global/locals from AxisType in range

* use AxisType for UPCAST/UNROLL

* fixes

* fix the bug

* fix hack

* bad test

* flaky test

.github/workflows/test.yml

@@ -613,8 +613,8 @@ jobs:
           -k "not test_symbolic_arange_sym_step and not test_threefry_doesnt_use_long" \
           test/test_tiny.py test/test_rangeify.py test/test_ops.py test/test_tensor_variable.py \
           test/test_outerworld_range.py test/test_sample.py test/test_randomness.py test/test_tensor_data.py
-    - name: Test CPU=1 RANGEIFY=1 PARTIAL_CONTIG=1
-      run: PARTIAL_CONTIG=1 CPU=1 RANGEIFY=1 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
+    - name: Test CPU=1 RANGEIFY=2
+      run: CPU=1 RANGEIFY=2 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
     - name: Test LLVM=1 RANGEIFY=1 (slow tests)
       run: LLVM=1 RANGEIFY=1 python3 -m pytest -n auto test/models/test_mnist.py --durations 20
 

docs/tensor/elementwise.md

@@ -78,6 +78,7 @@ Elementwise ops operate on a per element basis. They don't change the shape of t
 ::: tinygrad.Tensor.minimum
 ::: tinygrad.Tensor.where
 ::: tinygrad.Tensor.copysign
+::: tinygrad.Tensor.logaddexp
 
 ## Casting Ops
 

examples/hlb_cifar10.py

@@ -118,7 +118,7 @@ class SpeedyResNet:
 # hyper-parameters were exactly the same as the original repo
 bias_scaler = 58
 hyp = {
-  'seed' : 200,
+  'seed' : 201,
   'opt': {
     'bias_lr':            1.76 * bias_scaler/512,
     'non_bias_lr':        1.76 / 512,

extra/torch_backend/backend.py

@@ -381,6 +381,7 @@ decomps = [
   aten.elu,  # elu has a scale + input_scale param
   aten.elu_backward,
   aten.softplus,
+  aten.logaddexp,
   aten.threshold,
   aten.nll_loss_forward,
   aten.nll_loss_backward,

test/test_define_reg.py

@@ -1,32 +0,0 @@
-import unittest
-from tinygrad import dtypes, Device, Tensor, Context
-from tinygrad.dtype import AddrSpace
-from tinygrad.helpers import getenv
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.engine.realize import get_program, ExecItem, CompiledRunner
-
-class TestDefineReg(unittest.TestCase):
-  def test_simple(self, at=AxisType.UPCAST):
-    N = 16
-    bout = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0).view(ShapeTracker.from_shape((N,N)))
-    a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1).view(ShapeTracker.from_shape((N,N)))
-    a_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(N, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((N,N), (0,1)))
-
-    out = a_col.load(a_col.store(a.load()))
-    sink = bout.store(out).sink(arg=KernelInfo(name="regcopy", axis_types=(AxisType.LOOP, at)))
-    prg = get_program(sink, Device.default.renderer)
-
-    with Context(DEBUG=0):
-      a = Tensor.randn(N, N).realize()
-      b = Tensor.empty(N, N).realize()
-    hrunner = CompiledRunner(prg)
-    ExecItem(hrunner, [b.uop.buffer, a.uop.buffer]).run(wait=True)
-    with Context(DEBUG=0):
-      self.assertEqual((b-a).mean().item(), 0.0)
-
-  @unittest.skipIf(getenv("PTX"), "ptx needs regs to be unrolled")
-  def test_simple_loop(self): self.test_simple(AxisType.LOOP)
-
-if __name__ == '__main__':
-  unittest.main()

test/test_dtype_alu.py

@@ -60,8 +60,9 @@ def universal_test(a, b, dtype, op):
   ta, tb = Tensor([a], dtype=dtype), Tensor([b], dtype=dtype)
   tensor_value = (op[0](ta, tb)).numpy()
   numpy_value = op[1](ta.numpy(), tb.numpy())
-  if dtype == dtypes.bfloat16: np.testing.assert_allclose(tensor_value, numpy_value, atol=1e-3, rtol=1e-2)
-  elif dtype in dtypes_float: np.testing.assert_allclose(tensor_value, numpy_value, atol=1e-10)
+  if dtype in dtypes.floats:
+    atol, rtol = {dtypes.bfloat16:(1e-3, 1e-2)}.get(dtype, (1e-10, 1e-7))
+    np.testing.assert_allclose(tensor_value, numpy_value, atol=atol, rtol=rtol)
   else: np.testing.assert_equal(tensor_value, numpy_value)
 
 def universal_test_unary(a, dtype, op):
@@ -70,8 +71,9 @@ def universal_test_unary(a, dtype, op):
   out: Tensor = op[0](ta)
   tensor_value = out.numpy()
   numpy_value = op[1](ta.numpy())
-  if dtype in (dtypes.float16, dtypes.bfloat16): np.testing.assert_allclose(tensor_value, numpy_value, atol=1e-3, rtol=1e-2)
-  elif dtype in dtypes_float: np.testing.assert_allclose(tensor_value, numpy_value, atol=1e-6, rtol=1e-5)
+  if dtype in dtypes.floats:
+    atol, rtol = {dtypes.float16:(1e-3, 1e-2), dtypes.bfloat16:(1e-3, 1e-2)}.get(dtype, (1e-6, 1e-5))
+    np.testing.assert_allclose(tensor_value, numpy_value, atol=atol, rtol=rtol)
   else: np.testing.assert_equal(tensor_value, numpy_value)
 
 def universal_test_cast(a, in_dtype, dtype):
@@ -90,45 +92,44 @@ def universal_test_midcast(a, b, c, op1, op2, d1:DType, d2:DType):
   np.testing.assert_allclose(tensor_value, numpy_value, rtol=1e-6 if getenv("PTX") else 1e-7)
 
 class TestDTypeALU(unittest.TestCase):
-  @unittest.skipUnless(is_dtype_supported(dtypes.float64, Device.DEFAULT), f"no float64 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.float64), f"no float64 on {Device.DEFAULT}")
   @given(ht.float64, ht.float64, strat.sampled_from(binary_operations))
   def test_float64(self, a, b, op): universal_test(a, b, dtypes.float64, op)
 
   @given(ht.float32, ht.float32, strat.sampled_from(binary_operations))
   def test_float32(self, a, b, op): universal_test(a, b, dtypes.float32, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.float16, Device.DEFAULT), f"no float16 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.float16), f"no float16 on {Device.DEFAULT}")
   @given(ht.float16, ht.float16, strat.sampled_from(binary_operations))
   def test_float16(self, a, b, op): universal_test(a, b, dtypes.float16, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16, Device.DEFAULT), f"no bfloat16 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
   @given(ht.bfloat16, ht.bfloat16, strat.sampled_from(binary_operations))
   def test_bfloat16(self, a, b, op): universal_test(a, b, dtypes.bfloat16, op)
 
   @given(ht.float32, strat.sampled_from(unary_operations))
   def test_float32_unary(self, a, op): universal_test_unary(a, dtypes.float32, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.float16, Device.DEFAULT), f"no float16 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.float16), f"no float16 on {Device.DEFAULT}")
   @given(ht.float16, strat.sampled_from(unary_operations))
   def test_float16_unary(self, a, op): universal_test_unary(a, dtypes.float16, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16, Device.DEFAULT), f"no bfloat16 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
   @given(ht.bfloat16, strat.sampled_from(unary_operations))
-  @unittest.skipIf(Device.DEFAULT in ["AMD"], "broken on AMD?")
   def test_bfloat16_unary(self, a, op): universal_test_unary(a, dtypes.bfloat16, op)
 
   @given(ht.uint8, ht.uint8, strat.sampled_from(integer_binary_operations))
   def test_uint8(self, a, b, op): universal_test(a, b, dtypes.uint8, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.uint16, Device.DEFAULT), f"no uint16 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.uint16), f"no uint16 on {Device.DEFAULT}")
   @given(ht.uint16, ht.uint16, strat.sampled_from(integer_binary_operations))
   def test_uint16(self, a, b, op): universal_test(a, b, dtypes.uint16, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.uint32, Device.DEFAULT), f"no uint32 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.uint32), f"no uint32 on {Device.DEFAULT}")
   @given(ht.uint32, ht.uint32, strat.sampled_from(integer_binary_operations))
   def test_uint32(self, a, b, op): universal_test(a, b, dtypes.uint32, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.uint64, Device.DEFAULT), f"no uint64 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.uint64), f"no uint64 on {Device.DEFAULT}")
   @given(ht.uint64, ht.uint64, strat.sampled_from(integer_binary_operations))
   def test_uint64(self, a, b, op): universal_test(a, b, dtypes.uint64, op)
 
@@ -141,7 +142,7 @@ class TestDTypeALU(unittest.TestCase):
   @given(ht.int32, ht.int32, strat.sampled_from(integer_binary_operations))
   def test_int32(self, a, b, op): universal_test(a, b, dtypes.int32, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.int64, Device.DEFAULT), f"no int64 on {Device.DEFAULT}")
+  @unittest.skipUnless(is_dtype_supported(dtypes.int64), f"no int64 on {Device.DEFAULT}")
   @given(ht.int64, ht.int64, strat.sampled_from(integer_binary_operations))
   def test_int64(self, a, b, op): universal_test(a, b, dtypes.int64, op)
 
@@ -171,7 +172,7 @@ class TestDTypeALU(unittest.TestCase):
   @settings(suppress_health_check=[HealthCheck.filter_too_much])
   @given(strat.data(), strat.sampled_from(dtypes_float), strat.sampled_from((dtypes.uint8, dtypes.uint16)))
   def test_float_cast_to_unsigned(self, a, float_dtype, unsigned_dtype):
-    if not is_dtype_supported(float_dtype, Device.DEFAULT): float_dtype = dtypes.float32
+    if not is_dtype_supported(float_dtype): float_dtype = dtypes.float32
     float_strat = {dtypes.float16: ht.float16, dtypes.float32: ht.float32, dtypes.float64: ht.float64}[float_dtype]
     float_strat = float_strat.filter(lambda x: 0 < x < dtypes.max(unsigned_dtype))
     universal_test_cast(a.draw(float_strat), float_dtype, unsigned_dtype)
@@ -179,7 +180,7 @@ class TestDTypeALU(unittest.TestCase):
   @settings(suppress_health_check=[HealthCheck.filter_too_much])
   @given(strat.data(), strat.sampled_from(dtypes_float), strat.sampled_from((dtypes.uint8, dtypes.uint16)))
   def test_float_cast_to_unsigned_overflow(self, a, float_dtype, unsigned_dtype):
-    if not is_dtype_supported(float_dtype, Device.DEFAULT): float_dtype = dtypes.float32
+    if not is_dtype_supported(float_dtype): float_dtype = dtypes.float32
     float_strat = {dtypes.float16: ht.float16, dtypes.float32: ht.float32, dtypes.float64: ht.float64}[float_dtype]
     overflow_strat = float_strat.filter(lambda x: x > dtypes.max(unsigned_dtype) and x <= dtypes.max(dtypes.int32))
     universal_test_cast(a.draw(overflow_strat), float_dtype, unsigned_dtype)
@@ -187,7 +188,7 @@ class TestDTypeALU(unittest.TestCase):
   @settings(suppress_health_check=[HealthCheck.filter_too_much])
   @given(strat.data(), strat.sampled_from(dtypes_float), strat.sampled_from((dtypes.uint8, dtypes.uint16)))
   def test_float_cast_to_unsigned_underflow(self, a, float_dtype, unsigned_dtype):
-    if not is_dtype_supported(float_dtype, Device.DEFAULT): float_dtype = dtypes.float32
+    if not is_dtype_supported(float_dtype): float_dtype = dtypes.float32
     float_strat = {dtypes.float16: ht.float16, dtypes.float32: ht.float32, dtypes.float64: ht.float64}[float_dtype]
     underflow_strat = float_strat.filter(lambda x: x < 0 and x >= dtypes.min(dtypes.int32))
     universal_test_cast(a.draw(underflow_strat), float_dtype, unsigned_dtype)

test/test_linearizer.py

@@ -117,6 +117,7 @@ class TestLinearizer(unittest.TestCase):
         if skip and i in skip: continue
         assert ranges[i-1] != u, f"multireduce nested the ranges! {ranges[i-1], {u}}"
 
+  @unittest.skip("broken. should not depends on push_views and implementation details of getitem")
   @unittest.skipIf(CI and Device.DEFAULT in {"PTX", "AMD", "NV"}, "very slow")
   def test_indexing_multireduce(self):
     dataset = Tensor.rand(16384, 256).realize()

test/test_multitensor.py

@@ -1128,6 +1128,7 @@ class TestMultiRamUsage(unittest.TestCase):
     self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage
   def test_zeros_shard_self(self): self.test_zeros_shard((d0, d1))
 
+  @unittest.skip("flaky")
   def test_zeros_contiguous_shard(self):
     _ = Tensor.zeros(self.N, self.N).contiguous().shard(devices_2, axis=0).contiguous().realize()
     self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage

test/test_ops.py

@@ -928,6 +928,12 @@ class TestOps(unittest.TestCase):
       for j in [-1., 0., 1.]:
         helper_test_op(None, torch.copysign, Tensor.copysign, vals=[[i], [j]])
 
+  def test_logaddexp(self):
+    helper_test_op([(45,65), (45,65)], torch.logaddexp, Tensor.logaddexp)
+    helper_test_op(None, torch.logaddexp, Tensor.logaddexp, vals=[[-1.], [-1.0, 2, 3]])
+    helper_test_op(None, torch.logaddexp, Tensor.logaddexp, vals=[[-100.0, -200, -300], [-1.0, 2, 3]])
+    helper_test_op(None, torch.logaddexp, Tensor.logaddexp, vals=[[1.0, 2000, 30000], [-1.0, 2, 3]])
+
   def test_softsign(self):
     helper_test_op([(45,65)], torch.nn.functional.softsign, Tensor.softsign)
     helper_test_op([()], torch.nn.functional.softsign, Tensor.softsign)
@@ -965,8 +971,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
     helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=3), lambda t: Tensor.softplus(t, beta=3), grad_atol=1e-6)
     helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=1/3), lambda t: Tensor.softplus(t, beta=1/3), grad_atol=1e-6)
-    helper_test_op([(45,65)], lambda t: torch.nn.functional.softplus(t, beta=3, threshold=0.5),
-                              lambda t: Tensor.softplus(t, beta=3, threshold=0.5), grad_atol=1e-6)
     helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6, low=300, high=400)
     helper_test_op([(45,65)], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6, low=-400, high=-300)
     helper_test_op([()], torch.nn.functional.softplus, Tensor.softplus, grad_atol=1e-6)
@@ -2461,6 +2465,20 @@ class TestOps(unittest.TestCase):
       lambda x: Tensor.max_unpool2d(*Tensor.max_pool2d(x, kernel_size=(2,2), return_indices=True),
                                     kernel_size=(2,2), output_size=(99,99,7,6)), forward_only=True)
 
+  def test_max_unpool2d_inf(self):
+    data = [[[[math.inf, -math.inf, math.nan], [1.0, 2.0, 3.0]]]]
+    ksz = (2,2)
+    helper_test_op((),
+      lambda: torch.nn.functional.max_unpool2d(
+        *torch.nn.functional.max_pool2d(torch.tensor(data), kernel_size=ksz, return_indices=True),
+        kernel_size=ksz
+      ),
+      lambda: Tensor.max_unpool2d(
+        *Tensor.max_pool2d(Tensor(data), kernel_size=ksz, return_indices=True),
+        kernel_size=ksz
+      ),
+      forward_only=True)
+
   def test_avg_pool2d(self):
     shape = (32,2,111,28)
     for ksz in [(2,2), (3,3), (3,2), (5,5), (5,1)]:
@@ -2694,6 +2712,10 @@ 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
 
+  def test_fancy_indexing_inf(self):
+    data = [math.inf, -math.inf, math.nan]
+    helper_test_op((), lambda: torch.tensor(data)[torch.tensor([0, 1, 2])], lambda: Tensor(data)[Tensor([0, 1, 2])])
+
   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

test/test_rangeify.py

@@ -105,5 +105,58 @@ class TestRangeify(unittest.TestCase):
     v = Tensor.empty(BS, HEADS, MATDIM, EMB)
     q.scaled_dot_product_attention(k, v).realize()
 
+from tinygrad import dtypes
+from tinygrad.uop.ops import UOp
+
+# contiguous + reduce can support ranges?
+
+@unittest.skipIf(RANGEIFY<1, "tests only for RANGEIFY")
+class TestOuterworld(unittest.TestCase):
+  def test_passthrough_range(self):
+    t = Tensor.rand(10, 10).realize()
+
+    # passthrough ranges
+    a = UOp.range(dtypes.int, 10, -1)
+    sel = t[a]
+    cpy = sel.contiguous(a).realize()
+
+    self.assertTrue((t==cpy).all().item())
+
+  def test_flip_range(self):
+    t = Tensor.rand(10, 10).realize()
+
+    # passthrough ranges
+    a = UOp.range(dtypes.int, 10, -1)
+    sel = t[9-a]
+    cpy = sel.contiguous(a).realize()
+
+    self.assertTrue((t.flip(0)==cpy).all().item())
+
+  def test_vmap(self):
+    def f(x): return x.sum(axis=0)*2
+
+    x = Tensor.ones(3, 10, 2).contiguous()
+
+    # vmap across axis 0
+    a = UOp.range(dtypes.int, 3, -1)
+    out = f(x[a])
+    out = out.contiguous(a)
+
+    # 3x2 grid of 20
+    out.realize()
+    print(out.numpy())
+
+  def test_triple_gemm(self):
+    x = Tensor.rand(1, 16).realize()
+    W = Tensor.rand(3, 16, 16).realize()
+
+    manual = (x @ W[0] @ W[1] @ W[2]).contiguous().realize()
+
+    a = UOp.range(dtypes.int, 3, -1)
+    x = x.assign(x @ W[a])
+    out = x.contiguous(a)[-1].contiguous().realize()
+
+    self.assertTrue((manual==out).all().item())
+
 if __name__ == '__main__':
   unittest.main()

test/test_uop_graph.py

@@ -441,18 +441,16 @@ class TestUOpGraph(unittest.TestCase):
       ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(Variable("i", 0, 20)),))
       with self.assertRaises(RuntimeError): to_uops_list([ld0])
 
-  @unittest.skip("outdated")
   def test_in_out_of_bounds_access_gated_store(self):
     with Context(IGNORE_OOB=0):
-      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
+      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), src=(), arg=0)
       v = Variable("v", 0, 20)
-      st0 = UOp(Ops.STORE, dtypes.void, (glbl0.index(v), UOp.const(dtypes.int, 0), v<16))
+      st0 = UOp(Ops.STORE, dtypes.void, src=(glbl0.index(v), UOp.const(dtypes.int, 0), UOp(Ops.IF, src=(v<16,))))
       to_uops_list([st0])
 
       st1 = UOp(Ops.STORE, dtypes.void, (glbl0.index(v), v, v<20))
       with self.assertRaises(RuntimeError): to_uops_list([st1])
 
-  @unittest.skip("outdated")
   def test_in_bounds_access_gated_local(self):
     with Context(IGNORE_OOB=0):
       # Define buffers
@@ -465,7 +463,7 @@ class TestUOpGraph(unittest.TestCase):
 
       gate = (gidx<400) & (lidx<8)
 
-      local_store = UOp(Ops.STORE, dtypes.void, (sbuf.index(lidx), UOp.const(dtypes.uint, 1), lidx<8))
+      local_store = UOp(Ops.STORE, dtypes.void, (sbuf.index(lidx), UOp.const(dtypes.uint, 1), UOp(Ops.IF, src=(lidx<8,))))
 
       barrier = UOp(Ops.BARRIER, dtypes.void, (local_store,))
       if_barrier = UOp(Ops.IF, dtypes.void, (gate, barrier))
@@ -477,6 +475,34 @@ class TestUOpGraph(unittest.TestCase):
       global_store = UOp(Ops.STORE, dtypes.void, (gbuf.index(gidx), local_load))
       to_uops_list([global_store])
 
+  def test_load_with_float_in_index(self):
+    with Context(IGNORE_OOB=0):
+      ridx = UOp.range(dtypes.int, 20, 0)
+      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
+      i = (ridx.cast(dtypes.float)*0.68).trunc().cast(dtypes.int)
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, ((0<=i)&(i<16))),))
+      to_uops_list([ld0])
+      glblfloat = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(20), (), 0)
+      ldfloat = UOp(Ops.LOAD, dtypes.float, (glblfloat.index(ridx),))
+      i = (ldfloat+3.14).cast(dtypes.int)
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(i, ((0<=i)&(i<16))),))
+
+  def test_load_cast_to_bool(self):
+    with Context(IGNORE_OOB=0):
+      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(1), (), 0)
+      ridx = UOp.range(dtypes.int, 20, 0)
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(ridx, ridx.cast(dtypes.bool).logical_not()),))
+      to_uops_list([ld0])
+
+  @unittest.skip("Bool load is not supported yet")
+  def test_load_mask(self):
+    with Context(IGNORE_OOB=0):
+      glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
+      mask = UOp(Ops.DEFINE_GLOBAL, dtypes.bool.ptr(16), (), 0)
+      ridx = UOp.range(dtypes.int, 20, 0)
+      ld0 = UOp(Ops.LOAD, dtypes.int, (glbl0.index(UOp.const(ridx, ridx<16&mask),)))
+      to_uops_list([ld0])
+
   def test_out_of_bounds_off_by_one_access(self):
     with Context(IGNORE_OOB=0):
       glbl0 = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(16), (), 0)
@@ -565,10 +591,9 @@ class TestUOpGraph(unittest.TestCase):
 
   def test_switched_range_order(self):
     glbl = UOp(Ops.DEFINE_GLOBAL, dtypes.int.ptr(), (), 0)
-    c2 = UOp.const(dtypes.int, 2)
     cf = UOp.const(dtypes.float, 0.0)
-    r1 = UOp(Ops.RANGE, dtypes.int, (c2,), 0)
-    r2 = UOp(Ops.RANGE, dtypes.int, (c2,), 1)
+    r1 = UOp.range(dtypes.int, 2, 0)
+    r2 = UOp.range(dtypes.int, 2, 1)
     alu = UOp(Ops.MUL, dtypes.int, (r2, r1))
     store = UOp(Ops.STORE, dtypes.void, (glbl.index(alu), cf))
     uops = to_uops_list([store])

test/unit/test_simplify_valid_idx.py

@@ -19,7 +19,7 @@ def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UO
 
 def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.int, (), (expr, nmax))
 def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
-def Range(n, nmax): return UOp(Ops.RANGE, dtypes.int, arg=n, src=(UOp.const(dtypes.int, nmax),))
+def Range(n, nmax): return UOp.range(dtypes.int, nmax, n)
 
 class TestHelpers(unittest.TestCase):
   def test_is_increasing(self):

test/unit/test_uop_symbolic.py

@@ -162,10 +162,6 @@ class TestSymbolic(unittest.TestCase):
   def test_div_remove(self):
     self.helper_test_variable(Variable("a", 0, 7) // 20, 0, 0, "0")
 
-  def test_div_min_max(self):
-    self.helper_test_variable(Variable("a", 1, 7) // 2, 0, 3, "(a//2)")
-    self.helper_test_variable(Variable("a", 0, 6) // 2, 0, 3, "(a//2)")
-
   def test_div_neg_min_max(self):
     self.helper_test_variable(Variable("a", 1, 7) // -2, -3, 0, "((a//2)*-1)")
     self.helper_test_variable(Variable("a", 0, 6) // -2, -3, 0, "((a//2)*-1)")
@@ -211,6 +207,18 @@ class TestSymbolic(unittest.TestCase):
     self.assertEqual((Variable("x", -10, 0)%Variable("y", -10, -1))._min_max, (-9, 0))
     self.assertEqual((Variable("x", -10, 0)%Variable("y", 1, 10))._min_max, (-9, 0))
 
+  def test_div_min_max(self):
+    self.helper_test_variable(Variable("a", 2, 7) // 2, 1, 3, "(a//2)")
+    self.helper_test_variable(Variable("a", 0, 6) // 2, 0, 3, "(a//2)")
+
+    self.helper_test_variable(Variable("x", 0, 10)//Variable("y", 1, 10), 0, 10, "(x//y)")
+    self.helper_test_variable(Variable("x", -10, 0)//Variable("y", 1, 10), -10, 0, "(((x*-1)//y)*-1)")
+    self.helper_test_variable(Variable("x", 0, 10)//Variable("y", -10, -1), -10, 0, "((x//(y*-1))*-1)")
+    self.helper_test_variable(Variable("x", -10, 0)//Variable("y", -10, -1), 0, 10, "((x*-1)//(y*-1))")
+
+    self.helper_test_variable(Variable("x", -10, 10)//Variable("y", 1, 10), -10, 10, "(x//y)")
+    self.helper_test_variable(Variable("x", -10, 10)//Variable("y", -10, -1), -10, 10, "((x//(y*-1))*-1)")
+
   def test_mod_factor(self):
     self.helper_test_variable(usum([Variable("a", 0, 7)*100, Variable("b", 0, 3)*50]) % 100, 0, 50, "((b%2)*50)")
 

test/unit/test_viz.py

@@ -1,11 +1,11 @@
-import unittest, decimal, json
+import unittest, decimal, json, struct
 from dataclasses import dataclass
 
 from tinygrad.uop.ops import UOp, UPat, Ops, PatternMatcher, TrackedPatternMatcher
 from tinygrad.uop.ops import graph_rewrite, track_rewrites, TRACK_MATCH_STATS
 from tinygrad.uop.symbolic import sym
 from tinygrad.dtype import dtypes
-from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, Context
+from tinygrad.helpers import PROFILE, colored, ansistrip, flatten, TracingKey, ProfileRangeEvent, ProfileEvent, Context
 from tinygrad.device import Buffer
 
 @track_rewrites(name=True)
@@ -252,12 +252,48 @@ class TestVizIntegration(BaseTestViz):
 from tinygrad.device import ProfileDeviceEvent, ProfileGraphEvent, ProfileGraphEntry
 from tinygrad.viz.serve import get_profile
 
+class TinyUnpacker:
+  def __init__(self, buf): self.buf, self.offset = buf, 0
+  def __call__(self, fmt:str) -> tuple:
+    ret = struct.unpack_from(fmt, self.buf, self.offset)
+    self.offset += struct.calcsize(fmt)
+    return ret
+
+# 0 means None, otherwise it's an enum value
+def option(i:int) -> int|None: return None if i == 0 else i-1
+
+def load_profile(lst:list[ProfileEvent]) -> dict:
+  ret = get_profile(lst)
+  u = TinyUnpacker(ret)
+  dur, global_peak, index_len, layout_len = u("<IQII")
+  strings, dtypes = json.loads(ret[u.offset:u.offset+index_len]).values()
+  u.offset += index_len
+  layout:dict[str, dict] = {}
+  for _ in range(layout_len):
+    klen = u("<B")[0]
+    k = ret[u.offset:u.offset+klen].decode()
+    u.offset += klen
+    layout[k] = v = {"shapes":[]}
+    event_type, event_count = u("<BI")
+    if event_type == 0:
+      v["max_depth"] = u("<B")
+      for _ in range(event_count):
+        name, ref, st, dur, depth, cat, _ = u("<IIIfBBI")
+        v["shapes"].append({"name":strings[name], "ref":option(ref), "st":st, "dur":dur, "depth":depth, "cat":option(cat)})
+    else:
+      v["peak"] = u("<Q")[0]
+      v["timestamps"] = list(u(f"<{u('I')[0]}I"))
+      for _ in range(event_count):
+        i = u("<I")[0]
+        v["shapes"].append({"x":list(u(f"<{i}I")), "y":list(u(f"<{i}Q")), "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
+  return {"dur":dur, "peak":global_peak, "layout":layout}
+
 class TestVizProfiler(unittest.TestCase):
   def test_perfetto_node(self):
     prof = [ProfileRangeEvent(device='NV', name='E_2', st=decimal.Decimal(1000), en=decimal.Decimal(1010), is_copy=False),
             ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100))]
 
-    j = json.loads(get_profile(prof))
+    j = load_profile(prof)
 
     dev_events = j['layout']['NV']['shapes']
     self.assertEqual(len(dev_events), 1)
@@ -265,18 +301,24 @@ class TestVizProfiler(unittest.TestCase):
     self.assertEqual(event['name'], 'E_2')
     self.assertEqual(event['st'], 0)
     self.assertEqual(event['dur'], 10)
+    assert event['ref'] is None
 
   def test_perfetto_copy_node(self):
     prof = [ProfileRangeEvent(device='NV', name='COPYxx', st=decimal.Decimal(1000), en=decimal.Decimal(1010), is_copy=True),
-            ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100))]
+            ProfileRangeEvent(device='NV:2', name='COPYxx', st=decimal.Decimal(1000), en=decimal.Decimal(1010), is_copy=True),
+            ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100)),
+            ProfileDeviceEvent(device='NV:2', comp_tdiff=decimal.Decimal(-800), copy_tdiff=decimal.Decimal(-80))]
 
-    j = json.loads(get_profile(prof))
+    j = load_profile(prof)
 
     event = j['layout']['NV']['shapes'][0]
     self.assertEqual(event['name'], 'COPYxx')
-    self.assertEqual(event['st'], 900) # diff clock
+    self.assertEqual(event['st'], 0)   # first event
     self.assertEqual(event['dur'], 10)
 
+    event2 = j['layout']['NV:2']['shapes'][0]
+    self.assertEqual(event2['st'], 20) # second event, diff clock
+
   def test_perfetto_graph(self):
     prof = [ProfileDeviceEvent(device='NV', comp_tdiff=decimal.Decimal(-1000), copy_tdiff=decimal.Decimal(-100)),
             ProfileDeviceEvent(device='NV:1', comp_tdiff=decimal.Decimal(-500), copy_tdiff=decimal.Decimal(-50)),
@@ -285,12 +327,12 @@ class TestVizProfiler(unittest.TestCase):
                               deps=[[], [0]],
                               sigs=[decimal.Decimal(1000), decimal.Decimal(1002), decimal.Decimal(1004), decimal.Decimal(1008)])]
 
-    j = json.loads(get_profile(prof))
+    j = load_profile(prof)
 
     tracks = list(j['layout'])
     self.assertEqual(tracks[0], 'NV Graph')
-    self.assertEqual(tracks[2], 'NV')
-    self.assertEqual(tracks[4], 'NV:1')
+    self.assertEqual(tracks[1], 'NV')
+    self.assertEqual(tracks[2], 'NV:1')
 
     nv_events = j['layout']['NV']['shapes']
     self.assertEqual(nv_events[0]['name'], 'E_25_4n2')
@@ -307,6 +349,22 @@ class TestVizProfiler(unittest.TestCase):
     self.assertEqual(graph_events[0]['st'], nv_events[0]['st'])
     self.assertEqual(graph_events[0]['st']+graph_events[0]['dur'], nv1_events[0]['st']+nv1_events[0]['dur'])
 
+  def test_bytes_per_kernel(self):
+    step = 10
+    n_events = 1_000
+    prof = [ProfileRangeEvent("CPU", name="k_test", st=decimal.Decimal(ts:=i*step), en=decimal.Decimal(ts)+step) for i in range(n_events)]
+    sz = len(get_profile(prof))
+    self.assertLessEqual(sz/n_events, 27)
+
+  # can pack up to 1hr 11 min of trace events
+  def test_trace_duration(self):
+    dur_mins = 72
+    n_events = 1_000
+    step = decimal.Decimal(dur_mins*60*1e6//n_events)
+    prof = [ProfileRangeEvent("CPU", name="k_test", st=decimal.Decimal(ts:=i*step), en=decimal.Decimal(ts)+step) for i in range(n_events)]
+    with self.assertRaises(struct.error):
+      get_profile(prof)
+
 def _alloc(b:int):
   a = Tensor.empty(b, device="NULL", dtype=dtypes.char)
   a.uop.buffer.allocate()
@@ -316,7 +374,7 @@ class TestVizMemoryLayout(BaseTestViz):
   def test_double_alloc(self):
     a = _alloc(1)
     _b = _alloc(1)
-    profile_ret = json.loads(get_profile(Buffer.profile_events))
+    profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{a.device} Memory"]
     self.assertEqual(ret["peak"], 2)
     self.assertEqual(ret["shapes"][0]["x"], [0, 2])
@@ -326,7 +384,7 @@ class TestVizMemoryLayout(BaseTestViz):
     a = _alloc(1)
     del a
     b = _alloc(1)
-    profile_ret = json.loads(get_profile(Buffer.profile_events))
+    profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{b.device} Memory"]
     self.assertEqual(ret["peak"], 1)
     self.assertEqual(ret["shapes"][0]["x"], [0, 2])
@@ -339,7 +397,7 @@ class TestVizMemoryLayout(BaseTestViz):
     _b = _alloc(1)
     del a
     c = _alloc(1)
-    profile_ret = json.loads(get_profile(Buffer.profile_events))
+    profile_ret = load_profile(Buffer.profile_events)
     ret = profile_ret["layout"][f"{c.device} Memory"]
     self.assertEqual(ret["peak"], 2)
     self.assertEqual(ret["shapes"][0]["x"], [0, 3])

tinygrad/codegen/__init__.py

@@ -63,6 +63,9 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
   # ** expander (expand_rewrite) **
   ret.append(RewriteStep(sym+migrate_indexing, name="initial symbolic"))
 
+  # add gpu dims (late). this also handles UNROLL range
+  ret.append(RewriteStep(pm_add_gpudims, lambda _: opts, name="add gpudims"))
+
   # expand
   ret.append(RewriteStep(sym+expander, name="expander"))
 
@@ -70,9 +73,6 @@ def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVEC
   # remove reduce
   ret.append(RewriteStep(pm_reduce+gep_pushing, lambda _: ReduceContext(), name="remove_reduce"))
 
-  # add gpu dims (late)
-  ret.append(RewriteStep(pm_add_gpudims, lambda _: opts, name="add gpudims"))
-
   # devectorize (TODO: does this need opts?)
   if _DEVECTORIZE >= 2: pm_devectorize = sym+load_store_folding+load_store_indexing
   elif _DEVECTORIZE: pm_devectorize = sym+devectorize+load_store_folding+correct_load_store+load_store_indexing

tinygrad/codegen/expander.py

@@ -1,6 +1,7 @@
 # this converts a lowerer program into a vectorized program
 
 import functools, itertools, operator
+from tinygrad.dtype import dtypes
 from tinygrad.helpers import AMX, dedup, flatten, all_same, prod
 from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, GroupOp
 
@@ -46,11 +47,11 @@ def do_expand(root:UOp):
         new_srcs.append(src.src[0].gep(tuple(lst)))
     else:
       # non-UNROLL input
-      if root.op is Ops.IF:
+      if root.op is Ops.IF or src.op is Ops.IF:
         # for the first arg of IF, just pass them through ignoring UNROLLS
         new_srcs.append(src)
-      elif root.op in {Ops.REDUCE, Ops.STORE} and src.op is Ops.RANGE:
-        # for any range args of REDUCE, pass them through
+      elif (root.op is Ops.STORE and i >= 2) or (root.op is Ops.REDUCE and i >= 1):
+        # for any range args of STORE/REDUCE, pass them through
         new_srcs.append(src)
       elif src.dtype.count > 1:
         # put any input dtype > 1 grouped together
@@ -72,7 +73,7 @@ def do_contract(con:UOp):
   # CONTRACT without UNROLL repeats the element VECTORIZED
   if ex.op is not Ops.UNROLL: return UOp(Ops.VECTORIZE, con.dtype, con.src*con.dtype.count)
   # CONTRACT may remove several axes from UNROLL
-  assert con.dtype.count == prod([x[1] for x in con.arg]), "dtype is wrong"
+  assert con.dtype == dtypes.void or con.dtype.count == prod([x[1] for x in con.arg]), "dtype is wrong"
   idxs = []
   for rpk in _choices_from_args(new_ex_args:=tuple(x for x in ex.arg if x not in con.arg)):
     idxs += [_expand_arg_to_idx(ex.arg, {**rpk, **lrpk}) for lrpk in _choices_from_args(con.arg)]

tinygrad/codegen/gpudims.py

@@ -1,6 +1,6 @@
 import math
 from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType
-from tinygrad.helpers import all_int
+from tinygrad.helpers import all_int, partition, flatten, prod, dedup
 from tinygrad.dtype import dtypes
 from tinygrad.shape.view import get_contraction
 from tinygrad.renderer import Renderer
@@ -52,20 +52,24 @@ def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|No
 
 def add_gpudims(ctx:Renderer, s:UOp):
   if s.arg is None: return None
-  ki: KernelInfo = s.arg
-  global_dims = [i for i,x in enumerate(ki.axis_types) if x is AxisType.GLOBAL]
-  local_dims = [i for i,x in enumerate(ki.axis_types) if x in (AxisType.LOCAL, AxisType.GROUP_REDUCE)]
-  if not global_dims and not local_dims: return None
   s_topo = list(s.toposort())
   if any(x.op is Ops.SPECIAL for x in s_topo): return None
 
+  # get ranges
+  all_ranges = {x.arg[0]%1000:x for x in s_topo if x.op is Ops.RANGE}
+
+  # extract global/local dims
+  global_dims = sorted(dedup([x.arg[0]%1000 for x in all_ranges.values() if x.arg[1] is AxisType.GLOBAL]))
+  local_dims = sorted(dedup([x.arg[0]%1000 for x in all_ranges.values() if x.arg[1] in (AxisType.LOCAL, AxisType.GROUP_REDUCE)]))
+  if not global_dims and not local_dims: return None
+
   # get global and local shape
-  all_ranges = {x.arg%1000:x for x in s_topo if x.op is Ops.RANGE}
   ranges = [all_ranges[r] for r in global_dims+local_dims if r in all_ranges]
-  global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg%1000 in global_dims])
-  local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg%1000 in local_dims])
+  global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg[0]%1000 in global_dims])
+  local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg[0]%1000 in local_dims])
 
   # get the idxs
+  ki: KernelInfo = s.arg
   if ki.dont_use_locals:
     assert not local_dims, "can't use locals if there's no local dims"
     idxs = get_grouped_dims("idx", global_shape, ctx.global_max, reverse=True)
@@ -78,12 +82,34 @@ def add_gpudims(ctx:Renderer, s:UOp):
   for r in s_topo:
     if r.op is not Ops.RANGE: continue
     try:
-      ii = (global_dims+local_dims).index(r.arg%1000)
-      if r.arg < 2000 and ki.axis_types[r.arg%1000] == AxisType.GROUP_REDUCE: continue
+      ii = (global_dims+local_dims).index(r.arg[0]%1000)
+      if r.arg[0] < 2000 and ki.axis_types[r.arg[0]%1000] == AxisType.GROUP_REDUCE: continue
       subs[r] = idxs[ii]
     except ValueError: continue
   return s.substitute(subs)
 
+def fix_reduce_unroll(x:UOp):
+  reduce_range, reduce_expand = partition(x.src[1:], lambda y: y.op is Ops.RANGE)
+  if len(reduce_expand) == 0: return None
+  assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
+  ret = x.src[0]
+  if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
+    ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis), tag=1)
+  # REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
+  return x.replace(src=(ret,)+tuple(reduce_range))
+
+def fix_store_unroll(x:UOp):
+  store_expand, store_range = partition(x.src[2:], lambda y: y.op is Ops.UNROLL)
+  if len(store_expand) == 0: return None
+  return UOp(Ops.CONTRACT, dtypes.void, (x.replace(src=x.src[:2]+tuple(store_range)),), tuple(flatten(x.arg for x in store_expand)), tag=1)
+
 pm_add_gpudims = PatternMatcher([
   (UPat(Ops.SINK, name="s"), add_gpudims),
+  # rewrite UPCAST/UNROLL range to something to be expanded
+  (UPat(Ops.RANGE, name="r"),
+   lambda r: UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s:=r.vmax+1), tuple(range(s))),), ((r.arg[0],s),)) \
+    if r.arg[1] in {AxisType.UNROLL, AxisType.UPCAST} else None),
+  # fix REDUCEs with UNROLLs
+  (UPat(Ops.REDUCE, name="x"), fix_reduce_unroll),
+  (UPat(Ops.STORE, name="x"), fix_store_unroll),
 ])

tinygrad/codegen/lowerer.py

@@ -4,7 +4,6 @@ from typing import cast
 from dataclasses import dataclass
 from tinygrad.dtype import dtypes, AddrSpace, PtrDType
 from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType, graph_rewrite
-from tinygrad.helpers import prod, partition, flatten
 
 # ***** indexing *****
 
@@ -15,16 +14,7 @@ class IndexContext:
   start: int = 0
 
 def shape_to_idx(s, axis_types, start=0):
-  # indexes
-  idxs = []
-  for i, (s, at) in enumerate(zip(s, axis_types)):
-    if at in (AxisType.UPCAST, AxisType.UNROLL):
-      assert isinstance(s, int), "needs to be int to upcast/unroll"
-      idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s), tuple(range(s))),), ((i,s),), tag=1))
-    else:
-      # all others are RANGES
-      idxs.append(UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), start+i))
-  return idxs
+  return [UOp.range(dtypes.int, sint_to_uop(s), start+i, axistype=at) for i, (s, at) in enumerate(zip(s, axis_types))]
 
 def get_index(ast:UOp) -> IndexContext:
   axis_types = ast.arg.axis_types if isinstance(ast.arg, KernelInfo) else ()
@@ -42,16 +32,8 @@ def lower_reduce_axis(ctx: IndexContext, x: UOp):
   new_idxs = shape_to_idx(x.src[0].shape, ctx.axis_types, ctx.start)
   full_new_idx = list(ctx.idxs)
   for a in x.axis_arg: full_new_idx[a] = new_idxs[a]
-
   ret = subblock(ctx, full_new_idx, x.src[0])
-
-  # NOTE: always using ridxs is fine here
-  reduce_range, reduce_expand = partition([full_new_idx[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
-  assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
-  if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
-    ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis), tag=1)
-  # REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
-  return UOp(Ops.REDUCE, x.dtype, (ret,)+tuple(reduce_range), x.arg[0])
+  return UOp(Ops.REDUCE, x.dtype, (ret,)+tuple([full_new_idx[i] for i in x.axis_arg]), x.arg[0])
 
 def lower_store(ctx: IndexContext, x: UOp, buf: UOp):
   # TODO: reenable after REDUCE_AXIS is fixed
@@ -71,9 +53,9 @@ def lower_store(ctx: IndexContext, x: UOp, buf: UOp):
 
   # insert BARRIER if we are ending a LOCAL, IF if we are ending a GROUP_REDUCE
   if cast(PtrDType, buf.dtype).addrspace == AddrSpace.LOCAL and \
-      any(ctx.axis_types[x.arg%1000] in {AxisType.GROUP_REDUCE, AxisType.LOCAL} for x in used_ranges):
+      any(ctx.axis_types[x.arg[0]%1000] in {AxisType.GROUP_REDUCE, AxisType.LOCAL} for x in used_ranges):
     ret = ret.barrier()
-    range_gates = [x.eq(0) for x in used_ranges if ctx.axis_types[x.arg%1000] == AxisType.GROUP_REDUCE]
+    range_gates = [x.eq(0) for x in used_ranges if ctx.axis_types[x.arg[0]%1000] == AxisType.GROUP_REDUCE]
     if len(range_gates): ret = UOp(Ops.IF, src=(functools.reduce(operator.and_, range_gates), ret))
   return ret
 
@@ -86,8 +68,8 @@ def fixup_wmma(ctx:IndexContext, x:UOp):
   srcs = subblock(ctx, full_new_idx, UOp.sink(*x.src)).src
 
   # NOTE: this assumes these are expanded. which now shouldn't change anything
-  new_x_arg_m2 = tuple([tuple([(full_new_idx[a].arg[0][0], sz) for a,sz in v]) for v in x.arg[-2]])
-  new_x_arg_m1 = tuple([full_new_idx[a].arg[0][0] for a in x.arg[-1]])
+  new_x_arg_m2 = tuple([tuple([(full_new_idx[a].arg[0], sz) for a,sz in v]) for v in x.arg[-2]])
+  new_x_arg_m1 = tuple([full_new_idx[a].arg[0] for a in x.arg[-1]])
   return x.replace(src=srcs, arg=x.arg[:-2]+(new_x_arg_m2, new_x_arg_m1), tag=1)
 
 pm_lowerer = PatternMatcher([
@@ -110,5 +92,5 @@ pm_lowerer = PatternMatcher([
 
   # axis fixups for WMMA
   (UPat((Ops.CONTRACT, Ops.UNROLL), name="x"),
-   lambda ctx,x: x.replace(tag=1, arg=tuple([(ctx.idxs[a].arg[0][0], sz) for a,sz in x.arg])) if x.tag is None else None),
+   lambda ctx,x: x.replace(tag=1, arg=tuple([(ctx.idxs[a].arg[0], sz) for a,sz in x.arg])) if x.tag is None else None),
 ])

tinygrad/device.py

@@ -139,7 +139,7 @@ class Buffer:
       if PROFILE:
         self._prof_num = num = len(Buffer.profile_events)
         ts = decimal.Decimal(time.perf_counter_ns())/1000
-        Buffer.profile_events.append(ProfilePointEvent(self.device, "alloc", ts, num, {"dtype":str(self.dtype),"sz":self.size,"nbytes":self.nbytes}))
+        Buffer.profile_events.append(ProfilePointEvent(self.device, "alloc", ts, num, {"dtype":self.dtype, "sz":self.size}))
     return self
   def deallocate(self):
     assert hasattr(self, '_buf'), "buffer must be allocated to deallocate"

tinygrad/helpers.py

@@ -140,7 +140,7 @@ DONT_REALIZE_EXPAND, DONT_GROUP_REDUCES = ContextVar("DONT_REALIZE_EXPAND", 0),
 QUANTIZE, VALIDATE_WITH_CPU, DISABLE_FAST_IDIV = ContextVar("QUANTIZE", 0), ContextVar("VALIDATE_WITH_CPU", 0), ContextVar("DISABLE_FAST_IDIV", 0)
 CORRECT_DIVMOD_FOLDING, FUSE_OPTIM = ContextVar("CORRECT_DIVMOD_FOLDING", 0), ContextVar("FUSE_OPTIM", 0)
 ALLOW_DEVICE_USAGE, MAX_BUFFER_SIZE, AMD_LLVM = ContextVar("ALLOW_DEVICE_USAGE", 1), ContextVar("MAX_BUFFER_SIZE", 0), ContextVar("AMD_LLVM", 1)
-RANGEIFY, PARTIAL_CONTIG = ContextVar("RANGEIFY", 0), ContextVar("PARTIAL_CONTIG", 0)
+RANGEIFY = ContextVar("RANGEIFY", 0)
 
 @dataclass(frozen=True)
 class Metadata:

tinygrad/renderer/cstyle.py

@@ -157,7 +157,7 @@ class CStyleLanguage(Renderer):
       # naming
       prefix = None
       if u.op is Ops.SPECIAL: r[u] = u.arg[0]
-      elif u.op is Ops.RANGE: r[u] = f"ridx{u.arg}"
+      elif u.op is Ops.RANGE: r[u] = f"ridx{u.arg[0]}" if u.arg[0] >= 0 else f"ridxm{-u.arg[0]}"
       else:
         prefix = {Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
                   Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.GEP: "gep", Ops.VECTORIZE: "cast", Ops.PRECAST: "precast",

tinygrad/renderer/llvmir.py

@@ -101,13 +101,13 @@ base_rewrite = PatternMatcher([
 
   # range
   (UPat(Ops.RANGE, name="x"), lambda ctx,x:
-   f"  br label %loop_entry_{x.arg}\nloop_entry_{x.arg}:\n"
-   f"  br label %loop_body_{x.arg}\nloop_body_{x.arg}:\n"
-   f"  {ctx[x]} = phi {ldt(x.dtype)} [ 0, %loop_entry_{x.arg} ], [ {ctx[x]}phi, %loop_latch_{x.arg} ]"),
+   f"  br label %loop_entry_{x.arg[0]}\nloop_entry_{x.arg[0]}:\n"
+   f"  br label %loop_body_{x.arg[0]}\nloop_body_{x.arg[0]}:\n"
+   f"  {ctx[x]} = phi {ldt(x.dtype)} [ 0, %loop_entry_{x.arg[0]} ], [ {ctx[x]}phi, %loop_latch_{x.arg[0]} ]"),
   (UPat(Ops.ENDRANGE, name="x"), lambda ctx,x:
-   f"  br label %loop_latch_{x.src[0].arg}\nloop_latch_{x.src[0].arg}:\n"
+   f"  br label %loop_latch_{x.src[0].arg[0]}\nloop_latch_{x.src[0].arg[0]}:\n"
    f"  {ctx[x.src[0]]}phi = add i32 {ctx[x.src[0]]}, 1\n  {ctx[x]} = icmp ult i32 {ctx[x.src[0]]}phi, {ctx[x.src[0].src[0]]}\n"
-   f"  br i1 {ctx[x]}, label %loop_body_{x.src[0].arg}, label %loop_exit_{x.src[0].arg}\nloop_exit_{x.src[0].arg}:"),
+   f"  br i1 {ctx[x]}, label %loop_body_{x.src[0].arg[0]}, label %loop_exit_{x.src[0].arg[0]}\nloop_exit_{x.src[0].arg[0]}:"),
 
   # if
   (UPat(Ops.IF, name="x"), lambda ctx,x: f"  br i1 {ctx[x.src[0]]}, label %ifbody_{ctx[x][1:]}, label %ifskip_{ctx[x][1:]}\nifbody_{ctx[x][1:]}:"),

tinygrad/runtime/support/am/amdev.py

@@ -104,7 +104,7 @@ class AMPageTableEntry:
   def entry(self, entry_id:int) -> int: return self.entries[entry_id]
   def valid(self, entry_id:int) -> bool: return (self.entries[entry_id] & am.AMDGPU_PTE_VALID) != 0
   def address(self, entry_id:int) -> int: return self.entries[entry_id] & 0x0000FFFFFFFFF000
-  def is_huge_page(self, entry_id:int) -> bool: return self.lv == am.AMDGPU_VM_PTB or self.adev.gmc.is_pte_huge_page(self.entries[entry_id])
+  def is_page(self, entry_id:int) -> bool: return self.lv == am.AMDGPU_VM_PTB or self.adev.gmc.is_pte_huge_page(self.entries[entry_id])
   def supports_huge_page(self, paddr:int): return self.lv >= am.AMDGPU_VM_PDB2
 
 class AMMemoryManager(MemoryManager):

tinygrad/runtime/support/memory.py

@@ -118,7 +118,7 @@ class PageTableTraverseContext:
       assert self.create_pts, "Not allowed to create new page table"
       pt.set_entry(pte_idx, self.dev.mm.palloc(0x1000, zero=True, boot=self.boot), table=True, valid=True)
 
-    assert not pt.is_huge_page(pte_idx), f"Must be table pt={pt.paddr:#x}, {pt.lv=} {pte_idx=} {pt.read_fields(pte_idx)}"
+    assert not pt.is_page(pte_idx), f"Must be table pt={pt.paddr:#x}, {pt.lv=} {pte_idx=} {pt.read_fields(pte_idx)}"
     child_page_table = self.dev.mm.pt_t(self.dev, pt.address(pte_idx), lv=pt.lv+1)
 
     self.pt_stack.append((child_page_table, self._pt_pte_idx(child_page_table, self.vaddr), self._pt_pte_size(child_page_table)))
@@ -145,7 +145,7 @@ class PageTableTraverseContext:
         assert paddr is not None, "paddr must be provided when allocating new page tables"
         while pte_covers > size or not pt.supports_huge_page(paddr+off) or self.vaddr&(pte_covers-1) != 0: pt, pte_idx, pte_covers = self.level_down()
       else:
-        while not pt.is_huge_page(pte_idx): pt, pte_idx, pte_covers = self.level_down()
+        while not pt.is_page(pte_idx): pt, pte_idx, pte_covers = self.level_down()
 
       entries = min(size // pte_covers, self._pt_pte_cnt(pt.lv) - pte_idx)
       assert entries > 0, f"Invalid entries {size=:#x}, {pte_covers=:#x}"

tinygrad/runtime/support/nv/nvdev.py

@@ -51,14 +51,14 @@ class NVPageTableEntry:
     return (self.entries[2*entry_id+1]<<64) | self.entries[2*entry_id] if self._is_dual_pde() else self.entries[entry_id]
 
   def read_fields(self, entry_id:int) -> dict:
-    if self.is_huge_page(entry_id): return self.nvdev.pte_t.decode(self.entry(entry_id))
+    if self.is_page(entry_id): return self.nvdev.pte_t.decode(self.entry(entry_id))
     return (self.nvdev.dual_pde_t if self._is_dual_pde() else self.nvdev.pde_t).decode(self.entry(entry_id))
 
-  def is_huge_page(self, entry_id) -> bool: return (self.entry(entry_id) & 1 == 1) if self.lv < self.nvdev.mm.level_cnt - 1 else True
+  def is_page(self, entry_id) -> bool: return (self.entry(entry_id) & 1 == 1) if self.lv < self.nvdev.mm.level_cnt - 1 else True
   def supports_huge_page(self, paddr:int): return self.lv >= self.nvdev.mm.level_cnt - 3 and paddr % self.nvdev.mm.pte_covers[self.lv] == 0
 
   def valid(self, entry_id):
-    if self.is_huge_page(entry_id): return self.read_fields(entry_id)['valid']
+    if self.is_page(entry_id): return self.read_fields(entry_id)['valid']
     return self.read_fields(entry_id)['aperture_small' if self._is_dual_pde() else 'aperture'] != 0
 
   def address(self, entry_id:int) -> int:

tinygrad/runtime/support/system.py

@@ -85,7 +85,7 @@ class PCIDevice:
       if FileIOInterface.exists(rpath:=f"/sys/bus/pci/devices/{self.pcibus}/resource{i}_resize"):
         try: FileIOInterface(rpath, os.O_RDWR).write(str(int(FileIOInterface(rpath, os.O_RDONLY).read(), 16).bit_length() - 1))
         except OSError as e:
-          if e.errno == errno.EPERM:
+          if e.errno in {errno.EPERM, errno.EACCES}:
             raise RuntimeError(f"Cannot resize BAR {i}: {e}. Permission error: run `extra/amdpci/setup_python_cap.sh`"
                                 " to allow python accessing device or run with sudo") from e
           raise RuntimeError(f"Cannot resize BAR {i}: {e}. Ensure the resizable BAR option is enabled on your system.") from e

tinygrad/schedule/rangeify.py

@@ -1,8 +1,8 @@
 from typing import Any
 from dataclasses import dataclass, field
 from tinygrad.dtype import dtypes, PtrDType
-from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, RewriteNotReady, _substitute
-from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, colored, PARTIAL_CONTIG
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, RewriteNotReady, _substitute, AxisType
+from tinygrad.helpers import argsort, prod, all_same, pluralize, getenv, colored, RANGEIFY
 from tinygrad.schedule.multi import multi_pm
 
 from tinygrad.schedule.kernelize import Kernel
@@ -58,7 +58,7 @@ def realize_assign(ctx:dict[UOp, None], a:UOp) -> None:
 do_realize = PatternMatcher([
   # always realize SINK parents
   (UPat(Ops.SINK, name="s"), lambda ctx,s: ctx.update((x.base, None) for x in s.src if x.base.op not in ALWAYS_CONTIGUOUS)),
-  # always realize ASSIGN/CONTIGUOUS/COPY/BUFFER_VIEW
+  # always realize ASSIGN/COPY/BUFFER_VIEW
   (UPat({Ops.ASSIGN, Ops.COPY, Ops.BUFFER_VIEW}, name="tr"), realize),
   # realize parents of COPY, MSELECT, MSTACK
   (UPat((Ops.COPY, Ops.MSELECT, Ops.MSTACK), name="rb"), realize_parents),
@@ -70,7 +70,6 @@ add_contiguous = PatternMatcher([
   (UPat(GroupOp.All-{Ops.CONTIGUOUS}, name="x"), lambda ctx,x: x.replace(tag=1).contiguous() if x in ctx and x.tag is None else None),
 ])
 remove_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
-early_cleanups = PatternMatcher([(UPat().contiguous(name="c").contiguous(), lambda c: c),])
 
 # 2. mark all children
 
@@ -109,8 +108,8 @@ class RangeifyContext:
 
   # create ranges
   range_idx: int = 0
-  def new_range(self, s:sint):
-    ret = UOp.range(dtypes.int, s, self.range_idx)
+  def new_range(self, s:sint, axistype:AxisType=AxisType.LOOP):
+    ret = UOp.range(dtypes.int, s, self.range_idx, axistype)
     self.range_idx += 1
     return ret
 
@@ -178,7 +177,7 @@ pm_mops = PatternMatcher([
 def map_partial_contiguous(ctx:RangeifyContext, x:UOp, idx:UOp):
   if x.arg is None: return None  # map_contiguous can handle this
   # NOTE: all partial contiguous can safely be replaced by full contiguous. we should be able to match old functionality like this
-  if not PARTIAL_CONTIG: return idx.replace(src=(x.replace(arg=None),)+idx.src[1:])
+  if not (RANGEIFY > 1): return idx.replace(src=(x.replace(arg=None),)+idx.src[1:])
   ranges = []
   new_ranges = []
   passthrough_idx = []
@@ -195,16 +194,16 @@ def map_partial_contiguous(ctx:RangeifyContext, x:UOp, idx:UOp):
 def map_contiguous(ctx:RangeifyContext, x:UOp):
   if x.arg is not None: return None
   ranges = []
-  for s in x.shape:
+  for s in x.shape[len(x.src)-1:]:
     ranges.append(ctx.new_range(s) if resolve(s!=1) else UOp.const(dtypes.int, 0))
-  return x.src[0].index(*ranges).bufferize(*[x for x in ranges if x.op is not Ops.CONST], arg=x.device).forced_reshape(x.shape)
+  return x.src[0].index(*ranges).bufferize(*x.src[1:], *[x for x in ranges if x.op is not Ops.CONST], arg=x.device).forced_reshape(x.shape)
 
 def map_reduce(ctx:RangeifyContext, idx:UOp, red:UOp):
   rngs = list(idx.src[1:])
   new_ranges = []
   for i,s in enumerate(red.src[0].shape):
     if i in red.arg[1]:
-      rngs[i] = ctx.new_range(s)
+      rngs[i] = ctx.new_range(s, axistype=AxisType.REDUCE)
       new_ranges.append(rngs[i])
   return UOp(Ops.REDUCE, red.dtype, src=(red.src[0].index(*rngs),)+tuple(new_ranges), arg=red.arg[0])
 
@@ -260,7 +259,7 @@ def might_end_axis(idx:UOp):
 
 pm_rangeify = pm_mops+PatternMatcher([
   # sink contigs to kick it off
-  (UPat(Ops.CONTIGUOUS, src=(UPat(),), name="x"), map_contiguous),
+  (UPat(Ops.CONTIGUOUS, src=(UPat(),), name="x", allow_any_len=True), map_contiguous),
   # if there's an INDEX it can support partial contig
   (UPat(Ops.INDEX, src=(UPat(Ops.CONTIGUOUS, src=(UPat(),), name="x"),), allow_any_len=True, name="idx"), map_partial_contiguous),
 
@@ -419,7 +418,7 @@ def get_rangeify_map(sink:UOp) -> dict[UOp, UOp]:
   realize_map: dict[UOp, UOp] = {}
   graph_rewrite(tensor_map[sink], do_realize, ctx=realize_map, name="Input Graph")
   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], early_cleanups+remove_tags, input_map=tensor_map, name="cleanup")
+  tensor_map = graph_rewrite_map(tensor_map[sink], remove_tags, input_map=tensor_map, name="cleanup")
   tensor_map = graph_rewrite_map(tensor_map[sink], pm_children, ctx=ChildrenContext(), bottom_up=True, input_map=tensor_map, name="children")
   tensor_map = graph_rewrite_map(tensor_map[sink], pm_rangeify, ctx=RangeifyContext(), bottom_up=True, input_map=tensor_map, name="rangeify")
   # NOTE: running symbolic can break the graph, leaving RANGE/INDEX/BUFFERIZE in the final graph

tinygrad/shape/shapetracker.py

@@ -44,13 +44,13 @@ def views_to_real_strides(views: tuple[View, ...], ignore_valid=False) -> tuple[
   ret: list[sint|None] = [None] * len(views[-1].shape)
   idx, valid = views_to_indexed_uops(views)
   for c in split_uop(idx, Ops.ADD):
-    if c.op is Ops.RANGE: ret[c.arg] = 1
-    if c.op is Ops.MUL and c.src[0].op is Ops.RANGE and c.src[1].op is Ops.CONST: ret[c.src[0].arg] = c.src[1].arg
-    if c.op is Ops.MUL and c.src[1].op is Ops.RANGE and c.src[0].op is Ops.CONST: ret[c.src[1].arg] = c.src[0].arg
-  used_ranges = [x.arg for x in idx.toposort() if x.op is Ops.RANGE]
+    if c.op is Ops.RANGE: ret[c.arg[0]] = 1
+    if c.op is Ops.MUL and c.src[0].op is Ops.RANGE and c.src[1].op is Ops.CONST: ret[c.src[0].arg[0]] = c.src[1].arg
+    if c.op is Ops.MUL and c.src[1].op is Ops.RANGE and c.src[0].op is Ops.CONST: ret[c.src[1].arg[0]] = c.src[0].arg
+  used_ranges = [x.arg[0] for x in idx.toposort() if x.op is Ops.RANGE]
   ret = [x if i in used_ranges else 0 for i,x in enumerate(ret)]
   if not ignore_valid:
-    for masked_axis in [x.arg for x in valid.toposort() if x.op is Ops.RANGE]: ret[masked_axis] = None
+    for masked_axis in [x.arg[0] for x in valid.toposort() if x.op is Ops.RANGE]: ret[masked_axis] = None
   return tuple(ret)
 
 @dataclass(frozen=True, order=True)
@@ -112,7 +112,7 @@ class ShapeTracker:
   def axis_is_masked(self, axis:int) -> bool:
     with Context(TRACK_MATCH_STATS=0):
       _, valid = self.to_indexed_uops()
-      return axis in [x.arg for x in graph_rewrite(valid, symbolic_flat).toposort() if x.op is Ops.RANGE]
+      return axis in [x.arg[0] for x in graph_rewrite(valid, symbolic_flat).toposort() if x.op is Ops.RANGE]
 
   def simplify(self) -> ShapeTracker:
     if len(self.views) >= 2 and (new_view := self.views[-2] + self.views[-1]) is not None:

tinygrad/tensor.py

@@ -177,8 +177,8 @@ class Tensor(MathTrait):
     all_tensors[weakref.ref(self)] = None
   def __del__(self): all_tensors.pop(weakref.ref(self), None)
 
-  def _apply_uop(self, fxn:Callable, *x:Tensor, **kwargs) -> Tensor:
-    new_uop: UOp = fxn(*[t.uop for t in (self,)+x], **kwargs)
+  def _apply_uop(self, fxn:Callable, *x:Tensor, extra_args=(), **kwargs) -> Tensor:
+    new_uop: UOp = fxn(*[t.uop for t in (self,)+x], *extra_args, **kwargs)
     if (metadata:=_METADATA.get()) is not None: all_metadata[new_uop] = (metadata,)
     needs_input_grad = [t.requires_grad for t in (self,)+x]
     return Tensor(new_uop, device=new_uop.device, requires_grad=True if any(needs_input_grad) else None if None in needs_input_grad else False)
@@ -1192,7 +1192,7 @@ class Tensor(MathTrait):
         x = x.shrink(tuple(flatten(((0, s), (0, 1)) for s in x.shape[::2]))).reshape(x.shape[::2])
 
     # dim injection from None by including None dim size (which is 1) and dim collapse by skipping int dim size
-    x = x.reshape(tuple(index['size'] for index in indices_parsed if not isinstance(index['index'], (int, UOp))))
+    x = x.reshape(tuple(index['size'] for index in indices_parsed if not isinstance(index['index'], sint)))
 
     # tensor indexing
     if tops := [(d,i) for d,i in enumerate(i_ for i_ in indices_parsed if not isinstance(i_['index'], int)) if isinstance(i['index'], Tensor)]:
@@ -1212,7 +1212,7 @@ class Tensor(MathTrait):
       # inject 1's for the extra dims added in create masks
       reshape_arg = x.shape[:dims[0]] + (1,) * len(big_shape) + x.shape[dims[0]:]
       # sum reduce the extra dims introduced in create masks
-      x = (x.reshape(reshape_arg) * mask).sum(sum_axis:=tuple(d + len(big_shape) for d in dims), dtype=x.dtype)
+      x = (mask.where(x.reshape(reshape_arg), 0)).sum(sum_axis:=tuple(d + len(big_shape) for d in dims), dtype=x.dtype)
 
       # special permute case
       if dims[0] != 0 and len(dims) != 1 and tuple(dims) != tuple(range(dims[0], dims[-1]+1)):
@@ -2437,7 +2437,7 @@ class Tensor(MathTrait):
       # https://arxiv.org/pdf/1603.07285 inverse of relationship 15 in section 5.1.
       output_size = tuple((i-1)*s - (pB+pA) + (d*(k-1)+1) for i,k,d,s,(pA,pB) in zip(spatial_shape,k_,d_,s_,p_))
     else: output_size = output_size[-len(spatial_shape):]
-    ret = (indices.reshape(bs,c,1,-1)._one_hot_along_dim(prod(output_size), 2) * self.reshape(bs,c,1,-1)).sum(3)
+    ret = (indices.reshape(bs,c,1,-1)._one_hot_along_dim(prod(output_size), 2).where(self.reshape(bs,c,1,-1), 0)).sum(3)
     return ret.reshape(bs,c,*output_size)
 
   def conv2d(self, weight:Tensor, bias:Tensor|None=None, groups=1, stride=1, dilation=1, padding:int|tuple[int, ...]=0,
@@ -2941,11 +2941,11 @@ class Tensor(MathTrait):
     """
     return self*-1 if self.dtype != dtypes.bool else self.logical_not()
 
-  def contiguous(self, **kwargs) -> Tensor:
+  def contiguous(self, *args, **kwargs) -> Tensor:
     """
     Returns a contiguous tensor.
     """
-    return self._apply_uop(UOp.contiguous, **kwargs)
+    return self._apply_uop(UOp.contiguous, extra_args=args, **kwargs)
 
   def fuse(self) -> Tensor:
     """
@@ -2996,6 +2996,9 @@ class Tensor(MathTrait):
     print(Tensor([0., 1., 2., 3.]).exp().numpy())
     ```
     """
+    # TODO: make it generic, and same thing to log and cos
+    if self.is_floating_point(): return self.cast(least_upper_dtype(self.dtype, dtypes.float32)).mul(1/math.log(2)).exp2().cast(self.dtype)
+    # TODO: behavior when DEFAULT_FLOAT is bfloat16 and input is int32?
     return self.mul(1/math.log(2)).exp2()
 
   def exp2(self) -> Tensor:
@@ -3515,16 +3518,15 @@ class Tensor(MathTrait):
     """
     return self * self.softplus().tanh()
 
-  def softplus(self, beta=1.0, threshold=20.0) -> Tensor:
+  def softplus(self, beta=1.0) -> Tensor:
     """
     Applies the Softplus function element-wise.
-    For numerical stability, the implementation folds into identity function when `self * beta > threshold`.
 
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softplus().numpy())
     ```
     """
-    return (self * beta > threshold).where(self, (1/beta) * (1 + (self*beta).exp()).log())
+    return (1/beta) * (self*beta).logaddexp(0.0)
 
   def softsign(self) -> Tensor:
     """
@@ -3756,6 +3758,13 @@ class Tensor(MathTrait):
     # TODO: remove other*0?
     return (other < 0).where(-self.abs(), self.abs()) + other*0
 
+  def logaddexp(self, other) -> Tensor:
+    """
+    Calculates (self.exp()+other.exp()).log(), elementwise.
+    """
+    m = self.maximum(other)
+    return ((self-m).exp() + (self._broadcasted(other)[1]-m).exp()).log() + m
+
   # ***** op wrappers *****
 
   def __invert__(self) -> Tensor: return self.bitwise_not()

tinygrad/uop/__init__.py

@@ -109,6 +109,9 @@ class GroupOp:
   # BinaryOps that satisfy f(x,x)=x see https://en.wikipedia.org/wiki/Idempotence
   Idempotent = {Ops.OR, Ops.AND, Ops.MAX}
 
+  # These can change the dtype to bool
+  Comparison = {Ops.CMPLT, Ops.CMPNE, Ops.CMPEQ}
+
   # do not preserve f(0) = 0
   UnsafePad = {Ops.RECIP, Ops.LOG2, Ops.EXP2, Ops.IDIV, Ops.POW}
 

tinygrad/uop/decompositions.py

@@ -315,8 +315,12 @@ def threefry2x32(x: UOp, key: UOp):
 powers_of_two = {2**i:i for i in range(64)}
 @functools.cache
 def get_late_rewrite_patterns(ops:tuple[Ops, ...], force_transcendental=False):
-  pat: list[tuple[UPat, Callable]] = [(UPat(op, dtype=TRANSCENDENTAL_DTYPES, src=(UPat.var("d"),)), f) for op,f in \
-           ((Ops.EXP2, xexp2), (Ops.LOG2, xlog2), (Ops.SIN, xsin)) if op not in ops or force_transcendental]
+  pat: list[tuple[UPat, Callable]] = []
+  for op,f in ((Ops.EXP2, xexp2), (Ops.LOG2, xlog2), (Ops.SIN, xsin)):
+    if op not in ops or force_transcendental:
+      pat += [(UPat(op, dtype=TRANSCENDENTAL_DTYPES, src=(UPat.var("d"),)), f),
+              (UPat(op, dtype=tuple(dt for dt in dtypes.floats if dt not in TRANSCENDENTAL_DTYPES), src=(UPat.var("d"),), name="x"),
+                lambda x,d: d.cast(dtypes.float32).alu(x.op).cast(x.dtype))]
   # no real hardware supports THREEFRY, but NullRenderer does
   if Ops.THREEFRY not in ops: pat.append((UPat(Ops.THREEFRY, dtype=dtypes.uint64, src=(UPat.var("x"), UPat.var("key"))), threefry2x32))
   # MAX can be rewritten as CMPLT + WHERE (max function is annoying on many cstyle backends)

tinygrad/uop/ops.py

@@ -12,6 +12,9 @@ if TYPE_CHECKING:
   from tinygrad.shape.shapetracker import ShapeTracker
   from tinygrad.device import Buffer, MultiBuffer
 
+class AxisType(Enum):
+  GLOBAL = auto(); LOCAL = auto(); LOOP = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto()  # noqa: E702
+
 # https://en.wikipedia.org/wiki/Identity_element
 def identity_element(op:Ops, dt:DType) -> ConstType: return dtypes.as_const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dtypes.min(dt)}[op], dt)
 
@@ -162,6 +165,11 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       sz = cast(PtrDType, self.dtype).size
       return ShapeTracker.from_shape((sz,)) if sz > 0 else None
 
+    # CONTIGUOUS with RANGE
+    # TODO: how are these not RANGE?
+    if self.op is Ops.CONTIGUOUS and len(self.src) > 1 and all(x.op is Ops.RANGE for x in self.src[1:]):
+      return ShapeTracker.from_shape((tuple([int(x.vmax+1) for x in self.src[1:]])+self.src[0].shape))
+
     # hack for PTX, CASTing the ptr loses the shape
     if self.op is Ops.CAST and self.src[0].op is Ops.DEFINE_GLOBAL: return None
 
@@ -291,7 +299,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       else: ret = ret.replace(src=(UOp(Ops.DEVICE, arg=device),))
     return ret
   @staticmethod
-  def range(dtype:DType, end:sint, idx:int): return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end),), arg=idx)
+  def range(dtype:DType, end:sint, idx:int, axistype:AxisType=AxisType.LOOP):
+    return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end),), arg=(idx, axistype))
   def r(self, op:Ops, axis:tuple[int, ...]):
     axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
     if len(axis) == 0: return self
@@ -379,11 +388,12 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     return ret
 
   def forced_reshape(self, arg:tuple[sint, ...], **kwargs): return UOp(Ops.RESHAPE, kwargs.pop("dtype", self.dtype), src=(self,), arg=arg)
+
   def reshape(self, arg:tuple[sint, ...]): return self._mop(Ops.RESHAPE, arg)
-  def pad(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.PAD, arg)
   def expand(self, arg:tuple[sint, ...]): return self._mop(Ops.EXPAND, arg)
-  def permute(self, arg:tuple[sint, ...]): return self._mop(Ops.PERMUTE, arg)
   def shrink(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.SHRINK, arg)
+  def pad(self, arg:tuple[tuple[sint, sint], ...]): return self._mop(Ops.PAD, arg)
+  def permute(self, arg:tuple[int, ...]): return self._mop(Ops.PERMUTE, arg)
   def flip(self, arg:tuple[bool, ...]): return self._mop(Ops.FLIP, arg)
 
   # *** uop UNIQUE ***
@@ -529,13 +539,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
         if s1_vmax < 0: return (0, -s1_vmin-1) if s0_vmin >= 0 else (-(-s1_vmin-1), 0) if s0_vmax <= 0 else (-(-s1_vmin-1), -s1_vmin-1)
       if self.op is Ops.IDIV:
         assert isinstance(s0_vmin, int) and isinstance(s0_vmax, int) and isinstance(s1_vmin, int) and isinstance(s1_vmax, int)
-        if (c:=s1_vmin) == s1_vmax:  # s1 is a const
-          if c > 0: return cdiv(s0_vmin, c), cdiv(s0_vmax, c)
-          if c < 0: return cdiv(s0_vmax, c), cdiv(s0_vmin, c)
-        if (s0_vmax <= 0 and s1_vmax < 0): return cdiv(s0_vmax, s1_vmin), cdiv(s0_vmin, s1_vmax)
-        if (s0_vmin >= 0 and s1_vmin > 0): return cdiv(s0_vmin, s1_vmax), cdiv(s0_vmax, s1_vmin)
-        if (s0_vmax <= 0 and s1_vmin > 0): return cdiv(s0_vmin, s1_vmin), cdiv(s0_vmax, s1_vmax)
-        if (s0_vmin >= 0 and s1_vmax < 0): return cdiv(s0_vmax, s1_vmax), cdiv(s0_vmin, s1_vmin)
+        if s1_vmin*s1_vmax>0:
+          return min(vals:=(cdiv(s0_vmin, s1_vmin), cdiv(s0_vmin, s1_vmax), cdiv(s0_vmax, s1_vmin), cdiv(s0_vmax, s1_vmax))), max(vals)
       if self.op is Ops.MAX: return max(s0_vmin, s1_vmin), max(s0_vmax, s1_vmax)
       if self.op is Ops.CMPLT: return (s0_vmax<s1_vmin, s0_vmin<s1_vmax)
       if self.op is Ops.CMPNE: return ((s0_vmax < s1_vmin) or (s1_vmax < s0_vmin), not (s0_vmin == s0_vmax == s1_vmin == s1_vmax))
@@ -574,9 +579,6 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       ret = graph_rewrite(self.simplify() if simplify else self, renderer if pm is None else pm)
     return ret.arg if ret.op is Ops.NOOP else str(ret)
 
-class AxisType(Enum):
-  GLOBAL = auto(); LOCAL = auto(); LOOP = auto(); GROUP_REDUCE = auto(); REDUCE = auto(); UPCAST = auto(); UNROLL = auto()  # noqa: E702
-
 @dataclass(frozen=True)
 class KernelInfo:
   name: str = "test"            # name of the kernel
@@ -1009,7 +1011,7 @@ syms = { Ops.ADD: "+", Ops.SUB: "-", Ops.IDIV: "//", Ops.MOD: "%", Ops.SHL: "<<"
          Ops.MUL: "*", Ops.CMPLT: "<", Ops.CMPNE: "!=", Ops.AND: "&", Ops.OR: "|", Ops.XOR: "^"}
 renderer = PatternMatcher([
   (UPat((Ops.DEFINE_VAR, Ops.SPECIAL), name="x"), lambda x: UOp(Ops.NOOP, arg=x.arg[0])),
-  (UPat(Ops.RANGE, name="x"), lambda x: UOp(Ops.NOOP, arg=f"ridx{x.arg}")),
+  (UPat(Ops.RANGE, name="x"), lambda x: UOp(Ops.NOOP, arg=f"ridx{x.arg[0]}" if x.arg[0] >= 0 else f"ridxm{-x.arg[0]}")),
   (UPat((Ops.CONST, Ops.VCONST), name="x"), lambda x: UOp(Ops.NOOP, arg=str(x.arg))),
   (UPat(Ops.UNROLL, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UNROLL({x.src[0].arg}, {x.arg})")),
   (UPat(Ops.CAST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"({str(x.dtype)[7:]})({x.src[0].arg})")),

tinygrad/uop/spec.py

@@ -23,14 +23,25 @@ try:
     (UPat(Ops.SPECIAL, src=UPat(Ops.NOOP), name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=create_bounded(x.arg, 0, x.src[0].arg-1, ctx[0]))),
     (UPat(Ops.DEFINE_VAR, name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=create_bounded(x.arg[0], x.arg[1], x.arg[2], ctx[0]))),
     (UPat(Ops.RANGE, name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=create_bounded(f"ridx{x.arg}", 0, x.src[0].arg-1, ctx[0]))),
+    # float loads only become a variable when they get cast to int/bool
     (UPat(Ops.LOAD, dtypes.ints, name="x"),
-      lambda x,ctx: UOp(Ops.NOOP, arg=create_bounded(f"load{ctx[1].setdefault(x, len(ctx[1]))}", x.vmin, x.vmax, ctx[0]))),
-    (UPat(Ops.CONST, name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=(z3.BoolVal if dtypes.is_bool(x.dtype) else z3.IntVal)(x.arg, ctx=ctx[0].ctx))),
-    (UPat(Ops.CAST, dtype=dtypes.ints+(dtypes.bool,), src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
-    (UPat(Ops.CAST, name="x"), lambda x,ctx: UOp(Ops.NOOP, arg=create_bounded(f"cast{ctx[1].setdefault(x, len(ctx[1]))}", x.vmin, x.vmax, ctx[0]))),
+      lambda x,ctx: UOp(Ops.NOOP, arg=create_bounded(f"load{ctx[1].setdefault(x, len(ctx[1]))}", x.dtype.min, x.dtype.max, ctx[0]))),
+    (UPat(Ops.CONST, dtype=dtypes.ints+(dtypes.bool,), name="x"),
+      lambda x,ctx: UOp(Ops.NOOP, arg=(z3.BoolVal if dtypes.is_bool(x.dtype) else z3.IntVal)(x.arg, ctx=ctx[0].ctx))),
+    # z3 can cast from bool to int automatically
+    (UPat(Ops.CAST, dtype=dtypes.ints, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
+    (UPat(Ops.CAST, dtype=dtypes.bool, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=(x.src[0].arg!=0))),
+    # if the source of the cast is not a noop it means that it is a float and so we create a new variable
+    (UPat(Ops.CAST, dtype=dtypes.ints, name="x"), lambda x,ctx:
+      UOp(Ops.NOOP, arg=create_bounded(f"cast{ctx[1].setdefault(x, len(ctx[1]))}", x.dtype.min, x.dtype.max, ctx[0]))),
+    (UPat(Ops.CAST, dtype=dtypes.bool, name="x"), lambda x,ctx:
+      UOp(Ops.NOOP, arg=z3.Bool(f"cast{ctx[1].setdefault(x, len(ctx[1]))}",ctx=ctx[0].ctx))),
     (UPat(Ops.XOR, src=UPat(Ops.NOOP), name="x"),
       lambda x: UOp(Ops.NOOP, arg=z3.BV2Int(z3_alu[x.op](*(z3.Int2BV(s.arg, x.dtype.itemsize*8) for s in x.src))))),
     (UPat(GroupOp.ALU, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=z3_alu[x.op](*(s.arg for s in x.src)))),
+    # A comparison between floats introduces a new bool variable
+    (UPat(GroupOp.Comparison, src=UPat(dtype=dtypes.floats), name="x"), lambda x,ctx:
+      UOp(Ops.NOOP, arg=z3.Bool(f"float_cmp{ctx[1].setdefault(x, len(ctx[1]))}",ctx=ctx[0].ctx))),
   ])
 
   z3_imported = True
@@ -90,6 +101,10 @@ tensor_uop_spec = buffer_spec+assign_spec+PatternMatcher([
   (UPat((Ops.DETACH, Ops.CONTIGUOUS, Ops.CONTIGUOUS_BACKWARD, Ops.FUSE), name="root", src=(UPat.var("x"),), arg=None),
    lambda root,x: root.dtype == x.dtype),
 
+  # CONTIGUOUS with a range
+  (UPat(Ops.CONTIGUOUS, name="root", src=(UPat.var("x"),), allow_any_len=True, arg=None),
+   lambda root,x: root.dtype == x.dtype and all(u.op is Ops.RANGE for u in root.src[1:])),
+
   # COPY/ALLREDUCE/MULTI
   (UPat(Ops.COPY, name="copy", src=(UPat.var("x"), UPat(Ops.DEVICE)), arg=None), lambda copy,x: copy.dtype == x.dtype),
   (UPat(Ops.ALLREDUCE, name="red", src=(UPat.var("x"), UPat(Ops.DEVICE))), lambda red,x: red.dtype == x.dtype and isinstance(red.arg, Ops)),
@@ -136,7 +151,7 @@ spec = PatternMatcher([
   (UPat(Ops.DEFINE_REG, src=()), lambda: True),
   (UPat(Ops.DEFINE_VAR, name="x"), lambda x: isinstance(x.arg[1], int) and isinstance(x.arg[2], int)),
 
-  (UPat(Ops.RANGE, src=(UPat.var("x"),), name="rng"), lambda rng,x: rng.dtype == x.dtype and isinstance(rng.arg, int)),
+  (UPat(Ops.RANGE, src=(UPat.var("x"),), name="rng"), lambda rng,x: rng.dtype == x.dtype and isinstance(rng.arg, tuple)),
   (UPat(Ops.SPECIAL, src=()), lambda: True),
 
   (UPat(Ops.VIEW, dtypes.void, src=(), name="x"), lambda x: isinstance(x.arg, ShapeTracker)),

tinygrad/viz/js/index.js

@@ -151,7 +151,17 @@ async function renderProfiler() {
   // layout once!
   if (data != null) return;
   const profiler = d3.select(".profiler").html("");
-  const { layout, st, et } = await (await fetch("/get_profile")).json();
+  const buf = await (await fetch("/get_profile")).arrayBuffer();
+  const view = new DataView(buf);
+  let offset = 0;
+  const u8 = () => { const ret = view.getUint8(offset); offset += 1; return ret; }
+  const u32 = () => { const ret = view.getUint32(offset, true); offset += 4; return ret; }
+  const u64 = () => { const ret = new Number(view.getBigUint64(offset, true)); offset += 8; return ret; }
+  const f32 = () => { const ret = view.getFloat32(offset, true); offset += 4; return ret; }
+  const optional = (i) => i === 0 ? null : i-1;
+  const dur = u32(), peak = u64(), indexLen = u32(), layoutsLen = u32();
+  const textDecoder = new TextDecoder("utf-8");
+  const { strings, dtypes }  = JSON.parse(textDecoder.decode(new Uint8Array(buf, offset, indexLen))); offset += indexLen;
   // place devices on the y axis and set vertical positions
   const [tickSize, padding] = [10, 8];
   const deviceList = profiler.append("div").attr("id", "device-list").style("padding-top", tickSize+padding+"px");
@@ -162,19 +172,24 @@ async function renderProfiler() {
   const canvasTop = rect(canvas).top;
   // color by key (name/category/device)
   const colorMap = new Map();
-  data = {tracks:new Map(), axes:{}, st, et};
-  const heightScale = d3.scaleLinear().domain([0, Object.entries(layout).reduce((peak, [_,d]) => Math.max(peak, d.peak||0), 0)]).range([4,maxheight=100]);
-  for (const [k, v] of Object.entries(layout)) {
-    if (v.shapes.length === 0) continue;
+  data = {tracks:new Map(), axes:{}};
+  const heightScale = d3.scaleLinear().domain([0, peak]).range([4,maxheight=100]);
+  for (let i=0; i<layoutsLen; i++) {
+    const nameLen = view.getUint8(offset, true); offset += 1;
+    const k = textDecoder.decode(new Uint8Array(buf, offset, nameLen)); offset += nameLen;
     const div = deviceList.append("div").attr("id", k).text(k).style("padding", padding+"px");
     const { y:baseY, height:baseHeight } = rect(div.node());
     const offsetY = baseY-canvasTop+padding/2;
-    if (v.shapes[0].dur != null) {
+    const EventTypes = {TIMELINE:0, MEMORY:1};
+    const eventType = u8(), eventsLen = u32();
+    if (eventType === EventTypes.TIMELINE) {
+      const maxDepth = u8();
       const levelHeight = baseHeight-padding;
       const shapes = [];
       data.tracks.set(k, { shapes, offsetY });
       let colorKey, ref;
-      for (const e of v.shapes) {
+      for (let j=0; j<eventsLen; j++) {
+        const e = {name:strings[u32()], ref:optional(u32()), st:u32(), dur:f32(), depth:u8(), cat:optional(u8()), info:strings[u32()] || null};
         if (e.depth === 0) colorKey = e.cat ?? e.name;
         if (!colorMap.has(colorKey)) colorMap.set(colorKey, cycleColors(colorScheme[k] ?? colorScheme.DEFAULT, colorMap.size));
         const fillColor = d3.color(colorMap.get(colorKey)).brighter(e.depth).toString();
@@ -187,19 +202,24 @@ async function renderProfiler() {
         }
         const arg = { tooltipText:formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...ref };
         // offset y by depth
-        shapes.push({x:e.st-st, y:levelHeight*e.depth, width:e.dur, height:levelHeight, arg, label, fillColor });
+        shapes.push({x:e.st, y:levelHeight*e.depth, width:e.dur, height:levelHeight, arg, label, fillColor });
       }
-      div.style("height", levelHeight*v.maxDepth+padding+"px").style("pointerEvents", "none");
+      div.style("height", levelHeight*maxDepth+padding+"px").style("pointerEvents", "none");
     } else {
-      const height = heightScale(v.peak);
-      const yscale = d3.scaleLinear().domain([0, v.peak]).range([height, 0]);
+      const peak = u64();
+      const height = heightScale(peak);
+      const yscale = d3.scaleLinear().domain([0, peak]).range([height, 0]);
+      const timestamps = Array.from({length:u32()}, u32);
       const shapes = [];
-      for (const [i,e] of v.shapes.entries()) {
-        const x = e.x.map(tsIdx => v.timestamps[tsIdx]-st);
+      for (let j=0; j<eventsLen; j++) {
+        const length = u32();
+        const x = Array.from({ length }, () => timestamps[u32()]);
+        const e = {y:Array.from({ length }, u64), arg:{dtype:strings[u32()], sz:u64()}};
+        const nbytes = dtypes[e.arg.dtype]*e.arg.sz;
         const arg = {tooltipText:`${e.arg.dtype} len:${formatUnit(e.arg.sz)}\n${formatUnit(e.arg.nbytes, "B")}`};
-        shapes.push({ x, y0:e.y.map(yscale), y1:e.y.map(y => yscale(y+e.arg.nbytes)), arg, fillColor:cycleColors(colorScheme.BUFFER, i) });
+        shapes.push({ x, y0:e.y.map(yscale), y1:e.y.map(y => yscale(y+nbytes)), arg, fillColor:cycleColors(colorScheme.BUFFER, j) });
       }
-      data.tracks.set(k, { shapes, offsetY, height, peak:v.peak, scaleFactor:maxheight*4/height });
+      data.tracks.set(k, { shapes, offsetY, height, peak, scaleFactor:maxheight*4/height });
       div.style("height", height+padding+"px").style("cursor", "pointer").on("click", (e) => {
         const newFocus = e.currentTarget.id === focusedDevice ? null : e.currentTarget.id;
         let offset = 0;
@@ -225,7 +245,7 @@ async function renderProfiler() {
     ctx.save();
     ctx.clearRect(0, 0, canvas.clientWidth, canvas.clientHeight);
     // rescale to match current zoom
-    const xscale = d3.scaleLinear().domain([0, et-st]).range([0, canvas.clientWidth]);
+    const xscale = d3.scaleLinear().domain([0, dur]).range([0, canvas.clientWidth]);
     xscale.domain(xscale.range().map(zoomLevel.invertX, zoomLevel).map(xscale.invert, xscale));
     const zoomDomain = transform != null ? xscale.domain() : null;
     let yscale = null;
@@ -289,7 +309,7 @@ async function renderProfiler() {
       // tick label
       ctx.textBaseline = "top";
       ctx.textAlign = "left";
-      ctx.fillText(formatTime(tick, et-st), x+ctx.lineWidth+2, tickSize);
+      ctx.fillText(formatTime(tick, dur), x+ctx.lineWidth+2, tickSize);
     }
     if (yscale != null) {
       drawLine(ctx, [0, 0], yscale.range());

tinygrad/viz/serve.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python3
-import multiprocessing, pickle, difflib, os, threading, json, time, sys, webbrowser, socket, argparse, socketserver, functools, codecs, io
+import multiprocessing, pickle, difflib, os, threading, json, time, sys, webbrowser, socket, argparse, socketserver, functools, codecs, io, struct
 import subprocess, ctypes
 from contextlib import redirect_stdout
 from decimal import Decimal
@@ -79,7 +79,7 @@ def uop_to_json(x:UOp) -> dict[int, dict]:
       if u.op not in {Ops.VIEW, Ops.BUFFER, Ops.KERNEL, Ops.ASSIGN, Ops.COPY, Ops.SINK, *GroupOp.Buffer} and u.st is not None:
         label += f"\n{shape_to_str(u.shape)}"
       elif len(rngs:=u.ranges):
-        label += f"\n{str(sorted([x.arg for x in rngs]))}"
+        label += f"\n{str(sorted([x.arg[0] for x in rngs]))}"
     except Exception:
       label += "\n<ISSUE GETTING LABEL>"
     if (ref:=ref_map.get(u.arg.ast) if u.op is Ops.KERNEL else None) is not None: label += f"\ncodegen@{ctxs[ref]['name']}"
@@ -106,6 +106,15 @@ def get_details(ctx:TrackedGraphRewrite) -> Generator[GraphRewriteDetails, None,
            "diff":list(difflib.unified_diff(str(u0).splitlines(), str(u1).splitlines())), "upat":(upat_loc, printable(upat_loc))}
     if not ctx.bottom_up: next_sink = new_sink
 
+# encoder helpers
+
+def enum_str(s, cache:dict[str, int]) -> int:
+  if (cret:=cache.get(s)) is not None: return cret
+  cache[s] = ret = len(cache)
+  return ret
+
+def option(s:int|None) -> int: return 0 if s is None else s+1
+
 # Profiler API
 
 device_ts_diffs:dict[str, tuple[Decimal, Decimal]] = {}
@@ -123,10 +132,11 @@ def flatten_events(profile:list[ProfileEvent]) -> Generator[tuple[Decimal, Decim
       for i,ent in enumerate(e.ents): yield (cpu_ts[i*2], cpu_ts[i*2+1], ent)
 
 # timeline layout stacks events in a contiguous block. When a late starter finishes late, there is whitespace in the higher levels.
-def timeline_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
-  shapes:list[dict] = []
+def timeline_layout(events:list[tuple[int, int, float, DevEvent]], start_ts:int, scache:dict[str, int]) -> bytes|None:
+  shapes:list[bytes] = []
   levels:list[int] = []
   exec_points:dict[str, dict] = {}
+  category_enum:dict[str, int] = {}
   for st,et,dur,e in events:
     if isinstance(e, ProfilePointEvent) and e.name == "exec": exec_points[e.key] = e.arg
     if dur == 0: continue
@@ -143,10 +153,12 @@ def timeline_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
     elif isinstance(e.name, TracingKey):
       name, cat = e.name.display_name, e.name.cat
       ref = next((v for k in e.name.keys if (v:=ref_map.get(k)) is not None), None)
-    shapes.append({"name":name, "ref":ref, "st":st, "dur":dur, "depth":depth, "cat":cat, "info":info})
-  return {"shapes":shapes, "maxDepth":len(levels)}
+    shapes.append(struct.pack("<IIIfBBI", enum_str(name,scache), option(ref), st-start_ts, dur, depth,
+                              option(None if cat is None else enum_str(cat, category_enum)), enum_str(info or "",scache)))
+  return struct.pack("<BIB", 0, len(shapes), len(levels))+b"".join(shapes) if shapes else None
 
-def mem_layout(events:list[tuple[int, int, float, DevEvent]], max_ts:int) -> dict:
+def mem_layout(events:list[tuple[int, int, float, DevEvent]], start_ts:int, end_ts:int, peaks:list[int], dtypes_map:dict[str, int],
+               scache:dict[str, int]) -> bytes|None:
   step, peak, mem = 0, 0, 0
   shps:dict[int, dict] = {}
   temp:dict[int, dict] = {}
@@ -154,26 +166,30 @@ def mem_layout(events:list[tuple[int, int, float, DevEvent]], max_ts:int) -> dic
   for st,_,_,e in events:
     if not isinstance(e, ProfilePointEvent): continue
     if e.name == "alloc":
-      shps[e.key] = temp[e.key] = {"x":[step], "y":[mem], "arg":e.arg}
-      timestamps.append(int(e.ts))
+      shps[e.key] = temp[e.key] = {"x":[step], "y":[mem], "arg":{"dtype":e.arg["dtype"].name, "sz":e.arg["sz"]}}
+      dtypes_map.setdefault(e.arg["dtype"].name, e.arg["dtype"].itemsize)
+      timestamps.append(int(e.ts)-start_ts)
       step += 1
-      mem += e.arg["nbytes"]
+      mem += e.arg["sz"]*e.arg["dtype"].itemsize
       if mem > peak: peak = mem
     if e.name == "free":
-      timestamps.append(int(e.ts))
+      timestamps.append(int(e.ts)-start_ts)
       step += 1
-      mem -= (removed:=temp.pop(e.key))["arg"]["nbytes"]
+      mem -= (free_nbytes:=(removed:=temp.pop(e.key))["arg"]["sz"]*dtypes_map[removed["arg"]["dtype"]])
       removed["x"].append(step)
       removed["y"].append(removed["y"][-1])
       for k,v in temp.items():
         if k > e.key:
           v["x"] += [step, step]
-          v["y"] += [v["y"][-1], v["y"][-1]-removed["arg"]["nbytes"]]
+          v["y"] += [v["y"][-1], v["y"][-1]-free_nbytes]
   for v in temp.values():
     v["x"].append(step)
     v["y"].append(v["y"][-1])
-  timestamps.append(max_ts)
-  return {"shapes":list(shps.values()), "peak":peak, "timestamps":timestamps}
+  timestamps.append(end_ts-start_ts)
+  peaks.append(peak)
+  bufs = [struct.pack("<I"+str(i:=len(v['x']))+f"I{i}QIQ", i, *v["x"], *v["y"], enum_str(v["arg"]["dtype"], scache),
+                      v["arg"]["sz"]) for v in shps.values()]
+  return struct.pack("<BIQI", 1, len(shps), peak, len(timestamps))+struct.pack(f"<{len(timestamps)}I", *timestamps)+b"".join(bufs) if bufs else None
 
 def get_profile(profile:list[ProfileEvent]) -> bytes|None:
   # start by getting the time diffs
@@ -181,20 +197,25 @@ def get_profile(profile:list[ProfileEvent]) -> bytes|None:
     if isinstance(ev,ProfileDeviceEvent): device_ts_diffs[ev.device] = (ev.comp_tdiff, ev.copy_tdiff if ev.copy_tdiff is not None else ev.comp_tdiff)
   # map events per device
   dev_events:dict[str, list[tuple[int, int, float, DevEvent]]] = {}
-  min_ts:int|None = None
-  max_ts:int|None = None
+  start_ts:int|None = None
+  end_ts:int|None = None
   for ts,en,e in flatten_events(profile):
     dev_events.setdefault(e.device,[]).append((st:=int(ts), et:=int(en), float(en-ts), e))
-    if min_ts is None or st < min_ts: min_ts = st
-    if max_ts is None or et > max_ts: max_ts = et
-  if min_ts is None: return None
+    if start_ts is None or st < start_ts: start_ts = st
+    if end_ts is None or et > end_ts: end_ts = et
+  if start_ts is None: return None
   # return layout of per device events
-  layout:dict[str, dict] = {}
+  layout:dict[str, bytes|None] = {}
+  scache:dict[str, int] = {}
+  peaks:list[int] = []
+  dtypes_map:dict[str, int] = {}
   for k,v in dev_events.items():
     v.sort(key=lambda e:e[0])
-    layout[k] = timeline_layout(v)
-    layout[f"{k} Memory"] = mem_layout(v, unwrap(max_ts))
-  return json.dumps({"layout":layout, "st":min_ts, "et":max_ts}).encode("utf-8")
+    layout[k] = timeline_layout(v, start_ts, scache)
+    layout[f"{k} Memory"] = mem_layout(v, start_ts, unwrap(end_ts), peaks, dtypes_map, scache)
+  ret = [b"".join([struct.pack("<B", len(k)), k.encode(), v]) for k,v in layout.items() if v is not None]
+  index = json.dumps({"strings":list(scache), "dtypes":dtypes_map}).encode()
+  return struct.pack("<IQII", unwrap(end_ts)-start_ts, max(peaks,default=0), len(index), len(ret))+index+b"".join(ret)
 
 def get_runtime_stats(key) -> list[dict]:
   ret:list[dict] = []