Merge branch 'master' into no_late_simplify_marg

.github/workflows/benchmark.yml

@@ -238,8 +238,6 @@ jobs:
       run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_beam.txt
     - name: Run LLaMA-3 8B on 4 GPUs with BEAM
       run: BENCHMARK_LOG=llama3_beam_4gpu NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_four_gpu.txt
-    - name: Run quantized LLaMA3
-      run: BENCHMARK_LOG=llama3_fp8 python3 examples/llama3.py --size 8B --model weights/LLaMA-3/8B-SF-DPO/ --temperature 0 --benchmark --quantize fp8 | tee llama3_fp8.txt
     # - name: Run LLaMA-3 8B on 6 GPUs
     #   run: NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0 | tee llama3_six_gpu.txt
     # - name: Run LLaMA-2 70B
@@ -273,7 +271,6 @@ jobs:
           llama3_beam.txt
           llama3_four_gpu.txt
           llama3_six_gpu.txt
-          llama3_fp8.txt
           llama_2_70B.txt
           mixtral.txt
           gpt2_unjitted.txt

.github/workflows/test.yml

@@ -264,6 +264,8 @@ jobs:
       run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
     - name: Run unit tests
       run: CPU=1 python -m pytest -n=auto test/unit/ --durations=20
+    - name: Check SPEC=2
+      run: SPEC=2 python3 test/test_tiny.py
     - name: Run targetted tests on NULL backend
       run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step test/device/test_null.py
     # TODO: too slow
@@ -292,21 +294,6 @@ jobs:
     - name: Repo line count < 18000 lines
       run: MAX_LINE_COUNT=18000 python sz.py
 
-  spec:
-    name: SPEC=2
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: spec-unit
-        deps: testing_unit
-    - name: Test SPEC=2
-      run: SPEC=2 PYTHONPATH="." pytest --maxfail=10 -n auto --durations=30 --ignore=test/models --ignore test/unit/test_hashing.py --ignore test/test_nn.py --timeout 40 -k "not test_setitem_big"
-
   fuzzing:
     name: Fuzzing
     runs-on: ubuntu-latest

docs/env_vars.md

@@ -41,7 +41,7 @@ BEAM                | [#]        | number of beams in kernel beam search
 DEFAULT_FLOAT       | [HALF, ...]| specify the default float dtype (FLOAT32, HALF, BFLOAT16, FLOAT64, ...), default to FLOAT32
 IMAGE               | [1-2]      | enable 2d specific optimizations
 FLOAT16             | [1]        | use float16 for images instead of float32
-HCQ_VISIBLE_DEVICES | [list[int]]| restricts the HCQ devices that are available. The format is a comma-separated list of identifiers (indexing starts with 0).
+VISIBLE_DEVICES     | [list[int]]| restricts the NV/AMD devices that are available. The format is a comma-separated list of identifiers (indexing starts with 0).
 JIT                 | [0-2]      | 0=disabled, 1=[jit enabled](quickstart.md#jit) (default), 2=jit enabled, but graphs are disabled
 VIZ                 | [1]        | 0=disabled, 1=[viz enabled](https://github.com/tinygrad/tinygrad/tree/master/tinygrad/viz)
 ALLOW_TF32          | [1]        | enable TensorFloat-32 tensor cores on Ampere or newer GPUs.

examples/llama3.py

@@ -145,41 +145,6 @@ def NF4Linear(block_size):
       return new_state_dict
   return _NF4Linear
 
-def quantize_to_fp8(x: Tensor, dtype=dtypes.fp8e4m3):
-  fp8_min = -448.0 if dtype == dtypes.fp8e4m3 else -57344.0
-  fp8_max = 448.0 if dtype == dtypes.fp8e4m3 else 57344.0
-  scale = fp8_max / x.abs().max()
-  x_scl_sat = (x * scale).clamp(fp8_min, fp8_max)
-  return x_scl_sat.cast(dtype), scale.float().reciprocal()
-
-class FP8Linear:
-  def __init__(self, in_features, out_features, bias=True):
-    self.weight = Tensor.empty(out_features, in_features, dtype=dtypes.fp8e4m3)
-    self.bias = Tensor.empty(out_features, dtype=dtypes.float16) if bias else None
-    self.weight_scale = Tensor.empty((), dtype=dtypes.float16)
-
-  def __call__(self, x:Tensor):
-    y = x.dot(self.weight.T.cast(dtypes.float32)) * self.weight_scale
-    if self.bias is not None: y = y + self.bias.cast(y.dtype)
-    return y.cast(x.dtype)
-
-  @staticmethod
-  def quantize(tensors, device, scale_dtype=dtypes.float16, quantize_embeds=False):
-    assert not quantize_embeds
-    new_tensors = {}
-    for name,v in tensors.items():
-      if "feed_forward" in name or "attention.w" in name:
-        assert "weight" in name, name
-        fp8_weight, scale = quantize_to_fp8(v)
-        new_tensors[name] = fp8_weight
-        new_tensors[name.replace('weight', 'weight_scale')] = scale.cast(scale_dtype)
-        if isinstance(device, tuple):
-          new_tensors[name].shard_(device, axis=-1)
-          new_tensors[name.replace('weight', 'weight_scale')].shard_(device, axis=None)
-      else:
-        new_tensors[name] = v
-    return new_tensors
-
 MODEL_PARAMS = {
   "1B": {
     "args": {"dim": 2048, "n_heads": 32, "n_kv_heads": 8, "n_layers": 16, "norm_eps": 1e-5, "rope_theta": 500000, "vocab_size": 128256, "hidden_dim": 8192},
@@ -202,7 +167,6 @@ def build_transformer(model_path: Path, model_size="8B", quantize=None, scale_dt
   # build model
   if quantize == "int8": linear, embedding, quantize_embeds = Int8Linear, Int8Embedding, True
   elif quantize == "nf4": linear, embedding, quantize_embeds = NF4Linear(64), nn.Embedding, False
-  elif quantize == "fp8": linear, embedding, quantize_embeds = FP8Linear, nn.Embedding, False
   else: linear, embedding, quantize_embeds = nn.Linear, nn.Embedding, False
   model = Transformer(**MODEL_PARAMS[model_size]["args"], linear=linear, embedding=embedding, max_context=max_context, jit=True)
 
@@ -278,7 +242,7 @@ if __name__ == "__main__":
   parser.add_argument("--model", type=Path, help="Model path")
   parser.add_argument("--size", choices=["1B", "8B", "70B", "405B"], default="1B", help="Model size")
   parser.add_argument("--shard", type=int, default=1, help="Shard the model across multiple devices")
-  parser.add_argument("--quantize", choices=["int8", "nf4", "float16", "fp8"], help="Quantization method")
+  parser.add_argument("--quantize", choices=["int8", "nf4", "float16"], help="Quantization method")
   parser.add_argument("--no_api", action="store_true", help="Disable the api and run a cli test interface")
   parser.add_argument("--host", type=str, default="0.0.0.0", help="Web server bind address")
   parser.add_argument("--port", type=int, default=7776, help="Web server port")

extra/gemm/torch_gemm.py

@@ -8,22 +8,19 @@ import torch
 torch.set_num_threads(1)
 from tinygrad.helpers import getenv
 CUDA = getenv("CUDA", 1)
-MPS = getenv("MPS", 0)
 
-for dtype in [torch.float32, torch.float16, torch.bfloat16]:
+for dtype in [torch.float32, torch.float16]:
   for N in [256, 512, 1024, 2048, 4096]:
     FLOPS = N*N*N*2
 
     b = torch.rand((N,N), dtype=dtype)
     c = torch.rand((N,N), dtype=dtype)
     if CUDA: b,c = b.cuda(),c.cuda()
-    if MPS: b,c = b.to('mps'),c.to('mps')
 
     def torch_prog(b, c):
       st = time.perf_counter()
       a = b@c
       if CUDA: torch.cuda.synchronize()
-      if MPS: torch.mps.synchronize()
       return time.perf_counter() - st
     tm = min([torch_prog(b, c) for _ in range(20)])
     print(f"{N*N:10d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS {N:4d}x{N:4d}x{N:4d} matmul in {dtype}")

extra/thunder/cuda/include/ops/group/group.cuh

@@ -46,9 +46,9 @@ __device__ static inline void arrive(int id) {
 #include "memory/memory.cuh"
 #include "shared/shared.cuh"
 #include "register/register.cuh"
-#include "mma/mma.cuh"
 
 #ifdef KITTENS_HOPPER
+#include "mma/mma.cuh"
 
 template<int n_reg> __device__ static inline void increase_registers() {
     static_assert(n_reg % 8 == 0, "n_reg must be a multiple of 8");
@@ -93,4 +93,4 @@ __device__ static inline void sync() {
 using warp = group<1>;      // scope used by most pre-Hopper GPUs, and also for most register operations.
 using warpgroup = group<4>; // special scope commonly used by Hopper and later.
 
-}
+}

extra/thunder/cuda/matmul.py

@@ -6,7 +6,7 @@ from tinygrad.runtime.support.compiler_cuda import pretty_ptx, NVCCCompiler
 if __name__ == "__main__":
   code = (pathlib.Path(__file__).parent / "matmul.cu").read_text()
   device = Device["CUDA"]
-  kitten_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "--expt-relaxed-constexpr"]
+  kitten_args = [f"-I{(pathlib.Path(__file__).parent / 'include').as_posix()}", "-std=c++20", "--expt-relaxed-constexpr", "-DKITTENS_HOPPER"]
   lib = NVCCCompiler(device.compiler.arch, kitten_args).compile(code)
   kernel_name = lib.decode().split(".globl\t")[1].split("\n")[0]
   print("kernel name", kernel_name)

test/external/external_benchmark_schedule.py

@@ -3,7 +3,7 @@ from tinygrad import Tensor, nn, Device
 from tinygrad.helpers import Profiling, Timing, getenv
 from tinygrad.uop.ops import Ops
 from tinygrad.codegen import full_rewrite_to_sink
-from tinygrad.codegen.late.linearizer import linearize
+from tinygrad.codegen.late.control_flow import linearize
 from tinygrad.uop.spec import type_verify, program_spec
 
 if __name__ == "__main__":

test/test_uop_graph.py

@@ -264,7 +264,6 @@ class TestUOpGraph(unittest.TestCase):
     uops = to_uops_list([out])
     self.assertEqual(len([x for x in uops if x.op is Ops.VECTORIZE]), 0)
 
-  @unittest.skip("this test isn't valid uops")
   def test_gep_vec_fold(self):
     d0 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 0)
     d1 = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), (), 1)

test/test_uops.py

@@ -548,16 +548,10 @@ class TestUopsObject(unittest.TestCase):
 class TestUOpRender(unittest.TestCase):
   def test_render_vectorize_same(self):
     u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0)))
-    self.assertEqual(u.render(simplify=False), "{0, ...}")
+    self.assertEqual(u.render(), "{0, ...}")
   def test_render_vectorize_different(self):
     u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1), UOp.const(dtypes.int, 2)))
-    self.assertEqual(u.render(simplify=False), "{0,1,2}")
-  def test_render_vectorize_same_simplified(self):
-    u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 0)))
-    self.assertEqual(u.render(), "0")
-  def test_render_vectorize_different_simplified(self):
-    u = UOp(Ops.VECTORIZE, dtype=dtypes.int.vec(3), src=(UOp.const(dtypes.int, 0), UOp.const(dtypes.int, 1), UOp.const(dtypes.int, 2)))
-    self.assertEqual(u.render(), "(0, 1, 2)")
+    self.assertEqual(u.render(), "{0,1,2}")
 
 if __name__ == '__main__':
   unittest.main(verbosity=2)

tinygrad/codegen/__init__.py

@@ -1,10 +1,7 @@
-from typing import cast
 from tinygrad.helpers import QUANTIZE, DEVECTORIZE, TRANSCENDENTAL, SPEC
-from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp, pm_lower_index_dtype, Ops, UPat
+from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp, pm_lower_index_dtype
 from tinygrad.uop.spec import type_verify, program_spec, kernel_spec
 from tinygrad.renderer import Renderer
-from tinygrad.dtype import dtypes
-from tinygrad.helpers import panic
 
 # import all pattern matchers here
 from tinygrad.codegen.quantize import pm_quant
@@ -17,12 +14,12 @@ from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_in
 from tinygrad.codegen.opt.postrange import apply_opts
 from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_split_ranges, pm_load_collapse
 from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen
-from tinygrad.codegen.late.linearizer import CFGContext, pm_split_ends, pm_add_control_flow, linearize
+from tinygrad.codegen.late.control_flow import CFGContext, pm_split_ends, pm_add_control_flow, linearize
 
 def full_rewrite_to_sink(sink:UOp, ren:Renderer|None=None, optimize:bool=True) -> UOp:
   if ren is None: ren = Renderer()
 
-  if SPEC: type_verify(sink, kernel_spec)
+  if SPEC: type_verify(list(sink.toposort()), kernel_spec)
 
   # first we optimize
   if optimize:
@@ -82,35 +79,16 @@ def full_rewrite_to_sink(sink:UOp, ren:Renderer|None=None, optimize:bool=True) -
 
   # final rules for the renderer (without sym)
   extra_matcher = ren.extra_matcher if ren.extra_matcher is not None else PatternMatcher([])
-  pm_final_rewrite = pm_decomp+pm_render+extra_matcher+pm_split_ends
+  pm_final_rewrite = pm_decomp+pm_render+extra_matcher
   sink = graph_rewrite(sink, pm_final_rewrite, ctx=ren.device, name="final rewrite")
 
   # this was the linearizer
+  sink = graph_rewrite(sink, pm_split_ends, name="split ends of ranges")
   sink = graph_rewrite(sink, pm_add_control_flow, ctx=CFGContext(sink), name="add control flow", bottom_up=True)
 
   # return the rewritten sink
   return sink
 
-# inject IF/ENDIF. only needed if device doesn't support gated stores
-pm_linearize_cleanups = PatternMatcher([
-  # if statements are not allowed in the graph
-  (UPat((Ops.IF, Ops.ENDIF)), lambda: panic(RuntimeError("if not allowed in graph"))),
-  # gated INDEX becomes IF-STORE-ENDIF. this is the only use of IF-ENDIF
-  (UPat(Ops.STORE, name="u", src=(UPat(Ops.INDEX, src=(UPat(), UPat(), UPat(name="gate", dtype=dtypes.bool))).or_casted(), UPat()),
-        allow_any_len=True), lambda u, gate: (u, [mif:=UOp(Ops.IF, src=(gate, u.src[0])), u, UOp(Ops.ENDIF, src=(mif,))]))
-])
-
-# requires lst be toposorted. like graph rewrite, but for lines
-def line_rewrite(lst:list[UOp], pm:PatternMatcher) -> list[UOp]:
-  newlst = []
-  replaced: dict[UOp, UOp] = {}
-  for u in lst:
-    nu = u.replace(src=tuple([replaced[x] for x in u.src]))
-    ret: tuple[UOp, list[UOp]] = cast(tuple[UOp, list[UOp]]|None, pm.rewrite(nu)) or (nu, [nu])
-    replaced[u] = ret[0]
-    newlst.extend(ret[1])
-  return newlst
-
 def full_rewrite(sink:UOp, ren:Renderer|None=None) -> list[UOp]:
   """
   Function to transform the Kernel UOp graph into a linearized program.
@@ -125,6 +103,6 @@ def full_rewrite(sink:UOp, ren:Renderer|None=None) -> list[UOp]:
 
   full_sink = full_rewrite_to_sink(sink, ren, optimize=sink.tag is None)
   assert len(full_sink.ranges) == 0, "all ranges must end by the sink"
-  lst = line_rewrite(linearize(full_sink), pm_linearize_cleanups)
+  lst = linearize(full_sink)
   if SPEC: type_verify(lst, program_spec)
   return lst

tinygrad/codegen/late/control_flow.py

@@ -1,9 +1,31 @@
 import heapq
+from typing import cast
 from collections import defaultdict
+from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import PatternMatcher, UOp, Ops, UPat
+from tinygrad.helpers import panic
+
+# only needed if device doesn't support gated stores
+pm_linearize_cleanups = PatternMatcher([
+  # if statements are not allowed in the graph
+  (UPat((Ops.IF, Ops.ENDIF)), lambda: panic(RuntimeError("if not allowed in graph"))),
+  # gated INDEX becomes IF-STORE-ENDIF. this is the only use of IF-ENDIF
+  (UPat(Ops.STORE, name="u", src=(UPat(Ops.INDEX, src=(UPat(), UPat(), UPat(name="gate", dtype=dtypes.bool))).or_casted(), UPat()),
+        allow_any_len=True), lambda u, gate: (u, [mif:=UOp(Ops.IF, src=(gate, u.src[0])), u, UOp(Ops.ENDIF, src=(mif,))]))
+])
+
+# requires lst be toposorted. like graph rewrite, but for lines
+def line_rewrite(lst:list[UOp], pm:PatternMatcher) -> list[UOp]:
+  newlst = []
+  replaced: dict[UOp, UOp] = {}
+  for u in lst:
+    nu = u.replace(src=tuple([replaced[x] for x in u.src]))
+    ret: tuple[UOp, list[UOp]] = cast(tuple[UOp, list[UOp]]|None, pm.rewrite(nu)) or (nu, [nu])
+    replaced[u] = ret[0]
+    newlst.extend(ret[1])
+  return newlst
 
 def linearize(u:UOp) -> list[UOp]:
-  # this is a toposort with priority
   lst = list(u.toposort())
   consumers: defaultdict[UOp, list[UOp]] = defaultdict(list)
   in_degree:dict[UOp, int] = {}
@@ -36,8 +58,9 @@ def linearize(u:UOp) -> list[UOp]:
     for v in consumers[u]:
       in_degree[v] -= 1
       if in_degree[v] == 0: heapq.heappush(heap, (nkey[v],v))
+
   assert len(newlst) == len(lst), f"len mismatch {len(newlst)} != {len(lst)}"
-  return newlst
+  return line_rewrite(newlst, pm_linearize_cleanups)
 
 class CFGContext:
   def __init__(self, sink:UOp):
@@ -78,4 +101,4 @@ def do_split_ends(e:UOp):
 pm_split_ends = PatternMatcher([
   # split the ends
   (UPat(Ops.END, name="e"), do_split_ends),
-])
+])

tinygrad/codegen/opt/search.py

@@ -137,7 +137,7 @@ def beam_search(lin:Scheduler, rawbufs:list[Buffer], amt:int, allow_test_size=Tr
   min_progress = getenv("BEAM_MIN_PROGRESS", 0.01)/1e6
   if BEAM_DEBUG:
     print("BEAM_SEARCH:")
-    print(pyrender(lin.ast.replace(arg=None)))
+    print('\n'.join(pyrender(lin.ast.replace(arg=None))))
   if DEBUG >= 2: print(f"   0.00s:                from   1 ->   1 actions {lin.colored_shape()}")
 
   try:

tinygrad/dtype.py

@@ -14,8 +14,6 @@ class InvalidTypeMetaClass(type):
 
 class InvalidType(metaclass=InvalidTypeMetaClass):
   def __eq__(self, other): return self is other
-  def __lt__(self, other): return self is not other
-  def __gt__(self, other): return self is not other
   def __hash__(self): return id(self)
   def __repr__(self): return "Invalid"
   def __reduce__(self): return (InvalidType, ())  # Return the global Invalid instance

tinygrad/engine/realize.py

@@ -26,7 +26,7 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
   """
 
   if getenv("VIZ"): graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
-  if DEBUG >= 5: print(pyrender(ast))
+  if DEBUG >= 5: print('\n'.join(pyrender(ast)))
 
   # linearize
   if renderer is None: renderer = Device.default.renderer
@@ -38,7 +38,7 @@ def get_program(ast:UOp, renderer:Renderer|None=None, opts:list[Opt]|None=None)
   except RuntimeError as e:
     print("***** LINEARIZE FAILURE *****")
     print(e)
-    print(pyrender(ast))
+    print('\n'.join(pyrender(ast)))
     raise
   assert uops[-1].op is Ops.SINK, "last uop must be sink"
 

tinygrad/helpers.py

@@ -184,6 +184,7 @@ class Metadata:
   caller: str
   backward: bool = False
   def __hash__(self): return hash(self.name)
+  def __repr__(self): return str(self) + (f" - {self.caller}" if self.caller else "")
   def __str__(self): return self.name + (" bw" if self.backward else "")
 
 # **************** global state Counters ****************

tinygrad/renderer/__init__.py

@@ -81,12 +81,8 @@ class ProgramSpec:
       for u in self.uops:
         if u.op is Ops.DEFINE_VAR: self.vars.append(u)
         if u.op is Ops.DEFINE_GLOBAL: self.globals.append(u.arg)
-        if u.op is Ops.STORE and (u.src[0].op is Ops.INDEX or (u.src[0].op is Ops.CAST and u.src[0].src[0].op is Ops.INDEX)):
-          idx = u.src[0] if u.src[0].op is Ops.INDEX else u.src[0].src[0]
-          if (buf:=idx.src[0]).op is Ops.DEFINE_GLOBAL: self.outs.append(buf.arg)
-        if u.op is Ops.LOAD and (u.src[0].op is Ops.INDEX or (u.src[0].op is Ops.CAST and u.src[0].src[0].op is Ops.INDEX)):
-          idx = u.src[0] if u.src[0].op is Ops.INDEX else u.src[0].src[0]
-          if (buf:=idx.src[0]).op is Ops.DEFINE_GLOBAL: self.ins.append(buf.arg)
+        if u.op is Ops.STORE: self.outs.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
+        if u.op is Ops.LOAD: self.ins.extend([x.arg for x in u.src[0].toposort() if x.op is Ops.DEFINE_GLOBAL])
         if u.op is Ops.SPECIAL:
           # NOTE: you have to set local_size and global_size to the base [1,1,1] outside this
           if u.arg[0] == 'i': self.local_size = None

tinygrad/runtime/ops_amd.py

@@ -4,7 +4,7 @@ import os, ctypes, struct, hashlib, functools, importlib, mmap, errno, array, co
 assert sys.platform != 'win32'
 from dataclasses import dataclass
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQBuffer, HWQueue, CLikeArgsState, HCQSignal, HCQProgram, FileIOInterface
-from tinygrad.runtime.support.hcq import MMIOInterface, BumpAllocator, hcq_filter_visible_devices
+from tinygrad.runtime.support.hcq import MMIOInterface, BumpAllocator
 from tinygrad.uop.ops import sint
 from tinygrad.device import Compiled, DMAFdRef, BufferSpec, CompilerPairT
 from tinygrad.helpers import getenv, round_up, data64_le, DEBUG, PROFILE, ProfileEvent, suppress_finalizing, lo32, hi32, colored
@@ -575,7 +575,9 @@ class KFDIface:
     if KFDIface.kfd is None:
       KFDIface.kfd = FileIOInterface("/dev/kfd", os.O_RDWR)
       gpus = [g for g in FileIOInterface(kfd_topo_path).listdir() if self._is_usable_gpu(FileIOInterface(f"{kfd_topo_path}/{g}/gpu_id"))]
-      KFDIface.gpus = hcq_filter_visible_devices(sorted(gpus, key=lambda x: int(x.split('/')[-1])))
+      gpus = sorted(gpus, key=lambda x: int(x.split('/')[-1]))
+      visible_devices = [int(x) for x in (getenv('VISIBLE_DEVICES', getenv('HIP_VISIBLE_DEVICES', ''))).split(',') if x.strip()]
+      KFDIface.gpus = [gpus[x] for x in visible_devices] if visible_devices else gpus
 
     if device_id >= len(KFDIface.gpus): raise RuntimeError(f"No device found for {device_id}. Requesting more devices than the system has?")
 

tinygrad/runtime/ops_nv.py

@@ -4,7 +4,7 @@ assert sys.platform != 'win32'
 from typing import cast, ClassVar
 from dataclasses import dataclass
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQBuffer, HWQueue, CLikeArgsState, HCQProgram, HCQSignal, BumpAllocator
-from tinygrad.runtime.support.hcq import MMIOInterface, FileIOInterface, MOCKGPU, hcq_filter_visible_devices
+from tinygrad.runtime.support.hcq import MMIOInterface, FileIOInterface, MOCKGPU
 from tinygrad.uop.ops import sint
 from tinygrad.device import BufferSpec, CompilerPairT
 from tinygrad.helpers import getenv, mv_address, round_up, data64, data64_le, prod, OSX, to_mv, hi32, lo32, suppress_finalizing
@@ -321,7 +321,8 @@ class NVKIface:
       with contextlib.suppress(RuntimeError): uvm.mm_initialize(self.fd_uvm_2, uvmFd=self.fd_uvm.fd) # this error is okay, CUDA hits it too
 
       nv_iowr(NVKIface.fd_ctl, nv_gpu.NV_ESC_CARD_INFO, gpus_info:=(nv_gpu.nv_ioctl_card_info_t*64)())
-      NVKIface.gpus_info = hcq_filter_visible_devices(gpus_info)
+      visible_devices = [int(x) for x in (getenv('VISIBLE_DEVICES', getenv('CUDA_VISIBLE_DEVICES', ''))).split(',') if x.strip()]
+      NVKIface.gpus_info = [gpus_info[x] for x in visible_devices] if visible_devices else gpus_info
 
     self.dev, self.device_id = dev, device_id
     if self.device_id >= len(NVKIface.gpus_info) or not NVKIface.gpus_info[self.device_id].valid:

tinygrad/runtime/support/amd.py

@@ -35,7 +35,7 @@ def fixup_ip_version(ip:str, version:tuple[int, ...]) -> list[tuple[int, ...]]:
       if version[:len(ver)] == ver: return ovrd_ver
     return version
 
-  if ip in ['nbio', 'nbif']: version = _apply_ovrd({(3,3): (2,3,0), (7,3): (7,2,0)})
+  if ip in ['nbio', 'nbif']: version = _apply_ovrd({(3,3): (2,3,0)})
   elif ip in ['mp', 'smu']: version = _apply_ovrd({(14,0,3): (14,0,2)})
   elif ip in ['gc']: version = _apply_ovrd({(9,5,0): (9,4,3)})
 

tinygrad/runtime/support/hcq.py

@@ -57,9 +57,6 @@ if MOCKGPU:=getenv("MOCKGPU"): from test.mockgpu.mockgpu import MockFileIOInterf
 
 # **************** for HCQ Compatible Devices ****************
 
-def hcq_filter_visible_devices(dev):
-  return [dev[x] for x in ids] if (ids:=[int(x) for x in (getenv('HCQ_VISIBLE_DEVICES', '')).split(',') if x.strip()]) else dev
-
 SignalType = TypeVar('SignalType', bound='HCQSignal')
 HCQDeviceType = TypeVar('HCQDeviceType', bound='HCQCompiled')
 ProgramType = TypeVar('ProgramType', bound='HCQProgram')

tinygrad/runtime/support/nv/nvdev.py

@@ -71,7 +71,7 @@ class NVMemoryManager(MemoryManager):
 
 class NVDev(PCIDevImplBase):
   def __init__(self, pci_dev:PCIDevice):
-    self.pci_dev, self.devfmt, self.mmio = pci_dev, pci_dev.pcibus, pci_dev.map_bar(0, fmt='I')
+    self.pci_dev, self.devfmt, self.vram, self.mmio = pci_dev, pci_dev.pcibus, pci_dev.map_bar(1), pci_dev.map_bar(0, fmt='I')
 
     self.lock_fd = System.flock_acquire(f"nv_{self.devfmt}.lock")
 
@@ -134,8 +134,6 @@ class NVDev(PCIDevImplBase):
     self.pte_t, self.pde_t, self.dual_pde_t = tuple([self.__dict__[name] for name in mmu_pd_names])
 
     self.vram_size = self.reg("NV_PGC6_AON_SECURE_SCRATCH_GROUP_42").read() << 20
-
-    self.vram, self.mmio = self.pci_dev.map_bar(1), self.pci_dev.map_bar(0, fmt='I')
     self.large_bar = self.vram.nbytes >= self.vram_size
 
   def _alloc_boot_struct(self, struct:ctypes.Structure) -> tuple[ctypes.Structure, int]:

tinygrad/runtime/support/system.py

@@ -2,7 +2,7 @@ import os, mmap, array, functools, ctypes, select, contextlib, dataclasses, sys,
 from typing import cast, ClassVar
 from tinygrad.helpers import round_up, getenv, OSX, temp, ceildiv
 from tinygrad.runtime.autogen import libc, vfio, pci
-from tinygrad.runtime.support.hcq import FileIOInterface, MMIOInterface, HCQBuffer, hcq_filter_visible_devices
+from tinygrad.runtime.support.hcq import FileIOInterface, MMIOInterface, HCQBuffer
 from tinygrad.runtime.support.memory import MemoryManager, VirtMapping
 from tinygrad.runtime.support.usb import ASM24Controller, USBMMIOInterface
 
@@ -243,7 +243,9 @@ class LNXPCIIfaceBase:
 
   def __init__(self, dev, dev_id, vendor, devices, bars, vram_bar, va_start, va_size):
     if len((cls:=type(self)).gpus) == 0:
-      cls.gpus = hcq_filter_visible_devices(System.pci_scan_bus(vendor, devices))
+      cls.gpus = System.pci_scan_bus(vendor, devices)
+      visible_devices = [int(x) for x in (getenv('VISIBLE_DEVICES', '')).split(',') if x.strip()]
+      cls.gpus = [cls.gpus[x] for x in visible_devices] if visible_devices else cls.gpus
 
       # Acquire va range to avoid collisions.
       FileIOInterface.anon_mmap(va_start, va_size, 0, mmap.MAP_PRIVATE | mmap.MAP_ANONYMOUS | MAP_NORESERVE | MAP_FIXED, 0)

tinygrad/tensor.py

@@ -229,7 +229,7 @@ class Tensor(MathTrait):
     big_sink = UOp.sink(*[x.uop for x in (self,)+lst])
 
     # verify Tensors match the spec
-    if SPEC: type_verify(big_sink, tensor_spec)
+    if SPEC: type_verify(list(big_sink.toposort()), tensor_spec)
 
     if any(isinstance(x._device, tuple) for x in big_sink.toposort()):
       _apply_map_to_tensors(get_multi_map(big_sink), "Apply Multi Map")

tinygrad/uop/__init__.py

@@ -3,7 +3,6 @@ from enum import auto, IntEnum, Enum
 # wrapper around IntEnum that preserves Enum.__str__ and makes auto() unique across all FastEnum subclasses
 class FastEnum(IntEnum):
   def __str__(self): return Enum.__str__(self)
-  def __repr__(x): return str(x)
   @staticmethod
   def _generate_next_value_(_, __, ___, last_values): return 1 + max([0, *last_values, *[max(c) for c in FastEnum.__subclasses__()]])
 

tinygrad/uop/ops.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-from typing import Any, Callable, cast, TYPE_CHECKING, Type, Sequence, Iterable
+from typing import Any, Callable, cast, TYPE_CHECKING, Type, Sequence
 import sys, time, functools, itertools, math, operator, hashlib, os, types, pickle, pathlib, inspect, weakref, collections
 from dataclasses import dataclass
 from enum import Enum, auto
@@ -42,13 +42,6 @@ def sym_infer(uop: UOp|int, var_vals: dict[str, int]) -> int: return uop.sym_inf
 
 def range_str(u:UOp) -> str: return '_'.join([str(x) if x >= 0 else "m"+str(-x) for x in u.arg[0:-1]])
 
-def consumer_map_from_toposort(lst:Iterable[UOp]):
-  ret: dict[UOp, dict[UOp, None]] = {}
-  for u in lst:
-    ret[u] = {}
-    for s in u.src: ret[s][u] = None
-  return ret
-
 # used for UOp and UPat
 def pretty_print(x:Any, rep:Callable, srcfn=lambda x: x.src, cache=None, d=0)->str:
   def dfs(x:Any, cache:dict):
@@ -72,8 +65,7 @@ class UOpMetaClass(type):
       assert op is Ops.BUFFER, f"trying to set Buffer {_buffer} for {op}"
       buffers[created] = _buffer
     if SPEC > 1:
-      from tinygrad.uop.spec import full_spec, test_pyrender
-      if SPEC > 2: test_pyrender(created)
+      from tinygrad.uop.spec import full_spec
       with Context(IGNORE_OOB=1): ret = full_spec.rewrite(created)
       if cast(bool|None, ret) is not True: raise RuntimeError(f"SPEC ISSUE {ret}: {created}")
     return created
@@ -152,7 +144,12 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     return ret
 
   # returns map of UOps to their consumers in the graph rooted by self
-  def get_consumer_map(self) -> dict[UOp, dict[UOp, None]]: return consumer_map_from_toposort(self.toposort())
+  def get_consumer_map(self) -> dict[UOp, dict[UOp, None]]:
+    ret: dict[UOp, dict[UOp, None]] = {}
+    for u in self.toposort():
+      ret[u] = {}
+      for s in u.src: ret[s][u] = None
+    return ret
 
   def reverse_toposort(self, consumer_map) -> dict[UOp, None]:
     ret: dict[UOp, None] = {}
@@ -363,7 +360,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
   def end(self, *src:UOp):
     if len(src) == 0: return self
     return UOp(Ops.END, src=(self,)+src)
-  def after(self, *src:UOp, **kwargs): return UOp(Ops.AFTER, self.dtype, (self,)+src, **kwargs)
+  def after(self, *src:UOp): return UOp(Ops.AFTER, self.dtype, (self,)+src)
   def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))
   def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
   def alu(self, op, *src:UOp, **kwargs):
@@ -381,10 +378,10 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if shape is not None: ret = ret.reshape((1,)*len(shape)).expand(shape)
     return ret
   @staticmethod
-  def range(end:sint, *arg, dtype=dtypes.index, src=(), **kwargs):
+  def range(end:sint, *arg, dtype=dtypes.index, **kwargs):
     if len(arg) == 0: raise RuntimeError("range needs an arg")
     if len(arg) == 1: arg = arg+(AxisType.LOOP,)
-    return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end, dtype),)+src, arg=arg, **kwargs)
+    return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end, dtype),), arg=arg, **kwargs)
   @staticmethod
   def special(end:sint, name:str, dtype=dtypes.index): return UOp(Ops.SPECIAL, dtype=dtype, src=(sint_to_uop(end, dtype),), arg=name)
   def r(self, op:Ops, axis:tuple[int, ...]):
@@ -1237,101 +1234,44 @@ renderer_infer = PatternMatcher([
   *renderer.patterns
 ])
 
-# *** pyrender ***
-
-def render_marg(ctx,x:UOp):
-  if x.op in {Ops.PERMUTE, Ops.FLIP}: return str(x.marg)
-  pieces = []
-  if x.op in {Ops.RESHAPE, Ops.EXPAND}:
-    pieces = [f"{ctx[a] if isinstance(a, UOp) else str(a)}" for a in x.marg]
-  if x.op in {Ops.PAD, Ops.SHRINK}:
-    pieces = [f"({ctx[a[0]] if isinstance(a[0], UOp) else str(a[0])}, {ctx[a[1]] if isinstance(a[1], UOp) else str(a[1])})" for a in x.marg]
-  return f"({','.join(pieces)})" if len(pieces) != 1 else f"({pieces[0]},)"
-
-# TODO: use this more in pyrender
-def srcs(ctx, src): return f"({ctx[src[0]]},)" if len(src) == 1 else f"({', '.join([ctx[x] for x in src])})"
-
-sugar = {Ops.SINK, Ops.END, Ops.STORE, Ops.LOAD, Ops.UNIQUE, Ops.SQRT, Ops.INDEX, Ops.REDUCE, Ops.AFTER,
-         Ops.WHERE, Ops.RECIPROCAL, Ops.EXP2, Ops.LOG2, Ops.SIN, Ops.CONTIGUOUS, Ops.BARRIER}
-pm_pyrender_extra = PatternMatcher([
-  (UPat(Ops.CONST, src=(UPat(Ops.DEVICE, name="d"),), name="x"), lambda x,d: f"UOp.const({x.dtype}, {x.arg}, device={repr(d.arg)})"),
-  (UPat(Ops.CONST, name="x"), lambda x: f"UOp.const({x.dtype}, {x.arg})"),
-  (UPat(Ops.DEFINE_VAR, src=(), name="x"), lambda x:
-    f"UOp.variable(\"{x.arg[0]}\", {x.arg[1]}, {x.arg[2]}{', dtype='+str(x.dtype) if x.dtype is not dtypes.index else ''})"),
-  (UPat((Ops.CAST, Ops.BITCAST), name="x"), lambda ctx,x: f"{ctx[x.src[0]]}.{x.op.name.lower()}({x.dtype})"),
-  (UPat(Ops.SPECIAL, src=(UPat(Ops.CONST),), name="x"), lambda x: f"UOp.special({x.src[0].arg}, {repr(x.arg)}, dtype={x.dtype})"),
-  (UPat(Ops.BUFFER, src=(UPat(Ops.UNIQUE, name="u"), UPat(Ops.DEVICE, name="d")), name="x"), lambda x,u,d:
-    f"UOp.new_buffer({repr(d.arg)}, {x.size}, {x.dtype}, {u.arg})"),
-  (UPat(Ops.COPY, src=(UPat(name="x"), UPat(Ops.DEVICE, name="d"))), lambda ctx,x,d: f"{ctx[x]}.copy_to_device({repr(d.arg)})"),
-  (UPat(Ops.REDUCE_AXIS, name="r"), lambda ctx,r: f"{ctx[r.src[0]]}.r({r.arg[0]}, {r.arg[1]})"),
-  # NOTE: range has srcs sometimes after control flow
-  (UPat(Ops.RANGE, src=(UPat(Ops.CONST, name="c"),), allow_any_len=True, name="x"), lambda ctx,x,c:
-    "UOp.range("+', '.join([str(c.arg)] + [str(y) for y in x.arg])+
-      (f', src={srcs(ctx, x.src[1:])}' if len(x.src) > 1 else '')+\
-      (', dtype='+str(x.dtype) if x.dtype is not dtypes.index else '')+\
-      (', tag='+str(x.tag) if x.tag is not None else '')+")"),
-  # TODO: index shouldn't mismatch dtype
-  (UPat(Ops.INDEX, src=(UPat(), UPat()), name="x"), lambda ctx,x:
-   f"{ctx[x.src[0]]}.index({ctx[x.src[1]]}, dtype={x.dtype})" if x.src[0].dtype != x.dtype else None),
-  # TODO: fix forced_reshape
-  (UPat(Ops.RESHAPE, name="x"), lambda ctx,x: f"{ctx[x.src[0]]}.forced_reshape({render_marg(ctx,x)})" if x.src[0].shape == x.shape else None),
-  (UPat(GroupOp.Movement, name="x"), lambda ctx,x: f"{ctx[x.src[0]]}.{x.op.name.lower()}({render_marg(ctx,x)})"),
-  # NOTE: CMPNE doesn't work cause there's no __rne__
-  (UPat(set(syms.keys())-{Ops.SUB, Ops.CMPNE}, src=(UPat(Ops.CONST, name="y"), UPat(name="z")), name="x"),
-   lambda ctx,x,y,z: f"({y.arg}{syms[x.op]}{ctx[z]})"),
-  # NOTE: sub doesn't work cause it's written as add/mul
-  (UPat(set(syms.keys())-{Ops.SUB}, src=(UPat(name="y"), UPat(Ops.CONST, name="z")), name="x"), lambda ctx,x,y,z: f"({ctx[y]}{syms[x.op]}{z.arg})"),
-  (UPat(set(syms.keys())-{Ops.SUB}, name="x"), lambda ctx,x: f"({ctx[x.src[0]]}{syms[x.op]}{ctx[x.src[1]]})"),
-  (UPat(sugar, src=(), name="x"), lambda x: f"UOp.{x.op.name.lower()}("+', '.join( \
-    ([f'arg={repr(x.arg)}'] if x.arg is not None else []) + ([f'tag={repr(x.tag)}'] if x.tag is not None else []))+")"),
-  (UPat(sugar, name="x"), lambda ctx,x: f"{ctx[x.src[0]]}.{x.op.name.lower()}("+', '.join([ctx[y] for y in x.src[1:]] + \
-    ([f'arg={repr(x.arg)}'] if x.arg is not None else []) + ([f'tag={repr(x.tag)}'] if x.tag is not None else []))+")"),
+sugar = { Ops.SINK: "sink", Ops.STORE: "store", Ops.LOAD: "load", Ops.SQRT: "sqrt", Ops.INDEX: "index", Ops.REDUCE: "reduce",
+          Ops.WHERE: "where", Ops.RECIPROCAL: "reciprocal", Ops.EXP2: "exp2", Ops.LOG2: "log2", Ops.SIN: "sin"}
+pm_pyrender = PatternMatcher([
+  (UPat(Ops.CONST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg}, src={x.src[0].arg})")),
+  (UPat(Ops.CONST, name="x"), lambda x: UOp(Ops.NOOP, arg=f"UOp.const({x.dtype}, {x.arg})")),
+  (UPat(Ops.END, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.end({', '.join([y.arg for y in x.src[1:]])})")),
+  (UPat(Ops.CAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.cast({x.dtype})")),
+  (UPat(Ops.BITCAST, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.bitcast({x.dtype})")),
+  (UPat({Ops.MAX, Ops.THREEFRY, Ops.CMPLT, Ops.CMPNE, Ops.POW}, src=UPat(Ops.NOOP), name="x"),
+   lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.alu({x.op}, {x.src[1].arg})")),
+  (UPat(Ops.RANGE, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg=
+    f"UOp.range({x.src[0].arg}, {str(x.arg[0])}, {str(x.arg[1])}{', dtype='+str(x.dtype) if x.dtype is not dtypes.index else ''})")),
+  (UPat(Ops.SPECIAL, src=(UPat(Ops.NOOP),), name="x"), lambda x: UOp(Ops.NOOP, arg= f"UOp.special({x.src[0].arg}, \"{x.arg}\", dtype={x.dtype})")),
+  (UPat(Ops.DEFINE_VAR, name="x"), lambda x: UOp(Ops.NOOP, arg=
+    f"UOp.variable(\"{x.arg[0]}\", {x.arg[1]}, {x.arg[2]}{', dtype='+str(x.dtype) if x.dtype is not dtypes.index else ''})")),
+  (UPat(set(sugar.keys()), src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP,
+    arg=f"{x.src[0].arg}.{sugar[x.op]}({', '.join([y.arg for y in x.src[1:]] + ([f'arg={str(x.arg)}'] if x.arg is not None else []))})")),
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.NOOP),), name="x"),
+   lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}.f({x.op}, arg=({', '.join([str(y) for y in x.arg])}))")),
 ])
 
-# NOTE: you can remove pm_pyrender_extra and it'll still be correct
-pm_pyrender = pm_pyrender_extra+PatternMatcher([
-  (UPat(Ops.KERNEL, name="u"), lambda ctx,u: "UOp(Ops.KERNEL, src="+', '.join( \
-    ([f"({ctx[u.src[0]]},)"] if len(u.src) == 1 else ([f"({', '.join([ctx[x] for x in u.src])})"] if len(u.src) > 1 else []))) + \
-    f", arg=Kernel({ctx[u.arg.ast]}(), {u.arg.metadata})"+(f", tag={repr(u.tag)}" if u.tag is not None else "")+")"),
-  (UPat(GroupOp.All, name="u"), lambda ctx,u: "UOp("+', '.join([str(u.op), str(u.dtype)] + \
-    ([f"({ctx[u.src[0]]},)"] if len(u.src) == 1 else ([f"({', '.join([ctx[x] for x in u.src])})"] if len(u.src) > 1 else [])) + \
-    ([f"arg={repr(u.arg)}"] if u.arg is not None else []) + ([f"tag={repr(u.tag)}"] if u.tag is not None else []))+")"),
-])
-
-def pyrender(ast:UOp) -> str:
-  lst = list(ast.toposort())
-
-  cmap = consumer_map_from_toposort(lst)
-  not_rendered = {Ops.CONST, Ops.VCONST, Ops.DEVICE}
-  always_rendered = {Ops.DEFINE_GLOBAL, Ops.LOAD, Ops.SPECIAL, Ops.RANGE, Ops.CONTIGUOUS, Ops.BUFFER, Ops.COPY, Ops.KERNEL, Ops.WHERE, Ops.END}
-
-  to_render: set[UOp] = {ast}
-  for u in lst:
+@Context(SPEC=0)
+def pyrender(ast:UOp) -> list[str]:
+  cmap = ast.get_consumer_map()
+  to_render = set()
+  for u in ast.toposort():
+    if u.op is Ops.STORE: to_render.add(u.src[1])
+    if len(cmap[u]) == 1 and u.op not in {Ops.DEFINE_GLOBAL, Ops.LOAD} or u.op in {Ops.CONST}: continue
     if u.op in {Ops.SINK}:
       for s in u.src: to_render.add(s)
-    if u.op is Ops.STORE: to_render.add(u.src[1])
-    if u.op in {Ops.REDUCE, Ops.REDUCE_AXIS}: to_render.add(u.src[0])
-    if u.op in not_rendered: continue
-    # checking the consumers is not enough, you have to make sure it's not used twice by the one consumer
-    if len(cmap[u]) == 1 and len([x for x in list(cmap[u].keys())[0].src if x is u]) == 1 and u.op not in always_rendered: continue
     to_render.add(u)
-
-  kernels: dict[UOp, tuple[str, str]] = {}
-  r: dict[UOp, str] = {}
-  ret: dict[str, str] = {}
-  for i,u in enumerate(lst):
-    if u.op is Ops.KERNEL:
-      if u.arg.ast not in kernels:
-        kernels[u.arg.ast] = (f"k{len(kernels)}", f"def k{len(kernels)}():\n  " + pyrender(u.arg.ast).replace('\n', '\n  ') + "\n  return ast\n\n")
-      r[u.arg.ast] = kernels[u.arg.ast][0]
-    ren = cast(str, pm_pyrender.rewrite(u, ctx=r))
-    assert isinstance(ren, str)
-    #if u.tag is not None: ren += f".rtag({u.tag})"
-    if u not in to_render: r[u] = ren
-    else:
-      r[u] = f"c{i}" if u is not lst[-1] else "ast"
-      ret[r[u]] = ren
-  return ''.join([v[1] for v in kernels.values()]) + '\n'.join([f"{k} = {v}" for k,v in ret.items()])
+  ret: list[str] = []
+  rep: dict[UOp, UOp] = {}
+  for u in ast.toposort():
+    if u not in to_render: continue
+    ret.append(f"c{len(ret)} = {u.substitute(rep).render(simplify=False, pm=pm_pyrender+renderer)}")
+    rep[u] = UOp(Ops.NOOP, arg=f"c{len(ret)-1}")
+  return ret[0:-1] + ["ast ="+ret[-1].split("=", 1)[1]]
 
 # *** what was symbolic.py ***
 

tinygrad/uop/spec.py

@@ -1,8 +1,7 @@
-import math
-from typing import cast, Any
-from tinygrad.uop.ops import PatternMatcher, UPat, GroupOp, Ops, UOp, print_uops, AxisType, KernelInfo, pyrender
+from typing import cast
+from tinygrad.uop.ops import PatternMatcher, UPat, GroupOp, Ops, UOp, print_uops, AxisType
 from tinygrad.dtype import DType, ImageDType, dtypes, PtrDType, AddrSpace, Invalid
-from tinygrad.helpers import DEBUG, Context, prod, SPEC, Metadata
+from tinygrad.helpers import DEBUG, Context, prod
 from tinygrad.uop.validate import validate_index
 
 # four specs:
@@ -234,32 +233,9 @@ full_spec = PatternMatcher([
 
 # ***** uop helpers *****
 
-def type_verify(ast:UOp|list[UOp], check_spec:PatternMatcher):
-  lst = list(ast.toposort()) if isinstance(ast, UOp) else ast
-  if SPEC > 1: test_pyrender(lst[-1])  # assume this is the sink
-
-  for i,u in enumerate(lst):
+def type_verify(uops:list[UOp], check_spec:PatternMatcher):
+  for i,u in enumerate(uops):
     with Context(TRACK_MATCH_STATS=0): ret = check_spec.rewrite(u)
     if cast(bool|None, ret) is not True:
-      if DEBUG >= 3: print_uops(lst)
+      if DEBUG >= 3: print_uops(uops)
       raise RuntimeError(f"UOp verification failed at {i} on {u.op} {u.dtype} {len(u.src)} {[(x.op, x.dtype, x.arg) for x in u.src]} {u.arg}")
-
-# late imports to avoid circular import
-from tinygrad.codegen.opt import Opt, OptOps
-from tinygrad.schedule.rangeify import BufferizeOpts, Kernel
-glbls:dict[str, Any] = {"inf": math.inf, "nan": math.nan, "KernelInfo": KernelInfo, "Kernel": Kernel, "Metadata": Metadata,
-                        "UOp": UOp, "dtypes": dtypes, "Ops": Ops, "AxisType": AxisType, "Invalid": Invalid,
-                        "Opt": Opt, "OptOps": OptOps, "BufferizeOpts": BufferizeOpts, "AddrSpace": AddrSpace}
-def eval_pyrender(code:str) -> UOp:
-  lcls:dict[str, Any] = {}
-  exec(code, glbls, lcls)
-  return lcls['ast']
-
-def test_pyrender(test_ast:UOp, assert_parents=True):
-  code = pyrender(test_ast)
-  ast:UOp = eval_pyrender(code)
-  if ast is not test_ast:
-    if assert_parents:
-      for u in test_ast.toposort(): test_pyrender(u, assert_parents=False)
-    raise RuntimeError(f"PYRENDER ISSUE:\nSTR MATCH: {str(test_ast) == str(ast)}\nUOP:\n{test_ast}\nPRODUCED:\n{ast}\nCODE:\n{code}")
-  return code

tinygrad/uop/symbolic.py

@@ -382,8 +382,6 @@ symbolic = symbolic_simple+commutative+PatternMatcher([
     tuple(flatten([(y,) if y.op in {Ops.RANGE, Ops.IF, Ops.STORE, Ops.KERNEL, Ops.BARRIER, Ops.END, Ops.UNROLL} else y.src for y in x.src[1:]])))),
   # after with 1 src is just src[0]
   (UPat(Ops.AFTER, src=(UPat.var("s"),)), lambda s: s),
-  # VECTORIZE/CONST
-  (UPat(Ops.VECTORIZE, src=UPat(Ops.CONST), name="vec"), lambda vec: UOp.const(vec.dtype, tuple(x.arg for x in vec.src))),
 ])+gep_pushing
 
 symbolic_flat = symbolic+PatternMatcher([
@@ -513,7 +511,8 @@ sym = symbolic_flat+pm_simplify_valid+PatternMatcher([
   # LOAD/STORE -> NOOP
   (UPat.var('x').store(UPat.var('x').load(), allow_any_len=True), lambda x: None if x.dtype.addrspace != AddrSpace.REG else x.src[0].src[0]),
   (UPat(Ops.LOAD, src=(UPat.cvar('c'))), lambda c: c),
-  # VECTORIZE/GEP
+  # VECTORIZE/CONST, VECTORIZE/GEP
+  (UPat(Ops.VECTORIZE, src=UPat(Ops.CONST), name="vec"), lambda vec: UOp.const(vec.dtype, tuple(x.arg for x in vec.src))),
   (UPat(Ops.VECTORIZE, src=UPat(Ops.GEP, src=(UPat.var("x"),)), name="vec"), lambda vec,x: x.gep(tuple(y.arg[0] for y in vec.src))),
   # reorder ALU/VECTORIZE
   (UPat(GroupOp.ALU, src=(UPat(Ops.VECTORIZE, src=UPat(name='x')), UPat(Ops.VECTORIZE, src=UPat(name='y'))), name='alu'),

tinygrad/viz/index.html

@@ -241,9 +241,6 @@
     max-height: 30vh;
     padding: 8px;
   }
-  pre.full-height code.hljs {
-    max-height: none;
-  }
   #progress-message {
     position: absolute;
     z-index: 2;

tinygrad/viz/js/index.js

@@ -70,11 +70,10 @@ const drawGraph = (data) => {
   nodes.selectAll("rect").data(d => [d]).join("rect").attr("width", d => d.width).attr("height", d => d.height).attr("fill", d => d.color)
     .attr("x", d => -d.width/2).attr("y", d => -d.height/2);
   const STROKE_WIDTH = 1.4;
-  nodes.selectAll("g.label").data(d => [d]).join("g").attr("class", "label").attr("transform", d => {
-    const x = d.labelWidth/2;
-    const y = d.labelHeight/2+STROKE_WIDTH*2;
-    return `translate(-${x}, -${y})`;
-  }).selectAll("text").data(d => {
+  const labels = nodes.selectAll("g.label").data(d => [d]).join("g").attr("class", "label").attr("transform", d => {
+    return d.labelWidth != null ? `translate(-${d.labelWidth/2}, -${d.labelHeight/2+STROKE_WIDTH*2})` : null;
+  });
+  labels.selectAll("text").data(d => {
     const ret = [[]];
     for (const { st, color } of parseColors(d.label, defaultColor="initial")) {
       const lines = st.split("\n");
@@ -732,8 +731,8 @@ async function main() {
   if (ret.length === 0) return;
   renderDag(ret[currentRewrite].graph, ret[currentRewrite].changed_nodes ?? [], currentRewrite === 0);
   // ** right sidebar code blocks
-  const codeElement = codeBlock(ret[currentRewrite].uop, "python", { wrap:false });
-  metadata.replaceChildren(codeBlock(step.code_line, "python", { loc:step.loc, wrap:true }), codeElement);
+  metadata.replaceChildren(codeBlock(step.code_line, "python", { loc:step.loc, wrap:true }),
+                           codeBlock(ret[currentRewrite].uop, "python", { wrap:false }));
   // ** rewrite steps
   if (step.match_count >= 1) {
     const rewriteList = metadata.appendChild(document.createElement("div"));
@@ -756,7 +755,7 @@ async function main() {
         diffCode.className = "wrap";
       }
     }
-  } else codeElement.classList.add("full-height");
+  }
 }
 
 // **** collapse/expand

tinygrad/viz/js/worker.js

@@ -8,7 +8,7 @@ onmessage = (e) => {
   const { graph, additions } = e.data;
   const g = new dagre.graphlib.Graph({ compound: true });
   g.setGraph({ rankdir: "LR" }).setDefaultEdgeLabel(function() { return {}; });
-  if (additions.length !== 0) g.setNode("addition", {label:"", labelWidth:0, labelHeight:0, className:"overlay"});
+  if (additions.length !== 0) g.setNode("addition", {label:"", className:"overlay"});
   for (let [k, {label, src, ref, ...rest }] of Object.entries(graph)) {
     // adjust node dims by label size (excluding escape codes) + add padding
     let [width, height] = [0, 0];

tinygrad/viz/serve.py

@@ -53,8 +53,10 @@ class GraphRewriteDetails(TypedDict):
 def shape_to_str(s:tuple[sint, ...]): return "(" + ','.join(srender(x) for x in s) + ")"
 def mask_to_str(s:tuple[tuple[sint, sint], ...]): return "(" + ','.join(shape_to_str(x) for x in s) + ")"
 def pystr(u:UOp, i:int) -> str:
-  try: return pyrender(u)
-  except Exception: return str(u)
+  if isinstance(trace.keys[i].ret, ProgramSpec):
+    try: return "\n".join(pyrender(u))
+    except Exception: pass
+  return str(u)
 
 def uop_to_json(x:UOp, ignore_indexing=False) -> dict[int, dict]:
   assert isinstance(x, UOp)