works

.github/actions/setup-tinygrad/action.yml

@@ -45,10 +45,6 @@ inputs:
     description: "Install mesa"
     required: false
     default: 'false'
-  tinydreno:
-    description: "Install tinydreno"
-    required: false
-    default: 'false'
 runs:
   using: "composite"
   steps:
@@ -66,14 +62,14 @@ runs:
       uses: actions/cache/restore@v4
       with:
         path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-${{ runner.arch }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
+        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
     - name: Cache Python packages
       if: github.event_name != 'pull_request'
       id: restore-venv
       uses: actions/cache@v4
       with:
         path: ${{ github.workspace }}/.venv
-        key: venv-${{ runner.os }}-${{ runner.arch }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
+        key: venv-${{ runner.os }}-python-${{ steps.setup-python.outputs.python-version }}-${{ inputs.deps }}-${{ inputs.pydeps }}-${{ env.CACHE_VERSION }}
 
     # **** Caching downloads ****
 
@@ -199,13 +195,13 @@ runs:
       uses: actions/cache/restore@v4
       with:
         path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-${{ runner.arch }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
+        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
     - name: Cache apt
       if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true') && github.event_name != 'pull_request'
       uses: actions/cache@v4
       with:
         path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-${{ runner.arch }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
+        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
 
     - name: Run apt Update + Install
       if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
@@ -330,9 +326,3 @@ runs:
       if: inputs.mesa == 'true' && runner.os == 'macOS'
       shell: bash
       run: brew install sirhcm/tinymesa/tinymesa_cpu
-
-    # *** tinydreno ***
-    - name: Install tinydreno (linux)
-      if: inputs.tinydreno == 'true' && runner.os == 'Linux'
-      shell: bash
-      run: sudo curl -fL https://github.com/sirhcm/tinydreno/raw/refs/heads/master/libllvm-qcom.so -o /usr/lib/libllvm-qcom.so

.github/workflows/benchmark.yml

@@ -332,7 +332,7 @@ jobs:
     # - name: Fuzz Padded Tensor Core GEMM (PTX)
     #   run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
     - name: HEVC Decode Benchmark
-      run: VALIDATE=1 MAX_FRAMES=100 ASSERT_FPS=1400 JITBEAM=1 NV=1 PYTHONPATH=. python3 extra/hevc/decode.py
+      run: VALIDATE=1 MAX_FRAMES=100 JITBEAM=1 NV=1 PYTHONPATH=. python3 extra/hevc/decode.py
     - name: Train MNIST
       run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py
     - name: Run 10 CIFAR training steps

.github/workflows/test.yml

@@ -1,7 +1,7 @@
 name: Unit Tests
 env:
   # increment this when downloads substantially change to avoid the internet
-  CACHE_VERSION: '18'
+  CACHE_VERSION: '17'
   CAPTURE_PROCESS_REPLAY: 1
   GH_TOKEN: ${{ secrets.GITHUB_TOKEN }}
   PYTHONPATH: ${{ github.workspace }}
@@ -1011,26 +1011,3 @@ jobs:
           python -c "from tinygrad import Device; assert Device.DEFAULT == 'NULL'"
           DEBUG=4 python3 test/backend/test_ops.py TestOps.test_add
           python -m pytest -n=auto test/backend/test_ops.py --durations=20
-  qcomclcompiletests:
-    name: Compile-only (QCOM CL)
-    runs-on: ubuntu-24.04-arm
-    timeout-minutes: 15
-    steps:
-      - name: Checkout Code
-        uses: actions/checkout@v4
-      - name: Setup Environment
-        uses: ./.github/actions/setup-tinygrad
-        with:
-          key: compile-qcomcl
-          deps: testing_unit
-          tinydreno: 'true'
-          python-version: '3.12'
-      - name: Set env
-        shell: bash
-        run: printf "NULL=1\nNULL_ALLOW_COPYOUT=1\nNULL_QCOMCL=1" >> $GITHUB_ENV
-      - name: Run test_ops
-        shell: bash
-        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == 'NULL'"
-          DEBUG=4 python3 test/backend/test_ops.py TestOps.test_add
-          python -m pytest -n=auto test/backend/test_ops.py --durations=20

examples/mlperf/model_train.py

@@ -1282,7 +1282,7 @@ def train_bert():
         previous_step = i
 
 def train_llama3():
-  from examples.mlperf.models.llama import Transformer
+  from extra.models.llama import Transformer
   from examples.llama3 import MODEL_PARAMS
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
   from examples.mlperf.optim import GradAccClipAdamW
@@ -1343,8 +1343,7 @@ def train_llama3():
   if (MP := getenv("MP", 1)) > 1: model_params['vocab_size'] = round_up(model_params['vocab_size'], 256 * MP)
   vocab_mask:Tensor = Tensor.arange(model_params['vocab_size']).reshape(1, 1, -1) >= real_vocab_size
 
-  model = Transformer(**model_params, max_context=SEQLEN)
-
+  model = Transformer(**model_params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
   params = get_parameters(model)
   # weights are all bfloat16 for now
   assert params and all(p.dtype == dtypes.bfloat16 for p in params)
@@ -1382,20 +1381,16 @@ def train_llama3():
 
     vocab_mask.shard_(device, axis=2).realize()
 
-  is_offload_optim = bool(getenv("OFFLOAD_OPTIM"))
-  is_fake_offload = Device.DEFAULT == "NULL"
-  optim_device = ("CPU" if not is_fake_offload else "NULL:99") if is_offload_optim else None
+  optim_device = "CPU" if getenv("OFFLOAD_OPTIM") else None
   optim = GradAccClipAdamW(get_parameters(model), lr=0.0, b1=opt_adamw_beta_1, b2=opt_adamw_beta_2,
                            eps=opt_adamw_epsilon, weight_decay=opt_adamw_weight_decay, grad_acc=grad_acc, device=optim_device)
 
   # init grads
-  if is_offload_optim:
-    for p in optim.params:
-      p.grad = Tensor.zeros(p.shape, dtype=p.dtype, device=optim_device, requires_grad=False).contiguous().realize()
-  else:
-    for p in optim.params:
-      p.grad = p.zeros_like().contiguous().realize()
+  for p in optim.params:
+    p.grad = p.empty_like().realize()
   grads: list[Tensor] = [p.grad for p in optim.params]
+  for p in optim.params:
+    p.grad.assign(p.grad.zeros_like()).realize()
 
   scheduler = CosineAnnealingLRWithWarmup(optim, opt_base_learning_rate, opt_end_learning_rate, opt_learning_rate_warmup_steps, opt_learning_rate_decay_steps)
 
@@ -1417,13 +1412,12 @@ def train_llama3():
       device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
       tokens = tokens.shard(device)
     if DP == 1 and MP == 1: tokens = tokens.to(None)
-    logits:Tensor = model(tokens[:, :-1])
+    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
     loss = vocab_mask.where(-1e9, logits).sparse_categorical_crossentropy(tokens[:, 1:])
     loss.backward()
     assert all(p.grad is g for p,g in zip(optim.params, grads))
-    loss_cpu = loss.flatten().float().to("CPU")
-    Tensor.realize(loss_cpu, *grads)
-    return loss_cpu
+    Tensor.realize(loss, *grads)
+    return loss.flatten().float().to("CPU")
 
   @TinyJit
   def optim_step():
@@ -1431,13 +1425,12 @@ def train_llama3():
     scheduler.step()
 
     for g in grads:
-      g.assign(g.zeros_like())
+      g.assign(g.zeros_like()).realize()
 
-    lr_cpu = optim.lr.float().to("CPU")
-    grad_norm_cpu = grad_norm.float().to("CPU")
-    Tensor.realize(lr_cpu, grad_norm_cpu, *grads)
+    lr = optim.lr
+    Tensor.realize(lr, *grads)
 
-    return lr_cpu, grad_norm_cpu
+    return lr.float().to("CPU"), grad_norm.float().to("CPU")
 
   @TinyJit
   @Tensor.train(False)
@@ -1449,7 +1442,7 @@ def train_llama3():
       device = tuple(f"{Device.DEFAULT}:{i}" for i in range(MP))
       tokens = tokens.shard(device)
     if DP == 1 and MP == 1: tokens = tokens.to(None)
-    logits:Tensor = model(tokens[:, :-1])
+    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
     loss = vocab_mask.where(-1e9, logits).sparse_categorical_crossentropy(tokens[:, 1:])
     return loss.flatten().float().to("CPU")
 
@@ -1485,14 +1478,13 @@ def train_llama3():
   step_times = []
   while i < MAX_STEPS:
     GlobalCounters.reset()
-    actual_gbs = GBS if i >= 2 else BS
     if getenv("TRAIN", 1):
       profile_marker(f"train @ {i}")
       st = time.perf_counter()
 
       stopped = False
       losses, data_time, dev_time = [], 0, 0
-      for _ in range(grad_acc if i >= 2 else 1):
+      for _ in range(grad_acc if i >= 3 else 1):
         ist = time.perf_counter()
         try: tokens = next(train_iter)
         except StopIteration:
@@ -1517,7 +1509,7 @@ def train_llama3():
       if BENCHMARK: step_times.append(step_time)
 
       i += 1
-      sequences_seen += actual_gbs
+      sequences_seen += GBS
 
       mem_gb = GlobalCounters.mem_used / 1e9
       gflops = GlobalCounters.global_ops / 1e9 / dev_time
@@ -1560,7 +1552,7 @@ def train_llama3():
         print(f"epoch global_ops: {GlobalCounters.global_ops:_}, "
               f"epoch global_mem: {GlobalCounters.global_mem:_}")
 
-    if (sequences_seen // EVAL_FREQ != (sequences_seen - actual_gbs) // EVAL_FREQ and (i != 1 or EVAL_FREQ == 1)) or (BENCHMARK and i == BENCHMARK):
+    if (sequences_seen % EVAL_FREQ == 0 and (i != 1 or EVAL_FREQ == 1)) or (BENCHMARK and i == BENCHMARK):
       if EVAL_BS == 0: return
       tqdm.write(f"evaluating after {sequences_seen} sequences")
       profile_marker(f"eval @ {i}")

examples/mlperf/models/llama.py

@@ -1,80 +0,0 @@
-from tinygrad import Tensor, nn
-from tinygrad.helpers import getenv
-from extra.models.llama import apply_rotary_emb, precompute_freqs_cis
-
-class Attention:
-  def __init__(self, dim:int, n_heads:int, n_kv_heads:int|None=None, linear=nn.Linear):
-    self.n_heads = n_heads
-    self.n_kv_heads = n_kv_heads if n_kv_heads is not None else n_heads # n_kv_heads != n_heads implies MQA [arxiv/2307.09288, A.2.1]
-    self.head_dim = dim // n_heads
-    self.n_rep = self.n_heads // self.n_kv_heads
-
-    if getenv("WQKV"):
-      self.wqkv = linear(dim, self.n_heads * self.head_dim + self.n_kv_heads * self.head_dim * 2, bias=False)
-    else:
-      self.wq = linear(dim, self.n_heads * self.head_dim, bias=False)
-      self.wk = linear(dim, self.n_kv_heads * self.head_dim, bias=False)
-      self.wv = linear(dim, self.n_kv_heads * self.head_dim, bias=False)
-
-    self.wo = linear(self.n_heads * self.head_dim, dim, bias=False)
-
-  def __call__(self, x:Tensor, freqs_cis:Tensor) -> Tensor:
-    if getenv("WQKV"):
-      xqkv = self.wqkv(x)
-      xqkv = xqkv.reshape(xqkv.shape[0], xqkv.shape[1], self.n_kv_heads, self.n_rep + 2, self.head_dim)
-      xq = xqkv[:, :, :, :self.n_rep].reshape(xqkv.shape[0], xqkv.shape[1], -1)
-      xk = xqkv[:, :, :, self.n_rep:self.n_rep+1].reshape(xqkv.shape[0], xqkv.shape[1], -1)
-      xv = xqkv[:, :, :, self.n_rep+1:self.n_rep+2].reshape(xqkv.shape[0], xqkv.shape[1], -1)
-    else:
-      xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
-
-    xq = xq.reshape(xq.shape[0], xq.shape[1], self.n_heads, self.head_dim)
-    xk = xk.reshape(xk.shape[0], xk.shape[1], self.n_kv_heads, self.head_dim)
-    xv = xv.reshape(xv.shape[0], xv.shape[1], self.n_kv_heads, self.head_dim)
-
-    xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
-    bsz, seqlen, _, _ = xq.shape
-
-    xq, xk, xv = xq.transpose(1, 2), xk.transpose(1, 2), xv.transpose(1, 2)
-    attn = xq.scaled_dot_product_attention(xk, xv, is_causal=True, enable_gqa=True).transpose(1, 2)
-
-    attn = attn.reshape(bsz, seqlen, -1)
-    return self.wo(attn)
-
-class FeedForward:
-  def __init__(self, dim:int, hidden_dim:int, linear=nn.Linear):
-    self.w1 = linear(dim, hidden_dim, bias=False)
-    self.w2 = linear(hidden_dim, dim, bias=False)
-    self.w3 = linear(dim, hidden_dim, bias=False) # the gate in Gated Linear Unit
-
-  def __call__(self, x:Tensor) -> Tensor:
-    w1 = self.w1(x).silu()
-    w3 = self.w3(x)
-    return self.w2(w1 * w3)
-
-class TransformerBlock:
-  def __init__(self, dim:int, hidden_dim:int, n_heads:int, n_kv_heads:int|None, norm_eps:float, linear=nn.Linear):
-    self.attention = Attention(dim, n_heads, n_kv_heads, linear)
-    self.feed_forward = FeedForward(dim, hidden_dim, linear)
-    self.attention_norm = nn.RMSNorm(dim, norm_eps)
-    self.ffn_norm = nn.RMSNorm(dim, norm_eps)
-
-  def __call__(self, x:Tensor, freqs_cis:Tensor):
-    h = x + self.attention(self.attention_norm(x), freqs_cis)
-    return h + self.feed_forward(self.ffn_norm(h))
-
-class Transformer:
-  def __init__(self, dim:int, hidden_dim:int, n_heads:int, n_layers:int, norm_eps:float, vocab_size:int, n_kv_heads:int|None=None,
-               rope_theta:int=10000, max_context:int=1024, linear=nn.Linear, embedding=nn.Embedding):
-    self.layers = [TransformerBlock(dim, hidden_dim, n_heads, n_kv_heads, norm_eps, linear) for _ in range(n_layers)]
-    self.norm = nn.RMSNorm(dim, norm_eps)
-    self.tok_embeddings = embedding(vocab_size, dim)
-    self.output = nn.Linear(dim, vocab_size, bias=False) if embedding == nn.Embedding else linear(dim, vocab_size, bias=False)
-    self.freqs_cis = precompute_freqs_cis(dim // n_heads, max_context * 2, rope_theta).contiguous().requires_grad_(False)
-
-  def __call__(self, tokens:Tensor):
-    h = self.tok_embeddings(tokens)
-    freqs_cis = self.freqs_cis.cast(h.dtype)[:, :tokens.shape[1], :, :, :]
-    for layer in self.layers: h = layer(h, freqs_cis)
-    logits = self.output(self.norm(h))
-    return logits

examples/mlperf/optim.py

@@ -34,10 +34,10 @@ class GradAccClipAdamW(Optimizer):
       grads[0].assign((grads[0] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[0].dtype))
     else:
       for i in range(len(grads)):
-        grads[i].assign(grads[i] / self.grad_acc)
-      total_norm = Tensor.stack(*[g.float().square().sum() for g in grads]).sum().sqrt().contiguous()
+        grads[i].assign(grads[i] / self.grad_acc).realize()
+      total_norm = Tensor.stack(*[g.float().square().sum() for g in grads]).sum().sqrt().contiguous().realize()
       for i in range(len(grads)):
-        grads[i].assign((grads[i] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[i].dtype))
+        grads[i].assign((grads[i] * (self.clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(grads[i].dtype)).realize()
 
     ret = []
     self.b1_t *= self.b1
@@ -45,13 +45,12 @@ class GradAccClipAdamW(Optimizer):
     for i, g in enumerate(grads):
       self.m[i].assign((self.b1 * self.m[i] + (1.0 - self.b1) * g).cast(self.m[i].dtype))
       self.v[i].assign((self.b2 * self.v[i] + (1.0 - self.b2) * (g * g)).cast(self.v[i].dtype))
-      m_hat = (self.m[i] / (1.0 - self.b1_t)).cast(self.m[i].dtype)
-      v_hat = (self.v[i] / (1.0 - self.b2_t)).cast(self.v[i].dtype)
+      m_hat = self.m[i] / (1.0 - self.b1_t)
+      v_hat = self.v[i] / (1.0 - self.b2_t)
       up = m_hat / (v_hat.sqrt() + self.eps)
       ret.append((self.lr * up).cast(g.dtype))
     return ret, [self.b1_t, self.b2_t] + self.m + self.v + [total_norm]
 
   def _apply_update(self, t:Tensor, up:Tensor) -> Tensor:
-    wd = self.wd if t.ndim >= 2 else 0.0
-    up = up.shard_like(t) + self.lr.to(t.device) * wd * t.detach()
+    up = up.shard_like(t) + self.lr.to(t.device) * self.wd * t.detach()
     return t.detach() - up.cast(t.dtype)

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama405b/implementations/tinybox_8xMI350X/dev_beam.sh

@@ -1,37 +0,0 @@
-#!/usr/bin/env bash
-
-export PYTHONPATH="."
-export DEV=${DEV:-AMD}
-export EMULATE="AMD_CDNA4"
-export CHECK_OOB=0
-export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
-
-export DEBUG=${DEBUG:-2}
-export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
-export ALL2ALL=${ALL2ALL:-1}
-export USE_ATOMICS=${USE_ATOMICS:-0}
-export ASM_GEMM=${ASM_GEMM:-1}
-export WQKV=${WQKV:-1}
-export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
-
-export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-1} MP=${MP:-8}
-export BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
-
-export MODEL="llama3"
-export BASEDIR="/raid/datasets/c4/"
-export LLAMA3_SIZE=${LLAMA3_SIZE:-"405B"}
-export SEQLEN=${SEQLEN:-8192}
-
-export SEED=${SEED:-5760}
-export DATA_SEED=${DATA_SEED:-5760}
-
-export JITBEAM=${JITBEAM:-3}
-export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
-
-export FAKEDATA=1 BENCHMARK=10
-if [ -z "$FULL_LAYERS" ]; then
-  export LLAMA_LAYERS=2
-fi
-
-python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama405b/implementations/tinybox_8xMI350X/dev_run.sh

@@ -1,32 +0,0 @@
-#!/usr/bin/env bash
-
-export PYTHONPATH="."
-export DEV=${DEV:-AMD}
-export EMULATE="AMD_CDNA4"
-export CHECK_OOB=0
-export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
-
-export DEBUG=${DEBUG:-0}
-export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
-export ALL2ALL=${ALL2ALL:-1}
-export USE_ATOMICS=${USE_ATOMICS:-0}
-export ASM_GEMM=${ASM_GEMM:-1}
-export WQKV=${WQKV:-1}
-export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
-
-export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-1} MP=${MP:-8}
-export BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-1152}
-
-export MODEL="llama3"
-export BASEDIR="/raid/datasets/c4/"
-export LLAMA3_SIZE=${LLAMA3_SIZE:-"405B"}
-export SEQLEN=${SEQLEN:-8192}
-
-export SEED=${SEED:-$RANDOM}
-export DATA_SEED=${DATA_SEED:-5760}
-
-export JITBEAM=${JITBEAM:-3}
-export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
-
-python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_beam.sh

@@ -5,7 +5,6 @@ export DEV=${DEV:-AMD}
 export EMULATE="AMD_CDNA4"
 export CHECK_OOB=0
 export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
-export DEVICE_IN_FUNCTION_BUG=1
 
 export DEBUG=${DEBUG:-2}
 export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
@@ -15,7 +14,7 @@ export ASM_GEMM=${ASM_GEMM:-1}
 export WQKV=${WQKV:-0}
 
 export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-8} MP=${MP:-1} BS=${BS:-8} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
+export DP=${DP:-8} BS=${BS:-8} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
 export GBS=$((BS * GRADIENT_ACC_STEPS))
 
 export MODEL="llama3"
@@ -23,7 +22,7 @@ export BASEDIR="/raid/datasets/c4-8b/"
 export SMALL=1
 export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
 export EVAL_TARGET=3.3 EVAL_FREQ=12288
-export LR="1e-3" END_LR="1e-4" WARMUP_SAMPLES=4096 MAX_STEPS=1200000
+export LR="4e-4" END_LR="4e-5" WARMUP_SAMPLES=256 MAX_STEPS=1200000
 export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
 export SAMPLES=$((MAX_STEPS * GBS))
 export SEQLEN=${SEQLEN:-8192}

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_beam_mp.sh

@@ -1,43 +0,0 @@
-#!/usr/bin/env bash
-
-export PYTHONPATH="."
-export DEV=${DEV:-AMD}
-export EMULATE="AMD_CDNA4"
-export CHECK_OOB=0
-export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
-export DEVICE_IN_FUNCTION_BUG=1
-
-export DEBUG=${DEBUG:-2}
-export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
-export ALL2ALL=${ALL2ALL:-1}
-export USE_ATOMICS=${USE_ATOMICS:-0}
-export ASM_GEMM=${ASM_GEMM:-1}
-export WQKV=${WQKV:-1}
-export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
-
-export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-1} MP=${MP:-8} BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
-export GBS=$((BS * GRADIENT_ACC_STEPS))
-
-export MODEL="llama3"
-export BASEDIR="/raid/datasets/c4-8b/"
-export SMALL=1
-export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
-export EVAL_TARGET=3.3 EVAL_FREQ=12288
-export LR="1e-3" END_LR="1e-4" WARMUP_SAMPLES=4096 MAX_STEPS=1200000
-export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
-export SAMPLES=$((MAX_STEPS * GBS))
-export SEQLEN=${SEQLEN:-8192}
-
-export SEED=${SEED:-5760}
-export DATA_SEED=${DATA_SEED:-5760}
-
-export JITBEAM=${JITBEAM:-3}
-export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
-
-export FAKEDATA=1 BENCHMARK=10
-if [ -z "$FULL_LAYERS" ]; then
-  export LLAMA_LAYERS=2
-fi
-
-python3 examples/mlperf/model_train.py

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_run.sh

@@ -15,7 +15,7 @@ export ASM_GEMM=${ASM_GEMM:-1}
 export WQKV=${WQKV:-0}
 
 export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-8} MP=${MP:-1} BS=${BS:-8} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-4}
+export DP=${DP:-8} BS=${BS:-8} EVAL_BS=${EVAL_BS:-8} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-2}
 export GBS=$((BS * GRADIENT_ACC_STEPS))
 
 export MODEL="llama3"
@@ -23,7 +23,7 @@ export BASEDIR="/raid/datasets/c4-8b/"
 export SMALL=1
 export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
 export EVAL_TARGET=3.3 EVAL_FREQ=12288
-export LR="1e-3" END_LR="1e-4" WARMUP_SAMPLES=4096 MAX_STEPS=1200000
+export LR="4e-4" END_LR="4e-5" WARMUP_SAMPLES=256 MAX_STEPS=1200000
 export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
 export SAMPLES=$((MAX_STEPS * GBS))
 export SEQLEN=${SEQLEN:-8192}

examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama8b/implementations/tinybox_8xMI350X/dev_run_mp.sh

@@ -1,38 +0,0 @@
-#!/usr/bin/env bash
-
-export PYTHONPATH="."
-export DEV=${DEV:-AMD}
-export EMULATE="AMD_CDNA4"
-export CHECK_OOB=0
-export REWRITE_STACK_LIMIT=5000000 HCQDEV_WAIT_TIMEOUT_MS=240000
-export DEVICE_IN_FUNCTION_BUG=1
-
-export DEBUG=${DEBUG:-0}
-export HK_FLASH_ATTENTION=${HK_FLASH_ATTENTION:-1}
-export ALL2ALL=${ALL2ALL:-1}
-export USE_ATOMICS=${USE_ATOMICS:-0}
-export ASM_GEMM=${ASM_GEMM:-1}
-export WQKV=${WQKV:-1}
-export OFFLOAD_OPTIM=${OFFLOAD_OPTIM:-1}
-
-export DEFAULT_FLOAT="bfloat16" OPTIM_DTYPE="bfloat16"
-export DP=${DP:-1} MP=${MP:-8} BS=${BS:-1} EVAL_BS=${EVAL_BS:-1} GRADIENT_ACC_STEPS=${GRADIENT_ACC_STEPS:-32}
-export GBS=$((BS * GRADIENT_ACC_STEPS))
-
-export MODEL="llama3"
-export BASEDIR="/raid/datasets/c4-8b/"
-export SMALL=1
-export LLAMA3_SIZE=${LLAMA3_SIZE:-"8B"}
-export EVAL_TARGET=3.3 EVAL_FREQ=12288
-export LR="1e-3" END_LR="1e-4" WARMUP_SAMPLES=4096 MAX_STEPS=1200000
-export WARMUP_STEPS=$((WARMUP_SAMPLES / GBS))
-export SAMPLES=$((MAX_STEPS * GBS))
-export SEQLEN=${SEQLEN:-8192}
-
-export SEED=${SEED:-$RANDOM}
-export DATA_SEED=${DATA_SEED:-5760}
-
-export JITBEAM=${JITBEAM:-3}
-export BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=1
-
-python3 examples/mlperf/model_train.py

examples/openpilot/compile3.py

@@ -31,7 +31,7 @@ def compile(onnx_file):
   for i in range(3):
     GlobalCounters.reset()
     print(f"run {i}")
-    with Context(DEBUG=max(DEBUG.value, 2 if i == 2 else 1), OPENPILOT_HACKS=1):
+    with Context(DEBUG=max(DEBUG.value, 2 if i == 2 else 1)):
       ret = run_onnx_jit(**inputs).numpy()
     # copy i == 1 so use of JITBEAM is okay
     if i == 1: test_val = np.copy(ret)

extra/amdpci/am_smi.py

@@ -65,7 +65,7 @@ def get_bar0_size(pcibus):
 class AMSMI(AMDev):
   def __init__(self, pcibus, vram_bar:MMIOInterface, doorbell_bar:MMIOInterface, mmio_bar:MMIOInterface):
     self.pcibus = pcibus
-    self.vram, self.doorbell64, self.mmio = vram_bar, doorbell_bar, mmio_bar
+    self.vram, self.doorbell64, self.mmio, self.dma_regions = vram_bar, doorbell_bar, mmio_bar, None
     self.pci_state = self.read_pci_state()
     if self.pci_state == "D0": self._init_from_d0()
 

extra/amdpci/proclogs.py

@@ -7,8 +7,8 @@ class GFXFake:
   def __init__(self): self.xccs = 8
 
 class AMDFake(AMDev):
-  def __init__(self, pci_dev):
-    self.pci_dev, self.devfmt = pci_dev, pci_dev.pcibus
+  def __init__(self, pci_dev, dma_regions=None):
+    self.pci_dev, self.devfmt, self.dma_regions = pci_dev, pci_dev.pcibus, dma_regions
     self.vram, self.doorbell64, self.mmio = self.pci_dev.map_bar(0), self.pci_dev.map_bar(2, fmt='Q'), self.pci_dev.map_bar(5, fmt='I')
     self._run_discovery()
     self._build_regs()

extra/gemm/amd_copy_matmul.py

@@ -1,84 +0,0 @@
-from tinygrad import UOp, getenv
-from tinygrad.uop.ops import AxisType, KernelInfo, Ops
-from tinygrad.dtype import AddrSpace, dtypes
-
-N = getenv("N", 4096)
-M = getenv("M", N)
-K = getenv("K", N)
-
-WARP_SIZE = 32
-BLOCK_M, BLOCK_N, BLOCK_K = 128, 128, 8
-TM, TN = 4, 4
-LANES_PER_WAVE_M, LANES_PER_WAVE_N = 4, 8
-assert N % BLOCK_N == 0 and M % BLOCK_M == 0 and K % BLOCK_K == 0
-
-is_kernel5 = getenv("K5", 0)
-THREADS_PER_BLOCK = 128 if is_kernel5 else 256
-WAVES_PER_BLOCK_N = 1 if is_kernel5 else 2
-WAVES_PER_BLOCK_M = THREADS_PER_BLOCK // WARP_SIZE // WAVES_PER_BLOCK_N
-REG_TILES_PER_WAVE_N = BLOCK_N // (WAVES_PER_BLOCK_N * LANES_PER_WAVE_N * TN)
-REG_TILES_PER_WAVE_M = BLOCK_M // (WAVES_PER_BLOCK_M * LANES_PER_WAVE_M * TM)
-
-assert WAVES_PER_BLOCK_M*REG_TILES_PER_WAVE_M*LANES_PER_WAVE_M*TM == BLOCK_M, "M reshape is wrong"
-assert WAVES_PER_BLOCK_N*REG_TILES_PER_WAVE_N*LANES_PER_WAVE_N*TN == BLOCK_N, "N reshape is wrong"
-
-consts = {"wpb_m":WAVES_PER_BLOCK_M, "lpw_m":LANES_PER_WAVE_M, "rt_m":REG_TILES_PER_WAVE_M, "t_m": TM,
-          "wpb_n":WAVES_PER_BLOCK_N, "lpw_n":LANES_PER_WAVE_N, "rt_n":REG_TILES_PER_WAVE_N, "t_n": TN}
-
-# 128x128 out, kx128, kx128 in
-def block_128x128_gemm(c:UOp, a:UOp, b:UOp) -> UOp:
-  tid = UOp.range(THREADS_PER_BLOCK, 2, AxisType.LOCAL)
-
-  #tid = UOp.special(THREADS_PER_BLOCK, "lidx0")
-  warp, lane = tid // WARP_SIZE, tid % WARP_SIZE
-  wave_n, wave_m = warp % WAVES_PER_BLOCK_N, warp // WAVES_PER_BLOCK_N
-  lane_n, lane_m = lane % LANES_PER_WAVE_N, lane // LANES_PER_WAVE_N
-
-  # define locals
-  A_local = UOp.placeholder((BLOCK_K, BLOCK_M), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL)
-  B_local = UOp.placeholder((BLOCK_K, BLOCK_N), dtypes.float, slot=1, addrspace=AddrSpace.LOCAL)
-
-  # open the main reduction range and copy in GLOBAL -> LOCAL
-  a = a.reshape(K // BLOCK_K, BLOCK_K, BLOCK_M)
-  b = b.reshape(K // BLOCK_K, BLOCK_K, BLOCK_N)
-  k_tile_range = UOp.range(K // BLOCK_K, 3, AxisType.REDUCE)
-  A_store = A_local.reshape(-1, THREADS_PER_BLOCK)[:, tid].store(a[k_tile_range].reshape(-1, THREADS_PER_BLOCK)[:, tid])
-  B_store = B_local.reshape(-1, THREADS_PER_BLOCK)[:, tid].store(b[k_tile_range].reshape(-1, THREADS_PER_BLOCK)[:, tid])
-  barrier = UOp.barrier(A_store, B_store)
-  A_local, B_local = A_local.after(barrier), B_local.after(barrier)
-
-  # define accumulator (128x128), but broadcast across tid
-  c_regs = UOp.placeholder((REG_TILES_PER_WAVE_M*TM, REG_TILES_PER_WAVE_N*TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
-  c_regs = c_regs.after(c_regs.store(UOp.const(dtypes.float, 0).reshape((1,)*len(c_regs.shape)).expand(c_regs.shape)))
-
-  # define registers (NOTE: the thread count is the device count for this multi, it's sharded across the THREADS_PER_BLOCK)
-  A_col = UOp.placeholder((REG_TILES_PER_WAVE_M, TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
-  B_row = UOp.placeholder((REG_TILES_PER_WAVE_N, TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
-
-  # LOCAL -> REGS
-  k = UOp.range(BLOCK_K, 4, AxisType.REDUCE)
-  A_col = A_col.after(A_col.store(A_local[k].reshape(WAVES_PER_BLOCK_M, REG_TILES_PER_WAVE_M, LANES_PER_WAVE_M, TM)[wave_m, :, lane_m, :]))
-  B_row = B_row.after(B_row.store(B_local[k].reshape(WAVES_PER_BLOCK_N, REG_TILES_PER_WAVE_N, LANES_PER_WAVE_N, TN)[wave_n, :, lane_n, :]))
-
-  # do FMA
-  A_col = A_col.reshape(REG_TILES_PER_WAVE_M*TM, 1).expand(REG_TILES_PER_WAVE_M*TM, REG_TILES_PER_WAVE_N*TN)
-  B_row = B_row.reshape(1, REG_TILES_PER_WAVE_N*TN).expand(REG_TILES_PER_WAVE_M*TM, REG_TILES_PER_WAVE_N*TN)
-  c_regs = c_regs.after(c_regs.store(c_regs.after(k) + (A_col * B_row)).end(k).barrier().end(k_tile_range))
-
-  # store back to c
-  c_store = c.rearrange("(wpb_m rt_m lpw_m t_m) (wpb_n rt_n lpw_n t_n) -> (wpb_m wpb_n lpw_m lpw_n) (rt_m t_m) (rt_n t_n)", **consts)
-  return c_store[tid].store(c_regs).end(tid)
-
-def amd_copy_matmul(c:UOp, a:UOp, b:UOp) -> UOp:
-  block_id_n = UOp.range(N // BLOCK_N, 0, AxisType.GLOBAL)
-  block_id_m = UOp.range(M // BLOCK_M, 1, AxisType.GLOBAL)
-
-  # index the output with the globals
-  c = c.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_N, BLOCK_N)[block_id_m, :, block_id_n, :]
-  a = a.T.reshape(K, M // BLOCK_M, BLOCK_M)[:, block_id_m, :]
-  b = b.reshape(K, N // BLOCK_N, BLOCK_N)[:, block_id_n, :]
-  return block_128x128_gemm(c, a, b).end(block_id_n, block_id_m).sink(arg=KernelInfo(opts_to_apply=()))
-
-if __name__ == "__main__":
-  from amd_uop_matmul import eval_custom_matmul
-  eval_custom_matmul(amd_copy_matmul)

extra/gemm/amd_uop_matmul.py

@@ -1,74 +1,98 @@
-from tinygrad import Tensor, Context, GlobalCounters, dtypes
+import numpy as np
+from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
 from tinygrad.uop.ops import UOp, KernelInfo, sint, AxisType
+from tinygrad.engine.realize import ExecItem, get_runner
 from tinygrad.dtype import AddrSpace
-from tinygrad.helpers import DEBUG, getenv
+from tinygrad.helpers import getenv
 
 N = getenv("N", 4096)
-M = getenv("M", N)
-K = getenv("K", N)
-NUM_RUNS = getenv("CNT", 5)
+M = K = N
+run_count = getenv("CNT", 5)
 
 # ---------------------------
 # launch/config constants
 # ---------------------------
 
 WARP_SIZE = 32
-BLOCK_M, BLOCK_N, BLOCK_K = 128, 128, 8
-TM, TN = 4, 4
-LANES_PER_WAVE_M, LANES_PER_WAVE_N = 4, 8
-assert N % BLOCK_N == 0 and M % BLOCK_M == 0 and K % BLOCK_K == 0
+
+# Threadblock tile sizes (block-level tile of C that a block computes)
+BLOCK_N = 128   # columns of C (N-dim) per block
+BLOCK_M = 128   # rows of C (M-dim) per block
+BLOCK_K = 8     # K-slice per block iteration
+
+# Register tile sizes (per-thread accumulator tile of C)
+TN = 4     # columns per thread
+TM = 4     # rows per thread
 
 is_kernel5 = getenv("K5", 0)
 THREADS_PER_BLOCK = 128 if is_kernel5 else 256
-WAVES_PER_BLOCK_N = 1 if is_kernel5 else 2
-WAVES_PER_BLOCK_M = THREADS_PER_BLOCK // WARP_SIZE // WAVES_PER_BLOCK_N
-REG_TILES_PER_WAVE_N = BLOCK_N // (WAVES_PER_BLOCK_N * LANES_PER_WAVE_N * TN)
-REG_TILES_PER_WAVE_M = BLOCK_M // (WAVES_PER_BLOCK_M * LANES_PER_WAVE_M * TM)
+assert THREADS_PER_BLOCK % BLOCK_N == 0, "THREADS_PER_BLOCK must be divisible by BLOCK_N"
+assert THREADS_PER_BLOCK % BLOCK_K == 0, "THREADS_PER_BLOCK must be divisible by BLOCK_K"
+assert (BLOCK_N * BLOCK_K) % THREADS_PER_BLOCK == 0
+assert (BLOCK_M * BLOCK_K) % THREADS_PER_BLOCK == 0
 
-assert WAVES_PER_BLOCK_M*REG_TILES_PER_WAVE_M*LANES_PER_WAVE_M*TM == BLOCK_M, "M reshape is wrong"
-assert WAVES_PER_BLOCK_N*REG_TILES_PER_WAVE_N*LANES_PER_WAVE_N*TN == BLOCK_N, "N reshape is wrong"
+WARPS_PER_BLOCK = THREADS_PER_BLOCK // WARP_SIZE
+WAVE_TILE_N = 128 if is_kernel5 else 64
+WAVE_TILE_M = BLOCK_N * BLOCK_M // WARPS_PER_BLOCK // WAVE_TILE_N
+assert BLOCK_N % WAVE_TILE_N == 0, "BN must be a multiple of WN"
+assert BLOCK_M % WAVE_TILE_M == 0, "BM must be a multiple of WM"
+WAVES_IN_BLOCK_X = BLOCK_N // WAVE_TILE_N
+WAVES_IN_BLOCK_Y = BLOCK_M // WAVE_TILE_M
+assert WAVES_IN_BLOCK_X * WAVES_IN_BLOCK_Y == WARPS_PER_BLOCK, "wave grid must match warps/block"
+
+LANES_PER_WAVE_X = 8
+LANES_PER_WAVE_Y = 4
+ITERS_PER_WAVE_N = WAVE_TILE_N // (LANES_PER_WAVE_X * TN)
+ITERS_PER_WAVE_M = WAVE_TILE_M // (LANES_PER_WAVE_Y * TM)
+assert WAVE_TILE_N % (LANES_PER_WAVE_X * TN) == 0, "WAVE_TILE_N must be divisible by LANES_PER_WAVE_X*TN"
+assert WAVE_TILE_M % (LANES_PER_WAVE_Y * TM) == 0, "WAVE_TILE_M must be divisible by LANES_PER_WAVE_Y*TM"
 
 def rngs_for_shape(shape:tuple[sint, ...], rng:int, axis_type=AxisType.LOOP): return [UOp.range(s, rng+i, axis_type) for i,s in enumerate(shape)]
-def copy(dest:UOp, src:UOp, rng:int, upcast=False):
+def copy(dest:UOp, src:UOp, rng:int, set=False, upcast=False):
   assert dest.shape == src.shape
   rngs = rngs_for_shape(src.shape, rng, AxisType.UPCAST if upcast else AxisType.LOOP)
-  return dest[*rngs].store(src[*rngs]).end(*rngs)
+  copy = dest[*rngs].store(src[*rngs]).end(*rngs)
+  return dest.after(copy) if set else copy
 
-def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
+def hand_spec_kernel3():
   # ---------------------------
-  # block indices
+  # block indices & placeholders
   # ---------------------------
-  block_id_n = UOp.special(N // BLOCK_N, "gidx0")
-  block_id_m = UOp.special(M // BLOCK_M, "gidx1")
+  blockIdx_x = UOp.special(N // BLOCK_N, "gidx0")
+  blockIdx_y = UOp.special(N // BLOCK_M, "gidx1")
+
+  a = UOp.placeholder((N, N), dtypes.float, slot=1)
+  b = UOp.placeholder((N, N), dtypes.float, slot=2)
+  c = UOp.placeholder((N, N), dtypes.float, slot=0)
 
   # index the output with the globals
-  c = c.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_N, BLOCK_N)[block_id_m, :, block_id_n, :]
+  c = c.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_N, BLOCK_N)[blockIdx_y, :, blockIdx_x, :]
 
   # open the main reduction range
-  k_tile_range = UOp.range(K // BLOCK_K, 0, AxisType.REDUCE)
-  a = a.reshape(M // BLOCK_M, BLOCK_M, K // BLOCK_K, BLOCK_K)[block_id_m, :, k_tile_range, :]
-  b = b.reshape(K // BLOCK_K, BLOCK_K, N // BLOCK_N, BLOCK_N)[k_tile_range, :, block_id_n, :]
+  k_tile_range = UOp.range(N // BLOCK_K, 0, AxisType.REDUCE)
+  a = a.reshape(M // BLOCK_M, BLOCK_M, N // BLOCK_K, BLOCK_K)[blockIdx_y, :, k_tile_range, :]
+  b = b.reshape(N // BLOCK_K, BLOCK_K, N // BLOCK_N, BLOCK_N)[k_tile_range, :, blockIdx_x, :]
 
   # globals are no longer used, they are already in the indexes
-  del block_id_m, block_id_n
+  del blockIdx_y, blockIdx_x
 
   # ---------------------------
-  # GLOBAL -> LOCAL (A_local, B_local)
+  # GLOBAL -> LOCAL (As, Bs)
   # ---------------------------
   tid = UOp.special(THREADS_PER_BLOCK, "lidx0")
 
   # A: read BM x BK tiles (permute on store into locals)
-  BM_A_local_stride = (BLOCK_M + 4) if is_kernel5 else BLOCK_M
-  A_local = UOp.placeholder((BLOCK_K, BM_A_local_stride), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL).shrink_to((BLOCK_K, BLOCK_M))
-  A_local_store = copy(A_local.permute((1,0)).reshape(-1, THREADS_PER_BLOCK)[:, tid], a.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=100)
+  BM_As_stride = (BLOCK_M + 4) if is_kernel5 else BLOCK_M
+  As = UOp.placeholder((BLOCK_K, BM_As_stride), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL).shrink_to((BLOCK_K, BLOCK_M))
+  As_store = copy(As.permute((1,0)).reshape(-1, THREADS_PER_BLOCK)[:, tid], a.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=100)
 
   # B: read BK x BN tiles
-  B_local = UOp.placeholder((BLOCK_K, BLOCK_N), dtypes.float, slot=1, addrspace=AddrSpace.LOCAL)
-  B_local_store = copy(B_local.reshape(-1, THREADS_PER_BLOCK)[:, tid], b.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=200)
+  Bs = UOp.placeholder((BLOCK_K, BLOCK_N), dtypes.float, slot=1, addrspace=AddrSpace.LOCAL)
+  Bs_store = copy(Bs.reshape(-1, THREADS_PER_BLOCK)[:, tid], b.reshape(-1, THREADS_PER_BLOCK)[:, tid], rng=200)
 
   # TODO: can we automate barrier?
-  barrier = UOp.barrier(A_local_store, B_local_store)
-  A_local, B_local = A_local.after(barrier), B_local.after(barrier)
+  barrier = UOp.barrier(As_store, Bs_store)
+  As, Bs = As.after(barrier), Bs.after(barrier)
 
   # open inner k range
   k = UOp.range(BLOCK_K, 3, AxisType.REDUCE)
@@ -76,30 +100,31 @@ def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
   # ---------------------------
   # LOCAL -> REG (per-wave tiles)
   # ---------------------------
-  warp, lane = tid // WARP_SIZE, tid % WARP_SIZE
-  waveIdx, waveIdy = warp % WAVES_PER_BLOCK_N, warp // WAVES_PER_BLOCK_N
-  laneIdx, laneIdy = lane % LANES_PER_WAVE_N, lane // LANES_PER_WAVE_N
-  assert waveIdy.vmax+1 == WAVES_PER_BLOCK_M and laneIdy.vmax+1 == LANES_PER_WAVE_M
+  waveIdx = (tid // WARP_SIZE) % WAVES_IN_BLOCK_X
+  waveIdy = (tid // WARP_SIZE) // WAVES_IN_BLOCK_X
+  assert waveIdy.vmax+1 == WAVES_IN_BLOCK_Y
 
-  A_col = UOp.placeholder((REG_TILES_PER_WAVE_M, TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
-  A_local_slice = A_local[k, :].reshape(WAVES_PER_BLOCK_M, REG_TILES_PER_WAVE_M, LANES_PER_WAVE_M, TM)[waveIdy, :, laneIdy, :]
-  A_col = A_col.after(copy(A_col, A_local_slice, 300, upcast=True))
+  laneIdx = (tid % WARP_SIZE) % LANES_PER_WAVE_X
+  laneIdy = (tid % WARP_SIZE) // LANES_PER_WAVE_X
+  assert laneIdy.vmax+1 == LANES_PER_WAVE_Y
 
-  B_row = UOp.placeholder((REG_TILES_PER_WAVE_N, TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
-  B_local_slice = B_local[k, :].reshape(WAVES_PER_BLOCK_N, REG_TILES_PER_WAVE_N, LANES_PER_WAVE_N, TN)[waveIdx, :, laneIdx, :]
-  B_row = B_row.after(copy(B_row, B_local_slice, 400, upcast=True))
+  A_col = UOp.placeholder((ITERS_PER_WAVE_M, TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
+  A_col = copy(A_col, As[k, :].reshape(WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM)[waveIdy, :, laneIdy, :], 300, set=True, upcast=True)
+
+  B_row = UOp.placeholder((ITERS_PER_WAVE_N, TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
+  B_row = copy(B_row, Bs[k, :].reshape(WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)[waveIdx, :, laneIdx, :], 400, set=True, upcast=True)
 
   # ---------------------------
   # FMA: c_regs += A_col * B_row
   # ---------------------------
-  c_regs = UOp.placeholder((REG_TILES_PER_WAVE_M, TM, REG_TILES_PER_WAVE_N, TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
+  c_regs = UOp.placeholder((ITERS_PER_WAVE_M, TM, ITERS_PER_WAVE_N, TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
   i = UOp.range(c_regs.size, 16)
   c_regs = c_regs.after(c_regs.flatten()[i].store(0.0).end(i))
 
   # TODO: why don't these work as upcast?
   # why if the ranges merge is it slow?!? (if you change the order on end, they will merge. big slowdown on METAL)
-  iter_m, t_m, iter_n, t_n = rngs = rngs_for_shape(c_regs.shape, 500)
-  sink = c_regs[*rngs].store(c_regs.after(k)[*rngs] + A_col[iter_m, t_m] * B_row[iter_n, t_n]).end(iter_m, iter_n, t_m, t_n)
+  iterWaveM, yt, iterWaveN, xt = rngs = rngs_for_shape(c_regs.shape, 500)
+  sink = c_regs[*rngs].store(c_regs.after(k)[*rngs] + A_col[iterWaveM, yt] * B_row[iterWaveN, xt]).end(iterWaveM, iterWaveN, yt, xt)
 
   # Close k, sync, and close K tiles
   sink = sink.end(k).barrier().end(k_tile_range)
@@ -107,37 +132,38 @@ def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
   # ---------------------------
   # REG -> GLOBAL (epilogue)
   # ---------------------------
-  c = c.reshape(WAVES_PER_BLOCK_M, REG_TILES_PER_WAVE_M, LANES_PER_WAVE_M, TM,
-                WAVES_PER_BLOCK_N, REG_TILES_PER_WAVE_N, LANES_PER_WAVE_N, TN)
+  c = c.reshape(WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM,
+                WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)
   c = c[waveIdy, :, laneIdy, :,
         waveIdx, :, laneIdx, :]
   sink = copy(c, c_regs.after(sink), rng=600)
 
   return sink.sink(arg=KernelInfo(opts_to_apply=())).simplify()
 
-def eval_custom_matmul(fxn):
-  a = Tensor.randn(M, K, dtype=dtypes.float)
-  b = Tensor.randn(K, N, dtype=dtypes.float)
-  c = Tensor.empty(M, N, dtype=dtypes.float)
-  with Context(DEBUG=0): Tensor.realize(a, b)
+def test_matmul(sink:UOp, dtype=dtypes.float32, N=N):
+  rng = np.random.default_rng()
+  a = Tensor(rng.random((N, N), dtype=np.float32)-0.5, dtype=dtype)
+  b = Tensor(rng.random((N, N), dtype=np.float32)-0.5, dtype=dtype)
+  hc = Tensor.empty(N, N, dtype=dtype)
+  Tensor.realize(a, b, hc)
+
+  ei = ExecItem(sink, [t.uop.buffer for t in [hc, a, b]], prg=get_runner(Device.DEFAULT, sink))
 
   ets = []
-  with Context(DEBUG=max(2, DEBUG.value)):
-    for _ in range(NUM_RUNS):
-      GlobalCounters.reset()
-      tst = Tensor.custom_kernel(c, a, b, fxn=fxn)[0].realize()
-      ets.append(GlobalCounters.time_sum_s)
-  print(f"REAL TFLOPS {M * N * K * 2 / min(ets) * 1e-12:.2f}")
+  with Context(DEBUG=2):
+    for _ in range(run_count):
+      ets.append(ei.run(wait=True))
+  print(f"REAL TFLOPS {N * N * N * 2 / min(ets) * 1e-12:.2f}")
 
   if getenv("VERIFY", 1):
     GlobalCounters.reset()
     with Context(DEBUG=2):
       tc = (a @ b).realize()
     with Context(DEBUG=0):
-      err = (tc - tst).square().mean().item()
+      err = (hc - tc).square().mean().item()
     print(f"mean squared error {err}")
     if err > 1e-06:
       raise RuntimeError("matmul is wrong!")
 
 if __name__ == "__main__":
-  eval_custom_matmul(hand_spec_kernel3)
+  test_matmul(hand_spec_kernel3(), N=N)

extra/gemm/asm/cdna/asm.py

@@ -1,14 +1,5 @@
-import atexit, functools
-from tinygrad import Tensor, Device, dtypes
-from tinygrad.dtype import AddrSpace
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from tinygrad.renderer import Estimates
-from tinygrad.helpers import getenv, all_same, DEBUG
 from tinygrad.runtime.autogen.amd.cdna.ins import *
-
-# ** CDNA4 assembly gemm
-
-WORKGROUP_SIZE = 256
+from tinygrad.dtype import dtypes
 
 # M0 is encoded with 124 (NULL in RDNA) in CDNA
 M0 = NULL
@@ -2606,117 +2597,3 @@ def build_kernel(batch, M, N, K, dtype):
   k.label('KernelEnd')
   k.emit(s_endpgm())
   return k.finalize()
-
-# ** ASM_GEMM custom kernel
-
-@functools.cache
-def custom_asm_gemm(C:UOp, A:UOp, B:UOp, dname:str) -> UOp:
-  batch, M, K = A.shape
-  K2, N = B.shape[(1 if B.ndim == 3 else 0):]
-  assert K == K2
-  lidx = UOp.special(WORKGROUP_SIZE, "lidx0")
-  gidx = UOp.special(NUM_WG, "gidx0")
-  insts = build_kernel(batch, M, N, K, A.dtype.base)
-  lds = UOp(Ops.DEFINE_LOCAL, dtypes.uint8.ptr(size=133_120, addrspace=AddrSpace.LOCAL), (), 'lds')
-  sink = UOp.sink(C.base, A.base, B.base, lds, lidx, gidx,
-                  arg=KernelInfo(name=f"gemm_{batch}_{M}_{N}_{K}", estimates=Estimates(ops=2*batch*M*N*K, mem=(batch*M*K + K*N + batch*M*N)*2)))
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=dname),
-                                UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=x) for x in insts]))))
-
-counters = {"used":0, "todos":[]}
-def todo(msg:str) -> bool: counters["todos"].append(msg); return False
-def _asm_gemm_report():
-  print(f'asm_gemm: {counters["used"]} used, {len(counters["todos"])} not used')
-  if DEBUG >= 2 and counters["todos"]:
-    from collections import Counter
-    for msg, cnt in Counter(counters["todos"]).most_common(): print(f'  {cnt:3d}x {msg}')
-atexit.register(_asm_gemm_report)
-
-def can_use_asm_gemm(a:Tensor, b:Tensor) -> bool:
-  if a.dtype != b.dtype: return todo(f"dtypes must match {a.dtype} != {b.dtype}")
-  if a.dtype not in {dtypes.bfloat16, dtypes.float16}: return todo(f"only bfloat16/float16, got {a.dtype}")
-  batch, M, K = (1, *a.shape) if a.ndim == 2 else a.shape
-  N = b.shape[1]
-  if isinstance(a.device, tuple):
-    if a.ndim == 2 and a.uop.axis == 0 and b.uop.axis is None: M //= len(a.device)
-    elif a.ndim == 2 and a.uop.axis == 1 and b.uop.axis == 0: K //= len(a.device)
-    elif a.ndim == 2 and a.uop.axis is None and b.uop.axis == 1: N //= len(a.device)
-    elif a.ndim == 3 and a.uop.axis == 0 and b.uop.axis is None: batch //= len(a.device)
-    elif a.ndim == 3 and a.uop.axis is None and b.uop.axis == 1: N //= len(a.device)
-    elif a.ndim == 3 and a.uop.axis == 2 and b.uop.axis == 0: K //= len(a.device)
-    else: return todo(f"sharding mismatch a.ndim={a.ndim} a.uop.axis={a.uop.axis} b.uop.axis={b.uop.axis}")
-    dname = a.device[0]
-  else: dname = a.device
-  arch = getattr(Device[dname].renderer, "arch", "")
-  if batch not in {1, 2}: return todo(f"GEMM batch size {batch}")
-  if (M % TILE_M != 0 or N % TILE_N != 0 or K % TILE_K != 0) and arch == "gfx950":
-    return todo(f"GEMM shape ({M},{N},{K}) not a multiple of ({TILE_M},{TILE_N},{TILE_K})")
-  return True
-
-# ** UOp gemm to test Tensor.custom_kernel multi and backward correctness on non cdna4
-# note: this can be removed after we have GEMM on mixins
-
-def custom_uop_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
-  M, K = A.shape[0]*A.shape[1], A.shape[2]
-  K2, N = B.shape[(1 if B.ndim == 3 else 0):]
-  assert K == K2
-  m = UOp.range(M, 1, AxisType.LOOP)
-  n = UOp.range(N, 2, AxisType.LOOP)
-  k = UOp.range(K, 0, AxisType.REDUCE)
-  mul = (A.flatten().index((m*UOp.const(dtypes.index, K)+k))*
-         B.flatten().index((k*UOp.const(dtypes.index, N)+n))).cast(dtypes.float32)
-  red = mul.reduce(k, arg=Ops.ADD, dtype=dtypes.float32).cast(C.dtype.base)
-  store = C.flatten().index((m*UOp.const(dtypes.index, N)+n), ptr=True).store(red).end(m, n)
-  return store.sink(arg=KernelInfo(name=f'uop_gemm_{M}_{N}_{K}'))
-
-# ** backward gemm, might use the asm gemm
-
-def custom_gemm_bw(gradient:UOp, kernel:UOp):
-  out, a, b = kernel.src[1:]
-  assert all_same([gradient.device, a.device, b.device, out.device])
-  a_t, b_t, g_t = Tensor(a, device=a.device), Tensor(b, device=a.device), Tensor(gradient, device=a.device)
-  # TODO: this needs to be cleaned up and done properly, the batch dim of grad and a multi need to align
-  g_t = g_t[:a.shape[0]]
-  grad_a = (g_t @ b_t.T).uop
-  grad_b = (a_t.permute(2, 0, 1).reshape(a_t.shape[2], -1) @ g_t.reshape(-1, g_t.shape[-1])).uop
-  return (None, grad_a, grad_b)
-
-# ** main gemm function
-
-def asm_gemm(a:Tensor, b:Tensor) -> Tensor:
-  assert can_use_asm_gemm(a, b), f"{counters['todos'][-1]}"
-  counters["used"] += 1
-  unfold_batch = a.ndim == 3 and isinstance(a.device, tuple) and a.uop.axis == 2 and b.uop.axis == 0
-  if unfold_batch:
-    orig_batch = a.shape[0]
-    a = a.reshape(a.shape[0]*a.shape[1], a.shape[2])
-  squeeze = a.ndim == 2
-  if squeeze: a = a.unsqueeze(0)
-
-  batch, M, K = a.shape
-  N = b.shape[1]
-  is_multi = isinstance(a.device, tuple)
-  if (k_sharded:=is_multi and a.uop.axis == 2): K //= len(a.device)
-  if (m_sharded:=is_multi and a.uop.axis == 1): M //= len(a.device)
-  n_sharded = is_multi and b.uop.axis == 1
-
-  if is_multi:
-    if n_sharded:
-      out = Tensor(Tensor.empty(batch, M, N//len(a.device), dtype=a.dtype, device=a.device).uop.multi(2), device=a.device)
-    elif m_sharded:
-      out = Tensor(Tensor.empty(batch, M, N, dtype=a.dtype, device=a.device).uop.multi(1), device=a.device)
-    else:
-      out = Tensor(Tensor.empty(batch//len(a.device) if a.uop.axis==0 else batch, M, N, dtype=a.dtype, device=a.device).uop.multi(0), device=a.device)
-  else:
-    out = Tensor.empty(batch, M, N, dtype=a.dtype, device=a.device)
-
-  renderer = Device[a.device[0] if is_multi else a.device].renderer
-  dname, arch = renderer.device, getattr(renderer, "arch", "")
-  if arch.startswith("gfx950") and getenv("USE_ASM", 1):
-    out = Tensor.custom_kernel(out, a, b, fxn=functools.partial(custom_asm_gemm, dname=dname), grad_fxn=custom_gemm_bw)[0]
-  else:
-    out = Tensor.custom_kernel(out, a, b, fxn=custom_uop_gemm, grad_fxn=custom_gemm_bw)[0]
-  if k_sharded: out = out.sum(0)
-  out = out.squeeze(0) if squeeze else out
-  if unfold_batch: out = out.reshape(orig_batch, -1, out.shape[-1])
-  return out

extra/gemm/asm/cdna/gemm.py

@@ -0,0 +1,122 @@
+import atexit, functools
+from tinygrad import Tensor, Device, dtypes
+from tinygrad.dtype import AddrSpace
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
+from tinygrad.renderer import Estimates
+from tinygrad.helpers import getenv, all_same, DEBUG
+from extra.gemm.asm.cdna.asm import build_kernel, TILE_M, TILE_N, TILE_K, NUM_WG
+
+# ** CDNA4 assembly gemm
+
+WORKGROUP_SIZE = 256
+
+@functools.cache
+def custom_asm_gemm(C:UOp, A:UOp, B:UOp, dname:str) -> UOp:
+  batch, M, K = A.shape
+  K2, N = B.shape[(1 if B.ndim == 3 else 0):]
+  assert K == K2
+  lidx = UOp.special(WORKGROUP_SIZE, "lidx0")
+  gidx = UOp.special(NUM_WG, "gidx0")
+  insts = build_kernel(batch, M, N, K, A.dtype.base)
+  lds = UOp(Ops.DEFINE_LOCAL, dtypes.uint8.ptr(size=133_120, addrspace=AddrSpace.LOCAL), (), 'lds')
+  sink = UOp.sink(C.base, A.base, B.base, lds, lidx, gidx,
+                  arg=KernelInfo(name=f"gemm_{batch}_{M}_{N}_{K}", estimates=Estimates(ops=2*batch*M*N*K, mem=(batch*M*K + K*N + batch*M*N)*2)))
+  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=dname),
+                                UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=x) for x in insts]))))
+
+counters = {"used":0, "todos":[]}
+def todo(msg:str) -> bool: counters["todos"].append(msg); return False
+def _asm_gemm_report():
+  print(f'asm_gemm: {counters["used"]} used, {len(counters["todos"])} not used')
+  if DEBUG >= 2 and counters["todos"]:
+    from collections import Counter
+    for msg, cnt in Counter(counters["todos"]).most_common(): print(f'  {cnt:3d}x {msg}')
+atexit.register(_asm_gemm_report)
+
+def can_use_asm_gemm(a:Tensor, b:Tensor) -> bool:
+  if a.dtype != b.dtype: return todo(f"dtypes must match {a.dtype} != {b.dtype}")
+  if a.dtype not in {dtypes.bfloat16, dtypes.float16}: return todo(f"only bfloat16/float16, got {a.dtype}")
+  batch, M, K = (1, *a.shape) if a.ndim == 2 else a.shape
+  N = b.shape[1]
+  if isinstance(a.device, tuple):
+    if a.ndim == 2 and a.uop.axis == 0 and b.uop.axis is None: M //= len(a.device)
+    elif a.ndim == 2 and a.uop.axis == 1 and b.uop.axis == 0: K //= len(a.device)
+    elif a.ndim == 2 and a.uop.axis is None and b.uop.axis == 1: N //= len(a.device)
+    elif a.ndim == 3 and a.uop.axis == 0 and b.uop.axis is None: batch //= len(a.device)
+    elif a.ndim == 3 and a.uop.axis is None and b.uop.axis == 1: N //= len(a.device)
+    elif a.ndim == 3 and a.uop.axis == 2 and b.uop.axis == 0: K //= len(a.device)
+    else: return todo(f"sharding mismatch a.ndim={a.ndim} a.uop.axis={a.uop.axis} b.uop.axis={b.uop.axis}")
+    dname = a.device[0]
+  else: dname = a.device
+  arch = getattr(Device[dname].renderer, "arch", "")
+  if batch not in {1, 2}: return todo(f"GEMM batch size {batch}")
+  if (M % TILE_M != 0 or N % TILE_N != 0 or K % TILE_K != 0) and arch == "gfx950":
+    return todo(f"GEMM shape ({M},{N},{K}) not a multiple of ({TILE_M},{TILE_N},{TILE_K})")
+  return True
+
+# ** UOp gemm to test Tensor.custom_kernel multi and backward correctness on non cdna4
+# note: this can be removed after we have GEMM on mixins
+
+def custom_uop_gemm(C:UOp, A:UOp, B:UOp) -> UOp:
+  M, K = A.shape[0]*A.shape[1], A.shape[2]
+  K2, N = B.shape[(1 if B.ndim == 3 else 0):]
+  assert K == K2
+  m = UOp.range(M, 1, AxisType.LOOP)
+  n = UOp.range(N, 2, AxisType.LOOP)
+  k = UOp.range(K, 0, AxisType.REDUCE)
+  mul = (A.index((m*UOp.const(dtypes.index, K)+k))*B.index((k*UOp.const(dtypes.index, N)+n))).cast(dtypes.float32)
+  red = mul.reduce(k, arg=Ops.ADD, dtype=dtypes.float32).cast(C.dtype.base)
+  store = C.index((m*UOp.const(dtypes.index, N)+n), ptr=True).store(red).end(m, n)
+  return store.sink(arg=KernelInfo(name=f'uop_gemm_{M}_{N}_{K}'))
+
+# ** backward gemm, might use the asm gemm
+
+def custom_gemm_bw(gradient:UOp, kernel:UOp):
+  out, a, b = kernel.src[1:]
+  assert all_same([gradient.device, a.device, b.device, out.device])
+  a_t, b_t, g_t = Tensor(a, device=a.device), Tensor(b, device=a.device), Tensor(gradient, device=a.device)
+  # TODO: this needs to be cleaned up and done properly, the batch dim of grad and a multi need to align
+  g_t = g_t[:a.shape[0]]
+  grad_a = (g_t @ b_t.T).uop
+  grad_b = (a_t.permute(2, 0, 1).reshape(a_t.shape[2], -1) @ g_t.reshape(-1, g_t.shape[-1])).uop
+  return (None, grad_a, grad_b)
+
+# ** main gemm function
+
+def asm_gemm(a:Tensor, b:Tensor) -> Tensor:
+  assert can_use_asm_gemm(a, b), f"{counters['todos'][-1]}"
+  counters["used"] += 1
+  unfold_batch = a.ndim == 3 and isinstance(a.device, tuple) and a.uop.axis == 2 and b.uop.axis == 0
+  if unfold_batch:
+    orig_batch = a.shape[0]
+    a = a.reshape(a.shape[0]*a.shape[1], a.shape[2])
+  squeeze = a.ndim == 2
+  if squeeze: a = a.unsqueeze(0)
+
+  batch, M, K = a.shape
+  N = b.shape[1]
+  is_multi = isinstance(a.device, tuple)
+  if (k_sharded:=is_multi and a.uop.axis == 2): K //= len(a.device)
+  if (m_sharded:=is_multi and a.uop.axis == 1): M //= len(a.device)
+  n_sharded = is_multi and b.uop.axis == 1
+
+  if is_multi:
+    if n_sharded:
+      out = Tensor(Tensor.empty(batch, M, N//len(a.device), dtype=a.dtype, device=a.device).uop.multi(2), device=a.device)
+    elif m_sharded:
+      out = Tensor(Tensor.empty(batch, M, N, dtype=a.dtype, device=a.device).uop.multi(1), device=a.device)
+    else:
+      out = Tensor(Tensor.empty(batch//len(a.device) if a.uop.axis==0 else batch, M, N, dtype=a.dtype, device=a.device).uop.multi(0), device=a.device)
+  else:
+    out = Tensor.empty(batch, M, N, dtype=a.dtype, device=a.device)
+
+  renderer = Device[a.device[0] if is_multi else a.device].renderer
+  dname, arch = renderer.device, getattr(renderer, "arch", "")
+  if arch.startswith("gfx950") and getenv("USE_ASM", 1):
+    out = Tensor.custom_kernel(out, a, b, fxn=functools.partial(custom_asm_gemm, dname=dname), grad_fxn=custom_gemm_bw)[0]
+  else:
+    out = Tensor.custom_kernel(out, a, b, fxn=custom_uop_gemm, grad_fxn=custom_gemm_bw)[0]
+  if k_sharded: out = out.sum(0)
+  out = out.squeeze(0) if squeeze else out
+  if unfold_batch: out = out.reshape(orig_batch, -1, out.shape[-1])
+  return out

extra/gemm/asm/rdna3/template.s

@@ -0,0 +1,76 @@
+.text
+.section	.text.
+.global	gemm
+.p2align	8
+.type	gemm,@function
+
+gemm:
+INSTRUCTIONS
+
+.section .rodata,"a",@progbits
+.p2align 6, 0x0
+.amdhsa_kernel gemm
+  # basic memory requirements
+  .amdhsa_group_segment_fixed_size 30336
+  .amdhsa_private_segment_fixed_size 0
+  .amdhsa_kernarg_size 32
+  # register usage (RSRC1)
+  .amdhsa_next_free_vgpr 256
+  .amdhsa_next_free_sgpr 100
+  # workgroup / workitem IDs (RSRC2)
+  .amdhsa_system_sgpr_workgroup_id_x 1
+  .amdhsa_system_sgpr_workgroup_id_y 1
+  .amdhsa_system_sgpr_workgroup_id_z 1
+  # user SGPRs: kernarg ptr in s[0:1]
+  .amdhsa_user_sgpr_kernarg_segment_ptr 1
+  .amdhsa_user_sgpr_count 2
+  # gfx10+ / gfx11 specifics (RSRC1[29..31])
+  .amdhsa_wavefront_size32 1
+  .amdhsa_workgroup_processor_mode 1
+  .amdhsa_memory_ordered 1
+  .amdhsa_forward_progress 1
+  # misc for gfx11
+  .amdhsa_dx10_clamp 1
+  .amdhsa_ieee_mode 1
+  .amdhsa_uses_dynamic_stack 0
+.end_amdhsa_kernel
+
+.amdgpu_metadata
+---
+amdhsa.kernels:
+  - .args:
+      - .address_space:  generic
+        .name:           C
+        .offset:         0
+        .size:           8
+        .value_kind:     global_buffer
+        .value_type:     f16
+      - .address_space:  generic
+        .name:           A
+        .offset:         8
+        .size:           8
+        .value_kind:     global_buffer
+        .value_type:     f16
+      - .address_space:  generic
+        .name:           B
+        .offset:         16
+        .size:           8
+        .value_kind:     global_buffer
+        .value_type:     f16
+    .group_segment_fixed_size: 30336
+    .kernarg_segment_align: 8
+    .kernarg_segment_size: 32
+    .max_flat_workgroup_size: 128
+    .name:           gemm
+    .private_segment_fixed_size: 0
+    .sgpr_count:     70
+    .sgpr_spill_count: 0
+    .symbol:         gemm.kd
+    .vgpr_count:     256
+    .vgpr_spill_count: 0
+    .wavefront_size: 32
+amdhsa.version:
+  - 1
+  - 1
+...
+.end_amdgpu_metadata

extra/gemm/asm/rdna3/test.py

@@ -0,0 +1,30 @@
+import math, pathlib
+
+from tinygrad import Device, dtypes
+from tinygrad.uop.ops import UOp, Ops, KernelInfo
+
+from extra.gemm.amd_uop_matmul import test_matmul
+
+N = 4096
+TN = 96
+THREADS_PER_WG = 128
+NUM_WG = math.ceil(N / TN) * math.ceil(N / TN)
+
+dname:str = Device.DEFAULT
+template:str = (pathlib.Path(__file__).parent/"template.s").read_text()
+
+def asm_kernel() -> UOp:
+  lidx = UOp.special(THREADS_PER_WG, "lidx0")
+  gidx = UOp.special(NUM_WG, "gidx0")
+
+  a = UOp.placeholder((N*N,), dtypes.half, slot=1)
+  b = UOp.placeholder((N*N,), dtypes.half, slot=2)
+  c = UOp.placeholder((N*N,), dtypes.half, slot=0)
+
+  src = template.replace("INSTRUCTIONS", (pathlib.Path(__file__).parent/"gemm.s").read_text())
+
+  sink = UOp.sink(a, b, c, lidx, gidx, arg=KernelInfo(name="gemm"))
+  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=dname), UOp(Ops.LINEAR, src=(*sink.src, sink)), UOp(Ops.SOURCE, arg=src)))
+
+if __name__ == "__main__":
+  test_matmul(asm_kernel(), dtype=dtypes.half, N=N)

extra/gemm/asm/unpack_kd.py

@@ -0,0 +1,179 @@
+# unpack the complete kernel descriptor of an amdgpu ELF
+# https://rocm.docs.amd.com/projects/llvm-project/en/latest/LLVM/llvm/html/AMDGPUUsage.html#code-object-v3-kernel-descriptor
+import struct, pathlib, sys
+from tinygrad.runtime.support.elf import elf_loader
+
+def bits(x, lo, hi): return (x >> lo) & ((1 << (hi - lo + 1)) - 1)
+def assert_zero(x, lo, hi): assert bits(x, lo, hi) == 0
+
+with open(sys.argv[1], "rb") as f:
+  lib = f.read()
+
+image, sections, relocs = elf_loader(lib)
+rodata_entry = next((sh.header.sh_addr for sh in sections if sh.name == ".rodata"))
+
+# rodata is exactly 64 bytes
+kd = image[rodata_entry:rodata_entry+64]
+desc = int.from_bytes(kd, byteorder="little")
+
+group_segment_fixed_size = bits(desc, 0, 31)
+private_segment_fixed_size = bits(desc, 32, 63)
+kernarg_size = bits(desc, 64, 95)
+reserved_127_96 = bits(desc, 96, 127)
+assert reserved_127_96 == 0
+
+print("GROUP_SEGMENT_FIXED_SIZE:", group_segment_fixed_size)
+print("PRIVATE_SEGMENT_FIXED_SIZE:", private_segment_fixed_size)
+print("KERNARG_SIZE:", kernarg_size)
+print("RESERVED 127:96:", reserved_127_96)
+
+entry_off = bits(desc, 128, 191)
+
+# sign-extend manually if needed
+if entry_off & (1 << 63):
+  entry_off -= 1 << 64
+
+print("KERNEL_CODE_ENTRY_BYTE_OFFSET:", entry_off)
+
+kd_addr = 0x1840
+entry_addr = kd_addr + entry_off
+
+print("Computed entry address: 0x%016x" % entry_addr)
+print("256B aligned:", entry_addr % 256 == 0)
+
+pgm_rsrc3 = bits(desc, 352, 383)
+pgm_rsrc1 = bits(desc, 384, 415)
+pgm_rsrc2 = bits(desc, 416, 447)
+
+print("COMPUTE_PGM_RSRC3: 0x%08x" % pgm_rsrc3)
+print("COMPUTE_PGM_RSRC1: 0x%08x" % pgm_rsrc1)
+print("COMPUTE_PGM_RSRC2: 0x%08x" % pgm_rsrc2)
+
+# rsrc 3 (gfx950)
+
+accum_offset_raw = bits(pgm_rsrc3, 0, 5)
+assert_zero(pgm_rsrc3, 6, 15)
+tg_split = bits(pgm_rsrc3, 16, 16)
+accum_offset_vgprs = (accum_offset_raw + 1) * 4
+print("RSRC3.ACCUM_OFFSET (AccVGPR index):", accum_offset_vgprs)
+print("RSRC3.TG_SPLIT:", tg_split)
+
+# rsrc 1
+
+vgpr_gran = bits(pgm_rsrc1, 0, 5)
+sgpr_gran = bits(pgm_rsrc1, 6, 9)
+assert_zero(pgm_rsrc1, 27, 28)
+
+# NOTE: this is vgprs + agprs
+vgprs_used = (vgpr_gran + 1) * 8
+assert 0 <= vgprs_used <= 512
+
+k = sgpr_gran // 2
+sgprs_used = (k + 1) * 16
+
+print("RSRC1.VGPRS:", vgprs_used)
+print("RSRC1.SGPRS:", sgprs_used)
+
+assert_zero(pgm_rsrc1, 10, 11)
+
+float_round_mode_32 = bits(pgm_rsrc1, 12, 13)
+float_round_mode_16_64 = bits(pgm_rsrc1, 15, 14)
+float_denorm_mode_32 = bits(pgm_rsrc1, 16, 17)
+float_denorm_mode_16_64 = bits(pgm_rsrc1, 18, 19)
+
+priv = bits(pgm_rsrc1, 20, 20)
+assert priv == 0
+enable_dx10_clamp_wg_rr_en = bits(pgm_rsrc1, 21, 21)
+debug_mode = bits(pgm_rsrc1, 22, 22)
+enable_ieee_mode = bits(pgm_rsrc1, 23, 23)
+bulky = bits(pgm_rsrc1, 24, 24)
+assert bulky == 0
+cdbg_user = bits(pgm_rsrc1, 25, 25)
+assert cdbg_user == 0
+fp16_ovfl = bits(pgm_rsrc1, 26, 26)
+assert_zero(pgm_rsrc1, 27, 28)  # reserved
+assert_zero(pgm_rsrc1, 29, 29)  # WGP_MODE (reserved on gfx9)
+assert_zero(pgm_rsrc1, 30, 30)  # MEM_ORDERED (reserved on gfx9)
+assert_zero(pgm_rsrc1, 31, 31)  # FWD_PROGRESS (reserved on gfx9)
+
+# rsrc 2
+
+enable_private_segment = bits(pgm_rsrc2, 0, 0)  # SCRATCH_EN
+user_sgpr_count = bits(pgm_rsrc2, 1, 5)         # USER_SGPR
+enable_trap_handler = bits(pgm_rsrc2, 6, 6)     # TRAP_PRESENT (must be 0 here)
+assert enable_trap_handler == 0
+
+enable_sgpr_workgroup_id_x = bits(pgm_rsrc2, 7, 7)
+enable_sgpr_workgroup_id_y = bits(pgm_rsrc2, 8, 8)
+enable_sgpr_workgroup_id_z = bits(pgm_rsrc2, 9, 9)
+enable_sgpr_workgroup_info = bits(pgm_rsrc2, 10, 10)
+
+enable_vgpr_workitem_id = bits(pgm_rsrc2, 11, 12)  # TIDIG_CMP_CNT enum (0..3)
+
+enable_exception_address_watch = bits(pgm_rsrc2, 13, 13)
+assert enable_exception_address_watch == 0
+enable_exception_memory = bits(pgm_rsrc2, 14, 14)
+assert enable_exception_memory == 0
+
+granulated_lds_size = bits(pgm_rsrc2, 15, 23)
+assert granulated_lds_size == 0  # spec: must be 0; CP uses dispatch packet rounding
+
+enable_exception_fp_invalid = bits(pgm_rsrc2, 24, 24)
+enable_exception_fp_denorm_src = bits(pgm_rsrc2, 25, 25)
+enable_exception_fp_div0 = bits(pgm_rsrc2, 26, 26)
+enable_exception_fp_overflow = bits(pgm_rsrc2, 27, 27)
+enable_exception_fp_underflow = bits(pgm_rsrc2, 28, 28)
+enable_exception_fp_inexact = bits(pgm_rsrc2, 29, 29)
+enable_exception_int_div0 = bits(pgm_rsrc2, 30, 30)
+
+assert_zero(pgm_rsrc2, 31, 31)
+
+print("RSRC2.ENABLE_PRIVATE_SEGMENT:", enable_private_segment)
+print("RSRC2.USER_SGPR_COUNT:", user_sgpr_count)
+print("RSRC2.ENABLE_SGPR_WORKGROUP_ID_X:", enable_sgpr_workgroup_id_x)
+print("RSRC2.ENABLE_SGPR_WORKGROUP_ID_Y:", enable_sgpr_workgroup_id_y)
+print("RSRC2.ENABLE_SGPR_WORKGROUP_ID_Z:", enable_sgpr_workgroup_id_z)
+print("RSRC2.ENABLE_SGPR_WORKGROUP_INFO:", enable_sgpr_workgroup_info)
+print("RSRC2.ENABLE_VGPR_WORKITEM_ID (enum):", enable_vgpr_workitem_id)
+
+print("RSRC2.EXC_FP_INVALID:", enable_exception_fp_invalid)
+print("RSRC2.EXC_FP_DENORM_SRC:", enable_exception_fp_denorm_src)
+print("RSRC2.EXC_FP_DIV0:", enable_exception_fp_div0)
+print("RSRC2.EXC_FP_OVERFLOW:", enable_exception_fp_overflow)
+print("RSRC2.EXC_FP_UNDERFLOW:", enable_exception_fp_underflow)
+print("RSRC2.EXC_FP_INEXACT:", enable_exception_fp_inexact)
+print("RSRC2.EXC_INT_DIV0:", enable_exception_int_div0)
+
+# user sgprs
+
+enable_sgpr_private_segment_buffer = bits(desc, 448, 448)
+enable_sgpr_dispatch_ptr = bits(desc, 449, 449)
+enable_sgpr_queue_ptr = bits(desc, 450, 450)
+enable_sgpr_kernarg_segment_ptr = bits(desc, 451, 451)
+enable_sgpr_dispatch_id = bits(desc, 452, 452)
+enable_sgpr_flat_scratch_init = bits(desc, 453, 453)
+enable_sgpr_private_segment_size = bits(desc, 454, 454)
+
+assert_zero(desc, 455, 457)
+
+print("DESC.ENABLE_SGPR_PRIVATE_SEGMENT_BUFFER:", enable_sgpr_private_segment_buffer)
+print("DESC.ENABLE_SGPR_DISPATCH_PTR:", enable_sgpr_dispatch_ptr)
+print("DESC.ENABLE_SGPR_QUEUE_PTR:", enable_sgpr_queue_ptr)
+print("DESC.ENABLE_SGPR_KERNARG_SEGMENT_PTR:", enable_sgpr_kernarg_segment_ptr)
+print("DESC.ENABLE_SGPR_DISPATCH_ID:", enable_sgpr_dispatch_id)
+print("DESC.ENABLE_SGPR_FLAT_SCRATCH_INIT:", enable_sgpr_flat_scratch_init)
+print("DESC.ENABLE_SGPR_PRIVATE_SEGMENT_SIZE:", enable_sgpr_private_segment_size)
+
+assert_zero(desc, 458, 459)
+
+uses_dynamic_stack = bits(desc, 459, 460)
+print("DESC.USES_DYNAMIC_STACK:", uses_dynamic_stack)
+
+# gfx950 only
+assert_zero(desc, 460, 463)
+kernarg_preload_spec_length = bits(desc, 464, 470)
+print("DESC.KERNARG_PRELOAD_SPEC_LENGTH:", kernarg_preload_spec_length)
+kernarg_preload_spec_offset = bits(desc, 471, 479)
+print("DESC.KERNARG_PRELOAD_SPEC_OFFSET:", kernarg_preload_spec_offset)
+
+assert_zero(desc, 480, 511)

extra/hevc/decode.py

@@ -10,9 +10,9 @@ HEVC_ROUNDUP = getenv("DATA_ROUNDUP", 32)
 @functools.cache
 def _hevc_jitted_decoder(out_image_size:tuple[int, int], max_hist:int, inplace:bool):
   def hevc_decode_frame(pos:Variable, hevc_tensor:Tensor, offset:Variable, sz:Variable, opaque:Tensor, i:Variable, *hist:Tensor, outbuf:Tensor|None=None):
-    x = hevc_tensor[offset:offset+sz*HEVC_ROUNDUP].decode_hevc_frame(pos, out_image_size, opaque[i], hist).realize()
+    x = hevc_tensor[offset:offset+sz*HEVC_ROUNDUP].decode_hevc_frame(pos, out_image_size, opaque[i], hist)
     if outbuf is not None: outbuf.assign(x).realize()
-    return x
+    return x.realize()
   return TinyJit(hevc_decode_frame)
 
 def hevc_decode(hevc_tensor:Tensor, opaque:Tensor, frame_info:list, luma_h:int, luma_w:int,
@@ -74,14 +74,10 @@ if __name__ == "__main__":
   Device.default.synchronize()
 
   # decode all frames using the iterator
-  tm = Timing("decoding whole file: ", on_exit=(lambda et: f", {len(frame_info)} frames, {len(frame_info)/(et/1e9):.2f} fps"))
-  with tm:
+  with Timing("decoding whole file: ", on_exit=(lambda et: f", {len(frame_info)} frames, {len(frame_info)/(et/1e9):.2f} fps")):
     images = list(hevc_decode(hevc_tensor, opaque_nv, frame_info, luma_h, luma_w, history=hist, preallocated_outputs=out_images))
     Device.default.synchronize()
 
-  fps = len(frame_info)/(tm.et/1e9)
-  assert fps >= getenv("ASSERT_FPS", 0), f"HEVC decode too slow: {fps:.2f} fps"
-
   # validation
   if getenv("VALIDATE", 0):
     import pickle

extra/sqtt/examples/discover_ops.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+# Run all ALU and memory instructions in the ISA
+import functools, inspect
+from enum import Enum
+from tinygrad import Tensor, Device, dtypes
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, AddrSpace
+from tinygrad.renderer.amd.dsl import Inst, Reg, OPERANDS, SrcField, VGPRField, SGPRField, SSrcField, SBaseField, AlignedSGPRField, BitField
+from tinygrad.renderer.amd.dsl import FixedBitField, EnumBitField, s, v, NULL, VCC_LO
+from extra.gemm.amd_asm_matmul import Kernel
+
+# skip instructions that mutate wave state (PC, EXEC, allocations, signals)
+SKIP = {"S_SETPC_B64", "S_SWAPPC_B64", "S_RFE_B64", "S_BARRIER_SIGNAL_ISFIRST", "S_GET_BARRIER_STATE", "S_ALLOC_VGPR", "S_SLEEP_VAR", "S_GETPC_B64",
+        "S_SENDMSG_RTN_B32", "S_SENDMSG_RTN_B64"}
+# skip barriers, s_waits, wrap level atomics, and ray tracing (bvh)
+SKIP_SUBSTR = ["SAVEEXEC", "CMPX", "WREXEC", "MOVREL", "ATOMIC", "S_BUFFER_", "S_ATC_PROBE", "BARRIER", "S_WAITCNT", "BVH",
+               "DS_CMPSTORE_RTN", "DS_WRAP_RTN_B32", "DS_ORDERED_COUNT", "DS_GWS", "GS_REG", "GLOBAL_LOAD_LDS", "GLOBAL_STORE_BLOCK"]
+
+ALU_FORMATS = {"VOP1", "VOP1_LIT", "VOP1_SDST", "VOP2", "VOP2_LIT", "VOP3", "VOP3_SDST", "VOP3SD", "VOP3P", "VOP3P_MFMA", "VOP3PX2",
+               "VOPC", "SOP1", "SOP1_LIT", "SOP2", "SOP2_LIT", "SOPC", "SOPC_LIT", "SOPK", "SOPK_LIT", "VINTERP"}
+# intentionally not testing scratch memory ops
+MEM_FORMATS = {"VGLOBAL", "GLOBAL", "SMEM", "DS"}
+
+def should_skip(op:Enum) -> bool: return (name:=op.name) in SKIP or any(sub in name for sub in SKIP_SUBSTR)
+
+# ** named register assignments
+
+# ALU operands
+ALU_VGPR_STRIDE = 16          # v[0], v[16], v[32], ... per ALU operand slot
+ALU_SGPR_STRIDE = 4           # s[0], s[4], s[8], ... per ALU operand slot
+
+# memory address registers
+S_KERNARG_PTR = (0, 1)
+S_BUF_PTR = (2, 3)
+V_VADDR = (0, 1)
+V_DS_ADDR = 0
+
+# memory data registers
+MEM_VGPR_BASE = 32            # v[32], v[48], ... for vdst/vdata/vsrc
+MEM_VGPR_STRIDE = 16          # spacing between memory data vgpr slots
+MEM_SGPR_BASE = 8             # s[8], s[10], ... for SMEM sdata
+MEM_SGPR_STRIDE = 2           # spacing between memory data sgpr slots
+
+# ** create an ALU instruction based on the operands
+
+def create_alu_inst(op:Enum, builder:functools.partial[Inst]) -> Inst:
+  inst_cls, operands, slot = builder.func, OPERANDS[op], 0
+  kwargs:dict[str, Reg|int] = {}
+  for name, field in inst_cls._fields:
+    if isinstance(field, (FixedBitField, EnumBitField)): continue
+    nregs = max(1, operands[name][1] // 32) if name in operands else 1
+    is_sreg = name in operands and "SREG" in str(operands[name][2])
+    base_v, base_s = slot * ALU_VGPR_STRIDE, slot * ALU_SGPR_STRIDE
+    if name == "sdst" and isinstance(field, SGPRField): reg = VCC_LO
+    elif is_sreg and not isinstance(field, VGPRField): reg = VCC_LO
+    elif isinstance(field, VGPRField): reg = v[base_v:base_v+nregs-1] if nregs > 1 else v[base_v]
+    elif isinstance(field, SSrcField): reg = VCC_LO if nregs <= 2 else s[base_s:base_s+nregs-1] if nregs > 1 else s[base_s]
+    elif isinstance(field, SGPRField): reg = s[base_s:base_s+nregs-1] if nregs > 1 else s[base_s]
+    elif isinstance(field, SrcField): reg = v[base_v:base_v+nregs-1] if nregs > 1 else v[base_v]
+    else: reg = None
+    if reg is not None: kwargs[name] = reg; slot += 1
+    elif isinstance(field, BitField): kwargs[name] = field.default
+  return builder(**kwargs)
+
+# ** create a memory instruction with pre set address registers
+
+MEM_PRESET_REGS:dict[str, dict[str, Reg]] = {
+  "VGLOBAL":{"saddr":s[S_BUF_PTR[0]:S_BUF_PTR[1]], "vaddr":v[V_VADDR[0]:V_VADDR[1]]},
+  "GLOBAL":{"saddr":s[S_BUF_PTR[0]:S_BUF_PTR[1]], "addr":v[V_DS_ADDR]},  # addr is 32-bit offset when saddr is valid SGPR
+  "DS":{"addr":v[V_DS_ADDR]},
+  "SMEM":{"sbase":s[S_KERNARG_PTR[0]:S_KERNARG_PTR[1]], "soffset":NULL},
+}
+
+def create_mem_inst(op:Enum, builder:functools.partial[Inst]) -> Inst:
+  inst_cls, operands, field_map = builder.func, OPERANDS.get(op, {}), MEM_PRESET_REGS.get(builder.func.__name__, {})
+  kwargs:dict[str, Reg|int] = {}
+  vslot, sslot = 0, 0
+  for name, field in inst_cls._fields:
+    if isinstance(field, (FixedBitField, EnumBitField)): continue
+    if name in field_map:
+      kwargs[name] = field_map[name]
+      continue
+    nregs = max(1, operands[name][1] // 32) if name in operands else 1
+    if isinstance(field, VGPRField):
+      vi = MEM_VGPR_BASE + vslot * MEM_VGPR_STRIDE
+      kwargs[name] = v[vi:vi+nregs-1] if nregs > 1 else v[vi]
+      vslot += 1
+    elif isinstance(field, (SGPRField, AlignedSGPRField, SBaseField)):
+      si = MEM_SGPR_BASE + sslot * MEM_SGPR_STRIDE
+      kwargs[name] = s[si:si+nregs-1] if nregs > 1 else s[si]
+      sslot += 1
+    elif isinstance(field, BitField): kwargs[name] = field.default
+  return builder(**kwargs)
+
+# ** collect all memory and ALU instructions from the ISA autogen
+
+def collect_instructions() -> tuple[list[Inst], list[Inst], list[str]]:
+  op_map:dict[Enum, functools.partial[Inst]] = {}
+  for name, obj in inspect.getmembers(all_insts):
+    if isinstance(obj, functools.partial) and len(obj.args) == 1: op_map[obj.args[0]] = obj
+  alu_insts:list[Inst] = []
+  mem_insts:list[Inst] = []
+  skipped:list[str] = []
+  for op_enum, builder in op_map.items():
+    if should_skip(op_enum) or op_enum not in OPERANDS: skipped.append(op_enum.name); continue
+    fmt = builder.func.__name__
+    if fmt in ALU_FORMATS: alu_insts.append(create_alu_inst(op_enum, builder))
+    elif fmt in MEM_FORMATS: mem_insts.append(create_mem_inst(op_enum, builder))
+  return alu_insts, mem_insts, skipped
+
+def exec_insts(insts:list):
+  k = Kernel(arch)
+  # ** prologue for global memory
+  k.emit(s_load_b64(sdata=s[S_BUF_PTR[0]:S_BUF_PTR[1]], sbase=s[S_KERNARG_PTR[0]:S_KERNARG_PTR[1]], soffset=NULL))
+  k.waitcnt(lgkm=0)
+  k.emit(v_mov_b32_e32(v[V_VADDR[0]], 0))
+  k.emit(v_mov_b32_e32(v[V_VADDR[1]], 0))
+  # ** emit
+  for inst in insts: k.emit(inst)
+  k.emit(s_endpgm())
+  # ** run
+  NUM_THREADS, NUM_GRIDS, BUF_SIZE = 32, 1, 1024*1024
+  def fxn(A:UOp, B:UOp, C:UOp) -> UOp:
+    lidx, gidx = UOp.special(NUM_THREADS, "lidx0"), UOp.special(NUM_GRIDS, "gidx0")
+    lds = UOp(Ops.DEFINE_LOCAL, dtypes.uint8.ptr(size=BUF_SIZE, addrspace=AddrSpace.LOCAL), (), "lds")
+    sink = UOp.sink(A.base, B.base, C.base, lds, lidx, gidx, arg=KernelInfo(name="discover_ops"))
+    return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg="AMD"), UOp(Ops.LINEAR, src=tuple(UOp(Ops.INS, arg=x) for x in k.finalize()))))
+  A = Tensor.empty(BUF_SIZE, dtype=dtypes.uint8)
+  B = Tensor.empty(1, dtype=dtypes.uint8)
+  C = Tensor.empty(1, dtype=dtypes.uint8)
+  Tensor.custom_kernel(A, B, C, fxn=fxn)[0].realize()
+
+if __name__ == "__main__":
+  import sys
+  arch = Device[Device.DEFAULT].renderer.arch
+  if arch.startswith("gfx12"):
+    from tinygrad.runtime.autogen.amd.rdna4.ins import *
+    import tinygrad.runtime.autogen.amd.rdna4.ins as all_insts
+  elif arch.startswith("gfx11"):
+    from tinygrad.runtime.autogen.amd.rdna3.ins import *
+    import tinygrad.runtime.autogen.amd.rdna3.ins as all_insts
+    # these don"t exist in RDNA3, only RDNA3.5 and above
+    SKIP.update(["S_FMAAK_F32", "S_FMAMK_F32"])
+  else:
+    print(f"{arch} not supported yet")
+    sys.exit(0)
+  alu_insts, mem_insts, skipped = collect_instructions()
+  print(f"collected {len(alu_insts)} ALU + {len(mem_insts)} memory instructions ({len(skipped)} skipped)")
+  exec_insts(mem_insts+alu_insts)

extra/sqtt/examples/generate_examples.py

@@ -1,25 +1,25 @@
-import os, subprocess, sys, shlex
+import os, subprocess, sys
 from pathlib import Path
 from tinygrad.helpers import temp
 
 EXAMPLES_DIR = Path(__file__).parent
 PROFILE_PATH = Path(temp("profile.pkl", append_user=True))
 
-EXAMPLES = {
-  "empty":"test/backend/test_custom_kernel.py TestCustomKernel.test_empty",
-  "plus":"test/test_tiny.py TestTiny.test_plus",
-  "gemm":"-c \"from tinygrad import Tensor; (Tensor.empty(N:=32, N)@Tensor.empty(N, N)).realize()\"",
-  "sync":"test/amd/test_custom_kernel.py TestCustomKernel.test_wave_sync",
-}
+EXAMPLES = [
+  "test/backend/test_custom_kernel.py TestCustomKernel.test_empty",
+  "test/test_tiny.py TestTiny.test_plus",
+  "test/test_tiny.py TestTiny.test_gemm",
+  "extra/sqtt/examples/discover_ops.py"
+]
 
 if __name__ == "__main__":
   arch = subprocess.check_output(["python", "-c", "from tinygrad import Device; print(Device['AMD'].arch)"], text=True,
                                  env={**os.environ, "DEBUG":"0"}).rstrip()
   (EXAMPLES_DIR/arch).mkdir(exist_ok=True)
-  for name,test in EXAMPLES.items():
+  for test in EXAMPLES:
     for i in range(2):
       # AM_RESET=1 gets a clear trace, does not work on mi300 machines
-      subprocess.run([sys.executable, *shlex.split(test)], cwd=EXAMPLES_DIR.parent.parent.parent,
+      subprocess.run([sys.executable, *test.split()], cwd=EXAMPLES_DIR.parent.parent.parent,
                      env={**os.environ, "AMD":"1", "AM_RESET":"1" if not arch.startswith("gfx9") else "0", "VIZ":"-2", "PYTHONPATH":"."})
-      PROFILE_PATH.rename(dest:=EXAMPLES_DIR/arch/f"profile_{name}_run_{i}.pkl")
+      PROFILE_PATH.rename(dest:=EXAMPLES_DIR/arch/f"profile_{test.split('.')[-1].replace('test_', '')}_run_{i}.pkl")
       print(f"saved SQTT trace to {dest}")

extra/thunder/amd/fa.py

@@ -19,38 +19,6 @@ def _sharded_empty(shape:Tensor, ref:Tensor, axis:int|None, dtype:DTypeLike|None
 def _sharded_empty_like(ref:Tensor, axis:int|None=None) -> Tensor:
   return _sharded_empty(ref.shape, ref, axis)
 
-@functools.cache
-def _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch):
-  def grad(dou:UOp, ker:UOp) -> tuple[None, None, UOp, UOp, UOp]:
-    do = Tensor(dou, device=dou.device)
-    attn = Tensor(ker.src[1].after(ker), device=ker.src[1].device)
-    l_vec = Tensor(ker.src[2].after(ker), device=ker.src[2].device)
-    xq = Tensor(ker.src[3], device=ker.src[3].device)
-    xk = Tensor(ker.src[4], device=ker.src[4].device)
-    xv = Tensor(ker.src[5], device=ker.src[5].device)
-
-    dq = _sharded_empty((B, H, N, D), xq, axis=shard_axis_t)
-    GROUP_SIZE = H_local // H_KV_local
-    dk_partial = _sharded_empty((B * GROUP_SIZE, N, H_KV, D), xk, axis=shard_axis)
-    dv_partial = _sharded_empty((B * GROUP_SIZE, N, H_KV, D), xv, axis=shard_axis)
-
-    # delta_vec = (do * attn).sum(-1, dtype=dtypes.float32).transpose(1, 2).unsqueeze(-2).detach()
-    delta_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
-    delta_vec, dq = Tensor.custom_kernel(delta_vec, dq, attn, do, fxn=functools.partial(custom_fa_backward_pre, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:2]
-
-    dq, dk_partial, dv_partial = Tensor.custom_kernel(dq, dk_partial, dv_partial, do, xq, xk, xv, l_vec, delta_vec, fxn=functools.partial(custom_fa_backward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:3]
-
-    # unshuffle dq: atomic_pk_add_bf16_with_warpid creates a shuffled layout within each 16x128 tile
-    # decompose each tile into (j=4, a=2, b=2, d=4, e=4, k=4, c=2) and permute to (e, k, j, a, d, b, c) = standard row-major
-    dq = dq.reshape(B, H, N//16, 4, 2, 2, 4, 4, 4, 2).permute(0, 1, 2, 7, 8, 3, 4, 6, 5, 9).reshape(B, H, N, D).transpose(1, 2)
-
-    # reduce partial dK/dV across GROUP_SIZE query heads
-    dk = dk_partial.reshape(B, GROUP_SIZE, N, H_KV, D).sum(1)
-    dv = dv_partial.reshape(B, GROUP_SIZE, N, H_KV, D).sum(1)
-
-    return None, None, dq.uop, dk.uop, dv.uop
-  return grad
-
 def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False):
   assert attn_mask is None, "attn_mask not supported"
   assert is_causal, "only causal attention supported"
@@ -77,7 +45,23 @@ def flash_attention(xq, xk, xv, attn_mask:Tensor|None=None, is_causal:bool=False
   attn = _sharded_empty_like(xq, axis=shard_axis)
   l_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
 
-  grad = _fa_grad_fxn(B, H, N, D, H_local, H_KV_local, H_KV, B_local, shard_axis, shard_axis_t, single_device, arch)
+  def grad(dou:UOp, _) -> tuple[None, None, UOp, UOp, UOp]:
+    do = Tensor(dou, device=dou.device)
+    dq_in = _sharded_empty((B, H, N, D), xq, axis=shard_axis_t)
+    dq = _sharded_empty_like(xq, axis=shard_axis)
+    dk = _sharded_empty_like(xk, axis=shard_axis)
+    dv = _sharded_empty_like(xv, axis=shard_axis)
+
+    # delta_vec = (do * attn).sum(-1, dtype=dtypes.float32).transpose(1, 2).unsqueeze(-2).detach()
+    delta_vec = _sharded_empty((B, H, 1, N), xq, dtype=dtypes.float32, axis=shard_axis_t)
+    delta_vec, dq_in = Tensor.custom_kernel(delta_vec, dq_in, attn, do, fxn=functools.partial(custom_fa_backward_pre, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:2]
+
+    dq_in, dk, dv = Tensor.custom_kernel(dq_in, dk, dv, do, xq, xk, xv, l_vec, delta_vec, fxn=functools.partial(custom_fa_backward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[:3]
+
+    # unshuffle dq
+    dq = Tensor.custom_kernel(dq, dq_in, fxn=functools.partial(custom_fa_backward_post, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D))[0]
+
+    return None, None, dq.uop, dk.uop, dv.uop
 
   attn, l_vec = Tensor.custom_kernel(attn, l_vec, xq, xk, xv, fxn=functools.partial(custom_fa_forward, device=single_device, arch=arch, B=B_local, N=N, H=H_local, H_KV=H_KV_local, D=D), grad_fxn=grad)[:2]
 
@@ -105,6 +89,7 @@ def custom_fa_forward(o:UOp, l_vec:UOp, q:UOp, k:UOp, v:UOp, device:str, arch:st
                   arg=KernelInfo(name="custom_fa_forward", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
+
   lib = bytearray(lib)
   rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
   struct.pack_into('<I', lib, rodata_off, 160000)
@@ -135,6 +120,7 @@ def custom_fa_backward_pre(delta_vec:UOp, dq:UOp, o:UOp, do:UOp, device:str, arc
                   arg=KernelInfo(name="custom_fa_backward_pre", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
+
   lib = bytearray(lib)
   rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
   struct.pack_into('<I', lib, rodata_off, 160000)
@@ -152,7 +138,7 @@ def custom_fa_backward(dq:UOp, dk:UOp, dv:UOp, do:UOp, q:UOp, k:UOp, v:UOp, l_ve
   BLOCK_SIZE_KV = 256
   NUM_WARPS = 4
   NUM_THREADS = 64 * NUM_WARPS
-  gsz = (H, N // BLOCK_SIZE_KV, B)
+  gsz = (H_KV, N // BLOCK_SIZE_KV, B)
   lsz = (NUM_THREADS, 1, 1)
   threadIdx_x = UOp.special(lsz[0], "lidx0")
   blockIdx_x, blockIdx_y, blockIdx_z = UOp.special(gsz[0], "gidx0"), UOp.special(gsz[1], "gidx1"), UOp.special(gsz[2], "gidx2")
@@ -165,6 +151,7 @@ def custom_fa_backward(dq:UOp, dk:UOp, dv:UOp, do:UOp, q:UOp, k:UOp, v:UOp, l_ve
                   arg=KernelInfo(name="custom_fa_backward", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
+
   lib = bytearray(lib)
   rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
   struct.pack_into('<I', lib, rodata_off, 160000)
@@ -195,6 +182,7 @@ def custom_fa_backward_post(dq_out:UOp, dq_in:UOp, device:str, arch:str, B:int,
                   arg=KernelInfo(name="custom_fa_backward_post", estimates=estimates))
 
   lib = HIPCCCompiler(arch, compile_args).compile_cached(code)
+
   lib = bytearray(lib)
   rodata_off = next(sh.header.sh_offset for sh in elf_loader(bytes(lib))[1] if sh.name == ".rodata")
   struct.pack_into('<I', lib, rodata_off, 160000)

extra/thunder/amd/fa_bwd_causal.cpp

@@ -37,7 +37,7 @@ using namespace kittens;
 using _gl_QdO  = gl<bf16, ATTN_B, ATTN_N, ATTN_H, ATTN_D>;
 using _gl_KV   = gl<bf16, ATTN_B, ATTN_N, ATTN_H_KV, ATTN_D>;
 using _gl_dQ   = gl<bf16, ATTN_B, ATTN_H, ATTN_N, ATTN_D>;
-using _gl_dKV  = gl<bf16, ATTN_B * GROUP_SIZE, ATTN_N, ATTN_H_KV, ATTN_D>;
+using _gl_dKV  = gl<bf16, ATTN_B, ATTN_N, ATTN_H_KV, ATTN_D>;
 using _gl_Lvec = gl<float, ATTN_B, ATTN_H, 1, ATTN_N>;
 
 template<int D> struct attn_bwd_combined_globals {
@@ -47,7 +47,7 @@ template<int D> struct attn_bwd_combined_globals {
   _gl_dQ dQg;
   _gl_dKV dKg, dVg;
   _gl_Lvec L_vec, delta_vec;
-  dim3 grid() { return dim3(ATTN_H, (ATTN_N / BLOCK_SIZE_KV), ATTN_B); }
+  dim3 grid() { return dim3(ATTN_H_KV, (ATTN_N / BLOCK_SIZE_KV), ATTN_B); }
   dim3 block() { return dim3(NUM_THREADS); }
   size_t dynamic_shared_memory() { return MAX_SHARED_MEMORY; }
 };
@@ -55,12 +55,10 @@ template<int D> struct attn_bwd_combined_globals {
 template<int D> __launch_bounds__(NUM_THREADS, 1)
 __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr, bf16 *dO_ptr, bf16 *Q_ptr, bf16 *K_ptr, bf16 *V_ptr, float *L_vec_ptr, float *delta_vec_ptr) {
 
-  const int q_head_idx_fixed = blockIdx.x;  // This is the query head index [0, ATTN_H)
-  const int kv_head_idx = q_head_idx_fixed / GROUP_SIZE;
-  const int q_head_in_group = q_head_idx_fixed % GROUP_SIZE;
+  const int kv_head_idx = blockIdx.x;  // This is the KV head index
   const int seq_idx = blockIdx.y;
   const int batch_idx = blockIdx.z;
-  const int first_q_head = q_head_idx_fixed;
+  const int first_q_head = kv_head_idx * GROUP_SIZE;
 
   const int warpid = kittens::warpid();
   const int j = seq_idx * NUM_WARPS + warpid;
@@ -72,7 +70,7 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
   // first Q step that can overlap this K_span:
   const int first_step = max(0, k_start_min / STEP_QO);
   const int num_steps_per_head = total_steps_per_head - first_step;
-  const int num_steps = num_steps_per_head;
+  const int num_steps = num_steps_per_head * GROUP_SIZE;
   const int k_pos = j * WARP_SIZE_KV;
 
   constexpr float L_SCALE_FACTOR = 1.44269504089f;
@@ -3357,14 +3355,14 @@ __global__ void attend_bwd_combined_ker(bf16 *dQ_ptr, bf16 *dK_ptr, bf16 *dV_ptr
     }
   }
 
-  store<1>(g.dVg, dV_j, {batch_idx * GROUP_SIZE + q_head_in_group, 0, kv_head_idx, 0}, {0, j, 0, 0});
+  store<1>(g.dVg, dV_j, {batch_idx, 0, kv_head_idx, 0}, {0, j, 0, 0});
   __builtin_amdgcn_s_waitcnt(0);
   __builtin_amdgcn_s_barrier();
 
   // We first copy dV_j_T from accumulator GPRs to vector GPRs and then perform the store
   accvgpr_read(dV_j_T, dK_j_T);
   mul(dV_j_T, dV_j_T, dP_SCALE_FACTOR);
-  store<1>(g.dKg, dV_j, {batch_idx * GROUP_SIZE + q_head_in_group, 0, kv_head_idx, 0}, {0, j, 0, 0});
+  store<1>(g.dKg, dV_j, {batch_idx, 0, kv_head_idx, 0}, {0, j, 0, 0});
 
   // Write out final dQ_i slice
   mul(dQ_i_T, dQ_i_T, dP_SCALE_FACTOR);

extra/thunder/amd/fa_fwd_causal.cpp

@@ -66,7 +66,7 @@ template<int D, typename T=bf16, typename L=row_l, typename S=rt_32x16_s> using
 template<int D, typename T=bf16, typename L=col_l, typename S=rt_16x32_s> using qo_tile_transposed = rt<T, D, Q_BLOCK_SIZE, L, S>;
 template<int D, typename T=bf16, typename L=row_l, typename S=rt_32x16_s> using kv_tile = rt<T, KV_BLOCK_SIZE, D, L, S>;
 template<int D, typename T=bf16, typename L=col_l, typename S=rt_16x32_s> using kv_tile_transposed = rt<T, D, KV_BLOCK_SIZE, L, S>;
-template<typename T=float, typename L=col_l, typename S=rt_16x32_4_s> using attn_tile = rt<T, KV_BLOCK_SIZE, Q_BLOCK_SIZE, L, S>;
+template<int D, typename T=float, typename L=col_l, typename S=rt_16x32_4_s> using attn_tile = rt<T, KV_BLOCK_SIZE, Q_BLOCK_SIZE, L, S>;
 
 /**********************************************************/
 template<int THR_X, int THR_Y>
@@ -103,7 +103,7 @@ __device__ inline void mask_kv_tile(RT &dst, int q_abs, int k_abs, uint32_t neg_
 
     #pragma unroll
     for (int i = 0; i < dst.height; ++i) {
-        // Row base of the 32x* chunk produced by MFMA
+        // Row base of the 32x* chunk produced by MFMA 
         const int row_base = (i * 32) + ((lane >> 5) << 2); // multiplesof 4
 
         // Relative index of the FIRST element in this row-chunk w.r.t. q_pos
@@ -148,7 +148,7 @@ __device__ inline void mask_kv_tile(RT &dst, int q_abs, int k_abs, uint32_t neg_
 /**********************************************************/
 
 template<int D> struct attn_globals {
-    _gl_QKVO Qg, Kg, Vg, Og;
+    _gl_QKVO Qg, Kg, Vg, Og; 
     gl<float, -1, -1, -1, -1> L_vec;
     dim3 grid() { return dim3(ATTN_H, ((ATTN_N / Q_BLOCK_SIZE + NUM_WARPS - 1) / NUM_WARPS), ATTN_B); }
     dim3 block() { return dim3(NUM_THREADS); }
@@ -196,10 +196,10 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
 
     kv_tile<D, bf16, col_l, rt_16x32_4_s> v_reg;
     qo_tile_transposed<D, float, col_l, rt_32x32_s> o_reg; // Output tile.
-    attn_tile<float, col_l, rt_32x32_s> att_block[2]; // attention tile, in float.
-    attn_tile<bf16, col_l, rt_32x32_s> att_block_bf16;
-    attn_tile<bf16, col_l, rt_16x32_4_s> att_block_bf16_in;
-    typename attn_tile<float, col_l, rt_32x32_s>::row_vec max_vec, norm_vec, max_vec_prev, scale_vec;
+    attn_tile<D, float, col_l, rt_32x32_s> att_block[2]; // attention tile, in float.
+    attn_tile<D, bf16, col_l, rt_32x32_s> att_block_bf16;
+    attn_tile<D, bf16, col_l, rt_16x32_4_s> att_block_bf16_in;
+    typename attn_tile<D, float, col_l, rt_32x32_s>::row_vec max_vec, norm_vec, max_vec_prev, scale_vec;
 
     zero(o_reg);
     zero(norm_vec);
@@ -241,8 +241,8 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     zero(att_block[0]);
     transpose(k_reg_transposed, k_reg);
     mma_AtB(att_block[0], k_reg_transposed, q_reg_transposed, att_block[0]);
-    __builtin_amdgcn_sched_barrier(0);
-    if constexpr (causal) {
+    __builtin_amdgcn_sched_barrier(0); 
+    if constexpr (causal) { 
         const int kv_end_pos = (1) * KV_BLOCK_SIZE;
         if (__builtin_expect(q_start_pos < kv_end_pos, 0)) {  // Only mask if needed
             mask_kv_tile(att_block[0], tile_idx, 0, neg_inf_v, lane);
@@ -269,7 +269,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     load(k_reg, k_smem[1]);
     // All warps then collaboratively load in the third slice of K (K2) into shared memory
     G::load<1, false>(k_smem[0], g.Kg, {batch_idx, 2, head_idx_kv, 0}, swizzled_offsets_K);
-    // All warps then collaboratively load in the second slice of V (V1) into shared memory
+    // All warps then collaboratively load in the second slice of V (V1) into shared memory 
     G::load<1, false>(v_smem[1], g.Vg, {batch_idx, 1, head_idx_kv, 0}, swizzled_offsets_V);
     asm volatile("s_waitcnt lgkmcnt(0)");
     asm volatile("s_waitcnt vmcnt(4)");
@@ -288,7 +288,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
         mul(norm_vec, norm_vec, scale_vec);
         col_sum(norm_vec, att_block[0], norm_vec);
         copy(att_block_bf16, att_block[0]);
-        att_block_bf16_in = *reinterpret_cast<attn_tile< bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
+        att_block_bf16_in = *reinterpret_cast<attn_tile<D, bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
         sched_barrier_exp_pairs<6, 3, 1>();
         sched_barrier_pairs<10, 5, 1>();
         __builtin_amdgcn_sched_barrier(0);
@@ -296,7 +296,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
         __builtin_amdgcn_sched_barrier(0);
 
         // Cluster 1:
-        //      Load K3 into shared
+        //      Load K3 into shared 
         G::load<1, false>(k_smem[1], g.Kg, {batch_idx, j, head_idx_kv, 0}, swizzled_offsets_K);
         //      Load V0 into registers
         load(v_reg, v_smem[0]);
@@ -348,7 +348,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
         mul(norm_vec, norm_vec, scale_vec);
         col_sum(norm_vec, att_block[1], norm_vec);
         copy(att_block_bf16, att_block[1]);
-        att_block_bf16_in = *reinterpret_cast<attn_tile<bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
+        att_block_bf16_in = *reinterpret_cast<attn_tile<D, bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
         sched_barrier_exp_pairs<6, 3, 3>();
         sched_barrier_pairs<10, 5, 3>();
         __builtin_amdgcn_s_setprio(0);
@@ -417,7 +417,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
 
     col_sum(norm_vec, att_block[0], norm_vec);
     copy(att_block_bf16, att_block[0]);
-    att_block_bf16_in = *reinterpret_cast<attn_tile<bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
+    att_block_bf16_in = *reinterpret_cast<attn_tile<D, bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
     sched_barrier_exp_pairs<6, 3, 5>();
     sched_barrier_pairs<10, 5, 5>();
     __builtin_amdgcn_sched_barrier(0);
@@ -482,7 +482,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     mul(norm_vec, norm_vec, scale_vec);
     col_sum(norm_vec, att_block[1], norm_vec);
     copy(att_block_bf16, att_block[1]);
-    att_block_bf16_in = *reinterpret_cast<attn_tile<bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
+    att_block_bf16_in = *reinterpret_cast<attn_tile<D, bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
     sched_barrier_exp_pairs<6, 3, 7>();
     sched_barrier_pairs<10, 5, 7>();
     __builtin_amdgcn_sched_barrier(0);
@@ -544,7 +544,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
     mul(norm_vec, norm_vec, scale_vec);
     col_sum(norm_vec, att_block[0], norm_vec);
     copy(att_block_bf16, att_block[0]);
-    att_block_bf16_in = *reinterpret_cast<attn_tile<bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
+    att_block_bf16_in = *reinterpret_cast<attn_tile<D, bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
     sched_barrier_exp_pairs<6, 3, 9>();
     sched_barrier_pairs<10, 5, 9>();
     __builtin_amdgcn_sched_barrier(0);
@@ -586,7 +586,7 @@ __global__ void attend_ker(bf16 *O_ptr, float *L_vec_ptr, bf16 *Q_ptr, bf16 *K_p
 
     col_sum(norm_vec, att_block[1], norm_vec);
     copy(att_block_bf16, att_block[1]);
-    att_block_bf16_in = *reinterpret_cast<attn_tile<bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
+    att_block_bf16_in = *reinterpret_cast<attn_tile<D, bf16, col_l, rt_16x32_4_s>*>(&att_block_bf16);
 
     __builtin_amdgcn_sched_barrier(0);
     mul_col(o_reg, o_reg, scale_vec);

extra/torch_backend/backend.py

@@ -505,9 +505,7 @@ tiny_backend_out = {**{f"aten.{x}.out":getattr(Tensor,x) for x in simple_tensor_
   "aten.lt.Tensor_out": Tensor.__lt__, "aten.lt.Scalar_out": Tensor.__lt__,
   "aten.le.Tensor_out": Tensor.__le__, "aten.le.Scalar_out": Tensor.__le__,
   "aten.clamp_max.Tensor_out": lambda input,max_: input.clamp(max_=max_),
-  "aten.clamp_max.out": lambda input,max_: input.clamp(max_=max_),
   "aten.clamp_min.Tensor_out": lambda input,min_: input.clamp(min_=min_),
-  "aten.clamp_min.out": lambda input,min_: input.clamp(min_=min_),
   "aten.fmod.Tensor_out": lambda input,other: input-input.div(other, rounding_mode="trunc")*other,
   # TODO: this might result in overflow issues
   "aten.round.decimals_out": lambda self,decimals: (self*10**decimals).round()/10**decimals,
@@ -590,7 +588,7 @@ tiny_backend = {**{k:wrap_out(v) for k,v in tiny_backend_out.items()}, **{
   "aten.repeat": lambda x,*repeats: Tensor.repeat(x,*repeats).contiguous(), # not a view
   "aten._softmax": lambda self,dim,half_to_float: self.softmax(dim),
   "aten._log_softmax": lambda self,dim,half_to_float: self.log_softmax(dim),
-  "aten.random_": lambda self: Tensor.randint(*self.shape, low=self.dtype.min, high=self.dtype.max, device=self.device, dtype=self.dtype),
+  "aten.random_": lambda self: Tensor.randint(*self.shape, low=dtypes.min(self.dtype), high=dtypes.max(self.dtype), device=self.device, dtype=self.dtype),
   "aten.random_.from": lambda self, from_, to: Tensor.randint(*self.shape, low=from_, high=to, device=self.device, dtype=self.dtype),
   "aten.uniform_": lambda self, low=0, high=1: Tensor.uniform(*self.shape, low=low, high=high, dtype=self.dtype),
   "aten.normal_": lambda self, mean=0, std=1: Tensor.normal(*self.shape, mean=mean, std=std, dtype=self.dtype),

extra/usbgpu/tbgpu/installer/notary_tool.sh

@@ -3,7 +3,3 @@ set -e
 
 ditto -c -k --keepParent ./build/Release/TinyGPU.app ./build/Release/TinyGPU.zip
 xcrun notarytool submit ./build/Release/TinyGPU.zip --keychain-profile "hgwJFhdheiIEy82nDN" --wait
-
-rm ./build/Release/TinyGPU.zip
-xcrun stapler staple ./build/Release/TinyGPU.app
-ditto -c -k --keepParent ./build/Release/TinyGPU.app ./build/Release/TinyGPU.zip

extra/viz/cli.py

@@ -48,16 +48,11 @@ def decode_profile(data:bytes) -> dict:
         name, ref, key, st, dur, fmt = u("<IIIIfI")
         v["events"].append({"name":strings[name], "ref":option(ref), "key":option(key), "st":st, "dur":dur, "fmt":strings[fmt]})
     else:
-      v["linear"] = u("<B")[0]
       v["peak"] = u("<Q")[0]
       for _ in range(event_count):
-        if v["linear"]:
-          ts, value = u("<IQ")
-          v["events"].append({"event":"freq", "ts":ts, "value":value})
-        else:
-          alloc, ts, key = u("<BII")
-          if alloc: v["events"].append({"event":"alloc", "ts":ts, "key":key, "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
-          else: v["events"].append({"event":"free", "ts":ts, "key":key, "arg": {"users":[u("<IIIB") for _ in range(u("<I")[0])]}})
+        alloc, ts, key = u("<BII")
+        if alloc: v["events"].append({"event":"alloc", "ts":ts, "key":key, "arg": {"dtype":strings[u("<I")[0]], "sz":u("<Q")[0]}})
+        else: v["events"].append({"event":"free", "ts":ts, "key":key, "arg": {"users":[u("<IIIB") for _ in range(u("<I")[0])]}})
   return {"dur":total_dur, "peak":global_peak, "layout":layout, "markers":markers}
 
 if __name__ == "__main__":

pyproject.toml

@@ -74,7 +74,7 @@ testing_minimal = [
   "hypothesis>=6.148.9",
   "z3-solver<4.15.4",  # 4.15.4 has a segfault when creating many z3.Context()
 ]
-testing_unit = ["tinygrad[testing_minimal]", "tqdm", "safetensors", "tabulate", "openai", "gguf>=0.18"]
+testing_unit = ["tinygrad[testing_minimal]", "tqdm", "safetensors", "tabulate", "openai", "gguf"]
 testing = [
   "tinygrad[testing_unit]",
   "pillow",

test/amd/helpers.py

@@ -6,7 +6,7 @@ from tinygrad.runtime.support.elf import elf_loader
 
 ARCH_TO_TARGET:dict[str, list[str]] = {
   "rdna3":["gfx1100"],
-  "rdna4":["gfx1200", "gfx1201"],
+  "rdna4":["gfx1200"],
   "cdna":["gfx950", "gfx942"],
 }
 

test/amd/test_custom_kernel.py

@@ -1,12 +1,10 @@
 import unittest
-import functools
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.uop.ops import UOp, Ops, KernelInfo
 from tinygrad.renderer import Estimates
+
 from tinygrad.runtime.autogen.amd.rdna3.ins import *
-from tinygrad.runtime.autogen.amd.rdna4.ins import s_barrier_wait, s_barrier_signal
 from tinygrad.renderer.amd.dsl import s, v
-from test.amd.helpers import TARGET_TO_ARCH
 
 def custom_add_one(A:UOp) -> UOp:
   A = A.flatten()
@@ -45,26 +43,9 @@ def custom_add_var(A:UOp, B:UOp) -> UOp:
   sink = UOp.sink(A.base, B.base, var, threads, arg=KernelInfo(f"custom_add_var_{A.size}"))
   return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg="AMD"), UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=x) for x in insts]))))
 
-def custom_wave_sync(A:UOp, arch:str) -> UOp:
-  # 4 waves across 1024 WG — enough to saturate a SIMD with many concurrent WGs
-  # s_sleep yields the SIMD so waves from different WGs interleave, causing barrier packet reordering
-  threads = UOp.special(128, "lidx0")
-  wg = UOp.special(1024, "gidx0")
-  insts = []
-  for _ in range(4):
-    insts.append(s_sleep(4))
-    insts += [s_barrier()] if arch == "rdna3" else [s_barrier_signal(), s_barrier_wait()]
-    insts += [s_nop(0)]*4
-  insts.append(s_endpgm())
-  sink = UOp.sink(A.base, threads, wg, arg=KernelInfo("custom_wave_sync"))
-  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg="AMD"), UOp(Ops.LINEAR, src=tuple([UOp(Ops.INS, arg=x) for x in insts]))))
-
 @unittest.skipUnless(Device.DEFAULT == "AMD", "requires AMD device")
 class TestCustomKernel(unittest.TestCase):
-  def setUp(self): self.arch = TARGET_TO_ARCH[Device["AMD"].arch]
-
   def test_simple(self):
-    if self.arch != "rdna3": self.skipTest("only rdna3")
     a = Tensor.full((16, 16), 1.).contiguous().realize()
     a = Tensor.custom_kernel(a, fxn=custom_add_one)[0]
     ei = a.schedule()[-1].lower()
@@ -74,7 +55,6 @@ class TestCustomKernel(unittest.TestCase):
     self.assertTrue((a.numpy() == 2.).all())
 
   def test_variable(self):
-    if self.arch != "rdna3": self.skipTest("only rdna3")
     b = Tensor.full((16, 16), 1, dtype=dtypes.uint32).contiguous().realize()
     a = Tensor.zeros_like(b).contiguous().realize()
     a = Tensor.custom_kernel(a, b, fxn=custom_add_var)[0]
@@ -83,9 +63,5 @@ class TestCustomKernel(unittest.TestCase):
       ei.run({"var":i})
       self.assertTrue((a.numpy() == 1+i).all())
 
-  def test_wave_sync(self):
-    if self.arch not in {"rdna3", "rdna4"}: self.skipTest("only rdna3 or rdna4")
-    Tensor.empty(1).custom_kernel(fxn=functools.partial(custom_wave_sync, arch=self.arch))[0].realize()
-
 if __name__ == "__main__":
   unittest.main()

test/amd/test_sqtt_examples.py

@@ -9,8 +9,8 @@ from tinygrad.renderer.amd import decode_inst
 from tinygrad.runtime.autogen.amd.rdna3.ins import SOPP
 from tinygrad.runtime.autogen.amd.rdna3.enum import SOPPOp
 from tinygrad.renderer.amd.sqtt import (decode, LAYOUT_HEADER, WAVESTART, WAVESTART_RDNA4, WAVEEND, INST, INST_RDNA4, VALUINST,
-                                     IMMEDIATE, IMMEDIATE_MASK, PACKET_TYPES_RDNA3, PACKET_TYPES_RDNA4, PACKET_TYPES_CDNA, CDNA_WAVESTART,
-                                     InstOp, InstOpRDNA4, print_packets, CDNA_WAVEEND, CDNA_INST)
+                                     IMMEDIATE, IMMEDIATE_MASK, PACKET_TYPES_RDNA3, PACKET_TYPES_RDNA4,
+                                     InstOp, InstOpRDNA4, print_packets)
 from test.amd.helpers import TARGET_TO_ARCH
 
 import tinygrad
@@ -21,7 +21,7 @@ OTHER_SIMD_OPS = {InstOp.OTHER_LDS_LOAD, InstOp.OTHER_LDS_STORE, InstOp.OTHER_LD
                   InstOp.OTHER_FLAT_STORE_128, InstOp.OTHER_GLOBAL_LOAD, InstOp.OTHER_GLOBAL_LOAD_VADDR,
                   InstOp.OTHER_GLOBAL_STORE_64, InstOp.OTHER_GLOBAL_STORE_96, InstOp.OTHER_GLOBAL_STORE_128,
                   InstOp.OTHER_GLOBAL_STORE_VADDR_128}
-OTHER_SIMD_OPS_RDNA4 = {InstOpRDNA4.OTHER_VMEM, InstOpRDNA4.OTHER_VMEM_5, InstOpRDNA4.OTHER_LDS_1, InstOpRDNA4.OTHER_LDS_2}
+OTHER_SIMD_OPS_RDNA4 = {InstOpRDNA4.OTHER_VMEM}
 
 # ═══════════════════════════════════════════════════════════════════════════════
 # ROCPROF DECODER
@@ -125,7 +125,7 @@ class SQTTExamplesTestBase(unittest.TestCase):
           self.assertIsInstance(packets[0], LAYOUT_HEADER, f"first packet should be LAYOUT_HEADER in {name}")
 
   def test_packet_types_valid(self):
-    all_classes = set(PACKET_TYPES_RDNA3.values()) | set(PACKET_TYPES_RDNA4.values()) | set(PACKET_TYPES_CDNA.values())
+    all_classes = set(PACKET_TYPES_RDNA3.values()) | set(PACKET_TYPES_RDNA4.values())
     for name, (events, *_) in self.examples.items():
       for i, event in enumerate(events):
         with self.subTest(example=name, event=i):
@@ -138,8 +138,8 @@ class SQTTExamplesTestBase(unittest.TestCase):
       if "empty" in name: continue
       with self.subTest(example=name):
         all_packets = [p for e in events for p in decode(e.blob)]
-        self.assertGreater(len([p for p in all_packets if isinstance(p, (WAVESTART, WAVESTART_RDNA4, CDNA_WAVESTART))]), 0, f"no WAVESTART in {name}")
-        self.assertGreater(len([p for p in all_packets if isinstance(p, (WAVEEND, CDNA_WAVEEND))]), 0, f"no WAVEEND in {name}")
+        self.assertGreater(len([p for p in all_packets if isinstance(p, (WAVESTART, WAVESTART_RDNA4))]), 0, f"no WAVESTART in {name}")
+        self.assertGreater(len([p for p in all_packets if isinstance(p, WAVEEND)]), 0, f"no WAVEEND in {name}")
 
   def test_time_monotonic(self):
     for name, (events, *_) in self.examples.items():
@@ -153,10 +153,7 @@ class SQTTExamplesTestBase(unittest.TestCase):
       if "gemm" not in name: continue
       with self.subTest(example=name):
         all_packets = [p for e in events for p in decode(e.blob)]
-        inst_packets = [p for p in all_packets if isinstance(p, (INST, INST_RDNA4, CDNA_INST))]
-        self.assertGreater(len(inst_packets), 0, f"no INST packets in {name}")
-        if isinstance(inst_packets[0], (INST, INST_RDNA4)):
-          self.assertGreater(len([p for p in inst_packets if p.op.name.startswith("JUMP")]), 0, f"no JUMP packets in {name}")
+        self.assertGreater(len([p for p in all_packets if isinstance(p, (INST, INST_RDNA4))]), 0, f"no INST packets in {name}")
 
   expected: dict[str, list[int]] = {}  # override in subclasses
   def test_packet_counts(self):
@@ -184,8 +181,8 @@ class SQTTExamplesTestBase(unittest.TestCase):
         for event in events:
           wave_starts: dict[tuple[int, int, int], int] = {}
           for p in decode(event.blob):
-            if isinstance(p, (WAVESTART, CDNA_WAVESTART, WAVESTART_RDNA4)): wave_starts[(p.wave, p.simd, p.cu)] = p._time
-            elif isinstance(p, (WAVEEND, CDNA_WAVEEND)) and (key := (p.wave, p.simd, p.cu)) in wave_starts:
+            if isinstance(p, (WAVESTART, WAVESTART_RDNA4)): wave_starts[(p.wave, p.simd, p.cu)] = p._time
+            elif isinstance(p, WAVEEND) and (key := (p.wave, p.simd, p.cu)) in wave_starts:
               our_waves.append((wave_starts[key], p._time))
         self.assertEqual(sorted(our_waves), sorted(roc_waves), f"wave times mismatch in {name}")
 
@@ -211,22 +208,17 @@ class SQTTExamplesTestBase(unittest.TestCase):
 class TestSQTTExamplesRDNA3(SQTTExamplesTestBase):
   target = "gfx1100"
   expected = {
-    "profile_empty_run_0": [1880, 1867, 1920, 1971, 1998, 1904],
-    "profile_empty_run_1": [1880, 1867, 1920, 1971, 1998, 1904],
-    "profile_gemm_run_0": [3275, 3278, 2426, 2475, 2511, 2431],
-    "profile_gemm_run_1": [3264, 3268, 2420, 2469, 2504, 2401],
-    "profile_ops_run_0": [1944, 4903, 1984, 2035, 2062, 1968],
-    "profile_ops_run_1": [1944, 4918, 1984, 2035, 2062, 1968],
-    "profile_plus_run_0": [1938, 1932, 1978, 2029, 2056, 1962],
-    "profile_plus_run_1": [1891, 1874, 1931, 1982, 2009, 1915],
+    "profile_empty_run_0": [1744, 1801, 1854, 1890, 1917, 1822],
+    "profile_empty_run_1": [1744, 1801, 1854, 1886, 1921, 1906],
+    "profile_gemm_run_0": [1800, 1867, 1899, 1898, 1914, 1895, 1694, 1779, 1819, 1872, 1877, 1858, 1750, 1834, 1866, 1834, 1911, 1796],
+    "profile_gemm_run_1": [1806, 1874, 1837, 1885, 1907, 1906, 1694, 1778, 1810, 1873, 1885, 1867, 1750, 1834, 1866, 1856, 1903, 1897],
+    "profile_plus_run_0": [1744, 1878, 1854, 1890, 1878, 1910],
+    "profile_plus_run_1": [1744, 1878, 1854, 1886, 1921, 1909],
   }
 
 class TestSQTTExamplesRDNA4(SQTTExamplesTestBase): target = "gfx1200"
-
-class TestSQTTExamplesCDNA(SQTTExamplesTestBase):
-  target = "gfx950"
-  def test_rocprof_wave_times_match(self): self.skipTest("TODO: requires timestamp patching")
-  def test_rocprof_inst_times_match(self): self.skipTest("TODO: requires timestamp patching")
+@unittest.skip("TODO: fix CDNA")
+class TestSQTTExamplesCDNA(SQTTExamplesTestBase): target = "gfx950"
 
 if __name__ == "__main__":
   unittest.main()

test/amd/test_sqttmap.py

@@ -2,16 +2,15 @@
 import unittest, pickle
 from typing import Iterator
 from pathlib import Path
-from tinygrad.helpers import DEBUG, getenv, temp
+from tinygrad.helpers import DEBUG, OSX
 from tinygrad.renderer.amd.sqtt import print_packets, map_insts
 from tinygrad.runtime.autogen.amd.rdna3.ins import s_endpgm
-from tinygrad.viz.serve import sqtt_timeline
 from test.amd.disasm import disasm
 
 import tinygrad
 EXAMPLES_DIR = Path(tinygrad.__file__).parent.parent / "extra/sqtt/examples"
 
-def rocprof_inst_traces_match(sqtt, prg, target):
+def rocprof_inst_traces_match(sqtt, prg, target, pass_rocprof_err=False):
   from tinygrad.viz.serve import amd_decode
   from extra.sqtt.roc import decode as roc_decode, InstExec
   addr_table = amd_decode(prg.lib, target)
@@ -31,7 +30,7 @@ def rocprof_inst_traces_match(sqtt, prg, target):
     rocprof_inst = next(rwaves_iter[info.wave][0])
     ref_pc = rocprof_inst.pc-prg.base
     # always check pc matches
-    assert ref_pc == info.pc, f"pc mismatch {ref_pc}:{disasm_map[rocprof_inst.pc]} != {info.pc}:{disasm(info.inst)}"
+    assert ref_pc == info.pc or pass_rocprof_err, f"pc mismatch {ref_pc}:{disasm_map[rocprof_inst.pc]} != {info.pc}:{disasm(info.inst)}"
     # special handling for s_endpgm, it marks the wave completion.
     if info.inst == s_endpgm():
       completed_wave = list(rwaves_iter[info.wave].pop(0))
@@ -54,7 +53,7 @@ class TestSQTTMapBase(unittest.TestCase):
   def setUpClass(cls):
     if cls is TestSQTTMapBase: raise unittest.SkipTest("base class")
     cls.examples = {}
-    for pkl_path in ([Path(temp("profile.pkl", append_user=True))] if getenv("LOAD_PROFILE") else sorted((EXAMPLES_DIR/cls.target).glob("*.pkl"))):
+    for pkl_path in sorted((EXAMPLES_DIR/cls.target).glob("*.pkl")):
       with open(pkl_path, "rb") as f:
         data = pickle.load(f)
       sqtt_events = [e for e in data if type(e).__name__ == "ProfileSQTTEvent"]
@@ -64,48 +63,15 @@ class TestSQTTMapBase(unittest.TestCase):
 
   def test_rocprof_inst_traces_match(self):
     for name, (events, kern_events, target) in self.examples.items():
-      if "sync" in name and self.target.startswith("gfx12"):
-        self.skipTest("our timestamps are off by a few cycles because rocprof patches timestamps for rdna4 barriers")
       for event in events:
         if not event.itrace: continue
         if event.kern not in kern_events: continue
         with self.subTest(example=name, kern=event.kern):
-          passed_insts, n_waves, n_units = rocprof_inst_traces_match(event, kern_events[event.kern], target)
+          # rocprof OSX has a bug for sopk decoding, linux rocprof works
+          pass_rocprof_err = OSX and target == "gfx1200" and name.startswith("profile_py")
+          passed_insts, n_waves, n_units = rocprof_inst_traces_match(event, kern_events[event.kern], target, pass_rocprof_err)
           if n_waves: print(f"{name}: passed for {passed_insts} instructions across {n_waves} waves scheduled on {n_units} wave units")
 
-  def test_sqtt_timeline(self):
-    for name, (events, kern_events, target) in self.examples.items():
-      for event in events:
-        if (p:=kern_events.get(event.kern)) is None: continue
-        with self.subTest(example=name, kern=event.kern):
-          if not (timeline:=sqtt_timeline(event.blob, p.lib, target)): continue
-          frequency = [e.key for e in timeline if type(e).__name__ == "ProfilePointEvent" and e.name == "freq_hz"]
-          mean = sum(frequency) / len(frequency)
-          variance = sum((v - mean) ** 2 for v in frequency) / len(frequency)
-          self.assertGreater(mean, 0)
-          if DEBUG >= 2: print(f"{name:20s} SE:{event.se} {mean/1e9:.2f} GHz mean, {variance/1e18:.2f} GHz^2 variance")
-          events = [e for e in timeline if type(e).__name__ == "ProfileRangeEvent"]
-          insts, execs = 0, 0
-          for e in events:
-            if "EXEC" in e.device:
-              if "ALT" not in e.name.display_name: execs += 1
-            elif "WAVE" in e.device:
-              # sopk/immediates don't get ALU/MEM EXEC
-              if e.name.display_name not in {"IMMEDIATE", "IMMEDIATE_MASK", "JUMP", "JUMP_NO", "MESSAGE", "BARRIER", "BARRIER_SIGNAL"}: insts += 1
-            else: raise Exception(f"timeline row must be INST or EXEC, got {e.device}")
-          self.assertEqual(execs, insts)
-
-  def test_wave_sync(self):
-    for name, (events, kern_events, target) in self.examples.items():
-      for event in events:
-        wave_barriers = {}
-        for e in sqtt_timeline(event.blob, kern_events[event.kern].lib, target):
-          if type(e).__name__ == "ProfileRangeEvent" and e.name.display_name == "BARRIER": wave_barriers.setdefault(e.device, []).append(e)
-        if not wave_barriers: continue
-        for row, events in wave_barriers.items():
-          for e in events:
-            assert e.en-e.st > 1, f"all barriers must have a duration greater than 1, got {e}"
-
 class TestSQTTMapRDNA3(TestSQTTMapBase): target = "gfx1100"
 
 class TestSQTTMapRDNA4(TestSQTTMapBase): target = "gfx1200"

test/backend/test_asm_gemm.py

@@ -2,7 +2,7 @@ import unittest
 from tinygrad import Tensor, Device, dtypes, Context
 from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv
-from extra.gemm.cdna_asm_gemm import asm_gemm
+from extra.gemm.asm.cdna.gemm import asm_gemm
 from test.helpers import needs_second_gpu
 
 # On non CDNA4 it will only validate the Tensor.custom_kernel integration
@@ -157,7 +157,7 @@ class TestGemmLarge(unittest.TestCase):
 
 class TestMagicGu(unittest.TestCase):
   def test_magicgu_matches_old(self):
-    from extra.gemm.cdna_asm_gemm import _magicgu_mulhi, TILE_M, TILE_N, TILE_K
+    from extra.gemm.asm.cdna.asm import _magicgu_mulhi, TILE_M, TILE_N, TILE_K
     old_iters_args = {64: (67108864, 0), 128: (33554432, 0), 224: (613566757, 2147483656)}
     old_gemm_shapes = [
       (8192, 4096, 4096), (8192, 14336, 4096), (8192, 4096, 14336),

test/backend/test_dtype.py

@@ -10,7 +10,7 @@ from tinygrad.renderer.nir import NIRRenderer
 from tinygrad import Context, Device, Tensor, dtypes
 from hypothesis import given, settings, strategies as strat
 from test.helpers import rand_for_dtype
-from test.unit.test_dtype_spec import _assert_eq, core_dtypes, dtype_ints, dtype_floats, FP8E4M3_MAX, FP8E5M2_MAX, FP8E4M3FNUZ_MAX, FP8E5M2FNUZ_MAX
+from test.unit.test_dtype_spec import _assert_eq, core_dtypes, dtype_ints, dtype_floats, FP8E4M3_MAX, FP8E5M2_MAX
 import pytest
 pytestmark = pytest.mark.filterwarnings("ignore")
 
@@ -101,14 +101,14 @@ class TestDType(unittest.TestCase):
   @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, (PTXRenderer, NIRRenderer)), "skip for now")
   def test_uint_overflow(self):
     if not dtypes.is_unsigned(self.DTYPE): raise unittest.SkipTest("only for unsigned")
-    v = self.DTYPE.max
+    v = dtypes.max(self.DTYPE)
     _test_to_np(Tensor(v, dtype=self.DTYPE)+2, _to_np_dtype(self.DTYPE), np.array(v, dtype=_to_np_dtype(self.DTYPE))+2)
     _test_to_np(Tensor(v, dtype=self.DTYPE)*2, _to_np_dtype(self.DTYPE), np.array(v, dtype=_to_np_dtype(self.DTYPE))*2)
 
   def test_dtypes_DTYPES_DICT(self):
     self.assertIn("float", DTYPES_DICT)
     self.assertIn("float32", DTYPES_DICT)
-    self.assertEqual(len(DTYPES_DICT), 28)
+    self.assertEqual(len(DTYPES_DICT), 26)
     self.assertTrue(all(isinstance(value, DType) for value in DTYPES_DICT.values()))
     self.assertTrue(all(issubclass(_to_np_dtype(value), np.generic) for value in DTYPES_DICT.values() if _to_np_dtype(value) is not None))
 
@@ -143,8 +143,6 @@ def _test_ops(a_dtype:DType, b_dtype:DType, target_dtype=None):
 class TestFp8s(unittest.TestCase):
   def test_fp8e4m3_creation(self): assert Tensor([-1, 1, 2], dtype=dtypes.fp8e4m3).dtype == dtypes.fp8e4m3
   def test_fp8e5m2_creation(self): assert Tensor([-1, 1, 2], dtype=dtypes.fp8e5m2).dtype == dtypes.fp8e5m2
-  def test_fp8e4m3fnuz_creation(self): assert Tensor([-1, 1, 2], dtype=dtypes.fp8e4m3fnuz).dtype == dtypes.fp8e4m3fnuz
-  def test_fp8e5m2fnuz_creation(self): assert Tensor([-1, 1, 2], dtype=dtypes.fp8e5m2fnuz).dtype == dtypes.fp8e5m2fnuz
 
 class TestFp8sConversions(unittest.TestCase):
   @given(strat.floats(width=32, allow_subnormal=True, allow_nan=False, allow_infinity=False, min_value=-FP8E4M3_MAX, max_value=FP8E4M3_MAX))
@@ -171,30 +169,6 @@ class TestFp8sConversions(unittest.TestCase):
   def test_fp8e5m2_to_float(self, x):
     np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e5m2), torch.tensor(x, dtype=torch.uint8).view(torch.float8_e5m2).float().item())
 
-  @given(strat.floats(width=32, allow_subnormal=True, allow_nan=False, allow_infinity=False, min_value=-FP8E4M3FNUZ_MAX, max_value=FP8E4M3FNUZ_MAX))
-  def test_float_to_fp8e4m3fnuz(self, x):
-    np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e4m3fnuz), torch.tensor(x, dtype=torch.float8_e4m3fnuz).view(torch.uint8).item())
-
-  def test_float_to_fp8e4m3fnuz_extreme_values(self):
-    for x in [FP8E4M3FNUZ_MAX, FP8E4M3FNUZ_MAX*1.01, -FP8E4M3FNUZ_MAX, -FP8E4M3FNUZ_MAX*1.01, math.inf, -math.inf, math.nan, 0.0, -0.0]:
-      np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e4m3fnuz), torch.tensor(x, dtype=torch.float8_e4m3fnuz).view(torch.uint8).item())
-
-  @given(strat.floats(width=32, allow_subnormal=True, allow_nan=False, allow_infinity=False, min_value=-FP8E5M2FNUZ_MAX, max_value=FP8E5M2FNUZ_MAX))
-  def test_float_to_fp8e5m2fnuz(self, x):
-    np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e5m2fnuz), torch.tensor(x, dtype=torch.float8_e5m2fnuz).view(torch.uint8).item())
-
-  def test_float_to_fp8e5m2fnuz_extreme_values(self):
-    for x in [FP8E5M2FNUZ_MAX, FP8E5M2FNUZ_MAX*1.01, -FP8E5M2FNUZ_MAX, -FP8E5M2FNUZ_MAX*1.01, math.inf, -math.inf, math.nan, 0.0, -0.0]:
-      np.testing.assert_equal(float_to_fp8(x, dtypes.fp8e5m2fnuz), torch.tensor(x, dtype=torch.float8_e5m2fnuz).view(torch.uint8).item())
-
-  @given(strat.integers(min_value=0, max_value=255))
-  def test_fp8e4m3fnuz_to_float(self, x):
-    np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e4m3fnuz), torch.tensor(x, dtype=torch.uint8).view(torch.float8_e4m3fnuz).float().item())
-
-  @given(strat.integers(min_value=0, max_value=255))
-  def test_fp8e5m2fnuz_to_float(self, x):
-    np.testing.assert_equal(fp8_to_float(x, dtypes.fp8e5m2fnuz), torch.tensor(x, dtype=torch.uint8).view(torch.float8_e5m2fnuz).float().item())
-
 @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), "bfloat16 not supported")
 class TestBFloat16(unittest.TestCase):
   def test_bf16_creation_numpy(self):
@@ -516,3 +490,4 @@ class TestOpsBFloat16(unittest.TestCase):
 
 if __name__ == '__main__':
   unittest.main()
+

test/backend/test_dtype_alu.py

@@ -52,8 +52,6 @@ class ht:
 ht.bfloat16 = ht.uint16.filter(lambda x: ((x >> 7) & 0xFF) != 0)  # filter subnormal bfloat16
 ht.fp8e4m3 = ht.uint8
 ht.fp8e5m2 = ht.uint8
-ht.fp8e4m3fnuz = ht.uint8
-ht.fp8e5m2fnuz = ht.uint8
 
 def universal_test(a, b, dtype, op):
   if not isinstance(op, tuple): op = (op, op)
@@ -69,8 +67,7 @@ def universal_test(a, b, dtype, op):
     if not is_dtype_supported(dtype) or dtype in EMULATED_DTYPES.tolist(dtypes): # denormals are zero
       fe, fm = dtypes.finfo(dtype)
       atol, rtol = 2 ** (2 - (1 << (fe - 1))), 2 ** (-fm)
-    else: atol, rtol = {dtypes.bfloat16:(1e-3, 1e-2), dtypes.fp8e4m3:(1e-1, 1e-1), dtypes.fp8e5m2:(1.0, 5e-1),
-                        dtypes.fp8e4m3fnuz:(1e-1, 1e-1), dtypes.fp8e5m2fnuz:(5e-1, 5e-1)}.get(dtype, (1e-10, 1e-7))
+    else: atol, rtol = {dtypes.bfloat16:(1e-3, 1e-2), dtypes.fp8e4m3:(1e-1, 1e-1), dtypes.fp8e5m2:(1.0, 5e-1)}.get(dtype, (1e-10, 1e-7))
     np.testing.assert_allclose(tensor_value, numpy_value, atol=atol, rtol=rtol)
   else: np.testing.assert_equal(tensor_value, numpy_value)
 
@@ -90,8 +87,7 @@ def universal_test_unary(a, dtype, op):
   else: tensor_value, numpy_value = op[0](ta).numpy(), op[1](ta.numpy())
   if dtype in dtypes.floats:
     atol, rtol = { dtypes.float16:(1e-3, 1e-2), dtypes.bfloat16:(1e-3, 2e-2),
-      dtypes.fp8e4m3:(1e-1, 1e-1), dtypes.fp8e5m2: (1.0, 5e-1),
-      dtypes.fp8e4m3fnuz:(1e-1, 1e-1), dtypes.fp8e5m2fnuz: (5e-1, 5e-1)}.get(dtype, (1e-6, 1e-5))
+      dtypes.fp8e4m3:(1e-1, 1e-1), dtypes.fp8e5m2: (1.0, 5e-1)}.get(dtype, (1e-6, 1e-5))
     np.testing.assert_allclose(tensor_value, numpy_value, atol=atol, rtol=rtol)
   else: np.testing.assert_equal(tensor_value, numpy_value)
 
@@ -159,26 +155,6 @@ class TestDTypeALU(unittest.TestCase):
   def test_emulated_fp8e5m2(self, a, b, op):
     universal_test(from_storage_scalar(a, dtypes.fp8e5m2), from_storage_scalar(b, dtypes.fp8e5m2), dtypes.fp8e5m2, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.fp8e4m3fnuz), f"no fp8e4m3fnuz on {Device.DEFAULT}")
-  @given(ht.fp8e4m3fnuz, ht.fp8e4m3fnuz, strat.sampled_from(binary_operations))
-  def test_fp8e4m3fnuz(self, a, b, op):
-    universal_test(from_storage_scalar(a, dtypes.fp8e4m3fnuz), from_storage_scalar(b, dtypes.fp8e4m3fnuz), dtypes.fp8e4m3fnuz, op)
-
-  @unittest.skipUnless(is_dtype_supported(dtypes.fp8e5m2fnuz), f"no fp8e5m2fnuz on {Device.DEFAULT}")
-  @given(ht.fp8e5m2fnuz, ht.fp8e5m2fnuz, strat.sampled_from(binary_operations))
-  def test_fp8e5m2fnuz(self, a, b, op):
-    universal_test(from_storage_scalar(a, dtypes.fp8e5m2fnuz), from_storage_scalar(b, dtypes.fp8e5m2fnuz), dtypes.fp8e5m2fnuz, op)
-
-  @given(ht.fp8e4m3fnuz, ht.fp8e4m3fnuz, strat.sampled_from(binary_operations))
-  @Context(EMULATED_DTYPES="fp8e4m3fnuz")
-  def test_emulated_fp8e4m3fnuz(self, a, b, op):
-    universal_test(from_storage_scalar(a, dtypes.fp8e4m3fnuz), from_storage_scalar(b, dtypes.fp8e4m3fnuz), dtypes.fp8e4m3fnuz, op)
-
-  @given(ht.fp8e5m2fnuz, ht.fp8e5m2fnuz, strat.sampled_from(binary_operations))
-  @Context(EMULATED_DTYPES="fp8e5m2fnuz")
-  def test_emulated_fp8e5m2fnuz(self, a, b, op):
-    universal_test(from_storage_scalar(a, dtypes.fp8e5m2fnuz), from_storage_scalar(b, dtypes.fp8e5m2fnuz), dtypes.fp8e5m2fnuz, op)
-
   @given(ht.float32, strat.sampled_from(unary_operations))
   def test_float32_unary(self, a, op): universal_test_unary(a, dtypes.float32, op)
 
@@ -222,30 +198,6 @@ class TestDTypeALU(unittest.TestCase):
     if op[1] == np.reciprocal: assume(from_storage_scalar(a, dtype=dtypes.fp8e5m2) != 0.0)
     universal_test_unary(from_storage_scalar(a, dtype=dtypes.fp8e5m2), dtypes.fp8e5m2, op)
 
-  @unittest.skipUnless(is_dtype_supported(dtypes.fp8e4m3fnuz), f"no fp8e4m3fnuz on {Device.DEFAULT}")
-  @given(ht.fp8e4m3fnuz, strat.sampled_from(unary_operations))
-  def test_fp8e4m3fnuz_unary(self, a, op):
-    if op[1] == np.reciprocal: assume(from_storage_scalar(a, dtype=dtypes.fp8e4m3fnuz) != 0.0)
-    universal_test_unary(from_storage_scalar(a, dtype=dtypes.fp8e4m3fnuz), dtypes.fp8e4m3fnuz, op)
-
-  @unittest.skipUnless(is_dtype_supported(dtypes.fp8e5m2fnuz), f"no fp8e5m2fnuz on {Device.DEFAULT}")
-  @given(ht.fp8e5m2fnuz, strat.sampled_from(unary_operations))
-  def test_fp8e5m2fnuz_unary(self, a, op):
-    if op[1] == np.reciprocal: assume(from_storage_scalar(a, dtype=dtypes.fp8e5m2fnuz) != 0.0)
-    universal_test_unary(from_storage_scalar(a, dtype=dtypes.fp8e5m2fnuz), dtypes.fp8e5m2fnuz, op)
-
-  @given(ht.fp8e4m3fnuz, strat.sampled_from(unary_operations))
-  @Context(EMULATED_DTYPES="fp8e4m3fnuz")
-  def test_emulated_fp8e4m3fnuz_unary(self, a, op):
-    if op[1] == np.reciprocal: assume(from_storage_scalar(a, dtype=dtypes.fp8e4m3fnuz) != 0.0)
-    universal_test_unary(from_storage_scalar(a, dtype=dtypes.fp8e4m3fnuz), dtypes.fp8e4m3fnuz, op)
-
-  @given(ht.fp8e5m2fnuz, strat.sampled_from(unary_operations))
-  @Context(EMULATED_DTYPES="fp8e5m2fnuz")
-  def test_emulated_fp8e5m2fnuz_unary(self, a, op):
-    if op[1] == np.reciprocal: assume(from_storage_scalar(a, dtype=dtypes.fp8e5m2fnuz) != 0.0)
-    universal_test_unary(from_storage_scalar(a, dtype=dtypes.fp8e5m2fnuz), dtypes.fp8e5m2fnuz, op)
-
   @given(ht.uint8, ht.uint8, strat.sampled_from(integer_binary_operations))
   def test_uint8(self, a, b, op): universal_test(a, b, dtypes.uint8, op)
 
@@ -366,7 +318,7 @@ class TestDTypeALU(unittest.TestCase):
 
   @unittest.expectedFailure
   def test_unsafe_cast_float_to_int_failure(self):
-    val = float(dtypes.int32.max - 1)
+    val = float(dtypes.max(dtypes.int32) - 1)
     t1 = Tensor([val], dtype=dtypes.float32).cast(dtypes.int32)
     t2 = Tensor(val, dtype=dtypes.float32).cast(dtypes.int32)
     np.testing.assert_equal(t1.item(), t2.item())

test/backend/test_graph.py

@@ -15,7 +15,7 @@ from test.helpers import needs_second_gpu
 np.random.seed(1337)
 Tensor.manual_seed(1337)
 BUF_SIZE = 4096
-RUN_CNT = 5
+RUN_CNT = 4
 
 cached_prgs = {}
 def helper_exec_op(device, outbuf, inbufs):
@@ -47,17 +47,6 @@ def helper_create_offset_rawbuffer(base, offset=0):
   x = Buffer(base.device, base.size-offset, base.dtype, base=base, offset=offset)
   return x.ensure_allocated()
 
-def helper_alloc_rawbuffer_sized(device, size, fill=False):
-  rawbuf = Buffer(device, size, dtypes.int).ensure_allocated()
-  if fill:
-    with Context(DEBUG=0):
-      data = np.random.randint(-10000, 10000, size=rawbuf.size, dtype=_to_np_dtype(rawbuf.dtype))
-      rawbuf.copyin(Tensor(data).realize().uop.base.realized.as_memoryview())
-  return rawbuf
-
-def helper_make_view(base, offset_elems, size_elems):
-  return Buffer(base.device, size_elems, base.dtype, base=base, offset=offset_elems * base.dtype.itemsize).ensure_allocated()
-
 def helper_run_jit(jis, bufs, out_buffers):
   for rawbuf in out_buffers:
     mv = memoryview(bytearray(rawbuf.size * rawbuf.dtype.itemsize))
@@ -91,14 +80,6 @@ def helper_test_graphs(graph_impl, graphs, runs=RUN_CNT):
 
 @unittest.skipUnless(Device[Device.DEFAULT].graph is not None, "graph support required")
 class TestGraph(unittest.TestCase):
-  def skip_if_no_offset(self):
-    if not hasattr(Device[Device.DEFAULT].allocator, "_offset"): self.skipTest("device does not support _offset")
-
-  def skip_if_not_multigraph(self):
-    graph = g.func if isinstance(g:=(d:=Device[Device.DEFAULT]).graph, functools.partial) else g
-    if not issubclass(graph, MultiGraphRunner): self.skipTest("graph is not supported (not MultiGraphRunner)")
-    if not hasattr(d.allocator, '_transfer') or not d.allocator.supports_transfer: self.skipTest("device is not supported (no transfers)")
-
   def test_order_2_writes_to_same_buf(self):
     d0 = Device.DEFAULT
     b0 = [helper_alloc_rawbuffer(d0, fill=True) for _ in range(5)]
@@ -129,6 +110,11 @@ class TestGraph(unittest.TestCase):
 
     helper_test_graphs(Device[d0].graph, graphs)
 
+  def skip_if_not_multigraph(self):
+    graph = g.func if isinstance(g:=(d:=Device[Device.DEFAULT]).graph, functools.partial) else g
+    if not issubclass(graph, MultiGraphRunner): self.skipTest("graph is not supported (not MultiGraphRunner)")
+    if not hasattr(d.allocator, '_transfer') or not d.allocator.supports_transfer: self.skipTest("device is not supported (no transfers)")
+
   def test_order_copy_writed(self):
     self.skip_if_not_multigraph()
 
@@ -279,58 +265,5 @@ class TestGraph(unittest.TestCase):
 
     helper_test_graphs(Device[d0].graph, graphs)
 
-  def test_partial_write_preserves_write_dep(self):
-    self.skip_if_not_multigraph()
-    self.skip_if_no_offset()
-    d0 = Device.DEFAULT
-
-    base = helper_alloc_rawbuffer_sized(d0, BUF_SIZE * 2, fill=True)
-    copy_src_full = helper_alloc_rawbuffer_sized(d0, BUF_SIZE * 2, fill=True)
-    copy_src_lo = helper_alloc_rawbuffer(d0, fill=True)
-    v_lo = helper_make_view(base, 0, BUF_SIZE)
-    v_hi = helper_make_view(base, BUF_SIZE, BUF_SIZE)
-    a, c = [helper_alloc_rawbuffer(d0, fill=True) for _ in range(2)]
-
-    graphs = [
-      [helper_copy_op(d0, base, copy_src_full), helper_copy_op(d0, v_lo, copy_src_lo), helper_exec_op(d0, c, [v_hi, a])]
-    ]
-    helper_test_graphs(Device[d0].graph, graphs)
-
-  def test_partial_write_preserves_read_dep(self):
-    self.skip_if_not_multigraph()
-    self.skip_if_no_offset()
-    d0 = Device.DEFAULT
-
-    base = helper_alloc_rawbuffer_sized(d0, BUF_SIZE * 2, fill=True)
-    copy_dst = helper_alloc_rawbuffer_sized(d0, BUF_SIZE * 2, fill=True)
-    copy_src_lo = helper_alloc_rawbuffer(d0, fill=True)
-    v_lo = helper_make_view(base, 0, BUF_SIZE)
-    v_hi = helper_make_view(base, BUF_SIZE, BUF_SIZE)
-    a, b = [helper_alloc_rawbuffer(d0, fill=True) for _ in range(2)]
-
-    graphs = [
-      [helper_copy_op(d0, copy_dst, base), helper_copy_op(d0, v_lo, copy_src_lo), helper_exec_op(d0, v_hi, [a, b])]
-    ]
-    helper_test_graphs(Device[d0].graph, graphs)
-
-  def test_middle_write_splits_write_dep(self):
-    self.skip_if_not_multigraph()
-    self.skip_if_no_offset()
-    d0 = Device.DEFAULT
-
-    base = helper_alloc_rawbuffer_sized(d0, BUF_SIZE * 3, fill=True)
-    copy_src_full = helper_alloc_rawbuffer_sized(d0, BUF_SIZE * 3, fill=True)
-    copy_src_mid = helper_alloc_rawbuffer(d0, fill=True)
-    v_lo = helper_make_view(base, 0, BUF_SIZE)
-    v_mid = helper_make_view(base, BUF_SIZE, BUF_SIZE)
-    v_hi = helper_make_view(base, BUF_SIZE * 2, BUF_SIZE)
-    a, c, e = [helper_alloc_rawbuffer(d0, fill=True) for _ in range(3)]
-
-    graphs = [
-      [helper_copy_op(d0, base, copy_src_full), helper_copy_op(d0, v_mid, copy_src_mid),
-       helper_exec_op(d0, c, [v_lo, a]), helper_exec_op(d0, e, [v_hi, a])]
-    ]
-    helper_test_graphs(Device[d0].graph, graphs)
-
 if __name__ == '__main__':
   unittest.main()

test/backend/test_ops.py

@@ -6,7 +6,6 @@ from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, CPU_LLVM, AMD_LL
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
-from tinygrad.renderer.cstyle import QCOMCLRenderer
 from tinygrad.renderer.nir import NIRRenderer
 
 TINY_BACKEND = getenv("TINY_BACKEND")
@@ -437,7 +436,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(45,35), (45,35), (45,35)], lambda x,y,z: x.lerp(y,z))
     helper_test_op(None, lambda x,y,z: x.lerp(y,z), vals=[[1.,2.,3.], [4.,5.,6.], 0.5])
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
+  @unittest.skipIf(Device.DEFAULT == "QCOM", "OpenCL fails to compile this (both on GPU(qcom)/QCOM backends)")
   def test_tril(self):
     helper_test_op([(3,3)], lambda x: x.tril())
     helper_test_op([(3,3)], lambda x: x.tril(1))
@@ -455,7 +454,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(5,3,3)], lambda x: x.tril(1))
     helper_test_op(None, lambda x: x.tril(), vals=[[[True] * 3] * 3], forward_only=True)
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
+  @unittest.skipIf(Device.DEFAULT == "QCOM", "OpenCL fails to compile this (both on GPU(qcom)/QCOM backends)")
   def test_triu(self):
     helper_test_op([(3,3)], lambda x: x.triu())
     helper_test_op([(3,3)], lambda x: x.triu(1))
@@ -479,9 +478,9 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[1., 0., 3., -4.], 3.])
     helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[1., 0., 3., -4.], [-1., -2., 3., 0.]])
     helper_test_op(None, torch.maximum, Tensor.maximum,
-                   vals=[[-1234, 0, 1234, dtypes.int.max, dtypes.int.min], dtypes.int.max], forward_only=True)
+                   vals=[[-1234, 0, 1234, dtypes.max(dtypes.int), dtypes.min(dtypes.int)], dtypes.max(dtypes.int)], forward_only=True)
     helper_test_op(None, torch.maximum, Tensor.maximum,
-                   vals=[[-1234, 0, 1234, dtypes.int.max, dtypes.int.min], dtypes.int.min], forward_only=True)
+                   vals=[[-1234, 0, 1234, dtypes.max(dtypes.int), dtypes.min(dtypes.int)], dtypes.min(dtypes.int)], forward_only=True)
     helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[True, False, False], True], forward_only=True)
     helper_test_op(None, torch.maximum, Tensor.maximum, vals=[[True, False, False], [True, True, False]], forward_only=True)
 
@@ -496,9 +495,9 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[1., 0., 3., -4.], 3.])
     helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[1., 0., 3., -4.], [-1., -2., 3., 0.]])
     helper_test_op(None, torch.minimum, Tensor.minimum,
-                   vals=[[-1234, 0, 1234, dtypes.int.max, dtypes.int.min], dtypes.int.max], forward_only=True)
+                   vals=[[-1234, 0, 1234, dtypes.max(dtypes.int), dtypes.min(dtypes.int)], dtypes.max(dtypes.int)], forward_only=True)
     helper_test_op(None, torch.minimum, Tensor.minimum,
-                   vals=[[-1234, 0, 1234, dtypes.int.max, dtypes.int.min], dtypes.int.min], forward_only=True)
+                   vals=[[-1234, 0, 1234, dtypes.max(dtypes.int), dtypes.min(dtypes.int)], dtypes.min(dtypes.int)], forward_only=True)
     helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[True, False, False], True], forward_only=True)
     helper_test_op(None, torch.minimum, Tensor.minimum, vals=[[True, False, False], [True, True, False]], forward_only=True)
 
@@ -766,7 +765,6 @@ class TestOps(unittest.TestCase):
 
     self.helper_test_exception([(4), (4)], lambda x,y: x.bitwise_xor(y), expected=RuntimeError)
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_and(self):
     data = [[1,-8,1],[32,1,6]]
     tor = torch.tensor(data, dtype=torch.int)
@@ -784,7 +782,6 @@ class TestOps(unittest.TestCase):
 
     self.helper_test_exception([(4), (4)], lambda x,y: x.bitwise_and(y), expected=RuntimeError)
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_or(self):
     data = [[1,-8,1],[32,1,6]]
     tor = torch.tensor(data, dtype=torch.int)
@@ -1173,7 +1170,6 @@ class TestOps(unittest.TestCase):
     helper_test_op(None, lambda x: x.type(torch.int32).argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True, vals=[[False, True]])
     helper_test_op(None, lambda x: x.type(torch.int32).argmax().type(torch.int32), lambda x: x.argmax(), forward_only=True, vals=[[True, False]])
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_argmin(self):
     # check if it returns the first index for multiple occurrences
     helper_test_op(None, lambda x: x.argmin().type(torch.int32), lambda x: x.argmin(), forward_only=True, vals=[[2, 2]])
@@ -1479,7 +1475,6 @@ class TestOps(unittest.TestCase):
   def test_prod_dtype_arg(self):
     with self.assertRaises(AttributeError): Tensor([1.0, 2.0]).prod(dtype="")
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_min(self):
     helper_test_op([(3,3)], lambda x: x.min())
     helper_test_op([(45,3)], lambda x: x.min())
@@ -1508,6 +1503,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(3,3)], lambda x: torch.full_like(x, 2).prod(), lambda x: (x.full_like(2)).prod(), forward_only=True)
     helper_test_op([(3,3)], lambda x: torch.full_like(x, 2).max(), lambda x: (x.full_like(2)).max(), forward_only=True)
 
+  @unittest.skipIf(Device.DEFAULT == "QCOM", "OpenCL fails to compile this (both on GPU(qcom)/QCOM backends)")
   def test_any(self):
     helper_test_op([(3,4,5,6)], lambda x: x.any(), forward_only=True)
     helper_test_op(None, lambda x: x.any(), vals=[[True, True]], forward_only=True)
@@ -1519,7 +1515,7 @@ class TestOps(unittest.TestCase):
   def test_any_zero_axis(self):
     helper_test_op([(1,0,3,0,5)], lambda x: x.any(axis=(1,3)), forward_only=True)
 
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
+  @unittest.skipIf(Device.DEFAULT == "QCOM", "OpenCL fails to compile this (both on GPU(qcom)/QCOM backends)")
   def test_all(self):
     helper_test_op([(3,4,5,6)], lambda x: x.all(), forward_only=True)
     helper_test_op(None, lambda x: x.all(), vals=[[True, True]], forward_only=True)
@@ -1669,15 +1665,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(10,10,10)], lambda x: x.log_softmax(1), atol=1e-7, grad_atol=1e-7)
     helper_test_op([(10,10,10)], lambda x: x.log_softmax(2), atol=1e-7, grad_atol=1e-7)
 
-  def test_normalize(self):
-    helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x), lambda x: x.normalize(), atol=1e-7, grad_atol=1e-7)
-    helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x, dim=0), lambda x: x.normalize(dim=0), atol=1e-7, grad_atol=1e-7)
-    helper_test_op([(10,10,10)], lambda x: torch.nn.functional.normalize(x, dim=2), lambda x: x.normalize(dim=2), atol=1e-7, grad_atol=1e-7)
-    helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x, p=1), lambda x: x.normalize(p=1), atol=1e-7, grad_atol=1e-7)
-    helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x, p=3, dim=0), lambda x: x.normalize(p=3, dim=0), atol=1e-7, grad_atol=1e-7)
-    helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x, p=0), lambda x: x.normalize(p=0), atol=1e-7, grad_atol=1e-7)
-    helper_test_op([(45,65)], lambda x: torch.nn.functional.normalize(x, p=-1), lambda x: x.normalize(p=-1), atol=1e-7, grad_atol=1e-7)
-
   def test_logsumexp(self):
     helper_test_op([(45,65)], lambda x: torch.logsumexp(x, dim=0), lambda x: x.logsumexp(0), atol=1e-7, grad_atol=1e-7)
     helper_test_op([(45,65)], lambda x: torch.logsumexp(x, dim=0, keepdim=True), lambda x: x.logsumexp(0, True), atol=1e-7, grad_atol=1e-7)
@@ -2893,7 +2880,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[...,c,:,e], lambda x: x[...,k,:,p])
 
   @slow_test
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_dim_collapse_int(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # dim collapse from int
@@ -2904,7 +2890,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,:,3:11:2,d,0:2], lambda x: x[1,:,3:11:2,o,0:2])
 
   @slow_test
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_dim_inject_none(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # dim injection from None
@@ -2939,7 +2924,6 @@ class TestOps(unittest.TestCase):
                             lambda x: x[Tensor([[0,1,-1],[-1,-2,0]]), Tensor([2,1,-1])])
 
   @slow_test
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_list_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[((0,),)])
@@ -2951,7 +2935,6 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,(2,1,0),c,(-2,1,0),e], lambda x: x[i,(2,1,0),k,(-2,1,0),p])
 
   @slow_test
-  @unittest.skipIf(isinstance(Device[Device.DEFAULT].renderer, QCOMCLRenderer), "QCOM CL vectorized bool bug")
   def test_slice_fancy_indexing_tuple_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[(((0,),),)], lambda x: x[(((0,),),)])
@@ -3293,6 +3276,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(20,)], lambda x: (x>0.5).nonzero().int(), lambda x: (x>0.5).nonzero(), forward_only=True)
     helper_test_op([(10, 5, 3)], lambda x: (x>0.5).nonzero().int(), lambda x: (x>0.5).nonzero(), forward_only=True)
 
+  @unittest.skipIf(Device.DEFAULT == "QCOM", "OpenCL fails to compile this (both on GPU(qcom)/QCOM backends)")
   def test_cast(self):
     helper_test_op([(3, 3)], lambda x: x.float())
     helper_test_op(None, lambda x: x.float(), vals=[[0, 1, 2, 3]], forward_only=True)

test/backend/test_quantize_onnx.py

@@ -204,13 +204,13 @@ class TestQuantizeOnnx(unittest.TestCase):
     W = Tensor(m2:=(np.random.uniform(0, 255, size=(N,N)).astype(wi))).realize()
     tg_dtype = dtypes.int8 if xi == np.int8 else dtypes.uint8
     out = (X.int().matmul(W.int())//1000)
-    if clip: out = out.clip(tg_dtype.min, tg_dtype.max)
+    if clip: out = out.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
     out = out.cast(tg_dtype)
     opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts
     sexec(out, opts, replace_src, run_count=1)
     tout = out.numpy()
     mout = ((m1.astype(np.int32) @ m2.astype(np.int32)) // 1000)
-    if clip: mout = mout.clip(tg_dtype.min, tg_dtype.max)
+    if clip: mout = mout.clip(dtypes.min(tg_dtype),dtypes.max(tg_dtype))
     mout = mout.astype(xi)
     print(tout)
     print(mout)

test/backend/test_renderer_failures.py

@@ -62,7 +62,7 @@ class TestRendererFailures(unittest.TestCase):
 class TestCStyleFailures(unittest.TestCase):
   def test_inline_const_alu(self):
     # CPU doesn't use the max function
-    ret = _setup_and_test_alu(Ops.MAX, 1, UOp.const(dtypes.int, dtypes.int.min+1))
+    ret = _setup_and_test_alu(Ops.MAX, 1, UOp.const(dtypes.int, dtypes.min(dtypes.int)+1))
     self.assertEqual(ret[0], 1)
 
   def _test_src_strip_paren(self, op: Ops, should_strip_paren:bool=True):

test/backend/test_schedule.py

@@ -797,7 +797,7 @@ class TestSchedule(unittest.TestCase):
 
   @unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
   def test_image_dot_f16_fusion(self):
-    with Context(FLOAT16=1, OPENPILOT_HACKS=1):
+    with Context(FLOAT16=1):
       def cnt():
         x, y, z = Tensor.empty((64, 64), dtype='float'), Tensor.empty((64, 64), dtype='float'), Tensor.empty((64, 64), dtype='float')
         a = (x @ y).relu()
@@ -811,42 +811,21 @@ class TestSchedule(unittest.TestCase):
       self.assertEqual(cnt1, 5)
       self.assertEqual(cnt2, 5)
 
-  @unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
-  def test_image_f16_residual_fusion(self):
-    with Context(FLOAT16=1, OPENPILOT_HACKS=1):
-      def cnt():
-        inp = Tensor.empty((512,), dtype='float')
-        b1, b2 = Tensor.empty((512, 1024), dtype='float'), Tensor.empty((1024, 512), dtype='float')
-        c1, c2 = Tensor.empty((1024,), dtype='float'), Tensor.empty((512,), dtype='float')
-        rb = (((((inp @ b1) + c1).relu() @ b2) + c2).relu() + inp).relu()
-        b16, c16 = Tensor.empty((512, 16), dtype='float'), Tensor.empty((16,), dtype='float')
-        b32, c32 = Tensor.empty((512, 32), dtype='float'), Tensor.empty((32,), dtype='float')
-        sched = Tensor.schedule((rb @ b16 + c16).relu(), (rb @ b32 + c32).relu())
-        for si in sched: si.lower()
-        return len([si for si in sched if isinstance(si.prg, CompiledRunner)])
-
-      with Context(IMAGE=1): cnt1 = cnt()
-      with Context(IMAGE=2): cnt2 = cnt()
-
-      self.assertEqual(cnt1, 9)
-      self.assertEqual(cnt2, 9)
-
   @unittest.skipIf(Device.DEFAULT != "CL", "image only supported on CL")
   @unittest.expectedFailure
   def test_image_conv_fusion(self):
-    with Context(OPENPILOT_HACKS=1):
-      def cnt():
-        x, y, z = Tensor.empty((1, 4, 3, 3)), Tensor.empty((4, 1, 3, 3)), Tensor.empty((4, 1, 7, 7))
-        a = x.conv2d(y, Tensor.empty(4), groups=4, padding=1)
-        b = a.conv2d(z, groups=4, padding=3)
-        sched = (a + b).schedule()
-        for si in sched: si.lower()
-        return len([si for si in sched if isinstance(si.prg, CompiledRunner)])
+    def cnt():
+      x, y, z = Tensor.empty((1, 4, 3, 3)), Tensor.empty((4, 1, 3, 3)), Tensor.empty((4, 1, 7, 7))
+      a = x.conv2d(y, Tensor.empty(4), groups=4, padding=1)
+      b = a.conv2d(z, groups=4, padding=3)
+      sched = (a + b).schedule()
+      for si in sched: si.lower()
+      return len([si for si in sched if isinstance(si.prg, CompiledRunner)])
 
-      with Context(IMAGE=1): cnt1 = cnt()
-      with Context(IMAGE=2): cnt2 = cnt()
+    with Context(IMAGE=1): cnt1 = cnt()
+    with Context(IMAGE=2): cnt2 = cnt()
 
-      self.assertEqual(cnt1, cnt2)
+    self.assertEqual(cnt1, cnt2)
 
   def _test_fusion(self, shapes, f, cnt):
     with Context(DEBUG=0, TRACK_MATCH_STATS=0): args = [Tensor.randn(s).realize() for s in shapes]

test/backend/test_setitem.py

@@ -251,11 +251,8 @@ class TestSetitem(unittest.TestCase):
     s1 = t.sum()
     t[3:].assign(2.0)
     s2 = t.sum()
-    try:
-      np.testing.assert_allclose([s0.item(), s1.item(), s2.item()], [0.0, 3.0, 9.0])
-    except AssertionError:
-      # TODO: broken now, lazy sums all see final buffer state
-      np.testing.assert_allclose([s0.item(), s1.item(), s2.item()], [9.0, 9.0, 9.0])
+    # TODO: s0 and s1 see final buffer state, should be [0.0, 3.0, 9.0]
+    np.testing.assert_allclose([s0.item(), s1.item(), s2.item()], [9.0, 9.0, 9.0])
 
     # eager version
     t = Tensor.zeros(6).contiguous().realize()
@@ -276,11 +273,8 @@ class TestSetitem(unittest.TestCase):
     a.assign(new_a)
     b.assign(new_b)
     np.testing.assert_allclose(a.numpy(), [4, 6, 8, 10])
-    try:
-      np.testing.assert_allclose(b.numpy(), [0, 2, 4, 6])
-    except AssertionError:
-      # TODO: broken now, new_b sees mutated a
-      np.testing.assert_allclose(b.numpy(), [8, 12, 16, 20])
+    # TODO: new_b sees mutated a, should be [0, 2, 4, 6]
+    np.testing.assert_allclose(b.numpy(), [8, 12, 16, 20])
 
     # eager version
     a = Tensor.arange(4, dtype=dtypes.float).contiguous().realize()

test/mockgpu/amd/amdgpu.py

@@ -271,7 +271,6 @@ class SDMAExecutor(AMDQueue):
       elif op == amd_gpu.SDMA_OP_GCR: self._execute_gcr()
       elif op == amd_gpu.SDMA_OP_COPY: self._execute_copy()
       elif op == amd_gpu.SDMA_OP_TIMESTAMP: self._execute_timestamp()
-      elif op == 32: self.rptr[0] += 4  # SDMA_OP_DUMMY_TRAP: pipeline flush, no interrupt
       else: raise RuntimeError(f"Unknown SDMA op {op}")
     return self.rptr[0] - prev_rptr
 

test/null/test_dtype_spec.py

@@ -75,20 +75,20 @@ class TestHelpers(unittest.TestCase):
   def test_dtype_range(self):
     for dt in core_dtypes:
       if dtypes.is_float(dt):
-        np.testing.assert_equal(dt.min, -math.inf)
-        np.testing.assert_equal(dt.max, math.inf)
+        np.testing.assert_equal(dtypes.min(dt), -math.inf)
+        np.testing.assert_equal(dtypes.max(dt), math.inf)
         np.testing.assert_equal(dt.min, -math.inf)
         np.testing.assert_equal(dt.max, math.inf)
       elif dtypes.is_int(dt):
         info = np.iinfo(_to_np_dtype(dt))
-        np.testing.assert_equal(dt.min, info.min)
-        np.testing.assert_equal(dt.max, info.max)
+        np.testing.assert_equal(dtypes.min(dt), info.min)
+        np.testing.assert_equal(dtypes.max(dt), info.max)
         np.testing.assert_equal(dt.min, info.min)
         np.testing.assert_equal(dt.max, info.max)
       else:
         assert dt == dtypes.bool, dt
-        np.testing.assert_equal(dt.min, False)
-        np.testing.assert_equal(dt.max, True)
+        np.testing.assert_equal(dtypes.min(dt), False)
+        np.testing.assert_equal(dtypes.max(dt), True)
         np.testing.assert_equal(dt.min, False)
         np.testing.assert_equal(dt.max, True)
 

test/null/test_llm_server.py

@@ -14,7 +14,6 @@ class TestLLMServer(unittest.TestCase):
 
     cls.mock_model = Mock()
     cls.mock_model.generate = Mock(side_effect=lambda ids, **kwargs: iter([300, 301, 999]))
-    cls.mock_model.get_start_pos = Mock(return_value=0)
 
     cls.bos_id = 1
     cls.eos_id = 999

test/null/test_memory_planner.py

@@ -11,8 +11,8 @@ def b(i, base=None, offset=0, pin=False, size=16):
   if pin: global_map[i].ref(1)
   return global_map[i]
 
-def check_assign(buffers:list[list[Buffer]|tuple[Buffer, ...]], copies:list[tuple[Buffer, Buffer]]|None=None):
-  assigned = _internal_memory_planner(buffers, copies=copies)
+def check_assign(buffers:list[list[Buffer]|tuple[Buffer, ...]]):
+  assigned = _internal_memory_planner(buffers, noopt_buffers=None)
 
   taken_parts = set()
   first_appearance, last_appearance = {}, {}
@@ -134,75 +134,5 @@ class TestMemoryPlanner(unittest.TestCase):
     ]
     check_assign(bs)
 
-  def test_copy_bufs_separate_from_compute(self):
-    bs = [
-      [b(0), b(1)],
-      [b(1), b(2)],
-      [b(3), b(2)],
-    ]
-    assigned = _internal_memory_planner(bs, copies=[(b(1), b(0))])
-    r1, r2 = assigned.get(b(1), b(1)), assigned.get(b(2), b(2))
-    assert r1.base != r2.base
-
-  def test_copy_bufs_reuse_among_copies(self):
-    bs = [
-      [b(0), b(1)],
-      [b(2), b(1)],
-      [b(3), b(2)],
-    ]
-    assigned = _internal_memory_planner(bs, copies=[(b(1), b(0)), (b(2), b(1))])
-    r1, r2 = assigned.get(b(1), b(1)), assigned.get(b(2), b(2))
-    assert r1.base == r2.base
-
-  def test_compute_bufs_reuse_among_compute(self):
-    bs = [
-      [b(0), b(1)],
-      [b(2), b(1)],
-      [b(3), b(2)],
-      [b(4), b(3)],
-    ]
-    assigned = _internal_memory_planner(bs, copies=[(b(1), b(0))])
-    r2, r3 = assigned.get(b(2), b(2)), assigned.get(b(3), b(3))
-    assert r2.base == r3.base
-
-  def test_copy_and_compute_no_cross_reuse(self):
-    bs = [
-      [b(0), b(1)],
-      [b(2), b(1)],
-      [b(3), b(2)],
-    ]
-    assigned = _internal_memory_planner(bs, copies=[(b(2), b(1))])
-    r0, r2 = assigned.get(b(0), b(0)), assigned.get(b(2), b(2))
-    assert r0.base != r2.base
-
-  def test_multiple_copy_bufs_with_offsets(self):
-    bs = [
-      [b(0, pin=True), b(1), b(2)],
-      [b(3, base=0, offset=1, size=8), b(1), b(2)],
-      [b(4), b(3)],
-      [b(5), b(4)],
-    ]
-    check_assign(bs, copies=[(b(1), b(0)), (b(2), b(0))])
-
-  def test_copy_bufs_pinned_mixed(self):
-    bs = [
-      [b(0, pin=True), b(1), b(2)],
-      [b(1), b(3), b(2)],
-      [b(4), b(3)],
-      [b(5), b(4), b(0)],
-    ]
-    check_assign(bs, copies=[(b(1), b(0)), (b(3), b(1))])
-
-  def test_deferred_copy_frees_chain(self):
-    bs = []
-    copies = []
-    for i in range(6):
-      copy_buf, compute_buf = b(i * 2 + 1), b(i * 2 + 2)
-      bs.append([copy_buf, b(0, pin=True)])
-      bs.append([compute_buf, copy_buf])
-      copies.append((copy_buf, b(0, pin=True)))
-    bs.append([b(100, pin=True)])
-    check_assign(bs, copies=copies)
-
 if __name__ == "__main__":
   unittest.main()

test/null/test_multitensor.py

@@ -1,6 +1,5 @@
 import gc, unittest
 from tinygrad import Tensor, GlobalCounters, dtypes
-from tinygrad.engine.jit import TinyJit
 
 class TestMultiRamUsage(unittest.TestCase):
   def setUp(self):
@@ -108,37 +107,6 @@ class TestMultiRamUsage(unittest.TestCase):
   def test_matmul_half(self): self._test_matmul_half(dev_count=2)
   def test_matmul_half_alt(self): self._test_matmul_half(dev_count=4)
 
-  def test_multi_layer_allreduce(self):
-    N = 32
-    devices_2 = ("NULL:1", "NULL:2")
-
-    def make_inp():
-      x = Tensor.zeros(N, N).contiguous().shard(devices_2, axis=None).realize()
-      w1 = Tensor.zeros(N, N).contiguous().shard(devices_2, axis=1).realize()
-      w2 = Tensor.zeros(N, N).contiguous().shard(devices_2, axis=0).realize()
-      return x, w1, w2
-
-    def run_layers(n_layers):
-      GlobalCounters.reset()
-
-      @TinyJit
-      def f(x, w1, w2):
-        for _ in range(n_layers):
-          x = (x @ w1 @ w2)
-        return x.contiguous()
-
-      for _ in range(3):
-        a = make_inp()
-        r = f(*a)
-        del a, r
-
-      gc.collect()
-      return GlobalCounters.mem_used
-
-    mem_2 = run_layers(2)
-    mem_4 = run_layers(4)
-    self.assertEqual(mem_2, mem_4, f"graph memory should not grow with layers: 2 layers={mem_2}, 4 layers={mem_4}")
-
 class TestMultiAxis(unittest.TestCase):
   def test_reshape_shard_invalid(self):
     devices = ("NULL:0", "NULL:1")

test/null/test_schedule.py

@@ -580,22 +580,21 @@ class TestSchedule(unittest.TestCase):
 
   # this is the failing case in openpilot...it's very simple like this
   def test_image_conv_fusion(self):
-    with Context(OPENPILOT_HACKS=1):
-      w1 = Tensor.empty(16, 16, 1, 1)
-      b1 = Tensor.empty(16)
-      w2 = Tensor.empty(16, 16, 1, 1)
-      b2 = Tensor.empty(16)
-      w3 = Tensor.empty(16, 16, 1, 1)
-      b3 = Tensor.empty(16)
+    w1 = Tensor.empty(16, 16, 1, 1)
+    b1 = Tensor.empty(16)
+    w2 = Tensor.empty(16, 16, 1, 1)
+    b2 = Tensor.empty(16)
+    w3 = Tensor.empty(16, 16, 1, 1)
+    b3 = Tensor.empty(16)
 
-      x = Tensor.empty(1, 16, 32, 32)
-      x = base = x.image_conv2d(w1, b1)
-      x = x.image_conv2d(w2, b2) + base
-      x = x.image_conv2d(w3, b3)
+    x = Tensor.empty(1, 16, 32, 32)
+    x = base = x.image_conv2d(w1, b1)
+    x = x.image_conv2d(w2, b2) + base
+    x = x.image_conv2d(w3, b3)
 
-      # NOOP, 3 convs, contiguous
-      #check_schedule(x, 5)
-      check_schedule(x, 7)
+    # NOOP, 3 convs, contiguous
+    #check_schedule(x, 5)
+    check_schedule(x, 7)
 
   def test_image_conv_fusion_minimal(self):
     b1 = Tensor.empty(16)
@@ -1189,11 +1188,5 @@ class TestBufferView(unittest.TestCase):
     b = a.shrink(((200, 800),)).shrink(((0, 300),)).reshape((30, 10)).shrink(((20, 25), (0, 10))).contiguous()
     run_schedule(check_schedule(b, 0))
 
-class TestInvalidTensor(unittest.TestCase):
-  def test_full_invalid_is_zero_kernels(self):
-    from tinygrad.dtype import Invalid
-    t = Tensor.full((4,), Invalid, dtype=dtypes.float)
-    check_schedule(t, 0)
-
 if __name__ == '__main__':
   unittest.main(verbosity=2)

test/null/test_simplify_valid_idx.py

@@ -46,9 +46,9 @@ class TestHelpers(unittest.TestCase):
     self.assertTrue((rng+2).is_increasing())
 
 class TestValidIdxSimplification(unittest.TestCase):
-  def check(self, load, sidx, svalid, extra=()):
+  def check(self, load, sidx, svalid):
     with Context(NOOPT=1, SPEC=0):
-      load = full_rewrite_to_sink(UOp.sink(load, *extra)).src[0]
+      load = full_rewrite_to_sink(load.sink()).src[0]
     idx, valid = load.src[0].src[1], load.src[0].src[2]
     check_uop_against_string(self, idx, sidx)
     check_uop_against_string(self, valid, svalid)
@@ -156,12 +156,9 @@ class TestValidIdxSimplification(unittest.TestCase):
     idx = (alu15*-31)+(((((alu11+218)//224)+ridx0)%30)*1568)
     valid = (ridx2<1)&(ridx1<6)
     load = get_gated_load_uop(valid, idx)
-    # prevent ridx1 and ridx2 from being shrunk
-    red = UOp(Ops.REDUCE, dtypes.float, (load, ridx1, ridx2), Ops.ADD)
     self.check(load,
       "(r0*1568)",
-      "((r2<1)&(r1<6))",
-      extra=(red,))
+      "((r2<1)&(r1<6))")
 
   def test_valid_becomes_const1_z3(self):
     from z3 import Ints, Solver, And, If, Not, unsat
@@ -354,7 +351,7 @@ class TestImageSimplification(unittest.TestCase):
 
     self.check(load,
                "((((idx2*2)+r0)<11)&((((idx1*8)+r1)<3)!=True))",
-               "(idx0+(idx1*512+r1*64)+-192)",
+               "(((idx0+((idx1*512)+(r1*64)))+832)%1024)",
                "((((idx2*2)+r0)+(((idx1+((r1+5)//8))+1)//2))+-4)")
 
   def test_simplify1(self):
@@ -391,17 +388,18 @@ class TestImageSimplification(unittest.TestCase):
     alu8 = (idx0//8%32//4)
     alu9 = idx0<256
 
+    # TODO: can this be simplified further?
     load = get_load_image_uop(shape, alu9, (((alu8+(alu2*8))%64),(alu2//8)))
-    self.check(load, "(idx0<256)", "(idx0%2*32+idx0//32+8)", "(idx0//2%4)")
+    self.check(load, "(idx0<256)", "(((((idx0%8)*32)+(idx0//32))+8)%64)", "((idx0%8)//2)")
 
     load = get_load_image_uop(shape, alu9, (((alu8+(alu3*8))%64),(alu3//8)))
-    self.check(load, "(idx0<256)", "(idx0%2*32+idx0//32+16)", "(idx0//2%4)")
+    self.check(load, "(idx0<256)", "(((((idx0%8)*32)+(idx0//32))+16)%64)", "((idx0%8)//2)")
 
     load = get_load_image_uop(shape, alu9, (((alu8+(alu4*8))%64),(alu4//8)))
-    self.check(load, "(idx0<256)", "(idx0%2*32+idx0//32+24)", "(idx0//2%4)")
+    self.check(load, "(idx0<256)", "(((((idx0%8)*32)+(idx0//32))+24)%64)", "((idx0%8)//2)")
 
     load = get_load_image_uop(shape, alu9, (((alu8+(alu5*8))%64),(alu5//8)))
-    self.check(load, "(idx0<256)", "(idx0%2*32+idx0//32)", "(idx0//2%4)")
+    self.check(load, "(idx0<256)", "((((idx0%8)*32)+(idx0//32))%64)", "((idx0%8)//2)")
 
   def test_simplify5(self):
     # openpilot 0.9.7, chunk replacement to simplify
@@ -416,7 +414,7 @@ class TestImageSimplification(unittest.TestCase):
     valid = alu3<640
 
     load = get_load_image_uop(shape, valid, idx)
-    self.check(load, None, "((idx0+((idx1//3)*16))+128)", "((idx1%3)*4)")
+    self.check(load, "(((idx0+(idx1*64))%192)<160)", "((idx0+((idx1//3)*16))+128)", "(((idx0+(idx1*64))%192)//16)")
 
   def test_simplify6(self):
     # from openpilot
@@ -486,79 +484,5 @@ class TestDropTrueGate(unittest.TestCase):
     # the True gate should be dropped (INDEX should only have 2 sources)
     self.assertEqual(len(result.src), 2, "True gate should be dropped from INDEX")
 
-class TestRangeShrink(unittest.TestCase):
-  def get_ranges(self, sink):
-    with Context(NOOPT=1, SPEC=0):
-      result = full_rewrite_to_sink(sink)
-    return [u for u in result.toposort() if u.op is Ops.RANGE]
-
-  def test_range_shrink_single_guard(self):
-    # range 0..203 guarded by r < 4 everywhere -> shrink to 0..3
-    r = Range(0, 204)
-    load = get_gated_load_uop(r < UOp.const(dtypes.index, 4), r)
-    ranges = self.get_ranges(load.sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 4)
-
-  def test_range_shrink_picks_max_guard(self):
-    # two loads guard the same range with r < 4 and r < 8 -> shrink to max(4, 8) = 8
-    r = Range(0, 204)
-    load1 = get_gated_load_uop(r < UOp.const(dtypes.index, 4), r)
-    load2 = get_gated_load_uop(r < UOp.const(dtypes.index, 8), r)
-    ranges = self.get_ranges(UOp.sink(load1, load2))
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 8)
-
-  def test_range_no_shrink_guard_ge_max(self):
-    # guard r < 300 with range max 204 -> no shrink (guard doesn't constrain)
-    r = Range(0, 204)
-    load = get_gated_load_uop(r < UOp.const(dtypes.index, 300), r)
-    ranges = self.get_ranges(load.sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 204)
-
-  def test_range_no_shrink_when_unguarded_elsewhere(self):
-    # one load guards r < 4, but another load uses r without a gate -> no shrink
-    r = Range(0, 204)
-    load1 = get_gated_load_uop(r < UOp.const(dtypes.index, 4), r)
-    load2 = UOp(Ops.LOAD, dtypes.float, (UOp(Ops.PARAM, dtypes.float.ptr(), arg=1).index(r, ptr=True),))
-    ranges = self.get_ranges(UOp.sink(load1, load2))
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 204)
-
-  def test_range_no_shrink_when_used_in_reduce(self):
-    # range used in both a gated load AND directly in the reduce expression -> no shrink
-    r = Range(0, 204)
-    gated_load = get_gated_load_uop(r < UOp.const(dtypes.index, 4), r)
-    red = UOp(Ops.REDUCE, dtypes.float, (r.cast(dtypes.float) + gated_load, r), Ops.ADD)
-    ranges = self.get_ranges(red.sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 204)
-
-  def test_range_shrink_to_single_iteration(self):
-    # guard r < 1 shrinks range to 1 -> single iteration, range eliminated entirely
-    r = Range(0, 204)
-    load = get_gated_load_uop(r < UOp.const(dtypes.index, 1), r)
-    ranges = self.get_ranges(load.sink())
-    self.assertEqual(len(ranges), 0)
-
-  def test_range_shrink_store_where_invalid(self):
-    # emulates mask.where(x.pad_to(mask.shape), Invalid): range should shrink accordingly
-    from tinygrad.dtype import Invalid
-    r = Range(0, 204)
-    x = (r < 4).where(UOp.const(dtypes.float, 1), Invalid)
-    ranges = self.get_ranges(UOp(Ops.PARAM, dtypes.float.ptr(), arg=0).index(r).store((r < 4).where(x, 0)).sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 4)
-
-  def test_range_shrink_store_where_invalid_flipped(self):
-    # above, but flipped
-    from tinygrad.dtype import Invalid
-    r = Range(0, 204)
-    x = (r < 4).where(UOp.const(dtypes.float, 1), Invalid)
-    ranges = self.get_ranges(UOp(Ops.PARAM, dtypes.float.ptr(), arg=0).index(r).store((r < 4).where(0, x)).sink())
-    self.assertEqual(len(ranges), 1)
-    self.assertEqual(ranges[0].src[0].arg, 4)
-
 if __name__ == '__main__':
   unittest.main()

test/null/test_tqdm.py

@@ -315,7 +315,7 @@ class TestProgressBar(unittest.TestCase):
     for _ in tinytqdm(range(100)): pass
     tinytqdm_time = time.perf_counter() - st
 
-    assert tinytqdm_time < 5 * tqdm_time
+    assert tinytqdm_time < 2 * tqdm_time
 
   def test_tqdm_perf_high_iter(self):
     st = time.perf_counter()

test/null/test_uop_graph.py

@@ -423,12 +423,10 @@ class TestUOpGraph(unittest.TestCase):
     d0 = UOp(Ops.PARAM, dtypes.long.ptr(), (), 0)
     ld = d0.index(ridx0.valid(ridx0<50))
     w = (ridx0<50).where(ld, 5)
-    # prevent ridx0 from being shrunk
-    red = UOp(Ops.REDUCE, dtypes.long, (ridx0.cast(dtypes.long), ridx0), Ops.ADD)
-    uops = to_uops_list([w, red])
+    uops = to_uops_list([w])
     for u in uops:
       assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD and u.src[0].src[0].op is Ops.PARAM: assert u.src[1].arg==5
+      if u.op is Ops.LOAD: assert u.src[1].arg==5
 
   def test_where_on_gated_load_folds_swapped_branches(self):
     ridx0 = UOp.range(100, 0)
@@ -446,12 +444,10 @@ class TestUOpGraph(unittest.TestCase):
     gate_idx = ridx0.valid((ridx0<50))
     ld = d0.index(gate_idx).cast(dtypes.float)
     w = (ridx0<50).where(ld, 5.0)
-    # prevent ridx0 from being shrunk
-    red = UOp(Ops.REDUCE, dtypes.long, (ridx0.cast(dtypes.long), ridx0), Ops.ADD)
-    uops = to_uops_list([w, red])
+    uops = to_uops_list([w])
     for u in uops:
       assert u.op is not Ops.WHERE
-      if u.op is Ops.LOAD and u.src[0].src[0].op is Ops.PARAM: assert u.src[1].arg == 5
+      if u.op is Ops.LOAD: assert u.src[1].arg == 5
 
   def test_where_in_store_becomes_gate(self):
     ridx0 = UOp.range(100, 0)
@@ -760,7 +756,7 @@ class TestLoadStoreFolding(unittest.TestCase):
     self.assertEqual(len(gated_load.src), 2)  # PTRCAT + alt
     result = graph_rewrite(gated_load, load_store_folding, name='test')
     # After rewrite, should be CAT of LOADs, each preserving alt
-    self.assertEqual(result.op, Ops.VCAT)
+    self.assertEqual(result.op, Ops.CAT)
     for inner_load in result.src:
       self.assertEqual(inner_load.op, Ops.LOAD)
       self.assertEqual(len(inner_load.src), 2)  # INDEX + alt

test/null/test_uop_symbolic.py

@@ -220,7 +220,7 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(usum([Variable("a", 0, 7)*4, Variable("b", 0, 3)*4]) % 2, 0, 0, "0")
 
   def test_sum_div_some_factor(self):
-    self.helper_test_variable(usum([Variable("a", 0, 7)*5, Variable("b", 0, 3)*4]) // 2, 0, 23, "((a*2)+(b*2)+(a//2))")
+    self.helper_test_variable(usum([Variable("a", 0, 7)*5, Variable("b", 0, 3)*4]) // 2, 0, 23, "(((a*5)//2)+(b*2))")
 
   def test_sum_div_trim_const(self):
     self.helper_test_variable((Variable("a", 0, 7)*4 + Variable("b", 0, 3)*4 + 7) // 16, 0, 2, "(((a+b)+1)//4)")
@@ -228,10 +228,10 @@ class TestSymbolic(unittest.TestCase):
   def test_sum_div_some_partial_factor(self):
     self.helper_test_variable(usum([Variable("a", 0, 7)*6, Variable("b", 0, 7)*6]) // 16, 0, 5, "(((a*3)+(b*3))//8)")
     self.helper_test_variable(usum([uconst(16), Variable("a", 0, 7)*6, Variable("b", 0, 7)*6]) // 16, 1, 6, "((((a*3)+(b*3))//8)+1)")
-    self.helper_test_variable((Variable("a", 0, 7)*30+20)//20, 1, 11, "((a+(a//2))+1)")
+    self.helper_test_variable((Variable("a", 0, 7)*30+20)//20, 1, 11, "(((a*3)//2)+1)")
 
   def test_sum_div_no_factor(self):
-    self.helper_test_variable(usum([Variable("a", 0, 7)*5, Variable("b", 0, 3)*5]) // 2, 0, 25, "((a*2)+(b*2)+((a+b)//2))")
+    self.helper_test_variable(usum([Variable("a", 0, 7)*5, Variable("b", 0, 3)*5]) // 2, 0, 25, "(((a*5)+(b*5))//2)")
 
   def test_mod_min_max(self):
     self.helper_test_variable(Variable("x", 0, 10)%Variable("y", 1, 10), 0, 9, "(x%y)")
@@ -286,11 +286,6 @@ class TestSymbolic(unittest.TestCase):
                               "(((z+(x*-1))+(y*-1))+7)")
     self.helper_test_variable((10+12*Variable("x",0,2)+Variable("y", 0, 4)%3)%13, 8, 12, "(((x*-1)+(y%3))+10)")
 
-  def test_mod_congruence_tied_remainder(self):
-    # when f%c == c/2, both r and r-c have equal abs — try both signs
-    self.helper_test_variable((3+2*Variable("x",0,1)+3*Variable("y",0,1))%4, 0, 3, "((x*-2)+(y*-1)+3)")
-    self.helper_test_variable((3+6*Variable("x",0,1)+7*Variable("y",0,1))%4, 0, 3, "((x*-2)+(y*-1)+3)")
-
   def test_div_congruence(self):
     self.helper_test_variable((3+3*Variable("a",0,3))//4, 0, 3, "a")
     self.helper_test_variable((18+17*Variable("a",0,2)+17)//18, 1, 3, "(a+1)")
@@ -302,9 +297,6 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable((3+Variable("a",0,1))%4, 0, 3, "((a*-3)+3)")
     self.helper_test_variable((3+Variable("a",4,5))%4, 0, 3, "((a*-3)+15)")
 
-  def test_div_binary_expression(self):
-    self.helper_test_variable((3+Variable("a",0,1))//4, 0, 1, "a")
-
   def test_sum_div_const(self):
     self.helper_test_variable(usum([Variable("a", 0, 7)*4, uconst(3)]) // 4, 0, 7, "a")
 
@@ -553,13 +545,6 @@ class TestSymbolic(unittest.TestCase):
   def test_div_into_mod(self):
     self.helper_test_variable((Variable("idx", 0, 16)*4)%8//4, 0, 1, "(idx%2)")
 
-  def test_mod_div_reorder(self):
-    # (x % (a*b)) // a -> (x // a) % b, enables div-mod recombine
-    x = Variable("x", 0, 23)
-    self.helper_test_variable(x % 6 // 3, 0, 1, "(x//3%2)")
-    self.helper_test_variable(x % 12 // 4, 0, 2, "(x//4%3)")
-    self.helper_test_variable(x%12//4*4 + x%4 + x//12*12, 0, 23, "x")
-
   def test_div_neg_cancel(self):
     self.helper_test_variable((-Variable("idx", 0, 100)+199)//-4 + 50, 1, 26, "((idx//4)+1)")
     self.helper_test_variable((-Variable("idx", 0, 100)+200)//-4 + 50, 0, 25, "((idx+3)//4)")
@@ -603,7 +588,8 @@ class TestSymbolic(unittest.TestCase):
     gidx0 = Variable("gidx0", 0, 2)
     lidx2 = Variable("lidx2", 0, 12)
     lidx3 = Variable("lidx3", 0, 12)
-    self.helper_test_variable((gidx0*3+lidx2*19+lidx3*38)//(3*19), 0, 12, "((lidx2+(lidx3*2))//3)")
+    # TODO: improve nest_div_by_smallest_factor to get ((lidx2+(lidx3*2))//3)
+    self.helper_test_variable((gidx0*3+lidx2*19+lidx3*38)//(3*19), 0, 12, "((gidx0+(lidx2*19+lidx3*38)//3)//19)")
 
   def test_sum_mul_distribute(self):
     gidx0 = Variable("gidx0", 0, 7)
@@ -620,12 +606,6 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable((idx0*v+idx1)//v, 0, 2, "(idx0)")
     self.helper_test_variable((idx0*v+idx1)%v, 0, start_pos, "idx1")
 
-  def test_mod_variable_denom_factor_remainder(self):
-    d = Variable("d", 2, 5)
-    a = Variable("a", 0, 3)
-    b = Variable("b", 0, 1)
-    self.helper_test_variable((d*a+b)%d, 0, 1, "b")
-
   def test_divmod_variable_denom_fold_to_const(self):
     x = Variable("x", 20, 23)
     y = Variable("y", 8, 10)
@@ -675,7 +655,8 @@ class TestSymbolic(unittest.TestCase):
     a = Variable("a", 0, 2)
     b = Variable("b", 0, 100)
     self.helper_test_variable((31 * a + 1) % 30 + ((31 * a + 1) // 30) * 30, 1, 63, "((a*31)+1)")
-    self.helper_test_variable((31 * b + 1) % 18 + ((31 * b + 1) // 18) * 18, 1, 3101, "((b*31)+1)")
+    with self.assertRaises(AssertionError):
+      self.helper_test_variable((31 * b + 1) % 18 + ((31 * b + 1) // 18) * 18, 1, 3101, "((b*31)+1)")
 
   def test_div_mod_recombine_3level(self):
     gidx = Variable("gidx", 0, 150527)
@@ -699,120 +680,8 @@ class TestSymbolic(unittest.TestCase):
     b = Variable("b", 0, 100)
     exp = (16 * b + 2) % 18 + ((16 * b + 2) // 18) * 18
     self.helper_test_variable(exp, 2, 1602, "((b*16)+2)")
-    self.helper_test_variable((30 * b + 1) % 18 + ((30 * b + 1) // 18) * 18, 1, 3001, "((b*30)+1)")
-
-  def test_div_partial_quotient(self):
-    # IDIV should extract partial quotients when const_factor > divisor, matching what MOD already does
-    # (f*x+c)//d -> (f%d*x+c)//d + (f//d)*x  when f >= d
-    b = Variable("b", 0, 100)
-    self.helper_test_variable((31*b+1)//18, 0, 172, "(((b*13)+1)//18+b)")
-    self.helper_test_variable((19*b+3)//7, 0, 271, "(((b*5)+3)//7+(b*2))")
-
-  def test_gcd_with_remainder(self):
-    # gcd_with_remainder: factor GCD out of non-constant terms and denominator
-    a = Variable("a", 0, 2)
-    self.helper_test_variable((a*4)//6, 0, 1, "(a*2//3)")
-    self.helper_test_variable((a*4+1)//6, 0, 1, "(a*2//3)")
-    self.helper_test_variable((a*4+2)//6, 0, 1, "((a*2+1)//3)")
-    self.helper_test_variable((a*4+3)//6, 0, 1, "((a*2+1)//3)")
-    self.helper_test_variable((a*4)%6, 0, 4, "(a*2%3*2)")
-    self.helper_test_variable((a*4+1)%6, 1, 5, "(a*2%3*2+1)")
-    self.helper_test_variable((a*4+2)%6, 0, 4, "((a*2+1)%3*2)")
-    self.helper_test_variable((a*4+3)%6, 1, 5, "((a*2+1)%3*2+1)")
-
-  def test_div_by_factor_tie_break(self):
-    a = Variable("a", 0, 1)
-    b = Variable("b", 0, 1)
-    with Context(CORRECT_DIVMOD_FOLDING=1):
-      self.helper_test_variable((a*2+b*3+2)//6, 0, 1, "((a+b+1)//3)")
-
-  def test_div_mod_recombine_large_coeff(self):
-    # recombine must work even when coeff > divisor: both mod and div reduce the coeff the same way
-    b = Variable("b", 0, 100)
-    self.helper_test_variable((19*b+3)%7 + ((19*b+3)//7)*7, 3, 1903, "((b*19)+3)")
-    a = Variable("a", 0, 10)
-    self.helper_test_variable((25*a+3)%10 + ((25*a+3)//10)*10, 3, 253, "((a*25)+3)")
-
-  def test_mod_nest_by_factor(self):
-    # (a*f+b) % (f*k) = (a%k)*f + b when 0<=b<f — mirrors nest_div_by_factor for MOD
-    gidx0 = Variable("gidx0", 0, 15)
-    lidx0 = Variable("lidx0", 0, 3)
-    # f=4, k=2, c=8: (gidx0*4+lidx0)%8 = (gidx0%2)*4 + lidx0
-    self.helper_test_variable((gidx0*4+lidx0)%8, 0, 7, "(lidx0+gidx0%2*4)")
-    # f=2, k=4: (gidx0*2+lidx0)%8 where lidx0 in [0,1]
-    lidx1 = Variable("lidx1", 0, 1)
-    self.helper_test_variable((gidx0*2+lidx1)%8, 0, 7, "(lidx1+gidx0%4*2)")
-    # f=3, k=3: (a*3+b)%9 where b in [0,2]
-    a = Variable("a", 0, 10)
-    b = Variable("b", 0, 2)
-    self.helper_test_variable((a*3+b)%9, 0, 8, "(b+a%3*3)")
-
-  def test_mod_nest_by_factor_with_const(self):
-    # nest_by_factor MOD with non-zero constant offset: (a*f+b+const) % (f*k) = (a%k)*f + b + const when 0<=b+const<f
-    a = Variable("a", 0, 7)
-    b = Variable("b", 0, 1)
-    # f=4, k=2, const=2: (a*4+b+2)%8 = (a%2)*4 + b + 2
-    self.helper_test_variable((a*4+b+2)%8, 2, 7, "(b+a%2*4+2)")
-    # f=6, k=2, const=3: (a*6+b+3)%12 = (a%2)*6 + b + 3
-    b2 = Variable("b", 0, 2)
-    self.helper_test_variable((a*6+b2+3)%12, 3, 11, "(b+a%2*6+3)")
-    # f=3, k=2, const=1: (a*3+b+1)%6 = (a%2)*3 + b + 1
-    self.helper_test_variable((a*3+b+1)%6, 1, 5, "(b+a%2*3+1)")
-
-  def test_div_nest_by_factor_with_const(self):
-    # nest_by_factor IDIV: (160*a + 5*b + 4*c + K) // 60 should pick div=5 (clean) over div=4 (dirty)
-    a = Variable("a", 0, 2)
-    b = Variable("b", 0, 31)
-    c = Variable("c", 0, 1)
-    self.helper_test_variable((160*a + 5*b + 4*c) // 60, 0, 7, "(a*2+(b+a*8)//12)")
-    self.helper_test_variable((160*a + 5*b + 4*c + 1) // 60, 0, 8, "(a*2+(b+c+a*8)//12)")
-    self.helper_test_variable((160*a + 5*b + 4*c + 2) // 60, 0, 8, "(a*2+(b+c+a*8)//12)")
-    self.helper_test_variable((160*a + 5*b + 4*c + 3) // 60, 0, 8, "(a*2+(b+c+a*8)//12)")
-    self.helper_test_variable((160*a + 5*b + 4*c + 59) // 60, 0, 8, "(a*2+(b+c+a*8+11)//12)")
-
-  def test_div_mod_recombine_after_nesting(self):
-    # when nest_div_by_factor simplifies the div, the mod must also nest so recombine can fire
-    gidx0 = Variable("gidx0", 0, 15)
-    lidx0 = Variable("lidx0", 0, 3)
-    x = gidx0*4+lidx0
-    # div nests: x//8 -> gidx0//2, mod nests: x%8 -> (gidx0%2)*4+lidx0, then recombine gives x back
-    self.helper_test_variable((x//8)*8 + x%8, 0, 63, "(lidx0+gidx0*4)")
-    # with a scaling factor: recombine gives x*2
-    self.helper_test_variable((x//8)*16 + (x%8)*2, 0, 126, "(gidx0*8+lidx0*2)")
-    # two variables with different factors
-    a = Variable("a", 0, 7)
-    b = Variable("b", 0, 1)
-    y = a*6+b
-    # div nests: y//12 -> a//2, mod nests: y%12 -> (a%2)*6+b, recombine
-    self.helper_test_variable((y//12)*12 + y%12, 0, 43, "(b+a*6)")
-
-  def test_div_mod_recombine_after_asymmetric_fold(self):
-    a = Variable("a", 0, 7)
-    b = Variable("b", 0, 14)
-    x = a*15+b
-    # TODO: expected "(b+a*15)"
-    self.helper_test_variable((x//10)*10 + x%10, 0, 119, "(a*10+(a+b//5)//2*10+(b+a*5)%10)")
-    self.helper_test_variable((x//10)*2 + (x//5)%2, 0, 23, "(a*3+b//5)")
-
-  def test_div_mod_recombine_in_additive_sum(self):
-    x = Variable("x", 0, 31)
-    y = Variable("y", 0, 5)
-    # recombine should work inside larger additive sums, not just in the two special y+... tree shapes
-    self.helper_test_variable((x//8)*4 + y + (x//2)%4, 0, 20, "(y+x//2)")
-    self.helper_test_variable(y + (x//8)*4 + (x//2)%4, 0, 20, "(y+x//2)")
-
-  def test_div_mod_recompose_low_order_remainder(self):
-    x = Variable("x", 0, 127)
-    self.helper_test_variable((x//2)%4*2 + x%2, 0, 7, "(x%8)")
-
-  def test_reshape_index_roundtrip(self):
-    # simulate reshape index decompose then recompose — the core pattern this enables
-    # (8,8) decomposed for (16,4): combined=r0*8+r1, div and mod by 4
-    r0 = Variable("r0", 0, 7)
-    r1 = Variable("r1", 0, 7)
-    combined = r0*8+r1
-    src_idx = (combined//4)*4 + combined%4
-    self.helper_test_variable(src_idx, 0, 63, "(r1+r0*8)")
+    with self.assertRaises(AssertionError):
+      self.helper_test_variable((30 * b + 1) % 18 + ((30 * b + 1) // 18) * 18, 1, 3001, "((b*30)+1)")
 
   def test_gated_load(self):
     idx = Variable("idx", 0, 24)
@@ -960,12 +829,6 @@ class TestSymbolic(unittest.TestCase):
     self.assertIn((a.cast(dtypes.long)+b.cast(dtypes.long)).render(), "(long)((a+b))")
     self.assertIn((a.cast(dtypes.long)*b.cast(dtypes.long)).render(), "(long)((a*b))")
 
-  def test_nested_mod_negative_range(self):
-    # (x%(k*c))%c = x%c holds for cmod regardless of signs since sign(x%(k*c)) = sign(x)
-    x = Variable("x", 0, 1575)
-    self.helper_test_variable(((x + (-1064)) % 512) % 4, -3, 3, "((x+-1064)%4)")
-    self.helper_test_variable(((x + (-1064)) % 512) % 128, -127, 127, "((x+-1064)%128)")
-
 class TestSymbolicNumeric(unittest.TestCase):
   def helper_test_numeric(self, f):
     MIN, MAX = 0, 10
@@ -1057,17 +920,6 @@ class TestSymInfer(unittest.TestCase):
 
     assert sym_infer(UOp.const(dtypes.float, 1.5).bitcast(dtypes.uint), {}) == 1069547520
 
-  def test_sym_infer_deeply_nested(self):
-    # build an expression that exceeds Python's nested parentheses limit for eval
-    # max(x, negative_const) can't be simplified when x can be negative, so nesting compounds
-    a = Variable("a", 1, 8192)
-    b = Variable("b", 0, 8191)
-    expr = a
-    for _ in range(200):
-      expr = (expr * (b + a)).maximum(uconst(-33554432)) * uconst(-1) + a
-    result = sym_infer(expr, {"a": 1, "b": 0})
-    assert isinstance(result, int)
-
 """
 @unittest.skip("not supported on uops yet")
 class TestSymbolicSymbolicOps(unittest.TestCase):

test/null/test_uop_vmin_vmax.py

@@ -64,8 +64,8 @@ class TestVminVmaxProperties(unittest.TestCase):
 
     # negative mask: x & -1 could be anything since -1 has all bits set
     uop = x & -1
-    self.assertEqual(uop.vmin, dtypes.int32.min)
-    self.assertEqual(uop.vmax, dtypes.int32.max)
+    self.assertEqual(uop.vmin, dtypes.min(dtypes.int32))
+    self.assertEqual(uop.vmax, dtypes.max(dtypes.int32))
 
   def test_vmin_vmax_multiplication_with_variable(self):
     # vmin and vmax for multiplication with a variable
@@ -136,8 +136,8 @@ class TestVminVmaxProperties(unittest.TestCase):
     self.assertEqual(x_bool.vmin, False)
     self.assertEqual(x_bool.vmax, True)
     x_uint = x.cast(dtypes.uint)
-    self.assertEqual(x_uint.vmin, dtypes.uint.min)
-    self.assertEqual(x_uint.vmax, dtypes.uint.max)
+    self.assertEqual(x_uint.vmin, dtypes.min(dtypes.uint))
+    self.assertEqual(x_uint.vmax, dtypes.max(dtypes.uint))
 
   def test_vmin_vmax_invalid(self):
     i = UOp.invalid()

test/null/test_viz.py

@@ -1,4 +1,4 @@
-import unittest, decimal, sys, json
+import unittest, decimal, sys
 from dataclasses import dataclass
 from typing import Generator
 
@@ -509,15 +509,9 @@ class TestVizProfiler(BaseTestViz):
 
   def test_calltrace(self):
     def fxn(): return Tensor.empty(10).mul(2).realize()
-    with cpu_profile(TracingKey("test_fxn"), "CUSTOM"):
-      fxn()
-    codegen_trace = get_viz_list()[0]["steps"][0]["trace"]
-    assert any(fxn.__code__.co_filename == f and fxn.__code__.co_firstlineno == l for f,l,*_ in codegen_trace), str(codegen_trace)
-    profile_ret = load_profile(cpu_events)
-    e = profile_ret["layout"]["CUSTOM"]["events"][0]
-    self.assertEqual(e["name"], "test_fxn")
-    runtime_trace = json.loads(e["fmt"].replace("TB:", ""))
-    assert any(fxn.__code__.co_filename == f and fxn.__code__.co_firstlineno+1 == l for f,l,*_ in runtime_trace), str(runtime_trace)
+    fxn()
+    trace = get_viz_list()[0]["steps"][0]["trace"]
+    assert any(fxn.__code__.co_filename == f and fxn.__code__.co_firstlineno == l for f,l,*_ in trace), str(trace)
 
   # can pack up to 1hr 11 min of trace events
   def test_trace_duration(self):

test/testextra/test_cfg_viz.py

@@ -58,8 +58,7 @@ class TestCfg(unittest.TestCase):
     k.emit(s_endpgm())
     k.emit(s_code_end())
     ei = run_asm("diamond", k)
-    ret = amdgpu_cfg(ei.prg.p.lib, self.arch)
-    cfg = ret["data"]
+    cfg = amdgpu_cfg(ei.prg.p.lib, self.arch)["data"]
     self.assertEqual(len(cfg["blocks"]), 5)
     edge_count = sum(len(v) for v in cfg["paths"].values())
     self.assertEqual(edge_count, 5)
@@ -70,11 +69,8 @@ class TestCfg(unittest.TestCase):
     self.assertEqual(len(references["r0"]), 2)
     insts = [cfg["pc_tokens"][pc][0]["st"] for pc in references["r0"]]
     self.assertEqual(insts, ['s_mov_b32', 's_cmp_eq_u64'])
-    end_block = [" ".join(t["st"] for t in cfg["pc_tokens"][pc]) for pc in list(cfg["blocks"].values())[-1]]
-    code_line = ret["src"].splitlines()[-1]
-    self.assertEqual(len(end_block), 2)
-    for st in [end_block[-1], code_line]:
-      assert st.startswith("s_code_end") and st.endswith("x)"), st
+    end_block_content = "\n".join(" ".join(t["st"] for t in cfg["pc_tokens"][pc]) for pc in list(cfg["blocks"].values())[-1])
+    self.assertEqual(end_block_content, "s_endpgm\ns_code_end (217x)")
 
   def test_loop(self):
     k = Kernel(arch=Device["AMD"].arch)

test/unit/test_assign.py

@@ -280,11 +280,6 @@ class TestAssign(unittest.TestCase):
     t.uop = t.uop.after(t[:5].uop.assign(Tensor.ones(5).uop))
     np.testing.assert_allclose(t.numpy(), [1.,1.,1.,1.,1.,0.,0.,0.,0.,0.])
 
-  def test_assign_after_target_chain(self):
-    t = Tensor.arange(16).reshape(4, 4).permute(1, 0).contiguous()
-    t.assign(t + 100)
-    np.testing.assert_equal(t.numpy(), [[100, 104, 108, 112], [101, 105, 109, 113], [102, 106, 110, 114], [103, 107, 111, 115]])
-
   def test_assign_contiguous(self):
     b = Tensor.arange(16).reshape(4,4).contiguous().realize()
     a = (Tensor.arange(16).reshape(4,4).contiguous().realize() + 1)
@@ -504,11 +499,7 @@ class TestAssign(unittest.TestCase):
     # assign to a shape-changing bitcast view (only works on DISK currently)
     a = Tensor([0]*8, dtype=dtypes.uint8).realize()
     a.bitcast(dtypes.int64).assign(Tensor([12345], dtype=dtypes.int64)).realize()
-    try:
-      np.testing.assert_equal(a.numpy(), [57, 48, 0, 0, 0, 0, 0, 0])
-    except AssertionError:
-      # TODO: broken now
-      np.testing.assert_equal(a.numpy(), [0]*8)
+    np.testing.assert_equal(a.numpy(), [0]*8)  # TODO: should be [57, 48, 0, 0, 0, 0, 0, 0] (little-endian 12345)
 
   @unittest.skip("don't use output buffer, and mismatch dtype no longer supported")
   def test_cast_assignment(self):
@@ -700,11 +691,7 @@ class TestAssignOrdering(unittest.TestCase):
     right = buf[4:8].clone()  # lazy - not captured yet
     buf[0:4].assign(right).realize()  # this works
     buf[4:8].assign(left).realize()  # left now reads from modified buf!
-    try:
-      np.testing.assert_equal(buf.numpy(), [5, 6, 7, 8, 1, 2, 3, 4])
-    except AssertionError:
-      # TODO: broken now
-      np.testing.assert_equal(buf.numpy(), [5, 6, 7, 8, 5, 6, 7, 8])
+    np.testing.assert_equal(buf.numpy(), [5, 6, 7, 8, 5, 6, 7, 8])  # TODO: wrong! should be [5,6,7,8,1,2,3,4]
 
     # with .realize() on temps: values captured before writes
     buf = Tensor([1, 2, 3, 4, 5, 6, 7, 8]).contiguous().realize()
@@ -822,55 +809,40 @@ class TestAssignToUnrealizedView(unittest.TestCase):
     c = t.to("CPU:1")  # unrealized COPY
     self.assertIs(c.uop.base.op, Ops.COPY)
     c[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).to("CPU:1").contiguous().realize())
-    try:
-      self.assertEqual(c.tolist(), [[0,1],[0,1]])
-    except AssertionError:
-      # TODO: broken now
-      self.assertEqual(c.tolist(), [[0,0],[0,0]])
+    # TODO: should be [[0,1],[0,1]]
+    self.assertEqual(c.tolist(), [[0,0],[0,0]])
 
   def test_contiguous(self):
     t = Tensor([[1,2],[3,4]]).contiguous().realize()
     c = t.permute(1,0).contiguous()  # unrealized CONTIGUOUS
     self.assertIs(c.uop.base.op, Ops.CONTIGUOUS)
     c[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
-    try:
-      self.assertEqual(c.tolist(), [[1,1],[2,1]])
-    except AssertionError:
-      # TODO: broken now
-      self.assertEqual(c.tolist(), [[1,3],[2,4]])
+    # TODO: should be [[1,1],[2,1]]
+    self.assertEqual(c.tolist(), [[1,3],[2,4]])
 
   def test_contiguous_backward(self):
     t = Tensor([[1,2],[3,4]]).contiguous().realize()
     cb = t.contiguous_backward()  # unrealized CONTIGUOUS_BACKWARD
     self.assertIs(cb.uop.base.op, Ops.CONTIGUOUS_BACKWARD)
     cb[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
-    try:
-      self.assertEqual(cb.tolist(), [[1,1],[3,1]])
-    except AssertionError:
-      # TODO: broken now
-      self.assertEqual(cb.tolist(), [[1,2],[3,4]])
+    # TODO: should be [[1,1],[3,1]]
+    self.assertEqual(cb.tolist(), [[1,2],[3,4]])
 
   def test_detach_copy(self):
     t = Tensor.zeros(2,2, dtype=dtypes.int).to("CPU:0").contiguous().realize()
     d = t.to("CPU:1").detach()  # DETACH(unrealized COPY)
     self.assertIs(d.uop.base.op, Ops.COPY)
     d[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).to("CPU:1").contiguous().realize())
-    try:
-      self.assertEqual(d.tolist(), [[0,1],[0,1]])
-    except AssertionError:
-      # TODO: broken now
-      self.assertEqual(d.tolist(), [[0,0],[0,0]])
+    # TODO: should be [[0,1],[0,1]]
+    self.assertEqual(d.tolist(), [[0,0],[0,0]])
 
   def test_detach_contiguous(self):
     t = Tensor([[1,2],[3,4]]).contiguous().realize()
     d = t.permute(1,0).contiguous().detach()  # DETACH(unrealized CONTIGUOUS)
     self.assertIs(d.uop.base.op, Ops.CONTIGUOUS)
     d[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
-    try:
-      self.assertEqual(d.tolist(), [[1,1],[2,1]])
-    except AssertionError:
-      # TODO: broken now
-      self.assertEqual(d.tolist(), [[1,3],[2,4]])
+    # TODO: should be [[1,1],[2,1]]
+    self.assertEqual(d.tolist(), [[1,3],[2,4]])
 
   def test_alu(self):
     a = Tensor([1,2,3,4]).contiguous().realize()
@@ -878,74 +850,31 @@ class TestAssignToUnrealizedView(unittest.TestCase):
     c = a + b  # unrealized ADD
     self.assertIs(c.uop.base.op, Ops.ADD)
     c[:2].assign(Tensor([99, 99]).realize())
-    try:
-      self.assertEqual(c.tolist(), [99,99,10,12])
-    except AssertionError:
-      # TODO: broken now, silently dropped
-      self.assertEqual(c.tolist(), [6,8,10,12])
+    # TODO: silently dropped, should be [99,99,10,12] or raise an error
+    self.assertEqual(c.tolist(), [6,8,10,12])
 
   def test_reduce(self):
     a = Tensor([[1,2],[3,4]]).contiguous().realize()
     r = a.sum(axis=0)  # unrealized REDUCE_AXIS
     self.assertIs(r.uop.base.op, Ops.REDUCE_AXIS)
     r[:1].assign(Tensor([99]).realize())
-    try:
-      self.assertEqual(r.tolist(), [99,6])
-    except AssertionError:
-      # TODO: broken now, silently dropped
-      self.assertEqual(r.tolist(), [4,6])
+    # TODO: silently dropped, should be [99,6] or raise an error
+    self.assertEqual(r.tolist(), [4,6])
 
   def test_cast(self):
     a = Tensor([1,2,3,4]).contiguous().realize()
     c = a.float()  # unrealized CAST
     self.assertIs(c.uop.base.op, Ops.CAST)
     c[:2].assign(Tensor([99, 99], dtype=dtypes.float).realize())
-    try:
-      self.assertEqual(c.tolist(), [99,99,3,4])
-    except AssertionError:
-      # TODO: broken now, silently dropped
-      self.assertEqual(c.tolist(), [1,2,3,4])
+    # TODO: silently dropped, should be [99,99,3,4] or raise an error
+    self.assertEqual(c.tolist(), [1,2,3,4])
 
   def test_const(self):
     c = Tensor(5).reshape(1, 1).expand(2, 2)
     self.assertIs(c.uop.base.op, Ops.CONST)
     c[:, 1:2].assign(Tensor.ones(2,1, dtype=dtypes.int).contiguous().realize())
-    try:
-      self.assertEqual(c.tolist(), [[5,1],[5,1]])
-    except AssertionError:
-      # TODO: broken now, silently dropped
-      self.assertEqual(c.tolist(), [[5,5],[5,5]])
-
-class TestPartialAssignToSharedBuffer(unittest.TestCase):
-  def test_five_slices(self):
-    big = Tensor.zeros(50).contiguous().realize()
-    views = [big[i*10:(i+1)*10].reshape(2, 5) for i in range(5)]
-    for v in views: v.assign(v + 1)
-    Tensor.realize(*views)
-    for v in views:
-      np.testing.assert_allclose(v.numpy(), np.ones((2, 5)))
-
-  def test_many_slices(self):
-    n_params = 10
-    big = Tensor.zeros(n_params * 12).contiguous().realize()
-    grads = [big[i*12:(i+1)*12].reshape(3, 4) for i in range(n_params)]
-    for g in grads: g.assign(g + 1)
-    Tensor.realize(*grads)
-    for g in grads:
-      np.testing.assert_allclose(g.numpy(), np.ones((3, 4)))
-
-  def test_mixed_shapes(self):
-    big = Tensor.zeros(100).contiguous().realize()
-    shapes = [(3, 4), (4, 6), (6, 4), (2, 5), (4, 3)]
-    pos, views = 0, []
-    for s in shapes:
-      n = s[0] * s[1]
-      views.append(big[pos:pos+n].reshape(*s))
-      pos += n
-    for v in views: v.assign(v + 1)
-    Tensor.realize(*views)
-    for v, s in zip(views, shapes):
-      np.testing.assert_allclose(v.numpy(), np.ones(s))
+    # TODO: silently dropped, should be [[5,1],[5,1]] or raise an error
+    self.assertEqual(c.tolist(), [[5,5],[5,5]])
 
 if __name__ == "__main__":
   unittest.main()

test/unit/test_call.py

@@ -2,7 +2,7 @@ import unittest
 import numpy as np
 from tinygrad import Tensor, function
 from tinygrad.dtype import dtypes
-from tinygrad.uop.ops import UOp, Ops
+from tinygrad.uop.ops import UOp
 
 class TestCall(unittest.TestCase):
   def test_call_plus(self):
@@ -100,42 +100,6 @@ class TestCall(unittest.TestCase):
     c = Tensor.call(a, b, fxn=a.as_param(0) + b.as_param(1))
     np.testing.assert_equal(c.numpy(), 2 * np.ones((10, 10)))
 
-class TestCallShape(unittest.TestCase):
-  def test_call_shape_int(self):
-    # fixed-shape function: shape passes through unchanged
-    @function
-    def f(x:Tensor) -> Tensor: return x * 2
-    self.assertEqual(f(Tensor.empty(4, 8)).shape, (4, 8))
-
-  def test_call_shape_param_substitution(self):
-    # symbolic shape dimension is substituted: inner PARAM replaced with the BIND arg
-    @function
-    def f(x:Tensor) -> Tensor: return x * 2
-    sz = UOp.variable("sz", 1, 8)
-    shape = f(Tensor.empty(8)[:sz.bind(5)]).shape
-    # the PARAM should be gone, replaced with the BIND from the call arg
-    self.assertIsInstance(shape[0], UOp)
-    self.assertNotEqual(shape[0].op, Ops.PARAM)
-    self.assertEqual(shape[0], sz.bind(5))
-
-  def test_call_shape_expr_substitution(self):
-    # expression containing PARAMs in shape gets fully substituted
-    @function
-    def f(x:Tensor) -> Tensor: return x + 1
-    sz = UOp.variable("sz", 1, 10)
-    shape = f(Tensor.empty(10, 4)[:sz.bind(3)]).shape
-    self.assertIsInstance(shape[0], UOp)
-    self.assertNotEqual(shape[0].op, Ops.PARAM)
-    self.assertEqual(shape[1], 4)
-
-  def test_call_shape_no_param_passthrough(self):
-    # a non-PARAM UOp shape element passes through unchanged
-    @function
-    def f(x:Tensor) -> Tensor: return x * 3
-    sz = UOp.variable("sz", 1, 8)
-    shape = f(Tensor.empty(8)[:sz.bind(5)]).shape
-    self.assertEqual(shape[0], sz.bind(5))
-
 class TestCallSchedule(unittest.TestCase):
   def test_reshape_precompile(self):
     a = Tensor.empty(4, 8).realize()
@@ -174,76 +138,5 @@ class TestCallSchedule(unittest.TestCase):
     (out-ref).square().mean().backward()
     out.realize(a.grad, b.grad, c.grad)
 
-  def test_precompile_symbolic_shape(self):
-    """precompile with a symbolic-shaped input produces correct values and shape"""
-    @function(precompile=True)
-    def f(x:Tensor) -> Tensor: return x * 2
-    sz = UOp.variable("sz", 1, 8)
-    a = Tensor([1., 2., 3., 4., 5., 6., 7., 8.])[:sz.bind(5)]
-    out = f(a)
-    self.assertIsInstance(out.shape[0], UOp)
-    np.testing.assert_allclose(out[:5].numpy(), [2., 4., 6., 8., 10.])
-
-  def test_precompile_symbolic_shape_contiguous(self):
-    """precompile with a .contiguous() inside the function body on a symbolic-shaped input"""
-    @function(precompile=True)
-    def f(x:Tensor) -> Tensor: return (x * 2).contiguous() + 1
-    sz = UOp.variable("sz", 1, 8)
-    a = Tensor([1., 2., 3., 4., 5., 6., 7., 8.])[:sz.bind(3)]
-    out = f(a)
-    self.assertIsInstance(out.shape[0], UOp)
-    np.testing.assert_allclose(out[:3].numpy(), [3., 5., 7.])
-
-  def test_precompile_symbolic_shape_chain(self):
-    """precompiled symbolic result used in downstream ops (tests AFTER has correct symbolic shape)"""
-    @function(precompile=True)
-    def f(x:Tensor) -> Tensor: return x * 2
-    sz = UOp.variable("sz", 1, 8)
-    a = Tensor([1., 2., 3., 4., 5., 6., 7., 8.])[:sz.bind(4)]
-    out = f(a) + 10  # downstream op on the precompiled result
-    self.assertIsInstance(out.shape[0], UOp)
-    np.testing.assert_allclose(out[:4].numpy(), [12., 14., 16., 18.])
-
-  def test_precompile_bind_arg(self):
-    """precompile with a BIND (scalar variable) as a function argument"""
-    @function(precompile=True)
-    def f(x:Tensor, scale:UOp) -> Tensor: return x * scale
-    v = UOp.variable("scale", 1, 100)
-    a = Tensor([1., 2., 3.])
-    out = f(a, v.bind(5))
-    np.testing.assert_allclose(out.numpy(), [5., 10., 15.])
-
-  def test_precompile_schedule_cache_hit(self):
-    """two instances of the same @function should produce identical function body keys (schedule cache hit)"""
-    @function(precompile=True)
-    def f(x:Tensor) -> Tensor: return x + Tensor.full(x.shape, -1.0)
-    a = Tensor.empty(4, 8)
-    b = Tensor.empty(4, 8)
-    r0, r1 = f(a), f(b)
-    # find the CALL nodes
-    c0 = next(u for u in r0.uop.toposort() if u.op is Ops.CALL)
-    c1 = next(u for u in r1.uop.toposort() if u.op is Ops.CALL)
-    # the function bodies (src[0]) should have identical keys — unique consts must not leak through
-    self.assertEqual(c0.src[0].key, c1.src[0].key)
-
-  def test_precompile_symbolic_2d(self):
-    """precompile with symbolic shapes in 2D (tests debuf reshape with symbolic PARAM)"""
-    @function(precompile=True)
-    def f(x:Tensor) -> Tensor: return x * 2 + 1
-    sz = UOp.variable("sz", 1, 16)
-    a = Tensor.arange(16*4).reshape(16, 4).float()[:sz.bind(5)]
-    out = f(a)
-    # result shape should have the symbolic dim, not the max
-    self.assertIsInstance(out.shape[0], UOp)
-    np.testing.assert_allclose(out[:5].numpy(), (np.arange(16*4).reshape(16, 4)[:5] * 2 + 1).astype(np.float32))
-
-  def test_precompile_multi_sharded(self):
-    @function(precompile=True)
-    def f(x:Tensor) -> Tensor: return x + 1
-    devs = ("CPU:0", "CPU:1")
-    a = Tensor.arange(8).reshape(4, 2).float().shard(devs, axis=0)
-    out = f(a) + 2
-    np.testing.assert_allclose(out.numpy(), np.arange(8, dtype=np.float32).reshape(4, 2) + 3)
-
 if __name__ == '__main__':
   unittest.main()

test/unit/test_disk_tensor.py

@@ -269,16 +269,22 @@ class TestDiskTensor(TempDirTestCase):
     assert tout == list([(x+1,x) for x in range(32,64,2)])
 
   def test_strided_read(self):
-    # test non-contiguous (strided) read raises
+    # test non-contiguous (strided) read - should read elements at indices 0, 2, 4
     dt = Tensor([0, 1, 2, 3, 4, 5]).to(f"disk:{self.tmp('dt_strided_read')}")
-    with self.assertRaisesRegex(RuntimeError, "non-contiguous view is not supported"):
-      dt[::2].tolist()
+    with self.assertRaises(RuntimeError):
+      result = dt[::2].tolist()
+      # TODO: dt[::2] selects indices 0, 2, 4, so result should be [0, 2, 4]
+      # self.assertEqual(result, [0, 2, 4])
+      self.assertEqual(result, [0, 1, 2])  # wrong!
 
   def test_permuted_read(self):
-    # test non-contiguous (permuted) read raises
+    # test non-contiguous (permuted) read - should read transposed
     dt = Tensor([[0, 1, 2], [3, 4, 5]]).to(f"disk:{self.tmp('dt_permuted_read')}")
-    with self.assertRaisesRegex(RuntimeError, "non-contiguous view is not supported"):
-      dt.T.tolist()
+    with self.assertRaises(RuntimeError):
+      result = dt.T.tolist()
+      # TODO: transpose should give [[0, 3], [1, 4], [2, 5]]
+      # self.assertEqual(result, [[0, 3], [1, 4], [2, 5]])
+      self.assertEqual(result, [[0, 1], [2, 3], [4, 5]])  # wrong!
 
   def test_write_ones(self):
     out = Tensor.ones(10, 10, device="CPU").contiguous()
@@ -304,10 +310,13 @@ class TestDiskTensor(TempDirTestCase):
     self.assertEqual(dt.tolist(), [[1], [3]])
 
   def test_strided_setitem(self):
-    # test non-contiguous (strided) setitem raises
+    # test non-contiguous (strided) setitem - should set elements at indices 0, 2, 4
     dt = Tensor([1, 2, 3, 4, 5, 6]).to(f"disk:{self.tmp('dt_strided_setitem')}")
-    with self.assertRaisesRegex(RuntimeError, "non-contiguous view is not supported"):
+    with self.assertRaises(RuntimeError):
       dt[::2] = Tensor([10, 20, 30])
+      # TODO: dt[::2] selects indices 0, 2, 4, so result should be [10, 2, 20, 4, 30, 6]
+      # self.assertEqual(dt.tolist(), [10, 2, 20, 4, 30, 6])
+      self.assertEqual(dt.tolist(), [10, 20, 30, 4, 5, 6])  # wrong!
 
   def test_advanced_setitem_not_supported(self):
     dt = Tensor.arange(12).reshape(3, 4).to(f"disk:{self.tmp('dt_advanced_setitem')}")

test/unit/test_dtype_spec.py

@@ -17,15 +17,13 @@ dtype_floats = [dt for dt in core_dtypes if dtypes.is_float(dt) and is_dtype_sup
 
 FP8E4M3_MAX = 448.0
 FP8E5M2_MAX = 57344.0
-FP8E4M3FNUZ_MAX = 240.0
-FP8E5M2FNUZ_MAX = 57344.0
 
 def _assert_eq(tensor:Tensor, target_dtype:DType, target, tol_target_dtype:float=1e-7):
   if DEBUG >= 2: print(tensor.numpy())
   try:
     assert tensor.dtype == target_dtype
     np.testing.assert_allclose(tensor.numpy(), target, rtol={dtypes.float16:1e-3, dtypes.bfloat16:1e-2,
-      dtypes.fp8e4m3:1e-1, dtypes.fp8e5m2:5e-1, dtypes.fp8e4m3fnuz:1e-1, dtypes.fp8e5m2fnuz:5e-1}.get(target_dtype, tol_target_dtype))
+      dtypes.fp8e4m3:1e-1, dtypes.fp8e5m2:5e-1}.get(target_dtype, tol_target_dtype))
 
   except AssertionError as e:
     raise AssertionError(f"\ntensor {tensor.numpy()} dtype {tensor.dtype} does not match target {target} with dtype {target_dtype}") from e

test/unit/test_function.py

@@ -3,7 +3,6 @@ import unittest
 from tinygrad.function import function
 from tinygrad import Tensor
 from tinygrad.uop.ops import UOp
-from tinygrad.helpers import GlobalCounters
 
 class TestFunction(unittest.TestCase):
   def test_simple(self):
@@ -336,100 +335,5 @@ class TestFunctionMulti(unittest.TestCase):
       f(x).sum().backward()
       np.testing.assert_allclose(x.grad.numpy(), expected)
 
-class TestFunctionTuple(unittest.TestCase):
-  def test_tuple(self, precompile=False):
-    x = Tensor.ones(3).contiguous()
-    @function(precompile=precompile)
-    def f(t:Tensor): return (t+1, t+2)
-    t1, t2 = f(x)
-    t1.realize(t2)
-    print(t1.tolist(), t2.tolist())
-    assert t1.tolist() == [2,2,2]
-    assert t2.tolist() == [3,3,3]
-  def test_tuple_precompile(self): self.test_tuple(True)
-
-class TestFunctionBackward(unittest.TestCase):
-  def test_backward_has_grad(self):
-    N = 16
-    x = Tensor.empty(N, N)
-    w1 = Tensor.empty(N, N, requires_grad=True)
-    @function
-    def f(t:Tensor, w1:Tensor): return (t@w1)
-    f(x, w1).sum().backward()
-    assert w1.grad is not None
-
-  def test_double_matmul_backward(self):
-    N = 16
-    x = Tensor.empty(N, N)
-    w1 = Tensor.empty(N, N, requires_grad=True)
-    w2 = Tensor.empty(N, N, requires_grad=True)
-    ref = Tensor.empty(N)
-
-    @function(precompile=True)
-    def f(t:Tensor, w1:Tensor, w2:Tensor): return (t@w1)@w2
-    loss = (f(x, w1, w2)-ref).square().mean().backward()
-    loss.realize(w1.grad, w2.grad)
-
-  def test_backward_single_call(self):
-    N = 4
-    x = Tensor.arange(N*N).reshape(N, N).float()
-    w1 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_()
-    w2 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_()
-    xn, w1n, w2n = x.numpy(), w1.numpy(), w2.numpy()
-    @function
-    def f(t:Tensor, w1:Tensor, w2:Tensor): return (t@w1)@w2
-    f(x, w1, w2).sum().backward()
-    assert w1.grad is not None and w2.grad is not None
-    np.testing.assert_allclose(w1.grad.numpy(), xn.T @ np.ones((N,N)) @ w2n.T, atol=1e-3)
-    np.testing.assert_allclose(w2.grad.numpy(), (xn @ w1n).T @ np.ones((N,N)), atol=1e-3)
-
-  def test_backward_precompile_backward(self):
-    N = 4
-    x = Tensor.arange(N*N).reshape(N, N).float().contiguous()
-    w1 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_().contiguous()
-    w2 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_().contiguous()
-    Tensor.realize(x, w1, w2)
-    xn, w1n, w2n = x.numpy(), w1.numpy(), w2.numpy()
-    @function(precompile=True, precompile_backward=True)
-    def f(t:Tensor, w1:Tensor, w2:Tensor): return (t@w1)@w2
-    loss = f(x, w1, w2).sum().backward()
-    assert w1.grad is not None and w2.grad is not None
-    GlobalCounters.reset()
-    Tensor.realize(loss, w1.grad, w2.grad)
-    np.testing.assert_allclose(w1.grad.numpy(), xn.T @ np.ones((N,N)) @ w2n.T, atol=1e-3)
-    np.testing.assert_allclose(w2.grad.numpy(), (xn @ w1n).T @ np.ones((N,N)), atol=1e-3)
-
-  def test_backward_precompile_backward_tuple(self):
-    N = 4
-    x = Tensor.arange(N*N).reshape(N, N).float().contiguous()
-    w1 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_().contiguous()
-    w2 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_().contiguous()
-    Tensor.realize(x, w1, w2)
-    xn, w1n, w2n = x.numpy(), w1.numpy(), w2.numpy()
-    # non-tuple reference
-    ref_w1 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_().contiguous()
-    ref_w2 = Tensor.arange(N*N).reshape(N, N).float().requires_grad_().contiguous()
-    Tensor.realize(ref_w1, ref_w2)
-    @function(precompile=True, precompile_backward=True)
-    def g(t:Tensor, w1:Tensor, w2:Tensor): return (t@w1)@w2
-    g(x, ref_w1, ref_w2).sum().backward()
-    GlobalCounters.reset()
-    Tensor.realize(ref_w1.grad, ref_w2.grad)
-    ref_ops = GlobalCounters.global_ops
-    # tuple version with intermediate — should not redo forward compute
-    @function(precompile=True, precompile_backward=True)
-    def f(t:Tensor, w1:Tensor, w2:Tensor):
-      h = t@w1
-      return (h, h@w2)
-    h, out = f(x, w1, w2)
-    loss = out.sum().backward()
-    assert w1.grad is not None and w2.grad is not None
-    GlobalCounters.reset()
-    Tensor.realize(loss, w1.grad, w2.grad)
-    np.testing.assert_allclose(w1.grad.numpy(), xn.T @ np.ones((N,N)) @ w2n.T, atol=1e-3)
-    np.testing.assert_allclose(w2.grad.numpy(), (xn @ w1n).T @ np.ones((N,N)), atol=1e-3)
-    # tuple version should have fewer ops than non-tuple (saves recomputing h=t@w1 in backward)
-    self.assertLessEqual(GlobalCounters.global_ops, ref_ops)
-
 if __name__ == '__main__':
   unittest.main()

test/unit/test_gguf.py

@@ -26,16 +26,16 @@ class TestGGUF(unittest.TestCase):
     # codes 0-7 = [0, 1, 2, 3, 4, 6, 8, 12], codes 8-15 are their negatives
     block = np.array([0x80] + list(range(16)), dtype=np.uint8)  # E=128, nibbles 0-15 in low, zeros in high
     expected = np.array([0., 1., 2., 3., 4., 6., 8., 12., -0., -1., -2., -3., -4., -6., -8., -12.] + [0.]*16, dtype=np.float32)
-    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 32, GGMLQuantizationType.MXFP4.value).numpy().flatten(), expected)
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 32, 39).numpy().flatten(), expected)
 
   def test_dequantization_q4_0(self): self._test_dequantization(GGMLQuantizationType.Q4_0)
   def test_dequantization_q4_1(self): self._test_dequantization(GGMLQuantizationType.Q4_1)
   def test_dequantization_q8_0(self): self._test_dequantization(GGMLQuantizationType.Q8_0)
   def test_dequantization_q4_k(self): self._test_dequantization(GGMLQuantizationType.Q4_K)
-  def test_dequantization_q5_k(self): self._test_dequantization(GGMLQuantizationType.Q5_K)
   def test_dequantization_q6_k(self): self._test_dequantization(GGMLQuantizationType.Q6_K)
-  def test_dequantization_mxfp4(self): self._test_dequantization(GGMLQuantizationType.MXFP4)
-  def test_dequantization_mxfp4_old(self):
+  def test_dequantization_mxfp4(self):
+    MXFP4 = 39
+
     def encode(nibbles, E):
       packed = [(low & 0xF) | ((high & 0xF) << 4) for low, high in zip(nibbles[:16], nibbles[16:])]
       return np.array([E] + packed, dtype=np.uint8)
@@ -56,10 +56,12 @@ class TestGGUF(unittest.TestCase):
       blocks.append(encode(codes, E))
       expected.extend(decode(c, E) for c in codes)
     tensor = Tensor(np.concatenate(blocks))
-    out = ggml_data_to_tensor(tensor, len(expected), GGMLQuantizationType.MXFP4.value)
-    np.testing.assert_equal(out.numpy(), expected)
+    out = ggml_data_to_tensor(tensor, len(expected), MXFP4)
+    # TODO: should this be exact equal? somehow failed on CI
+    np.testing.assert_allclose(out.numpy(), expected, atol=0.0, rtol=1e-6)
 
   def test_dequantization_mxfp4_block(self):
+    MXFP4 = 39
     # https://gist.github.com/Ananta-Ranganathan/3317b6ed51a3b033e9c2564fafb4e043
     # used the above script to download the first block of blk.0.attn_k_b.weight from
     # https://huggingface.co/unsloth/GLM-4.7-Flash-GGUF/blob/main/GLM-4.7-Flash-MXFP4_MOE.gguf
@@ -74,8 +76,9 @@ class TestGGUF(unittest.TestCase):
                         0.03125000,  0.00000000, 0.06250000,  0.01562500,
                         -0.06250000,  0.00000000, 0.00000000, -0.01562500,
                         0.04687500,  0.00000000, 0.00000000,  0.01562500], dtype=np.float32)
-    out = ggml_data_to_tensor(Tensor(block), 32, GGMLQuantizationType.MXFP4.value)
-    np.testing.assert_equal(out.numpy(), expected)
+    out = ggml_data_to_tensor(Tensor(block), 32, MXFP4)
+    # TODO: similar to previous test fails on Mac CI with assert_equal for unclear reason
+    np.testing.assert_allclose(out.numpy(), expected, atol=0.0, rtol=1e-6)
 
   def test_expected_failure_unknown_type(self):
     with self.assertRaises(ValueError):
@@ -127,9 +130,8 @@ class TestGGUFGEMV(unittest.TestCase):
     q_data = rng.integers(0, 256, size=n_blocks * type_size, dtype=np.uint8).reshape(n_blocks, type_size)
     scales = np.float16(rng.standard_normal(n_blocks * 4)).view(np.uint8).reshape(n_blocks, -1)
     if qtype == GGMLQuantizationType.Q8_0: q_data[:, :2] = scales[:, :2]                    # d at offset 0
-    elif qtype in (GGMLQuantizationType.Q4_K, GGMLQuantizationType.Q5_K): q_data[:, :4] = scales[:, :4] # d, dmin at offset 0
+    elif qtype == GGMLQuantizationType.Q4_K: q_data[:, :4] = scales[:, :4]                  # d, dmin at offset 0
     elif qtype == GGMLQuantizationType.Q6_K: q_data[:, -2:] = scales[:, :2]                 # d at end
-    elif qtype == GGMLQuantizationType.MXFP4: q_data[:, 0] = rng.integers(120, 136, size=n_blocks, dtype=np.uint8) # constrain byte0
     q_data = q_data.flatten()
     ref = dequantize(q_data, qtype).reshape(rows, cols)
 
@@ -149,13 +151,10 @@ class TestGGUFGEMV(unittest.TestCase):
     x = rng.standard_normal(cols).astype(np.float32)
     np.testing.assert_allclose((tensors["weight"] @ Tensor(x)).numpy(), ref @ x, atol=1e-2, rtol=1e-2)
     np.testing.assert_equal(tensors["weight"].numpy(), ref)
-    assert np.isfinite(ref).all() and np.isfinite(tensors["weight"].numpy()).all(), f"{qtype.name} has NaN/Inf"
 
   def test_gguf_gemv_q8_0(self): self._test_gguf_gemv(GGMLQuantizationType.Q8_0)
   def test_gguf_gemv_q4_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q4_K)
-  def test_gguf_gemv_q5_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q5_K)
   def test_gguf_gemv_q6_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q6_K)
-  def test_gguf_gemv_mxfp4(self): self._test_gguf_gemv(GGMLQuantizationType.MXFP4)
 
 if __name__ == '__main__':
   unittest.main()

test/unit/test_invalid_tensor.py

@@ -1,126 +0,0 @@
-import unittest
-from tinygrad import Tensor
-from tinygrad.dtype import Invalid, dtypes
-from tinygrad.engine.realize import run_schedule
-
-class TestInvalidTensor(unittest.TestCase):
-  def _invalid_test_helper(self, out, expected):
-    sched = out.schedule()
-    buf = out.uop.buffer
-    buf.allocate()
-    sentinel = memoryview(bytearray(b'\x42' * buf.nbytes))
-    buf.copyin(sentinel)
-    before = buf.as_memoryview().cast(out.dtype.fmt).tolist()
-    run_schedule(sched)
-    ret = buf.as_memoryview().cast(out.dtype.fmt).tolist()
-
-    for i,v in enumerate(expected): self.assertEqual(ret[i], before[i] if v is None else v)
-
-  def test_where_x_invalid(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Invalid)
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_where_invalid_x(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Invalid, Tensor([1.0, 2.0, 3.0, 4.0]))
-    self._invalid_test_helper(out, [None, None, 3.0, 4.0])
-
-  def test_where_invalid_2d(self):
-    mask = Tensor.arange(6).reshape(2, 3) < 3
-    vals = Tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
-    out = mask.where(vals, Invalid)
-    self._invalid_test_helper(out, [1.0, 2.0, 3.0, None, None, None])
-
-  def test_where_invalid_int(self):
-    mask = Tensor.arange(3) < 2
-    out = mask.where(Tensor([10, 20, 30]), Invalid)
-    self._invalid_test_helper(out, [10, 20, None])
-
-  def test_where_invalid_add(self):
-    mask = Tensor.arange(3) < 2
-    mixed = mask.where(Tensor([10.0, 20.0, 30.0]), Invalid)
-    out = mixed + Tensor([1.0, 2.0, 3.0])
-    self._invalid_test_helper(out, [11.0, 22.0, None])
-
-  def test_where_invalid_add_left(self):
-    mask = Tensor.arange(3) < 2
-    mixed = mask.where(Tensor([10.0, 20.0, 30.0]), Invalid)
-    out = Tensor([1.0, 2.0, 3.0]) + mixed
-    self._invalid_test_helper(out, [11.0, 22.0, None])
-
-  def test_where_always_true(self):
-    mask = Tensor.arange(3) < 10
-    out = mask.where(Tensor([10.0, 20.0, 30.0]), Invalid)
-    self._invalid_test_helper(out, [10.0, 20.0, 30.0])
-
-  def test_where_cast(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Invalid).cast(dtypes.int)
-    self._invalid_test_helper(out, [1, 2, None, None])
-
-  def test_where_compare(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Invalid) > 1
-    self._invalid_test_helper(out, [False, True, None, None])
-
-  def test_where_unary(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 4.0, 9.0, 16.0]), Invalid).sqrt()
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_where_where(self):
-    mask1 = Tensor.arange(4) < 2
-    mask2 = Tensor.arange(4) > 0
-    out = mask2.where(mask1.where(Tensor([1.0, 2.0, 3.0, 4.0]), Invalid), Invalid)
-    self._invalid_test_helper(out, [None, 2.0, None, None])
-
-  def test_where_reduce_always_true(self):
-    mask = Tensor.arange(4) < 9
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Invalid).sum()
-    self._invalid_test_helper(out, [10.0])
-
-  def test_invalid_unary(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Tensor.full((4,), Invalid, dtype=dtypes.float).sqrt())
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_invalid_binary(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Tensor.full((4,), Invalid, dtype=dtypes.float) + 2)
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_invalid_binary_left(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), 2 + Tensor.full((4,), Invalid, dtype=dtypes.float))
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_invalid_reshape(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Invalid).reshape(2,2)
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_invalid_cast(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Tensor.full((4,), Invalid, dtype=dtypes.int).cast(dtypes.float))
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_invalid_bitcast(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Tensor.full((4,), Invalid, dtype=dtypes.int).bitcast(dtypes.float))
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-  def test_where_bitcast(self):
-    mask = Tensor.arange(4) < 2
-    out = mask.where(Tensor([1.0, 2.0, 3.0, 4.0]), Tensor.full((4,), Invalid, dtype=dtypes.int)).bitcast(dtypes.int)
-    self._invalid_test_helper(out, [0x3f800000, 0x40000000, None, None])
-
-  # tensor indexing uses reduce, so the entire result becomes invalid
-  @unittest.expectedFailure
-  def test_tensor_index(self):
-    idx = (Tensor.arange(4) < 2).where(Tensor([0, 1, 2, 3]), Invalid)
-    out = Tensor([1.0, 2.0, 3.0, 4.0])[idx]
-    self._invalid_test_helper(out, [1.0, 2.0, None, None])
-
-if __name__ == '__main__':
-  unittest.main()

test/unit/test_llm_server.py

@@ -1,112 +1,29 @@
 import unittest
 from unittest.mock import patch
-from tinygrad import Tensor, UOp
-from tinygrad.engine.schedule import schedule_cache
+from tinygrad import Tensor
 
 class TestTransformerGenerate(unittest.TestCase):
-  def test_kv_cache_reuse(self):
-    """Test that generate reuses the KV cache when tokens extend the cached prefix."""
+  def test_start_pos_parameter_is_used(self):
+    """Test that start_pos parameter is not ignored (regression test for always resetting to 0)."""
     from tinygrad.apps.llm import Transformer
+    # Create a minimal transformer
     model = Transformer(num_blocks=1, dim=64, hidden_dim=128, n_heads=2, n_kv_heads=2,
                         norm_eps=1e-5, vocab_size=100, head_dim=32, rope_theta=10000.0, max_context=32)
 
     captured_inputs = []
     def mock_call(self, tokens, start_pos):
-      captured_inputs.append((tokens.shape, start_pos if isinstance(start_pos, int) else start_pos.val))
-      return Tensor([[42]])
+      captured_inputs.append((tokens.shape, start_pos if isinstance(start_pos, int) else start_pos.bind_val))
+      return Tensor([[42]])  # return a fake next token
 
     with patch.object(Transformer, '__call__', mock_call):
-      # first conversation: prefill 5 tokens + 1 decode
       tokens = [1, 2, 3, 4, 5]
-      gen = model.generate(tokens)
-      next(gen)  # prefill
-      next(gen)  # decode
+      gen = model.generate(tokens, start_pos=3)
+      next(gen)  # get first token
 
-      # second call extends the conversation — cached prefix should be reused
-      captured_inputs.clear()
-      tokens = [1, 2, 3, 4, 5, 42, 42, 10, 11, 12]
-      gen = model.generate(tokens)
-      next(gen)
-
-    # should only process tokens[7:] = [10, 11, 12] since first 7 are cached
-    toks_shape = captured_inputs[0][0][-1]
-    self.assertEqual(toks_shape.val if isinstance(toks_shape, UOp) else toks_shape, 3)
-    self.assertEqual(captured_inputs[0][1], 7)
-
-  def test_kv_cache_invalidation(self):
-    """Test that generate invalidates the KV cache when tokens diverge from the cached prefix."""
-    from tinygrad.apps.llm import Transformer
-    model = Transformer(num_blocks=1, dim=64, hidden_dim=128, n_heads=2, n_kv_heads=2,
-                        norm_eps=1e-5, vocab_size=100, head_dim=32, rope_theta=10000.0, max_context=32)
-
-    captured_inputs = []
-    def mock_call(self, tokens, start_pos):
-      captured_inputs.append((tokens.shape, start_pos if isinstance(start_pos, int) else start_pos.val))
-      return Tensor([[42]])
-
-    with patch.object(Transformer, '__call__', mock_call):
-      # first conversation
-      gen = model.generate([1, 2, 3, 4, 5])
-      next(gen)
-
-      # completely different prompt — KV cache should be invalidated
-      captured_inputs.clear()
-      gen = model.generate([10, 20, 30])
-      next(gen)
-
-    # should process all 3 tokens from start
-    toks_shape = captured_inputs[0][0][-1]
-    self.assertEqual(toks_shape.val if isinstance(toks_shape, UOp) else toks_shape, 3)
-    self.assertEqual(captured_inputs[0][1], 0)
-
-  def test_two_prompts_schedule_cache(self):
-    """Third prompt should hit the schedule cache, not miss (first two warm up both jits: prefill + decode)."""
-    from tinygrad.apps.llm import Transformer
-    model = Transformer(num_blocks=1, dim=64, hidden_dim=128, n_heads=2, n_kv_heads=2,
-                        norm_eps=1e-5, vocab_size=100, head_dim=32, rope_theta=10000.0, max_context=64)
-
-    # first two prompts warm up both jits (prefill + decode)
-    ids = list(range(1, 6))
-    gen = model.generate(ids)
-    for _ in range(3): next(gen)
-
-    ids += list(range(10, 15))
-    gen = model.generate(ids)
-    for _ in range(3): next(gen)
-    cache_size_after_warmup = len(schedule_cache)
-
-    # third prompt should reuse the same schedule cache entries, not create new ones
-    ids += list(range(20, 25))
-    gen = model.generate(ids)
-    for _ in range(3): next(gen)
-
-    self.assertEqual(cache_size_after_warmup, len(schedule_cache),
-      f"third prompt added {len(schedule_cache) - cache_size_after_warmup} new schedule cache entries (expected 0)")
-
-  def test_chunked_prefill(self):
-    """When prompt > chunk_size, all chunks should be prefill"""
-    from tinygrad.apps.llm import Transformer
-    from tinygrad.uop.ops import resolve
-    model = Transformer(num_blocks=1, dim=64, hidden_dim=128, n_heads=2, n_kv_heads=2,
-                        norm_eps=1e-5, vocab_size=100, head_dim=32, rope_theta=10000.0, max_context=64)
-
-    def get_prefill_flags(tokens, chunk_size):
-      is_prefill = []
-      def mock_call(self, tokens, start_pos):
-        is_prefill.append(resolve(tokens.shape[1] != 1))
-        return Tensor([[42]])
-      with patch.object(Transformer, '__call__', mock_call):
-        gen = model.generate(tokens, chunk_size=chunk_size)
-        for _ in range(3): next(gen)
-      model._cached_tokens = []
-      return is_prefill
-
-    # 8 tokens, chunk_size=4 -> 2 prefill chunks
-    self.assertEqual(get_prefill_flags(list(range(8)), 4), [True, True, False, False])
-    # 9 tokens, chunk_size=4 -> 3 prefill chunks (4+4+1)
-    self.assertEqual(get_prefill_flags(list(range(9)), 4), [True, True, True, False, False])
-    # 4 tokens, chunk_size=4 -> 1 prefill chunk
-    self.assertEqual(get_prefill_flags(list(range(4)), 4), [True, False, False])
+    # With start_pos=3, the initial tensor should only have tokens[3:] = [4, 5] (length 2)
+    # If the bug existed (start_pos always reset to 0), it would have all 5 tokens
+    self.assertEqual(captured_inputs[0][0][-1], 2)  # shape should be (1, 2)
+    self.assertEqual(captured_inputs[0][1], 3)  # start_pos should be 3, not 0
 
 if __name__ == '__main__':
   unittest.main()

test/unit/test_metal_graph.py

@@ -1,34 +0,0 @@
-import unittest
-from unittest.mock import MagicMock
-from tinygrad import Device
-from tinygrad.engine.realize import CompiledRunner
-
-@unittest.skipUnless(Device.DEFAULT == "METAL", "Metal device required to run")
-class TestMetalGraph(unittest.TestCase):
-  def setUp(self):
-    from tinygrad.runtime.graph.metal import MetalGraph
-    from tinygrad.runtime.ops_metal import MetalBuffer
-    self.MetalGraph = MetalGraph
-    self.MetalBuffer = MetalBuffer
-    self.dev = Device[Device.DEFAULT]
-
-  def metal_buf(self, offset): return MagicMock(_buf=self.MetalBuffer(MagicMock(), 4, offset))
-
-  def ei(self, *bufs):
-    ei = MagicMock()
-    ei.prg = MagicMock(spec=CompiledRunner)
-    ei.bufs = list(bufs)
-    return ei
-
-  def test_supports_exec_item_normal_offset(self):
-    assert self.MetalGraph.supports_exec_item([self.dev], self.ei(self.metal_buf(0), self.metal_buf(100), self.metal_buf(0xFFFFFFFF))) is True
-
-  def test_supports_exec_item_overflow_offset(self):
-    assert self.MetalGraph.supports_exec_item([self.dev], self.ei(self.metal_buf(0), self.metal_buf(0x100000000))) is False
-
-  def test_supports_exec_item_nonmetal_buf(self):
-    # HCQBuffer.offset is a method, not an int — must not crash
-    self.MetalGraph.supports_exec_item([self.dev], self.ei(MagicMock(**{"_buf.offset": lambda: 0})))
-
-if __name__ == "__main__":
-  unittest.main()

test/unit/test_setitem_schedule.py

@@ -36,7 +36,8 @@ class TestSetitemInto(unittest.TestCase):
     self.assertEqual(GlobalCounters.kernel_count, 0)
     t.realize()
     self.assertEqual(GlobalCounters.kernel_count, 1)
-    self.assertEqual(GlobalCounters.global_mem, 4)
+    # TODO: this can be just 4 if empty goes through is_realized setitem path
+    self.assertEqual(GlobalCounters.global_mem, 4*(3*2+1)) # 3 elements had +1, 1 is assigned directly
     t[1].realize()
     t.realize()
     self.assertEqual(GlobalCounters.kernel_count, 1)

tinygrad/apps/llm.py

@@ -1,8 +1,7 @@
 from __future__ import annotations
-import sys, argparse, typing, re, unicodedata, json, uuid, time, functools, itertools
+import sys, argparse, typing, re, unicodedata, json, uuid, time, functools
 from tinygrad import Tensor, nn, UOp, TinyJit, getenv, function
-from tinygrad.uop.ops import resolve
-from tinygrad.helpers import partition, DEBUG, Timing, GlobalCounters, stderr_log, colored, Context
+from tinygrad.helpers import partition, DEBUG, Timing, GlobalCounters, stderr_log, colored
 from tinygrad.viz.serve import TCPServerWithReuse, HTTPRequestHandler
 
 class SimpleTokenizer:
@@ -117,7 +116,7 @@ class TransformerBlock:
       self.ffn_up      = nn.Linear(dim, hidden_dim, bias=False)
       self.ffn_down    = nn.Linear(hidden_dim, dim, bias=False)
 
-  @function(precompile=bool(getenv("PRECOMPILE", 0)))
+  @function
   def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor:
     x_norm = self.attn_norm(x)                       # (B,T,D)
     q, k, v = self.attn_q(x_norm), self.attn_k(x_norm), self.attn_v(x_norm)
@@ -145,7 +144,7 @@ class TransformerBlock:
 
     # NOTE: this mask is causal_lower_right, not the causal_upper_left generated by is_casual = True
     # TODO: this if statement should be removed and it shouldn't generate extra kernels
-    mask = Tensor.full((1, 1, T, start_pos+T), float("-inf"), dtype=x.dtype, device=x.device).triu(start_pos+1) if resolve(T != 1) else None
+    mask = Tensor.full((1, 1, T, start_pos+T), float("-inf"), dtype=x.dtype, device=x.device).triu(start_pos+1) if T > 1 else None
     attn = q.scaled_dot_product_attention(k, v, attn_mask=mask, enable_gqa=True)     # (B,H,T,Hd)
     attn = attn.transpose(1, 2).reshape(B, T, -1)                                    # back to (B,T,D)
     attn = self.attn_output(attn)
@@ -166,8 +165,7 @@ class TransformerBlock:
   def __call__(self, x: Tensor, start_pos: int|UOp):
     if not hasattr(self, "cache_kv"):
       # TODO: how is the dtype of this determined?
-      # NOTE: clone is used to promise the creation of a specific buffer
-      self.cache_kv = Tensor.zeros(2, x.shape[0], self.n_kv_heads, self.max_context, self.head_dim, device=x.device).clone()
+      self.cache_kv = Tensor.zeros(2, x.shape[0], self.n_kv_heads, self.max_context, self.head_dim, device=x.device).contiguous().realize()
     return self._feed_forward(self._attention(x, start_pos)).contiguous()
 
 class Transformer:
@@ -179,10 +177,8 @@ class Transformer:
     self.output_norm = nn.RMSNorm(dim, norm_eps)
     self.output = nn.Linear(dim, vocab_size, bias=False)
     self.max_context = max_context
-    self._cached_tokens: list[int] = []
-    # we specialize the JIT for prefill and rollout
-    self.prefill_jit = TinyJit(self.forward)
-    self.rollout_jit = TinyJit(self.forward)
+    # JIT is used if T=1 and start_pos is a UOp. TODO: make this not needed by including T in the JIT and making start_pos always a UOp
+    self.forward_jit = TinyJit(self.forward)
 
   def forward(self, tokens:Tensor, start_pos:int|UOp) -> Tensor:
     x = self.token_embd(tokens)                           # (B, T, D)
@@ -191,10 +187,10 @@ class Transformer:
     return self.output(self.output_norm(x))[:, -1, :].softmax(-1, dtype="float").argmax(-1, keepdim=True)
 
   def __call__(self, tokens:Tensor, start_pos:int|UOp=0) -> Tensor:
-    return (self.prefill_jit if resolve(tokens.shape[1] != 1) else self.rollout_jit)(tokens, start_pos)
+    return (self.forward_jit if getenv("JIT", 1) and tokens.shape[1] == 1 and isinstance(start_pos, UOp) else self.forward)(tokens, start_pos)
 
   @staticmethod
-  def from_gguf(gguf:Tensor, max_context:int|None=None, realize=bool(getenv("REALIZE", 0))) -> tuple[Transformer, dict]:
+  def from_gguf(gguf:Tensor, max_context:int|None=None, realize=bool(getenv("REALIZE", 1))) -> tuple[Transformer, dict]:
     # TODO: remove the need for copy to default device
     kv, state_dict = nn.state.gguf_load(gguf.to(None).realize())
 
@@ -229,26 +225,15 @@ class Transformer:
       Tensor.realize(*params)
     return model, kv
 
-  def get_start_pos(self, tokens:list[int]):
-    return sum(1 for _ in itertools.takewhile(lambda ab: ab[0] == ab[1], zip(tokens[:-1], self._cached_tokens)))
-
-  def generate(self, tokens:list[int], chunk_size:int=32):
-    v_start_pos = UOp.variable("start_pos", 0, self.max_context-1)
-    v_toks = UOp.variable("toks", 1, chunk_size)
-    # assign all input tokens once, then slice from start_pos for the model call
-    t = Tensor(tokens + [0] * (self.max_context - len(tokens)), dtype="int32").reshape(1, self.max_context)
-    # recompute start_pos from what's currently valid in the kv cache
-    start_pos = self.get_start_pos(tokens)
-    out, prompt_len = None, len(tokens)
+  def generate(self, tokens:list[int], start_pos=0):
+    v_start_pos = UOp.variable("start_pos", 1, self.max_context-1)
+    t = Tensor([tokens[start_pos:]], dtype="int32")
     while len(tokens) < self.max_context:
-      sp, nt = v_start_pos.bind(start_pos), v_toks.bind(min(chunk_size, len(tokens) - start_pos))
-      out = self(t[:, sp:sp+nt] if start_pos < prompt_len or out is None else out, sp).realize()
-      start_pos += nt.val
-      # chunked prefill: keep processing until all prompt tokens are consumed
-      if start_pos < len(tokens): continue
-      tokens.append(int(out.item()))
-      self._cached_tokens = tokens[:]
-      yield tokens[-1]
+      t = self(t, v_start_pos.bind(start_pos) if getenv("SYM", 1) and start_pos != 0 and t.shape[-1] == 1 else start_pos)
+      next_id = int(t.item())
+      tokens.append(next_id)
+      start_pos = len(tokens) - 1
+      yield next_id
 
 models = {
   "llama3.2:1b": "https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q6_K.gguf",
@@ -277,7 +262,7 @@ CHAT_HTML = b'''<!DOCTYPE html><html><head><title>tinygrad chat</title><style>
            background: #2f2f2f; color: inherit; font: inherit;
            border: none; outline: none; resize: none; border-radius: 24px; field-sizing: content }
 </style></head><body><div id="chat"></div>
-<textarea id="input" rows="1" placeholder="Ask anything" autofocus></textarea>
+<textarea id="input" rows="1" placeholder="Ask anything"></textarea>
 <script>
   input.onkeydown = (e) => { if (e.key === 'Enter' && !e.shiftKey && !e.isComposing) { e.preventDefault(); send() } }
   const msgs = [];
@@ -305,22 +290,20 @@ class Handler(HTTPRequestHandler):
   def log_request(self, code='-', size='-'): pass
   def do_GET(self): self.send_data(CHAT_HTML, content_type="text/html")
   def run_model(self, ids:list[int], model_name:str, include_usage=False):
-    cache_start_pos = model.get_start_pos(ids)
-    stderr_log(f"{self.path}  {colored('--', 'BLACK')}  "
-               f"in:{colored(f'{cache_start_pos:5d}', 'green')} +{len(ids)-cache_start_pos:5d}  {colored('--', 'BLACK')}  ")
+    stderr_log(f"{self.path}  {colored('--', 'BLACK')}  in:{len(ids):5d}  {colored('--', 'BLACK')}  ")
     tmpl = {"id":f"chatcmpl-{uuid.uuid4().hex[:24]}", "object":"chat.completion.chunk", "created":int(time.time()), "model":model_name}
     yield {"choices": [{"index":0, "delta":{"role":"assistant","content":""}, "finish_reason":None}], **tmpl}
     out: list[int] = []
     st = time.perf_counter()
     for next_id in model.generate(ids):
-      if len(out) == 0: stderr_log(f"prefill:{(len(ids)-cache_start_pos)/((pt:=time.perf_counter())-st):4.0f} tok/s  {colored('--', 'BLACK')}  ")
+      if len(out) == 0: stderr_log(f"prefill:{len(ids)/((pt:=time.perf_counter())-st):4.0f} tok/s  {colored('--', 'BLACK')}  ")
       if next_id == eos_id: break
       out.append(next_id)
       yield {"choices": [{"index":0, "delta":{"content":tok.decode([next_id])}, "finish_reason":None}], **tmpl}
     yield {"choices": [{"index":0, "delta":{},"finish_reason":"stop"}], **tmpl}
     if include_usage:
       yield {"choices": [], "usage": {"prompt_tokens": len(ids), "completion_tokens": len(out), "total_tokens": len(ids) + len(out)}, **tmpl}
-    stderr_log(f"gen:{len(out)/(time.perf_counter()-pt):4.0f} tok/s  {colored('--', 'BLACK')}  out:{len(out):5d}\n")
+    stderr_log(f"out:{len(out):5d}  {colored('--', 'BLACK')}  gen: {len(out)/(time.perf_counter()-pt):4.0f} tok/s\n")
 
   def do_POST(self):
     raw_body = self.rfile.read(int(self.headers.get("Content-Length", "0")))
@@ -379,7 +362,7 @@ if __name__ == "__main__":
 
   # do benchmark
   if args.benchmark:
-    gen = model.generate(toks:=[bos_id or 0])
+    gen = model.generate(toks:=[bos_id or 0], 0)
     for _ in range(args.benchmark):
       GlobalCounters.reset()
       with Timing(on_exit=lambda x: f", {1e9/x:6.2f} tok/s, {GlobalCounters.global_mem/x:7.2f} GB/s,"
@@ -388,20 +371,17 @@ if __name__ == "__main__":
     exit(0)
 
   # start server
-  if args.serve:
-    # warmup: run 2 tokens through the model twice to capture the JIT before serving
-    with Context(DEBUG=max(DEBUG.value, 1)):
-      for _ in range(2): list(zip(range(2), model.generate([0])))
-    TCPServerWithReuse(('', args.serve), Handler).serve_forever()
+  if args.serve: TCPServerWithReuse(('', args.serve), Handler).serve_forever()
 
   # interactive chat
   ids: list[int] = [bos_id] if bos_id is not None else []
   while 1:
+    start_pos = max(len(ids) - 1, 0)
     try:
       ids += tok.role("user") + tok.encode(input('>>> ')) + tok.end_turn(eos_id) + tok.role("assistant")
     except EOFError:
       break
-    for next_id in model.generate(ids):
+    for next_id in model.generate(ids, start_pos):
       sys.stdout.write(tok.decode([next_id]) if next_id != eos_id else "\n\n")
       sys.stdout.flush()
       if next_id == eos_id: break

tinygrad/codegen/__init__.py

@@ -19,7 +19,7 @@ from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_in
   ReduceContext, correct_load_store, pm_render, pm_add_loads
 from tinygrad.codegen.opt.postrange import apply_opts, pm_make_images
 from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_split_ranges, pm_load_collapse
-from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen, pm_mops, pm_syntactic_sugar, pm_store_ranges
+from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen, pm_mops, pm_syntactic_sugar
 from tinygrad.codegen.late.linearizer import CFGContext, pm_split_ends, pm_add_control_flow, linearize
 from tinygrad.renderer.amd.elf import do_assemble_amd
 
@@ -31,7 +31,7 @@ def full_rewrite_to_sink(sink:UOp, ren:Renderer|None=None, optimize:bool=True) -
   if SPEC: type_verify(sink, kernel_spec)
 
   # preprocess
-  sink = graph_rewrite(sink, pm_mops+pm_syntactic_sugar+pm_store_ranges, ctx=itertools.count(1000), name="early movement ops", bottom_up=True)
+  sink = graph_rewrite(sink, pm_mops+pm_syntactic_sugar, name="early movement ops", bottom_up=True)
 
   # first we optimize
   if optimize:
@@ -48,7 +48,7 @@ def full_rewrite_to_sink(sink:UOp, ren:Renderer|None=None, optimize:bool=True) -
     sink = graph_rewrite(sink, sym+pm_flatten_range, name="initial symbolic")
 
     # optimize (schedule) the AST
-    sink = graph_rewrite(sink, pm_flatten_range+pm_simplify_ranges, ctx={}, name="simplify ranges")
+    sink = graph_rewrite(sink, pm_simplify_ranges, name="simplify ranges")
 
     # do postrange optimization, BEAM or hand_coded_optimizations
     sink = apply_opts(sink, ren)

tinygrad/codegen/gpudims.py

@@ -1,15 +1,15 @@
 import math
 from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType, sint_to_uop
-from tinygrad.helpers import dedup, get_contraction
+from tinygrad.helpers import all_int, dedup, get_contraction
 from tinygrad.dtype import dtypes, AddrSpace, Invalid
 from tinygrad.renderer import Renderer
 
-def _dim_max(d:sint) -> int: return d if isinstance(d, int) else int(d.vmax)
-
 def _group_dims(dims:tuple[sint, ...], max_sizes:tuple[int, ...]):
+  # TODO: symbolic shape
+  if not all_int(dims): return dims
   while len(dims) > len(max_sizes) or any(d > m for d,m in zip(dims, max_sizes)):
     for i,m in enumerate(max_sizes):
-      if i < (len(dims)-1) and _dim_max(dims[i]) * _dim_max(dims[i+1]) <= m:
+      if i < (len(dims)-1) and dims[i] * dims[i+1] <= m:
         dims = dims[:i] + (dims[i]*dims[i+1],) + dims[i+2:]
         break
     else: return None

tinygrad/codegen/late/devectorizer.py

@@ -130,7 +130,7 @@ load_store_folding = PatternMatcher([
   (UPat(Ops.STORE, src=(UPat(Ops.GEP, name="gep"), UPat.var("st")), name="sto"), gep_on_store),
   # put PTRCAT after LOAD
   (UPat(Ops.LOAD, src=(UPat(Ops.PTRCAT, name="cat"),), name="ld", allow_any_len=True),
-   lambda cat,ld: UOp(Ops.VCAT, cat.dtype.base.vec(cat.dtype.vcount), tuple(ld.replace(dtype=x.dtype.base, src=(x,)+ld.src[1:]) for x in cat.src))),
+   lambda cat,ld: UOp(Ops.CAT, cat.dtype.base.vec(cat.dtype.vcount), tuple(ld.replace(dtype=x.dtype.base, src=(x,)+ld.src[1:]) for x in cat.src))),
   # put PTRCAT after STORE
   (UPat(Ops.STORE, src=(UPat(Ops.PTRCAT, name="cat"), UPat(name="data")), name="sto"), cat_after_store),
 ])
@@ -181,7 +181,7 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
 
   # if it wasn't split, we return None. otherwise we CAT them
   if len(ret) <= 1: return None
-  return UOp(Ops.VCAT, ls.dtype, tuple(ret)) if ls.op is Ops.LOAD else UOp.group(*ret)
+  return UOp(Ops.CAT, ls.dtype, tuple(ret)) if ls.op is Ops.LOAD else UOp.group(*ret)
 
 def _do_image_fixup(dt:ImageDType, idx:UOp) -> tuple[UOp, UOp, int, int]:
   buf = idx.src[0]
@@ -189,10 +189,7 @@ def _do_image_fixup(dt:ImageDType, idx:UOp) -> tuple[UOp, UOp, int, int]:
   h, w = dt.shape[0], dt.shape[1]
   if IMAGE == 1 and valid is not None:
     h, w = max(ImageDType.valid_dims(dt), key=lambda hw:
-                # maximize number of valids removed
-               (len(_drop_valid_stmts(valid, idx:=uop_given_valid(valid, UOp.vectorize((x//4)%hw[1], x//(4*hw[1])).simplify()), *hw)),
-                # and minimize idx complexity (number of nodes)
-                -len(idx.gep(1).backward_slice)))
+               (len(_drop_valid_stmts(valid, idx:=uop_given_valid(valid, UOp.vectorize((x//4)%hw[1], x//(4*hw[1]))), *hw)), -len(idx.backward_slice)))
     buf = buf.replace(dtype=(dtypes.imageh if dt.itemsize == 2 else dtypes.imagef)((h, w, 4), w * 4 * dt.itemsize))
   oidx = UOp(Ops.VECTORIZE, dtypes.index.vec(2), ((x // 4) % w, (x // (4*w))))
   return x, idx.replace(src=(buf, oidx.valid(valid))), w, h

tinygrad/codegen/late/expander.py

@@ -51,7 +51,7 @@ def do_expand(root:UOp):
         new_srcs.append(src)
       elif src.dtype.count > 1:
         # put any input dtype > 1 grouped together
-        new_srcs.append(UOp(Ops.VCAT, src.dtype.scalar().vec(expand_sz*src.dtype.count), (src,)*expand_sz))
+        new_srcs.append(UOp(Ops.CAT, src.dtype.scalar().vec(expand_sz*src.dtype.count), (src,)*expand_sz))
       else:
         # repeat the arg
         new_srcs.append(src.broadcast(expand_sz))