Merge branch 'master' into codegen2

.github/workflows/test.yml

@@ -133,26 +133,46 @@ jobs:
       run: SKIP_SLOW_TEST=1 DEV=PYTHON python3 -m pytest -n=auto test/backend/test_dtype.py test/backend/test_dtype_alu.py test/backend/test_ops.py test/backend/test_uops.py test/backend/test_symbolic_ops.py test/backend/test_renderer_failures.py::TestRendererFailures --durations=20
     - name: Test IMAGE support
       run: IMAGE=1 DEV=PYTHON python3 test/backend/test_ops.py TestOps.test_gemm TestOps.test_simple_conv2d
-    - name: Test emulated tensor cores
+    - name: Test emulated METAL tensor cores
       env:
-        DEBUG: 2
+        DEV: 'PYTHON::METAL'
-        N: 64
-        CNT: 1
-        SHOULD_USE_TC: 1
       run: |
-        parallel -k --link --tagstring '[{1}]' '{2} python3 ./extra/gemm/simple_matmul.py' \
+        DEBUG=2 python3 test/backend/test_ops.py TestOps.test_big_gemm
-          ::: metal gfx950 gfx1100 gfx1100_acchalf gfx1201 gfx1201_acchalf sm_75 sm_80_half sm_80_tf32 \
+        python3 -m pytest -nauto test/opt/test_tensor_cores.py
-          ::: 'DEV=PYTHON::METAL' 'DEV=PYTHON::gfx950 HALF=1 ACC_HALF=0' \
+    - name: Test emulated AMD tensor cores
-              'DEV=PYTHON::gfx1100 HALF=1 ACC_HALF=0' 'DEV=PYTHON::gfx1100 HALF=1 ACC_HALF=1 ATOL=1e-3' \
+      env:
-              'DEV=PYTHON::gfx1201 HALF=1 ACC_HALF=0' 'DEV=PYTHON::gfx1201 HALF=1 ACC_HALF=1 ATOL=1e-3' \
+        DEV: 'PYTHON::gfx1100'
-              'DEV=PYTHON::sm_75 HALF=1' 'DEV=PYTHON::sm_80 HALF=1' 'DEV=PYTHON::sm_80 ALLOW_TF32=1'
-    - name: Run additional tensor core tests
       run: |
-        DEV=PYTHON::METAL python3 -m pytest -nauto test/opt/test_tensor_cores.py test/null/test_uops_stats.py::TestUOpsStatsMatmulHalf
+        DEBUG=2 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        DEV=PYTHON::gfx1100 python3 -m pytest -nauto test/opt/test_tensor_cores.py test/null/test_uops_stats.py::TestUOpsStatsMatmulHalf
+        DEBUG=2 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
-        DEV=PYTHON::gfx950 python3 -m pytest -nauto test/opt/test_tensor_cores.py
+        DEBUG=2 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
-        DEV=PYTHON::gfx1201 python3 -m pytest -nauto test/opt/test_tensor_cores.py
+        DEBUG=2 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        python3 -m pytest -nauto test/opt/test_tensor_cores.py
+    - name: Test emulated AMD MFMA tensor cores
+      env:
+        DEV: 'PYTHON::gfx950'
+      run: |
+        DEBUG=2 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        python3 -m pytest -nauto test/opt/test_tensor_cores.py
+    - name: Test emulated AMD RDNA4 tensor cores
+      env:
+        DEV: 'PYTHON::gfx1201'
+      run: |
+        DEBUG=2 N=16 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 N=64 HALF=1 ACC_HALF=0 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 N=16 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        DEBUG=2 N=64 HALF=1 ACC_HALF=1 ATOL=1e-3 python3 ./extra/gemm/simple_matmul.py
+        python3 -m pytest -nauto test/opt/test_tensor_cores.py
+    - name: Test emulated CUDA tensor cores
+      run: |
+        DEBUG=2 DEV=PYTHON::sm_80 python3 test/backend/test_ops.py TestOps.test_gemm_fp16
+        DEBUG=2 ALLOW_TF32=1 DEV=PYTHON::sm_80 python3 test/backend/test_ops.py TestOps.test_gemm
+        DEBUG=2 DEV=PYTHON::sm_75 python3 test/backend/test_ops.py TestOps.test_gemm_fp16
         ALLOW_TF32=1 DEV=PYTHON::sm_89 python3 -m pytest -nauto test/opt/test_tensor_cores.py
+    - name: Test device flop counts
+      run: |
+        DEBUG=2 DEV=PYTHON::METAL python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
+        DEBUG=2 DEV=PYTHON::gfx1100 python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
         DEBUG=2 DEV=PYTHON::sm_80 python3 ./test/null/test_uops_stats.py TestUOpsStatsMatmulHalf
 
   linter:
@@ -247,6 +267,13 @@ jobs:
       run: python3 test/external/external_benchmark_schedule.py
     - name: Run process replay tests
       uses: ./.github/actions/process-replay
+    - name: Regen dataset on test_tiny
+      run: |
+        test/external/process_replay/reset.py
+        CAPTURE_PROCESS_REPLAY=1 python test/test_tiny.py TestTiny.test_plus
+        python extra/optimization/extract_dataset.py
+        gzip -c /tmp/sops > extra/datasets/sops.gz
+        #DEBUG=1 MIN_ASTS=1 python extra/optimization/get_action_space.py
     - name: Repo line count < 25000 lines
       run: MAX_LINE_COUNT=25000 python sz.py
 
@@ -311,6 +338,31 @@ jobs:
       - name: Run process replay tests
         uses: ./.github/actions/process-replay
 
+  testgpumisc:
+    name: CL Misc tests
+    runs-on: *linux
+    timeout-minutes: 10
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v6
+      - name: Setup Environment
+        uses: ./.github/actions/setup-tinygrad
+        with:
+          key: gen-dataset
+          deps: testing
+          opencl: 'true'
+      - name: Generate Dataset
+        run: DEV=CL extra/optimization/generate_dataset.sh
+      - name: Run Kernel Count Test
+        run: DEV=CL python -m pytest -n=auto test/external/external_test_opt.py
+      - name: Run fused optimizer tests
+        run: DEV=CL FUSE_OPTIM=1 python -m pytest -n=auto test/models/test_mnist.py test/backend/test_optim.py -k "not muon"
+      - name: Upload artifact
+        uses: actions/upload-artifact@v7
+        with:
+          name: sops.gz
+          path: /tmp/sops.gz
+
   testopenpilot:
     name: openpilot Compile Tests
     runs-on: *linux
@@ -327,7 +379,7 @@ jobs:
           llvm: 'true'
       - name: Test openpilot model kernel count and gate usage
         run: |
-          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1361 ALLOWED_GATED_READ_IMAGE=55 FLOAT16=1 DEV=CL IMAGE=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1468 ALLOWED_GATED_READ_IMAGE=10 FLOAT16=1 DEV=CL IMAGE=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
       - name: Test openpilot CL compile fp32 (test correctness)
         run: |
           DEV=CL IMAGE=1 SELFTEST=1 python examples/openpilot/compile3.py https://github.com/haraschax/filedump/raw/refs/heads/master/driving_vision_fp32.onnx
@@ -370,6 +422,7 @@ jobs:
         with:
           key: optim
           deps: testing
+          pydeps: "tensorflow==2.19"
           opencl: 'true'
       #- name: Test Optimization Helpers
       #  run: DEBUG=1 python3 extra/optimization/test_helpers.py
@@ -378,7 +431,7 @@ jobs:
       - name: Test Beam Search
         run: DEV=CL IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
       - name: Test MLPerf stuff
-        run: DEV=CL python -m pytest -n=auto test/external/external_test_lr_schedule.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
+        run: DEV=CL python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
       - name: DEV=NULL beautiful_mnist_multigpu
         run: DEV=NULL NULL_ALLOW_COPYOUT=1 python examples/beautiful_mnist_multigpu.py
       - name: Test Bert training

README.md

@@ -72,7 +72,7 @@ As it turns out, 90% of what you need for neural networks are a decent autograd/
 Throw in an optimizer, a data loader, and some compute, and you have all you need.
 
 ```python
-from tinygrad import Tensor, nn, Context
+from tinygrad import Tensor, nn
 
 class LinearNet:
   def __init__(self):
@@ -86,7 +86,7 @@ optim = nn.optim.Adam([model.l1, model.l2], lr=0.001)
 
 x, y = Tensor.rand(4, 1, 28, 28), Tensor([2,4,3,7])  # replace with real mnist dataloader
 
-with Context(TRAINING=1):
+with Tensor.train():
   for i in range(10):
     optim.zero_grad()
     loss = model(x).sparse_categorical_crossentropy(y).backward()

docs/quickstart.md

@@ -165,14 +165,13 @@ from extra.datasets import fetch_mnist
 Now we have everything we need to start training our neural network.
 We will be training for 1000 steps with a batch size of 64.
 
-We use `with Context(TRAINING=1)` to set the internal flag `Tensor.training` to `True` during training.
+We use `with Tensor.train()` to set the internal flag `Tensor.training` to `True` during training.
 Upon exit, the flag is restored to its previous value by the context manager.
 
 ```python
-from tinygrad import Context
 X_train, Y_train, X_test, Y_test = fetch_mnist()
 
-with Context(TRAINING=1):
+with Tensor.train():
   for step in range(1000):
     # random sample a batch
     samp = np.random.randint(0, X_train.shape[0], size=(64))

examples/beautiful_cartpole.py

@@ -1,6 +1,6 @@
 from typing import Tuple
 import time
-from tinygrad import Tensor, TinyJit, nn, Context
+from tinygrad import Tensor, TinyJit, nn
 import gymnasium as gym
 from tinygrad.helpers import trange
 import numpy as np  # TODO: remove numpy import
@@ -55,7 +55,7 @@ if __name__ == "__main__":
 
   @TinyJit
   def train_step(x:Tensor, selected_action:Tensor, reward:Tensor, old_log_dist:Tensor) -> Tuple[Tensor, Tensor, Tensor]:
-    with Context(TRAINING=1):
+    with Tensor.train():
       log_dist, value = model(x)
       action_mask = (selected_action.reshape(-1, 1) == Tensor.arange(log_dist.shape[1]).reshape(1, -1).expand(selected_action.shape[0], -1)).float()
 

examples/beautiful_cifar.py

@@ -122,7 +122,7 @@ if __name__ == "__main__":
     return ret.mul(hyp['opt']['loss_scale_scaler']*loss_batchsize_scaler).sum().div(hyp['opt']['loss_scale_scaler'])
 
   @TinyJit
-  @Context(TRAINING=1)
+  @Tensor.train()
   def train_step(idxs:Tensor) -> Tensor:
     X, Y = X_train[idxs], Y_train[idxs]
     if len(GPUS) > 1:

examples/beautiful_mnist.py

@@ -1,6 +1,6 @@
 # model based off https://medium.com/data-science/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
 from typing import Callable
-from tinygrad import Tensor, TinyJit, nn, GlobalCounters, function, Context
+from tinygrad import Tensor, TinyJit, nn, GlobalCounters, function
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist
 
@@ -19,7 +19,7 @@ class Model:
   def __call__(self, x:Tensor) -> Tensor: return x.sequential(self.layers)
 
   @TinyJit
-  @Context(TRAINING=1)
+  @Tensor.train()
   def train_step(self, X_train:Tensor, Y_train:Tensor) -> Tensor:
     opt.zero_grad()
     samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])

examples/beautiful_mnist_multigpu.py

@@ -1,6 +1,6 @@
 # model based off https://towardsdatascience.com/going-beyond-99-mnist-handwritten-digits-recognition-cfff96337392
 from typing import List, Callable
-from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Device, Context
+from tinygrad import Tensor, TinyJit, nn, GlobalCounters, Device
 from tinygrad.helpers import getenv, colored, trange
 from tinygrad.nn.datasets import mnist
 
@@ -31,7 +31,7 @@ if __name__ == "__main__":
 
   @TinyJit
   def train_step() -> Tensor:
-    with Context(TRAINING=1):
+    with Tensor.train():
       opt.zero_grad()
       samples = Tensor.randint(getenv("BS", 512), high=X_train.shape[0])
       Xt, Yt = X_train[samples].shard_(GPUS, axis=0), Y_train[samples].shard_(GPUS, axis=0)  # we shard the data on axis 0

examples/gradaccum_mnist.py

@@ -1,6 +1,6 @@
 import itertools
 from typing import Callable
-from tinygrad import nn, Tensor, dtypes, Device, TinyJit, Context
+from tinygrad import nn, Tensor, dtypes, Device, TinyJit
 from tinygrad.helpers import getenv, trange, partition
 
 class Model:
@@ -59,7 +59,7 @@ if __name__ == "__main__":
   Tensor.realize(*params, *buffers, *adam_params, loss, grads)
 
   @TinyJit
-  @Context(TRAINING=1)
+  @Tensor.train()
   def microbatch():
     samples = Tensor.randint(BS // ACC_STEPS, high=X_train.shape[0])
     for t in params: t.grad = None

examples/hlb_cifar10.py

@@ -359,7 +359,7 @@ def train_cifar():
   i = 0
   eval_acc_pct = 0.0
   batcher = fetch_batches(X_train, Y_train, BS=BS, is_train=True)
-  with Context(TRAINING=1):
+  with Tensor.train():
     st = time.monotonic()
     while i <= STEPS:
       if i % getenv("EVAL_STEPS", STEPS) == 0 and i > 1 and not getenv("DISABLE_BACKWARD"):

examples/llm.c/train_gpt2.py

@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 import os, math, time
 import numpy as np
-from tinygrad import Tensor, nn, fetch, Device, TinyJit, GlobalCounters, Context
+from tinygrad import Tensor, nn, fetch, Device, TinyJit, GlobalCounters
 from dataclasses import dataclass
 
 @dataclass
@@ -177,7 +177,7 @@ if __name__ == "__main__":
   if args.gpus > 1: x, y = x.shard(GPUS, axis=0), y.shard(GPUS, axis=0)
 
   @TinyJit
-  @Context(TRAINING=1)
+  @Tensor.train()
   def step(x:Tensor, y:Tensor) -> Tensor:
     _, loss = model(x, y)
     optimizer.zero_grad()
@@ -204,3 +204,4 @@ if __name__ == "__main__":
     top_k = 40
     y = model.generate(x, max_new_tokens, temperature=temperature, top_k=top_k)
     print(decode(y[0].tolist()))
+

examples/minrf.py

@@ -1,5 +1,5 @@
 # much taken from https://github.com/cloneofsimo/minRF
-from tinygrad import Tensor, nn, GlobalCounters, TinyJit, Context
+from tinygrad import Tensor, nn, GlobalCounters, TinyJit
 from tinygrad.helpers import getenv, trange
 from extra.models.llama import Attention, FeedForward, precompute_freqs_cis
 
@@ -135,7 +135,7 @@ if __name__ == "__main__":
   optimizer = nn.optim.Adam(nn.state.get_parameters(model), lr=5e-4)
 
   @TinyJit
-  @Context(TRAINING=1)
+  @Tensor.train()
   def train_step():
     if getenv("OVERFIT"): samples = Tensor.zeros(getenv("BS", 256), dtype='int')
     else: samples = Tensor.randint(getenv("BS", 256), high=X_train.shape[0])

examples/mlperf/model_eval.py

@@ -358,7 +358,7 @@ def eval_stable_diffusion():
       batch = batch.cat(batch[-1:].expand(bs - unpadded_bs, *batch[-1].shape))
     return batch, unpadded_bs 
 
-  @Context(TRAINING=0)
+  @Tensor.train(mode=False)
   def eval_unet(eval_inputs:list[dict], unet:UNetModel, cond_stage:FrozenOpenClipEmbedder, first_stage:AutoencoderKL,
                 inception:FidInceptionV3, clip:OpenClipEncoder) -> tuple[float, float]:
     # Eval is divided into 5 jits, one per model

examples/mlperf/model_train.py

@@ -2,7 +2,7 @@ import os, time, math, functools, random, contextlib
 from pathlib import Path
 import multiprocessing
 
-from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes, Context
+from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
 from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, Profiling, profile_marker, DEBUG
 from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
 from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW
@@ -614,7 +614,7 @@ def train_retinanet():
 
       if getenv("RESET_STEP", 1): _train_step.reset()
 
-      with Context(TRAINING=0):
+      with Tensor.train(mode=False):
         if not RUNMLPERF:
           i, proc = 0, _fake_data_get(EVAL_BS, val=(val:=True))
         else:
@@ -784,7 +784,7 @@ def train_unet3d():
     return x.shard(GPUS, axis=0).realize(), y.shard(GPUS, axis=0), cookie
 
   @TinyJit
-  @Context(TRAINING=1)
+  @Tensor.train()
   def train_step(model, x, y):
     optim.zero_grad()
 
@@ -795,7 +795,7 @@ def train_unet3d():
     optim.step()
     return loss.realize()
 
-  @Context(TRAINING=0)
+  @Tensor.train(mode=False)
   def eval_step(model, x, y):
     y_hat, y = sliding_window_inference(model, x, y, gpus=GPUS)
     y_hat, y = Tensor(y_hat), Tensor(y)
@@ -1490,7 +1490,7 @@ def train_llama3():
     return lr_cpu, grad_norm_cpu
 
   @TinyJit
-  @Context(TRAINING=0)
+  @Tensor.train(False)
   def eval_step(tokens:Tensor):
     if is_dp: tokens = tokens.to(None).shard(device, 0)
     if is_mp: tokens = tokens.shard(device)
@@ -1803,7 +1803,7 @@ if __name__ == "__main__":
   elif getenv("RUNMLPERF"): bench_log_manager = WallTimeEvent(BenchEvent.MLPERF_RUN)
   else: bench_log_manager = contextlib.nullcontext()
 
-  with Context(TRAINING=1):
+  with Tensor.train():
     for m in getenv("MODEL", "resnet,retinanet,unet3d,rnnt,bert,maskrcnn,stable_diffusion").split(","):
       nm = f"train_{m}"
       if nm in globals():

examples/mlperf/models/flat_llama.py

@@ -38,7 +38,7 @@ def quantize_fp8(x:Tensor, amax_state:Tensor|None=None):
 
 def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_scale:Tensor|None=None,
            x_fp8:Tensor|None=None, x_new_amax:Tensor|None=None,
-           grad_amax_state:Tensor|None=None, x_prequant_mx:tuple|None=None) -> tuple[Tensor,...]:
+           grad_amax_state:Tensor|None=None) -> tuple[Tensor,...]:
   if not fp8:
     if ASM_GEMM:
       from extra.gemm.cdna_asm_gemm import can_use_asm_gemm, asm_gemm
@@ -47,14 +47,12 @@ def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_sca
   assert w_inv_scale is not None, "fp8 matmul requires w_inv_scale (weights must be stored in fp8 with per-tensor scale)"
   if MXFP8:
     from extra.gemm.cdna_asm_gemm import asm_gemm, quantize_mxfp8, mx_pack, can_use_asm_gemm, _mx_block_scale
-    if x_prequant_mx is not None: x_q, x_e8, x_si = x_prequant_mx       # fused producer already quantized (2d)
+    x_q, x_e8, x_si = quantize_mxfp8(x.reshape(-1, x.shape[-1]))
-    else: x_q, x_e8, x_si = quantize_mxfp8(x.reshape(-1, x.shape[-1]))
-    l_shape = x.shape[:-1] if x is not None else x_q.shape[:-1]
     if can_use_asm_gemm(x_q, w.T):
       out = asm_gemm(x_q, w.T, mx=True, mx_scales=(x_si, x_e8, mx_pack(w_inv_scale), w_inv_scale),
-                     mx_w_stored=True).reshape(*l_shape, w.shape[0])
+                     mx_w_stored=True).reshape(*x.shape[:-1], w.shape[0])
     else:
-      x_phys = (x_q.cast(dtypes.bfloat16) * _mx_block_scale(x_e8)).reshape(*l_shape, x_q.shape[-1])
+      x_phys = (x_q.cast(dtypes.bfloat16) * _mx_block_scale(x_e8)).reshape(*x.shape[:-1], x.shape[-1])
       out = x_phys @ (w.cast(dtypes.bfloat16) * _mx_block_scale(w_inv_scale)).T
     return out, (amax_x.detach() if amax_x is not None else None), x_q
   if x_fp8 is None:
@@ -128,8 +126,10 @@ class FlatTransformer:
 
     # FeedForward
     if SPLIT_W13:
-      self.w1, s_1 = self.lin_per_layer(dim, hidden_dim)
+      if getenv("ZEROS"): w13_raw = Tensor.zeros(2, self.n_layers, hidden_dim, dim)
-      self.w3, s_3 = self.lin_per_layer(dim, hidden_dim)
+      else: w13_raw = Tensor.normal(2, self.n_layers, hidden_dim, dim, mean=0.0, std=0.02)
+      self.w1, s_1 = self.lin_per_layer(dim, hidden_dim, w=w13_raw[0])
+      self.w3, s_3 = self.lin_per_layer(dim, hidden_dim, w=w13_raw[1])
     else:
       self.w13, s_13 = self.lin_per_layer(dim, hidden_dim * 2)
     self.w2, s_2 = self.lin_per_layer(hidden_dim, dim, std=scaled_std)
@@ -160,7 +160,7 @@ class FlatTransformer:
   def lin_per_layer(self, in_features:int, out_features:int, std:float=0.02, w:Tensor|None=None):
     if w is None:
       if getenv("ZEROS"): w = Tensor.zeros(self.n_layers, out_features, in_features)
-      else: w = Tensor.normal(self.n_layers, out_features, in_features, mean=0.0, std=std)
+      else: w = Tensor.normal(self.n_layers, out_features, in_features, mean=0.0, std=std).realize()
     if MXFP8:
       from extra.gemm.cdna_asm_gemm import quantize_mxfp8
       w_q, w_e8, _ = quantize_mxfp8(w.reshape(self.n_layers * out_features, in_features))
@@ -216,15 +216,8 @@ class FlatTransformer:
       x_w3, new_amax, *s = matmul(inp, kwargs["w3"], amax_x=kwargs["amax_x3"], w_inv_scale=kwargs["s_3"], grad_amax_state=kwargs["grad_amax_xw3"])
       amaxs.append(new_amax)
       saves.extend([*s, x_w3])
-      if FUSED_SILU_W13 and MXFP8:
+      out, new_amax, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], amax_x=kwargs["amax_x2"], w_inv_scale=kwargs["s_2"],
-        from extra.llama_kernels.fused_silu_mul_quantize_mxfp8 import fused_silu_mul_quantize_mxfp8
+                                 grad_amax_state=kwargs["grad_amax_xout"])
-        aq, ae8, asi = fused_silu_mul_quantize_mxfp8(x_w1.reshape(-1, x_w1.shape[-1]), x_w3.reshape(-1, x_w3.shape[-1]))
-        out, new_amax, *s = matmul(None, kwargs["w2"], x_prequant_mx=(aq, ae8, asi), amax_x=kwargs["amax_x2"],
-                                   w_inv_scale=kwargs["s_2"], grad_amax_state=kwargs["grad_amax_xout"])
-        out = out.reshape(*x_w1.shape[:-1], kwargs["w2"].shape[0])
-      else:
-        out, new_amax, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], amax_x=kwargs["amax_x2"], w_inv_scale=kwargs["s_2"],
-                                   grad_amax_state=kwargs["grad_amax_xout"])
       amaxs.append(new_amax)
       saves.extend([*s, out])
     else:
@@ -254,30 +247,20 @@ class FlatTransformer:
       for v in get_parameters(self): v.shard_(device, axis=None)
     else:
       # flat per-layer weights: axis 0 is n_layers, so shard axes are +1 vs per-layer Transformer
-      def _shard_fp8(name:str, axis:int, std:float=0.02):
+      def _shard_fp8(name:str, axis:int):
-        w = getattr(self, name)
+        getattr(self, name).shard_(device, axis=axis)
-        if MXFP8:
+        scale_axis = axis if MXFP8 else (1 if axis == 1 else None) if COLUMNWISE_WEIGHT_SCALE else None
-          from extra.gemm.cdna_asm_gemm import quantize_mxfp8
+        self._fp8_inv_scale[name] = self._fp8_inv_scale[name].shard(device, axis=scale_axis).contiguous().is_param_(False)
-          w_bf16 = Tensor.empty(self.n_layers, w.shape[1], w.shape[2], dtype=dtypes.bfloat16).shard(device, axis=axis).randn_like() * std
+        self._fp8_next_inv_scale[name] = self._fp8_next_inv_scale[name].shard(device, axis=scale_axis).contiguous().is_param_(False)
-          w_q, w_e8, _ = quantize_mxfp8(w_bf16)
+        Tensor.realize(getattr(self, name), self._fp8_inv_scale[name], self._fp8_next_inv_scale[name])
-          w.replace(w_q)
-          self._fp8_inv_scale[name].replace(w_e8.contiguous()).is_param_(False)
-          self._fp8_next_inv_scale[name].replace(w_e8.contiguous()).is_param_(False)
-        else:
-          w.shard_(device, axis=axis)
-          scale_axis = (1 if axis == 1 else None) if COLUMNWISE_WEIGHT_SCALE else None
-          self._fp8_inv_scale[name] = self._fp8_inv_scale[name].shard(device, axis=scale_axis).contiguous().is_param_(False)
-          self._fp8_next_inv_scale[name] = self._fp8_next_inv_scale[name].shard(device, axis=scale_axis).contiguous().is_param_(False)
-        Tensor.realize(w, self._fp8_inv_scale[name], self._fp8_next_inv_scale[name])
-      sstd = 0.02 / math.sqrt(2 * self.n_layers)
       _shard_fp8("wqkv", 1)          # (n_layers, out, dim) shard out
-      _shard_fp8("wo", 2, sstd)      # (n_layers, dim, in) shard in
+      _shard_fp8("wo", 2)            # (n_layers, dim, in) shard in
       if SPLIT_W13:
         _shard_fp8("w1", 1)
         _shard_fp8("w3", 1)
       else:
         _shard_fp8("w13", 1)         # (n_layers, hidden*2, dim) shard out
-      _shard_fp8("w2", 2, sstd)      # (n_layers, dim, hidden) shard in
+      _shard_fp8("w2", 2)            # (n_layers, dim, hidden) shard in
       self.attention_norm.shard_(device, axis=None).realize()
       self.ffn_norm.shard_(device, axis=None).realize()
       self.norm.weight.shard_(device, axis=None).realize()

examples/mnist_gan.py

@@ -3,7 +3,7 @@ import torch
 from torchvision.utils import make_grid, save_image
 from tinygrad.nn.state import get_parameters
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import trange, Context
+from tinygrad.helpers import trange
 from tinygrad.nn import optim
 from tinygrad.nn.datasets import mnist
 
@@ -86,7 +86,7 @@ if __name__ == "__main__":
   optim_g = optim.Adam(get_parameters(generator), lr=0.0002, b1=0.5)  # 0.0002 for equilibrium!
   optim_d = optim.Adam(get_parameters(discriminator), lr=0.0002, b1=0.5)
   # training loop
-  with Context(TRAINING=1):
+  with Tensor.train():
     for epoch in (t := trange(epochs)):
       loss_g, loss_d = 0.0, 0.0
       for _ in range(n_steps):

examples/stunning_mnist.py

@@ -5,7 +5,7 @@
 #  - symbolic removal
 
 from examples.beautiful_mnist import Model
-from tinygrad import Tensor, nn, getenv, GlobalCounters, Variable, Context
+from tinygrad import Tensor, nn, getenv, GlobalCounters, Variable
 from tinygrad.nn.datasets import mnist
 from tinygrad.helpers import trange
 
@@ -26,7 +26,7 @@ if __name__ == "__main__":
   X_samp, Y_samp = X_train[samples], Y_train[samples]
   print("*** got samples")
 
-  with Context(TRAINING=1):
+  with Tensor.train():
     """
     i = UOp.range(samples.shape[0])  # TODO: fix range function on UOp
     losses = model(X_samp[i]).sparse_categorical_crossentropy(Y_samp[i]).backward().contract(i)

extra/gemm/amd_copy_matmul.py

@@ -66,7 +66,7 @@ def block_128x128_gemm(c:UOp, a:UOp, b:UOp) -> UOp:
 
   # accumulator (unified: both paths use (TM, TN) with scalar dtypes.float)
   acc = UOp.placeholder((TM, TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
-  acc = acc.after(acc.store(acc.zeros_like(buffer=False)))
+  acc = acc.after(acc.store(acc.zeros_like()))
 
   if use_wmma:
     k = UOp.range(BLOCK_K // WMMA_K, 101, AxisType.REDUCE)

extra/gemm/cdna_asm_gemm.py

@@ -2675,8 +2675,8 @@ def custom_hk_mxfp8_gemm(C:UOp, A:UOp, B:UOp, scale_A:UOp, scale_B:UOp, *extra:U
 def quantize_mxfp8(x:Tensor) -> tuple[Tensor, Tensor, Tensor]:
   # 1x32 block scaling along the last axis
   *batch, K = x.shape
-  scale_K = K // 32
+  scale_K, k_iters = K // 32, K // 128
-  amax = x.detach().float().reshape(*batch, scale_K, 32).abs().max(axis=-1)
+  amax = x.detach().float().reshape(rows, scale_K, 32).abs().max(axis=-1)
   e8 = (amax.maximum(1e-38).log2().floor() + 127).clamp(0, 254).cast(dtypes.uint8)
   qscale = (127.0 - e8.cast(dtypes.float32)).exp2().reshape(*batch, scale_K, 1).expand(*batch, scale_K, 32).reshape(*batch, K)
   x_scaled = x.float() * qscale

extra/hcq2/hcq2.py

@@ -143,17 +143,14 @@ def make_getaddr(u, device=None):
 def make_ins(op, *srcs):
   return UOp(Ops.INS, dtypes.void, tuple(UOp.const(dtypes.uint32, s) if isinstance(s, int) else s.cast(dtypes.uint32) for s in srcs), op)
 
-def make_patch(buf:UOp, off:sint, val:UOp, dtype=None) -> UOp:
-  dt = dtype or val.dtype
-  return UOp(Ops.SHRINK, buf.dtype.base, (buf, UOp.const(dtypes.int, off), UOp.const(dtypes.int, dt.itemsize))).bitcast(dt).store(val.cast(dt))
-
 def make_cmdbuf(lin, devs, tag):
   blob, patches = b'', []
   for s in (s for ins in lin.src for s in ins.src):
     if s.op is not Ops.CONST: patches.append((len(blob), s))
     blob += struct.pack(f'<{s.dtype.fmt}', s.arg if s.op is Ops.CONST else 0x0)
   buf = UOp.new_buffer(devs, len(blob), dtypes.uint8).rtag(tag)
-  return buf.after(buf.store(UOp(Ops.BINARY, dtypes.void, src=(), arg=blob)), *[make_patch(buf, off, s) for off, s in patches])
+  stores = [buf.index(UOp.const(dtypes.int, off), dtype=buf.dtype.ptr()).cast(s.dtype.ptr()).store(s) for off, s in patches]
+  return buf.after(buf.store(UOp(Ops.BINARY, dtypes.void, src=(), arg=blob)), *stores)
 
 def make_mstack(uops): return uops[0] if len(uops) == 1 else UOp(Ops.MSTACK, uops[0].dtype, tuple(uops))
 
@@ -214,11 +211,15 @@ def prep_program(call:UOp, prg:UOp) -> UOp|None:
   return prg.replace(src=(buf.after(buf.store(blob)),), arg=(data, prg.arg)).call(*call.src[1:], aux=HCQInfo.from_call(call))
 
 def prep_kernargs(call:UOp, prg:UOp) -> UOp:
-  (data, info), dev_uop = prg.arg, UOp(Ops.DEVICE, arg=call.src[1].device)
+  data, info = prg.arg
-  buf = UOp.new_buffer(dev_uop.arg, data.kernargs_alloc_size, dtypes.uint8).rtag("kernargs")
+  patches = [(i*dtypes.uint64.itemsize, UOp(Ops.GETADDR, dtypes.uint64, src=(call.src[1+gi], UOp(Ops.DEVICE, arg=call.src[1+gi].device))),
-  patches = [make_patch(buf, i*8, UOp(Ops.GETADDR, dtypes.uint64, src=(call.src[1+gi], dev_uop))) for i,gi in enumerate(info.globals)] \
+              dtypes.uint64) for i,gi in enumerate(info.globals)] \
-          + [make_patch(buf, len(info.globals)*8 + i*4, v, dtypes.uint32) for i,v in enumerate(info.vars)]
+          + [(len(info.globals)*dtypes.uint64.itemsize + i*dtypes.uint32.itemsize, v, dtypes.uint32) for i,v in enumerate(info.vars)]
-  return call.replace(src=(prg.replace(src=prg.src + (buf.after(*patches),), arg=(data, info)),) + call.src[1:])
+
+  buf = UOp.new_buffer(call.src[1].device, data.kernargs_alloc_size, dtypes.uint8).rtag("kernargs")
+  kernargs = buf.after(*tuple(buf.index(UOp.const(dtypes.int, o), dtype=buf.dtype.ptr()).cast(dt.ptr()).store(val.cast(dt)) for o, val, dt in patches))
+
+  return call.replace(src=(prg.replace(src=prg.src + (kernargs,), arg=(data, info)),) + call.src[1:])
 
 pm_prep_runtime = PatternMatcher([
   # bind generic PROGRAM device to the call's actual dev(s), then run device-specific lowering
@@ -306,7 +307,7 @@ def schedule_inner_sync(ctx:DepsCtx, linear:UOp) -> UOp:
     for (_, lane), dep in latest.items(): deps[dep] += (lane,)
 
     if deps: new_q = new_q.after(*deps, arg=tuple(deps.values())).rtag("deps")
-    new_src.append(call.replace(src=(call.src[0].substitute({q:new_q}),)))
+    new_src.append(call.replace(src=(call.src[0].substitute({q:new_q}), *call.src[1:])))
   return linear.replace(src=tuple(new_src))
 pm_schedule_inner_sync = PatternMatcher([(UPat(Ops.LINEAR, name="linear"), schedule_inner_sync)])
 
@@ -531,9 +532,9 @@ pm_resolve_patches = PatternMatcher([
   (UPat(GroupOp.ALU, src=[UPat(Ops.STACK, name="s"), UPat(Ops.CONST)], name="op"), push_stack),
   (UPat(Ops.CAST, src=(UPat(Ops.STACK, name="s"),), name="op"), push_stack),
 
-  # shrink on slice is shrink on base at offset
+  # index on slice is index
-  (UPat(Ops.SHRINK, src=(UPat(Ops.SLICE, name="bv"), UPat(), UPat()), name="shr"),
+  (UPat(Ops.INDEX, src=(UPat(Ops.SLICE, name="bv"), UPat()), name="idx", allow_any_len=True),
-    lambda shr, bv: shr.replace(src=(bv.src[0], shr.src[1] + bv.src[1].cast(shr.src[1].dtype), shr.src[2]))),
+    lambda idx, bv: idx.replace(src=(bv.src[0], idx.src[1] + bv.src[1].cast(idx.src[1].dtype), *idx.src[2:]))),
 
   # getaddr
   (UPat(Ops.GETADDR, src=(UPat(Ops.SLICE, name="bv"), UPat(Ops.DEVICE, name="dev"))), resolve_getaddr_slice), # getaddr(slice(x)) -> offset+getaddr(x)
@@ -541,8 +542,8 @@ pm_resolve_patches = PatternMatcher([
 
   # folders
   (UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf").store(UPat(Ops.BINARY, name="blob")), fold_blob_store),
-  (UPat(Ops.SHRINK, src=(UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf"), UPat.cvar("off"), UPat(Ops.CONST))).bitcast()
+  (UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf").index(UPat.cvar("off")).or_casted().store(UPat.any(UPat.cvar("val"), UPat(Ops.STACK, name="val"))),
-    .store(UPat.any(UPat.cvar("val"), UPat(Ops.STACK, name="val"))), fold_const_store),
+    fold_const_store),
 ]) + symbolic_simple
 
 # *****************

extra/llama_kernels/fused_silu_mul_quantize_mxfp8/__init__.py

@@ -1,104 +0,0 @@
-import functools
-from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from extra.llama_kernels import FP8_MAX, THREADS_PER_WG, alloc_like
-
-BLK = 32
-PACK = 4
-LOG2E = 1.4426950408889634
-
-@functools.cache
-def _custom_silu_mul_quantize_mxfp8(fp8_out:UOp, e8_out:UOp, si_out:UOp, x_w1:UOp, x_w3:UOp) -> UOp:
-  rows, K = x_w1.shape
-  scale_K = K // BLK
-  n_elems = rows * K
-  n_super = n_elems // (BLK * PACK)
-  sk4 = scale_K // PACK
-  assert n_super % THREADS_PER_WG == 0, f"{n_super=} must divide over {THREADS_PER_WG=}"
-  nwg = n_super // THREADS_PER_WG
-
-  x_w1, x_w3 = x_w1.reshape(n_elems), x_w3.reshape(n_elems)
-  fp8_out = fp8_out.reshape(n_elems)
-  e8_out = e8_out.reshape(rows * scale_K)
-  si_out = si_out.reshape(sk4 * rows)
-
-  wg = UOp.range(nwg, 0, AxisType.GLOBAL)
-  tid = UOp.range(THREADS_PER_WG, 1, AxisType.LOCAL)
-  sb = UOp.range(PACK, 2, AxisType.UNROLL)
-  lane = UOp.range(BLK, 3, AxisType.UNROLL)
-
-  super_idx = wg * THREADS_PER_WG + tid
-  idx = super_idx * (BLK * PACK) + sb * BLK + lane
-
-  w1 = x_w1[idx].cast(dtypes.float)
-  w3 = x_w3[idx].cast(dtypes.float)
-  sig = (1.0 + (w1 * -LOG2E).exp2()).reciprocal()
-  act = w1 * sig * w3
-  abs_a = (act < 0.0).where(-act, act)
-  blk_max = abs_a.reduce(lane, arg=Ops.MAX)
-  e8f = (blk_max.maximum(1e-38).log2().floor() + 127.0).maximum(0.0).minimum(254.0)
-  qscale = (127.0 - e8f).exp2()
-  scaled = (act * qscale).maximum(-FP8_MAX).minimum(FP8_MAX)
-  e8u8 = e8f.cast(dtypes.uint8)
-
-  fp8_store = fp8_out[idx].store(scaled.cast(fp8_out.dtype.base)).end(lane)
-  e8_store = e8_out.after(fp8_store)[super_idx * PACK + sb].store(e8u8)
-  packed = (e8u8.cast(dtypes.uint32) << (sb.cast(dtypes.uint32) * 8)).reduce(sb, arg=Ops.ADD)
-  row, col4 = super_idx // sk4, super_idx % sk4
-  si_store = si_out.after(e8_store.end(sb))[col4 * rows + row].store(packed)
-  return si_store.end(tid, wg).sink(arg=KernelInfo(f"silu_mul_quantize_mxfp8_{n_elems}", opts_to_apply=()))
-
-@functools.cache
-def _custom_silu_mul_bwd_mxfp8(gx1_out:UOp, gx3_out:UOp, x_w1:UOp, x_w3:UOp, grad_aq:UOp, e8:UOp) -> UOp:
-  rows, K = x_w1.shape
-  scale_K = K // BLK
-  n_elems = rows * K
-  VEC = 8
-  assert n_elems % (THREADS_PER_WG * VEC) == 0, f"{n_elems=} must divide {THREADS_PER_WG*VEC=}"
-  nwg = n_elems // (THREADS_PER_WG * VEC)
-  x_w1, x_w3, grad_aq = x_w1.reshape(n_elems), x_w3.reshape(n_elems), grad_aq.reshape(n_elems)
-  gx1_out, gx3_out, e8 = gx1_out.reshape(n_elems), gx3_out.reshape(n_elems), e8.reshape(rows * scale_K)
-
-  wg = UOp.range(nwg, 0, AxisType.GLOBAL)
-  tid = UOp.range(THREADS_PER_WG, 1, AxisType.LOCAL)
-  lane = UOp.range(VEC, 2, AxisType.UNROLL)
-  idx = (wg * THREADS_PER_WG + tid) * VEC + lane
-
-  e8v = e8[idx // BLK].cast(dtypes.float)
-  qscale = (127.0 - e8v).exp2()
-  ga = grad_aq[idx].cast(dtypes.float) * qscale
-  w1 = x_w1[idx].cast(dtypes.float)
-  w3 = x_w3[idx].cast(dtypes.float)
-  sig = (1.0 + (w1 * -LOG2E).exp2()).reciprocal()
-  s = w1 * sig
-  sprime = sig * (1.0 + w1 * (1.0 - sig))
-  gx1 = gx1_out[idx].store((ga * sprime * w3).cast(gx1_out.dtype.base))
-  gx3 = gx3_out.after(gx1)[idx].store((ga * s).cast(gx3_out.dtype.base))
-  return gx3.end(lane, tid, wg).sink(arg=KernelInfo(f"silu_mul_bwd_mxfp8_{n_elems}", opts_to_apply=()))
-
-def _silu_mul_quantize_mxfp8_bwd(gradient:UOp, kernel:UOp):
-  _, e8_out, _, x_w1, x_w3 = kernel.src[1:]
-  device = x_w1.device
-  rows, K = x_w1.shape
-  axis = x_w1.axis if isinstance(device, tuple) else None
-  gx1 = alloc_like((rows, K), dtypes.bfloat16, device, axis)
-  gx3 = alloc_like((rows, K), dtypes.bfloat16, device, axis)
-  gx1, gx3, *_ = Tensor.custom_kernel(gx1, gx3, Tensor(x_w1, device=device), Tensor(x_w3, device=device),
-                                      Tensor(gradient, device=device).cast(dtypes.bfloat16), Tensor(e8_out.after(kernel), device=device),
-                                      fxn=_custom_silu_mul_bwd_mxfp8)
-  return (None, None, None, gx1.uop, gx3.uop)
-
-def fused_silu_mul_quantize_mxfp8(x_w1:Tensor, x_w3:Tensor) -> tuple[Tensor, Tensor, Tensor]:
-  assert x_w1.shape == x_w3.shape, f"{x_w1.shape} != {x_w3.shape}"
-  assert x_w1.dtype == dtypes.bfloat16 and x_w3.dtype == dtypes.bfloat16
-  assert x_w1.ndim == 2, f"expected 2d, got {x_w1.shape}"
-  from extra.gemm.cdna_asm_gemm import FP8_DTYPE
-  rows, K = x_w1.shape
-  scale_K = K // BLK
-  axis = x_w1.uop.axis if isinstance(x_w1.device, tuple) else None
-  fp8_out = alloc_like((rows, K), FP8_DTYPE, x_w1.device, axis)
-  e8_out = alloc_like((rows, scale_K), dtypes.uint8, x_w1.device, axis)
-  si_out = alloc_like((scale_K // PACK, rows), dtypes.uint32, x_w1.device, None if axis is None else (1 if axis == 0 else 0))
-  fp8_out, e8_out, si_out, *_ = Tensor.custom_kernel(fp8_out, e8_out, si_out, x_w1, x_w3,
-                                                     fxn=_custom_silu_mul_quantize_mxfp8, grad_fxn=_silu_mul_quantize_mxfp8_bwd)
-  return fp8_out, e8_out, si_out

extra/llama_kernels/quantize_fp8_delayed/__init__.py

@@ -42,8 +42,8 @@ def _custom_quantize_fp8_with_amax(fp8_out:UOp, amax_partial:UOp, x:UOp, amax_st
   step = THREADS_PER_WG // 2
   while step:
     active = tid < step
-    other = lds[(tid + step).valid(active)].load()
+    other = lds[tid + step].load(UOp.const(dtypes.float, 0.0), active)
-    lds = lds.after(lds[tid.valid(active)].store(lds[tid].maximum(other)).barrier())
+    lds = lds.after(lds[tid].store(lds[tid].maximum(other), gate=active).barrier())
     step //= 2
 
   amax_store = amax_partial[tid.eq(0).where(wg, UOp.invalid())].store(lds[0])

extra/training.py

@@ -1,6 +1,6 @@
 import numpy as np
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import trange, Context
+from tinygrad.helpers import trange
 from tinygrad.engine.jit import TinyJit
 
 
@@ -22,7 +22,7 @@ def train(model, X_train, Y_train, optim, steps, BS=128, lossfn=lambda out,y: ou
 
   if allow_jit: train_step = TinyJit(train_step)
 
-  with Context(TRAINING=1):
+  with Tensor.train():
     losses, accuracies = [], []
     for i in (t := trange(steps, disable=None)):
       samp = np.random.randint(0, X_train.shape[0], size=(BS))
@@ -55,3 +55,4 @@ def evaluate(model, X_test, Y_test, num_classes=None, BS=128, return_predict=Fal
   acc, Y_test_pred = numpy_eval(Y_test, num_classes)
   print("test set accuracy is %f" % acc)
   return (acc, Y_test_pred) if return_predict else acc
+

test/backend/test_edgecases.py

@@ -25,7 +25,7 @@
 import unittest
 import numpy as np
 import torch
-from tinygrad import Tensor, dtypes, nn, Context
+from tinygrad import Tensor, dtypes, nn
 from tinygrad.device import Device
 from tinygrad.helpers import DEV
 from tinygrad.renderer.nir import NIRRenderer
@@ -101,7 +101,7 @@ class TestDropoutProbabilityEdgeCases(unittest.TestCase):
   # we don't need more of these
 
   def test_dropout_rate_one(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       out = Tensor.ones(100).dropout(1.0)
       np.testing.assert_allclose(out.numpy(), np.zeros(100))
 
@@ -109,7 +109,7 @@ class TestDropoutProbabilityEdgeCases(unittest.TestCase):
     with self.assertRaises(ValueError):
       torch.nn.functional.dropout(torch.ones(10), -0.1, True)
     with self.assertRaises(ValueError):
-      with Context(TRAINING=1):
+      with Tensor.train():
         Tensor.ones(10).dropout(-0.1)
 
 class TestInputValidation(unittest.TestCase):

test/backend/test_linearizer.py

@@ -140,7 +140,7 @@ class TestLinearizer(unittest.TestCase):
                        renderer=Device[Device.DEFAULT].renderer).src[2].src)
     num_loads = len([uop for uop in uops if uop.op is Ops.LOAD])
     assert num_loads <= 4, "more load uops than needed"
-    assert num_loads >= 1, "expected at least one load uop"
+    assert num_loads >= 4, "unexpected number of uops, maybe this test needs updating?"
 
   @unittest.skip("this is handled at higher level now")
   def test_upcast_cse(self):

test/backend/test_nn.py

@@ -14,7 +14,7 @@ from test.helpers import not_support_multi_device, needs_second_gpu, slow
 @slow
 class TestNN(unittest.TestCase):
   def test_batchnorm2d(self, training=False, threed=False, track_running_stats=True):
-    with Context(TRAINING=training):
+    with Tensor.train(training):
       szs = [4, 8, 16, 32]
       for sz in szs:
         # create in tinygrad

test/backend/test_stunning.py

@@ -41,7 +41,7 @@ class TestStunning(unittest.TestCase):
     X_samp, Y_samp = X_train[samples], Y_train[samples]
     vi = Variable('i', 0, samples.shape[0]-1)
     with Context(SPLIT_REDUCEOP=0):
-      with Context(TRAINING=1):
+      with Tensor.train():
         losses = []
         for i in range(samples.shape[0]):
           vib = vi.bind(i)

test/backend/test_symbolic_ops.py

@@ -1,5 +1,5 @@
 import unittest
-from tinygrad import Tensor, Variable, GlobalCounters, Context
+from tinygrad import Tensor, Variable, GlobalCounters
 from tinygrad.uop.ops import sym_infer
 from tinygrad.dtype import dtypes
 from examples.gpt2 import Attention
@@ -63,7 +63,7 @@ class TestSymbolicOps(unittest.TestCase):
     self.test_attention(imin=4, imax=5, use_symbolic=True)
 
   def test_attention_training(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       self.test_attention(dropout_p=0.0)
       with self.assertRaises(ValueError):
         # symbolic shape dropout is not supported

test/backend/test_tensor.py

@@ -1,7 +1,7 @@
 import numpy as np
 import torch
 import unittest, copy, mmap, random, math, array
-from tinygrad import Tensor, Device, dtypes, nn, Context
+from tinygrad import Tensor, Device, dtypes, nn
 from tinygrad.helpers import getenv, temp, mv_address
 from extra.gradcheck import numerical_jacobian, jacobian, gradcheck
 from hypothesis import given, settings, strategies as strat
@@ -203,7 +203,7 @@ class TestTinygrad(unittest.TestCase):
       np.testing.assert_allclose(x, y, atol=1e-5)
 
   def test_dropout(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       n, rate = 1_000_000, 0.1
       w = Tensor.ones(n).dropout(rate)
       non_zeros = np.count_nonzero(w.numpy())

test/external/external_test_lr_schedule.py

@@ -1,67 +0,0 @@
-import unittest, math
-import numpy as np
-from tinygrad.tensor import Tensor
-from tinygrad.nn.optim import AdamW
-from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup, LambdaLR, LambdaLinearScheduler
-
-np.random.seed(1337)
-x_init = np.random.randn(1,4).astype(np.float32)
-W_init = np.random.randn(4,4).astype(np.float32)
-m_init = np.random.randn(1,4).astype(np.float32)
-
-class TinyNet:
-  def __init__(self):
-    self.x = Tensor(x_init.copy())
-    self.W = Tensor(W_init.copy())
-    self.m = Tensor(m_init.copy())
-
-  def forward(self):
-    out = self.x.matmul(self.W).relu()
-    out = out.log_softmax(1)
-    out = out.mul(self.m).add(self.m).sum()
-    return out
-
-class TestCosineAnnealingLRWithWarmup(unittest.TestCase):
-  # only tests the lr
-  def _test_lr(self, base_lr, end_lr, warmup_steps, decay_steps):
-    net = TinyNet()
-    optim = AdamW([net.W], lr=0.0)
-    tiny_lr = CosineAnnealingLRWithWarmup(optim, base_lr, end_lr, warmup_steps, decay_steps)
-    lr = []
-    for _ in range(warmup_steps+decay_steps):
-      lr.append(optim.lr.item())
-      tiny_lr.step()
-    # reimplemented in python
-    expected = []
-    for i in range(warmup_steps): expected.append((i+1)/warmup_steps*base_lr)
-    for i in range(decay_steps): expected.append(end_lr+(base_lr-end_lr)*(1+math.cos((i+1)/decay_steps*math.pi))/2)
-    np.testing.assert_allclose(lr, expected, rtol=1e-5)
-
-  def test_lr_0(self): self._test_lr(3e-4, 8e-5, 3, 5)
-  def test_lr_1(self): self._test_lr(3e-4, 8e-5, 10, 20)
-  def test_lr_llama3(self): self._test_lr(8e-5, 8e-7, 20, 100)
-
-class TestLambdaLRLinearWarmup(unittest.TestCase):
-  def test_linear_lr_warmup(self):
-    BS, BASE_LR = 304, 2.5e-7
-    lr = BS * BASE_LR
-    # Use a dummy Tensor parameter for optimizer because the lr_scheduler only needs the optimizer's device and lr, the params aren't touched.
-    optimizer = AdamW([Tensor([1.])])
-    lambda_lr_callback = LambdaLinearScheduler(1000, 1.0, 1.0, 1e-06, 10000000000000).schedule
-    lr_scheduler = LambdaLR(optimizer, Tensor(lr, device=optimizer.device), lambda_lr_callback)
-    lrs = {}
-
-    # with above settings, optimizer.lr should warm up to lr over 1000 steps linearly
-    for i in range(1200):
-      lr_scheduler.step()
-      if i in {0, 499, 998, 999, 1000, 1199}:
-        lrs[i] = optimizer.lr.item()
-
-    np.testing.assert_allclose(lr, lrs[999], rtol=0, atol=1e-11)
-    np.testing.assert_equal(lrs[999], lrs[1000])
-    np.testing.assert_equal(lrs[999], lrs[1199])
-    np.testing.assert_allclose(lrs[999] / lrs[0], 1000, rtol=0, atol=1)
-    np.testing.assert_allclose(lrs[999] / lrs[499], 2, rtol=0, atol=1e-5)
-
-if __name__ == '__main__':
-  unittest.main()

test/external/external_test_optim.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-import unittest
+import unittest, math
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.optimizers import Lamb
@@ -7,11 +7,11 @@ from tensorflow.python.ops import math_ops
 from extra.lr_scheduler import LRSchedulerGroup
 
 from tinygrad.tensor import Tensor
-from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup
+from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, AdamW
 
 from test.external.mlperf_resnet.lars_optimizer import LARSOptimizer
 
-from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup
+from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup, CosineAnnealingLRWithWarmup, LambdaLR, LambdaLinearScheduler
 from test.external.mlperf_resnet.lars_util import PolynomialDecayWithWarmup as PolynomialDecayWithWarmup_tf
 
 np.random.seed(1337)
@@ -173,5 +173,48 @@ class ExternalTestOptim(unittest.TestCase):
       'warmup': steps_per_epoch * warmup_epochs,
     }, 1e-5, 1e-5, do_optim=False)
 
+
+class TestCosineAnnealingLRWithWarmup(unittest.TestCase):
+  # only tests the lr
+  def _test_lr(self, base_lr, end_lr, warmup_steps, decay_steps):
+    net = TinyNet()
+    optim = AdamW([net.W], lr=0.0)
+    tiny_lr = CosineAnnealingLRWithWarmup(optim, base_lr, end_lr, warmup_steps, decay_steps)
+    lr = []
+    for _ in range(warmup_steps+decay_steps):
+      lr.append(optim.lr.item())
+      tiny_lr.step()
+    # reimplemented in python
+    expected = []
+    for i in range(warmup_steps): expected.append((i+1)/warmup_steps*base_lr)
+    for i in range(decay_steps): expected.append(end_lr+(base_lr-end_lr)*(1+math.cos((i+1)/decay_steps*math.pi))/2)
+    np.testing.assert_allclose(lr, expected, rtol=1e-5)
+
+  def test_lr_0(self): self._test_lr(3e-4, 8e-5, 3, 5)
+  def test_lr_1(self): self._test_lr(3e-4, 8e-5, 10, 20)
+  def test_lr_llama3(self): self._test_lr(8e-5, 8e-7, 20, 100)
+
+class TestLambdaLRLinearWarmup(unittest.TestCase):
+  def test_linear_lr_warmup(self):
+    BS, BASE_LR = 304, 2.5e-7
+    lr = BS * BASE_LR
+    # Use a dummy Tensor parameter for optimizer because the lr_scheduler only needs the optimizer's device and lr, the params aren't touched.
+    optimizer = AdamW([Tensor([1.])])
+    lambda_lr_callback = LambdaLinearScheduler(1000, 1.0, 1.0, 1e-06, 10000000000000).schedule
+    lr_scheduler = LambdaLR(optimizer, Tensor(lr, device=optimizer.device), lambda_lr_callback)
+    lrs = {}
+
+    # with above settings, optimizer.lr should warm up to lr over 1000 steps linearly
+    for i in range(1200):
+      lr_scheduler.step()
+      if i in {0, 499, 998, 999, 1000, 1199}:
+        lrs[i] = optimizer.lr.item()
+
+    np.testing.assert_allclose(lr, lrs[999], rtol=0, atol=1e-11)
+    np.testing.assert_equal(lrs[999], lrs[1000])
+    np.testing.assert_equal(lrs[999], lrs[1199])
+    np.testing.assert_allclose(lrs[999] / lrs[0], 1000, rtol=0, atol=1)
+    np.testing.assert_allclose(lrs[999] / lrs[499], 2, rtol=0, atol=1e-5)
+
 if __name__ == '__main__':
   unittest.main()

test/external/mlperf_stable_diffusion/external_test_train.py

@@ -1,6 +1,6 @@
 import unittest, os
 from tempfile import TemporaryDirectory
-from tinygrad import Context
+from tinygrad import Tensor
 from tinygrad.helpers import getenv
 from examples.mlperf.model_train import train_stable_diffusion
 
@@ -14,10 +14,10 @@ class TestTrain(unittest.TestCase):
     if not getenv("CKPTDIR", ""): os.environ["CKPTDIR"] = "/raid/weights/stable_diffusion"
     with TemporaryDirectory(prefix="test-train") as tmp:
       os.environ["UNET_CKPTDIR"] = tmp
-      with Context(TRAINING=1):
+      with Tensor.train():
         saved_ckpts = train_stable_diffusion()
       expected_ckpt = f"{tmp}/{num_steps}.safetensors"
       assert len(saved_ckpts) == 1 and saved_ckpts[0] == expected_ckpt
 
 if __name__=="__main__":
-  unittest.main()
+  unittest.main()

test/models/test_efficientnet.py

@@ -3,7 +3,7 @@ import ast, pathlib, unittest
 import numpy as np
 from PIL import Image
 
-from tinygrad import Tensor, Context
+from tinygrad import Tensor
 from tinygrad.helpers import getenv
 from test.helpers import slow
 from extra.models.efficientnet import EfficientNet
@@ -40,7 +40,7 @@ def preprocess(img, new=False):
   return img
 
 def _infer(model: EfficientNet, img):
-  with Context(TRAINING=0):
+  with Tensor.train(False):
     out = model.forward(Tensor(img)).argmax(axis=-1)
   return out.tolist()
 

test/models/test_end2end.py

@@ -5,11 +5,10 @@ import numpy as np
 from tinygrad.nn.state import get_parameters, get_state_dict
 from tinygrad.nn import optim, Linear, Conv2d, BatchNorm2d
 from tinygrad.tensor import Tensor
-from tinygrad.helpers import Context
 from extra.datasets import fetch_mnist
 
 def compare_tiny_torch(model, model_torch, X, Y):
-  with Context(TRAINING=1):
+  with Tensor.train():
     model_torch.train()
     model_state_dict = get_state_dict(model)
     for k,v in model_torch.named_parameters():

test/null/test_real_world.py

@@ -106,7 +106,7 @@ class TestRealWorld(unittest.TestCase):
   @slow
   def test_train_mnist(self):
     from examples.beautiful_mnist import Model
-    with Context(TRAINING=1):
+    with Tensor.train():
       model = Model()
       optimizer = optim.Adam(get_parameters(model))
       BS = 32
@@ -125,7 +125,7 @@ class TestRealWorld(unittest.TestCase):
   def test_forward_cifar(self):
     BS = 32
     # with training batchnorm still though
-    with Context(TRAINING=1):
+    with Tensor.train():
       model = SpeedyResNet(Tensor.ones((12,3,2,2)))
       @TinyJit
       def run(X): return model(X)
@@ -133,7 +133,7 @@ class TestRealWorld(unittest.TestCase):
 
   @slow
   def test_train_cifar(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       model = SpeedyResNet(Tensor.ones((12,3,2,2)))
       optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=0.8, nesterov=True, weight_decay=0.15)
       BS = 32
@@ -151,7 +151,7 @@ class TestRealWorld(unittest.TestCase):
   @unittest.skipUnless(dtypes.float16 in supported_dtypes, "need dtypes.float16")
   def test_train_cifar_hyp(self):
     dtypes.default_float = dtypes.float16
-    with Context(TRAINING=1):
+    with Tensor.train():
       model = SpeedyResNet(Tensor.ones((12,3,2,2)))
       optimizer = optim.SGD(get_parameters(model), lr=0.01, momentum=hyp['opt']['momentum'], nesterov=True, weight_decay=hyp['opt']['bias_decay'])
       initial_div_factor = hyp['opt']['initial_div_factor']
@@ -163,7 +163,7 @@ class TestRealWorld(unittest.TestCase):
 
   @slow
   def test_bert(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       args_tiny = {"attention_probs_dropout_prob": 0.0, "hidden_dropout_prob": 0.0, "vocab_size": 30522, "type_vocab_size": 2,
                   "max_position_embeddings": 512, "hidden_size": 128, "intermediate_size": 512, "num_attention_heads": 2, "num_hidden_layers": 2}
       model = BertForPretraining(**args_tiny)

test/null/test_schedule.py

@@ -1093,14 +1093,14 @@ class TestSchedule(unittest.TestCase):
 
   #@unittest.skip("may want to reconsider this")
   def test_fold_batchnorm(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(1,32,4,4)
       bn = nn.BatchNorm2d(32, track_running_stats=False)
       out = bn(img)
       check_schedule(out, 3, nn.state.get_parameters(bn))
 
   def test_fold_conv_batchnorm_notrain(self):
-    with Context(TRAINING=0):
+    with Tensor.train(False):
       img = Tensor.empty(1,3,8,8)
       c1 = nn.Conv2d(3,32,3)
       bn = nn.BatchNorm2d(32, track_running_stats=True)
@@ -1108,7 +1108,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(out, 1, [c1.weight, c1.bias, *nn.state.get_parameters(bn)])
 
   def test_fold_conv_batchnorm_notrain_no_running_stats(self):
-    with Context(TRAINING=0):
+    with Tensor.train(False):
       img = Tensor.empty(1,3,8,8)
       c1 = nn.Conv2d(3,32,3)
       bn = nn.BatchNorm2d(32, track_running_stats=False)
@@ -1116,7 +1116,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(out, 4, [c1.weight, c1.bias, *nn.state.get_parameters(bn)])
 
   def test_fold_conv_batchnorm(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(1,3,8,8)
       c1 = nn.Conv2d(3,32,3)
       bn = nn.BatchNorm2d(32, track_running_stats=False)
@@ -1125,7 +1125,7 @@ class TestSchedule(unittest.TestCase):
 
   def test_fold_conv_batchnorm_optim(self, adam=False):
     optim, cnt = (nn.optim.Adam, 29) if adam else (nn.optim.SGD, 15)
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.ones(1,3,4,4)
       c1 = nn.Conv2d(3,32,3)
       bn = nn.BatchNorm2d(32, track_running_stats=False)
@@ -1139,7 +1139,7 @@ class TestSchedule(unittest.TestCase):
   def test_fold_conv_batchnorm_optim_adam(self): self.test_fold_conv_batchnorm_optim(True)
 
   def test_fold_batchnorm_backward(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       x = Tensor.empty((2, 16, 8, 8)).contiguous()
       bn = nn.BatchNorm2d(16)
       fw = bn(x).contiguous_backward().relu().contiguous()
@@ -1484,7 +1484,7 @@ class TestSchedule(unittest.TestCase):
     check_schedule(out, 4)
 
   def test_adam_step_fusion(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       x = Tensor.empty(4, 64, 32)
       layer = nn.Linear(32, 32*4)
       _realize_weights(layer)
@@ -1494,7 +1494,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 13)
 
   def test_adam_conv_fuse(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,4,4)
       c1 = nn.Conv2d(3,32,3)
       _realize_weights(c1)
@@ -1505,7 +1505,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 13)
 
   def test_adam_2convs_fuse(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,4,4)
       c1 = nn.Conv2d(3,16,3,bias=False)
       c2 = nn.Conv2d(16,32,2,bias=False)
@@ -1517,7 +1517,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 15)
 
   def test_sgd_conv_fuse(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,4,4)
       c1 = nn.Conv2d(3,32,3)
       _realize_weights(c1)
@@ -1527,7 +1527,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 5) # TODO: 3?
 
   def test_sgd_2convs_fuse(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,4,4)
       c1 = nn.Conv2d(3,16,3,bias=False)
       c2 = nn.Conv2d(16,32,2,bias=False)
@@ -1538,7 +1538,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 7)
 
   def test_fold_2convs_sgd_nesterov_momentum_wd(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,4,4)
       c1 = nn.Conv2d(3,16,3,bias=False)
       c2 = nn.Conv2d(16,32,2,bias=False)
@@ -1550,7 +1550,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 11)
 
   def test_sgd_4convs_fuse(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,16,16)
       c1 = nn.Conv2d(3,4,3,bias=False)
       c2 = nn.Conv2d(4,8,3,bias=False)
@@ -1563,7 +1563,7 @@ class TestSchedule(unittest.TestCase):
       check_schedule(opt.schedule_step(), 15)
 
   def test_sgd_4convs_fuse_conv_bw(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       img = Tensor.empty(2,3,16,16)
       c1 = nn.Conv2d(3,4,3,bias=False)
       c2 = nn.Conv2d(4,8,3,bias=False)
@@ -1664,7 +1664,7 @@ class TestSchedule(unittest.TestCase):
     self.assertEqual(len([x for x in linear.src[0].src[0].backward_slice_with_self if x.op is Ops.REDUCE]), 0)
 
   def test_resnet_block(self):
-    with Context(TRAINING=0):
+    with Tensor.train(False):
       in_planes, planes = 64, 64
       conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
       bn1 = nn.BatchNorm2d(planes)

test/null/test_simplify_valid_idx.py

@@ -16,17 +16,41 @@ def simplify_image_idx(sink: UOp) -> UOp: return graph_rewrite(sink, sym+pm_move
 def get_gated_load_uop(valid:UOp, idx:UOp):
   return UOp(Ops.LOAD, dtypes.float, (
     UOp.param(0, dtypes.float.ptr()).index(idx.valid(valid), ptr=True),
+    UOp.const(dtypes.float, 0.0)
   ))
 
 def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UOp]):
   return UOp(Ops.LOAD, dtypes.float.vec(4), (
     UOp.param(0, dtypes.imagef(image_shape)).index(idx[1].valid(valid), idx[0].valid(valid), ptr=True),
+    UOp(Ops.STACK, dtypes.float.vec(4), src=(UOp.const(dtypes.float, 0.0),) * 4)
   ))
 
 def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.weakint, (UOp.const(dtypes.weakint, nmax),), expr)
 def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
 def Range(n, nmax): return UOp.range(nmax, n)
 
+class TestHelpers(unittest.TestCase):
+  def test_is_increasing(self):
+    idx1 = Special("idx1", 32)
+    idx2 = Special("idx2", 64)
+    ridx0 = Variable("ridx0", 0, 5)
+    ridx1 = Variable("ridx1", 0, 2)
+    ridx2 = Variable("ridx2", 0, 2)
+    # (ridx0+(idx1*48)+(ridx2*6)+(-6)),((idx2*2)+ridx1+(-1)))
+    f0 = ((idx1*24)+(ridx2*3)+ridx0+765)%768
+    f1 = ridx0+(idx1*48)+(ridx2*6)+(-6)
+    f2 = (idx2*2)+ridx1+((idx1+((ridx2+7)//8)+31)//32)+(-2)
+    f3 = (idx2*2)+ridx1+(-1)
+
+    self.assertFalse(f0.is_increasing())
+    self.assertTrue(f1.is_increasing())
+    self.assertTrue(f2.is_increasing())
+    self.assertTrue(f3.is_increasing())
+
+    rng = UOp.range(5, 2)
+    self.assertTrue(rng.is_increasing())
+    self.assertTrue((rng+2).is_increasing())
+
 class TestValidIdxSimplification(unittest.TestCase):
   def check(self, load, sidx, svalid, extra=()):
     load = simplify_valid_idx(UOp.sink(load, *extra)).src[0]
@@ -482,16 +506,6 @@ class TestImageSimplification(unittest.TestCase):
       self.check(load, "(((lidx1<1)!=True)&(((lidx0+r0)<3)!=True)&((lidx0+r0)<11))",
                        "(lidx2+gidx0*4+lidx1*256+(lidx0*1024+r0*1024)+-3264)", "0")
 
-  def test_drop_non_monotonic_window(self):
-    # two-sided window valid (645 <= gidx0 < 653) on a non-monotonic index (lane split via %4 and //4):
-    # gidx0 outside the window pushes idx_x out of the (1, 48) image, so the gate is dropped
-    gidx0 = Special("gidx0", 1064)
-    r12 = Range(12, 3)
-    valid = ((gidx0 < 645).ne(True)) & (gidx0 < 653)
-    idx = (r12*4 + (gidx0+3)%4 + (gidx0+3)//4*24 - 3888, UOp.const(dtypes.weakint, 0))
-    load = get_load_image_uop((1, 48, 4), valid, idx)
-    self.check(load, None, "(r12*4+(gidx0+3)%4+(gidx0+3)//4*24+-3888)", "0")
-
 class TestDropTrueGate(unittest.TestCase):
   def test_drop_true_gate_on_index(self):
     # test that INDEX with a constant True valid gets simplified to drop the valid

test/null/test_tensor.py

@@ -1,7 +1,7 @@
 # tensor tests that pass on NULL backend (no copyout needed)
 import numpy as np
 import unittest
-from tinygrad import Tensor, Device, dtypes, Context
+from tinygrad import Tensor, Device, dtypes
 from tinygrad.uop.ops import Ops, UOp
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.nir import NIRRenderer
@@ -15,7 +15,7 @@ m_init = np.random.randn(1,3).astype(np.float32)
 class TestTrainMode(unittest.TestCase):
   def test_train_mode(self):
     assert not Tensor.training
-    @Context(TRAINING=1)
+    @Tensor.train()
     def f():
       assert Tensor.training
     f()

test/unit/test_multitensor.py

@@ -207,7 +207,7 @@ class TestMultiTensor(unittest.TestCase):
     out.numpy()
 
   def test_backprop_conv(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       conv = nn.Conv2d(3, 16, 3)
       for p in get_parameters(conv): p.shard_(devices_2)
       optim = nn.optim.Adam(get_parameters(conv))
@@ -511,7 +511,7 @@ class TestMultiTensor(unittest.TestCase):
   def test_full_like_on_shard_axis(self): self.test_full_like_on_shard(0)
 
   def test_dropout_on_shard(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       X = Tensor.ones(256).to(devices_2)
       output = X.dropout(0.5).numpy()
       unique, counts = np.unique(output, return_counts=True)
@@ -519,7 +519,7 @@ class TestMultiTensor(unittest.TestCase):
       assert 96 < counts[0] < 160, counts[0]
 
   def test_dropout_on_shard_axis(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       X = Tensor.ones(512).shard(devices_2, axis=0)
       output = X.dropout(0.5).numpy()
       unique, counts = np.unique(output, return_counts=True)
@@ -664,7 +664,7 @@ class TestBatchNorm(unittest.TestCase):
   def setUp(self): pass
 
   def test_unsynced_backprop_conv_bn(self):
-    with Context(TRAINING=1):
+    with Tensor.train():
       from extra.lr_scheduler import OneCycleLR
 
       convs = [nn.Conv2d(3, 16, 3), nn.Conv2d(3, 16, 3)]
@@ -709,7 +709,7 @@ class TestBatchNorm(unittest.TestCase):
           bn_ts.append(bni)
         return bn_ts[0].cat(*bn_ts[1:])
 
-    with Context(TRAINING=1):
+    with Tensor.train():
       conv = nn.Conv2d(3, 16, 3)
       bn = BatchNorm(16)
 
@@ -731,7 +731,7 @@ class TestBatchNorm(unittest.TestCase):
     from examples.hlb_cifar10 import UnsyncedBatchNorm
     GPUS = (d1, d2)
 
-    with Context(TRAINING=1):
+    with Tensor.train():
       conv = nn.Conv2d(3, 16, 3)
       bn = UnsyncedBatchNorm(16, num_devices=len(GPUS))
 
@@ -756,7 +756,7 @@ class TestBatchNorm(unittest.TestCase):
     devices = [f"{Device.DEFAULT}:{i}" for i in range(4)]
     x = Tensor.arange(4096).reshape(8, 8, 8, 8).clone().realize().shard(devices, axis=0)
 
-    with Context(TRAINING=is_training):
+    with Tensor.train(is_training):
       bns = []
       for _ in range(len(devices)):
         bn = nn.BatchNorm2d(8)
@@ -777,7 +777,7 @@ class TestBatchNorm(unittest.TestCase):
     devices = [f"{Device.DEFAULT}:{i}" for i in range(4)]
     x = Tensor.ones(8, 8, 8, 8).contiguous().realize().shard(devices, axis=0)
 
-    with Context(TRAINING=1):
+    with Tensor.train():
       synced_bn = BatchNorm2d(8)
       unsynced_bn = UnsyncedBatchNorm(8, num_devices=len(devices))
 

tinygrad/codegen/__init__.py

@@ -1,19 +1,21 @@
 from typing import cast
 from dataclasses import replace
 import itertools
+import functools
 from tinygrad.helpers import DISABLE_FAST_IDIV, TRANSCENDENTAL, SPEC, DEBUG, VIZ, IMAGE, NOOPT, EMULATED_DTYPES, NOLOCALS, USE_TC
-from tinygrad.helpers import ALLOW_TF32, TracingKey, Context, panic
+from tinygrad.helpers import ALLOW_TF32, TracingKey, Context, panic, all_same, flatten
 from tinygrad.uop.ops import PatternMatcher, graph_rewrite, UOp, pm_lower_index_dtype, Ops, UPat, track_rewrites, KernelInfo, ProgramInfo, GroupOp
+from tinygrad.uop.ops import AxisType, _align_left, _broadcast_shape, identity_element
 from tinygrad.uop.render import pyrender
 from tinygrad.uop.spec import type_verify, spec_tensor, spec_program
 from tinygrad.renderer import Renderer, Estimates
 from tinygrad.renderer.isa import ISARenderer, IselContext, PreRegAllocContext
-from tinygrad.dtype import dtypes, PtrDType, ImageDType
+from tinygrad.dtype import dtypes, PtrDType, ImageDType, AddrSpace
 
 # import all pattern matchers here
 from tinygrad.codegen.gpudims import pm_add_gpudims
 from tinygrad.uop.symbolic import sym, symbolic_simple, gep_pushing, symbolic, pm_move_where_on_load, pm_clean_up_group_sink, pm_remove_invalid
-from tinygrad.uop.decompositions import get_late_rewrite_patterns, get_transcendental_patterns, pm_dtype_decomps, get_simplifying_rewrite_patterns
+from tinygrad.uop.decompositions import get_late_rewrite_patterns, get_transcendental_patterns, pm_dtype_decomps
 from tinygrad.codegen.late.expander import expander, pm_pre_expander, pm_group_for_reduce
 from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_indexing, devectorize_buf_and_index, devectorize_alu, pm_reduce, \
   ReduceContext, correct_load_store, pm_render, pm_add_loads, pm_make_images
@@ -23,7 +25,6 @@ from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_s
 from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen, pm_mops, pm_syntactic_sugar, pm_store_ranges
 from tinygrad.codegen.late.linearizer import CFGContext, pm_split_ends, pm_add_control_flow, linearize
 from tinygrad.codegen.late.regalloc import LinearScanRegallocContext, pm_regalloc_rewrite
-from tinygrad.codegen.late.coalese import memory_coalesing
 
 pm_index_is_shrink = PatternMatcher([
   # rewrite non-image INDEX to SHRINK
@@ -44,6 +45,98 @@ pm_remove_vec_dtypes = PatternMatcher([
    lambda x: x.replace(dtype=x.dtype.base.scalar().base)),
 ])+pm_clean_up_group_sink
 
+def maybe_load(u:UOp): return u.load() if u.addrspace in (AddrSpace.GLOBAL, AddrSpace.LOCAL, AddrSpace.REG) else u
+pm_move_regs = PatternMatcher([
+  # BITCAST?
+  (UPat(GroupOp.Elementwise, name="x"), lambda x: x.replace(src=tuple([maybe_load(u) for u in x.src]))),
+  (UPat(Ops.STORE, name="x"), lambda x: x.replace(src=(x.src[0], maybe_load(x.src[1]))+x.src[2:])),
+])
+
+pm_lower_weakints = PatternMatcher([
+  (UPat(GroupOp.All, dtype=dtypes.weakint, name="x"), lambda x: x.replace(dtype=dtypes.int)),
+])
+
+def build_range_map(ctx, sink:UOp):
+  for x in sink.toposort():
+    if x.op is Ops.RANGE and x.arg[1] in {AxisType.UNROLL, AxisType.UPCAST}:
+      ctx[x.arg[0]] = len(ctx)
+
+def fix_reduce(ctx, r:UOp):
+  range_to_axis = {u:ctx[u.arg[0]] for u in r.ended_ranges if u.arg[0] in ctx if u.arg[1] == AxisType.UNROLL}
+  return r.replace(src=tuple([u for u in r.src if u not in range_to_axis]), arg=(r.arg[0], r.arg[1]+tuple(range_to_axis.values())))
+
+expander2 = PatternMatcher([
+  (UPat(Ops.SINK, name="sink"), build_range_map),
+  (UPat(Ops.REDUCE, name="r"), fix_reduce),
+  (UPat(Ops.RANGE, name="r"),
+   lambda ctx, r: UOp.const(r.dtype, tuple(range(r.vmax+1))) \
+    .reshape(tuple([r.vmax+1 if i == ctx[r.arg[0]] else 1 for i in range(len(ctx))])) if r.arg[0] in ctx else None),
+])+pm_flatten_range
+
+def broadcast_binary(x:UOp):
+  shapes = [u.shape for u in x.src]
+  if all_same(shapes): return None
+  shaped_aligned = _align_left(*shapes)
+  broadcasted = _broadcast_shape(*shapes)
+  src_reshaped = [u.reshape(shp).expand(broadcasted) for u,shp in zip(x.src, shaped_aligned)]
+  return x.replace(src=tuple(src_reshaped))
+
+unbroadcast = PatternMatcher([
+  (UPat(GroupOp.Binary|GroupOp.Ternary|{Ops.STORE}, name="x"), broadcast_binary),
+])
+
+def do_devectorize(b:UOp):
+  if b.shape == (): return None
+  # broadcasting needs to be already unpacked
+  if not all_same([x.shape for x in b.src]): return None
+  src = []
+  for idx in itertools.product(*[range(x) for x in b.shape]):
+    idx_c = [UOp.const(dtypes.weakint, i) for i in idx]
+    src.append(b.replace(src=tuple([x.index(*idx_c) for x in b.src])))
+  return UOp.vectorize(*src).reshape(b.shape)
+
+devectorizer2 = pm_mops+PatternMatcher([
+  # unpack broadcasting
+  (UPat(GroupOp.Elementwise|{Ops.LOAD, Ops.STORE}, name="b"), do_devectorize),
+  # INDEX into STACK is src
+  (UPat(Ops.INDEX, src=(UPat(Ops.STACK, name="a"), UPat.cvar("i"))), lambda a,i: a.src[i.arg]),
+  # stacked INDEX is many INDEX
+  (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.STACK, name="s"))),
+   lambda b,s: UOp.vectorize(*[b.index(u) for u in s.src])),
+  # INDEX into RESHAPE moves the RESHAPE
+  (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.RESHAPE, name="s"))),
+   lambda b,s: b.index(s.src[0]).reshape(s.shape)),
+  # RESHAPE a void is removed (hack for AFTER)
+  (UPat(Ops.RESHAPE, dtype=dtypes.void, name="x"), lambda x: x.src[0]),
+  # reshape of a single element shaped value to scalar is an index
+  (UPat(Ops.RESHAPE, name="x"), lambda x: x.src[0].index(UOp.const(dtypes.weakint, 0)) if x.marg == () and x.src[0].shape == (1,) else None),
+  # INDEX without src is nothing
+  (UPat(Ops.INDEX, src=(UPat.var('x'),)), lambda x: x),
+])
+
+def reduce_ranges_to_acc(ctx:ReduceContext, r:UOp):
+  acc = UOp.placeholder_like(r, ctx.acc_num, AddrSpace.REG)
+  ctx.acc_num += 1
+  topo = r.src[0].toposort()
+  ended_ranges = flatten([x.ended_ranges for x in topo if x.op is Ops.END])
+  input_ranges = tuple(x for x in topo if x.op is Ops.RANGE and x not in r.src[1:] and x not in ended_ranges)
+  acc_init = acc.after(*input_ranges).store(identity_element(r.arg[0], r.dtype.scalar()))
+  acc_initted = acc.after(acc_init, *r.src[1:])
+  inp = r.src[0].reduce(arg=r.arg) if r.arg[1] else r.src[0]
+  acc_out = acc_initted.store(acc_initted.alu(r.arg[0], inp)).end(*r.src[1:])
+  return acc.after(acc_out)
+
+def expand_horizontal_reduce(r:UOp):
+  axes = r.arg[1]
+  vals = [r.src[0].shrink(tuple((idx[axes.index(i)], idx[axes.index(i)]+1) if i in axes else None for i in range(r.src[0].ndim)))
+          for idx in itertools.product(*[range(r.src[0].max_shape[a]) for a in axes])]
+  return functools.reduce(lambda x,y: x.alu(r.arg[0], y), vals)
+
+pm_reduce_local = PatternMatcher([
+  (UPat(Ops.REDUCE, src=(UPat(), UPat()), allow_any_len=True, name="r"), reduce_ranges_to_acc),
+  (UPat(Ops.REDUCE, src=(UPat(),), name="r"), expand_horizontal_reduce),
+])+pm_clean_up_group_sink
+
 def do_number_param(ctx:list[int], x:UOp):
   if x.arg.slot != -1: return None
   ctx[0] += 1
@@ -53,14 +146,94 @@ pm_number_params = PatternMatcher([
   (UPat(Ops.PARAM, name="x"), do_number_param),
 ])
 
-pm_no_weakints = PatternMatcher([
-  (UPat(GroupOp.All, dtype=dtypes.weakint, name="x"), lambda x: x.replace(dtype=dtypes.int))
-])
-
 def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
   if VIZ: graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
   if DEBUG >= 5: print(pyrender(ast))
   if SPEC: type_verify(ast, spec_tensor)
+  sink = ast
+
+  # preprocess. we need to simplify these
+  sink = graph_rewrite(ast, pm_mops+pm_syntactic_sugar+pm_store_ranges, ctx=itertools.count(1000), name="early movement ops", bottom_up=True)
+
+  # this is new style
+  sink = graph_rewrite(sink, pm_index_is_shrink, name="index is shrink")
+  sink = graph_rewrite(sink, pm_remove_vec_dtypes, name="transform to new style")
+
+  # first we optimize
+  if optimize:
+    # do postrange optimization, BEAM or hand_coded_optimizations
+    sink = apply_opts(sink, ren, beam=ast.arg.beam)
+
+  # do expander
+  sink = graph_rewrite(sink, expander2, ctx={}, name="expander", bottom_up=True)
+
+  # add locals (STAGE -> BUFFER)
+  sink = graph_rewrite(sink, pm_add_buffers_local+rangeify_codegen, ctx=itertools.count(0), name="add local buffers")
+
+  # rewrite reduce after optimizations
+  sink = graph_rewrite(sink, pm_reduce_local, ctx=ReduceContext(), name="remove_reduce")
+
+  # add gpu dims
+  sink = graph_rewrite(sink, pm_add_gpudims, ctx=ren, name="add gpudims")
+
+  # add loads
+  sink = graph_rewrite(sink, pm_move_regs, name="move to registers", walk=True)
+
+  # symbolic (note: this does POW decomp)
+  sink = graph_rewrite(sink, sym, name="post index symbolic")
+
+  # ***** make it rendererable (within spec, tighten) *****
+
+  # decompositions
+  supported_ops = tuple(ren.code_for_op.keys())
+  pm_decomp = symbolic_simple+get_late_rewrite_patterns(supported_ops, bool(DISABLE_FAST_IDIV))
+  pm_transcendental = symbolic_simple+get_transcendental_patterns(supported_ops, TRANSCENDENTAL>=2)
+  sink = graph_rewrite(sink, pm_decomp, ctx=ren, name="*** decompositions")
+  sink = graph_rewrite(sink, pm_dtype_decomps, ctx=(set(), ren), name="decomp dtypes")
+  sink = graph_rewrite(sink, pm_transcendental, name="transcendental")
+  sink = graph_rewrite(sink, pm_decomp, ctx=ren, name="decompositions more")
+
+  # split ends
+  sink = graph_rewrite(sink, pm_split_ends, name="split ends")
+
+  # this was the linearizer
+  sink = graph_rewrite(sink, pm_add_control_flow, ctx=CFGContext(sink), name="add control flow", bottom_up=True)
+
+  # ***** this is where it gets large *****
+
+  # unbroadcast
+  sink = graph_rewrite(sink, unbroadcast, name="*** unbroadcast")
+
+  # devectorizer
+  sink = graph_rewrite(sink, symbolic_simple+devectorizer2, name="devectorizer")
+
+  # ***** make it rendererable (outside spec, transform) *****
+
+  # final symbolic
+  sink = graph_rewrite(sink, sym, name="post devectorizer sym")
+
+  # move gates from unrenderable INVALID where
+  sink = graph_rewrite(sink, pm_move_gates_from_index, name="move gates from index")
+
+  # put registers in slots
+  num_params = len([x for x in sink.toposort() if x.op is Ops.PARAM and x.arg.slot != -1])
+  name_to_slot = {x:x.replace(arg=replace(x.arg, slot=num_params+i))
+                  for i,x in enumerate(sorted([x for x in sink.toposort() if x.op is Ops.PARAM and x.arg.slot == -1]))}
+  sink = sink.substitute(name_to_slot, name="put variables in slots")
+
+  # remove all weakints
+  sink = graph_rewrite(sink, pm_lower_weakints, name="lower weakints", bottom_up=True)
+
+  if VIZ: graph_rewrite(sink, PatternMatcher([]), name="View Output AST")
+  if SPEC: type_verify(sink, spec_program)
+
+  # return the rewritten sink
+  return sink
+
+def old_full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
+  if VIZ: graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
+  if DEBUG >= 5: print(pyrender(ast))
+  if SPEC: type_verify(ast, spec_tensor)
 
   # preprocess
   sink = graph_rewrite(ast, pm_mops+pm_syntactic_sugar+pm_store_ranges, ctx=itertools.count(1000), name="early movement ops", bottom_up=True)
@@ -83,7 +256,7 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
     sink = apply_opts(sink, ren, beam=ast.arg.beam)
 
   # ** expander (expand_rewrite) **
-  sink = graph_rewrite(sink, sym+pm_move_where_on_load+pm_flatten_range, name="postopt symbolic")
+  sink = graph_rewrite(sink, sym+pm_move_where_on_load, name="postopt symbolic")
 
   # expand
   sink = graph_rewrite(sink, sym+pm_pre_expander+pm_group_for_reduce+expander, name="expander")
@@ -118,23 +291,18 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
   # optional pre matcher
   if ren.pre_matcher is not None: sink = graph_rewrite(sink, ren.pre_matcher, name="pre_matcher")
 
-  # floordiv+mod / dtype decomp (early)
+  # decompositions
   supported_ops = tuple(ren.code_for_op.keys())
-  pm_decomp = symbolic_simple+get_simplifying_rewrite_patterns(supported_ops)
+  pm_decomp = symbolic_simple+get_late_rewrite_patterns(supported_ops, bool(DISABLE_FAST_IDIV))
-  sink = graph_rewrite(sink, pm_decomp, name="early decompositions")
+  pm_transcendental = symbolic_simple+get_transcendental_patterns(supported_ops, TRANSCENDENTAL>=2)
+  sink = graph_rewrite(sink, pm_decomp, ctx=ren, name="decompositions")
   sink = graph_rewrite(sink, pm_dtype_decomps, ctx=(set(), ren), name="decomp dtypes")
+  sink = graph_rewrite(sink, pm_transcendental, name="transcendental")
 
-  # do memory coalesing (late)
+  # GEP/STACK stuff
-  sink = memory_coalesing(sink, ren)
-
-  # instruction selection decompositions
-  pm_decomp = pm_decomp+\
-    get_late_rewrite_patterns(supported_ops, bool(DISABLE_FAST_IDIV))+\
-    get_transcendental_patterns(supported_ops, TRANSCENDENTAL>=2)
-  sink = graph_rewrite(sink, pm_decomp, ctx=ren, name="late decompositions")
-
-  # this is new style (TODO: this should all be removed)
   sink = graph_rewrite(sink, pm_render, name="pm_render gep/stack")
+
+  # this is new style
   sink = graph_rewrite(sink, pm_index_is_shrink, name="index is shrink")
   sink = graph_rewrite(sink, pm_remove_vec_dtypes, name="transform to new style")
 
@@ -143,7 +311,7 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
 
   # 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+extra_matcher+pm_split_ends+pm_no_weakints
+  pm_final_rewrite = pm_decomp+extra_matcher+pm_split_ends
   sink = graph_rewrite(sink, pm_final_rewrite, ctx=ren, name="final rewrite")
 
   # this was the linearizer

tinygrad/codegen/late/coalese.py

@@ -1,73 +0,0 @@
-from typing import Any
-import itertools
-from collections import defaultdict
-from tinygrad.dtype import dtypes, AddrSpace, Invalid, ImageDType
-from tinygrad.uop.ops import UOp, Ops
-from tinygrad.helpers import getenv
-from tinygrad.renderer import Renderer
-
-def memory_coalesing(sink:UOp, ctx:Renderer) -> UOp:
-  if getenv("DMC"): return sink
-
-  # collect
-  memory: defaultdict[tuple[Ops, UOp, Any, Any], dict[int, list[UOp]]]  = defaultdict(dict)
-  for u in sink.toposort():
-    # TODO: this should handle images too, it's just memory coalesing
-    if u.op in {Ops.LOAD, Ops.STORE} and not isinstance(u.src[0].src[0].dtype, ImageDType):
-      assert len(u.src) == (2 if u.op is Ops.STORE else 1), "memory coalesing does not support gated loads/stores"
-      assert u.src[0].op is Ops.INDEX
-      buf, idx_u = u.src[0].src
-      if buf.addrspace == AddrSpace.REG: continue
-      idx: Any = idx_u.src[1] if idx_u.op is Ops.WHERE and idx_u.src[2].arg is Invalid else idx_u
-      valid: Any = idx_u.src[0] if idx_u.op is Ops.WHERE and idx_u.src[2].arg is Invalid else None
-      if idx.op is Ops.ADD and idx.src[1].op is Ops.CONST: root_src, arg = idx.src[0], idx.src[1].arg
-      elif idx.op is Ops.ADD and idx.src[0].op is Ops.CONST: root_src, arg = idx.src[1], idx.src[0].arg
-      elif idx.op is Ops.CONST and idx.arg is Invalid: root_src, arg = "INVALID", 0
-      elif idx.op is Ops.CONST: root_src, arg = "CONST", idx.arg
-      else: root_src, arg = idx, 0
-      memory[(u.op, buf, root_src, valid)].setdefault(arg, []).append(u)
-
-  # build replacements
-  replacements = {}
-  for (op,buf,base,valid),offsets in memory.items():
-    # allowed lengths (copied in)
-    lengths = []
-    must_divide = True
-    if ctx is not None and ctx.target.device == "DSP":
-      lengths = [128,64,32,16,8,4]
-      must_divide = False
-    elif buf.dtype.base not in (dtypes.float, dtypes.half, *dtypes.fp8s) and not isinstance(buf.dtype, ImageDType):
-      pass
-    elif buf.addrspace == AddrSpace.REG:
-      pass
-    elif isinstance(buf.dtype, ImageDType):
-      lengths = [4]
-    elif ctx is not None and ctx.supports_float4:
-      # TODO: a better way to get this than ctx
-      lengths = [8,4,2] if buf.dtype.base == dtypes.half and getenv("ALLOW_HALF8") else [4,2]
-    lengths.append(1)  # worst case, it's not folded
-    # do the grouping
-    grouped_offsets = [[x for _,x in group] for _,group in itertools.groupby(enumerate(sorted(offsets.keys())), lambda x: x[1]-x[0])]
-    for full_grp in grouped_offsets:
-      while len(full_grp):
-        offset = (base+full_grp[0]) if isinstance(base, UOp) else UOp.const(dtypes.int, full_grp[0])
-        length = [l for l in lengths if l <= len(full_grp) and (not must_divide or offset.divides(l) is not None)][0]
-        grp = full_grp[:length]
-        idx = buf._mop(Ops.SHRINK, arg=[(offset, len(grp))]) if len(grp) > 1 else buf.index(offset)
-        if op == Ops.STORE:
-          datas = []
-          for i,g in enumerate(grp):
-            assert len(offsets[g]) == 1, f"attempting multiple stores: {len(offsets[g])}"
-            datas.append(offsets[g][0].src[1])
-          data = UOp.vectorize(*datas) if len(datas) > 1 else datas[0]
-          store = idx.store(data, valid) if valid is not None else idx.store(data)
-          for i,g in enumerate(grp): replacements[offsets[g][0]] = store
-        else:
-          ld = idx.load(idx.vconst_like(0), valid) if valid is not None else idx.load()
-          for i,g in enumerate(grp):
-            for oo in offsets[g]:
-              replacements[oo] = ld.index(UOp.const(dtypes.int, i)) if len(grp) > 1 else ld
-        full_grp = full_grp[length:]
-
-  # apply
-  return sink.substitute(replacements, name="memory coalesing")

tinygrad/codegen/late/devectorizer.py

@@ -14,7 +14,7 @@ from tinygrad.renderer import Renderer
 def _drop_valid_stmts(valid:UOp, idx:UOp, height:int, width:int) -> list[UOp]:
   # can drop valid if idx is out of bound when valid is False
   drop_stmt = []
-  for i,stmt in enumerate(valid.split_uop(Ops.AND)):
+  for stmt in valid.split_uop(Ops.AND):
     if (res:=parse_valid(stmt)) is None: continue
     X, is_upper_bound, c = res
 
@@ -25,12 +25,12 @@ def _drop_valid_stmts(valid:UOp, idx:UOp, height:int, width:int) -> list[UOp]:
         drop_stmt.append(stmt)
         continue
 
-    # check if idx is out of bound when X is on the wrong side of the bound: X in [c+1, vmax] or [vmin, c-1]
+    # if X <= c, check if it's out of bound when X = c+1
-    lo, hi = (c + 1, X.vmax) if is_upper_bound else (X.vmin, c - 1)
+    # if X >= c, check if it's out of bound when X = c-1
-    if lo <= hi:
+    test_value = c + 1 if is_upper_bound else c - 1
-      fake = UOp.variable(f"fake{i}", lo, hi, X.dtype)
+    for i,b in zip(idx.src, (width, height)):
-      for coord,b in zip(idx.src, (width, height)):
+      if i.is_increasing():
-        rw = coord.substitute({X:fake}).simplify()
+        rw = i.substitute({X:X.const_like(test_value)})
         if rw.vmin >= b or rw.vmax < 0:
           drop_stmt.append(stmt)
           break
@@ -162,8 +162,18 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
   # determine fold lengths
   lengths = []
   must_divide = True
-  # TODO: this belongs in coalese
+  if ctx is not None and ctx.target.device == "DSP":
-  if isinstance(buf.dtype, ImageDType): lengths = [4]
+    lengths = [128,64,32,16,8,4]
+    must_divide = False
+  elif buf.dtype.base not in (dtypes.float, dtypes.half, *dtypes.fp8s) and not isinstance(buf.dtype, ImageDType):
+    pass
+  elif buf.addrspace == AddrSpace.REG:
+    pass
+  elif isinstance(buf.dtype, ImageDType):
+    lengths = [4]
+  elif ctx is not None and ctx.supports_float4:
+    # TODO: a better way to get this than ctx
+    lengths = [8,4,2] if buf.dtype.base == dtypes.half and getenv("ALLOW_HALF8") else [4,2]
   lengths.append(1)  # worst case, it's not folded
 
   # filter fold lengths that don't divide

tinygrad/codegen/opt/heuristic.py

@@ -101,12 +101,6 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
     # for Schedule, we check if the range is used in INDEX gates or WHERE gates
     is_masked = k.rngs[axis] in where_gate_rngs
     if k.full_shape[axis] <= 7 and is_masked and prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
-      # upcasting a masked global axis moves that range out of the launch grid into each work-item
-      # under IMAGE, skip the upcast unless enough global work-items remain after it to hide memory latency
-      if IMAGE and k.axis_types[axis] is AxisType.GLOBAL:
-        global_upcast = prod(k.full_shape[i] for i in to_upcast if k.axis_types[i] is AxisType.GLOBAL) * k.full_shape[axis]
-        global_items_after = prod(k.full_shape[i] for i in k.axes_of(AxisType.GLOBAL)) // global_upcast
-        if resolve(global_items_after < getenv("OCCUPANCY_FLOOR", 4096), False): continue
       if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
       to_upcast.append(axis)
   for axis in to_upcast[::-1]: k.apply_opt(Opt(OptOps.UPCAST, axis, 0))

tinygrad/helpers.py

@@ -240,7 +240,6 @@ DEV, DEBUG, BEAM, NOOPT = _DEV("DEV", ""), ContextVar("DEBUG", 0), ContextVar("B
 IMAGE, FLOAT16, OPENPILOT_HACKS = ContextVar("IMAGE", 0), ContextVar("FLOAT16", 0), ContextVar("OPENPILOT_HACKS", 0)
 JIT, JIT_BATCH_SIZE = ContextVar("JIT", 2 if OSX and ARCH_X86 else 1), ContextVar("JIT_BATCH_SIZE", 32)
 WINO, CAPTURING, TRACEMETA, NO_COLOR = ContextVar("WINO", 0), ContextVar("CAPTURING", 1), ContextVar("TRACEMETA", 1), ContextVar("NO_COLOR", 0)
-TRAINING = ContextVar("TRAINING", 0)
 USE_TC, TC_SELECT, TC_OPT = ContextVar("TC", 1), ContextVar("TC_SELECT", -1), ContextVar("TC_OPT", 0)
 TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS", 0)
 SPLIT_REDUCEOP, NO_MEMORY_PLANNER, LRU = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("LRU", 1)
@@ -275,7 +274,7 @@ SCACHE = ContextVar("SCACHE", 1)
 # allow use of atomics for embedding backward
 USE_ATOMICS = ContextVar("USE_ATOMICS", 0)
 # don't allow broadcast
-DISALLOW_BROADCAST = ContextVar("DISALLOW_BROADCAST", 1)
+DISALLOW_BROADCAST = ContextVar("DISALLOW_BROADCAST", 0)
 
 @dataclass(frozen=True)
 class Metadata:

tinygrad/mixin/__init__.py

@@ -6,7 +6,7 @@ from tinygrad.mixin.movement import MovementMixin
 from tinygrad.mixin.reduce import ReduceMixin
 from tinygrad.uop import Ops
 from tinygrad.uop.ops import _broadcast_shape, resolve, smax, smin, identity_element
-from tinygrad.dtype import ConstType, DTypeLike, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
+from tinygrad.dtype import ConstType, DTypeLike, Invalid, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
 from tinygrad.helpers import all_int, argfix, argsort, ceildiv, flatten, flat_to_grouped, fully_flatten, get_shape, make_tuple, merge_dicts, prod
 from tinygrad.helpers import resolve_pool_pads, round_up
 
@@ -17,6 +17,9 @@ ReductionStr = Literal["mean", "sum", "none"]
 
 
 class OpMixin(ElementwiseMixin, ReduceMixin):
+  @staticmethod
+  def const(dtype, b): raise NotImplementedError
+
   def data(self) -> memoryview: raise NotImplementedError("data requires Tensor realization to host memory")
 
   def item(self) -> PyConst:
@@ -31,6 +34,48 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     assert self.numel() == 1, "must have one element for item"
     return self.data()[(0,) * len(self.shape)]
 
+  def _multi_like(self, fxn:Callable[[tuple[sint, ...], str|None], Self]) -> Self:
+    from tinygrad.uop.ops import UOp
+    assert isinstance(self.device, tuple), f"_multi_like needs a multi device tensor, got {self.device}"
+    if self._uop.axis is None: return self._wrap_uop(fxn(self.shape, None)._uop.shard(self.device, None))
+    return self._wrap_uop(UOp.mstack(*[fxn(self._uop.shard_shape, d)._uop for d in self.device]).multi(self._uop.axis))
+
+  @classmethod
+  def invalids(cls, *shape, device:str|tuple[str, ...]|None=None, dtype:DTypeLike|None=None) -> Self:
+    """
+    Creates a tensor with the given shape, filled with Invalid.
+
+    This is an alternative to Tensor.empty when you want an "anonymous" buffer.
+
+    Eventually Tensor.empty will be replaced by this.
+    """
+    return cls.full(argfix(*shape), Invalid, dtype=dtype, device=device)
+
+  @classmethod
+  def full(cls, shape:tuple[sint, ...], fill_value:ConstType|UOp, dtype:DTypeLike|None=None,
+           device:str|tuple[str, ...]|None=None, buffer=True) -> Self:
+    """
+    Creates a tensor with the given shape, filled with the given value.
+
+    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
+    Pass `buffer=False` to get a broadcast const value instead of a materialized buffer.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor.full((2, 3), 42).numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor.full((2, 3), False).numpy())
+    ```
+    """
+    # TODO: enable this check
+    # if not buffer: assert device is None, "buffer=False does not support device specification"
+    from tinygrad.uop.ops import UOp
+    new_shape = argfix(shape)
+    dt = to_dtype(dtype) if dtype is not None else None
+    val = cls.const(dt or (fill_value.dtype if isinstance(fill_value, UOp) else dtypes.from_py(fill_value)), fill_value)
+    val = val.reshape((1,)*len(new_shape)).expand(new_shape)
+    return val.clone(device=device) if buffer else val
+
   def __getitem__(self, indices) -> Self:
     """
     Retrieves a sub-tensor using indexing.
@@ -162,6 +207,40 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
     vb = vb.pad(tuple((m['boundary'][0], self.shape[d] - m['boundary'][1]) for d, m in enumerate(mops)))
     return (type(self).uprod(*per_dim) if per_dim else type(self).const(dtypes.bool, True)).where(vb, self)
 
+  @classmethod
+  def zeros(cls, *shape, **kwargs) -> Self:
+    """
+    Creates a tensor with the given shape, filled with zeros.
+
+    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
+    Additionally, all other keyword arguments are passed to the constructor of the tensor.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor.zeros(2, 3).numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor.zeros(2, 3, dtype=dtypes.int32).numpy())
+    ```
+    """
+    return cls.full(argfix(*shape), 0.0, **kwargs)
+
+  @classmethod
+  def ones(cls, *shape, **kwargs) -> Self:
+    """
+    Creates a tensor with the given shape, filled with ones.
+
+    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
+    Additionally, all other keyword arguments are passed to the constructor of the tensor.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor.ones(2, 3).numpy())
+    ```
+    ```python exec="true" source="above" session="tensor" result="python"
+    print(Tensor.ones(2, 3, dtype=dtypes.int32).numpy())
+    ```
+    """
+    return cls.full(argfix(*shape), 1.0, **kwargs)
+
   @classmethod
   def arange(cls, start, stop=None, step=1, dtype:DTypeLike|None=None) -> Self:
     """

tinygrad/mixin/creation.py

@@ -1,24 +1,10 @@
-from typing import TYPE_CHECKING, Callable, Self
+from typing import Self
-from tinygrad.dtype import ConstType, DTypeLike, Invalid, dtypes, to_dtype
+from tinygrad.dtype import ConstType, DType, DTypeLike
-from tinygrad.helpers import argfix
 from tinygrad.mixin.dtype import DTypeMixin
-from tinygrad.mixin.movement import MovementMixin
-
-if TYPE_CHECKING:
-  from tinygrad.uop.ops import sint, UOp
-
-class CreationMixin(DTypeMixin, MovementMixin):
-  @staticmethod
-  def const(dtype, b): raise NotImplementedError
 
+class CreationMixin(DTypeMixin):
   def const_like(self, b: ConstType) -> Self: return self._wrap_uop(self._uop.const_like(b))
 
-  def _multi_like(self, fxn:'Callable[[tuple[sint, ...], str|None], Self]') -> Self:
-    from tinygrad.uop.ops import UOp
-    assert isinstance(self.device, tuple), f"_multi_like needs a multi device tensor, got {self.device}"
-    if self._uop.axis is None: return self._wrap_uop(fxn(self.shape, None)._uop.shard(self.device, None))
-    return self._wrap_uop(UOp.mstack(*[fxn(self._uop.shard_shape, d)._uop for d in self.device]).multi(self._uop.axis))
-
   def empty_like(self, dtype: DTypeLike|None=None, device: str|tuple[str, ...]|None=None) -> Self:
     """
     Creates an empty tensor with the same shape as `self`.
@@ -26,76 +12,9 @@ class CreationMixin(DTypeMixin, MovementMixin):
     """
     return self._wrap_uop(self._uop.empty_like(dtype, device))
 
-  @classmethod
+  def full_like(self, fill_value: ConstType, dtype: DType|None=None) -> Self:
-  def invalids(cls, *shape, device:str|tuple[str, ...]|None=None, dtype:DTypeLike|None=None) -> Self:
+    """Creates a tensor with the same shape as `self`, filled with the given value."""
-    """
+    return self.const_like(fill_value) if dtype is None else self.const_like(fill_value).cast(dtype)
-    Creates a tensor with the given shape, filled with Invalid.
-
-    This is an alternative to Tensor.empty when you want an "anonymous" buffer.
-
-    Eventually Tensor.empty will be replaced by this.
-    """
-    return cls.full(argfix(*shape), Invalid, dtype=dtype, device=device)
-
-  @classmethod
-  def full(cls, shape:'tuple[sint, ...]', fill_value:'ConstType|UOp', dtype:DTypeLike|None=None,
-           device:str|tuple[str, ...]|None=None, buffer=True) -> Self:
-    """
-    Creates a tensor with the given shape, filled with the given value.
-
-    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
-    Pass `buffer=False` to get a broadcast const value instead of a materialized buffer.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor.full((2, 3), 42).numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor.full((2, 3), False).numpy())
-    ```
-    """
-    # TODO: enable this check
-    # if not buffer: assert device is None, "buffer=False does not support device specification"
-    from tinygrad.uop.ops import UOp
-    new_shape = argfix(shape)
-    dt = to_dtype(dtype) if dtype is not None else None
-    val = cls.const(dt or (fill_value.dtype if isinstance(fill_value, UOp) else dtypes.from_py(fill_value)), fill_value)
-    val = val.reshape((1,)*len(new_shape)).expand(new_shape)
-    return val.clone(device=device) if buffer else val
-
-  def full_like(self, fill_value:ConstType, dtype:DTypeLike|None=None, device:str|tuple[str, ...]|None=None, buffer=True) -> Self:
-    """
-    Creates a tensor with the same shape as `self`, filled with the given value.
-    If `dtype` is not specified, the dtype of `self` is used.
-
-    You can pass in the `device` keyword argument to control device of the tensor.
-    Pass `buffer=False` to get a broadcast const value instead of a materialized buffer.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    t = Tensor.ones(2, 3)
-    print(Tensor.full_like(t, 42).numpy())
-    ```
-    """
-    if isinstance(self.device, tuple):
-      if device is not None: raise RuntimeError("cannot specify `device` on `*_like` of a multi device tensor")
-      return self._multi_like(lambda shape, dev: type(self).full(shape, fill_value, dtype=dtype or self.dtype, device=dev, buffer=buffer))
-    return type(self).full(self.shape, fill_value, dtype=dtype or self.dtype, device=self.device if device is None else device, buffer=buffer)
-
-  @classmethod
-  def zeros(cls, *shape, **kwargs) -> Self:
-    """
-    Creates a tensor with the given shape, filled with zeros.
-
-    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
-    Additionally, all other keyword arguments are passed to the constructor of the tensor.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor.zeros(2, 3).numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor.zeros(2, 3, dtype=dtypes.int32).numpy())
-    ```
-    """
-    return cls.full(argfix(*shape), 0.0, **kwargs)
 
   def zeros_like(self, **kwargs) -> Self:
     """
@@ -110,23 +29,6 @@ class CreationMixin(DTypeMixin, MovementMixin):
     """
     return self.full_like(0, **kwargs)
 
-  @classmethod
-  def ones(cls, *shape, **kwargs) -> Self:
-    """
-    Creates a tensor with the given shape, filled with ones.
-
-    You can pass in `dtype` and `device` keyword arguments to control the data type and device of the tensor.
-    Additionally, all other keyword arguments are passed to the constructor of the tensor.
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor.ones(2, 3).numpy())
-    ```
-    ```python exec="true" source="above" session="tensor" result="python"
-    print(Tensor.ones(2, 3, dtype=dtypes.int32).numpy())
-    ```
-    """
-    return cls.full(argfix(*shape), 1.0, **kwargs)
-
   def ones_like(self, **kwargs) -> Self:
     """
     Creates a tensor with the same shape as `self`, filled with ones.

tinygrad/mixin/elementwise.py

@@ -45,11 +45,7 @@ class ElementwiseMixin(CreationMixin):
     """
     return self.cast(dtypes.bool).ne(True)
 
-  def contiguous(self, **kwargs) -> Self:
+  def contiguous(self, *args, **kwargs) -> Self: raise NotImplementedError
-    """
-    Returns a contiguous tensor.
-    """
-    return self._wrap_uop(self._uop.contiguous(**kwargs))
 
   def contiguous_backward(self) -> Self:
     """

tinygrad/mixin/rand.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 import math
 from typing import Self, cast
-from tinygrad.dtype import DType, DTypeLike, dtypes, least_upper_dtype, to_dtype
+from tinygrad.dtype import DType, DTypeLike, dtypes, to_dtype
-from tinygrad.helpers import all_int, argfix, ceildiv, prod, TRAINING
+from tinygrad.helpers import all_int, argfix, ceildiv, prod
 from tinygrad.mixin import OpMixin
 from tinygrad.device import canonicalize_device
 
@@ -273,54 +273,3 @@ class RandMixin(OpMixin):
       # Efraimidis-Spirakis
       indices = (weight.rand_like(dtype=dtypes.float32).log2() / weight).topk(num_samples, dim=1)[1]
     return (indices.squeeze(0) if self.ndim == 1 else indices).cast(dtypes.int32)
-
-  def dropout(self, p=0.5) -> Self:
-    """
-    Applies dropout to `self`.
-
-    NOTE: dropout is only applied when `TRAINING` is set (e.g. inside `Context(TRAINING=1)`).
-
-    - Paper: https://jmlr.org/papers/v15/srivastava14a.html
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    Tensor.manual_seed(42)
-    t = Tensor.randn(2, 2)
-    with Context(TRAINING=1):
-      print(t.dropout().numpy())
-    ```
-    """
-    if not 0 <= p <= 1: raise ValueError(f"{p=} is out of range [0, 1]")
-    if not TRAINING or p == 0: return self
-    if p == 1: return self.const_like(0)
-    return (self.rand_like(dtype=dtypes.default_float, contiguous=False) >= p).contiguous().where(self, 0) / (1.0 - p)
-
-  def scaled_dot_product_attention(self, key:Self, value:Self, attn_mask:Self|None=None, dropout_p:float=0.0,
-                                   is_causal:bool=False, enable_gqa:bool=False) -> Self:
-    """
-    Computes scaled dot-product attention.
-    `self` is the query tensor, `key` is the key tensor, and `value` is the value tensor.
-
-    - Paper: https://arxiv.org/abs/1706.03762v7
-
-    ```python exec="true" source="above" session="tensor" result="python"
-    q = Tensor.randn(2, 4, 8)
-    k = Tensor.randn(2, 4, 8)
-    v = Tensor.randn(2, 4, 8)
-    print(q.scaled_dot_product_attention(k, v).numpy())
-    ```
-    """
-    # GQA: https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
-    if enable_gqa:
-      key = key.repeat_interleave(int(self.shape[-3] // key.shape[-3]), dim=-3)
-      value = value.repeat_interleave(int(self.shape[-3] // value.shape[-3]), dim=-3)
-
-    q = self
-    qk = q.matmul(key.transpose(-2,-1), dtype=least_upper_dtype(q.dtype, key.dtype, dtypes.float32)) / math.sqrt(q.shape[-1])
-    # handle attention mask
-    if is_causal:
-      if attn_mask is not None: raise RuntimeError("cannot set attn_mask when is_causal=True")
-      attn_mask = qk.const_like(1).cast(dtypes.bool).tril()
-    if attn_mask is not None:
-      if attn_mask.dtype == dtypes.bool: attn_mask = attn_mask.where(0, -float("inf"))
-      qk = qk + attn_mask
-    return qk.cast(self.dtype).softmax(-1).dropout(dropout_p) @ value

tinygrad/renderer/cstyle.py

@@ -22,7 +22,6 @@ base_rewrite = PatternMatcher([
   (UPat(Ops.CAST, name="x"), lambda ctx,x: f"__builtin_convertvector({ctx[x.src[0]]}, {ctx.render_type(x)})" \
     if x.max_numel() > 1 and x.addrspace is AddrSpace.REG else None),
   (UPat(Ops.CAST, name="x"), lambda ctx,x: f"({ctx.render_cast(x, ctx[x.src[0]])})"),
-  (UPat(Ops.BITCAST, name="x"), lambda ctx,x: ctx[x.src[0]] if x.addrspace in (AddrSpace.GLOBAL, AddrSpace.LOCAL) else None),
   (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"__builtin_bit_cast({ctx.render_type(x)}, ({ctx.render_type(x.src[0])})({ctx[x.src[0]]}))"),
 
   # GPU stuff

tinygrad/schedule/rangeify.py

@@ -18,7 +18,7 @@ import sys
 sys.setrecursionlimit(10000)
 
 def add_ranges_to_store(ctx, x):
-  if x.src[0]._shape is None or x.src[1]._shape is None or x.src[0].shape == () or x.src[0].max_numel() == x.src[1].max_numel() == 1: return None
+  if x.src[0]._shape is None or x.src[1]._shape is None or x.src[0].shape == (): return None
   assert x.src[0].shape == x.src[1].shape, "bad store shape"
   idxs = [UOp.range(r, next(ctx), AxisType.LOOP) for r in x.src[0].shape]
   return UOp.store(x.src[0].index(*idxs), x.src[1].index(*idxs)).end(*idxs)

tinygrad/tensor.py

@@ -1,13 +1,14 @@
 # inspired by https://github.com/karpathy/micrograd/blob/master/micrograd/engine.py
 from __future__ import annotations
 import time, math, itertools, functools, sys, inspect, pathlib, hashlib, weakref
-from typing import Any, Callable, Sequence, cast, get_args, ParamSpec, TypeVar, Generic, TYPE_CHECKING
+from contextlib import ContextDecorator
+from typing import Any, Callable, ClassVar, Sequence, cast, get_args, ParamSpec, TypeVar, Generic, TYPE_CHECKING
 if TYPE_CHECKING: import numpy
-from tinygrad.dtype import DType, DTypeLike, dtypes, ConstType, to_dtype
+from tinygrad.dtype import DType, DTypeLike, dtypes, ConstType, least_upper_dtype, to_dtype
 from tinygrad.dtype import _from_np_dtype, _to_np_dtype, PyConst, Invalid
 from tinygrad.helpers import argfix, flatten, prod, all_int, round_up, getenv, fully_flatten, ceildiv, fetch, flat_to_grouped
 from tinygrad.helpers import resolve_pool_pads, IMAGE, FLOAT16, WINO, Metadata, TRACEMETA, is_numpy_ndarray, TracingKey, cpu_profile
-from tinygrad.helpers import suppress_finalizing, disable_gc, TRAINING
+from tinygrad.helpers import suppress_finalizing, disable_gc
 from tinygrad.uop.ops import UOp, Ops, sint, all_metadata, _index_to_concrete_int, Variable, _broadcast_shape
 from tinygrad.mixin.rand import RandMixin
 from tinygrad.schedule import create_linear_with_vars
@@ -58,25 +59,19 @@ def _apply_winograd_matrix(mat, t:Tensor, dims:int) -> Tensor:
   assert isinstance(ret, Tensor), "sum didn't return a Tensor"
   return ret
 
-# TODO: deprecate this, always use TRAINING
+class Tensor(RandMixin):
-class TensorMeta(type):
-  @property
-  def training(cls) -> bool: return bool(TRAINING.value)
-  @training.setter
-  def training(cls, mode:bool): TRAINING.value = int(mode)
-
-class Tensor(RandMixin, metaclass=TensorMeta):
   """
   A `Tensor` is a multi-dimensional matrix containing elements of a single data type.
 
   ```python exec="true" session="tensor"
-  from tinygrad import Tensor, dtypes, nn, Context
+  from tinygrad import Tensor, dtypes, nn
   import numpy as np
   import math
   np.set_printoptions(precision=4)
   ```
   """
   __slots__ = "uop", "is_param", "grad"
+  training: ClassVar[bool] = False
 
   def __init__(self, data:ConstType|bytes|list|tuple|UOp|'numpy.ndarray'|pathlib.Path|None,
                device:str|tuple|list|None=None, dtype:DTypeLike|None=None):
@@ -130,9 +125,9 @@ class Tensor(RandMixin, metaclass=TensorMeta):
   @suppress_finalizing
   def __del__(self): all_tensors.pop(weakref.ref(self), None)
 
-  def _apply_uop(self, fxn:Callable[..., UOp], *x:Tensor, **kwargs) -> Tensor:
+  def _apply_uop(self, fxn:Callable[..., UOp], *x:Tensor, extra_args=(), **kwargs) -> Tensor:
     srcs = (self,)+x
-    new_uop: UOp = fxn(*[t.uop for t in srcs], **kwargs)
+    new_uop: UOp = fxn(*[t.uop for t in srcs], *extra_args, **kwargs)
     if TRACEMETA >= 1 and (metadata:=_METADATA.get()) is not None: all_metadata[new_uop] = (metadata,)
     # directly create the Tensor
     ret = Tensor.__new__(Tensor)
@@ -153,6 +148,11 @@ class Tensor(RandMixin, metaclass=TensorMeta):
     self.is_param = is_param
     return self
 
+  class train(ContextDecorator):
+    def __init__(self, mode:bool = True): self.mode = mode
+    def __enter__(self): self.prev, Tensor.training = Tensor.training, self.mode
+    def __exit__(self, exc_type, exc_value, traceback): Tensor.training = self.prev
+
   def __repr__(self):
     ld = self.uop
     ld_repr = f"<UOp {ld.device} {ld.shape} {str(ld.dtype)[7:]}>"
@@ -264,7 +264,6 @@ class Tensor(RandMixin, metaclass=TensorMeta):
     x = self.cast(self.dtype.base).contiguous()
     if self.uop.device is None or isinstance(self.device, tuple): x = x.clone("CPU")
     return cast(Buffer, x.realize().uop.buffer).ensure_allocated()
-
   def _data(self) -> memoryview: return self._buffer().as_memoryview()
 
   def data(self) -> memoryview:
@@ -280,7 +279,7 @@ class Tensor(RandMixin, metaclass=TensorMeta):
     assert all_int(self.shape), f"no data if shape is symbolic, {self.shape=}"
     assert self.dtype.base.fmt is not None, f"no fmt dtype for {self.dtype.base}"
     assert self.dtype.base.fmt != "e" or sys.version_info >= (3, 12)
-    return self._data().cast(self.dtype.base.fmt, self.shape)
+    return self._buffer().as_memoryview().cast(self.dtype.base.fmt, self.shape)
 
   # NOTE: list[Any] because return type is recursive (list[list[...]] for higher dimensions)
   def tolist(self) -> PyConst|list[Any]:
@@ -504,6 +503,25 @@ class Tensor(RandMixin, metaclass=TensorMeta):
     high = counter[1:2] - (num >> 32) - (counter[0] < (num & 0xffffffff))
     return Tensor._device_seeds[device], low.cat(high)
 
+  # ***** creation helper functions *****
+
+  def full_like(self, fill_value:ConstType, dtype=None, device=None) -> Tensor:
+    """
+    Creates a tensor with the same shape as `self`, filled with the given value.
+    If `dtype` is not specified, the dtype of `self` is used.
+
+    You can pass in the `device` keyword argument to control device of the tensor.
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    t = Tensor.ones(2, 3)
+    print(Tensor.full_like(t, 42).numpy())
+    ```
+    """
+    if isinstance(self.device, tuple):
+      if device is not None: raise RuntimeError("cannot specify `device` on `*_like` of a multi device tensor")
+      return self._multi_like(lambda shape, dev: Tensor.full(shape, fill_value, dtype=dtype or self.dtype, device=dev))
+    return Tensor.full(self.shape, fill_value, dtype=dtype or self.dtype, device=self.device if device is None else device)
+
   # ***** toposort and backward pass *****
 
   def backward(self, gradient:Tensor|None=None) -> Tensor:
@@ -530,7 +548,7 @@ class Tensor(RandMixin, metaclass=TensorMeta):
 
   # ***** movement ops *****
 
-  def _mop(self, op:Ops, arg) -> Tensor: return self._apply_uop(UOp._mop, op=op, arg=arg)
+  def _mop(self, op:Ops, arg) -> Tensor: return self._apply_uop(UOp._mop, extra_args=(op,), arg=arg)
   def _rop(self, op:Ops, axis:tuple[int, ...]) -> Tensor: return self._apply_uop(UOp._rop, op=op, axis=axis)
 
   def __setitem__(self, indices, v:Tensor|PyConst|list|tuple) -> None:
@@ -719,6 +737,14 @@ class Tensor(RandMixin, metaclass=TensorMeta):
     if IMAGE: return self.image_dot(w, dtype)
     return super().dot(w, dtype)
 
+  # ***** unary ops *****
+
+  def contiguous(self, *args, **kwargs) -> Tensor:
+    """
+    Returns a contiguous tensor.
+    """
+    return self._apply_uop(UOp.contiguous, extra_args=args, **kwargs)
+
   # ***** broadcasted elementwise ops *****
 
   def where(self:Tensor, x:Tensor|ConstType|sint, y:Tensor|ConstType|sint) -> Tensor:
@@ -778,6 +804,59 @@ class Tensor(RandMixin, metaclass=TensorMeta):
     fn = UOp(Ops.CUSTOM_FUNCTION, dtypes.void, src=(frame_pos.src[0], *[UOp.const(dtypes.int, s) for s in shape]), arg="encdec")
     return Tensor(out.uop.after(fn.call(*[s.uop for s in srcs], frame_pos)))
 
+  # ***** functional nn ops *****
+
+  def dropout(self, p=0.5) -> Tensor:
+    """
+    Applies dropout to `self`.
+
+    NOTE: dropout is only applied when `Tensor.training` is `True`.
+
+    - Paper: https://jmlr.org/papers/v15/srivastava14a.html
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    Tensor.manual_seed(42)
+    t = Tensor.randn(2, 2)
+    with Tensor.train():
+      print(t.dropout().numpy())
+    ```
+    """
+    if not 0 <= p <= 1: raise ValueError(f"{p=} is out of range [0, 1]")
+    if not Tensor.training or p == 0: return self
+    if p == 1: return self.const_like(0)
+    return (Tensor.rand_like(self, dtype=dtypes.default_float, contiguous=False) >= p).contiguous().where(self, 0) / (1.0 - p)
+
+  def scaled_dot_product_attention(self, key:Tensor, value:Tensor, attn_mask:Tensor|None=None, dropout_p:float=0.0,
+                                   is_causal:bool=False, enable_gqa:bool=False) -> Tensor:
+    """
+    Computes scaled dot-product attention.
+    `self` is the query tensor, `key` is the key tensor, and `value` is the value tensor.
+
+    - Paper: https://arxiv.org/abs/1706.03762v7
+
+    ```python exec="true" source="above" session="tensor" result="python"
+    q = Tensor.randn(2, 4, 8)
+    k = Tensor.randn(2, 4, 8)
+    v = Tensor.randn(2, 4, 8)
+    print(q.scaled_dot_product_attention(k, v).numpy())
+    ```
+    """
+    # GQA: https://docs.pytorch.org/docs/stable/generated/torch.nn.functional.scaled_dot_product_attention.html
+    if enable_gqa:
+      key = key.repeat_interleave(int(self.shape[-3] // key.shape[-3]), dim=-3)
+      value = value.repeat_interleave(int(self.shape[-3] // value.shape[-3]), dim=-3)
+
+    q = self
+    qk = q.matmul(key.transpose(-2,-1), dtype=least_upper_dtype(q.dtype, key.dtype, dtypes.float32)) / math.sqrt(q.shape[-1])
+    # handle attention mask
+    if is_causal:
+      if attn_mask is not None: raise RuntimeError("cannot set attn_mask when is_causal=True")
+      attn_mask = qk.const_like(1).cast(dtypes.bool).tril()
+    if attn_mask is not None:
+      if attn_mask.dtype == dtypes.bool: attn_mask = attn_mask.where(0, -float("inf"))
+      qk = qk + attn_mask
+    return qk.cast(self.dtype).softmax(-1).dropout(dropout_p) @ value
+
   # ***** cast ops *****
 
   def bitcast(self, dtype:DTypeLike) -> Tensor:

tinygrad/uop/decompositions.py

@@ -454,8 +454,7 @@ def floormod_to_mod(a:UOp, b:UOp) -> UOp:
 
 powers_of_two: dict[int, int] = {2**i:i for i in range(64)}
 @functools.cache
-def get_simplifying_rewrite_patterns(ops:tuple[Ops, ...]) -> PatternMatcher:
+def get_late_rewrite_patterns(ops:tuple[Ops, ...], disable_fast_idiv:bool) -> PatternMatcher:
-  # these are rewrites that make things simpler
   pat: list[tuple[UPat, Callable]] = [(UPat.var("a")//UPat.var("b"), floordiv_to_idiv)]
   # FLOORMOD by 2**y -> x & (2**y-1) (correct floor mod for any sign in two's complement); fires before floormod_to_mod
   if Ops.AND in ops: pat.append((UPat.var("x", dtypes.ints)%UPat.cvar("c"), lambda x,c: x & (c.arg-1) if c.arg in powers_of_two else None))
@@ -464,11 +463,6 @@ def get_simplifying_rewrite_patterns(ops:tuple[Ops, ...]) -> PatternMatcher:
   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)
   if Ops.MAX not in ops and Ops.CMPLT in ops: pat.append((UPat(Ops.MAX, name="m"), lambda m: (m.src[0] < m.src[1]).where(m.src[1], m.src[0])))
-  return PatternMatcher(pat)
-
-@functools.cache
-def get_late_rewrite_patterns(ops:tuple[Ops, ...], disable_fast_idiv:bool) -> PatternMatcher:
-  pat: list[tuple[UPat, Callable]] = []
   if Ops.OR in ops: pat += [(UPat.var("x", dtypes.bool).logical_not()&UPat.var("y", dtypes.bool).logical_not(),
     lambda x,y: (x | y).logical_not())]
   # rewrite MUL/CDIV to SHL+SHR: x*(2**y) -> shl(x,y) and x//(2**y) -> shr(x,y)

tinygrad/uop/divandmod.py

@@ -75,7 +75,9 @@ def fold_divmod_general(d: UOp) -> UOp|None:
   # divide_by_gcd: x//y -> (x//gcd)//(y//gcd)
   gcd = UOp.gcd(*all_uops, y).simplify()
   if not (gcd.op is Ops.CONST and gcd.arg==1):
-    ret = unwrap(x.divide_exact(gcd)).alu(d.op, unwrap(y.divide_exact(gcd)))
+    x_div, y_div = x.divide_exact(gcd), y.divide_exact(gcd)
+    if x_div is None or y_div is None: return None
+    ret = x_div.alu(d.op, y_div)
     return ret*gcd if d.op is Ops.FLOORMOD else ret
 
   # factor_remainder: (d*x+y)//d -> x+y//d

tinygrad/uop/ops.py

@@ -84,8 +84,8 @@ def multirange_str(rngs:Iterable[UOp], color=False, pad=None) -> str:
 
 def shape_to_shape_arg(arg:tuple[sint, ...]) -> UOp:
   if len(arg) == 0: return UOp(Ops.STACK)
-  elif all_int(arg): return UOp.const(dtypes.weakint.vec(len(arg)), arg)
+  elif all_int(arg): return UOp.const(dtypes.weakint, arg)
-  else: return UOp(Ops.STACK, dtypes.weakint.vec(len(arg)), tuple(UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in arg))
+  else: return UOp(Ops.STACK, dtypes.weakint, tuple(UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in arg))
 
 def consumer_map_from_toposort(lst:Iterable[UOp]):
   ret: dict[UOp, dict[UOp, None]] = {}
@@ -306,9 +306,10 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
            Ops.COPY | Ops.ALLREDUCE | Ops.STORE | Ops.END:
         return self.src[0]._shape
       # REDUCE with empty axis is passthrough (lowered form)
-      case Ops.REDUCE if len(self.arg[1]) == 0:
+      # no longer true
+      #case Ops.REDUCE if len(self.arg[1]) == 0:
         # these can mismatch if there's a horizonal reduce
-        return (self.dtype.count,) if self.dtype.count > 1 else ()
+        #return (self.dtype.count,) if self.dtype.count > 1 else ()
 
       # TODO: disallow shape changing bitcast
       case Ops.BITCAST:
@@ -473,12 +474,12 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
     if len(srcs) == 1 and isinstance(srcs[0], UOp): return srcs[0]
     return UOp(Ops.GROUP, dtypes.void, tuple([x for x in srcs if x is not None]))
   def vectorize(self, *srcs):
-    return UOp(Ops.STACK, self.dtype.vec(len(srcs)+1), (self,)+srcs)
+    return UOp(Ops.STACK, self.dtype, (self,)+srcs)
   def index(self, *srcs:UOp|None, ptr=False, **kwargs):
     return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype if ptr else self.dtype.base), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
   def __getitem__(self, idx):
     # pointers index into INDEX UOps (scalar lookup); everything else uses the shared mixin view path
-    if not isinstance(self.dtype, PtrDType): return super(UOp, self).__getitem__(idx)
+    #if not isinstance(self.dtype, PtrDType): return super(UOp, self).__getitem__(idx)
     idx = self._normalize_indices(list(argfix(idx)))
     if len(slice_idx:=[i for i,x in enumerate(idx) if isinstance(x, slice)]):
       # apply SHRINK for slices that aren't the full range
@@ -919,6 +920,12 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
 
   # *** uop symbolic stuff ***
 
+  def is_increasing(self:UOp) -> bool:
+    # is f a monotonically increasing function regards its input
+    if self.op in GroupOp.Irreducible: return True
+    if self.op is Ops.ADD: return self.src[0].is_increasing() and self.src[1].is_increasing()
+    if self.op in (Ops.MUL, Ops.CDIV, Ops.FLOORDIV) and self.src[1].op is Ops.CONST and self.src[1].arg >= 0: return self.src[0].is_increasing()
+    return False  # False if not sure
   def const_factor(self) -> int:
     """largest known int that divides self"""
     # TODO: for negatives it's not the largest
@@ -1184,9 +1191,8 @@ python_alu: dict[Ops, Callable]  = {
   Ops.MULACC: lambda x,y,z: (x*y)+z, Ops.WHERE: lambda x,y,z: y if x else z, Ops.CMPEQ: operator.eq}
 
 def exec_alu(op:Ops, dtype:DType, operands, truncate_output=True):
-  if any(isinstance(x, tuple) for x in operands):
+  if dtype.count > 1:
-    count = max(len(x) for x in operands if isinstance(x, tuple))
+    return tuple([exec_alu(op, dtype.scalar(), [x[i] if isinstance(x, tuple) else x for x in operands]) for i in range(dtype.count)])
-    return tuple([exec_alu(op, dtype.scalar(), [x[i] if isinstance(x, tuple) else x for x in operands]) for i in range(count)])
   if dtype==dtypes.weakint and op in GroupOp.Binary and Invalid in operands: return Invalid
   alu = python_alu[op](*operands)
   return truncate.get(dtype, lambda x: x)(alu) if truncate_output else alu
@@ -1667,8 +1673,6 @@ pm_lower_index_dtype = PatternMatcher([
     lambda var,val: var.bind(val).cast(dtypes.weakint)),
   # remove hanging casts
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("idx", dtypes.ints).cast()),), lambda buf,idx: buf.index(idx, ptr=True)),
-  (UPat(Ops.SHRINK, src=(UPat.var("buf"), UPat.var("idx", dtypes.ints).cast(), UPat.var("slen", dtypes.ints).cast(),), name="shrink"),
-   lambda shrink,buf,idx,slen: shrink.replace(src=(buf,idx,slen))),
   (UPat(Ops.INDEX, src=(UPat.var("buf"), UPat.var("gate").where(UPat.var("idx", dtypes.ints).cast(), UPat(Ops.CONST, arg=Invalid)))),
    lambda buf,idx,gate: buf.index(gate.where(idx, idx.const_like(Invalid)), ptr=True)),
   # remove hanging casts for images
@@ -1679,7 +1683,7 @@ pm_lower_index_dtype = PatternMatcher([
                         UPat.var("gate").where(UPat.var("idx_x", dtypes.ints).cast(), UPat(Ops.CONST, arg=Invalid)))),
    lambda buf,idx_x,idx_y,gate: buf.index(gate.where(idx_y, idx_y.const_like(Invalid)),
                                           gate.where(idx_x, idx_x.const_like(Invalid)), ptr=True)),
-  (UPat((Ops.SINK, Ops.NOOP, Ops.END), name="n"),
+  (UPat((Ops.SINK, Ops.NOOP, Ops.END, Ops.AFTER, Ops.BUFFER), name="n"),
    lambda n: n.replace(src=tuple(s.src[0] if s.op is Ops.CAST and s.dtype == dtypes.weakint else s for s in n.src))),
 ])
 def _index_to_concrete_int(u:UOp) -> UOp: return graph_rewrite(u.sink(), pm_lower_index_dtype).src[0]

tinygrad/uop/symbolic.py

@@ -29,7 +29,14 @@ def const_arg(u:UOp) -> ConstType|tuple[ConstType, ...]|None:
 
 def fold_const_alu(a:UOp) -> UOp|None:
   vals = [const_arg(s) for s in a.src]
-  return None if any(v is None for v in vals) else a.const_like(exec_alu(a.op, a.dtype, vals, False))
+  if any(v is None for v in vals): return None
+  if any(isinstance(v, tuple) for v in vals):
+    out_len = prod(a.shape)
+    if not all(not isinstance(v, tuple) or len(v) in {1, out_len} for v in vals): return None
+    return a.const_like(tuple(exec_alu(a.op, a.dtype.scalar(),
+      [v[0] if isinstance(v, tuple) and len(v) == 1 else v[i] if isinstance(v, tuple) else v for v in vals], False)
+      for i in range(out_len)))
+  return a.const_like(exec_alu(a.op, a.dtype, vals, False))
 
 invalid_pat = UPat(Ops.CONST, arg=Invalid, name="i")
 invalid_gate = UPat.var("cond").where(UPat.var("x"), invalid_pat)
@@ -121,8 +128,6 @@ symbolic_simple = propagate_invalid + PatternMatcher([
   # TODO: combine this with "# rules for threefry" below
   ((UPat.var("x") & UPat.cvar("mask")) >> UPat.cvar("k"),
    lambda x,mask,k: x >> k.arg if mask.arg | ((1 << k.arg) - 1) == -1 else None),
-  ((UPat.var("x") & UPat.cvar("mask")) // UPat.cvar("c"),
-   lambda x,mask,c: x // c.arg if c.arg > 0 and c.arg & (c.arg-1) == 0 and mask.arg | (c.arg-1) == -1 else None),
   (UPat.var("x", dtype=dtypes.ints+(dtypes.bool, dtypes.weakint)) != UPat.var("x"),
    lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x != x -> False (only ints)
   # ** constant folding **
@@ -162,7 +167,6 @@ symbolic_simple = propagate_invalid + PatternMatcher([
   (((UPat.var(None, dtypes.uint64)*(1<<32)) | UPat.var('y',  dtypes.uint32).cast(dtypes.uint64)).cast(dtypes.uint32), lambda y: y),
   (((UPat.var('x',  dtypes.uint64)*(1<<32)) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))//(1<<32), lambda x: x),
   (((UPat.var(None, dtypes.uint64)<<32) | UPat.var('y',  dtypes.uint32).cast(dtypes.uint64)).cast(dtypes.uint32), lambda y: y),
-  (((UPat.var('x',  dtypes.uint64)<<32) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))//(1<<32), lambda x: x),
   (((UPat.var('x',  dtypes.uint64)<<32) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))>>32, lambda x: x),
   # ** simple where folding **
   # a conditional with the same results either way is a noop, also fold const conditionals
@@ -170,9 +174,6 @@ symbolic_simple = propagate_invalid + PatternMatcher([
   (UPat.cvar("gate").where(UPat.var("c0"), UPat.var("c1")), lambda gate, c0, c1: c0 if gate.arg else c1),
   # a.where(b.where(c, d), d) -> (a & b).where(c, d)
   (UPat.var("a").where(UPat.var("b").where(UPat.var("c"), UPat.var("d")), UPat.var("d")), lambda a,b,c,d: (a&b).where(c,d)),
-  # STACK on INDEX CONST (TODO: remove all the GEP crap)
-  (UPat(Ops.STACK, src=UPat(Ops.INDEX, src=(UPat.var("src"), UPat(Ops.CONST))), name="stk"),
-   lambda src,stk: src if stk.shape == src.shape and list(range(len(stk.src))) == [x.src[1].arg for x in stk.src] else None),
 ])
 
 # ******** phase 2 builds on phase 1, it includes the old "symbolic", rules that match deeper ********