len(self.pci_dev.irq_poller.poll(timeout))):
@@ -717,16 +717,8 @@ class USBIface(PCIIface):
       view=USBMMIOInterface(self.usb, self.bars[0][0] + am_mapping.paddrs[0][0], size, fmt='B') if cpu_access else None, owner=self.dev)
 
   def create_queue(self, queue_type, ring, gart, eop_buffer=None, cwsr_buffer=None, ctl_stack_size=0, ctx_save_restore_size=0, xcc_id=0):
-    if queue_type == kfd.KFD_IOC_QUEUE_TYPE_SDMA:
-      self.dev_impl.sdma.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr, wptr_addr=gart.va_addr+0x10,
-                                    doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_sDMA_ENGINE0), pipe=0, queue=0)
-    else:
-      self.dev_impl.gfx.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr, wptr_addr=gart.va_addr+0x10,
-        eop_addr=eop_buffer.va_addr, eop_size=eop_buffer.size, doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_MEC_RING0), pipe=0, queue=0)
-      self.usb._pci_cacheable += [(ring.cpu_view().addr, ring.size)]
-
-    return AMDQueueDesc(ring=ring.cpu_view().view(fmt='I'), doorbells=[self.dev_impl.doorbell64.view(doorbell_index * 8, 8, fmt='Q')],
-      read_ptrs=[gart.cpu_view().view(size=8, fmt='Q')], write_ptrs=[gart.cpu_view().view(offset=0x10, size=8, fmt='Q')])
+    if queue_type == kfd.KFD_IOC_QUEUE_TYPE_COMPUTE: self.usb._pci_cacheable += [(ring.cpu_view().addr, ring.size)]
+    return super().create_queue(queue_type, ring, gart, eop_buffer, cwsr_buffer, ctl_stack_size, ctx_save_restore_size, xcc_id)
 
   def sleep(self, timeout): pass
 

tinygrad/runtime/ops_cpu.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-import platform, subprocess, sys, ctypes, functools, time, mmap
+import platform, subprocess, sys, ctypes, functools, time, mmap, threading, queue
 from tinygrad.helpers import capstone_flatdump, getenv, from_mv, to_mv, OSX, mv_address, wait_cond, cpu_profile
 from tinygrad.device import Compiler, BufferSpec, DMACPURef
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocatorBase, HCQBuffer, HWQueue, HCQArgsState, HCQSignal, HCQProgram, MMIOInterface
@@ -7,6 +7,10 @@ from tinygrad.runtime.support.elf import jit_loader
 from tinygrad.renderer.cstyle import ClangRenderer
 from tinygrad.uop.ops import sint
 
+class CPUSignal(HCQSignal):
+  def _sleep(self, time_spent_waiting_ms:int):
+    if self.is_timeline and self.owner is not None: self.owner.tasks.join()
+
 class ClangJITCompiler(Compiler):
   def __init__(self, cachekey="compile_clang_jit"): super().__init__(cachekey)
 
@@ -21,6 +25,19 @@ class ClangJITCompiler(Compiler):
 
   def disassemble(self, lib:bytes): return capstone_flatdump(lib)
 
+class CPUWorker(threading.Thread):
+  def __init__(self, dev):
+    super().__init__()
+    self.dev, self.tasks, self.daemon = dev, dev.tasks, True
+
+  def run(self):
+    while True:
+      cmd_iter = iter(self.tasks.get())
+      for cmd in cmd_iter:
+        args_cnt = next(cmd_iter)
+        cmd(*[next(cmd_iter) for _ in range(args_cnt)])
+      self.tasks.task_done()
+
 class CPUComputeQueue(HWQueue):
   def _exec(self, prg, bufs, *args):
     prg.fxn(*map(ctypes.c_uint64, args[:bufs]), *map(ctypes.c_int64 if platform.machine() == "arm64" else ctypes.c_int32, args[bufs:]))
@@ -37,13 +54,7 @@ class CPUComputeQueue(HWQueue):
   def wait(self, signal, value=0): return self.cmd(self._wait, signal.value_addr, value)
   def timestamp(self, signal): return self.cmd(self._timestamp, signal.timestamp_addr)
   def signal(self, signal, value:sint=0): return self.cmd(self._signal, signal.value_addr, value)
-
-  def _submit(self, dev):
-    # Execute the commands in the queue: fn, argc, args...
-    off = 0
-    while off < len(self._q):
-      self._q[off](*self._q[off + 2:off + 2 + self._q[off + 1]])
-      off += self._q[off + 1] + 2
+  def _submit(self, dev): dev.tasks.put(self._q[:])
 
 # NOTE: MAP_JIT is added to mmap module in python 3.13
 MAP_JIT = 0x0800
@@ -90,16 +101,23 @@ class CPUAllocator(HCQAllocatorBase):
     elif sys.platform == "win32": addr = mv_address(buf:=mmap.mmap(-1, size, access=mmap.ACCESS_WRITE))
     else: addr = mv_address(buf:=mmap.mmap(-1, size, mmap.MAP_ANON | mmap.MAP_PRIVATE, mmap.PROT_READ | mmap.PROT_WRITE))
     return HCQBuffer(va:=addr, sz:=size, meta=buf, view=MMIOInterface(va, sz, fmt='B'), owner=self.dev)
-  def _as_buffer(self, src) -> memoryview: return to_mv(src.va_addr, src.size)
-  def _as_dmaref(self, buf): return DMACPURef(buf.va_addr, buf.size)
+  def _as_buffer(self, src) -> memoryview:
+   self.dev.synchronize()
+   return to_mv(src.va_addr, src.size)
+  def _as_dmaref(self, buf):
+    self.dev.synchronize()
+    return DMACPURef(buf.va_addr, buf.size)
   def _copyin(self, dest, src:memoryview):
+    self.dev.synchronize()
     with cpu_profile('TINY -> CPU', self.dev.device, is_copy=True): ctypes.memmove(dest.va_addr, from_mv(src), len(src))
   def _copyout(self, dest:memoryview, src):
+    self.dev.synchronize()
     with cpu_profile('CPU -> TINY', self.dev.device, is_copy=True): ctypes.memmove(from_mv(dest), src.va_addr, len(dest))
   def _map(self, buf:HCQBuffer):
     if buf.view is None or not isinstance(buf.view, MMIOInterface): raise RuntimeError("Cannot map buffer without view to cpu")
 
 class CPUDevice(HCQCompiled):
   def __init__(self, device:str=""):
-    super().__init__(device, CPUAllocator(self), ClangRenderer(), ClangJITCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue,
-                     supports_graph=False)
+    self.tasks:queue.Queue = queue.Queue()
+    CPUWorker(self).start()
+    super().__init__(device, CPUAllocator(self), ClangRenderer(), ClangJITCompiler(), functools.partial(CPUProgram, self), CPUSignal, CPUComputeQueue)

tinygrad/runtime/ops_llvm.py

@@ -1,7 +1,7 @@
-import ctypes, platform, functools
+import ctypes, platform, functools, queue
 from tinygrad.device import Compiler
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQSignal
-from tinygrad.runtime.ops_cpu import CPUAllocator, CPUProgram, CPUComputeQueue
+from tinygrad.runtime.ops_cpu import CPUAllocator, CPUProgram, CPUComputeQueue, CPUWorker
 from tinygrad.helpers import OSX, getenv, capstone_flatdump, DEBUG
 from tinygrad.renderer.llvmir import LLVMRenderer
 import tinygrad.runtime.autogen.llvm as llvm
@@ -73,5 +73,6 @@ class HostLLVMCompiler(LLVMCompiler):
 
 class LLVMDevice(HCQCompiled):
   def __init__(self, device:str=""):
-    super().__init__(device, CPUAllocator(self), LLVMRenderer(), HostLLVMCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue,
-                     supports_graph=False)
+    self.tasks:queue.Queue = queue.Queue()
+    CPUWorker(self).start()
+    super().__init__(device, CPUAllocator(self), LLVMRenderer(), HostLLVMCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue)

tinygrad/runtime/ops_remote.py

@@ -225,7 +225,8 @@ class RemoteHandler:
             graph_cls = graph_class(Device[self.base_device])
             rp = RemoteProperties(
               real_device=dev.device, renderer=(cls.__module__, cls.__name__, args), offset_supported=hasattr(dev.allocator, '_offset'),
-              graph_supported=graph_cls is not None, graph_supports_multi=graph_cls is not None and issubclass(graph_cls, MultiGraphRunner),
+              graph_supported=graph_cls is not None,
+              graph_supports_multi=graph_cls is not None and issubclass(graph_cls, MultiGraphRunner) and hasattr(dev.allocator, '_transfer'),
               ib_gid=bytes(self.ib_ctx.gid_attr.raw) if self.ib_ctx is not None else None,
             )
             ret = repr(rp).encode()

tinygrad/runtime/support/hcq.py

@@ -358,14 +358,14 @@ class HCQCompiled(Compiled, Generic[SignalType]):
   peer_groups: dict[str, list[HCQCompiled]] = collections.defaultdict(list)
   signal_pages: dict[str, list[HCQBuffer]] = collections.defaultdict(list) # per peer group
   signal_pool: dict[str, list[HCQBuffer]] = collections.defaultdict(list) # per peer group
+  cpu_devices: list[HCQCompiled] = []
 
   def __init__(self, device:str, allocator:HCQAllocatorBase, renderer:Renderer, compiler:Compiler, runtime, signal_t:Type[SignalType],
-               comp_queue_t:Callable[[], HWQueue], copy_queue_t:Callable[[], HWQueue]|None=None, kernargs_size=(16 << 20), sigalloc_size=0x1000,
-               supports_graph=True):
+               comp_queue_t:Callable[[], HWQueue], copy_queue_t:Callable[[], HWQueue]|None=None, kernargs_size=(16 << 20), sigalloc_size=0x1000):
     self.device_id:int = int(device.split(":")[1]) if ":" in device else 0
 
     from tinygrad.runtime.graph.hcq import HCQGraph
-    super().__init__(device, allocator, renderer, compiler, runtime, HCQGraph if supports_graph else None)
+    super().__init__(device, allocator, renderer, compiler, runtime, HCQGraph)
 
     # TODO: peer logic is determined based on device name.
     self.peer_group = device.split(":")[0]
@@ -383,7 +383,13 @@ class HCQCompiled(Compiled, Generic[SignalType]):
     self.kernargs_buf:HCQBuffer = self.allocator.alloc(kernargs_size, BufferSpec(cpu_access=True))
     self.kernargs_offset_allocator:BumpAllocator = BumpAllocator(self.kernargs_buf.size, wrap=True)
 
+    if self._is_cpu(): HCQCompiled.cpu_devices.append(self)
+
   def synchronize(self):
+    # If we have any work on CPU devices, need to synchronize them. This is just an optimization to release GIL allowing to finish faster.
+    if not self._is_cpu():
+      for dev in HCQCompiled.cpu_devices: dev.synchronize()
+
     try: self.timeline_signal.wait(self.timeline_value - 1)
     except RuntimeError as e:
       if hasattr(self, 'on_device_hang'): self.on_device_hang()

tinygrad/runtime/support/nv/ip.py

@@ -60,7 +60,8 @@ class NVRpcQueue:
 
       # Handling special functions
       if hdr.function == nv.NV_VGPU_MSG_EVENT_GSP_RUN_CPU_SEQUENCER: self.gsp.run_cpu_seq(msg)
-      elif hdr.function == nv.NV_VGPU_MSG_EVENT_OS_ERROR_LOG: print(f"GSP LOG: {msg[12:].tobytes().rstrip(bytes([0])).decode('utf-8')}")
+      elif hdr.function == nv.NV_VGPU_MSG_EVENT_OS_ERROR_LOG:
+        print(f"nv {self.gsp.nvdev.devfmt}: GSP LOG: {msg[12:].tobytes().rstrip(bytes([0])).decode('utf-8')}")
 
       # Update the read pointer
       self.rx.readPtr = (self.rx.readPtr + round_up(hdr.length, self.tx.msgSize) // self.tx.msgSize) % self.tx.msgCount

tinygrad/runtime/support/nv/nvdev.py

@@ -66,7 +66,7 @@ class NVPageTableEntry:
     return self.read_fields(entry_id)[f'address{small}{sys}'] << 12
 
 class NVMemoryManager(MemoryManager):
-  va_allocator = TLSFAllocator((1 << 44), base=1 << 30) # global for all devices.
+  va_allocator = TLSFAllocator((1 << 44), base=0x1000000000) # global for all devices.
 
   def on_range_mapped(self): self.dev.NV_VIRTUAL_FUNCTION_PRIV_MMU_INVALIDATE.write((1 << 0) | (1 << 1) | (1 << 6) | (1 << 31))
 

tinygrad/schedule/grouper.py

@@ -3,7 +3,7 @@ from tinygrad.helpers import all_int, prod, unwrap, dedup, DONT_REALIZE_EXPAND,
 from tinygrad.shape.shapetracker import ShapeTracker
 
 ALWAYS_CONTIGUOUS = {Ops.CONTIGUOUS, Ops.ASSIGN, Ops.COPY, Ops.BUFFER, Ops.BUFFER_VIEW,
-                     Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK}
+                     Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK, Ops.DEFINE_GLOBAL}
 
 # **** Grouper decides which of the UOps realize
 

tinygrad/schedule/kernelize.py

@@ -1,14 +1,13 @@
 from dataclasses import dataclass
-from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve, sint
+from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve
 from tinygrad.uop.ops import track_rewrites, _substitute
 from tinygrad.uop.spec import type_verify, tensor_uop_spec
 from tinygrad.uop.symbolic import symbolic_simple
-from tinygrad.helpers import Metadata, all_int, all_same, colored, prod, dedup, unwrap, getenv, pluralize, FUSE_ARANGE, DEBUG, SPLIT_REDUCEOP
-from tinygrad.dtype import ImageDType, dtypes
+from tinygrad.helpers import Metadata, all_int, all_same, prod, dedup, unwrap, getenv, pluralize, FUSE_ARANGE, DEBUG, SPLIT_REDUCEOP
+from tinygrad.dtype import ImageDType
 from tinygrad.schedule.multi import multi_pm
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.shape.view import View, strides_for_shape, get_contraction_with_reduce
 from tinygrad.schedule.grouper import group_realizes, ALWAYS_CONTIGUOUS
+from tinygrad.opt.swizzler import merge_views, apply_swizzle, swizzle_reduceop
 
 # creation can recurse a lot
 import sys
@@ -148,138 +147,13 @@ create_kernels = PatternMatcher([
    lambda ms: UOp(Ops.MSTACK, ms.dtype, tuple(x.src[0] for x in ms.src)).reshape(ms.src[0].arg)),
 ])
 
-# **** swizzler
-
-merge_views = PatternMatcher([
-  # merge adjacent views
-  (UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
-  # replace MovementOps with VIEW
-  (UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
-  # remove NOOP views
-  (UPat.var("x").view(name="view"), lambda x,view: x if x.st is not None and view.st.contiguous and view.shape == x.shape else None),
-  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
-   lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
-  # only unmaksed VIEW on CONST replaces the ShapeTracker
-  (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
-   lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
-])
-
-def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
-  # contiguous, expand, and the same with ones removed
-  if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
-      tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
-    new_shape: list[sint] = []
-    new_reduce_axis = []
-    if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
-    for i,pairs in enumerate(contraction):
-      new_shape_chunk = [view.shape[p] for p in pairs]
-      if i in r.arg[1]:
-        # if this is a reduce axis, we need a 1 in the view here to put it
-        assert len(new_shape_chunk) > 0
-        new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
-        new_reduce_axis.append(len(new_shape)-1)
-      else:
-        # otherwise, pass through the new_shape_chunk
-        new_shape += new_shape_chunk
-    ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
-    assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
-    return ret
-  return None
-
-view_left = merge_views+PatternMatcher([
-  # view before elementwise and buffer ops
-  (UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID, Ops.SINK}, name="e"),), name="view"),
-   lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
-  # if there's ones added after reduce, put this before the reduce
-  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
-])
-
-def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
-
-# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
-def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
-  # contiguous and same size can push to children
-  # if there's a reduce child, shapes match with ones removed
-  if unwrap(view.st).contiguous and view.size == r.size and \
-      (not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
-       tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
-    return None
-  # swizzle the input
-  input_st = ShapeTracker.from_shape(src.shape)
-  tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
-  prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
-  strides = strides_for_shape(rshape)
-  nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
-                    v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
-  new_view = tmp + ShapeTracker(tuple(nv))
-  swizzled_input = apply_swizzle(src.view(new_view))
-  # create a new reduceop
-  new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
-  if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
-  else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
-  return red.reshape(view.shape)
-
-def reduceop_view_right(src:UOp, v:UOp, r:UOp):
-  assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
-  new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
-  return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
-
-def elementwise_view_right(root:UOp):
-  if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
-  assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
-  # place view after applying the elementwise op
-  new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
-  new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
-  # reshape to match downstream shapes
-  return root.replace(src=tuple(new_src)).reshape(root.shape)
-
-# push VIEW to children
-view_right = merge_views+PatternMatcher([
-  # push a non contiguous ShapeTracker through reduceop
-  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
-  # apply view after reduceops
-  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
-  # apply view after elementwise ops
-  (UPat(GroupOp.All-{Ops.SINK, Ops.REDUCE_AXIS}, name="root"), elementwise_view_right),
-  # merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
-  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
-   lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
-])
-
 # **** fix kernel AST
 
-add_buffer_ops = PatternMatcher([
+early_buffer_ops = PatternMatcher([
   # LOAD
-  (UPat(Ops.BUFFER, name="x"), lambda ctx,x: UOp.load(UOp(Ops.DEFINE_GLOBAL, x.dtype.ptr(x.size), (), ctx.index(x)).view(x.st),)),
-  # STORE (except for meta ops)
+  (UPat(Ops.BUFFER, name="x"), lambda ctx,x: UOp(Ops.DEFINE_GLOBAL, x.dtype.ptr(x.size), (), ctx.index(x), tag=1)),
+  # no SINK for meta ops
   (UPat(Ops.SINK, src=(UPat(Ops.CONTIGUOUS, src=(UPat(GroupOp.Meta, name="x"),),))), lambda x:x),
-  (UPat(Ops.SINK, src=UPat(GroupOp.All-{Ops.STORE}), name="sink"), lambda ctx,sink:
-   UOp.sink(*[UOp.store(UOp(Ops.DEFINE_GLOBAL, (s:=x.base).dtype.ptr(ctx[i].size), (), i).view(s.st), s) for i,x in enumerate(sink.src)])),
-  # passthrough ASSIGN
-  (UPat(Ops.ASSIGN, name="x"), lambda x: x.src[1]),
-  # VALID
-  (UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"),
-   lambda self: UOp.where(UOp(Ops.VALID, dtypes.bool, (UOp(Ops.VIEW, arg=self.st),)), self.const_like(self.base.arg), 0)),
-])
-
-def check_load_st(glbl:UOp, view:UOp):
-  if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
-  # if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
-  if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
-  # if it has a single view and it's equal when you shrink a contig, it's fine
-  if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
-  # otherwise, it's not fine
-  raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
-                     +colored("   - a += a.T\n", "red")+colored("   + a += a.T.contiguous()", "green"))
-
-fix_kernel_ops = PatternMatcher([
-  # remove CONTIGUOUS/DEVICE from kernel AST
-  (UPat((Ops.CONTIGUOUS, Ops.MSELECT), src=(UPat.var("x"),)), lambda x: x),
-  (UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),), name="view"), lambda view: view.replace(src=())),
-  # no ImageDType after index
-  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
-  # if this kernel also assigns to the loaded buffer, ensure we can index it correctly
-  (UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
 ])
 
 replace_globals = PatternMatcher([
@@ -291,10 +165,6 @@ replace_globals = PatternMatcher([
 
 def fix_kernel_ast(k:UOp) -> UOp|None:
   if k.arg.ast.op in GroupOp.Meta or all(s.op is Ops.STORE for s in k.arg.ast.src): return None
-  # replace global memory ops with the BUFFER they write to
-  ast = graph_rewrite(k.arg.ast, replace_globals, bottom_up=True, name="replace globals")
-  # push views to edges
-  ast = graph_rewrite(graph_rewrite(ast, view_left, name="Main View Left"), view_right, name="Main View Right")
   # replace buffer with define_global + add load/store last
   bufs = []
   for s in k.src:
@@ -302,7 +172,9 @@ def fix_kernel_ast(k:UOp) -> UOp|None:
     # traverse back through MSELECT and MSTACK. HACK: 0 branch of MSTACK only
     while s.op in {Ops.MSELECT, Ops.MSTACK}: s = s.src[0]
     bufs.append(s)
-  ast = graph_rewrite(ast, view_left+add_buffer_ops+fix_kernel_ops, bufs, bottom_up=True, name="replace buffer")
+  # replace global memory ops with the BUFFER they write to
+  ast = graph_rewrite(k.arg.ast, replace_globals, bottom_up=True, name="replace globals")
+  ast = graph_rewrite(ast, early_buffer_ops, bufs, bottom_up=True, name="replace buffer early")
   if ast.op is Ops.SINK and not all_same([x.device for x in k.src]):
     raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in k.src)}")
   return k.replace(arg=Kernel(ast, k.arg.metadata))
@@ -344,7 +216,7 @@ pm_fuse = PatternMatcher([
 def do_fusion(x:UOp):
   found_contiguous = {}
   def gate_contiguous(x):
-    if is_contiguous:=(x.op is Ops.CONTIGUOUS): found_contiguous[x] = x.replace(src=(UOp(Ops.VIEW, arg=x.st),))
+    if is_contiguous:=(x.op is Ops.CONTIGUOUS): found_contiguous[x] = x.replace(src=(UOp(Ops.VIEW, arg=x.st), UOp.unique()))
     return not is_contiguous
   x.toposort(gate=gate_contiguous)
   del gate_contiguous
@@ -440,8 +312,6 @@ def get_kernelize_map(sink:UOp) -> dict[UOp, UOp]:
   tensor_map = graph_rewrite_map(tensor_map[sink], add_contiguous, ctx=realize_map, bottom_up=True, input_map=tensor_map, name="add_contiguous")
   tensor_map = graph_rewrite_map(tensor_map[sink], finalize_contiguous+remove_tags, input_map=tensor_map, name="finalize_contiguous")
 
-  # TODO: move view_left/view_right here
-
   # group into kernels (this is context-free)
   tensor_map = graph_rewrite_map(tensor_map[sink], create_kernels, input_map=tensor_map, name="create_kernels")
 

tinygrad/tensor.py

@@ -2234,7 +2234,7 @@ class Tensor(MathTrait):
     """
     def parse_formula(formula:str, *operands:Tensor):
       if "..." in (formula := formula.replace(" ", "")):
-        ell_chars, ell_longest = "".join(set(string.ascii_letters) - set(formula)), 0
+        ell_chars, ell_longest = "".join(c for c in string.ascii_letters if c not in formula), 0
         for i, inp in enumerate(filter(lambda x: "..." in x, inputs := formula.split("->")[0].split(","))):
           if (ell_count := max(operands[i].ndim, 1) - (len(inp) - len("..."))) > ell_longest: ell_longest = ell_count
           inputs[i] = inp.replace("...", ell_chars[-ell_count:])
@@ -2332,8 +2332,6 @@ class Tensor(MathTrait):
 
     NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
 
-    See: https://paperswithcode.com/method/average-pooling
-
     ```python exec="true" source="above" session="tensor" result="python"
     t = Tensor.arange(25).reshape(1, 1, 5, 5)
     print(t.avg_pool2d().numpy())
@@ -2380,8 +2378,6 @@ class Tensor(MathTrait):
 
     NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
 
-    See: https://paperswithcode.com/method/max-pooling
-
     ```python exec="true" source="above" session="tensor" result="python"
     t = Tensor.arange(25).reshape(1, 1, 5, 5)
     print(t.max_pool2d().numpy())
@@ -3010,8 +3006,6 @@ class Tensor(MathTrait):
     """
     Applies the Rectified Linear Unit (ReLU) function element-wise.
 
-    - Described: https://paperswithcode.com/method/relu
-
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).relu().numpy())
     ```
@@ -3048,7 +3042,6 @@ class Tensor(MathTrait):
     Applies the Hardsigmoid function element-wise.
     NOTE: default `alpha` and `beta` values are taken from torch
 
-    - Described: https://paperswithcode.com/method/hard-sigmoid
     - See: https://pytorch.org/docs/stable/generated/torch.nn.functional.hardsigmoid.html
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3291,7 +3284,6 @@ class Tensor(MathTrait):
     """
     Applies the Exponential Linear Unit (ELU) function element-wise.
 
-    - Described: https://paperswithcode.com/method/elu
     - Paper: https://arxiv.org/abs/1511.07289v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3304,7 +3296,6 @@ class Tensor(MathTrait):
     """
     Applies the Continuously differentiable Exponential Linear Unit (CELU) function element-wise.
 
-    - Described: https://paperswithcode.com/method/celu
     - Paper: https://arxiv.org/abs/1704.07483
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3317,7 +3308,6 @@ class Tensor(MathTrait):
     """
     Applies the Scaled Exponential Linear Unit (SELU) function element-wise.
 
-    - Described: https://paperswithcode.com/method/selu
     - Paper: https://arxiv.org/abs/1706.02515v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3342,7 +3332,6 @@ class Tensor(MathTrait):
     """
     Applies the Sigmoid Linear Unit (SiLU) function element-wise.
 
-    - Described: https://paperswithcode.com/method/silu
     - Paper: https://arxiv.org/abs/1606.08415
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3355,7 +3344,6 @@ class Tensor(MathTrait):
     """
     Applies the ReLU6 function element-wise.
 
-    - Described: https://paperswithcode.com/method/relu6
     - Paper: https://arxiv.org/abs/1704.04861v1
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3368,7 +3356,6 @@ class Tensor(MathTrait):
     """
     Applies the Hardswish function element-wise.
 
-    - Described: https://paperswithcode.com/method/hard-swish
     - Paper: https://arxiv.org/abs/1905.02244v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3453,8 +3440,6 @@ class Tensor(MathTrait):
     """
     Applies the Hardtanh function element-wise.
 
-    - Described: https://paperswithcode.com/method/hardtanh-activation
-
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-1.5, -1.0, -0.5, 0., 0.5, 1.0, 1.5]).hardtanh().numpy())
     ```
@@ -3479,7 +3464,6 @@ class Tensor(MathTrait):
     """
     Applies the Gaussian Error Linear Unit (GELU) function element-wise.
 
-    - Described: https://paperswithcode.com/method/gelu
     - Paper: https://arxiv.org/abs/1606.08415v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3492,8 +3476,6 @@ class Tensor(MathTrait):
     """
     Applies the Sigmoid GELU approximation element-wise.
 
-    - Described: https://paperswithcode.com/method/gelu
-
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).quick_gelu().numpy())
     ```
@@ -3504,8 +3486,6 @@ class Tensor(MathTrait):
     """
     Applies the Leaky ReLU function element-wise.
 
-    - Described: https://paperswithcode.com/method/leaky-relu
-
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).leaky_relu().numpy())
     ```
@@ -3519,7 +3499,6 @@ class Tensor(MathTrait):
     """
     Applies the Mish function element-wise.
 
-    - Described: https://paperswithcode.com/method/mish
     - Paper: https://arxiv.org/abs/1908.08681v3
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3532,8 +3511,6 @@ class Tensor(MathTrait):
     """
     Applies the Softplus function element-wise.
 
-    - Described: https://paperswithcode.com/method/softplus
-
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softplus().numpy())
     ```
@@ -3544,8 +3521,6 @@ class Tensor(MathTrait):
     """
     Applies the Softsign function element-wise.
 
-    - Described: https://paperswithcode.com/method/softsign
-
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softsign().numpy())
     ```
@@ -3561,7 +3536,8 @@ class Tensor(MathTrait):
     # for each dimension, check either dim is 1, or it does not change
     if not all(resolve(s == ns) or resolve(s == 1) for s,ns in zip(shape, new_shape)):
       raise ValueError(f"cannot broadcast {self.shape} to {new_shape=}")
-    return self.reshape(shape)._apply_uop(UOp.expand, arg=new_shape)
+    # NOTE: this cast is no-op in forward and uses sum_acc_dtype in the backward sum
+    return self.reshape(shape).cast(sum_acc_dtype(self.dtype))._apply_uop(UOp.expand, arg=new_shape).cast(self.dtype)
 
   def _broadcasted(self, y:Tensor|ConstType|UOp, reverse:bool=False, match_dtype:bool=True) -> tuple[Tensor, Tensor]:
     x: Tensor = self
@@ -3838,7 +3814,6 @@ class Tensor(MathTrait):
     """
     Applies Layer Normalization over a mini-batch of inputs.
 
-    - Described: https://paperswithcode.com/method/layer-normalization
     - Paper: https://arxiv.org/abs/1607.06450v1
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3857,7 +3832,6 @@ class Tensor(MathTrait):
     """
     Applies Batch Normalization over a mini-batch of inputs.
 
-    - Described: https://paperswithcode.com/method/batch-normalization
     - Paper: https://arxiv.org/abs/1502.03167
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3882,7 +3856,6 @@ class Tensor(MathTrait):
 
     NOTE: dropout is only applied when `Tensor.training` is `True`.
 
-    - Described: https://paperswithcode.com/method/dropout
     - Paper: https://jmlr.org/papers/v15/srivastava14a.html
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3924,7 +3897,6 @@ class Tensor(MathTrait):
     Computes scaled dot-product attention.
     `self` is the query tensor, `key` is the key tensor, and `value` is the value tensor.
 
-    - Described: https://paperswithcode.com/method/scaled
     - Paper: https://arxiv.org/abs/1706.03762v7
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -4117,8 +4089,8 @@ class Tensor(MathTrait):
     #extract singular values and sort. construct U from Q
     S, indices = U.square().sum(-2).sqrt().sort(dim = -1, descending=True)
     new_indices = Tensor.arange(num).reshape((1,) * (self.ndim - 1) + (num,)).expand(b_shape + 2 * (num,)).contiguous()
-    new_indices[..., :num] = indices.reshape(b_shape + (1,) + (U.shape[0],)).expand(b_shape + 2 * (num,))
-    U,V = U.gather(-1, new_indices[...,0:num,0:num]) / S.unsqueeze(-2), V.gather(-1, new_indices[..., 0:num, 0:num])
+    new_indices[..., :num] = indices.reshape(b_shape + (1,) + (num,)).expand(b_shape + 2 * (num,))
+    U,V = U.gather(-1, new_indices[...,0:num,0:num]) / S.unsqueeze(-2), V.gather(-1, new_indices[..., 0:num, 0:num]).realize()
 
     padded_u = Tensor.eye(q_num, dtype = U.dtype).reshape((1,) * (self.ndim - 2) + 2 * (q_num,)).expand(b_shape + 2 * (q_num,)).contiguous()
     padded_u[..., 0:num, 0:num] = U
@@ -4316,6 +4288,11 @@ class Tensor(MathTrait):
     """
     return self.cast(dtypes.bool)
 
+  def bfloat16(self) -> Tensor: return self.cast(dtypes.bfloat16)
+  def double(self) -> Tensor: return self.cast(dtypes.double)
+  def long(self) -> Tensor: return self.cast(dtypes.long)
+  def short(self) -> Tensor: return self.cast(dtypes.short)
+
   # *** image Tensor function replacements ***
 
   def image_dot(self, w:Tensor, dtype:DTypeLike|None=None) -> Tensor:

tinygrad/uop/ops.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass, field
 from enum import Enum, auto
 from tinygrad.uop import Ops, GroupOp
 from tinygrad.uop.mathtraits import MathTrait
-from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate
+from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate, PtrDType
 from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten
 from tinygrad.helpers import PICKLE_BUFFERS, PROFILE, dedup, cdiv, cmod, diskcache_put, to_function_name, cpu_profile, TracingKey
 if TYPE_CHECKING:
@@ -150,7 +150,12 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     # BUFFER/BUFFER_VIEW and KERNEL only have a size
     if self.op in {Ops.BUFFER, Ops.BUFFER_VIEW}: return ShapeTracker.from_shape((self.size,))
     if self.op is Ops.KERNEL: return ShapeTracker.from_shape((self.arg.ast.size,))
-    #if self.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}: return ShapeTracker.from_shape((self.dtype.size,))
+    if self.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
+      sz = cast(PtrDType, self.dtype).size
+      return ShapeTracker.from_shape((sz,)) if sz > 0 else None
+
+    # hack for PTX, CASTing the ptr loses the shape. even worse hack with tag
+    if self.op is Ops.CAST and self.src[0].op is Ops.DEFINE_GLOBAL and self.src[0].tag is None: return None
 
     # otherwise we get the shape from sources
     if not (src_sts := [x.st for x in self.src if x.st is not None]): return None
@@ -171,7 +176,9 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     parent_shapes = [x.full_shape for x in self.src]
     return tuple(smax(x) for x in itertools.zip_longest(*parent_shapes, fillvalue=1))
   @property
-  def shape(self) -> tuple[sint, ...]: return unwrap(self.st).shape
+  def shape(self) -> tuple[sint, ...]:
+    assert self.st is not None, f"{self.op} doesn't have a shape"
+    return unwrap(self.st).shape
   @property
   def size(self) -> int: return self.arg[0] if self.op is Ops.BUFFER_VIEW else self.arg if self.op is Ops.BUFFER else unwrap(self.st).size
 
@@ -433,7 +440,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     all_vars = set([x for x in self.toposort() if x.op is Ops.DEFINE_VAR])
     return bound_vars.union(set([x for x in all_vars if x not in bound_var_base]))
   def variables(self) -> list[Variable]:
-    st_vars: list[set[Variable]] = [x.st_arg.vars() for x in self.toposort() if x.op in GroupOp.Buffer]
+    st_vars: list[set[Variable]] = [x.arg.vars() for x in self.toposort() if x.op is Ops.VIEW]
     return sorted(set.union(*st_vars, set([x.unbind()[0] if x.op is not Ops.DEFINE_VAR else x for x in self.vars()])), key=lambda v: v.arg)
 
   # *** uop symbolic stuff ***
@@ -635,6 +642,7 @@ class UPat(MathTrait):
   def const(dtype:DType|tuple[DType, ...]|None, b:ConstType): return UPat(Ops.CONST, dtype=dtype, arg=b)
 
   # copied from UOp
+  def sink(self, *srcs:UPat|None, **kwargs): return UPat(Ops.SINK, dtypes.void, (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
   def index(self, idx:UPat, valid:UPat|None=None): return UPat(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))
   def view(self, st=None, **kwargs): return UPat(Ops.VIEW, self.dtype, (self,), st, **kwargs)
   def cast(self, dtype=None, **kwargs): return UPat(Ops.CAST, dtype, (self,), **kwargs)
@@ -941,6 +949,7 @@ renderer = PatternMatcher([
   (UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
   #(UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}[={x.src[1].arg}]")),
   (UPat(Ops.NEG, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(-{x.src[0].arg})")),
+  (UPat(Ops.RECIP, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(1/{x.src[0].arg})")),
   (UPat(Ops.MAX, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"max({x.src[0].arg}, {x.src[1].arg})")),
   (UPat(Ops.MULACC, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}*{x.src[1].arg}+{x.src[2].arg})")),
   (UPat(Ops.WHERE, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[1].arg} if {x.src[0].arg} else {x.src[2].arg})")),

tinygrad/uop/symbolic.py

@@ -3,7 +3,7 @@ from typing import Any, Literal, cast
 import math, operator, struct, functools
 from collections import defaultdict
 from tinygrad.uop.ops import Ops, PatternMatcher, UPat, UOp, GroupOp, exec_alu
-from tinygrad.dtype import ConstType, dtypes, PtrDType, AddrSpace
+from tinygrad.dtype import ConstType, dtypes, PtrDType, AddrSpace, can_safe_cast
 from tinygrad.helpers import partition, all_same, prod, flatten, get_single_element, cdiv, cmod, CORRECT_DIVMOD_FOLDING
 from tinygrad.uop.transcendental import xpow
 
@@ -65,6 +65,8 @@ symbolic_simple = PatternMatcher([
   (UPat(Ops.CAST, name="root", src=(UPat.cvar("c"),)), lambda root, c: root.const_like(c.arg)),
   (UPat((Ops.CAST, Ops.BITCAST), name="root"), lambda root: root.src[0] if root.dtype == root.src[0].dtype else None),
   (UPat(Ops.BITCAST, name="root", src=(UPat.cvar("c"),)), fold_bitcast),
+  # b.cast(a).cast(b) -> b if a preserves all values in b
+  (UPat.var('x').cast().named('a').cast().named('b'), lambda x,a,b: x if x.dtype == b.dtype and can_safe_cast(b.dtype, a.dtype) else None),
   # ** pow **
   (UPat.var("x").alu(Ops.POW, UPat.cvar("c", vec=False)), simplify_pow),
   # positive const ** x
@@ -427,7 +429,6 @@ sym = symbolic_flat+PatternMatcher([
   (UPat(Ops.WMMA, src=(UPat.var(), UPat.const(None, 0.0), UPat.var("acc"))), lambda acc: acc),
   # threefry + remove longs
   (UPat(Ops.THREEFRY, dtype=dtypes.uint64, src=(UPat.var("x"), UPat.var("key"))), threefry2x32),
-  (UPat.var('x', dtypes.uint32).cast(dtypes.uint64).cast(dtypes.uint32), lambda x: x),   # cast there and back is noop (TODO: genericize)
   ((UPat.var('x', dtypes.uint64)&0xFFFFFFFF).cast(dtypes.uint32), lambda x: x.cast(dtypes.uint32)),  # cast does truncation
   (((UPat.var(None, dtypes.uint64)*(1<<32)) | UPat.var('y',  dtypes.uint32).cast(dtypes.uint64)).cast(dtypes.uint32), lambda y: y),
   (((UPat.var('x',  dtypes.uint64)*(1<<32)) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))//(1<<32), lambda x: x),
@@ -466,6 +467,7 @@ sym = symbolic_flat+PatternMatcher([
       if any(x.op in REMOVE_FROM_SINK for x in root.src) else None),
   ((UPat.var("x") * UPat.var("x")).reciprocal(), lambda x: x.reciprocal()*x.reciprocal()),  # 1/(x^c) -> (1/x)^c
   ((UPat.var("x") * UPat.var("x") * UPat.var("x")).reciprocal(), lambda x: x.reciprocal()*x.reciprocal()*x.reciprocal()),
+  ((UPat.var("x") * UPat.cvar("c")).reciprocal(), lambda x,c: x.reciprocal()*c.reciprocal()), # 1/(x*c) -> (1/c)*(1/x)
   (UPat.var("x") * ((1+UPat.var("x")).reciprocal().named("d")), lambda x,d: 1-d), # x*/(1+x) -> 1-1/(1+x)
   (UPat.var("x") * ((1+UPat.var("x")).reciprocal().named("d")*UPat.var("y")), lambda x,y,d: y*(1-d)),
   (UPat.var("x") * ((1+UPat.var("x")).reciprocal().named("d")+UPat.var("y")), lambda x,y,d: (1-d)+x*y),

tinygrad/viz/index.html

@@ -173,7 +173,7 @@
     background-color: #1a1b26;
     border: 1px solid #4a4b56;
     color: #f0f0f5;
-    border-radius: 8px;
+    border-radius: 4px;
     padding: 6px;
     cursor: pointer;
     height: 32px;
@@ -184,7 +184,6 @@
   }
   .btn:hover {
     background-color: #2a2b36;
-    border-color: #5a5b66;
   }
   .collapsed .container {
     display: none;
@@ -203,7 +202,6 @@
   pre code.hljs {
     overflow-y: auto;
     max-height: 30vh;
-    border-radius: 8px;
     padding: 8px;
   }
   .progress-message {
@@ -255,7 +253,7 @@
     font-size: 0.95em;
   }
   table td {
-    border-bottom: 1px solid #2c2f40;
+    border-bottom: 1px solid #4a4b56;
     vertical-align: top;
   }
   table tr:last-child > td {
@@ -291,7 +289,7 @@
     text-align: left;
     padding: 10px 12px;
     font-weight: 600;
-    border-bottom: 1px solid #3a3d52;
+    border-bottom: 1px solid #4a4b56;
     font-size: 0.95em;
     letter-spacing: 0.03em;
   }

tinygrad/viz/js/index.js

@@ -109,7 +109,7 @@ function formatTime(ts, dur=ts) {
 }
 const formatUnit = (d, unit="") => d3.format(".3~s")(d)+unit;
 
-const colorScheme = {TINY:["#1b5745", "#354f52", "#354f52", "#46acc2", "#1d2e62"],
+const colorScheme = {TINY:["#1b5745", "#354f52", "#354f52", "#46acc2", "#1d2e62", "#63b0cd"],
   DEFAULT:["#2b2e39", "#2c2f3a", "#31343f", "#323544", "#2d303a", "#2e313c", "#343746", "#353847", "#3c4050", "#404459", "#444862", "#4a4e65"],
   BUFFER:["#3A57B7","#5066C1","#6277CD","#7488D8","#8A9BE3","#A3B4F2"],
   CATEGORICAL:["#ff8080", "#F4A261", "#C8F9D4", "#8D99AE", "#F4A261", "#ffffa2", "#ffffc0", "#87CEEB"],}

tinygrad/viz/serve.py

@@ -30,10 +30,13 @@ def get_metadata(keys:list[TracingKey], contexts:list[list[TrackedGraphRewrite]]
   for i,(k,v) in enumerate(zip(keys, contexts)):
     steps = [{"name":s.name, "loc":s.loc, "depth":s.depth, "match_count":len(s.matches), "code_line":printable(s.loc),
               "query":f"/ctxs?ctx={i}&idx={j}"} for j,s in enumerate(v)]
-    if isinstance(k.ret, ProgramSpec): steps.append({"name":"View Disassembly", "query":f"/disasm?ctx={i}"})
-    ret.append(r:={"name":k.display_name, "fmt":k.fmt, "steps":steps})
+    ret.append(r:={"name":k.display_name, "steps":steps})
     # use the first key to get runtime profiling data about this context
     if getenv("PROFILE_VALUE") >= 2 and k.keys: r["runtime_stats"] = get_runtime_stats(k.keys[0])
+    # program spec metadata
+    if isinstance(k.ret, ProgramSpec):
+      steps.append({"name":"View Disassembly", "query":f"/disasm?ctx={i}"})
+      r["fmt"] = k.ret.src
     for key in k.keys: ref_map[key] = i
   return ret
 
@@ -129,7 +132,8 @@ def timeline_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
     name, cat, info = e.name, None, None
     if (ref:=ref_map.get(name)) is not None:
       name = ctxs[ref]["name"]
-      if isinstance(p:=contexts[0][ref].ret, ProgramSpec):
+      # TODO: support symbolic by capturing var_vals in profile events
+      if isinstance(p:=contexts[0][ref].ret, ProgramSpec) and all(isinstance(es,int) for es in [p.estimates.ops, p.estimates.mem, p.estimates.lds]):
         info = f"{p.estimates.ops/(t:=dur*1e3):.2f} GFLOPS {p.estimates.mem/t:4.1f}|{p.estimates.lds/t:.1f} GB/s"
     elif isinstance(e.name, TracingKey):
       name, cat = e.name.display_name, e.name.cat
@@ -189,6 +193,24 @@ def get_runtime_stats(key) -> list[dict]:
       ret.append({"device":e.device, "data":[{"name":"Duration", "value":float(e.en-e.st), "unit":"us"}]})
   return ret
 
+# ** Assembly analyzers
+
+def get_llvm_mca(asm:str, mtriple:str, mcpu:str) -> dict:
+  target_args = [f"-mtriple={mtriple}", f"-mcpu={mcpu}"]
+  # disassembly output can include headers / metadata, skip if llvm-mca can't parse those lines
+  data = json.loads(subprocess.check_output(["llvm-mca","-skip-unsupported-instructions=parse-failure","--json","-"]+target_args, input=asm.encode()))
+  cr = data["CodeRegions"][0]
+  rows:list = [{"data":[instr], "segs":{}} for instr in cr["Instructions"]]
+  for i,info in enumerate(cr["InstructionInfoView"]["InstructionList"]): rows[i]["data"].append(info["Latency"])
+  for d in cr["ResourcePressureView"]["ResourcePressureInfo"]:
+    i, r = d["InstructionIndex"], d["ResourceIndex"]
+    if i>len(rows)-1: continue
+    rows[i]["segs"][r] = rows[i]["segs"].get(r, 0)+d["ResourceUsage"]
+  # rescale segment width to 0-100
+  max_usage = max([sum(x["segs"].values()) for x in rows], default=0)
+  for x in rows: x["segs"] = {k:{"width":(v/max_usage)*100, "value":v} for k,v in x["segs"].items()}
+  return {"rows":rows, "cols":["Opcode", "Latency", "HW Resources"], "segments":data["TargetInfo"]["Resources"]}
+
 def get_disassembly(ctx:list[str]):
   if not isinstance(prg:=contexts[0][int(ctx[0])].ret, ProgramSpec): return
   lib = (compiler:=Device[prg.device].compiler).compile(prg.src)
@@ -198,22 +220,9 @@ def get_disassembly(ctx:list[str]):
   if isinstance(compiler, LLVMCompiler):
     mtriple = ctypes.string_at(llvm.LLVMGetTargetMachineTriple(tm:=compiler.target_machine)).decode()
     mcpu = ctypes.string_at(llvm.LLVMGetTargetMachineCPU(tm)).decode()
-    # NOTE: llvm-objdump may contain headers, skip if llvm-mca can't parse those lines
-    data = json.loads(subprocess.check_output(["llvm-mca", f"-mtriple={mtriple}", f"-mcpu={mcpu}", "-skip-unsupported-instructions=parse-failure",
-                                               "--json", "-"], input=disasm_str.encode()))
-    cr = data["CodeRegions"][0]
-    instrs:list = [{"data":[rep], "segs":{}} for rep in cr["Instructions"]]
-    for i,info in enumerate(cr["InstructionInfoView"]["InstructionList"]): instrs[i]["data"].append(info["Latency"])
-    for d in cr["ResourcePressureView"]["ResourcePressureInfo"]:
-      i, r = d["InstructionIndex"], d["ResourceIndex"]
-      if i>len(instrs)-1: continue
-      instrs[i]["segs"][r] = instrs[i]["segs"].get(r, 0)+d["ResourceUsage"]
-    # rescale segment width to 0-100
-    if instrs:
-      hi = max([sum(ins["segs"].values()) for ins in instrs])
-      for n in instrs: n["segs"] = {k:{"width":v/hi*100, "value":v} for k,v in n["segs"].items()}
-    return json.dumps({"rows":instrs, "cols":["Opcode", "Latency", "HW Resources"], "segments":data["TargetInfo"]["Resources"]}).encode()
-  return json.dumps({"src":disasm_str}).encode()
+    ret = get_llvm_mca(disasm_str, mtriple, mcpu)
+  else: ret = {"src":disasm_str}
+  return json.dumps(ret).encode()
 
 # ** HTTP server