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

@@ -66,14 +66,14 @@ 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 +199,13 @@ 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')

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)
 
@@ -1417,7 +1412,7 @@ 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:])
     loss.backward()
     assert all(p.grad is g for p,g in zip(optim.params, grads))
@@ -1431,7 +1426,7 @@ 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")
@@ -1449,7 +1444,7 @@ 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")
 

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
@@ -45,8 +45,8 @@ 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]

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

@@ -12,7 +12,6 @@ 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}

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

@@ -12,7 +12,6 @@ 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}

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/callrange/amd_call_matmul.py

@@ -0,0 +1,139 @@
+from typing import Callable
+from tinygrad import UOp, dtypes, Device, Tensor, getenv, function
+from tinygrad.uop.ops import AxisType, AddrSpace
+
+def simple_function(fxn:Callable[..., UOp]) -> Callable[..., UOp]:
+  def wrapper(*args:UOp) -> UOp:
+    params:list[UOp] = [x.param_like(i) for i,x in enumerate(args)]
+    return fxn(*params).call(*args)
+  return wrapper
+
+THREADS_PER_BLOCK = 128
+WARP_SIZE = 32
+
+# Register tile sizes (per-thread accumulator tile of C)
+TN = 4     # columns per thread
+TM = 4     # rows per thread
+
+WAVE_TILE_N = 128
+WAVE_TILE_M = 32
+
+LANES_PER_WAVE_X = 8
+LANES_PER_WAVE_Y = 4
+ITERS_PER_WAVE_N = 4 #WAVE_TILE_N // (LANES_PER_WAVE_X * TN)
+ITERS_PER_WAVE_M = 2 #WAVE_TILE_M // (LANES_PER_WAVE_Y * TM)
+
+WAVES_IN_BLOCK_Y = 4
+WAVES_IN_BLOCK_X = 1
+
+
+N = getenv("N", 4096)
+M = K = N
+
+# 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
+
+@simple_function
+def slice_matmul(c_regs, a_local, b_local):
+  # 2x
+  A_col = UOp.placeholder((ITERS_PER_WAVE_M, TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
+  B_row = UOp.placeholder((ITERS_PER_WAVE_N, TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
+
+
+  pass
+
+@simple_function
+def compute_local(c:UOp, a_local:UOp, b_local:UOp) -> UOp:
+  # this is the LID level on the GPU, here we can define regs
+  tid = UOp.special(THREADS_PER_BLOCK, "lidx0")
+  waveIdx = (tid // WARP_SIZE) % WAVES_IN_BLOCK_X
+  waveIdy = (tid // WARP_SIZE) // WAVES_IN_BLOCK_X
+  assert waveIdy.vmax+1 == WAVES_IN_BLOCK_Y
+
+  laneIdx = (tid % WARP_SIZE) % LANES_PER_WAVE_X
+  laneIdy = (tid % WARP_SIZE) // LANES_PER_WAVE_X
+  assert laneIdy.vmax+1 == LANES_PER_WAVE_Y
+
+  A_col = UOp.placeholder((ITERS_PER_WAVE_M*TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
+  B_row = UOp.placeholder((ITERS_PER_WAVE_N*TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
+
+  # do the math
+  A_col = A_col.assign(a_local[k_tile].reshape(WAVES_IN_BLOCK_Y, ITERS_PER_WAVE_M, LANES_PER_WAVE_Y, TM)[waveIdy, :, laneIdy, :].flatten())
+  B_row = B_row.assign(b_local[k_tile].reshape(WAVES_IN_BLOCK_X, ITERS_PER_WAVE_N, LANES_PER_WAVE_X, TN)[waveIdx, :, laneIdx, :].flatten())
+  c_regs += A_col.reshape(-1, 1) * B_row.reshape(1, -1)  #
+  c_regs
+
+
+
+@simple_function
+def load_local(a_local, b_local, a_global, b_global):
+  # NOTE: it ends this range, so there's a BARRIER
+  tid = UOp.special(THREADS_PER_BLOCK, "lidx0")
+  return UOp.group(
+    a_local[:, tid].store(a_global[tid, :]),
+    b_local[:, tid].store(b_global[:, tid]))
+
+@simple_function
+def reg_matmul(c_regs, a_local, b_local):
+  A_col = UOp.placeholder((ITERS_PER_WAVE_M*TM), dtypes.float, slot=0, addrspace=AddrSpace.REG)
+  B_row = UOp.placeholder((ITERS_PER_WAVE_N*TN), dtypes.float, slot=1, addrspace=AddrSpace.REG)
+
+
+
+@simple_function
+def local_matmul(c:UOp, a:UOp, b:UOp, a_local:UOp, b_local:UOp):
+  tid = UOp.special(THREADS_PER_BLOCK, "lidx0")
+  waveIdx = (tid // WARP_SIZE) % WAVES_IN_BLOCK_X
+  waveIdy = (tid // WARP_SIZE) // WAVES_IN_BLOCK_X
+  laneIdx = (tid % WARP_SIZE) % LANES_PER_WAVE_X
+  laneIdy = (tid % WARP_SIZE) // LANES_PER_WAVE_X
+
+  # this is the LID level on the GPU, this (and below) is where we define REGs
+  c_regs = UOp.placeholder((ITERS_PER_WAVE_M*TM, ITERS_PER_WAVE_N*TN), dtypes.float, slot=2, addrspace=AddrSpace.REG)
+
+  # 128x128, Kx128, Kx128
+  k_tile = UOp.range(N // BLOCK_K, 0, AxisType.REDUCE)*BLOCK_K
+  fxn = reg_matmul(c_regs.assign(0),
+                   a_local[:, tid].assign(a[k_tile:k_tile+BLOCK_K, tid]),
+                   b_local[:, tid].assign(b[k_tile:k_tile+BLOCK_K, tid]))
+
+  # do math
+  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)
+  return c[waveIdy, :, laneIdy, :, waveIdx, :, laneIdx, :].store(c_regs.after(fxn))
+
+@simple_function
+def global_matmul(c:UOp, a:UOp, b:UOp):
+  # this is the GID level on the GPU, this is where we define LOCAL buffers shared across lids
+  gx = UOp.range(N//BLOCK_N, 0, AxisType.GLOBAL) * BLOCK_N
+  gy = UOp.range(M//BLOCK_M, 1, AxisType.GLOBAL) * BLOCK_M
+  a_local = UOp.placeholder((BLOCK_K, BLOCK_N), dtypes.float, slot=0, addrspace=AddrSpace.LOCAL)
+  b_local = UOp.placeholder((BLOCK_K, BLOCK_M), dtypes.float, slot=1, addrspace=AddrSpace.LOCAL)
+  return local_matmul(c[gx:gx+BLOCK_N, gy:gy+BLOCK_M], a.permute(1,0)[:, gx:gx+BLOCK_N], b[:, gy:gy+BLOCK_M], a_local, b_local)
+
+  #ll = load_local(a_local, b_local, a.permute(1,0)[:, gx:gx+BLOCK_N], b[:, gy:gy+BLOCK_M])
+  #return compute_local(c[gx:gx+BLOCK_N, gy:gy+BLOCK_M], a_local.after(ll), b_local.after(ll))
+
+if __name__ == "__main__":
+  # this is the outer lvel on the GPU, this is where we define GLOBAL buffers
+  C = Tensor.empty(N, M)
+  A = Tensor.randn(N, K)
+  B = Tensor.randn(K, M)
+  c_out = C.call(A, B, fxn=global_matmul).numpy()
+
+  #C = UOp.new_buffer(Device.DEFAULT, N*M, dtypes.float).reshape(N,M)
+  #A = UOp.new_buffer(Device.DEFAULT, N*K, dtypes.float).reshape(N,K)
+  #B = UOp.new_buffer(Device.DEFAULT, K*M, dtypes.float).reshape(K,M)
+  #global_matmul(C, A, B).realize()
+
+  # input matmuls
+  #c = UOp.param(0, dtypes.float, (N, M))
+  #a = UOp.param(1, dtypes.float, (N, K))
+  #b = UOp.param(2, dtypes.float, (K, M))
+
+
+  #ba = a.rearrange("(n bn) (k bk) -> n k bn bk", bn=BLOCK_N, bk=BLOCK_K)[gx, k_tile_range]
+  #bb = b.rearrange("(k bk) (m bm) -> k m bk bm", bk=BLOCK_K, bm=BLOCK_M)[k_tile_range, gy]
+  #bc = c.rearrange("(n bn) (m bm) -> n m bn bm", bn=BLOCK_N, bm=BLOCK_M)[gx, gy]

extra/callrange/test.py

@@ -0,0 +1,85 @@
+from tinygrad import UOp, dtypes, Device, Tensor
+
+if __name__ == "__main__":
+  B0 = UOp.new_buffer(Device.DEFAULT, 100, dtypes.float).reshape(10,10)
+  B1 = UOp.new_buffer(Device.DEFAULT, 100, dtypes.float).reshape(10,10)
+
+
+  b0 = UOp.param(0, dtypes.float, (10,10))
+  b1 = UOp.param(1, dtypes.float, (10,10))
+  r0 = UOp.range(10, axis_id=0)
+  r1 = UOp.range(10, axis_id=1)
+
+  fxn = (b0[r0, r1] + b1[r0, r1]).call(B0, B1)
+  t = Tensor(fxn)
+  t.realize()
+
+# gemm (N,N)
+
+# (N//k, k, N//k, k)
+
+
+# what if call just implicitly ends all ranges and you don't need to connect them?
+# you do have to connect them, and it does end the ranges
+
+# if assign (store+after) is on call, we move the store into the call (indexed with the ranges) and replace the assign with an after
+
+
+def gemm(A, B):
+  N = 4096
+  k = 128
+
+  ia = UOp.param(0, dtypes.float, (k, k)).reshape(k, 1, k)
+  ib = UOp.param(1, dtypes.float, (k, k)).reshape(1, k, k)
+  gemm_fxn = (ia * ib).sum(2) # <-- rangeify this
+
+  a = UOp.param(0, dtypes.float, (N, N))
+  b = UOp.param(1, dtypes.float, (N, N))
+  r0 = UOp.range(N//k, 0)
+  r1 = UOp.range(N//k, 1)
+  local_fxn = gemm_fxn.call(a.reshape(N//k, k, N//k, k)[r0, :, r1, :], b.reshape(N//k, k, N//k, k)[r0, :, r1, :], r0, r1).permute(0,2,1,3).reshape(N,N)
+
+  fxn = local_fxn.call(A,B)
+
+
+
+  return
+
+
+
+
+
+  a = UOp.param(0, dtypes.float, (N//k, k, N//k, k))
+  b = UOp.param(1, dtypes.float, (N//k, k, N//k, k))
+
+
+
+  # inner kxk GEMM (are WMMAs calls?)
+  ia = UOp.param(0, dtypes.float, (k,k)).reshape(k, 1, k)
+  ib = UOp.param(1, dtypes.float, (k,k)).reshape(1, k, k)
+  r0 = UOp.range(N//k, 0)
+  r1 = UOp.range(N//k, 1)
+  fxn = (ia * ib).sum(2).call(a[:, r0, :, r1], b[:, r0, :, r1])   # this call ends these ranges implicitly
+  assert fxn.shape == (N//k, N//k, k, k)
+
+
+  #.call(A, B, UOp.range(N//k), UOp.range(N//k))
+
+  #r0 = UOp.param(2, dtypes.index, (), vmin_vmax=(0, N//k-1))
+  #r1 = UOp.param(3, dtypes.index, (), vmin_vmax=(0, N//k-1))
+
+
+
+
+
+
+
+
+
+
+# Q = [batch, seq_len, heads, dim]
+# K = [batch, seq_len, head_kv, dim]
+# V = [batch, seq_len, head_kv, dim]
+
+
+

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

@@ -1,7 +1,9 @@
-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)
@@ -13,34 +15,60 @@ NUM_RUNS = getenv("CNT", 5)
 # ---------------------------
 
 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
+assert N % BLOCK_N == 0, f"N ({N}) must be a multiple of BLOCK_N ({BLOCK_N})"
+assert M % BLOCK_M == 0, f"M ({M}) must be a multiple of BLOCK_M ({BLOCK_M})"
+assert K % BLOCK_K == 0, f"K ({K}) must be a multiple of BLOCK_K ({BLOCK_K})"
+
+# 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_PER_BLOCK_N = BLOCK_N // WAVE_TILE_N
+WAVES_PER_BLOCK_M = BLOCK_M // WAVE_TILE_M
+assert WAVES_PER_BLOCK_N * WAVES_PER_BLOCK_M == WARPS_PER_BLOCK, "wave grid must match warps/block"
+
+LANES_PER_WAVE_N = 8
+LANES_PER_WAVE_M = 4
+REG_TILES_PER_WAVE_N = WAVE_TILE_N // (LANES_PER_WAVE_N * TN)
+REG_TILES_PER_WAVE_M = WAVE_TILE_M // (LANES_PER_WAVE_M * TM)
+assert WAVE_TILE_N % (LANES_PER_WAVE_N * TN) == 0, "WAVE_TILE_N must be divisible by LANES_PER_WAVE_N*TN"
+assert WAVE_TILE_M % (LANES_PER_WAVE_M * TM) == 0, "WAVE_TILE_M must be divisible by LANES_PER_WAVE_M*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")
 
+  a = UOp.placeholder((M, K), dtypes.float, slot=1)
+  b = UOp.placeholder((K, N), dtypes.float, slot=2)
+  c = UOp.placeholder((M, 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, :]
 
@@ -76,18 +104,21 @@ 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_PER_BLOCK_N
+  waveIdy = (tid // WARP_SIZE) // WAVES_PER_BLOCK_N
+  assert waveIdy.vmax+1 == WAVES_PER_BLOCK_M
+
+  laneIdx = (tid % WARP_SIZE) % LANES_PER_WAVE_N
+  laneIdy = (tid % WARP_SIZE) // LANES_PER_WAVE_N
+  assert laneIdy.vmax+1 == LANES_PER_WAVE_M
 
   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))
+  A_col = copy(A_col, A_local_slice , 300, set=True, upcast=True)
 
   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))
+  B_row = copy(B_row, B_local_slice, 400, set=True, upcast=True)
 
   # ---------------------------
   # FMA: c_regs += A_col * B_row
@@ -115,18 +146,19 @@ def hand_spec_kernel3(c:UOp, a:UOp, b:UOp) -> UOp:
 
   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, M=M, N=N, K=K):
+  rng = np.random.default_rng()
+  a = Tensor(rng.random((M, K), dtype=np.float32)-0.5, dtype=dtype)
+  b = Tensor(rng.random((K, N), dtype=np.float32)-0.5, dtype=dtype)
+  hc = Tensor.empty(M, 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)):
+  with Context(DEBUG=2):
     for _ in range(NUM_RUNS):
-      GlobalCounters.reset()
-      tst = Tensor.custom_kernel(c, a, b, fxn=fxn)[0].realize()
-      ets.append(GlobalCounters.time_sum_s)
+      ets.append(ei.run(wait=True))
   print(f"REAL TFLOPS {M * N * K * 2 / min(ets) * 1e-12:.2f}")
 
   if getenv("VERIFY", 1):
@@ -134,10 +166,10 @@ def eval_custom_matmul(fxn):
     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/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

@@ -8,8 +8,8 @@ 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",
+  "gemm":"-c \"from tinygrad import Tensor; (Tensor.empty(N:=64, N)@Tensor.empty(N, N)).realize()\"",
+  "ops":"extra/sqtt/examples/discover_ops.py"
 }
 
 if __name__ == "__main__":

extra/torch_backend/backend.py

@@ -590,7 +590,7 @@ 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/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__":

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

@@ -10,7 +10,7 @@ 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)
+                                     InstOp, InstOpRDNA4, print_packets, CDNA_WAVEEND)
 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, InstOpRDNA4.OTHER_VMEM_5}
 
 # ═══════════════════════════════════════════════════════════════════════════════
 # ROCPROF DECODER
@@ -153,10 +153,9 @@ 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}")
+        inst_names = [p.op.name for p in all_packets if isinstance(p, (INST, INST_RDNA4))]
+        self.assertGreater(len(inst_names), 0, f"no INST packets in {name}")
+        self.assertGreater(len([n for n in inst_names if n.startswith("JUMP")]), 0, f"no JUMP packets in {name}")
 
   expected: dict[str, list[int]] = {}  # override in subclasses
   def test_packet_counts(self):
@@ -211,22 +210,23 @@ 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": [1974, 1961, 2014, 2065, 2092, 1998],
+    "profile_empty_run_1": [1979, 1972, 2019, 2070, 2097, 2003],
+    "profile_gemm_run_0": [2038, 11076, 2324, 2129, 2156, 2062],
+    "profile_gemm_run_1": [2038, 11037, 2318, 2129, 2156, 2062],
+    "profile_ops_run_0": [2038, 5070, 2078, 2129, 2156, 2062],
+    "profile_ops_run_1": [2038, 5007, 2078, 2129, 2156, 2062],
+    "profile_plus_run_0": [1979, 1979, 2030, 2070, 2097, 2003],
+    "profile_plus_run_1": [1979, 2043, 2030, 2070, 2097, 2003],
   }
 
 class TestSQTTExamplesRDNA4(SQTTExamplesTestBase): target = "gfx1200"
 
 class TestSQTTExamplesCDNA(SQTTExamplesTestBase):
   target = "gfx950"
+  def test_decode_all_examples(self): self.skipTest("TODO: correct deltas in the timestamp packet types, first packet is REGCS_CDNA")
+  def test_gemm_has_instructions(self): self.skipTest("TODO: decode CDNA inst packets")
   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")
 
 if __name__ == "__main__":
   unittest.main()

test/amd/test_sqttmap.py

@@ -2,7 +2,7 @@
 import unittest, pickle
 from typing import Iterator
 from pathlib import Path
-from tinygrad.helpers import DEBUG, getenv, temp
+from tinygrad.helpers import DEBUG, OSX, getenv, temp
 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
@@ -11,7 +11,7 @@ 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 +31,7 @@ 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))
@@ -64,13 +64,13 @@ 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_ops")
+          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):
@@ -78,34 +78,17 @@ class TestSQTTMapBase(unittest.TestCase):
       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"]
+          events = [e for e in sqtt_timeline(event.blob, p.lib, target) 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
+              if e.name.display_name not in {"IMMEDIATE", "IMMEDIATE_MASK", "JUMP", "JUMP_NO", "MESSAGE"}: 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_ops.py

@@ -479,9 +479,9 @@ 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 +496,9 @@ 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)
 

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/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_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
@@ -486,79 +483,5 @@ 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_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)

test/null/test_uop_symbolic.py

@@ -786,14 +786,6 @@ class TestSymbolic(unittest.TestCase):
     # 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)

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/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)

test/unit/test_call.py

@@ -237,13 +237,5 @@ class TestCallSchedule(unittest.TestCase):
     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_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_invalid_tensor.py

@@ -1,20 +1,27 @@
-import unittest
+import math, unittest
+from unittest.mock import patch
 from tinygrad import Tensor
+from tinygrad.device import Device
 from tinygrad.dtype import Invalid, dtypes
-from tinygrad.engine.realize import run_schedule
+from tinygrad.helpers import unwrap_class_type
 
 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()
+    before = None
+    original_call = (runtime_cls:=unwrap_class_type(Device[Device.DEFAULT].runtime)).__call__
 
-    for i,v in enumerate(expected): self.assertEqual(ret[i], before[i] if v is None else v)
+    def patched_call(self_prg, *bufs, **kwargs):
+      nonlocal before
+      before = Device[Device.DEFAULT].allocator._as_buffer(bufs[0]).cast(out.dtype.fmt).tolist()
+      return original_call(self_prg, *bufs, **kwargs)
+
+    with patch.object(runtime_cls, '__call__', patched_call): ret = out.tolist()
+
+    for i,v in enumerate(expected):
+      if v is None: assert before[i] == ret[i] or (math.isnan(before[i]) and math.isnan(ret[i]))
+      else: assert ret[i] == v
+
+    return before, ret
 
   def test_where_x_invalid(self):
     mask = Tensor.arange(4) < 2
@@ -30,7 +37,11 @@ class TestInvalidTensor(unittest.TestCase):
     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])
+    before, ret = self._invalid_test_helper(out, [])
+    assert ret[0] == [1.0, 2.0, 3.0]
+    assert before[3] == ret[1][0] or (math.isnan(before[3]) and math.isnan(ret[1][0]))
+    assert before[4] == ret[1][1] or (math.isnan(before[4]) and math.isnan(ret[1][1]))
+    assert before[5] == ret[1][2] or (math.isnan(before[5]) and math.isnan(ret[1][2]))
 
   def test_where_invalid_int(self):
     mask = Tensor.arange(3) < 2
@@ -78,7 +89,8 @@ class TestInvalidTensor(unittest.TestCase):
   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])
+    before, ret = self._invalid_test_helper(out, [])
+    assert ret == 10.0
 
   def test_invalid_unary(self):
     mask = Tensor.arange(4) < 2
@@ -98,7 +110,10 @@ class TestInvalidTensor(unittest.TestCase):
   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])
+    before, ret = self._invalid_test_helper(out, [])
+    assert ret[0] == [1.0, 2.0]
+    assert ret[1][0] == before[2] or (math.isnan(ret[1][0]) and math.isnan(before[2]))
+    assert ret[1][1] == before[3] or (math.isnan(ret[1][1]) and math.isnan(before[3]))
 
   def test_invalid_cast(self):
     mask = Tensor.arange(4) < 2

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/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/late/devectorizer.py

@@ -190,9 +190,9 @@ def _do_image_fixup(dt:ImageDType, idx:UOp) -> tuple[UOp, UOp, int, int]:
   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)),
+               (len(_drop_valid_stmts(valid, idx:=uop_given_valid(valid, UOp.vectorize((x//4)%hw[1], x//(4*hw[1]))), *hw)),
                 # and minimize idx complexity (number of nodes)
-                -len(idx.gep(1).backward_slice)))
+                -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/simplify.py

@@ -1,5 +1,4 @@
 import itertools
-from typing import Callable
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, graph_rewrite, _substitute, range_start
 from tinygrad.uop.symbolic import symbolic
 from tinygrad.helpers import partition
@@ -37,32 +36,22 @@ def simplify_merge_adjacent(u:UOp) -> UOp|None:
           u = nidx
   return u
 
-def mark_gated(ctx, idx):
-  if idx.src[1].op is Ops.WHERE:
-    x, cond = idx.src[1].get_idx(), idx.src[1].get_valid()
-    # get all ranges r with guards "r < c" for some const c
-    guards = {r:c for v in cond.split_uop(Ops.AND) if v.op is Ops.CMPLT and (r:=v.src[0]).op is Ops.RANGE and (c:=v.src[1]).op is Ops.CONST}
-  else: x, guards = idx, {}
-  # ensure that we choose max(c_i) for all i where r < c_i
-  ctx |= {r:c for r,c in guards.items() if (r not in ctx or ctx[r].arg < c.arg)}
-  # but if a range is ever ungated, we cannot shrink it
-  ctx |= {r:r.src[0] for r in x.ranges if r not in guards}
-
 pm_simplify_ranges = PatternMatcher([
   (UPat((Ops.END, Ops.REDUCE), name="u"), simplify_merge_adjacent),
-  (UPat(Ops.INDEX, name="idx"), mark_gated),
-  # reduce ranges can't be shrunk
-  (UPat(Ops.REDUCE, name="red"), lambda ctx, red: ctx.update({r:r.src[0] for r in red.src[1:]})),
-  (UPat(Ops.SINK, name="x"), lambda ctx, x: do_substitute(ctx, x, lambda r,c: r.replace(src=(c,)))),
 ])
 
 def mark_range_mod(ctx:dict[UOp, UOp|None], r:UOp, c:UOp) -> None:
   if r not in ctx and r.src[0].op is Ops.CONST and r.src[0].divides(c.arg) is not None: ctx[r] = c
 
-def do_substitute(ctx:dict, x: UOp, sub_fxn:Callable[[UOp, UOp], UOp]) -> UOp|None:
-  ret = x.substitute({k:sub_fxn(k,v) for k,v in ctx.items() if v is not None})
+def do_substitute(ctx:dict[UOp, UOp|None], x: UOp) -> UOp|None:
+  subs = {}
+  for k,v in ctx.items():
+    if v is not None:
+      subs[k] = k.replace(src=(k.src[0]//v,), arg=k.arg[0:-1]+(0,k.arg[-1]))*v + k.replace(src=(v,), arg=k.arg[0:-1]+(1,k.arg[-1]))
+  if not len(subs): return None
+  ret = x.substitute(subs).simplify()
   ctx.clear()
-  return None if ret is x else ret.simplify()
+  return ret
 
 def dont_sub_ranges_for_image(ctx:dict[UOp, UOp|None], x:UOp) -> None:
   if isinstance(x.src[0].src[0].dtype, ImageDType):
@@ -71,8 +60,7 @@ def dont_sub_ranges_for_image(ctx:dict[UOp, UOp|None], x:UOp) -> None:
 pm_split_ranges = PatternMatcher([
   (UPat(Ops.RANGE, name="r")%UPat.cvar("c"), mark_range_mod),
   (UPat(Ops.STORE, name="x"), dont_sub_ranges_for_image),
-  (UPat(Ops.SINK, name="x"), lambda ctx, x: do_substitute(ctx, x,
-    lambda k,v: k.replace(src=(k.src[0]//v,), arg=k.arg[0:-1]+(0,k.arg[-1]))*v + k.replace(src=(v,), arg=k.arg[0:-1]+(1,k.arg[-1])))),
+  (UPat(Ops.SINK, name="x"), do_substitute),
 ])
 
 # **** reduce simplification ****

tinygrad/device.py

@@ -353,12 +353,11 @@ def is_dtype_supported(dtype:DType, device:str|None=None) -> bool:
     if device == "NV": return not CI and not NV_PTX and not NV_NAK
     if device in {"CPU"}: return not CI and platform.machine() in {"arm", "arm64", "aarch64", "x86_64", "amd64"} and not CPU_LVP
     return device in {"AMD", "CL", "PYTHON", "NULL"}
-  if dtype in dtypes.fp8_ocp:
+  if dtype in dtypes.fp8s:
     if device == "CUDA": return not CI and not CUDA_PTX
     if device == "NV": return not CI and not NV_PTX and not NV_NAK
-    if device == "AMD": return not CI and getattr(Device["AMD"], "target") == (9,5,0)
+    if device == "AMD": return not CI and getattr(Device["AMD"], "target") in {(9,4,2), (9,5,0)}
     return device in {"PYTHON", "NULL"}
-  if dtype in dtypes.fp8_fnuz: return device in {"PYTHON", "NULL"}
   if device == "WEBGPU": return dtype in [dtypes.bool, dtypes.char, dtypes.uchar, dtypes.short,
                                           dtypes.ushort, dtypes.float, dtypes.int32, dtypes.uint32, dtypes.half]
   # for CI GPU and OSX, cl_khr_fp16 isn't supported

tinygrad/dtype.py

@@ -79,14 +79,10 @@ class DType(metaclass=DTypeMetaClass):
     return PtrDType(self.priority, self.bitsize, self.name, self.fmt, self.count, None, self, addrspace, 1, size)
   def scalar(self) -> DType: return self._scalar if self._scalar is not None else self
   def nbytes(self) -> int: raise RuntimeError("only ptr types have nbytes")
-  @functools.cached_property
-  def min(self):
-    if dtypes.is_int(self): return 0 if dtypes.is_unsigned(self) else -2**(self.scalar().bitsize-1)
-    return -float("inf") if dtypes.is_float(self) else False
-  @functools.cached_property
-  def max(self):
-    if dtypes.is_int(self): return 2**(self.scalar().bitsize)-1+self.min
-    return float("inf") if dtypes.is_float(self) else True
+  @property
+  def min(self): return dtypes.min(self)
+  @property
+  def max(self): return dtypes.max(self)
 
 @dataclass(frozen=True, eq=False)
 class PtrDType(DType):
@@ -176,11 +172,21 @@ class dtypes:
     # int is the default. wrap floats in ConstFloat to distinguish -0.0 from 0.0 in cache
     return ConstFloat(float(val)) if dtypes.is_float(dtype) else bool(val) if dtypes.is_bool(dtype) else int(val)
   @staticmethod
+  @functools.cache
+  def min(dtype:DType):
+    if dtypes.is_int(dtype): return 0 if dtypes.is_unsigned(dtype) else -2**(dtype.scalar().bitsize-1)
+    return -float("inf") if dtypes.is_float(dtype) else False
+  @staticmethod
+  @functools.cache
+  def max(dtype:DType):
+    if dtypes.is_int(dtype): return 2**(dtype.scalar().bitsize)-1+dtypes.min(dtype)
+    return float("inf") if dtypes.is_float(dtype) else True
+  @staticmethod
   def finfo(dtype:DType) -> tuple[int, int]:
     """(exponent, mantissa)"""
     if not dtypes.is_float(dtype): raise ValueError(f"{dtype} is not a floating point type")
     return {dtypes.float16: (5, 10), dtypes.bfloat16: (8, 7), dtypes.float32: (8, 23), dtypes.float64: (11, 52),
-            dtypes.fp8e4m3: (4, 3), dtypes.fp8e5m2: (5, 2), dtypes.fp8e4m3fnuz: (4, 3), dtypes.fp8e5m2fnuz: (5, 2)}[dtype]
+            dtypes.fp8e5m2: (5, 2), dtypes.fp8e4m3: (4, 3)}[dtype]
   void: Final[DType] = DType.new(-1, 0, "void", None)
   index: Final[DType] = DType.new(-1, 800, "index", None)
   bool: Final[DType] = DType.new(0, 1, "bool", '?')
@@ -196,8 +202,6 @@ class dtypes:
   _uint256: Final[DType] = DType.new(8, 256, "uint256", None)
   fp8e4m3: Final[DType] = DType.new(9, 8, "float8_e4m3", None)
   fp8e5m2: Final[DType] = DType.new(10, 8, "float8_e5m2", None)
-  fp8e4m3fnuz: Final[DType] = DType.new(9, 8, "float8_e4m3fnuz", None)
-  fp8e5m2fnuz: Final[DType] = DType.new(10, 8, "float8_e5m2fnuz", None)
   float16: Final[DType] = DType.new(11, 16, "half", 'e')
   # bfloat16 has higher priority than float16, so least_upper_dtype(dtypes.int64, dtypes.uint64) = dtypes.float16
   bfloat16: Final[DType] = DType.new(12, 16, "__bf16", None)
@@ -218,9 +222,7 @@ class dtypes:
   default_float: ClassVar[DType] = float32
   default_int: ClassVar[DType] = int32
 
-  fp8_ocp = (fp8e4m3, fp8e5m2)
-  fp8_fnuz = (fp8e4m3fnuz, fp8e5m2fnuz)
-  fp8s = fp8_ocp + fp8_fnuz
+  fp8s = (fp8e4m3, fp8e5m2)
   floats = fp8s + (float16, bfloat16, float32, float64)
   int8s = (uint8, int8)
   int16s = (uint16, int16)
@@ -242,9 +244,8 @@ def to_dtype(dtype:DTypeLike) -> DType: return dtype if isinstance(dtype, DType)
 # we don't support weak type and complex type
 promo_lattice = { dtypes.bool: [dtypes.int8, dtypes.uint8], dtypes.int8: [dtypes.int16], dtypes.int16: [dtypes.int32], dtypes.int32: [dtypes.int64],
   dtypes.int64: [dtypes.uint64], dtypes.uint8: [dtypes.int16, dtypes.uint16], dtypes.uint16: [dtypes.int32, dtypes.uint32],
-  dtypes.uint32: [dtypes.int64, dtypes.uint64], dtypes.uint64: [dtypes.fp8e4m3, dtypes.fp8e5m2, dtypes.fp8e4m3fnuz, dtypes.fp8e5m2fnuz],
-  dtypes.fp8e4m3: [dtypes.float16, dtypes.bfloat16], dtypes.fp8e5m2: [dtypes.float16, dtypes.bfloat16],
-  dtypes.fp8e4m3fnuz: [dtypes.float16, dtypes.bfloat16], dtypes.fp8e5m2fnuz: [dtypes.float16, dtypes.bfloat16],
+  dtypes.uint32: [dtypes.int64, dtypes.uint64], dtypes.uint64: [dtypes.fp8e4m3, dtypes.fp8e5m2],
+  dtypes.fp8e5m2: [dtypes.float16, dtypes.bfloat16], dtypes.fp8e4m3: [dtypes.float16, dtypes.bfloat16],
   dtypes.float16: [dtypes.float32], dtypes.bfloat16: [dtypes.float32], dtypes.float32: [dtypes.float64], }
 
 @functools.cache
@@ -299,14 +300,10 @@ def float_to_bf16(x):
 _fp8_cfg = {
   dtypes.fp8e4m3: (7, 4, 0x7, 0x3F50000000000000, 0x407D000000000000, 0x7E, 0x3F90000000000000),
   dtypes.fp8e5m2: (15, 3, 0x3, 0x3EE0000000000000, 0x40EE000000000000-1, 0x7B, 0x3F10000000000000),
-  dtypes.fp8e4m3fnuz: (8, 4, 0x7, 0x3F40000000000000, 0x406F000000000000-1, 0x7F, 0x3F80000000000000),
-  dtypes.fp8e5m2fnuz: (16, 3, 0x3, 0x3ED0000000000000, 0x40EE000000000000-1, 0x7F, 0x3F00000000000000),
 }
 
 def float_to_fp8(x: float, dtype: DType) -> int:
   assert dtype in dtypes.fp8s, "Only for fp8s"
-  if dtype in dtypes.fp8_fnuz and not math.isfinite(x): return 0x80
-  if dtype in dtypes.fp8_fnuz and x == 0.0: return 0x00
   # e4m3 don't support inf, return 0x7f(+NaN) and 0xff(-NaN) to match jax
   # NaN is unordered, can't compare with zero, use math.copysign to get sign
   if dtype == dtypes.fp8e4m3 and not math.isfinite(x): return 0x7f if math.copysign(1, x) > 0 else 0xff
@@ -326,17 +323,16 @@ def float_to_fp8(x: float, dtype: DType) -> int:
     res, half = mantissa >> shift, half_ulp << shift
     round_bits = (xbits | (1 << 52)) & ((half << 1) - 1)
     if round_bits > half or (round_bits == half and res & 1): res += 1
-  return 0 if dtype in dtypes.fp8_fnuz and res == 0 else int(res | sign)  # fnuz has no negative zero
+  return int(res | sign)
 
 def fp8_to_float(x: int, dtype: DType) -> float:
   assert dtype in dtypes.fp8s, "Only for fp8s"
-  if dtype in dtypes.fp8_fnuz and x == 0x80: return math.nan
   if (x & 0x7F) == 0: return -0.0 if x & 0x80 else 0.0
   bias, sig_bits, *_ = _fp8_cfg[dtype]
   mant_bits, exp_bits = sig_bits - 1, 8 - sig_bits
   exp_max, mant_max = (1 << exp_bits) - 1, (1 << mant_bits) - 1
   sign, exp, mantissa = (x >> 7) & 1, (x >> mant_bits) & exp_max, x & mant_max
-  if dtype not in dtypes.fp8_fnuz and exp == exp_max:
+  if exp == exp_max:
     if dtype == dtypes.fp8e5m2: return math.copysign(math.nan if mantissa else math.inf, -1 if sign else 1)
     if mantissa == mant_max: return math.nan
   val = (mantissa / (mant_max + 1)) * 2 ** (1 - bias) if exp == 0 else (1 + mantissa / (mant_max + 1)) * 2 ** (exp - bias)

tinygrad/engine/allocations.py

@@ -46,19 +46,18 @@ def _buffer_like(u:UOp) -> UOp:
     if prod(dtype.shape) != prod(u.max_shard_shape) or ([x for x in u.max_shard_shape if x != 1] or [1])[-1] % 4 != 0:
       if DEBUG >= 1: print(f"demoting Image {dtype} with shape {u.max_shard_shape}")
       dtype = dtype.base
-  buffer = UOp.new_buffer(u.device, u.shard_size, dtype).reshape(u.max_shard_shape).shrink_to(u.shard_shape)
+  buffer = UOp.new_buffer(u.device, u.shard_size, dtype).reshape(u.max_shard_shape)
   if isinstance(u.device, tuple) and u.axis is not None: buffer = buffer.multi(u.axis)
   return buffer
 
-def replace_contig_with_store_after(u:UOp):
+def replace_contig_with_assign(u:UOp):
   # can't allocate a buffer without a device (e.g., inside a CALL function body with only PARAMs)
   if u._device is None: return None
   # if size is 0, remove the contig
   if u.size == 0: return u.src[0]
   # no real contig for DISK/TINYFS tensors, they are left alone
   if isinstance(u._device, str) and u._device.startswith(("DISK", "TINYFS")): return u.rtag(None)
-  buf = _buffer_like(u)
-  return buf.after(buf.store(u.src[0])).rtag(u.tag)
+  return _buffer_like(u).assign(u.src[0]).rtag(u.tag)
 
 def replace_assign_with_contig(u:UOp):
   assigned_to = u
@@ -94,23 +93,9 @@ def contiguous_mops_to_view(c:UOp):
 def transform_precompiled_call(c:UOp) -> UOp|None:
   if not c.arg.precompile: return None
   if c.src[0].op is Ops.SINK: return None
-  input_buffers = tuple(x.contiguous() if x.op not in {Ops.AFTER, Ops.BIND} else x for x in c.src[1:])
-  # multi-output (TUPLE) precompiled calls: allocate a buffer per output and return a TUPLE of buffers
-  if c.src[0].op is Ops.TUPLE:
-    tuple_body = c.src[0]
-    out_bufs = []
-    sink_srcs = []
-    for i, elem in enumerate(tuple_body.src):
-      buf = UOp.new_buffer(c.device, prod(elem.max_shape), elem.dtype).reshape(elem.max_shape).shrink_to(elem.shape)
-      out_bufs.append(buf)
-      target = buf.param_like(len(c.src) - 1 + i).shrink_to(elem.shape)
-      sink_srcs.append(target.after(target.store(elem)))
-    fxn = UOp.sink(*sink_srcs)
-    new_call = c.replace(src=(fxn, *input_buffers, *out_bufs), dtype=dtypes.void, tag=None)
-    return UOp.maketuple(*[buf.after(new_call) for buf in out_bufs])
   out = _buffer_like(c)
-  target = out.param_like(len(c.src)-1).shrink_to(c.shape)
-  fxn = target.after(target.store(c.src[0])).sink()
+  input_buffers = tuple(x.contiguous() if x.op not in {Ops.AFTER, Ops.BIND} else x for x in c.src[1:])
+  fxn = out.param_like(len(c.src)-1).assign(c.src[0]).sink()
   ret = out.after(c.replace(src=(fxn, *input_buffers, out), dtype=dtypes.void, tag=None))
   # if the CALL has symbolic shapes, shrink the max-sized output to the actual symbolic shape
   if any(isinstance(s, UOp) for s in c.shape): ret = ret.shrink(tuple((0, s) for s in c.shape))
@@ -125,15 +110,14 @@ pm_early_transform_tensor_graph = PatternMatcher([
   (UPat(Ops.CONTIGUOUS, src=(UPat(GroupOp.Movement),), name="c"), contiguous_mops_to_view),
 
   # add CONTIGUOUS to tagged UOps
-  (UPat(GroupOp.All-{Ops.CONTIGUOUS, Ops.ASSIGN, Ops.AFTER, Ops.STORE}, name="x"),
-   lambda x: x.rtag(None).contiguous(tag=x.tag) if x.tag else x.replace(tag=None)),
-  # remove extra CONTIGUOUS on ASSIGN/AFTER (only when target is contiguous)
-  (UPat(Ops.CONTIGUOUS, src=(UPat({Ops.ASSIGN, Ops.AFTER}, name="a"),), name="c"),
+  (UPat(GroupOp.All-{Ops.CONTIGUOUS, Ops.ASSIGN}, name="x"), lambda x: x.rtag(None).contiguous(tag=x.tag) if x.tag else x.replace(tag=None)),
+  # remove extra CONTIGUOUS on ASSIGN (only when assign target is contiguous)
+  (UPat(Ops.CONTIGUOUS, src=(UPat(Ops.ASSIGN, name="a"),), name="c"),
    lambda a,c: a.replace(tag=(a.tag or ())+(c.tag or ())) if a.src[0].has_buffer_identity() else None),
   # replace ASSIGN with CONTIGUOUS
   (UPat(Ops.ASSIGN, name="u"), replace_assign_with_contig),
-  # replace CONTIGUOUS with STORE+AFTER
-  (UPat(Ops.CONTIGUOUS, name="u"), replace_contig_with_store_after),
+  # replace CONTIGUOUS with ASSIGNs
+  (UPat(Ops.CONTIGUOUS, name="u"), replace_contig_with_assign),
   # remove DETACH/CONTIGUOUS_BACKWARD (allows more contiguous removal)
   (UPat((Ops.DETACH, Ops.CONTIGUOUS_BACKWARD), name="x"), lambda x: x.src[0]),
 ])
@@ -144,9 +128,9 @@ def untag_and_append(ctx:AllocCtx, x:UOp):
   for t in x.tag:
     original_uop: UOp = ctx.uop_list[t]
     replace_uop = ret
-    while replace_uop.op in {Ops.ASSIGN, Ops.AFTER}: replace_uop = replace_uop.src[0]
+    while replace_uop.op is Ops.ASSIGN: replace_uop = replace_uop.src[0]
     ctx.buffer_map[original_uop] = replace_uop.shrink_to(original_uop.shape)
-  if ret.op is not Ops.AFTER: ctx.assigns.append(ret)  # AFTER gets appended by append_after
+  ctx.assigns.append(ret)
   return ret
 
 def append_after(ctx:AllocCtx, x:UOp):
@@ -158,7 +142,7 @@ def replace_input_buffer(ctx:AllocCtx, b:UOp):
                    b._min_max if b.op is Ops.BIND else None, b.src[0].arg[0] if b.op is Ops.BIND else None)
 
 pm_finalize_call = PatternMatcher([
-  (UPat({Ops.ASSIGN, Ops.AFTER}, name="x"), untag_and_append),
+  (UPat(Ops.ASSIGN, name="x"), untag_and_append),
   (UPat(Ops.AFTER, name="x"), append_after),
   (UPat(Ops.COPY, name="x"), lambda ctx,x: append_after(ctx,x) if isinstance(x.device, str) and x.device.startswith(("DISK", "TINYFS")) else None),
   # remove unique from const. TODO: this is copied in function.py

tinygrad/function.py

@@ -2,7 +2,6 @@ import functools
 from typing import Generic, TypeVar, Callable, cast, overload
 from tinygrad.helpers import Context, dedup, getenv
 from tinygrad.uop.ops import UOp, Ops, graph_rewrite, PatternMatcher, UPat
-from tinygrad.gradient import compute_gradient
 from tinygrad.tensor import Tensor
 
 def add_to_ctx(ctx, x:UOp):
@@ -20,10 +19,9 @@ pm_ctx = PatternMatcher([
 
 ReturnType = TypeVar('ReturnType')
 class _function(Generic[ReturnType]):
-  def __init__(self, fxn:Callable[..., ReturnType], *, precompile:bool=False, precompile_backward:bool=False):
+  def __init__(self, fxn:Callable[..., ReturnType], *, precompile:bool=False):
     self.fxn = fxn
     self.precompile = precompile
-    self.precompile_backward = precompile_backward
 
   def __get__(self, obj, objtype=None): return functools.partial(self.__call__, obj) if obj is not None else self
 
@@ -38,20 +36,15 @@ class _function(Generic[ReturnType]):
     call_uops: list[UOp] = dedup(input_uops)
 
     # disable realize/schedule while this is running
-    # run it and do surgery later
+    # run it and do surgery later. TODO: why am i not calling it with the params?
     with Context(ALLOW_DEVICE_USAGE=getenv("DEVICE_IN_FUNCTION_BUG", 0)):
       ret = self.fxn(*args, **kwargs)
-    if isinstance(ret, Tensor):
-      uret = ret.uop
-    elif isinstance(ret, tuple) and all(isinstance(x, Tensor) for x in ret):
-      uret = UOp.maketuple(*[x.uop for x in ret])
-    else:
-      raise RuntimeError(f"function return type {type(ret)} not supported")
+    assert isinstance(ret, Tensor), "only supports one tensor return for now"
 
     # replace the known inputs with params (using deduplicated slots)
     subs = {}
     for i,x in enumerate(call_uops): subs[x] = x.param_like(i)
-    uret = uret.substitute(subs)
+    uret = ret.uop.substitute(subs)
 
     # add contiguous to call_uops
     #call_uops = [x.contiguous() for x in call_uops]
@@ -67,77 +60,14 @@ class _function(Generic[ReturnType]):
     #call = assigned.call(*call_uops, buffer, name=name)
     #ret = buffer.after(call)
 
-    # precompute the backward: determine which inputs need gradients and build the backward CALL body
-    grad_fxn = None
-    inputs = [t for t in list(args)+[kwargs[k] for k in sorted(kwargs)] if isinstance(t, (Tensor, UOp))]
-    grad_params = {x.arg:x for x in uret.toposort(enter_calls=False) if x.op == Ops.PARAM}
-    # find which param slots correspond to requires_grad inputs
-    need_grad = {i for i, t in enumerate(inputs) if isinstance(t, Tensor) and t.requires_grad}
-    target_params = {grad_params[i] for i in need_grad if i in grad_params}
-    if target_params:
-      grad_fxn = self._make_grad_fxn(uret, len(call_uops), target_params, need_grad, name, self.precompile_backward)
-
-    fret = uret.call(*call_uops, name=name, precompile=self.precompile, precompile_backward=self.precompile_backward, grad_fxn=grad_fxn)
-    if isinstance(ret, tuple):
-      return cast(ReturnType, tuple(Tensor(fret.gettuple(i), device=fret.device) for i in range(len(ret))))
-    else:
-      return cast(ReturnType, Tensor(fret, device=fret.device))
-
-  @staticmethod
-  def _make_grad_fxn(uret:UOp, num_args:int, target_params:set[UOp], need_grad:set[int], name:str, precompile_backward:bool):
-    def grad_fxn(ctx, k):
-      fxn, args = k.src[0], k.src[1:]
-      params = {x.arg:x for x in fxn.toposort(enter_calls=False) if x.op == Ops.PARAM}
-      # compute gradients only for needed params
-      if isinstance(ctx, dict):
-        all_grads: dict[UOp, UOp] = {}
-        for idx, grad_out in ctx.items():
-          elem_grads = compute_gradient(fxn.src[idx], grad_out.param_like(len(args) + idx), target_params)
-          for p, g in elem_grads.items():
-            if p in all_grads: all_grads[p] = all_grads[p] + g
-            else: all_grads[p] = g
-        grads = all_grads
-        grad_ctx_inputs = tuple(ctx.get(i, fxn.src[i].const_like(0)) for i in range(len(fxn.src)))
-      else:
-        grads = compute_gradient(fxn, ctx.param_like(len(args)), target_params)
-        grad_ctx_inputs = (ctx,)
-      # collect gradients for needed params only
-      grad_indices: list[int] = []
-      grad_uops: list[UOp] = []
-      for i in range(len(args)):
-        if i in need_grad and (p:=params.get(i)) is not None and p in grads:
-          grad_indices.append(i)
-          grad_uops.append(grads[p])
-      if len(grad_uops) == 0: return (None,) * len(args)
-      # replace forward output references with PARAMs to avoid recomputation
-      if fxn.op is Ops.TUPLE:
-        fwd_subs = {elem: elem.param_like(len(args) + len(grad_ctx_inputs) + i) for i, elem in enumerate(fxn.src)}
-        fwd_inputs = tuple(k.gettuple(i) for i in range(len(fxn.src)))
-      else:
-        fwd_subs = {fxn: fxn.param_like(len(args) + 1)}
-        fwd_inputs = (k,)
-      grad_uops = [g.substitute(fwd_subs) for g in grad_uops]
-      # build a single backward CALL returning a TUPLE of all gradients
-      bwd_body = UOp.maketuple(*grad_uops)
-      bwd_call = bwd_body.call(*args, *grad_ctx_inputs, *fwd_inputs, name=name+"_backward", precompile=precompile_backward)
-      # extract each gradient via GETTUPLE
-      ret: list[UOp|None] = []
-      gi = 0
-      for i in range(len(args)):
-        if gi < len(grad_indices) and grad_indices[gi] == i:
-          ret.append(bwd_call.gettuple(gi))
-          gi += 1
-        else:
-          ret.append(None)
-      return tuple(ret)
-    return grad_fxn
+    ret = uret.call(*call_uops, name=name, precompile=self.precompile)
+    return cast(ReturnType, Tensor(ret, device=ret.device))
 
 # overload signatures support both @function and @function(precompile=True) syntax
 @overload
-def function(fxn:Callable[..., ReturnType], *, precompile:bool=False, precompile_backward:bool=False) -> _function[ReturnType]: ...
+def function(fxn:Callable[..., ReturnType], *, precompile:bool=False) -> _function[ReturnType]: ...
 @overload
-def function(fxn:None=None, *, precompile:bool=False, precompile_backward:bool=False) -> \
-  Callable[[Callable[..., ReturnType]], _function[ReturnType]]: ...
-def function(fxn=None, *, precompile:bool=False, precompile_backward:bool=False):
-  if fxn is None: return lambda f: _function(f, precompile=precompile, precompile_backward=precompile_backward)
-  return _function(fxn, precompile=precompile, precompile_backward=precompile_backward)
+def function(fxn:None=None, *, precompile:bool=False) -> Callable[[Callable[..., ReturnType]], _function[ReturnType]]: ...
+def function(fxn=None, *, precompile:bool=False):
+  if fxn is None: return lambda f: _function(f, precompile=precompile)
+  return _function(fxn, precompile=precompile)

tinygrad/gradient.py

@@ -13,53 +13,18 @@ def reduce_gradient(ctx:UOp, ret:UOp, op:Ops):
     return ((mask/broadcast_to_input(count)) * broadcast_to_input(ctx),)
   if op == Ops.MUL: return (broadcast_to_input(ctx * ret) / ret.src[0],)
 
-def call_gradient(ctx:UOp|dict[int, UOp], k:UOp) -> tuple[UOp|None, ...]:
-  if k.arg.grad_fxn is not None:
-    if isinstance(ctx, dict): return (None,) + k.arg.grad_fxn(ctx, k)
-    return (None,) + k.arg.grad_fxn(ctx, k)
+def call_gradient(ctx:UOp, k:UOp) -> tuple[UOp|None, ...]:
+  if k.arg.grad_fxn is not None: return (None,) + k.arg.grad_fxn(ctx, k)
   # auto-differentiate the function
   fxn, args = k.src[0], k.src[1:]
   params = {x.arg:x for x in fxn.toposort(enter_calls=False) if x.op == Ops.PARAM}
-  # for tuple-returning CALLs, ctx is a dict mapping output index to gradient; differentiate each output separately and sum
-  if isinstance(ctx, dict):
-    all_grads: dict[UOp, UOp] = {}
-    for idx, grad_out in ctx.items():
-      elem_grads = compute_gradient(fxn.src[idx], grad_out.param_like(len(args) + idx), set(params.values()))
-      for p, g in elem_grads.items():
-        if p in all_grads: all_grads[p] = all_grads[p] + g
-        else: all_grads[p] = g
-    grads = all_grads
-    grad_ctx_inputs = tuple(ctx.get(i, fxn.src[i].const_like(0)) for i in range(len(fxn.src)))
-  else:
-    grads = compute_gradient(fxn, ctx.param_like(len(args)), set(params.values()))
-    grad_ctx_inputs = (ctx,)
-  # collect which args have gradients
-  grad_indices: list[int] = []
-  grad_uops: list[UOp] = []
+  grads = compute_gradient(fxn, ctx.param_like(len(args)), set(params.values()))
+  ret: list[UOp|None] = [None]
   for i in range(len(args)):
     if (p:=params.get(i, None)) is not None and p in grads:
+      # TODO: compact the args and remove unused ones
       assert not grads[p].op_in_backward_slice_with_self(Ops.BUFFER), "BUG: BUFFER in backward slice of grad"
-      grad_indices.append(i)
-      grad_uops.append(grads[p])
-  if len(grad_uops) == 0: return (None,) * (len(args) + 1)
-  # replace forward output references with PARAMs, passing the forward CALL output(s) as inputs to avoid recomputation
-  if fxn.op is Ops.TUPLE:
-    fwd_subs = {elem: elem.param_like(len(args) + len(grad_ctx_inputs) + i) for i, elem in enumerate(fxn.src)}
-    fwd_inputs = tuple(k.gettuple(i) for i in range(len(fxn.src)))
-  else:
-    fwd_subs = {fxn: fxn.param_like(len(args) + 1)}
-    fwd_inputs = (k,)
-  grad_uops = [g.substitute(fwd_subs) for g in grad_uops]
-  # build a single backward CALL returning a TUPLE of all gradients
-  bwd_body = UOp.maketuple(*grad_uops)
-  bwd_call = bwd_body.call(*args, *grad_ctx_inputs, *fwd_inputs, name=(k.arg.name or "")+"_backward", precompile=k.arg.precompile_backward)
-  # extract each gradient via GETTUPLE
-  ret: list[UOp|None] = [None]
-  gi = 0
-  for i in range(len(args)):
-    if gi < len(grad_indices) and grad_indices[gi] == i:
-      ret.append(bwd_call.gettuple(gi))
-      gi += 1
+      ret.append(grads[p].call(*args, ctx, name=(k.arg.name or "")+f"_backward_{i}"))
     else:
       ret.append(None)
   return tuple(ret)
@@ -107,26 +72,11 @@ def _deepwalk(root:UOp, targets:set[UOp]) -> list[UOp]:
   return list(root.toposort(lambda node: node.op not in {Ops.DETACH, Ops.ASSIGN} and in_target_path[node]))
 
 def compute_gradient(root:UOp, root_grad:UOp, targets:set[UOp]) -> dict[UOp, UOp]:
-  grads: dict[UOp, UOp] = {root: root_grad}
-  # for GETTUPLE nodes on tuple-returning CALLs, collect per-output gradients
-  tuple_call_grads: dict[UOp, dict[int, UOp]] = {}
+  grads = {root: root_grad}
   for t0 in reversed(_deepwalk(root, targets)):
-    if t0 not in grads and t0 not in tuple_call_grads: continue
-    # for tuple-returning CALLs, use accumulated per-output gradients
-    if t0.op is Ops.CALL and t0 in tuple_call_grads:
-      lgrads = cast(tuple[UOp|None, ...], call_gradient(tuple_call_grads[t0], t0))
-    elif t0 not in grads:
-      continue
-    # for GETTUPLE on a CALL, accumulate gradient per output index instead of propagating to CALL directly
-    elif t0.op is Ops.GETTUPLE and t0.src[0].op is Ops.CALL:
-      call = t0.src[0]
-      if call not in tuple_call_grads: tuple_call_grads[call] = {}
-      if t0.arg in tuple_call_grads[call]: tuple_call_grads[call][t0.arg] = tuple_call_grads[call][t0.arg] + grads[t0]
-      else: tuple_call_grads[call][t0.arg] = grads[t0]
-      continue
-    else:
-      lgrads = cast(tuple[UOp|None, ...]|None, pm_gradient.rewrite(t0, ctx=grads[t0]))
-      if lgrads is None: raise RuntimeError(f"failed to compute gradient for {t0.op}\n\nin {str(t0)[0:1000]}...")
+    if t0 not in grads: continue
+    lgrads: tuple[UOp|None, ...]|None = cast(tuple[UOp|None, ...]|None, pm_gradient.rewrite(t0, ctx=grads[t0]))
+    if lgrads is None: raise RuntimeError(f"failed to compute gradient for {t0.op}\n\nin {str(t0)[0:1000]}...")
     assert len(lgrads) == len(t0.src), f"got {len(lgrads)} gradient, expected {len(t0.src)}"
     for k,v in zip(t0.src, lgrads):
       if v is None: continue

tinygrad/helpers.py

@@ -424,9 +424,7 @@ def fetch(url:str, name:pathlib.Path|str|None=None, subdir:str|None=None, gunzip
 
 def system(cmd:str, **kwargs) -> str:
   st = time.perf_counter()
-  try: ret = subprocess.check_output(cmd.split(), stderr=subprocess.STDOUT, **kwargs).decode().strip()
-  except subprocess.CalledProcessError as e:
-    raise RuntimeError(f"system: '{cmd}' failed with exit code {e.returncode}\n{(e.output or b'').decode().strip()}") from e
+  ret = subprocess.check_output(cmd.split(), **kwargs).decode().strip()
   if DEBUG >= 1: print(f"system: '{cmd}' returned {len(ret)} bytes in {(time.perf_counter() - st)*1e3:.2f} ms")
   return ret
 

tinygrad/nn/__init__.py

@@ -303,7 +303,7 @@ class RMSNorm:
     x = self._norm(x.float()).cast(x.dtype)
     return x if self.weight is None else x * self.weight
 
-from tinygrad.uop.ops import UOp, KernelInfo, Ops, AxisType
+from tinygrad.uop.ops import UOp, KernelInfo, Ops
 def _embedding_bwd(grad_emb:UOp, call:UOp) -> tuple:
   weight, idx = call.src[1:]
   # for multi-device: unshard inputs to one device
@@ -326,29 +326,15 @@ def _embedding_bwd(grad_emb:UOp, call:UOp) -> tuple:
   # this is the real atomic kernel
   def _embedding_bwd_kernel(grad_weight:UOp, grad_emb:UOp, idx:UOp) -> UOp:
     idx_flat, grad_emb_flat = idx.flatten(), grad_emb.reshape((idx.size, grad_weight.shape[-1]))
-
     i = UOp.range(grad_emb_flat.shape[0], 0)  # batch_size * sequence_length
     j = UOp.range(grad_emb_flat.shape[1], 1)  # embed_size
-
-    embed_size = grad_weight.shape[-1]
-    BLOCK_J = min(256, embed_size)
-    assert embed_size % BLOCK_J == 0, f"embed_size {embed_size} must be divisible by {BLOCK_J}"
-
-    n_j_blocks = embed_size // BLOCK_J
-    i = UOp.range(grad_emb_flat.shape[0], 0)         # batch_size * sequence_length -> GLOBAL
-    j_inner = UOp.range(BLOCK_J, 2, AxisType.LOOP if device in ("CPU", "NULL") else AxisType.LOCAL)  # BLOCK_J threads per workgroup
-    j_outer = UOp.range(n_j_blocks, 1)
-    j = j_outer * BLOCK_J + j_inner
-
     token_id = idx_flat[i].clip(0, grad_weight.shape[0]-1).cast(dtypes.index)
-
     # atomic scatter-add: grad_weight[token_id, j] += grad_emb_flat[i, j]
     if device in ("CPU", "NULL"): atomic_arg = "__atomic_fetch_add({0}, {1}, __ATOMIC_RELAXED);"
     elif device == "AMD": atomic_arg = "__hip_atomic_fetch_add({0}, {1}, __ATOMIC_RELAXED, __HIP_MEMORY_SCOPE_AGENT);"
     else: raise NotImplementedError(f"no atomics for device {device}")
     atomic = UOp(Ops.CUSTOM, dtypes.void, (grad_weight.index(token_id, j, ptr=True), grad_emb_flat[i, j].cast(dtypes.float)), arg = atomic_arg)
-    return atomic.end(i, j_outer, j_inner).sink(arg=KernelInfo(name="embedding_bwd", opts_to_apply=()))
-
+    return atomic.end(i, j).sink(arg=KernelInfo(name="embedding_bwd", opts_to_apply=()))
   grad_weight_uop = grad_weight_uop.custom_kernel(grad_emb, idx, fxn=_embedding_bwd_kernel)[0]
 
   return (grad_weight_uop.cast(weight.dtype), None)

tinygrad/nn/onnx.py

@@ -497,7 +497,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     o_ = [((i - 1) // s + 1) for i,s in zip(i_, s_)]
     return _onnx_pads_to_tiny_pads(_auto_pad([(o-1)*s+k-i for o,i,k,s in zip(o_, i_, k_, s_)], auto_pad))
 
-  def _clamp_cast(x:Tensor, dtype:DType): return x.clamp(dtype.min, dtype.max).cast(dtype)
+  def _clamp_cast(x:Tensor, dtype:DType): return x.clamp(dtypes.min(dtype), dtypes.max(dtype)).cast(dtype)
 
   def _prepare_quantize(x:Tensor, scale:Tensor, zero_point:Tensor|int, axis=1, block_size=0):
     if axis < 0: axis += x.ndim
@@ -1209,7 +1209,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
 
   def DynamicQuantizeLinear(x: Tensor):
     # only support uint8
-    qmin, qmax = dtypes.uint8.min, dtypes.uint8.max
+    qmin, qmax = dtypes.min(dtypes.uint8), dtypes.max(dtypes.uint8)
     scale = (x.max().maximum(0) + ((-x).max()).maximum(0)) / (qmax - qmin)
     zero_point = _clamp_cast((qmin - x.min() / scale).round(), dtypes.uint8)
     y = _clamp_cast((x / scale).round() + zero_point, dtypes.uint8)

tinygrad/renderer/amd/sqtt.py

@@ -126,9 +126,6 @@ class InstOpRDNA4(Enum):
   LDS_WR_3 = 0x2c
   LDS_WR_4 = 0x2d
   LDS_WR_5 = 0x2e
-  OTHER_LDS_1 = 0x50
-  OTHER_LDS_2 = 0x51
-  BARRIER_SIGNAL = 0x7a
   WMMA_8 = 0x8c
   WMMA_16 = 0x8d
   VALU_DPFP = 0x92
@@ -384,27 +381,25 @@ PACKET_TYPES_RDNA4: dict[int, type[PacketType]] = {
 # CDNA PACKET TYPE DEFINITIONS
 # ═══════════════════════════════════════════════════════════════════════════════
 
-class CDNA_MISC(PacketType):
-  """pkt_fmt=0: 16-bit (Misc)"""
+class CDNA_DELTA(PacketType):
+  """pkt_fmt=0: 16-bit timestamp delta packet"""
   encoding = bits[3:0] == 0
-  delta = bits[11:4]
-  sh = bits[12:12]
-  misc_type = bits[15:13]
+  delta = bits[11:4]      # (data >> 4) & 0xff
+  unk_0 = bits[12:12]     # (data >> 0xc) & 1
+  unk_1 = bits[15:13]     # (data >> 0xd)
 
 class CDNA_TIMESTAMP(PacketType):
   """pkt_fmt=1: 64-bit timestamp packet (case 0x0)"""
   encoding = bits[3:0] == 1
-  _reserved = bits[15:4]
+  unk_0 = bits[15:4]
   timestamp = bits[63:16]   # stored as (data_word >> 0x10) in low 46 bits of local_58
 
-class CDNA_REG(PacketType):
-  """pkt_fmt=2: 64-bit (Reg)"""
+class CDNA_PKT_2(PacketType):
+  """pkt_fmt=2: 64-bit packet (case 0x4)"""
   encoding = bits[3:0] == 2
-  pipe = bits[6:5]
-  _me_raw = bits[8:7]
-  _reserved = bits[15:9]
-  regaddr = bits[31:16]
-  regdata = bits[63:32]
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[7:7]       # (data >> 7) + 1 & 1
+  unk_padding = bits[63:8]
 
 class CDNA_WAVESTART(PacketType):
   """type 3: 32-bit wave start (Wave/group_id)"""
@@ -415,19 +410,19 @@ class CDNA_WAVESTART(PacketType):
   simd = bits[15:14]
   pipe = bits[17:16]
   me = bits[19:18]
-  _reserved = bits[21:20]
+  _gap = bits[21:20]
   count = bits[28:22]
   _padding = bits[31:29]
 
-class CDNA_WAVEALLOC(PacketType):
-  """pkt_fmt=4: 16-bit (Wave)"""
+class CDNA_PKT_4(PacketType):
+  """pkt_fmt=4: 16-bit packet (case 0xc, same as 0x8/0x14)"""
   encoding = bits[3:0] == 4
-  sh = bits[5:5]
-  cu = bits[9:6]
-  wave = bits[13:10]
-  simd = bits[15:14]
+  unk_0 = bits[5:5]       # (data_word >> 5) & 1
+  unk_1 = bits[9:6]       # (data_word >> 6) & 0xf
+  unk_2 = bits[13:10]     # (data_word >> 10) & 0xf
+  unk_3 = bits[15:14]     # (data_word >> 0xe)
 
-class CDNA_REG_CS(PacketType):
+class REGCS_CDNA(PacketType):
   """type 5: 48-bit register CS write (RegCs)"""
   encoding = bits[3:0] == 5
   pipe = bits[6:5]
@@ -443,86 +438,80 @@ class CDNA_WAVEEND(PacketType):
   wave = bits[13:10]
   simd = bits[15:14]
 
-class CDNA_INST(PacketType):
-  """pkt_fmt=10: 16-bit (MsgInst)"""
+class CDNA_EXEC(PacketType):
+  """pkt_fmt=10: 16-bit EXEC packet (case 0x24)"""
   encoding = bits[3:0] == 10
-  wave = bits[8:5]
-  simd = bits[10:9]
-  inst_type = bits[15:11]
+  unk_0 = bits[8:5]       # (data_word >> 5) & 0xf
+  unk_1 = bits[10:9]      # (data_word >> 9) & 3
+  unk_2 = bits[15:11]     # (data_word >> 0xb)
 
-class CDNA_INST_PC(PacketType):
-  """pkt_fmt=11: 64-bit (MsgInstPc)"""
+class CDNA_PKT_11(PacketType):
+  """pkt_fmt=11: 64-bit packet (case 0x28)"""
   encoding = bits[3:0] == 11
-  wave = bits[8:5]
-  simd = bits[10:9]
-  _reserved = bits[14:11]
-  err = bits[15:15]
-  pc = bits[63:16]
+  unk_0 = bits[8:5]       # (data_word >> 5) & 0xf
+  unk_1 = bits[10:9]      # (data_word >> 9) & 3
+  unk_2 = bits[15:15]     # (data_word >> 0xf) & 1
+  unk_padding = bits[63:16]
 
-class CDNA_ISSUE(PacketType):
-  """pkt_fmt=13: 32-bit (Issue)"""
+class CDNA_INST(PacketType):
+  """pkt_fmt=13: 32-bit INST packet (case 0x30)"""
   encoding = bits[3:0] == 13
-  simd = bits[6:5]
-  _gap = bits[7:7]
-  inst0 = bits[9:8]
-  inst1 = bits[11:10]
-  inst2 = bits[13:12]
-  inst3 = bits[15:14]
-  inst4 = bits[17:16]
-  inst5 = bits[19:18]
-  inst6 = bits[21:20]
-  inst7 = bits[23:22]
-  inst8 = bits[25:24]
-  inst9 = bits[27:26]
-  _padding = bits[31:28]
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[9:8]       # (data >> 8) & 3
+  unk_2 = bits[11:10]     # (data >> 10) & 3
+  unk_3 = bits[13:12]     # (data >> 0xc) & 3
+  unk_4 = bits[15:14]     # (data >> 0xe) & 3
+  unk_5 = bits[19:18]     # (data >> 0x12) & 3
+  unk_6 = bits[21:20]     # (data >> 0x14) & 3
+  unk_7 = bits[23:22]     # (data >> 0x16) & 3
+  unk_8 = bits[25:24]     # (data >> 0x18) & 3
+  unk_9 = bits[27:26]     # (data >> 0x1a) & 3
+  unk_padding = bits[31:28]
 
-class CDNA_PERF(PacketType):
-  """pkt_fmt=14: 64-bit (MsgPerf)"""
+class CDNA_PKT_14(PacketType):
+  """pkt_fmt=14: 64-bit packet (case 0x34)"""
   encoding = bits[3:0] == 14
-  sh = bits[5:5]
-  cu = bits[9:6]
-  cntr_bank = bits[11:10]
-  cntr0 = bits[24:12]
-  cntr1 = bits[37:25]
-  cntr2 = bits[50:38]
-  cntr3 = bits[63:51]
+  unk_0 = bits[5:5]       # (data >> 5) & 1
+  unk_1 = bits[9:6]       # (data >> 6) & 0xf
+  unk_2 = bits[11:10]     # (data >> 10) & 3
+  unk_3 = bits[24:12]     # (data >> 0xc) & 0x1fff
+  unk_4 = bits[37:25]     # (data >> 0x19) & 0x1fff
+  unk_5 = bits[50:38]     # (data >> 0x26) & 0x1fff
+  unk_6 = bits[51:51]     # (data >> 0x33) & 1
+  unk_padding = bits[63:52]
 
-class CDNA_EVENT(PacketType):
-  """pkt_fmt=7: 16-bit"""
+class CDNA_PKT_7(PacketType):
+  """pkt_fmt=7: 16-bit packet"""
   encoding = bits[3:0] == 7
-  _reserved = bits[15:4]
+  unk_padding = bits[15:4]
 
-class CDNA_EVENT_CS(PacketType):
-  """pkt_fmt=8: 16-bit"""
+class CDNA_PKT_8(PacketType):
+  """pkt_fmt=8: 16-bit packet"""
   encoding = bits[3:0] == 8
-  _reserved = bits[15:4]
+  unk_padding = bits[15:4]
 
-class CDNA_EVENT_GFX1(PacketType):
-  """pkt_fmt=9: 16-bit"""
+class CDNA_PKT_9(PacketType):
+  """pkt_fmt=9: 16-bit packet"""
   encoding = bits[3:0] == 9
-  _reserved = bits[15:4]
+  unk_padding = bits[15:4]
 
-class CDNA_USERDATA(PacketType):
-  """pkt_fmt=12: 48-bit (UserData)"""
+class CDNA_PKT_12(PacketType):
+  """pkt_fmt=12: 48-bit packet"""
   encoding = bits[3:0] == 12
-  sh = bits[5:5]
-  cu = bits[9:6]
-  wave = bits[13:10]
-  simd = bits[15:14]
-  data = bits[47:16]
+  unk_padding = bits[47:4]
 
-class CDNA_REG_CS_PRIV(PacketType):
-  """pkt_fmt=15: 48-bit (RegCs)"""
+class CDNA_PKT_15(PacketType):
+  """pkt_fmt=15: 48-bit packet (case 0x38, same as 0x10)"""
   encoding = bits[3:0] == 15
-  pipe = bits[6:5]
-  _me_raw = bits[8:7]
-  regaddr = bits[15:9]
-  regdata = bits[47:16]
+  unk_0 = bits[6:5]       # (data >> 5) & 3
+  unk_1 = bits[7:7]       # (data >> 7) + 1 & 1
+  unk_2 = bits[15:9]      # (data >> 9) & 0x7f
+  unk_padding = bits[47:16]
 
 PACKET_TYPES_CDNA: dict[int, type[PacketType]] = {
-  0: CDNA_MISC, 1: CDNA_TIMESTAMP, 2: CDNA_REG, 3: CDNA_WAVESTART, 4: CDNA_WAVEALLOC, 5: CDNA_REG_CS, 6: CDNA_WAVEEND,
-  7: CDNA_EVENT, 8: CDNA_EVENT_CS, 9: CDNA_EVENT_GFX1, 10: CDNA_INST, 11: CDNA_INST_PC, 12: CDNA_USERDATA,
-  13: CDNA_ISSUE, 14: CDNA_PERF, 15: CDNA_REG_CS_PRIV, 16: LAYOUT_HEADER,
+  0: CDNA_DELTA, 1: CDNA_TIMESTAMP, 2: CDNA_PKT_2, 3: CDNA_WAVESTART, 4: CDNA_PKT_4, 5: REGCS_CDNA, 6: CDNA_WAVEEND,
+  7: CDNA_PKT_7, 8: CDNA_PKT_8, 9: CDNA_PKT_9, 10: CDNA_EXEC, 11: CDNA_PKT_11, 12: CDNA_PKT_12,
+  13: CDNA_INST, 14: CDNA_PKT_14, 15: CDNA_PKT_15,
 }
 
 # ═══════════════════════════════════════════════════════════════════════════════
@@ -534,8 +523,8 @@ def _build_decode_tables(packet_types: dict[int, type[PacketType]]) -> tuple[dic
   sorted_types = sorted(packet_types.items(), key=lambda x: (-bin(x[1].encoding.mask).count('1'), x[0] == 16))
   state_table = bytes(next((op for op, cls in sorted_types if (b & cls.encoding.mask) == cls.encoding.default), 16) for b in range(256))
   # Build decode info: opcode -> (pkt_cls, nib_count, delta_lo, delta_mask, special_case)
-  # special_case: 0=none, 1=TS_DELTA_OR_MARK (check is_marker), 2=TS_DELTA_SHORT (add 8), 3=CDNA_MISC (*4), 4=CDNA_TIMESTAMP (absolute)
-  _special = {TS_DELTA_OR_MARK: 1, TS_DELTA_OR_MARK_RDNA4: 1, TS_DELTA_SHORT: 2, CDNA_MISC: 3, CDNA_TIMESTAMP: 4}
+  # special_case: 0=none, 1=TS_DELTA_OR_MARK (check is_marker), 2=TS_DELTA_SHORT (add 8), 3=CDNA_DELTA (*4), 4=CDNA_TIMESTAMP (absolute)
+  _special = {TS_DELTA_OR_MARK: 1, TS_DELTA_OR_MARK_RDNA4: 1, TS_DELTA_SHORT: 2, CDNA_DELTA: 3, CDNA_TIMESTAMP: 4}
   decode_info = {}
   for opcode, pkt_cls in packet_types.items():
     delta_field = getattr(pkt_cls, 'delta', None)

tinygrad/runtime/ops_amd.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-from typing import cast
+from typing import cast, ClassVar
 import os, ctypes, struct, hashlib, functools, importlib, mmap, errno, array, contextlib, sys, weakref, itertools, collections, atexit
 assert sys.platform != 'win32'
 from dataclasses import dataclass
@@ -11,7 +11,7 @@ from tinygrad.helpers import getenv, round_up, data64_le, DEBUG, PROFILE, Profil
 from tinygrad.helpers import VIZ, AMD_CC, AMD_LLVM, AMD_HIPCC, ceildiv, unwrap
 from tinygrad.renderer.cstyle import AMDHIPRenderer, AMDHIPCCRenderer
 from tinygrad.renderer.llvmir import AMDLLVMRenderer
-from tinygrad.runtime.autogen import kfd, hsa, sqtt, amdgpu_kd, amdgpu_drm
+from tinygrad.runtime.autogen import kfd, hsa, pci, sqtt, amdgpu_kd, amdgpu_drm
 from tinygrad.runtime.autogen.am import am
 from tinygrad.runtime.support.elf import elf_loader
 from tinygrad.runtime.support.am.amdev import AMDev, AMMemoryManager
@@ -824,16 +824,19 @@ class KFDIface:
   def is_wgp_active(self, xcc, se, sa, wgp) -> bool: return ((self.drm_dev_info.cu_bitmap[se % 4][sa + (se // 4) * 2] >> (2 * wgp)) & 0x3) == 0x3
 
 class PCIIface(PCIIfaceBase):
+  gpus:ClassVar[list[str]] = []
+
   def __init__(self, dev, dev_id):
     super().__init__(dev, dev_id, vendor=0x1002, devices=[(0xffff, [0x74a1, 0x744c, 0x7480, 0x7550, 0x7590, 0x75a0])], bars=[0, 2, 5], vram_bar=0,
       va_start=AMMemoryManager.va_allocator.base, va_size=AMMemoryManager.va_allocator.size)
     self._setup_adev(self.pci_dev)
+    self.pci_dev.write_config(pci.PCI_COMMAND, self.pci_dev.read_config(pci.PCI_COMMAND, 2) | pci.PCI_COMMAND_MASTER, 2)
 
   def require_profile_mode(self): return True
   def is_wgp_active(self, xcc, se, sa, wgp) -> bool: return True # TODO: account for WGP disablement on some asics.
 
-  def _setup_adev(self, pci_dev:PCIDevice):
-    self.dev_impl:AMDev = AMDev(pci_dev)
+  def _setup_adev(self, pci_dev:PCIDevice, dma_regions:list[tuple[int, MMIOInterface]]|None=None):
+    self.dev_impl:AMDev = AMDev(pci_dev, dma_regions)
     self.ip_versions = self.dev_impl.ip_ver
 
     gfxver = int(f"{self.dev_impl.ip_ver[am.GC_HWIP][0]:02d}{self.dev_impl.ip_ver[am.GC_HWIP][1]:02d}{self.dev_impl.ip_ver[am.GC_HWIP][2]:02d}")
@@ -891,7 +894,7 @@ class PCIIface(PCIIfaceBase):
 class USBIface(PCIIface):
   def __init__(self, dev, dev_id): # pylint: disable=super-init-not-called
     self.dev, self.pci_dev = dev, USBPCIDevice(dev.__class__.__name__[:2], f"usb:{dev_id}", bars=[0, 2, 5])
-    self._setup_adev(self.pci_dev)
+    self._setup_adev(self.pci_dev, dma_regions=[(0x200000, self.pci_dev.dma_view(0xf000, 0x80000))])
     self.pci_dev.usb._pci_cacheable += [(self.pci_dev.bar_info[2].addr, self.pci_dev.bar_info[2].size)] # doorbell region is cacheable
 
     # special regions
@@ -902,7 +905,7 @@ class USBIface(PCIIface):
     region = self.dev_impl.mm.map_range(vaddr:=self.dev_impl.mm.alloc_vaddr(size=size), size, [(sys_addr, size)], aspace=AddrSpace.SYS, uncached=True)
     return HCQBuffer(vaddr, size, meta=PCIAllocationMeta(region, has_cpu_mapping=False), view=self.pci_dev.dma_view(ctrl_addr, size), owner=self.dev)
 
-  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, force_devmem=False, **kwargs) -> HCQBuffer:
+  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, **kwargs) -> HCQBuffer:
     if (host or (uncached and cpu_access)) and self.sys_next_off + size < self.sys_buf.size:
       self.sys_next_off += size
       return self.sys_buf.offset(self.sys_next_off - size, size)

tinygrad/runtime/ops_nv.py

@@ -1,7 +1,7 @@
 from __future__ import annotations
 import os, ctypes, contextlib, re, functools, mmap, struct, array, sys, weakref
 assert sys.platform != 'win32'
-from typing import cast
+from typing import cast, ClassVar
 from dataclasses import dataclass
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQBuffer, HWQueue, CLikeArgsState, HCQProgram, HCQSignal, BumpAllocator
 from tinygrad.runtime.support.hcq import MMIOInterface, FileIOInterface, MOCKGPU, hcq_filter_visible_devices, hcq_profile
@@ -11,7 +11,7 @@ from tinygrad.helpers import getenv, mv_address, round_up, data64, data64_le, pr
 from tinygrad.helpers import ContextVar, VIZ, ProfileEvent
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.cstyle import CUDARenderer
-from tinygrad.runtime.autogen import nv_570, nv_580, mesa
+from tinygrad.runtime.autogen import nv_570, nv_580, pci, mesa
 from tinygrad.runtime.support.elf import elf_loader
 from tinygrad.runtime.support.nv.nvdev import NVDev, NVMemoryManager
 from tinygrad.runtime.support.system import System, PCIIfaceBase, MAP_FIXED
@@ -535,6 +535,8 @@ class NVKIface:
   def sleep(self, tm:int): pass
 
 class PCIIface(PCIIfaceBase):
+  gpus:ClassVar[list[str]] = []
+
   def __init__(self, dev, dev_id):
     # PCIIface's MAP_FIXED mmap will overwrite UVM allocations made by NVKIface, so don't try PCIIface if kernel driver was already used.
     if NVKIface.root is not None: raise RuntimeError("Cannot use PCIIface after NVKIface has been initialized (would corrupt UVM memory)")
@@ -542,6 +544,7 @@ class PCIIface(PCIIfaceBase):
       base_class=0x03, bars=[0, 1, 3], vram_bar=1, va_start=NVMemoryManager.va_allocator.base, va_size=NVMemoryManager.va_allocator.size)
     if not OSX: System.reserve_hugepages(64)
 
+    self.pci_dev.write_config(pci.PCI_COMMAND, self.pci_dev.read_config(pci.PCI_COMMAND, 2) | pci.PCI_COMMAND_MASTER, 2)
     self.dev_impl:NVDev = NVDev(self.pci_dev)
     self.root, self.gpu_instance = 0xc1000000, 0
     self.rm_alloc(0, nv_gpu.NV01_ROOT, nv_gpu.NV0000_ALLOC_PARAMETERS())
@@ -550,11 +553,11 @@ class PCIIface(PCIIfaceBase):
     self.gpfifo_class, self.compute_class, self.dma_class = (gsp:=self.dev_impl.gsp).gpfifo_class, gsp.compute_class, gsp.dma_class
     self.viddec_class = None
 
-  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, force_devmem=False, **kwargs) -> HCQBuffer:
+  def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, **kwargs) -> HCQBuffer:
     # Force use of huge pages for large allocations. NVDev will attempt to use huge pages in any case,
     # but if the size is not aligned, the tail will be allocated with 4KB pages, increasing TLB pressure.
-    return super().alloc(round_up(size, mmap.PAGESIZE if uncached or host else ((2 << 20) if size >= (8 << 20) else (4 << 10))),
-      host=host, uncached=uncached, cpu_access=cpu_access, contiguous=contiguous, force_devmem=force_devmem, **kwargs)
+    page_size = mmap.PAGESIZE if uncached or host else ((2 << 20) if size >= (8 << 20) else (4 << 10))
+    return super().alloc(round_up(size, page_size), host=host, uncached=uncached, cpu_access=cpu_access, contiguous=contiguous, **kwargs)
 
   def setup_usermode(self): return 0xce000000, self.pci_dev.map_bar(bar=0, fmt='I', off=0xbb0000, size=0x10000)
   def setup_vm(self, vaspace): pass

tinygrad/runtime/support/am/amdev.py

@@ -1,8 +1,8 @@
 from __future__ import annotations
 import ctypes, collections, dataclasses, functools, hashlib, array
 from tinygrad.helpers import mv_address, getenv, DEBUG, fetch, lo32, hi32
-from tinygrad.runtime.autogen import pci
 from tinygrad.runtime.autogen.am import am
+from tinygrad.runtime.support.hcq import MMIOInterface
 from tinygrad.runtime.support.amd import AMDReg, import_module, import_asic_regs
 from tinygrad.runtime.support.memory import TLSFAllocator, MemoryManager, AddrSpace
 from tinygrad.runtime.support.system import PCIDevice, PCIDevImplBase
@@ -146,8 +146,8 @@ class AMMemoryManager(MemoryManager):
 class AMDev(PCIDevImplBase):
   Version = 0xA0000008
 
-  def __init__(self, pci_dev:PCIDevice, reset_mode=False):
-    self.pci_dev, self.devfmt = pci_dev, pci_dev.pcibus
+  def __init__(self, pci_dev:PCIDevice, dma_regions:list[tuple[int, MMIOInterface]]|None=None, reset_mode=False):
+    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()
@@ -185,7 +185,6 @@ class AMDev(PCIDevImplBase):
 
     # Re-initialize main blocks
     self.init_hw(self.gfx, self.sdma)
-    self.pci_dev.write_config(pci.PCI_COMMAND, self.pci_dev.read_config(pci.PCI_COMMAND, 2) | pci.PCI_COMMAND_MASTER, 2)
 
     self.smu.set_clocks(level=-1) # last level, max perf.
     for ip in [self.soc, self.gfx]: ip.set_clockgating_state()

tinygrad/runtime/support/am/ip.py

@@ -547,10 +547,10 @@ class AM_PSP(AM_IP):
   def init_sw(self):
     self.reg_pref = "regMP0_SMN_C2PMSG" if self.adev.ip_ver[am.MP0_HWIP] < (14,0,0) else "regMPASP_SMN_C2PMSG"
 
-    if self.adev.devfmt.startswith("usb:"):
-      self.msg1_view, paddrs = self.adev.pci_dev.alloc_sysmem(512 << 10)
-      self.msg1_addr = self.adev.mm.alloc_vaddr(size=self.msg1_view.nbytes, align=am.PSP_1_MEG)
-      self.adev.mm.map_range(self.msg1_addr, self.msg1_view.nbytes, [(paddrs[0], self.msg1_view.nbytes)], AddrSpace.SYS, uncached=True, boot=True)
+    msg1_region = next((reg for reg in self.adev.dma_regions or [] if reg[1].nbytes >= (512 << 10)), None)
+    if msg1_region is not None:
+      self.msg1_addr, self.msg1_view = self.adev.mm.alloc_vaddr(size=msg1_region[1].nbytes, align=am.PSP_1_MEG), msg1_region[1]
+      self.adev.mm.map_range(self.msg1_addr, msg1_region[1].nbytes, [(msg1_region[0],msg1_region[1].nbytes)], AddrSpace.SYS, uncached=True, boot=True)
     else:
       self.msg1_paddr = self.adev.mm.palloc(am.PSP_1_MEG, align=am.PSP_1_MEG, zero=False, boot=True)
       self.msg1_addr, self.msg1_view = self.adev.paddr2mc(self.msg1_paddr), self.adev.vram.view(self.msg1_paddr, am.PSP_1_MEG, 'B')

tinygrad/runtime/support/c.py

@@ -8,7 +8,6 @@ def _do_ioctl(__idir, __base, __nr, __struct, __fd, *args, __payload=None, **kwa
   assert not WIN, "ioctl not supported"
   import tinygrad.runtime.support.hcq as hcq, fcntl
   ioctl = __fd.ioctl if isinstance(__fd, hcq.FileIOInterface) else functools.partial(fcntl.ioctl, __fd)
-  if __struct is None: return ioctl((__base<<8)|__nr, __payload or (args[0] if args else 0))
   if (rc:=ioctl((__idir<<30)|(ctypes.sizeof(out:=(__payload or __struct(*args, **kwargs)))<<16)|(__base<<8)|__nr, out)):
     raise RuntimeError(f"ioctl returned {rc}")
   return out

tinygrad/runtime/support/nv/nvdev.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 import ctypes, time, functools, re, gzip, struct
 from tinygrad.helpers import getenv, DEBUG, fetch, getbits
-from tinygrad.runtime.autogen import pci
 from tinygrad.runtime.support.memory import TLSFAllocator, MemoryManager, AddrSpace
 from tinygrad.runtime.support.nv.ip import NV_FLCN, NV_FLCN_COT, NV_GSP
 from tinygrad.runtime.support.system import PCIDevice, PCIDevImplBase, MMIOInterface
@@ -73,7 +72,6 @@ class NVMemoryManager(MemoryManager):
 class NVDev(PCIDevImplBase):
   def __init__(self, pci_dev:PCIDevice):
     self.pci_dev, self.devfmt, self.mmio = pci_dev, pci_dev.pcibus, pci_dev.map_bar(0, fmt='I')
-    self.pci_dev.write_config(pci.PCI_COMMAND, self.pci_dev.read_config(pci.PCI_COMMAND, 2) | pci.PCI_COMMAND_MASTER, 2)
 
     self.smi_dev, self.is_booting, self.is_err_state = False, True, False
     self._early_ip_init()

tinygrad/runtime/support/system.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 import os, mmap, array, functools, ctypes, select, contextlib, dataclasses, sys, itertools, struct, socket, subprocess, time, enum
-from tinygrad.helpers import round_up, getenv, OSX, temp, ceildiv, unwrap, fetch, system, _ensure_downloads_dir
+from typing import ClassVar
+from tinygrad.helpers import round_up, getenv, OSX, temp, ceildiv, unwrap, fetch, system
 from tinygrad.runtime.autogen import libc, pci, vfio, iokit, corefoundation
 from tinygrad.runtime.support.hcq import FileIOInterface, MMIOInterface, HCQBuffer, hcq_filter_visible_devices
-from tinygrad.runtime.support.memory import MemoryManager, VirtMapping, AddrSpace, BumpAllocator
+from tinygrad.runtime.support.memory import MemoryManager, VirtMapping, AddrSpace
 from tinygrad.runtime.support.usb import ASM24Controller, USBMMIOInterface
 
 MAP_FIXED, MAP_FIXED_NOREPLACE = 0x10, 0x100000
@@ -43,11 +44,6 @@ class _System:
 
   def reserve_hugepages(self, cnt): os.system(f"sudo sh -c 'echo {cnt} > /proc/sys/vm/nr_hugepages'")
 
-  @functools.cache
-  def reserve_va(self, va_start, va_size):
-    # cached, runs only once per range. used to not collide with other mappings.
-    FileIOInterface.anon_mmap(va_start, va_size, 0, mmap.MAP_PRIVATE | mmap.MAP_ANONYMOUS | MAP_NORESERVE | MAP_FIXED_NOREPLACE, 0)
-
   def memory_barrier(self): lib.atomic_thread_fence(__ATOMIC_SEQ_CST:=5) if (lib:=self.libsys if OSX else self.atomic_lib) is not None else None
 
   def lock_memory(self, addr:int, size:int):
@@ -76,13 +72,6 @@ class _System:
 
     return sorted([val for vndr, device, val in all_devs if vndr == vendor and any((device & mask) in devlist for mask, devlist in devices)])
 
-  def pci_probe_device(self, devpref:str, dev_id:int, vendor:int, devices:list[tuple[int, list[int]]], bars:list[int],
-                       resize_bars:list[int]|None=None, base_class:int|None=None):
-    gpus = hcq_filter_visible_devices(System.pci_scan_bus(vendor, devices, base_class))
-    if not gpus: raise RuntimeError("No supported GPUs found")
-    if OSX: return APLRemotePCIDevice(devpref, f'usb4:{dev_id}', bars)
-    return PCIDevice(devpref, gpus[dev_id], bars=bars, resize_bars=resize_bars)
-
   def pci_setup_usb_bars(self, usb:ASM24Controller, gpu_bus:int, mem_base:int, pref_mem_base:int) -> dict[int, PCIBarInfo]:
     for bus in range(gpu_bus):
       # All 3 values must be written at the same time.
@@ -183,8 +172,8 @@ class PCIDevice:
       self.irq_poller.register(self.irq_fd.fd, select.POLLIN)
 
       irqs = vfio.struct_vfio_irq_set(index=vfio.VFIO_PCI_MSI_IRQ_INDEX, flags=vfio.VFIO_IRQ_SET_DATA_EVENTFD|vfio.VFIO_IRQ_SET_ACTION_TRIGGER,
-        argsz=ctypes.sizeof(vfio.struct_vfio_irq_set) + ctypes.sizeof(ctypes.c_int), count=1)
-      vfio.VFIO_DEVICE_SET_IRQS(self.vfio_dev, (ctypes.c_byte * irqs.argsz).from_buffer(bytearray(bytes(irqs)) + struct.pack('i', self.irq_fd.fd)))
+        argsz=ctypes.sizeof(vfio.struct_vfio_irq_set), count=1, data=(ctypes.c_int * 1)(self.irq_fd.fd))
+      vfio.VFIO_DEVICE_SET_IRQS(self.vfio_dev, irqs)
     else: FileIOInterface(f"/sys/bus/pci/devices/{self.pcibus}/enable", os.O_RDWR).write("1")
 
     self.cfg_fd = FileIOInterface(f"/sys/bus/pci/devices/{self.pcibus}/config", os.O_RDWR | os.O_SYNC | os.O_CLOEXEC)
@@ -212,14 +201,9 @@ class USBPCIDevice(PCIDevice):
     self.lock_fd = System.flock_acquire(f"{devpref.lower()}_{pcibus.lower()}.lock")
     self.usb = ASM24Controller()
     self.pcibus, self.bar_info = pcibus, System.pci_setup_usb_bars(self.usb, gpu_bus=4, mem_base=0x10000000, pref_mem_base=(32 << 30))
-    self.sram = BumpAllocator(size=0x80000, wrap=False) # asm24 controller sram
-  def read_config(self, offset:int, size:int): return self.usb.pcie_cfg_req(offset, bus=4, dev=0, fn=0, size=size)
-  def write_config(self, offset:int, value:int, size:int): self.usb.pcie_cfg_req(offset, bus=4, dev=0, fn=0, value=value, size=size)
   def map_bar(self, bar, off=0, addr=0, size=None, fmt='B'):
     return USBMMIOInterface(self.usb, self.bar_info[bar].addr + off, size or self.bar_info[bar].size, fmt)
   def dma_view(self, ctrl_addr, size): return USBMMIOInterface(self.usb, ctrl_addr, size, fmt='B', pcimem=False)
-  def alloc_sysmem(self, size:int, vaddr:int=0, contiguous:bool=False) -> tuple[MMIOInterface, list[int]]:
-    return self.dma_view(0xf000 + (off:=self.sram.alloc(size)), size), [0x200000 + off]
 
 class PCIDevImplBase:
   mm: MemoryManager
@@ -227,20 +211,22 @@ class PCIDevImplBase:
 @dataclasses.dataclass
 class PCIAllocationMeta: mapping:VirtMapping; has_cpu_mapping:bool; hMemory:int=0 # noqa: E702
 
-class PCIIfaceBase:
+class LNXPCIIfaceBase:
   dev_impl:PCIDevImplBase
-
-  def is_local(self) -> bool: return not isinstance(self.pci_dev, RemotePCIDevice)
-  def is_bar_small(self) -> bool: return self.pci_dev.bar_info[self.vram_bar].size == (256 << 20)
+  gpus:ClassVar[list[str]] = []
 
   def __init__(self, dev, dev_id, vendor, devices:list[tuple[int, list[int]]], bars, vram_bar, va_start, va_size, base_class:int|None=None):
-    self.pci_dev = System.pci_probe_device(dev.__class__.__name__[:2], dev_id, vendor, devices, bars, resize_bars=[vram_bar], base_class=base_class)
-    if self.is_local(): System.reserve_va(va_start, va_size)
-    self.dev, self.vram_bar = dev, vram_bar
+    if len((cls:=type(self)).gpus) == 0:
+      cls.gpus = hcq_filter_visible_devices(System.pci_scan_bus(vendor, devices, base_class))
+
+      # Acquire va range to avoid collisions.
+      FileIOInterface.anon_mmap(va_start, va_size, 0, mmap.MAP_PRIVATE | mmap.MAP_ANONYMOUS | MAP_NORESERVE | MAP_FIXED_NOREPLACE, 0)
+    self.pci_dev, self.dev, self.vram_bar = PCIDevice(dev.__class__.__name__[:2], cls.gpus[dev_id], bars=bars, resize_bars=[vram_bar]), dev, vram_bar
     self.p2p_base_addr = self.pci_dev.bar_info[vram_bar].addr
 
   def alloc(self, size:int, host=False, uncached=False, cpu_access=False, contiguous=False, force_devmem=False, **kwargs) -> HCQBuffer:
-    should_use_sysmem = host or ((cpu_access if self.is_bar_small() else (uncached and cpu_access)) and not force_devmem)
+    # NOTE: logic on macos is different, since bar is small
+    should_use_sysmem = host or ((cpu_access if OSX else (uncached and cpu_access)) and not force_devmem)
     if should_use_sysmem:
       vaddr = self.dev_impl.mm.alloc_vaddr(size:=round_up(size, mmap.PAGESIZE), align=mmap.PAGESIZE)
       memview, paddrs = self.pci_dev.alloc_sysmem(size, vaddr=vaddr, contiguous=contiguous)
@@ -254,16 +240,14 @@ class PCIIfaceBase:
   def free(self, b:HCQBuffer):
     for dev in b.mapped_devs[1:]: dev.iface.dev_impl.mm.unmap_range(b.va_addr, b.size)
     if b.meta.mapping.aspace is AddrSpace.PHYS: self.dev_impl.mm.vfree(b.meta.mapping)
-    if self.is_local() and b.owner == self.dev and b.meta.has_cpu_mapping: FileIOInterface.munmap(b.va_addr, b.size)
+    if b.owner == self.dev and b.meta.has_cpu_mapping and not OSX: FileIOInterface.munmap(b.va_addr, b.size)
 
   def map(self, b:HCQBuffer):
-    if not self.is_local(): raise RuntimeError(f"P2P mapping not supported for remote devices: {b.owner} -> {self.dev}")
-
     if b.owner is not None and b.owner._is_cpu():
       System.lock_memory(int(b.va_addr), b.size)
       paddrs, aspace = [(x, 0x1000) for x in System.system_paddrs(int(b.va_addr), round_up(b.size, 0x1000))], AddrSpace.SYS
       snooped, uncached = True, True
-    elif (ifa:=getattr(b.owner, "iface", None)) is not None and isinstance(ifa, PCIIfaceBase):
+    elif (ifa:=getattr(b.owner, "iface", None)) is not None and isinstance(ifa, LNXPCIIfaceBase):
       snooped, uncached = True, b.meta.mapping.uncached
       if b.meta.mapping.aspace is AddrSpace.SYS: paddrs, aspace = b.meta.mapping.paddrs, AddrSpace.SYS
       elif hasattr(ifa.dev_impl, 'paddr2xgmi') and ifa.dev_impl.gmc.xgmi_seg_sz > 0:
@@ -339,16 +323,13 @@ class RemotePCIDevice(PCIDevice):
 class APLRemotePCIDevice(RemotePCIDevice):
   APP_PATH = "/Applications/TinyGPU.app/Contents/MacOS/TinyGPU"
 
-  @classmethod
-  def ensure_app(cls):
-    commit = "8120b5508b43149d27bf22f9a4e6d7c5a4b401e9"
-    if os.path.exists(cls.APP_PATH) and (_ensure_downloads_dir() / (app_name:=f"TinyGPU_{commit}.zip")).is_file(): return
+  @staticmethod
+  def install_tinygpu():
     print("Downloading TinyGPU.app...")
-    system(f"ditto -xk {fetch(f'https://github.com/nimlgen/tinygpu_releases/raw/{commit}/TinyGPU.zip', name=app_name)} /Applications")
-    print(system(f"{cls.APP_PATH} install"))
+    system(f"ditto -xk {fetch('https://github.com/nimlgen/tinygpu_releases/raw/8120b5508b43149d27bf22f9a4e6d7c5a4b401e9/TinyGPU.zip')} /Applications")
+    print(system(f"{APLRemotePCIDevice.APP_PATH} install"))
 
   def __init__(self, devpref:str, pcibus:str, bars:list[int], resize_bars:list[int]|None=None):
-    self.ensure_app()
     sock_path, sock = getenv("APL_REMOTE_SOCK", temp("tinygpu.sock")), socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
     for i in range(100):
       with contextlib.suppress(ConnectionRefusedError, FileNotFoundError):
@@ -358,3 +339,20 @@ class APLRemotePCIDevice(RemotePCIDevice):
       time.sleep(0.05)
     else: raise RuntimeError(f"Failed to connect to TinyGPU server at {sock_path}.")
     super().__init__(devpref, pcibus, bars, sock)
+
+class APLRemoteIfaceBase(LNXPCIIfaceBase):
+  def __init__(self, dev, dev_id, vendor, devices:list[tuple[int, list[int]]], bars, vram_bar, va_start, va_size, base_class:int|None=None):
+    if not (cls:=type(self)).gpus:
+      cls.gpus = System.pci_scan_bus(vendor, devices, base_class)
+      if not cls.gpus: raise RuntimeError("No supported GPUs found")
+      if not os.path.exists(APLRemotePCIDevice.APP_PATH): APLRemotePCIDevice.install_tinygpu()
+    if dev_id >= len(cls.gpus): raise RuntimeError(f"No device found for {dev_id}. Requesting more devices than the system has ({cls.gpus})?")
+    self.pci_dev = APLRemotePCIDevice(dev.__class__.__name__[:2], f'remote:{dev_id}', bars)
+    self.dev, self.vram_bar = dev, vram_bar
+
+  def free(self, b:HCQBuffer):
+    for dev in b.mapped_devs[1:]: dev.iface.dev_impl.mm.unmap_range(b.va_addr, b.size)
+
+  def map(self, b:HCQBuffer): raise RuntimeError(f"P2P mapping not supported for remote devices: {b.owner} -> {self.dev}")
+
+PCIIfaceBase:type = APLRemoteIfaceBase if OSX else LNXPCIIfaceBase

tinygrad/runtime/support/usb.py

@@ -107,7 +107,7 @@ class USB3:
       if self.use_bot:
         dir_in = rlen > 0
         data_len = rlen if dir_in else (len(send_data) if send_data is not None else 0)
-        assert not (rlen > 0 and send_data is not None), "BOT mode only supports either read or write per command"
+        assert (data_len == 0) if dir_in else (rlen == 0), "BOT mode only supports either read or write per command"
 
         # CBW
         self._tag += 1

tinygrad/schedule/indexing.py

@@ -7,7 +7,7 @@ from tinygrad.uop.ops import consumer_map_from_toposort, gate_kernel_sink
 from tinygrad.uop.symbolic import symbolic, pm_simplify_valid, pm_drop_and_clauses
 from tinygrad.helpers import argsort, all_same, cpu_profile, PCONTIG, colored
 
-ALWAYS_CONTIGUOUS: set[Ops] = {Ops.CONTIGUOUS, Ops.ASSIGN, Ops.AFTER, Ops.COPY, Ops.BUFFER, Ops.BUFFER_VIEW,
+ALWAYS_CONTIGUOUS: set[Ops] = {Ops.CONTIGUOUS, Ops.ASSIGN, Ops.COPY, Ops.BUFFER, Ops.BUFFER_VIEW,
                      Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK, Ops.PARAM,
                      Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.LOAD, Ops.CALL}
 
@@ -25,21 +25,15 @@ def realize_assign_src(ctx:dict[UOp, None], buf:UOp, x:UOp):
   # you don't usually have to do this for assign unless there's a WAR hazard like TestAssign.test_assign_double_diamond_reduce
   if buf.base in x.backward_slice_with_self: ctx[x] = None
 
-def unrealize_store_src(ctx:dict[UOp, None], x:UOp):
-  """Don't realize COPY/BUFFER_VIEW consumed by STORE inside AFTER — bufferize_to_store handles them."""
-  if x in ctx: del ctx[x]
-
 pm_generate_realize_map = PatternMatcher([
+  # always realize SINK src
+  (UPat(Ops.SINK, name="s"), lambda ctx,s: ctx.update((x.base, None) for x in s.src if x.base.op not in ALWAYS_CONTIGUOUS)),
   # always realize
-  (UPat({Ops.COPY, Ops.CONTIGUOUS, Ops.ASSIGN}, name="tr"), realize),
-  # realize AFTER of STORE+AFTER
-  (UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE)), allow_any_len=True, name="tr"), realize),
+  (UPat({Ops.COPY, Ops.BUFFER_VIEW, Ops.CONTIGUOUS, Ops.STORE, Ops.ASSIGN}, name="tr"), realize),
   # realize srcs of these
   (UPat((Ops.COPY, Ops.MSELECT, Ops.MSTACK), name="rb"), realize_srcs),
   # sometimes realize src of assign
   (UPat(Ops.ASSIGN, src=(UPat.var("buf"), UPat.var("x"))), realize_assign_src),
-  # don't realize COPY/BUFFER_VIEW consumed by STORE inside AFTER (like realize_assign_src for ASSIGN)
-  (UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE, src=(UPat(), UPat({Ops.COPY, Ops.BUFFER_VIEW}, name="x"))))), unrealize_store_src),
 ])
 
 @dataclass(frozen=True)
@@ -66,8 +60,7 @@ def create_bufferize_and_index_based_on_ranges(ctx:IndexingContext, x:UOp):
   new_srcs = []
   for s in x.src:
     new_src = s
-    if s.op in {Ops.PARAM, Ops.BUFFER_VIEW, Ops.MSTACK, Ops.MSELECT} or \
-       (s.op is Ops.AFTER and not any(c.op in {Ops.STORE, Ops.END} for c in s.src[1:])):
+    if s.op in {Ops.PARAM, Ops.BUFFER_VIEW, Ops.MSTACK, Ops.MSELECT, Ops.AFTER}:
       if x in ctx.range_map: new_src = new_src.index(*ctx.range_map[x][0])
     elif s in ctx.realize_map:
       realized_ranges = ctx.realize_map[s]
@@ -106,11 +99,25 @@ def convert_reduce_axis_to_reduce_with_ranges(ctx:IndexingContext, x:UOp):
 def remove_movement_op_after_rangeify(ctx:IndexingContext, x:UOp):
   if x in ctx.range_map or x.src[0].op is Ops.INDEX: return x.src[0]
 
+def handle_assign_mops(ctx:IndexingContext, assign:UOp, target:UOp, src:UOp):
+  if target.op in GroupOp.Movement and src.op is not Ops.CALL:
+    mops = []
+    while target.op in GroupOp.Movement:
+      mops.append((target.op, target.marg))
+      target = target.src[0]
+    if mops and assign in ctx.range_map:
+      ret = assign.replace(arg=tuple(mops))
+      ctx.range_map[ret] = ctx.range_map[assign]
+      return ret
+  return None
+
 pm_apply_rangeify = PatternMatcher([
   # REDUCE_AXIS -> REDUCE
   (UPat(Ops.REDUCE_AXIS, name="x"), convert_reduce_axis_to_reduce_with_ranges),
   # PAD -> WHERE
   (UPat(Ops.PAD, name="x"), convert_pad_to_where_to_keep_behavior_local),
+  # store movement ops in ASSIGN arg
+  (UPat(Ops.ASSIGN, src=(UPat(name="target"), UPat(name="src")), name="assign"), handle_assign_mops),
   # finally, apply_rangeify
   (UPat(GroupOp.All, name="x"), create_bufferize_and_index_based_on_ranges),
   # remove movement op
@@ -172,8 +179,8 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
     # no ranges on kernels, they are internal
     if x.op in {Ops.CALL, Ops.LINEAR}: continue
 
-    # only STORE+AFTER has range
-    if x.op is Ops.AFTER and all(s.op is not Ops.STORE for s in x.src[1:]): continue
+    # no range on after
+    if x.op is Ops.AFTER: continue
 
     # treat MSTACK/MSELECT like SINK
     if x.op in {Ops.MSTACK, Ops.MSELECT}: continue

tinygrad/schedule/multi.py

@@ -141,10 +141,13 @@ multi_pm = PatternMatcher([
     lambda multi,device,red: multi.src[0].allreduce(red.arg, device).multi(axis=multi.axis)),
   # rewrite into calls explicitly for MULTI
   (UPat(Ops.CALL, name="call"), rewrite_into_call),
-  (UPat((Ops.CALL, Ops.AFTER, Ops.STORE), src=(UPat(Ops.MULTI, name="multi"), ), name="root", allow_any_len=True), passthrough_multi),
+  (UPat(Ops.CALL, src=(UPat(Ops.MULTI, name="multi"), ), name="root", allow_any_len=True), passthrough_multi),
   # we just remove the MULTI from CALLs with dtypes.void and assume they are handled by the user for custom kernels
   (UPat(Ops.CALL, dtype=dtypes.void, name="root", custom_early_reject=set([Ops.MULTI])), lambda root:
     UOp(root.op, root.dtype, tuple(x.src[0] if x.op is Ops.MULTI else x for x in root.src), root.arg)),
   (UPat((Ops.CAST, Ops.BITCAST, Ops.CONTIGUOUS, Ops.DETACH, Ops.CONTIGUOUS_BACKWARD),
         src=(UPat(Ops.MULTI, name="multi"), ), name="root"), passthrough_multi),
+  # after CALL
+  (UPat(Ops.AFTER, src=(UPat(Ops.MULTI, name="multi"), UPat(Ops.CALL)), name="a"),
+    lambda multi,a: a.replace(src=(multi.src[0],)+a.src[1:]).multi(multi.axis)),
 ])+replace_allreduce

tinygrad/schedule/rangeify.py

@@ -16,39 +16,24 @@ from tinygrad.schedule.allreduce import create_allreduce_function
 import sys
 sys.setrecursionlimit(10000)
 
-def add_ranges_to_store(ctx, x):
-  if x.src[0]._shape is None or x.src[1]._shape is None or x.src[0].shape == (): return None
-  assert x.src[0].shape == x.src[1].shape, "bad store shape"
-  idxs = [UOp.range(r, next(ctx), AxisType.LOOP) for r in x.src[0].shape]
-  return UOp.store(x.src[0].index(*idxs), x.src[1].index(*idxs)).end(*idxs)
-
-pm_store_ranges = PatternMatcher([
-  (UPat(Ops.STORE, name="x"), add_ranges_to_store),
-])
-
 pm_syntactic_sugar = PatternMatcher([
   # INDEX on ptr INDEX concats them
   (UPat(Ops.INDEX, name="i1").f(Ops.INDEX, name="i2", allow_any_len=True),
    lambda i1,i2: i2.replace(src=i1.src+i2.src[1:]) if isinstance(i1.dtype, PtrDType) and not isinstance(i2.dtype, PtrDType) else None),
-  # early rangeify
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Elementwise | {Ops.CONST}, name="x"),), allow_any_len=True, name="idx"),
-   lambda idx,x: x.replace(src=tuple([s.index(*idx.src[1:]) for s in x.src]))),
 ])
 
 def found_assign(ctx:dict[UOp, UOp], assign:UOp, src:UOp):
   if (x:=src).op is Ops.CAST and x.dtype == dtypes.half and FLOAT16: x, assign = x.src[0], assign.cast(dtypes.float)
-  while True:
-    if x.op is Ops.PERMUTE: x, assign = x.src[0], assign.permute(argsort(x.marg))
-    elif x.op is Ops.RESHAPE: x, assign = x.src[0], assign.reshape(x.src[0].shape)
-    elif x.op is Ops.WHERE and x.src[2].base.arg == Invalid and x.src[1].op is Ops.PAD:
-      x, assign = x.src[1].src[0], assign.shrink(tuple((l, s-r) for (l,r),s in zip(x.src[1].marg, x.shape)))
-    else: break
+  while x is not x.base:
+    if x.op is Ops.PERMUTE: assign = assign.permute(argsort(x.marg))
+    elif x.op is Ops.RESHAPE: assign = assign.reshape(x.src[0].shape)
+    else: return None
+    x = x.src[0]
   ctx[x] = assign
 
-# *** fold moved ASSIGNs/AFTERs (hack for openpilot) ***
+# *** fold moved ASSIGNs (hack for openpilot) ***
 pm_fold_moved_assign = PatternMatcher([
   (UPat(Ops.ASSIGN, src=(UPat(), UPat((*GroupOp.Movement, Ops.CAST), name="src")), name="assign"), found_assign),
-  (UPat(Ops.AFTER, src=(UPat(), UPat(Ops.STORE, src=(UPat(), UPat((*GroupOp.Movement, Ops.CAST), name="src")))), name="assign"), found_assign),
   # replace ALU sources with assign versions found above
   (UPat(GroupOp.ALU, name="alu"), lambda ctx,alu: alu.replace(src=new_src) if (new_src:=tuple(ctx.get(s, s) for s in alu.src)) != alu.src else None),
 ])
@@ -56,12 +41,10 @@ pm_fold_moved_assign = PatternMatcher([
 # movement op on INDEX as a PatternMatcher
 pm_mops = PatternMatcher([
   (UPat(GroupOp.Movement, name="r").f(Ops.INDEX, allow_any_len=True, name="idx"),
-   lambda r,idx: r.src[0].index(*apply_movement_op(r.op, r.src[0].shape, r.marg, idx.src[1:]), dtype=idx.dtype, arg=idx.arg)
-     if len(idx.src[1:]) == len(r.shape) else None),
-  # move movement ops after AFTER (but not when AFTER has a raw STORE with shaped children — from replace_contig_with_store_after)
+   lambda r,idx: r.src[0].index(*apply_movement_op(r.op, r.src[0].shape, r.marg, idx.src[1:]), dtype=idx.dtype, arg=idx.arg)),
+  # move movement ops after AFTER
   (UPat(GroupOp.Movement, name="r").after(name="a", allow_any_len=True),
-   lambda r,a: UOp(r.op, r.dtype, (a.replace(src=(r.src[0],)+a.src[1:]),)+r.src[1:], r.arg)
-     if not any(s.op is Ops.STORE and s.src[0]._shape is not None for s in a.src[1:]) else None),
+   lambda r,a: UOp(r.op, r.dtype, (a.replace(src=(r.src[0],)+a.src[1:]),)+r.src[1:], r.arg)),
   (UPat(GroupOp.Movement, name="r").end(name="a", allow_any_len=True), lambda r,a: a.replace(src=(r.src[0],)+a.src[1:])),
 ])
 
@@ -71,12 +54,12 @@ pm_mops = PatternMatcher([
 def fix_assign_hazard(assign:UOp, target:UOp, src:UOp):
   # PERMUTE and FLIP reorder indices, SHRINK can have overlapping regions when dest is also shrunk
   unsafe = {Ops.PERMUTE, Ops.FLIP} | ({Ops.SHRINK} if target.op_in_backward_slice_with_self(Ops.SHRINK) else set())
-  if any(s.op in unsafe and target.base in s.backward_slice for s in src.toposort(gate=lambda s:s.op not in ALWAYS_CONTIGUOUS or s.op is Ops.AFTER)):
+  if any(s.op in unsafe and target.base in s.backward_slice for s in src.toposort(gate=lambda s:s.op not in ALWAYS_CONTIGUOUS)):
     return assign.replace(src=(target, src.contiguous()))
 
 def normalize_assign_target_chain(assign:UOp, target:UOp, src:UOp):
   root_target = target
-  while root_target.op in {Ops.ASSIGN, Ops.AFTER}: root_target = root_target.src[0]
+  while root_target.op is Ops.ASSIGN: root_target = root_target.src[0]
   # when RHS depends on the previous assign result, break with contiguous
   if target in src.toposort(): src = src.contiguous()
   return assign.replace(src=(root_target, src))
@@ -137,10 +120,8 @@ earliest_rewrites = mop_cleanup+PatternMatcher([
   # resolve calls
   (UPat(Ops.CALL, name="c"), resolve_call),
 
-  # resolve TUPLE+GETTUPLE
-  (UPat(Ops.GETTUPLE, src=(UPat(Ops.TUPLE, name="t"),), name="g"), lambda g,t: t.src[g.arg]),
-
   # resolve allreduce (must be bottom up)
+  (UPat(Ops.ASSIGN, src=(UPat.var("output"), UPat(Ops.ALLREDUCE, src=(UPat.var("buf"), UPat()), name="red"))), create_allreduce_function),
   (UPat(Ops.ALLREDUCE, src=(UPat.var("buf"), UPat()), name="red"), create_allreduce_function),
 
   # split_reduceop
@@ -175,9 +156,8 @@ earliest_rewrites = mop_cleanup+PatternMatcher([
   (UPat(Ops.ASSIGN, src=(UPat(Ops.BITCAST, src=(UPat(name="target"),)), UPat(name="src"))),
    lambda target, src: target.assign(src.bitcast(target.dtype))),
 
-  # if assign target is itself an ASSIGN/AFTER chain, canonicalize to the original buffer target
-  (UPat(Ops.ASSIGN, src=(UPat({Ops.ASSIGN, Ops.AFTER}, name="target"), UPat(name="src")), allow_any_len=True, name="assign"),
-   normalize_assign_target_chain),
+  # if assign target is itself an ASSIGN chain, canonicalize to the original buffer target
+  (UPat(Ops.ASSIGN, src=(UPat(Ops.ASSIGN, name="target"), UPat(name="src")), allow_any_len=True, name="assign"), normalize_assign_target_chain),
 
   # make source contiguous if it has hazardous movement ops on the dest buffer
   (UPat(Ops.ASSIGN, src=(UPat.var("target"), UPat.var("src")), name="assign"), fix_assign_hazard),
@@ -198,8 +178,8 @@ ALWAYS_RUN_OPS = {Ops.CONTIGUOUS, Ops.COPY, Ops.ASSIGN, Ops.NOOP}
 
 # you don't know in the first pass if axes are going to die, this happens if there's an EXPAND to the left
 def cleanup_dead_axes(b:UOp):
-  # don't optimize ALWAYS_RUN_OPS or AFTER (AFTER is a buffer identity — ranges define consumer access, not computation)
-  if b.src[0].op in ALWAYS_RUN_OPS or b.src[0].op is Ops.AFTER: return None
+  # don't optimize ALWAYS_RUN_OPS
+  if b.src[0].op in ALWAYS_RUN_OPS: return None
 
   new_rng = []
   hit = False
@@ -230,54 +210,56 @@ def remove_bufferize(src:UOp, buf:UOp, idx:UOp):
   # if it's user contiguous, we never remove it
   if src.op in ALWAYS_RUN_OPS or not buf.arg.removable: return None
 
-  # *** here is where we compute the cost ***
-  # if we return None, the bufferize is kept
+  # we don't want to bufferize threefry, also causes problems because not all platforms support long
+  if src.op is not Ops.THREEFRY:
+    # *** here is where we compute the cost ***
+    # if we return None, the bufferize is kept
 
-  accessed_buffers: list[UOp] = []
-  indexes: list[UOp] = []
-  reduces: list[UOp] = []
-  def red_gate(x:UOp):
-    if (x.op is Ops.BUFFERIZE and x.arg.addrspace == AddrSpace.GLOBAL) or x.op is Ops.MSTACK:
-      accessed_buffers.append(x)
-      return False
-    if x.op is Ops.PARAM:
-      accessed_buffers.append(x)
-    if x.op is Ops.INDEX:
-      indexes.append(x)
-    if x.op is Ops.REDUCE: reduces.append(x)
-    return True
-  src.toposort(gate=red_gate)
-  del red_gate
-  accessed_buffers = dedup(accessed_buffers)
+    accessed_buffers: list[UOp] = []
+    indexes: list[UOp] = []
+    reduces: list[UOp] = []
+    def red_gate(x:UOp):
+      if (x.op is Ops.BUFFERIZE and x.arg.addrspace == AddrSpace.GLOBAL) or x.op is Ops.MSTACK:
+        accessed_buffers.append(x)
+        return False
+      if x.op is Ops.PARAM:
+        accessed_buffers.append(x)
+      if x.op is Ops.INDEX:
+        indexes.append(x)
+      if x.op is Ops.REDUCE: reduces.append(x)
+      return True
+    src.toposort(gate=red_gate)
+    del red_gate
+    accessed_buffers = dedup(accessed_buffers)
 
-  # if this is generated from multiple buffers, don't remove this buffer
-  if len(accessed_buffers) > 3 and not (PCONTIG > 2): return None
+    # if this is generated from multiple buffers, don't remove this buffer
+    if len(accessed_buffers) > 3 and not (PCONTIG > 2): return None
 
-  # if any reduces access a buffer, don't remove this buffer
-  buffer_in_reduce = False
-  def buf_gate(x:UOp):
-    nonlocal buffer_in_reduce
-    if x.op in {Ops.PARAM, Ops.BUFFERIZE}: buffer_in_reduce = True
-    return not buffer_in_reduce
-  UOp.sink(*[x.src[0] for x in reduces]).toposort(gate=buf_gate)
-  del buf_gate
-  if buffer_in_reduce:
-    if PCONTIG > 2:
-      out_in_ratio = (prod(buf.shape)+1) / (sum([x.size for x in accessed_buffers])+1)
-      if out_in_ratio < 10: return None
-      # here we have to check the indexes, we might do a partial contig here
-      local_indexes = [x for x in indexes if x.src[0].op is Ops.BUFFERIZE and x.src[0].arg.addrspace == AddrSpace.LOCAL]
-      exclude_ranges = UOp.group(*[UOp.group(*x.src[1:]) for x in local_indexes]).ranges
-      subs = [(k,v) for k,v in zip(buf.src[1:], idx.src[1:]) if k.op is not Ops.CONST]
-      # if it's bufferized or a reduce, it's pcontig
-      is_pcontig, is_subs = partition(subs, lambda x: x[0] in exclude_ranges or any([r.arg[-1] == AxisType.REDUCE for r in x[1].ranges]))
-      if not len(is_subs):
+    # if any reduces access a buffer, don't remove this buffer
+    buffer_in_reduce = False
+    def buf_gate(x:UOp):
+      nonlocal buffer_in_reduce
+      if x.op in {Ops.PARAM, Ops.BUFFERIZE}: buffer_in_reduce = True
+      return not buffer_in_reduce
+    UOp.sink(*[x.src[0] for x in reduces]).toposort(gate=buf_gate)
+    del buf_gate
+    if buffer_in_reduce:
+      if PCONTIG > 2:
+        out_in_ratio = (prod(buf.shape)+1) / (sum([x.size for x in accessed_buffers])+1)
+        if out_in_ratio < 10: return None
+        # here we have to check the indexes, we might do a partial contig here
+        local_indexes = [x for x in indexes if x.src[0].op is Ops.BUFFERIZE and x.src[0].arg.addrspace == AddrSpace.LOCAL]
+        exclude_ranges = UOp.group(*[UOp.group(*x.src[1:]) for x in local_indexes]).ranges
+        subs = [(k,v) for k,v in zip(buf.src[1:], idx.src[1:]) if k.op is not Ops.CONST]
+        # if it's bufferized or a reduce, it's pcontig
+        is_pcontig, is_subs = partition(subs, lambda x: x[0] in exclude_ranges or any([r.arg[-1] == AxisType.REDUCE for r in x[1].ranges]))
+        if not len(is_subs):
+          return None
+        if len(is_pcontig):
+          ret = src.substitute(dict(is_subs), extra_pm=pm_gate_substitute)
+          return ret.bufferize(*[x[0] for x in is_pcontig], arg=BufferizeOpts(None, AddrSpace.LOCAL)).index(*[x[1] for x in is_pcontig])
+      else:
         return None
-      if len(is_pcontig):
-        ret = src.substitute(dict(is_subs), extra_pm=pm_gate_substitute)
-        return ret.bufferize(*[x[0] for x in is_pcontig], arg=BufferizeOpts(None, AddrSpace.LOCAL)).index(*[x[1] for x in is_pcontig])
-    else:
-      return None
 
   # if it makes it here, the bufferize is removed
   # this is the ranges replaced
@@ -373,24 +355,20 @@ def bufferize_to_store(ctx:itertools.count, x:UOp, idx:UOp, allow_locals=True):
   assert size > 0 and isinstance(size, int), f"no zero sized or symbolic sized buffers {size}"
 
   sdtype = x.dtype.ptr(size=size, addrspace=x.arg.addrspace)
-  # AFTER: add END to the existing STORE, return buffer with kernel dependency
-  if x.src[0].op is Ops.AFTER:
-    buf = x.src[0].src[0].buf_uop.base
-    stores = [s for s in x.src[0].src[1:] if s.op is Ops.STORE]
-    return buf.after(*[s.end(*rngs) for s in stores]) if stores else buf
   if (assign := x.src[0]).op is Ops.ASSIGN:
     assign_target, assign_src = assign.src[0], assign.src[1]
     assert assign_target.op is Ops.INDEX, f"{assign_target.op} is not index"
     while assign_src.op is Ops.NOOP: assign_src = assign_src.src[0]
 
     store_target = assign_target
-    if assign_target.src[0].op is Ops.BUFFERIZE and assign_target.src[0].src[0].op is Ops.INDEX:
+    if assign.arg and assign_target.src[0].op is Ops.BUFFERIZE and assign_target.src[0].src[0].op is Ops.INDEX:
       # BUFFERIZE(INDEX(...)); store through the underlying global index instead.
       store_target = assign_target.src[0].src[0]
 
     end_rngs = sorted(dedup(tuple(store_target.ranges) + tuple(rngs)), key=lambda x: x.arg)
     ret = store_target.buf_uop.base
-    if assign_src is not assign_target: ret = ret.after(store_target.replace(dtype=sdtype).store(assign_src).end(*end_rngs))
+    if assign_src is not store_target: ret = ret.after(store_target.replace(dtype=sdtype).store(assign_src).end(*end_rngs))
+    for op, marg in reversed(assign.arg or ()): ret = ret._mop(op, marg)
     return ret
 
   # NOTE: the DEFINE_LOCAL needs to be disambiguated here
@@ -540,8 +518,6 @@ pm_add_range_tags = PatternMatcher([
 def split_store(x:UOp) -> UOp|None:
   # if we have any open ranges here, we don't split
   if x.ranges: return None
-  # raw STORE (not from bufferize_to_store) should be processed through its END wrapper, not independently
-  if x.op is Ops.STORE and x.src[0]._shape is not None: return None
 
   # local kernel rewrite
   lctx = LocalAddBufferContext()

tinygrad/tensor.py

@@ -313,14 +313,7 @@ class Tensor(OpMixin):
     assign_uop = self.uop.assign(x.uop)
     base = self.uop.base
     if base.op in {Ops.BUFFER, Ops.AFTER} and not self.uop.has_buffer_identity():
-      original_uop = self.uop
-      assigned_base = base.after(assign_uop)
-      _apply_map_to_tensors({base: assigned_base}, name="Embed View Assign", walk=True)
-      def replace_view_base(u:UOp) -> UOp:
-        return u.replace(src=((assigned_base if u.src[0] is base else replace_view_base(u.src[0])),)+u.src[1:])
-      ret = Tensor(replace_view_base(original_uop), device=self.device, requires_grad=self.requires_grad)
-      self.replace(self._apply_uop(lambda *_: assign_uop, x))
-      return ret
+      _apply_map_to_tensors({base: base.after(assign_uop)}, name="Embed View Assign", walk=True)
     return self.replace(self._apply_uop(lambda *_: assign_uop, x))
 
   def detach(self) -> Tensor:
@@ -1071,7 +1064,7 @@ class Tensor(OpMixin):
     for t,g in zip(tensors_need_grad, self.gradient(*tensors_need_grad, gradient=gradient, materialize_grads=True)):
       assert g.shape == t.shape, f"grad shape must match tensor shape, {g.shape!r} != {t.shape!r}"
       if t.grad is None: t.grad = g
-      else: t.grad.assign(t.grad + g.to(t.grad.device))
+      else: t.grad.assign(t.grad + g)
     return self
 
   # ***** movement low level ops *****
@@ -1347,7 +1340,7 @@ class Tensor(OpMixin):
       if is_disk: raise RuntimeError("advanced setitem is not supported for DISK tensors")
       if not isinstance(v, Tensor): v = Tensor(v, device=self.device, dtype=self.dtype)
       self.assign(self._getitem(indices, v))
-    elif is_disk or self.uop.is_realized or self.uop.base.op in (Ops.AFTER, Ops.BUFFER): # basic setitem, self is realized
+    elif is_disk or self.uop.is_realized or self.uop.base.op is Ops.AFTER: # basic setitem, self is realized
       view = self[indices]
       if isinstance(v, Tensor) and v.uop.op is Ops.ASSIGN and v.uop in view.uop.base.src: return
       view.assign(v)
@@ -2116,7 +2109,7 @@ class Tensor(OpMixin):
     x_unsqueezed = x.unsqueeze(-2).expand((None,)*(self.ndim-1)+(last_dim_size, None))
     x_cummax, _ = x.cummax(-1)
     mask = Tensor.ones(last_dim_size, last_dim_size, requires_grad=False, device=self.device).tril()
-    ret = mask.where(x_unsqueezed - x_cummax.unsqueeze(-1), self.dtype.min).exp().sum(-1).log() + x_cummax
+    ret = mask.where(x_unsqueezed - x_cummax.unsqueeze(-1), dtypes.min(self.dtype)).exp().sum(-1).log() + x_cummax
     return ret.transpose(-1, axis)
 
   def argmax(self, axis=None, keepdim=False) -> Tensor:
@@ -2313,12 +2306,12 @@ class Tensor(OpMixin):
     axis = tuple(range(-len(k_ := make_tuple(kernel_size, 2)), 0))
     pads = self._resolve_pool_pads(padding, len(k_))
     if ceil_mode: pads = self._apply_ceil_mode(pads, k_, stride if stride is not None else k_, dilation)
-    pooled = self.pad(pads, value=self.dtype.min)._pool(k_, stride if stride is not None else k_, dilation)
+    pooled = self.pad(pads, value=dtypes.min(self.dtype))._pool(k_, stride if stride is not None else k_, dilation)
     if not return_indices: return pooled.max(axis)
     spatial_sz = int(math.prod(spatial_shape := self.shape[-len(k_):]))
     idx = Tensor.arange(spatial_sz,0,-1, requires_grad=False, device=self.device).reshape(spatial_shape)
     m = pooled == pooled.max(axis, keepdim=True)
-    idx = m * idx.pad(pads, value=idx.dtype.min)._pool(k_, stride if stride is not None else k_, dilation)
+    idx = m * idx.pad(pads, value=dtypes.min(idx.dtype))._pool(k_, stride if stride is not None else k_, dilation)
     return pooled.max(axis), spatial_sz - idx.max(axis)
 
   def max_unpool2d(self, indices:Tensor, kernel_size:tuple[int, ...]=(2,2), stride=None, dilation=1, padding:int|tuple[int, ...]=0, output_size=None):
@@ -2491,7 +2484,7 @@ class Tensor(OpMixin):
     """
     if IMAGE: return self.image_dot(w, dtype)
     if ASM_GEMM:
-      from extra.gemm.cdna_asm_gemm import can_use_asm_gemm, asm_gemm
+      from extra.gemm.asm.cdna.gemm import can_use_asm_gemm, asm_gemm
       if can_use_asm_gemm(self, w): return asm_gemm(self, w)
     x, dx, dw = self, self.ndim, w.ndim
     if not (dx > 0 and dw > 0): raise RuntimeError(f"both tensors need to be at least 1D, got {dx}D and {dw}D")
@@ -2759,8 +2752,8 @@ class Tensor(OpMixin):
     def _inv_mask(a:Tensor|PyConst, b:Tensor|PyConst) -> Tensor: return mask.any(-1).logical_not().where(a, b)
     if reduce == "sum": return mask.where(src, 0).sum(-1).add(self if include_self else _inv_mask(self, 0))
     if reduce == "prod": return mask.where(src, 1).prod(-1).mul(self if include_self else _inv_mask(self, 1))
-    if reduce == "amax": return mask.where(src, m := src.dtype.min).max(-1).maximum(self if include_self else _inv_mask(self, m))
-    if reduce == "amin": return mask.where(src, m := src.dtype.max).min(-1).minimum(self if include_self else _inv_mask(self, m))
+    if reduce == "amax": return mask.where(src, m := dtypes.min(src.dtype)).max(-1).maximum(self if include_self else _inv_mask(self, m))
+    if reduce == "amin": return mask.where(src, m := dtypes.max(src.dtype)).min(-1).minimum(self if include_self else _inv_mask(self, m))
     if reduce == "mean":
       count = mask.where(1, 0).sum(-1).add(1 if include_self else _inv_mask(1, 0))
       return mask.where(src, 0).sum(-1).add(self if include_self else _inv_mask(self, 0)).div(count)
@@ -2789,7 +2782,7 @@ class Tensor(OpMixin):
     # pad to power of 2
     n_stages = (orig_len-1).bit_length()
     pads = tuple((0, 2**n_stages - orig_len) if i == dim else None for i in range(x.ndim))
-    x = x.pad(pads, value=x.dtype.min if descending else x.dtype.max).unflatten(dim, (2,)*n_stages)
+    x = x.pad(pads, value=dtypes.min(x.dtype) if descending else dtypes.max(x.dtype)).unflatten(dim, (2,)*n_stages)
     # https://en.wikipedia.org/wiki/Bitonic_sorter#/media/File:BitonicSort1.svg
     for stage in range(1, n_stages+1):
       if stage != n_stages:
@@ -3662,27 +3655,21 @@ class Tensor(OpMixin):
 
     # contiguous creates the image, and early realize static weights (TODO: test for the static weight)
     if IMAGE == 1:
-      def is_pow2(v): return v > 0 and v & (v - 1) == 0
-      # pad dimension i to amt with invalids
-      def ipad(t, i, amt):
-        shape = (None,)*i + (amt,) + (None,)*(t.ndim-i-1)
-        return Tensor(True, device=t.device).expand(t.shape).pad_to(shape).where(t.pad_to(shape), Invalid) if amt != t.shape[i] else t
-      # align a dimension to 64 bytes
-      def pad_align(t, dim):
-        return ipad(t, dim, round_up(t.shape[dim], (64 // dtsz) // math.gcd(prod(t.shape) // t.shape[dim], (64 // dtsz))))
-
-      # bank conflicts
-      if cin >= 8 and is_pow2(cin // 4): x, w = ipad(x.reshape(bs, iy, ix, groups, cin // 4, 4), 4, cin // 4 + 1), ipad(w, 2, cin // 4 + 1)
-
-      # 64-byte pitch alignment
-      x, w = pad_align(x, 2), pad_align(w, 1)
+      # pad with Invalid
+      def _invalid_pad_to(t, shape):
+        if all(p is None or p == s for p,s in zip(shape, t.shape)): return t
+        return Tensor(True, device=t.device).expand(t.shape).pad_to(shape).where(t.pad_to(shape), Invalid)
+      # hacks for pitch alignment
+      assert isinstance(ix, int) and isinstance(H, int)
+      ALIGN = 64 // dtsz
+      x = _invalid_pad_to(x, (None, None, round_up(ix, ALIGN // math.gcd(groups * cin, ALIGN)), None))
+      w = _invalid_pad_to(w, (None, round_up(H, ALIGN // math.gcd(W * cin * 4, ALIGN))) + (None,) * (w.ndim - 2))
 
       if FLOAT16: x, w = x.cast(dtypes.half).contiguous().cast(dtypes.float), w.cast(dtypes.half).contiguous().cast(dtypes.float)
       else: x, w = x.contiguous(), w.contiguous()
 
       # undo alignment hacks
-      if cin >= 8 and is_pow2(cin // 4): x, w = x[:, :, :ix, :, :cin // 4, :], w[:, :H, :cin // 4, ...]
-      else: x, w = x[:, :, :ix, :], w[:, :H, ...]
+      x, w = x[:, :, :ix, :], w[:, :H, ...]
 
     elif IMAGE: x, w = x.cast(base_image_type((bs*iy, ix*groups*cin//4, 4))).contiguous(), w.cast(base_image_type((cout//4, H*W*cin, 4))).contiguous()
     else: x, w = x.contiguous(), w.contiguous()

tinygrad/uop/__init__.py

@@ -39,9 +39,6 @@ class Ops(FastEnum):
   # vector creation / item selection
   GEP = auto(); VECTORIZE = auto()
 
-  # tuple/gettuple for function with multiple returns
-  TUPLE = auto(); GETTUPLE = auto()
-
   # ** 3 -- load/store **
 
   # INDEX is a BinaryOp similar to ADD, but it operates on pointers

tinygrad/uop/decompositions.py

@@ -14,7 +14,7 @@ def _lazy_map_numbers(x:UOp, inf:UOp, _inf:UOp, nan:UOp, ratio:UOp):
 
 # *** helper functions for bit manipulation ***
 def mantissa_bits(d:DType) -> int: return dtypes.finfo(d.scalar())[1]
-def exponent_bias(d:DType) -> int: return (1 << (dtypes.finfo(d.scalar())[0] - 1)) - (0 if d.scalar() in dtypes.fp8_fnuz else 1)
+def exponent_bias(d:DType) -> int: return (1 << (dtypes.finfo(d.scalar())[0] - 1)) - 1
 def exponent_mask(d:DType) -> int: return (1 << dtypes.finfo(d.scalar())[0]) - 1
 
 # **** utils ****
@@ -287,7 +287,7 @@ def fast_idiv(device: str, x: UOp, d: int, dont_cast=False) -> UOp|None:
   assert d>0, "Sign should have been taken out of divisor"
   vmin,vmax = max(x.vmin, x.dtype.min), min(x.vmax, x.dtype.max)
   m,s = magicgu(max(vmax, abs(vmin)), d)
-  if m*vmin >= x.dtype.min and m*vmax <= x.dtype.max:
+  if m*vmin >= dtypes.min(x.dtype) and m*vmax <= dtypes.max(x.dtype):
     return ((x*m) >> s) if is_unsigned else ((x*m) >> s) + (x<0).where(x.ufix(1), 0)
   # before we try casting to a larger dtype (slow), we see if there are powers of two in d we can shift to make x smaller
   if (largest_factor_of_two_in_d := (d & -d)) > 1:
@@ -295,7 +295,7 @@ def fast_idiv(device: str, x: UOp, d: int, dont_cast=False) -> UOp|None:
   if dont_cast: return None
   # promo_lattice needs to return an unsigned type if the type is unsigned
   if dtypes.is_int(next_dtype := promo_lattice[x.dtype.scalar()][-1]) and is_dtype_supported(next_dtype, device):
-    if m*vmin >= next_dtype.min and m*vmax <= next_dtype.max:
+    if m*vmin >= dtypes.min(next_dtype) and m*vmax <= dtypes.max(next_dtype):
       return ((x.cast(next_dtype)*m) >> s).cast(x.dtype) if is_unsigned else ((x.cast(next_dtype)*m) >> s).cast(x.dtype) + (x<0).where(x.ufix(1), 0)
   return None
 
@@ -388,10 +388,6 @@ def f2f(v, fr:DType, to:DType):
     sign, nosign = shl((v & shl(1, fs-1)).cast(f2f_dt[to]), ts - fs), (v & (shl(1, fs-1) - 1)).cast(f2f_dt[to])
     exp, norm = shr(nosign, fm), shl(nosign, tm - fm) + shl(tb - fb, tm)
     nan = shl(nosign, tm - fm) | shl((shl(1, te) - 1), tm)
-    if fr in dtypes.fp8_fnuz:
-      fnuz_nan = sign.ne(0) & nosign.eq(0)
-      qnan = shl(shl(1, te) - 1, tm) | shl(1, tm - 1)
-      return fnuz_nan.where(qnan, sign | exp.eq(0).where(0, norm)).bitcast(to)
     # fp8e4m3 has only one nan
     is_nan = (nosign.eq(shl(1, fm + fe) - 1) if fr == dtypes.fp8e4m3 else exp.eq(shl(1, fe) - 1))
     return (sign | exp.eq(0).where(0, is_nan.where(nan, norm))).bitcast(to)
@@ -403,14 +399,12 @@ def f2f(v, fr:DType, to:DType):
     nan_mantissa = (shl(1, tm) - 1) if to == dtypes.fp8e4m3 else (shr(nosign, fm - tm) & (shl(1, tm) - 1))
     nan = (sign | nan_mantissa | shl(shl(1, te) - 1, tm)).cast(f2f_dt[to])
     is_nan = (shr(v, fm) & (shl(1, fe) - 1)).eq(shl(1, fe) - 1)
-    if to in dtypes.fp8_fnuz: return is_nan.where(shl(1, ts - 1), underflow.where(0, sign.cast(f2f_dt[to]) | norm))
     return is_nan.where(nan, sign.cast(f2f_dt[to]) | underflow.where(0, norm))
   else: raise NotImplementedError(f"unsupported decomp {fr} -> {to}")
 
 def f2f_clamp(val:UOp, dt:DType) -> UOp:
   e, m = dtypes.finfo(dt)
-  if dt in dtypes.fp8_fnuz: max_exp, max_man = (1 << e) - 1, (1 << m) - 1
-  else: max_exp, max_man = ((1 << e) - 1, (1 << m) - 2) if dt == dtypes.fp8e4m3 else ((1 << e) - 2, (1 << m) - 1)
+  max_exp, max_man = ((1 << e) - 1, (1 << m) - 2) if dt == dtypes.fp8e4m3 else ((1 << e) - 2, (1 << m) - 1)
   mx = val.const_like(2.0**(max_exp - exponent_bias(dt)) * (1.0 + max_man / (1 << m)))
   sat = mx if dt in dtypes.fp8s else val.const_like(float('inf'))
   # FIXME: CMPLT of nan is undefined

tinygrad/uop/ops.py

@@ -29,7 +29,7 @@ axis_to_pos = {AxisType.LOOP: -1, AxisType.THREAD: 0, AxisType.GLOBAL: 0, AxisTy
 range_start = {Ops.BUFFERIZE: 1, Ops.REDUCE: 1, Ops.STORE: 2, Ops.WMMA: 3, Ops.END: 1, Ops.CALL: 1, Ops.COPY: 2, Ops.BUFFER_VIEW: 1}
 
 # https://en.wikipedia.org/wiki/Identity_element
-def identity_element(op:Ops, dt:DType) -> PyConst: return dtypes.as_const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dt.min}[op], dt)
+def identity_element(op:Ops, dt:DType) -> PyConst: return dtypes.as_const({Ops.ADD:0, Ops.MUL:1, Ops.MAX:dtypes.min(dt)}[op], dt)
 
 # With True as the default, this matches the old symbolic behavior
 def resolve(x:UOp|bool, default:bool=True):
@@ -207,32 +207,28 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
   def _shape(self) -> tuple[sint, ...]|None:
     match self.op:
       # late ops don't have shape
-      case Ops.UNIQUE | Ops.LUNIQUE | Ops.DEVICE | Ops.RANGE | Ops.LOAD | Ops.STORE | Ops.IF | Ops.BARRIER | Ops.CUSTOM | Ops.CUSTOMI | \
+      case Ops.UNIQUE | Ops.LUNIQUE | Ops.DEVICE | Ops.LOAD | Ops.IF | Ops.BARRIER | Ops.CUSTOM | Ops.CUSTOMI | \
            Ops.VECTORIZE | Ops.GEP | Ops.SPECIAL | Ops.UNROLL | Ops.CONTRACT | Ops.SINK | \
-           Ops.LINEAR | Ops.PROGRAM | Ops.SOURCE | Ops.BINARY | Ops.INS | Ops.TUPLE:
+           Ops.LINEAR | Ops.PROGRAM | Ops.SOURCE | Ops.BINARY | Ops.INS:
         return None
 
-      case Ops.GETTUPLE:
-        # GETTUPLE extracts from a TUPLE
-        in_tuple = self.src[0].src[0] if self.src[0].op is Ops.CALL else self.src[0]
-        assert in_tuple.op is Ops.TUPLE
-        return in_tuple.src[self.arg]._shape
-
       case Ops.CAST:
         # when PTX casts from ptr to non ptr, remove the shape
         if isinstance(self.src[0].dtype, PtrDType) and not isinstance(self.src[0].dtype, ImageDType) and not isinstance(self.dtype, PtrDType):
           return None
 
       case Ops.INDEX:
-        # non pointer index doesn't have a shape
-        if not isinstance(self.dtype, PtrDType): return None
+        # non pointer index
+        if not isinstance(self.dtype, PtrDType):
+          idxs = flatten([d.shape for d in self.src[1:]])
+          return tuple(idxs) + self.src[0].shape[len(self.src)-1:]
         # fully indexed doesn't have a shape. TODO: remove this
         if self.src[0]._shape is None or len(self.src[1:]) == len(self.src[0].shape): return None
         # pointer index
         return self.src[0].shape[len(self.src[1:]):]
 
       # some ops init the shape
-      case Ops.CONST | Ops.VCONST | Ops.DEFINE_VAR | Ops.BIND: return ()
+      case Ops.CONST | Ops.VCONST | Ops.DEFINE_VAR | Ops.BIND | Ops.RANGE: return ()
       case Ops.BUFFER: return (self.arg,)
       case Ops.BUFFER_VIEW: return (self.arg[0],)
       case Ops.CUSTOM_FUNCTION: return None
@@ -252,7 +248,9 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
         inner_shape = self.src[0]._shape
         if inner_shape is None: return None
         # substitute internal PARAMs in the shape with corresponding args
-        return tuple(graph_rewrite(s, _pm_resolve_params, self.src[1:], walk=True) if isinstance(s, UOp) else s for s in inner_shape)
+        ret = tuple(graph_rewrite(s, _pm_resolve_params, self.src[1:], walk=True) if isinstance(s, UOp) else s for s in inner_shape)
+        prepend = tuple([x.vmax+1 for x in self.src[0].ranges])
+        return prepend+ret
 
       # TODO: disallow shape changing bitcast
       case Ops.BITCAST:
@@ -310,7 +308,7 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     if self.op is Ops.ASSIGN: return self.src[1]._shape
 
     # elementwise ops keep the shape the same. all inputs with shape must match
-    if self.op in GroupOp.ALU.union({Ops.CAST, Ops.COPY, Ops.NOOP, Ops.GROUP, Ops.SINK, Ops.ALLREDUCE}):
+    if self.op in GroupOp.ALU.union({Ops.CAST, Ops.COPY, Ops.NOOP, Ops.GROUP, Ops.SINK, Ops.ALLREDUCE, Ops.STORE}):
       input_shapes = [x._shape for x in self.src if x._shape is not None]
       if len(input_shapes) == 0: return None
       if not all_same(input_shapes): raise RuntimeError(f"shape mismatch at {self.op}: {input_shapes}")
@@ -354,7 +352,11 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
   @recursive_property
   def _ranges(self) -> dict[UOp, None]:
     ret: dict[UOp, None] = {}
-    for s in self.src: ret.update(s.ranges)
+    if self.op is Ops.CALL:
+      # ranges do not flow through calls
+      for s in self.src[1:]: ret.update(s.ranges)
+    else:
+      for s in self.src: ret.update(s.ranges)
     for er in self.ended_ranges:
       if er.op is Ops.RANGE:
         # if it's a single RANGE, we don't flow through it.
@@ -410,12 +412,6 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
 
   def sink(*srcs:UOp|None, **kwargs):  # pylint: disable=no-self-argument
     return UOp(Ops.SINK, dtypes.void, tuple([x for x in srcs if x is not None]), **kwargs)
-  def maketuple(*srcs:UOp):  # pylint: disable=no-self-argument
-    return UOp(Ops.TUPLE, dtypes.void, srcs)
-  def gettuple(self, idx:int) -> UOp:
-    in_tuple = self.src[0] if self.op is Ops.CALL else self
-    assert in_tuple.op is Ops.TUPLE, f"gettuple requires CALL or TUPLE source, got {self.op}"
-    return UOp(Ops.GETTUPLE, in_tuple.src[idx].dtype, (self,), idx)
   def group(*srcs:UOp|None):  # pylint: disable=no-self-argument
     if len(srcs) == 1 and isinstance(srcs[0], UOp): return srcs[0]
     return UOp(Ops.GROUP, dtypes.void, tuple([x for x in srcs if x is not None]))
@@ -426,9 +422,7 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype if ptr else self.dtype.base), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
   def __getitem__(self, idx):
     idx = argfix(idx)
-    # add : to the end
-    if len(idx) < len(self.shape): idx += tuple([slice(None)]*(len(self.shape)-len(idx)))
-    assert len(idx) == len(self.shape), f"__getitem__ shape mismatch, indexing {self.shape} with {len(idx)} args"
+    #assert len(idx) == len(self.shape), f"__getitem__ shape mismatch, indexing {self.shape} with {len(idx)} args"
     if len(slice_idx:=[i for i,x in enumerate(idx) if isinstance(x, slice)]):
       perm = self.permute(tuple([i for i in range(self.ndim) if i not in slice_idx] + slice_idx))
       return perm.index(*[UOp.const(dtypes.index, x) if isinstance(x, int) else x for x in idx if not isinstance(x, slice)], ptr=True)
@@ -857,8 +851,8 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     if self.op is Ops.GEP: return self.src[0]._min_max
     # TODO: CAST to bool/unsigned is not monotone, still some case can be simplified
     if self.op is Ops.CAST and self.dtype in dtypes.floats+dtypes.sints+(dtypes.index,):
-      return max(self.dtype.min, self.src[0].vmin), min(self.src[0].vmax, self.dtype.max)
-    return self.dtype.min, self.dtype.max
+      return max(dtypes.min(self.dtype), self.src[0].vmin), min(self.src[0].vmax, dtypes.max(self.dtype))
+    return dtypes.min(self.dtype), dtypes.max(self.dtype)
 
   @functools.cached_property
   def _sym_fxn(self):
@@ -915,10 +909,10 @@ class UOp(OpMixin, metaclass=UOpMetaClass):
     if self.axis is not None: p = p.replace(src=p.src + (UOp(Ops.MULTI, arg=self.axis),))
     return p
 
-  def call(self, *srcs:UOp, grad_fxn:Callable|None=None, metadata:tuple[Metadata, ...]=(),
-           name:str|None=None, precompile:bool=False, precompile_backward:bool=False) -> UOp:
-    assert len(self.ranges) == 0, f"ranges {self.ranges} are leaking out of the call in {self.pyrender()}"
-    return UOp(Ops.CALL, self.dtype, (self,)+srcs, CallInfo(grad_fxn, metadata, name, precompile, precompile_backward))
+  def call(self, *srcs:UOp, grad_fxn:Callable|None=None, metadata:tuple[Metadata, ...]=(), name:str|None=None, precompile:bool=False) -> UOp:
+    # ranges don't leak through calls, they end!
+    #assert len(self.ranges) == 0, f"ranges {self.ranges} are leaking out of the call in {self.pyrender()}"
+    return UOp(Ops.CALL, self.dtype, (self,)+srcs, CallInfo(grad_fxn, metadata, name, precompile))
   def custom_kernel(*srcs:UOp, fxn:Callable, grad_fxn:Callable|None=None) -> list[UOp]:
     contig_srcs = tuple(x.contiguous() if x.op is not Ops.AFTER else x for x in srcs)
     placeholders = [UOp.placeholder_like(s, slot=i) for i,s in enumerate(contig_srcs)]
@@ -942,12 +936,9 @@ class CallInfo:
   metadata: tuple[Metadata, ...] = ()
   name: str|None = None
   precompile: bool = False
-  precompile_backward: bool = False
   # grad_fxn can't be pickled, but metadata can
-  def __reduce__(self): return (CallInfo, (None, self.metadata, self.name, self.precompile, self.precompile_backward))
-  def __repr__(self):
-    gf = id(self.grad_fxn) if self.grad_fxn else None
-    return f"CallInfo({gf}, {self.metadata}, {repr(self.name)}, {self.precompile}, {self.precompile_backward})"
+  def __reduce__(self): return (CallInfo, (None, self.metadata, self.name, self.precompile))
+  def __repr__(self): return f"CallInfo({id(self.grad_fxn) if self.grad_fxn else None}, {self.metadata}, {repr(self.name)}, {self.precompile})"
 
 def should_resolve_call(c:UOp) -> bool:
   # don't resolve real kernel calls, sink or program
@@ -1480,6 +1471,7 @@ renderer = PatternMatcher([
   (UPat(Ops.PARAM, src=(UPat(), UPat(), UPat(), UPat(), UPat(Ops.NOOP, name="x"))), lambda x: x.arg),
   (UPat((Ops.SPECIAL), name="x"), lambda x: x.arg),
   (UPat(Ops.RANGE, name="x"), lambda x: f"r{range_str(x)}"),
+  (UPat(Ops.PARAM, name="x"), lambda x: f"p{x.arg}"),
   (UPat((Ops.CONST, Ops.VCONST), name="x"), lambda x: str(x.arg)),
   (UPat(Ops.UNROLL, name="x"), lambda ctx,x,u: f"UNROLL({ctx[x.src[0]]}, {u.arg})"),
   (UPat(Ops.CAST, name="x"), lambda ctx,x: f"({str(x.dtype)[7:]})({ctx[x.src[0]]})"),

tinygrad/uop/spec.py

@@ -59,9 +59,6 @@ shared_spec = PatternMatcher([
   # RANGE/SPECIAL define loops, END closes them
   (UPat(Ops.END, src=(UPat(), UPat(Ops.RANGE))), lambda: True),
 
-  # STORE in tensor graph: store a value into a target
-  (UPat(Ops.STORE, dtypes.void, (UPat(), UPat())), lambda: True),
-
   # NOOP
   (UPat(Ops.NOOP), lambda: True)
 ])
@@ -78,7 +75,7 @@ movement_ops = PatternMatcher([
   (UPat({Ops.ADD, Ops.MUL, Ops.IDIV}, dtype=dtypes.index), lambda: True),
 
   # AFTER on Movement Op or ASSIGN
-  (UPat(Ops.AFTER, src=(UPat(GroupOp.Movement.union({Ops.MULTI, Ops.CONTIGUOUS, Ops.ASSIGN, Ops.BUFFER})),), allow_any_len=True), lambda: True),
+  (UPat(Ops.AFTER, src=(UPat(GroupOp.Movement.union({Ops.MULTI, Ops.CONTIGUOUS, Ops.ASSIGN})),), allow_any_len=True), lambda: True),
 ])
 
 _tensor_spec = PatternMatcher([
@@ -139,10 +136,6 @@ _tensor_spec = PatternMatcher([
   (UPat(Ops.PARAM), lambda: True),
   (UPat(Ops.CUSTOM_FUNCTION, name="x"), lambda x: isinstance(x.arg, str)),
 
-  # TUPLE must have void dtype, GETTUPLE can only appear on CALL or TUPLE
-  (UPat(Ops.TUPLE, dtypes.void), lambda: True),
-  (UPat(Ops.GETTUPLE, src=(UPat((Ops.CALL, Ops.TUPLE)),), name="g"), lambda g: isinstance(g.arg, int)),
-
   # ** for custom kernels **
 
   # codegen: PROGRAM with progressive sources through the pipeline (SINK, DEVICE, LINEAR?, SOURCE?, BINARY?)
@@ -237,8 +230,8 @@ program_spec = PatternMatcher([
   # END closes ranges
   (UPat(Ops.END, src=(UPat(), UPat(Ops.RANGE)), dtype=dtypes.void), lambda: True),
 
-  # make sure all index dtypes have been lowered (except CONST/RANGE/DEFINE_VAR which are valid index-typed)
-  (UPat(GroupOp.All-{Ops.CONST, Ops.RANGE, Ops.DEFINE_VAR, Ops.VCONST, Ops.VECTORIZE}, dtype=dtypes.index), lambda: False),
+  # make sure all index dtypes have been lowered
+  (UPat(GroupOp.All, dtype=dtypes.index), lambda: False),
   (UPat(Ops.CONST, arg=Invalid), lambda: False),
   (UPat(Ops.VCONST, name="x"), lambda x: all(v is not Invalid for v in x.arg) and len(x.arg)==x.dtype.vcount>1 and
     type(x.arg) is type(dtypes.as_const(x.arg, x.dtype))),