work

* add llama layers

* add contig bw for speed

.github/workflows/benchmark.yml

@@ -617,6 +617,10 @@ jobs:
       run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/e8bea2c78ffa92685ece511e9b554122aaf1a79d/selfdrive/modeld/models/supercombo.onnx
     - name: openpilot dmonitoring compile3 0.9.7
       run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/dmonitoring_model.onnx
+    - name: openpilot compile3 Space Lab policy + vision
+      run: |
+        PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
+        PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/c824f68646a3b94f117f01c70dc8316fb466e05fbd42ccdba440b8a8dc86914b
     - name: benchmark MobileNetV2 on DSP
       run: |
         # generate quantized weights
@@ -649,10 +653,10 @@ jobs:
     steps:
     - name: Checkout Code
       uses: actions/checkout@v4
-    - name: Remove amdgpu
-      run: sudo rmmod amdgpu || true
-    - name: Cleanup running AM processes
-      run: python extra/amdpci/am_smi.py --pids --kill
+    - name: Remove amd modules
+      run: ./extra/hcq/hcq_smi.py amd rmmod
+    - name: Kill stale pids
+      run: ./extra/hcq/hcq_smi.py amd kill_pids
     - name: Symlink models and datasets
       run: |
         mkdir -p weights
@@ -716,7 +720,9 @@ jobs:
     - name: Checkout Code
       uses: actions/checkout@v4
     - name: Remove nv modules
-      run: ./extra/nvpci/nv_smi.py rmmod
+      run: ./extra/hcq/hcq_smi.py nv rmmod
+    - name: Kill stale pids
+      run: ./extra/hcq/hcq_smi.py nv kill_pids
     - name: Symlink models and datasets
       run: |
         mkdir -p weights

.github/workflows/mlperf.yml

@@ -12,7 +12,7 @@ jobs:
   run_script_job:
     runs-on: [self-hosted, Linux, tinybox]
     if: github.repository_owner == 'tinygrad'
-    timeout-minutes: 240
+    timeout-minutes: 360
 
     steps:
     - name: Checkout Code
@@ -27,4 +27,4 @@ jobs:
       run: |
         rm "~/.cache/tinygrad/cache_mlperf.db" || true
         BENCHMARK_LOG=mlpert_train_resnet LOGMLPERF=0 CACHEDB="~/.cache/tinygrad/cache_mlperf.db" examples/mlperf/training_submission_v5.1/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
-        rm "~/.cache/tinygrad/cache_mlperf.db"
+        rm "~/.cache/tinygrad/cache_mlperf.db"

examples/mlperf/dataloader.py

@@ -1,4 +1,6 @@
-import os, random, pickle, queue
+import functools
+import hashlib
+import os, random, pickle, queue, struct, math
 from typing import List
 from pathlib import Path
 from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu_count
@@ -6,6 +8,7 @@ from multiprocessing import Queue, Process, shared_memory, connection, Lock, cpu
 import numpy as np
 from tinygrad import dtypes, Tensor
 from tinygrad.helpers import getenv, prod, Context, round_up, tqdm, OSX
+from tinygrad.nn.state import TensorIO
 
 ### ResNet
 
@@ -510,6 +513,202 @@ def batch_load_retinanet(dataset, val:bool, base_dir:Path, batch_size:int=32, sh
       # happens with BENCHMARK set
       pass
 
+# llama3
+
+class BinIdxDataset:
+  def __init__(self, base_path:Path):
+    self.idx_t = Tensor(base_path.with_name(f"{base_path.name}.idx"))
+    self.idx = TensorIO(self.idx_t)
+
+    # parse idx file
+    magic = self.idx.read(9)
+    assert magic == b"MMIDIDX\x00\x00", "invalid index file format"
+    version, = struct.unpack("<Q", self.idx.read(8))
+    assert version == 1, "unsupported index version"
+    dtype_code, = struct.unpack("<B", self.idx.read(1))
+    self.dtype = {1:dtypes.uint8, 2:dtypes.int8, 3:dtypes.int16, 4:dtypes.int32, 5:dtypes.int64, 6:dtypes.float64, 7:dtypes.double, 8:dtypes.uint16}[dtype_code]
+    self.count, = struct.unpack("<Q", self.idx.read(8))
+    doc_count, = struct.unpack("<Q", self.idx.read(8))
+
+    start = self.idx.tell()
+    end = start + self.count * dtypes.int32.itemsize
+    self.sizes = self.idx_t[start:end].bitcast(dtypes.int32)
+
+    start = end
+    end = start + self.count * dtypes.int64.itemsize
+    self.pointers = self.idx_t[start:end].bitcast(dtypes.int64)
+
+    start = end
+    end = start + doc_count * dtypes.int64.itemsize
+    self.doc_idx = self.idx_t[start:end].bitcast(dtypes.int64)
+
+    # bin file
+    self.bin_t = Tensor(base_path.with_name(f"{base_path.name}.bin"))
+
+  def _index(self, idx) -> tuple[int, int]:
+    return self.pointers[idx].item(), self.sizes[idx].item()
+
+  def get(self, idx, offset:int=0, length:int|None=None):
+    ptr, size = self._index(idx)
+    if length is None: length = size - offset
+    ptr += offset * self.dtype.itemsize
+    return self.bin_t[ptr:ptr+length*self.dtype.itemsize].bitcast(self.dtype).to(None)
+
+# https://docs.nvidia.com/megatron-core/developer-guide/latest/api-guide/datasets.html
+class GPTDataset:
+  def __init__(self, base_path:Path, samples:int, seqlen:int, seed:int, shuffle:bool):
+    self.samples, self.seqlen = samples, seqlen
+    self.shuffle = shuffle
+    self.rng = np.random.RandomState(seed)
+
+    self.indexed_dataset = BinIdxDataset(base_path)
+
+    # check for cache
+    cache_hash = hashlib.sha256(f"{samples}:{seqlen}:{seed}:{shuffle}".encode()).hexdigest()
+    cache_path = base_path.with_name(f"{base_path.name}.{cache_hash}.index_cache")
+    if cache_path.exists():
+      with open(cache_path, "rb") as f:
+        self.doc_idx, self.sample_idx, self.shuffle_idx = pickle.load(f)
+    else:
+      self.doc_idx = self._build_doc_idx()
+      self.sample_idx = self._build_sample_idx()
+      self.shuffle_idx = self._build_shuffle_idx()
+      # save cache
+      with open(cache_path, "wb") as f:
+        pickle.dump((self.doc_idx, self.sample_idx, self.shuffle_idx), f)
+
+  def __getitem__(self, idx):
+    if idx is None:
+      text = self._get(0)
+    else:
+      text = self._get(idx)
+
+    return text
+
+  def _get(self, idx):
+    idx = self.shuffle_idx[idx]
+
+    doc_idx_beg, doc_idx_beg_offset = self.sample_idx[idx]
+    doc_idx_end, doc_idx_end_offset = self.sample_idx[idx + 1]
+
+    doc_ids, sample_parts = [], []
+
+    if doc_idx_beg == doc_idx_end:
+      doc_ids.append(self.doc_idx[doc_idx_beg])
+
+      sample_parts.append(
+          self.indexed_dataset.get(
+            int(self.doc_idx[doc_idx_beg]), offset=int(doc_idx_beg_offset), length=int(doc_idx_end_offset - doc_idx_beg_offset + 1)))
+    else:
+      for i in range(doc_idx_beg, doc_idx_end + 1):
+        doc_ids.append(self.doc_idx[i])
+
+        offset = 0 if i > doc_idx_beg else doc_idx_beg_offset
+        length = None if i < doc_idx_end else int(doc_idx_end_offset + 1)
+        sample_parts.append(self.indexed_dataset.get(int(self.doc_idx[i]), offset=int(offset), length=length))
+
+    # concat all parts
+    text = Tensor.cat(*sample_parts)
+
+    return text
+
+  @functools.cached_property
+  def tokens_per_epoch(self) -> int:
+    return sum(self.indexed_dataset.sizes.tolist())
+
+  @functools.cached_property
+  def num_epochs(self) -> int:
+    # we need enough epochs to cover the requested amount of tokens
+    num_epochs = 1
+    num_tokens = self.tokens_per_epoch
+    while num_tokens < self.samples * self.seqlen:
+      num_epochs += 1
+      num_tokens += self.tokens_per_epoch
+    return num_epochs
+
+  # https://github.com/NVIDIA/Megatron-LM/blob/94bd476bd840c2fd4c3ebfc7448c2af220f4832b/megatron/core/datasets/gpt_dataset.py#L558
+  def _build_doc_idx(self):
+    doc_idx = np.mgrid[:self.num_epochs, :self.indexed_dataset.count][1]
+    doc_idx = doc_idx.reshape(-1)
+    doc_idx = doc_idx.astype(np.int32)
+    if self.shuffle: self.rng.shuffle(doc_idx)
+    return doc_idx
+
+  def _build_sample_idx(self):
+    sample_idx = np.empty((self.samples + 1, 2), dtype=np.int32)
+
+    sample_idx_idx, doc_idx_idx, doc_offset = 0, 0, 0
+    sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
+    sample_idx_idx += 1
+
+    for _ in tqdm(range(1, self.samples + 1)):
+      remaining_seqlen = self.seqlen + 1
+      while remaining_seqlen > 0:
+        doc_idx = int(self.doc_idx[doc_idx_idx])
+        doc_len = self.indexed_dataset.sizes[doc_idx].item() - doc_offset
+        remaining_seqlen -= doc_len
+        if remaining_seqlen <= 0:
+          doc_offset += remaining_seqlen + doc_len - 1
+          remaining_seqlen = 0
+        else:
+          if doc_idx_idx == len(self.doc_idx) - 1:
+            assert sample_idx_idx == self.samples
+            doc_idx = int(self.doc_idx[doc_idx_idx])
+            doc_offset = self.indexed_dataset.sizes[doc_idx].item() - 1
+            break
+          doc_idx_idx += 1
+          doc_offset = 0
+
+      sample_idx[sample_idx_idx, 0], sample_idx[sample_idx_idx, 1] = doc_idx_idx, doc_offset
+      sample_idx_idx += 1
+
+    return sample_idx
+
+  def _build_shuffle_idx(self):
+    shuffle_idx = np.arange(self.samples, dtype=np.int32)
+    if self.shuffle: self.rng.shuffle(shuffle_idx)
+    return shuffle_idx
+
+class BlendedGPTDataset:
+  def __init__(self, paths:list[Path], weights:list[float], samples:int, seqlen:int, seed:int, shuffle:bool):
+    self.seed = seed
+
+    # normalize weights
+    total_weight = sum(weights)
+    self.weights = [w / total_weight for w in weights]
+
+    self.samples = samples
+    surplus = 0.005
+    samples_per_blend = [math.ceil(math.ceil(self.samples * w) * (1 + surplus)) for w in self.weights]
+
+    self.datasets = [GPTDataset(path, samples_per_blend[i], seqlen, seed + i, shuffle) for i,path in enumerate(paths)]
+
+  def get(self, idx:int):
+    tokens = self.datasets[0][idx]
+    return tokens
+
+def batch_load_llama3(bs:int, samples:int, seqlen:int, base_dir:Path, seed:int=0, val:bool=True):
+  if val:
+    dataset = BlendedGPTDataset([
+      base_dir / "validation" / "c4-validationn-91205-samples.en_text_document",
+    ], [
+      1.0
+    ], samples, seqlen, seed, False)
+  else:
+    dataset = BlendedGPTDataset([
+      base_dir / "c4-train.en_6_text_document",
+      base_dir / "c4-train.en_7_text_document",
+    ], [
+      1.0, 1.0
+    ], samples, seqlen, seed, True)
+
+  for b in range(math.ceil(samples / bs)):
+    batch = []
+    for i in range(bs):
+      tokens = dataset.get(b * bs + i)
+      batch.append(tokens)
+    yield Tensor.stack(batch, dim=0)
+
 if __name__ == "__main__":
   def load_unet3d(val):
     assert not val, "validation set is not supported due to different sizes on inputs"
@@ -538,6 +737,18 @@ if __name__ == "__main__":
       for x in batch_load_retinanet(dataset, val, base_dir):
         pbar.update(x[0].shape[0])
 
+  def load_llama3(val):
+    bs = 24
+    samples = 5760 if val else 1_200_000
+    seqlen = 512
+
+    max_, min_ = 0, math.inf
+    for tokens in tqdm(batch_load_llama3(bs, samples, seqlen, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=5760, val=bool(val)), total=samples//bs):
+      max_ = max(max_, tokens.shape[1])
+      min_ = min(min_, tokens.shape[1])
+    print(f"max seq length: {max_}")
+    print(f"min seq length: {min_}")
+
   load_fn_name = f"load_{getenv('MODEL', 'resnet')}"
   if load_fn_name in globals():
     globals()[load_fn_name](getenv("VAL", 1))

examples/mlperf/model_eval.py

@@ -1,4 +1,4 @@
-import time
+import time, math
 start = time.perf_counter()
 from pathlib import Path
 import numpy as np
@@ -241,6 +241,34 @@ def eval_mrcnn():
   evaluate_predictions_on_coco(bbox_output, iou_type='bbox')
   evaluate_predictions_on_coco(mask_output, iou_type='segm')
 
+def eval_llama3():
+  from extra.models.llama import Transformer
+  from examples.llama3 import MODEL_PARAMS
+  from tinygrad.helpers import tqdm
+
+  bs = 4
+  sequence_length = 512
+
+  model = Transformer(**(MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}), max_context=sequence_length, jit=False, disable_kv_cache=True)
+
+  @TinyJit
+  def eval_step(model, tokens):
+    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
+    loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
+    return loss.flatten()
+
+  from examples.mlperf.dataloader import batch_load_llama3
+  iter = batch_load_llama3(bs, 5760, sequence_length, Path(getenv("BASEDIR", "/raid/datasets/c4/")), True)
+
+  losses = []
+  for tokens in tqdm(iter, total=5760//bs):
+    GlobalCounters.reset()
+    losses += eval_step(model, tokens).tolist()
+    tqdm.write(f"loss: {np.mean(losses)}")
+
+  log_perplexity = Tensor(losses).mean()
+  print(f"Log Perplexity: {log_perplexity.item()}")
+
 if __name__ == "__main__":
   # inference only
   Tensor.training = False

examples/mlperf/model_train.py

@@ -1290,9 +1290,16 @@ def train_llama3():
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
 
   config = {}
-  BS                 = config["BS"]                     = getenv("BS", 4)
+  BS                 = config["BS"]                     = getenv("BS", 16)
   grad_acc           = config["GRADIENT_ACC_STEPS"]     = getenv("GRADIENT_ACC_STEPS", 1)
   GBS                = config["GLOBAL_BATCH_SIZE"]      = BS * grad_acc
+  SEED               = config["SEED"]                   = getenv("SEED", 5760)
+  SEQLEN             = config["SEQLEN"]                 = getenv("SEQLEN", 8192)
+  TRAIN_ON_VAL       = config["TRAIN_ON_VAL"]           = getenv("TRAIN_ON_VAL", 0)
+  SAMPLES            = config["SAMPLES"]                = getenv("SAMPLES", 5_760 if TRAIN_ON_VAL else 1_200_000)
+
+  # LR=1e-4 TRAIN_ON_VAL=1 DEFAULT_FLOAT=bfloat16 FUSE_ARANGE=1 JITBEAM=2 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B WARMUP_STEPS=36 DECAY_STEPS=360 SEQLEN=512 PYTHONPATH=. AMD=1 AMD_LLVM=0 MODEL=llama3 python3 examples/mlperf/model_train.py
+  # trains to 7
 
   opt_adamw_beta_1 = 0.9
   opt_adamw_beta_2 = 0.95
@@ -1300,7 +1307,6 @@ def train_llama3():
   opt_adamw_weight_decay = 0.1
 
   opt_gradient_clip_norm = 1.0
-  sequence_length = 8192
   opt_learning_rate_warmup_steps = getenv("WARMUP_STEPS", math.ceil(8000 * 1152 / GBS))
   opt_learning_rate_decay_steps = getenv("DECAY_STEPS", math.ceil(1_200_000 * 1152 / GBS) - opt_learning_rate_warmup_steps)
   opt_base_learning_rate = getenv("LR", 8e-5 * GBS / 1152)  # NOTE: cannot change for benchmark
@@ -1308,7 +1314,9 @@ def train_llama3():
 
   # TODO: confirm weights are in bf16
   # vocab_size from the mixtral tokenizer
-  model = Transformer(**(MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}), max_context=sequence_length, jit=False, disable_kv_cache=True)
+  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}
+  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
+  model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
 
   optim = AdamW(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)
@@ -1316,10 +1324,10 @@ def train_llama3():
 
   @TinyJit
   @Tensor.train()
-  def train_step(model, x, y):
+  def train_step(model, tokens):
     optim.zero_grad()
-    logits:Tensor = model(x, start_pos=0, temperature=math.nan)
-    loss = logits.cross_entropy(y)
+    logits:Tensor = model(tokens[:, :-1], start_pos=0, temperature=math.nan)
+    loss = logits.sparse_categorical_crossentropy(tokens[:, 1:])
     loss.backward()
 
     # L2 norm grad clip
@@ -1340,19 +1348,31 @@ def train_llama3():
     loss.realize(lr)
     return loss, lr
 
-  # overfitting this example should give cross_entropy log(BS)
-  fake_input = Tensor([list(range(getenv("SEQLEN", 10)))], dtype="int16").expand(BS, -1)
-  fake_label = Tensor(list(range(BS)), dtype="int16")
+  if getenv("FAKEDATA", 0):
+    def fake_data():
+      for _ in range(SAMPLES // GBS):
+        yield Tensor.randint(GBS, SEQLEN + 1, low=0, high=32000, dtype=dtypes.int32, device=Device.DEFAULT)
+    iter = fake_data()
+  else:
+    from examples.mlperf.dataloader import batch_load_llama3
+    iter = batch_load_llama3(GBS, SAMPLES, SEQLEN, Path(getenv("BASEDIR", "/raid/datasets/c4/")), seed=SEED, val=bool(TRAIN_ON_VAL))
 
-  for _ in range(100):
+  i = 0
+  for tokens in tqdm(iter, total=SAMPLES//BS):
     GlobalCounters.reset()
-    loss, lr = train_step(model, fake_input, fake_label)
-    # BS=2 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=8B WARMUP_STEPS=2 DECAY_STEPS=300 PYTHONPATH=. AMD=1 MODEL=llama3 python3 examples/mlperf/model_train.py
-    # uses 43% ~= 83GB
-    # 8B bf16 = 16GB. model + grad + optim m and v = 64GB
-    # TODO: this OOM
-    # BS=1 SEQLEN=4000 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=8B WARMUP_STEPS=2 DECAY_STEPS=300 PYTHONPATH=. AMD=1 MODEL=llama3 python3 examples/mlperf/model_train.py
-    print(loss.item(), lr.item(), f"{GlobalCounters.global_mem//10**9=}")
+    loss, lr = train_step(model, tokens)
+    # above as tqdm.write f-string
+    tqdm.write(f"{loss.item():.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used")
+    if (fname:=getenv("LOSS_FILE", "")):
+      with open(fname, "a") as f:
+        f.write(f"{i} {loss.item():.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
+
+    if getenv("CKPT") and (i % 200 == 0 or i == 10):
+      tqdm.write("saving checkpoint")
+      if not os.path.exists(ckpt_dir := "./ckpts"): os.mkdir(ckpt_dir)
+      fn = f"{ckpt_dir}/{i}.safe"
+      safe_save(get_state_dict(model), fn)
+    i += 1
 
 if __name__ == "__main__":
   multiprocessing.set_start_method('spawn')

extra/hcq/hcq_smi.py

@@ -0,0 +1,122 @@
+#!/usr/bin/env python3
+
+from tinygrad.runtime.support.system import System
+import argparse, glob, os, re, time, subprocess, sys
+
+def scan_devs_based_on_lock(prefix:str, args) -> list[str]:
+  target_dev = args.pci_bus if 'pci_bus' in args.__dir__() else ""
+
+  devs = []
+  for dev in glob.glob(f'/tmp/{prefix}_*.lock'):
+    dev_id = dev[8:-5]
+    if os.path.exists(f"/sys/bus/pci/devices/{dev_id}") and dev_id.startswith(target_dev): devs.append(dev_id)
+  return devs
+
+def _do_reset_device(pci_bus): System.pci_reset(pci_bus)
+def _is_module_loaded(name: str) -> bool: return os.path.isdir(f"/sys/module/{name}")
+
+def cmd_remove_module(args):
+  modules = ["nvidia_drm", "nvidia_modeset", "nvidia_uvm", "nvidia"] if args.backend == "nv" else ["amdgpu"]
+  to_unload = [m for m in modules if _is_module_loaded(m)]
+  if not to_unload: print("Kernel modules are not loaded")
+  else:
+    print("Removing kernel modules:", ", ".join(to_unload))
+    try: subprocess.run(["sudo", "modprobe", "-r", *to_unload], check=True)
+    except subprocess.CalledProcessError as e:
+      print("Failed to unload all modules — they may be in use.", file=sys.stderr)
+      sys.exit(e.returncode)
+
+def cmd_insert_module(args):
+  cmd_remove_module(args)
+  cmd_reset_devices(args)
+
+  module = "nvidia" if args.backend == "nv" else "amdgpu"
+  if _is_module_loaded(module):
+    print(f"{module} kernel module already loaded")
+    return
+
+  print(f"Inserting kernel module: {module}")
+  if args.backend == "nv":
+    subprocess.run(["nvidia-smi"], check=True)
+  elif args.backend == "amd":
+    subprocess.run(["sudo", "modprobe", "amdgpu"], check=True)
+
+def cmd_reset_devices(args):
+  devs = scan_devs_based_on_lock({"amd":"am", "nv":"nv"}[args.backend], args)
+
+  for dev in devs:
+    print(f"Resetting device {dev}")
+    if args.backend != "amd": _do_reset_device(dev)
+    time.sleep(0.2)
+
+def cmd_show_pids(args):
+  devs = scan_devs_based_on_lock(prefix:={"amd":"am", "nv":"nv"}[args.backend], args)
+
+  for dev in devs:
+    try:
+      pid = subprocess.check_output(['sudo', 'lsof', f'/tmp/{prefix}_{dev}.lock']).decode('utf-8').strip().split('\n')[1].split()[1]
+      print(f"{dev}: {pid}")
+    except subprocess.CalledProcessError: print(f"{dev}: No processes found using this device")
+
+def cmd_kill_pids(args):
+  devs = scan_devs_based_on_lock(prefix:={"amd":"am", "nv":"nv"}[args.backend], args)
+
+  for dev in devs:
+    try:
+      pid = subprocess.check_output(['sudo', 'lsof', f'/tmp/{prefix}_{dev}.lock']).decode('utf-8').strip().split('\n')[1].split()[1]
+      print(f"{dev}: {pid}")
+    except subprocess.CalledProcessError: print(f"{dev}: No processes found using this device")
+
+def cmd_kill_pids(args):
+  devs = scan_devs_based_on_lock(prefix:={"amd":"am", "nv":"nv"}[args.backend], args)
+
+  for dev in devs:
+    for i in range(128):
+      if i > 0: time.sleep(0.2)
+
+      try:
+        try: pid = subprocess.check_output(['sudo', 'lsof', f'/tmp/{prefix}_{dev}.lock']).decode('utf-8').strip().split('\n')[1].split()[1]
+        except subprocess.CalledProcessError: break
+
+        print(f"Killing process {pid} (which uses {dev})")
+        subprocess.run(['sudo', 'kill', '-9', pid], check=True)
+      except subprocess.CalledProcessError as e:
+        print(f"Failed to kill process for device {dev}: {e}", file=sys.stderr)
+
+def add_common_commands(parent_subparsers):
+  p_insmod = parent_subparsers.add_parser("insmod", help="Insert a kernel module")
+  p_insmod.set_defaults(func=cmd_insert_module)
+
+  p_rmmod = parent_subparsers.add_parser("rmmod", help="Remove a kernel module")
+  p_rmmod.set_defaults(func=cmd_remove_module)
+
+  p_reset = parent_subparsers.add_parser("reset", help="Reset a device")
+  p_reset.add_argument("--pci_bus", default="", help="PCI bus ID of the device to reset")
+  p_reset.set_defaults(func=cmd_reset_devices)
+
+  p_reset = parent_subparsers.add_parser("pids", help="Show pids of processes using the device")
+  p_reset.add_argument("--pci_bus", default="", help="PCI bus ID of the device")
+  p_reset.set_defaults(func=cmd_show_pids)
+
+  p_reset = parent_subparsers.add_parser("kill_pids", help="Kill pids of processes using the device")
+  p_reset.add_argument("--pci_bus", default="", help="PCI bus ID of the device")
+  p_reset.set_defaults(func=cmd_kill_pids)
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  backend_subparsers = parser.add_subparsers(dest="backend", required=True, metavar="{nv,amd}", help="Hardware backend to target")
+
+  nv_parser = backend_subparsers.add_parser("nv", help="NVIDIA GPUs")
+  nv_commands = nv_parser.add_subparsers(dest="command", required=True)
+  add_common_commands(nv_commands)
+
+  amd_parser = backend_subparsers.add_parser("amd", help="AMD GPUs")
+  amd_commands = amd_parser.add_subparsers(dest="command", required=True)
+  add_common_commands(amd_commands)
+
+  args = parser.parse_args()
+  if args.command is None:
+    parser.print_help(sys.stderr)
+    sys.exit(1)
+
+  args.func(args)

extra/models/llama.py

@@ -99,7 +99,9 @@ class FeedForward:
     self.w3 = linear(dim, hidden_dim, bias=False) # the gate in Gated Linear Unit
 
   def __call__(self, x:Tensor) -> Tensor:
-    return self.w2(self.w1(x).silu() * self.w3(x)) # SwiGLU [arxiv/2002.05202, eq (5)]
+    w1 = self.w1(x).silu()
+    w3 = self.w3(x.contiguous_backward())  # this fixes a strange fusion that makes tensor cores miss
+    return self.w2(w1 * w3)
 
 class TransformerBlock:
   def __init__(self, dim:int, hidden_dim:int, n_heads:int, n_kv_heads:int, norm_eps:float, max_context:int, linear=nn.Linear,
@@ -111,7 +113,7 @@ class TransformerBlock:
 
   def __call__(self, x:Tensor, start_pos:Union[Variable,int], freqs_cis:Tensor, mask:Optional[Tensor]):
     h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
-    return (h + self.feed_forward(self.ffn_norm(h))).contiguous()
+    return (h + self.feed_forward(self.ffn_norm(h))).contiguous().contiguous_backward()
 
 # standard openai sampling
 def sample(logits: Tensor, temp: float, k: int, p: float, af: float, ap: float):
@@ -185,10 +187,10 @@ class Transformer:
 
     mask = Tensor.full((1, 1, seqlen, start_pos+seqlen), float("-inf"), dtype=h.dtype, device=h.device).triu(start_pos+1) if seqlen > 1 else None
     for layer in self.layers: h = layer(h, start_pos, freqs_cis, mask)
-    logits = self.output(self.norm(h)).float()[:, -1, :]
+    logits = self.output(self.norm(h)).float()
     if math.isnan(temperature): return logits
 
-    return sample(logits.flatten(), temperature, top_k, top_p, alpha_f, alpha_p)
+    return sample(logits[:, -1, :].flatten(), temperature, top_k, top_p, alpha_f, alpha_p)
 
   def __call__(self, tokens:Tensor, start_pos:int, temperature:float=0.0, top_k:int=0, top_p:float=0.8, alpha_f:float=0.0, alpha_p:float=0.0):
     # TODO: better way to handle the first call v.s. the rest?

extra/nvpci/gpu_reset.sh

@@ -1,2 +0,0 @@
-GPU="$1"
-echo 1 | sudo tee /sys/bus/pci/devices/$GPU/reset 2>/dev/null

extra/nvpci/nv_smi.py

@@ -1,65 +0,0 @@
-#!/usr/bin/env python3
-
-from tinygrad.runtime.support.system import System
-import argparse, glob, os, re, time, subprocess, sys
-
-def scan_devs_based_on_lock(prefix:str) -> list[str]:
-  devs = []
-  for dev in glob.glob(f'/tmp/{prefix}_*.lock'):
-    dev_id = dev[8:-5]
-    if os.path.exists(f"/sys/bus/pci/devices/{dev_id}"): devs.append(dev_id)
-  return devs
-
-def _do_reset_device(pci_bus): System.pci_reset(pci_bus)
-def _is_module_loaded(name: str) -> bool: return os.path.isdir(f"/sys/module/{name}")
-
-def cmd_remove_module(args):
-  to_unload = [m for m in ["nvidia_drm", "nvidia_modeset", "nvidia_uvm", "nvidia"] if _is_module_loaded(m)]
-  if not to_unload:
-    print("NVIDIA kernel modules are not loaded")
-  else:
-    print("Removing NVIDIA kernel modules:", ", ".join(to_unload))
-    try: subprocess.run(["sudo", "modprobe", "-r", *to_unload], check=True)
-    except subprocess.CalledProcessError as e:
-      print("Failed to unload all modules — they may be in use.", file=sys.stderr)
-      sys.exit(e.returncode)
-
-def cmd_insert_module(args):
-  cmd_remove_module(args)
-  cmd_reset_devices(args)
-
-  if not os.path.exists("/sys/module/nvidia"):
-    print("Inserting nvidia kernel module")
-    subprocess.run(["nvidia-smi"], check=True)
-  else: print("Nvidia kernel module already loaded")
-
-def cmd_reset_devices(args):
-  devs = scan_devs_based_on_lock("nv")
-  dev_to_reset = args.pci_bus if 'pci_bus' in args.__dir__() else ""
-
-  for dev in devs:
-    if dev.startswith(dev_to_reset):
-      print(f"Resetting device {dev}")
-      _do_reset_device(dev)
-      time.sleep(0.2)
-
-if __name__ == "__main__":
-  parser = argparse.ArgumentParser()
-  subparsers = parser.add_subparsers(required=True, dest="cmd")
-
-  parser_insmod = subparsers.add_parser('insmod', help='Insert a nvidia kernel module')
-  parser_insmod.set_defaults(func=cmd_insert_module)
-
-  parser_rmmod = subparsers.add_parser('rmmod', help='Remove a nvidia kernel module')
-  parser_rmmod.set_defaults(func=cmd_remove_module)
-
-  parser_reset = subparsers.add_parser('reset', help='Reset a nvidia device')
-  parser_reset.add_argument('--pci_bus', type=str, default="", help='PCI bus ID of the device to reset')
-  parser_reset.set_defaults(func=cmd_reset_devices)
-
-  args = parser.parse_args()
-  if args.cmd is None:
-    parser.print_help(sys.stderr)
-    sys.exit(1)
-
-  args.func(args)

extra/torch_backend/backend.py

@@ -128,6 +128,12 @@ def _linalg_eigh(self, UPLO: str = 'U'):
   w, v = torch.linalg.eigh(self.cpu(), UPLO=UPLO)
   return w.tiny(), v.tiny()
 
+@torch.library.impl("aten::_linalg_det", "privateuseone")
+# TODO: move to tinygrad
+def _linalg_det(self: torch.Tensor):
+  result = aten._linalg_det(self.cpu())
+  return result[0].tiny(), result[1].tiny(), result[2].tiny()
+
 def upsample_backward(grad_out, output_size, input_size, *args, f=None): return f(grad_out.cpu(), output_size, input_size, *args).tiny()
 
 for i in [

extra/torch_backend/test.py

@@ -198,6 +198,11 @@ class TestTorchBackend(unittest.TestCase):
     recon = (v @ torch.diag(w) @ v.T).cpu().numpy()
     np.testing.assert_allclose(recon, a.cpu().numpy(), atol=1e-6)
 
+  def test_linalg_det(self):
+    a = torch.diag(torch.tensor([1,2,3,4,5], dtype = torch.float32, device=device))
+    b = torch.linalg.det(a)
+    np.testing.assert_equal(b.cpu().numpy(), 120.0)
+
   def test_scalar_assign(self):
     a = torch.tensor([1, 2, 3], device=device)
     a[1] = 4

test/test_search.py

@@ -34,25 +34,29 @@ class TestBEAM(unittest.TestCase):
     capturing.clear()
     self.assertNotEqual(k_beam_0[-1].prg.p.src, k_beam_1[-1].prg.p.src)
 
-  def test_get_kernel_actions(self):
+  def test_get_kernel_actions_dedup(self):
     from test.test_linearizer import helper_realized_ast
-    a = Tensor.rand(4, 3)
-    b = Tensor.rand(3)
-    realized_ast, _ = helper_realized_ast(a @ b)
     from tinygrad.opt.search import get_kernel_actions
-    lins = get_kernel_actions(Kernel(realized_ast), False).values()
+    a = Tensor.empty(4, 3)
+    b = Tensor.empty(3)
+    realized_ast, _ = helper_realized_ast(a @ b)
+    candidates = [
+      Opt(op=OptOps.UPCAST, axis=0, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=4),
+      Opt(op=OptOps.LOCAL, axis=0, arg=0), Opt(op=OptOps.LOCAL, axis=0, arg=4),
+      Opt(op=OptOps.UNROLL, axis=0, arg=0), Opt(op=OptOps.UNROLL, axis=0, arg=3),
+      Opt(op=OptOps.GROUP, axis=0, arg=0), Opt(op=OptOps.GROUP, axis=0, arg=3),
+      Opt(op=OptOps.GROUPTOP, axis=0, arg=0), Opt(op=OptOps.GROUPTOP, axis=0, arg=3),
+    ]
+    lins = get_kernel_actions(Kernel(realized_ast), include_0=False, candidates=candidates).values()
 
     # ensure amt=0 are not duplicated
-    if Opt(OptOps.UPCAST, 0, 0) in actions:
-      assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.UPCAST, axis=0, arg=4)]) == 0, "did not de-dup UPCAST"
-    if Opt(OptOps.LOCAL, 0, 0) in actions:
-      assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.LOCAL, axis=0, arg=4)]) == 0, "did not de-dup LOCAL"
-    if Opt(OptOps.UNROLL, 0, 0) in actions:
-      assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.UNROLL, axis=0, arg=3)]) == 0, "did not de-dup UNROLL"
-    if Opt(OptOps.GROUP, 0, 0) in actions:
-      assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.GROUP, axis=0, arg=3)]) == 0, "did not de-dup GROUP"
-    if Opt(OptOps.GROUPTOP, 0, 0) in actions:
-      assert len([x for x in lins if x.applied_opts[0] == Opt(OptOps.GROUPTOP, axis=0, arg=3)]) == 0, "did not de-dup GROUPTOP"
+    assert all(len(x.applied_opts) == 1 for x in lins)
+    kernel_actions = [x.applied_opts[0] for x in lins]
+    assert Opt(OptOps.UPCAST, axis=0, arg=4) not in kernel_actions, "did not de-dup UPCAST"
+    assert Opt(OptOps.LOCAL, axis=0, arg=4) not in kernel_actions, "did not de-dup LOCAL"
+    assert Opt(OptOps.UNROLL, axis=0, arg=3) not in kernel_actions, "did not de-dup UNROLL"
+    assert Opt(OptOps.GROUP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUP"
+    assert Opt(OptOps.GROUPTOP, axis=0, arg=3) not in kernel_actions, "did not de-dup GROUPTOP"
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.tensor_cores, "test requires tensor cores")
   def test_search_over_shape(self):

test/test_setitem.py

@@ -158,6 +158,13 @@ class TestSetitem(unittest.TestCase):
     t[:-1] = t[1:]
     self.assertEqual(t.tolist(), [[2.0], [1.0], [1.0]])
 
+  def test_setitem_big(self):
+    idx_size, val = 256, 4
+    t = Tensor.arange(0, idx_size+1)
+    idx = Tensor.arange(0, idx_size)
+    t[idx] = val
+    self.assertEqual(t.tolist(), [val]*idx_size+[idx_size])
+
 class TestWithGrad(unittest.TestCase):
   def test_no_requires_grad_works(self):
     z = Tensor.rand(8, 8)

test/test_tiny.py

@@ -33,6 +33,35 @@ class TestTiny(unittest.TestCase):
     self.assertListEqual((out:=a@b).flatten().tolist(), [1.0]*(N*N))
     if IMAGE < 2: self.assertEqual(out.dtype, out_dtype)
 
+  def test_double_gemm(self, N=64, BS=1):
+    a = Tensor.ones(BS,N,N).contiguous().realize()
+    b = Tensor.eye(N).contiguous().realize()
+    c = Tensor.eye(N).contiguous().realize()
+    d = Tensor.eye(N).contiguous().realize()
+    e = Tensor.eye(N).contiguous().realize()
+    f = Tensor.eye(N).contiguous().realize()
+    g = Tensor.eye(N).contiguous().realize()
+    out = ((a@b@c@d).contiguous()@e@f@g).contiguous().realize()
+    self.assertListEqual(out.flatten().tolist(), [1.0]*(BS*N*N))
+
+  def test_double_gemm_bs(self, N=64, BS=1): self.test_double_gemm(BS=4)
+
+  def test_conv2d(self):
+    N = 64
+    a = Tensor.ones(1,4,N,N).contiguous().realize()
+    w1 = Tensor.ones(16,4,3,3).contiguous().realize()
+    out = a.conv2d(w1).contiguous().realize()
+
+  def test_double_conv2d(self):
+    N = 64
+    a = Tensor.ones(1,4,N,N).contiguous().realize()
+    w1 = Tensor.ones(4,4,3,3).contiguous().realize()
+    w2 = Tensor.ones(4,4,3,3).contiguous().realize()
+    w3 = Tensor.ones(4,4,3,3).contiguous().realize()
+    w4 = Tensor.ones(4,4,3,3).contiguous().realize()
+    w5 = Tensor.ones(4,4,3,3).contiguous().realize()
+    out = a.conv2d(w1).conv2d(w2).conv2d(w3).conv2d(w4).conv2d(w5).contiguous().realize()
+
   # *** randomness ***
 
   def test_random(self):

test/unit/test_viz.py

@@ -138,6 +138,16 @@ class TestViz(BaseTestViz):
     nop = UOp(Ops.NOOP, arg="infinite loop in fixed_point_rewrite")
     self.assertEqual(graphs[2], uop_to_json(nop)[id(nop)])
 
+  def test_const_node_visibility(self):
+    a = UOp.variable("a", 0, 10)
+    z = UOp.const(dtypes.int, 0)
+    alu = a*z
+    exec_rewrite(alu, [sym])
+    graphs = [x["graph"] for x in get_details(tracked_ctxs[0][0])]
+    # embed const in the parent node when possible
+    self.assertEqual(list(graphs[0]), [id(a), id(alu)])
+    self.assertEqual(list(graphs[1]), [id(z)])
+
 # VIZ displays nested graph_rewrites in a tree view
 
 def leaf_rewrite(x:UOp): return x.rtag(1) if x.tag is None else None

tinygrad/codegen/linearize.py

@@ -90,9 +90,9 @@ class BlockContext:
       ctx.block_ctxs[u] = _sort_ctx(this_block_ctx) if u.op is not Ops.SINK else ()
 
       # RANGE/IF add to the next ctx
-      # STORE/ASSIGN subtract from the next ctx
+      # STORE/REDUCE_AXIS subtract from the next ctx
       if u.op in {Ops.RANGE, Ops.IF}: ctx.child_ctxs[u] = _sort_ctx(ctx.block_ctxs[u] + (u,))
-      elif u.op is Ops.STORE: ctx.child_ctxs[u] = tuple([y for y in ctx.block_ctxs[u] if y not in u.src])
+      elif u.op in {Ops.STORE, Ops.REDUCE_AXIS, Ops.CONTIGUOUS}: ctx.child_ctxs[u] = tuple([y for y in ctx.block_ctxs[u] if y not in u.src])
     return ctx
 
 # ***** make blocks *****

tinygrad/engine/realize.py

@@ -156,7 +156,7 @@ class ExecItem:
         lds_est = sym_infer(self.prg.estimates.lds, var_vals)
         mem_est = min(mem_est, lds_est)   # there can't be more memory accessed than loads/stores. remove this when symbolic is fixed
         ptm = colored(time_to_str(et, w=9), "yellow" if et > 0.01 else None) if et is not None else ""
-        print(f"{colored(f'*** {self.prg.device[:7]:7s} {GlobalCounters.kernel_count:4d}', 'magenta' if jit else ('green' if self.prg.first_run else None))} {self.prg.display_name+' '*(41-ansilen(self.prg.display_name))} arg {len(bufs):2d} mem {GlobalCounters.mem_used/1e9:5.2f} GB " +  # noqa: E501
+        print(f"{colored(f'*** {self.prg.device[:7]:7s} {GlobalCounters.kernel_count:4d}', 'magenta' if jit else ('green' if self.prg.first_run else None))} {self.prg.display_name+' '*(44-ansilen(self.prg.display_name))} arg {len(bufs):2d} mem {GlobalCounters.mem_used/1e9:5.2f} GB " +  # noqa: E501
               (str() if et is None else f"tm {ptm}/{GlobalCounters.time_sum_s*1e3:9.2f}ms ({op_est/((et or 1e-20)*1e9):9.2f} GFLOPS {mem_est/((et or 1e-20)*1e9):6.1f}|{lds_est/((et or 1e-20)*1e9):<7.1f} GB/s)" +  # noqa: E501
                f" {[repr(m) if TRACEMETA >= 2 else str(m) for m in self.metadata] if self.metadata else ''}"))
       self.prg.first_run = False

tinygrad/helpers.py

@@ -81,6 +81,13 @@ def word_wrap(x, wrap=80):
   while len(ansistrip(x[:i])) < wrap and i < len(x): i += 1
   return x[:i] + "\n" + word_wrap(x[i:], wrap)
 
+def suppress_finalizing(func):
+  def wrapper(*args, **kwargs):
+    try: return func(*args, **kwargs)
+    except (AttributeError, TypeError, ImportError):
+      if not getattr(sys, 'is_finalizing', lambda: True)(): raise # re-raise if not finalizing
+  return wrapper
+
 def pluralize(st:str, cnt:int): return f"{cnt} {st}"+('' if cnt == 1 else 's')
 
 class LazySeq(Generic[T]): # NOTE: Mapping requires __iter__ and __len__, Sequence requires supporting __len__ and slicing in __getitem__

tinygrad/opt/search.py

@@ -2,7 +2,7 @@ from typing import cast, Callable
 import itertools, functools, random, math, time, multiprocessing, traceback, signal, atexit
 from collections import defaultdict
 from dataclasses import replace
-from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer
+from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer, AxisType
 from tinygrad.device import Device, Buffer, Compiler
 from tinygrad.helpers import prod, flatten, DEBUG, CACHELEVEL, diskcache_get, diskcache_put, getenv, Context, colored, time_to_str
 from tinygrad.helpers import IGNORE_BEAM_CACHE, TC_SEARCH_OVER_SHAPE
@@ -83,7 +83,7 @@ def _try_compile_linearized_w_idx(x:tuple[int,Kernel], compiler:Compiler) -> tup
 
 # workers should not open devices and should ignore ctrl c and should not launch VIZ
 def _init_worker():
-  Context(ALLOW_DEVICE_USAGE=0, VIZ=0).__enter__()
+  Context(ALLOW_DEVICE_USAGE=0, VIZ=0, TRACK_MATCH_STATS=0).__enter__()
   signal.signal(signal.SIGINT, signal.SIG_IGN)
 
 def _ensure_buffer_alloc(bufs:list[Buffer]) -> list[Buffer]: return [buf.ensure_allocated() if buf is not None else buf for buf in bufs]
@@ -108,9 +108,9 @@ def bufs_from_lin(lin:Kernel, allocate:bool=True) -> list[Buffer]:
   return cast(list[Buffer], rawbufs)
 
 # get dictionary of all possible actions
-def get_kernel_actions(lin:Kernel, include_0=True) -> dict[int, Kernel]:
+def get_kernel_actions(lin:Kernel, include_0=True, candidates:list[Opt]|None=None) -> dict[int, Kernel]:
   acted_lins, max_up, max_lcl = {0:lin} if include_0 else {}, getenv("BEAM_UPCAST_MAX", 256), getenv("BEAM_LOCAL_MAX", 1024)
-  kernel_actions = actions.copy()
+  kernel_actions = (actions if candidates is None else candidates).copy()
 
   if TC_SEARCH_OVER_SHAPE and len(lin.applied_opts) == 0: # tensor core opts must be first
     for i, action in enumerate(kernel_actions):
@@ -123,14 +123,14 @@ def get_kernel_actions(lin:Kernel, include_0=True) -> dict[int, Kernel]:
     if a.axis is not None and a.op is not OptOps.TC:
       try: ax = lin.real_axis(a.op, a.axis)
       except KernelOptError: continue
-      if (ax >= lin.shape_len) or (lin.full_shape[ax] == a.arg and Opt(a.op, ax, 0) in kernel_actions): continue
+      if (ax >= lin.shape_len) or (lin.full_shape[ax] == a.arg and Opt(a.op, a.axis, 0) in kernel_actions): continue
     lin2 = lin.copy()
     try:
       lin2.apply_opt(a)
       up, lcl, tc_up = 1, 1, prod(tc.dims)//tc.threads if (tc:=lin2.tensor_core) else 1
-      for s,c in zip(lin2.full_shape, lin2.colors()):
-        if c in {"magenta", "yellow"}: up *= s
-        elif c in {"cyan", "green", "white"}: lcl *= s
+      for s,c in zip(lin2.full_shape, lin2.axis_types):
+        if c in (AxisType.UPCAST, AxisType.UNROLL): up *= s
+        elif c in (AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
       if up//tc_up > max_up or lcl > max_lcl:
         if getenv("BEAM_LOG_SURPASS_MAX"): print(f"too many upcast/local. {up//tc_up=}, {max_up=}, {lcl=}, {max_lcl=}")
         continue

tinygrad/opt/tc.py

@@ -37,6 +37,10 @@ class TensorCore: # D = A * B + C, A is (M x K), B is (K x N), C and D are (M x
     assert 2**local_axes == self.threads, f"{self.threads} threads construct the warp but found {2**local_axes} in {self.opts}"
     assert 2**upcast_axes == self.elements_per_thread[2], \
       f"{self.elements_per_thread[2]} elements from C are processed per thread but found {2**upcast_axes} in {self.opts}"
+    # check dims match opts
+    assert self.dims[0] == 2**len(gd:=[x for x in self.opts if x[1] == '0']), f"opts wrong on dims[0], {self.dims[0]} vs {gd}"
+    assert self.dims[1] == 2**len(gd:=[x for x in self.opts if x[1] == '1']), f"opts wrong on dims[1], {self.dims[1]} vs {gd}"
+    # NOTE: the K opts is implictly set by the dim
     # check swizzle
     assert len(self.swizzle[0]) == 3 and len(self.swizzle[1]) == 3, "swizzle has wrong part count"
     assert len(self.swizzle[0][0]) == len(self.swizzle[1][0]) == local_axes, "local swizzle size is wrong"
@@ -52,6 +56,7 @@ class TensorCore: # D = A * B + C, A is (M x K), B is (K x N), C and D are (M x
       if o[1] == '1': zero_stride_1.append(o[0] + str(un if o[0] == 'u' else ln))
       if o[0] == 'u': un += 1
       if o[0] == 'l': ln += 1
+    # NOTE: all the zero_stride dims can be placed in any order in the swizzle
     upcasted_0 = [x for x in (self.swizzle[0][1] + self.swizzle[0][2]) if x not in zero_stride_0 and x[0] != 'l']
     upcasted_1 = [x for x in (self.swizzle[1][1] + self.swizzle[1][2]) if x not in zero_stride_1 and x[0] != 'l']
     assert 2**len(upcasted_0) == self.elements_per_thread[0], f"mismatch in elements_per_thread[0], {upcasted_0} vs {self.elements_per_thread[0]}"

tinygrad/renderer/cstyle.py

@@ -141,7 +141,9 @@ class CStyleLanguage(Renderer):
     c: defaultdict[str, int] = defaultdict(int)
     name = "test"
     for u in uops:
-      if u.op is Ops.NOOP: continue
+      if u.op is Ops.NOOP:
+        if len(u.src): r[u] = r[u.src[0]]
+        continue
       if u.op is Ops.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue

tinygrad/runtime/ops_amd.py

@@ -7,7 +7,7 @@ from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQBuffer, H
 from tinygrad.runtime.support.hcq import MMIOInterface
 from tinygrad.uop.ops import sint
 from tinygrad.device import Compiled, DMAFdRef, BufferSpec
-from tinygrad.helpers import getenv, to_mv, round_up, data64_le, all_same, flatten, DEBUG, AMD_LLVM, PROFILE, ProfileEvent
+from tinygrad.helpers import getenv, to_mv, round_up, data64_le, all_same, flatten, DEBUG, AMD_LLVM, PROFILE, ProfileEvent, suppress_finalizing
 from tinygrad.renderer.cstyle import AMDRenderer
 from tinygrad.renderer.llvmir import AMDLLVMRenderer
 from tinygrad.runtime.autogen import kfd, hsa, pci, sqtt
@@ -473,11 +473,10 @@ class AMDAllocator(HCQAllocator['AMDDevice']):
   def _alloc(self, size:int, options:BufferSpec) -> HCQBuffer:
     return self.dev.iface.alloc(size, host=options.host, uncached=options.uncached, cpu_access=options.cpu_access)
 
+  @suppress_finalizing
   def _free(self, opaque, options:BufferSpec):
-    try:
-      self.dev.synchronize()
-      self.dev.iface.free(opaque)
-    except AttributeError: pass
+    self.dev.synchronize()
+    self.dev.iface.free(opaque)
 
   def _map(self, buf:HCQBuffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
 
@@ -593,7 +592,7 @@ class KFDIface:
 
   def free(self, mem):
     if len(mem.mapped_devs) > 0:
-      gpus = (ctypes.c_int32 * len(mem.mapped_devs))(*[x.gpu_id for x in mem.mapped_devs])
+      gpus = (ctypes.c_int32 * len(mem.mapped_devs))(*[x.iface.gpu_id for x in mem.mapped_devs])
       stm = kfd.AMDKFD_IOC_UNMAP_MEMORY_FROM_GPU(self.kfd, handle=mem.meta.handle, device_ids_array_ptr=ctypes.addressof(gpus), n_devices=len(gpus))
       assert stm.n_success == len(gpus)
     if mem.va_addr: FileIOInterface.munmap(mem.va_addr, mem.size)

tinygrad/runtime/ops_cuda.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import ctypes, ctypes.util, functools
-from tinygrad.helpers import DEBUG, getenv, mv_address, init_c_var, init_c_struct_t
+from tinygrad.helpers import DEBUG, getenv, mv_address, init_c_var, init_c_struct_t, suppress_finalizing
 from tinygrad.device import Compiled, BufferSpec, LRUAllocator
 from tinygrad.renderer.cstyle import CUDARenderer
 from tinygrad.renderer.ptx import PTXRenderer
@@ -45,9 +45,8 @@ class CUDAProgram:
     self.prg = prg
     if self.smem > 0: check(cuda.cuFuncSetAttribute(self.prg, cuda.CU_FUNC_ATTRIBUTE_MAX_DYNAMIC_SHARED_SIZE_BYTES, self.smem))
 
-  def __del__(self):
-    try: check(cuda.cuModuleUnload(self.module))
-    except AttributeError: pass
+  @suppress_finalizing
+  def __del__(self): check(cuda.cuModuleUnload(self.module))
 
   def __call__(self, *args, global_size:tuple[int,int,int]=(1,1,1), local_size:tuple[int,int,int]=(1,1,1), vals:tuple[int, ...]=(), wait=False):
     check(cuda.cuCtxSetCurrent(self.dev.context))

tinygrad/runtime/ops_gpu.py

@@ -2,7 +2,7 @@ from __future__ import annotations
 from typing import cast
 import ctypes, functools, hashlib
 from tinygrad.runtime.autogen import opencl as cl
-from tinygrad.helpers import init_c_var, to_char_p_p, from_mv, OSX, DEBUG, getenv, mv_address
+from tinygrad.helpers import init_c_var, to_char_p_p, from_mv, OSX, DEBUG, getenv, mv_address, suppress_finalizing
 from tinygrad.renderer.cstyle import OpenCLRenderer, IntelRenderer
 from tinygrad.device import BufferSpec, LRUAllocator, Compiled, Compiler, CompileError
 
@@ -69,9 +69,8 @@ class CLAllocator(LRUAllocator['CLDevice']):
                                         cl.cl_image_format(cl.CL_RGBA, {2: cl.CL_HALF_FLOAT, 4: cl.CL_FLOAT}[options.image.itemsize]),
                                         options.image.shape[1], options.image.shape[0], 0, None, status := ctypes.c_int32()), status), options)
     return (checked(cl.clCreateBuffer(self.dev.context, cl.CL_MEM_READ_WRITE, size, None, status := ctypes.c_int32()), status), options)
-  def _free(self, opaque:tuple[ctypes._CData, BufferSpec], options:BufferSpec):
-    try: check(cl.clReleaseMemObject(opaque[0]))
-    except AttributeError: pass
+  @suppress_finalizing
+  def _free(self, opaque:tuple[ctypes._CData, BufferSpec], options:BufferSpec): check(cl.clReleaseMemObject(opaque[0]))
   def _copyin(self, dest:tuple[ctypes._CData, BufferSpec], src:memoryview):
     if dest[1].image is not None:
       check(cl.clEnqueueWriteImage(self.dev.queue, dest[0], False, (ctypes.c_size_t * 3)(0,0,0),

tinygrad/runtime/ops_nv.py

@@ -7,7 +7,7 @@ from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQBuffer, H
 from tinygrad.runtime.support.hcq import MMIOInterface, FileIOInterface, MOCKGPU
 from tinygrad.uop.ops import sint
 from tinygrad.device import BufferSpec
-from tinygrad.helpers import getenv, mv_address, round_up, data64, data64_le, prod, OSX, to_mv, hi32, lo32
+from tinygrad.helpers import getenv, mv_address, round_up, data64, data64_le, prod, OSX, to_mv, hi32, lo32, suppress_finalizing
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.cstyle import NVRenderer
 from tinygrad.runtime.support.compiler_cuda import CUDACompiler, PTXCompiler, PTX, NVPTXCompiler, NVCompiler
@@ -276,11 +276,10 @@ class NVAllocator(HCQAllocator['NVDevice']):
   def _alloc(self, size:int, options:BufferSpec) -> HCQBuffer:
     return self.dev.iface.alloc(size, cpu_access=options.cpu_access, host=options.host)
 
+  @suppress_finalizing
   def _free(self, opaque:HCQBuffer, options:BufferSpec):
-    try:
-      self.dev.synchronize()
-      self.dev.iface.free(opaque)
-    except AttributeError: pass
+    self.dev.synchronize()
+    self.dev.iface.free(opaque)
 
   def _map(self, buf:HCQBuffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
 

tinygrad/runtime/ops_webgpu.py

@@ -1,7 +1,7 @@
 import functools, struct
 from tinygrad.device import  Compiled, Allocator, Compiler, BufferSpec
 from tinygrad.renderer.wgsl import WGSLRenderer
-from tinygrad.helpers import round_up
+from tinygrad.helpers import round_up, suppress_finalizing
 from tinygrad.runtime.autogen import webgpu
 from typing import List, Any, TypeAlias
 import ctypes
@@ -188,9 +188,8 @@ class WebGpuAllocator(Allocator['WGPUDevPtr']):
   def _copyout(self, dest:memoryview, src:WGPUBufPtr):
     buffer_data = read_buffer(self.dev, src)
     dest[:] = buffer_data[:dest.nbytes] if webgpu.wgpuBufferGetSize(src)  > dest.nbytes else buffer_data
-  def _free(self, opaque:WGPUBufPtr, options:BufferSpec):
-    try: webgpu.wgpuBufferDestroy(opaque)
-    except AttributeError: pass
+  @suppress_finalizing
+  def _free(self, opaque:WGPUBufPtr, options:BufferSpec): webgpu.wgpuBufferDestroy(opaque)
 
 class WebGpuDevice(Compiled):
   def __init__(self, device:str):

tinygrad/runtime/support/am/ip.py

@@ -169,12 +169,12 @@ class AM_SMU(AM_IP):
       self._send_msg(self.smu_mod.PPSMC_MSG_SetSoftMinByFreq, clck << 16 | (vals[level]))
       self._send_msg(self.smu_mod.PPSMC_MSG_SetSoftMaxByFreq, clck << 16 | (vals[level]))
 
-  def _smu_cmn_send_msg(self, msg, param=0, debug=False):
+  def _smu_cmn_send_msg(self, msg:int, param=0, debug=False):
     (self.adev.mmMP1_SMN_C2PMSG_90 if not debug else self.adev.mmMP1_SMN_C2PMSG_54).write(0) # resp reg
     (self.adev.mmMP1_SMN_C2PMSG_82 if not debug else self.adev.mmMP1_SMN_C2PMSG_53).write(param)
     (self.adev.mmMP1_SMN_C2PMSG_66 if not debug else self.adev.mmMP1_SMN_C2PMSG_75).write(msg)
 
-  def _send_msg(self, msg, param, read_back_arg=False, timeout=10000, debug=False): # 10s
+  def _send_msg(self, msg:int, param:int, read_back_arg=False, timeout=10000, debug=False): # default timeout is 10 seconds
     self._smu_cmn_send_msg(msg, param, debug=debug)
     wait_cond(lambda: (self.adev.mmMP1_SMN_C2PMSG_90 if not debug else self.adev.mmMP1_SMN_C2PMSG_54).read(), value=1, timeout_ms=timeout,
       msg=f"SMU msg {msg:#x} timeout")
@@ -414,12 +414,12 @@ class AM_PSP(AM_IP):
 
   def _wait_for_bootloader(self): wait_cond(lambda: self.adev.reg(f"{self.reg_pref}_35").read() & 0x80000000, value=0x80000000, msg="BL not ready")
 
-  def _prep_msg1(self, data):
+  def _prep_msg1(self, data:memoryview):
     assert len(data) <= self.msg1_view.nbytes, f"msg1 buffer is too small {len(data):#x} > {self.msg1_view.nbytes:#x}"
     self.msg1_view[:len(data)+4] = bytes(data) + b'\x00' * 4
     self.adev.gmc.flush_hdp()
 
-  def _bootloader_load_component(self, fw, compid):
+  def _bootloader_load_component(self, fw:int, compid:int):
     if fw not in self.adev.fw.sos_fw: return 0
 
     self._wait_for_bootloader()
@@ -458,7 +458,7 @@ class AM_PSP(AM_IP):
 
     wait_cond(lambda: self.adev.reg(f"{self.reg_pref}_64").read() & 0x8000FFFF, value=0x80000000, msg="sOS ring not created")
 
-  def _ring_submit(self, cmd):
+  def _ring_submit(self, cmd:am.struct_psp_gfx_cmd_resp) -> am.struct_psp_gfx_cmd_resp:
     msg = am.struct_psp_gfx_rb_frame(fence_value=(prev_wptr:=self.adev.reg(f"{self.reg_pref}_67").read()),
       cmd_buf_addr_lo=lo32(self.adev.paddr2mc(self.cmd_paddr)), cmd_buf_addr_hi=hi32(self.adev.paddr2mc(self.cmd_paddr)),
       fence_addr_lo=lo32(self.adev.paddr2mc(self.fence_paddr)), fence_addr_hi=hi32(self.adev.paddr2mc(self.fence_paddr)))
@@ -477,7 +477,7 @@ class AM_PSP(AM_IP):
 
     return resp
 
-  def _load_ip_fw_cmd(self, fw_types, fw_bytes):
+  def _load_ip_fw_cmd(self, fw_types:list[int], fw_bytes:memoryview):
     self._prep_msg1(fw_bytes)
     for fw_type in fw_types:
       if DEBUG >= 2: print(f"am {self.adev.devfmt}: loading fw: {am.psp_gfx_fw_type__enumvalues[fw_type]}")
@@ -487,7 +487,7 @@ class AM_PSP(AM_IP):
       cmd.cmd.cmd_load_ip_fw.fw_type = fw_type
       self._ring_submit(cmd)
 
-  def _tmr_load_cmd(self):
+  def _tmr_load_cmd(self) -> am.struct_psp_gfx_cmd_resp:
     cmd = am.struct_psp_gfx_cmd_resp(cmd_id=am.GFX_CMD_ID_SETUP_TMR)
     cmd.cmd.cmd_setup_tmr.buf_phy_addr_hi, cmd.cmd.cmd_setup_tmr.buf_phy_addr_lo = data64(self.adev.paddr2mc(self.tmr_paddr))
     cmd.cmd.cmd_setup_tmr.system_phy_addr_hi, cmd.cmd.cmd_setup_tmr.system_phy_addr_lo = data64(self.tmr_paddr)
@@ -495,7 +495,7 @@ class AM_PSP(AM_IP):
     cmd.cmd.cmd_setup_tmr.buf_size = self.tmr_size
     return self._ring_submit(cmd)
 
-  def _load_toc_cmd(self, toc_size):
+  def _load_toc_cmd(self, toc_size:int) -> am.struct_psp_gfx_cmd_resp:
     cmd = am.struct_psp_gfx_cmd_resp(cmd_id=am.GFX_CMD_ID_LOAD_TOC)
     cmd.cmd.cmd_load_toc.toc_phy_addr_hi, cmd.cmd.cmd_load_toc.toc_phy_addr_lo = data64(self.msg1_addr)
     cmd.cmd.cmd_load_toc.toc_size = toc_size

tinygrad/runtime/support/nv/ip.py

@@ -1,6 +1,6 @@
 from __future__ import annotations
 import ctypes, time, array, struct, itertools, dataclasses
-from typing import cast
+from typing import cast, Any
 from tinygrad.runtime.autogen.nv import nv
 from tinygrad.helpers import to_mv, lo32, hi32, DEBUG, round_up, round_down, mv_address, fetch, wait_cond
 from tinygrad.runtime.support.system import System
@@ -8,7 +8,7 @@ from tinygrad.runtime.support.elf import elf_loader
 from tinygrad.runtime.autogen import nv_gpu
 
 @dataclasses.dataclass(frozen=True)
-class GRBufDesc: size:int; v:int; p:int; lc:int=0 # noqa: E702
+class GRBufDesc: size:int; virt:bool; phys:bool; local:bool=False # noqa: E702
 
 class NV_IP:
   def __init__(self, nvdev): self.nvdev = nvdev
@@ -26,13 +26,13 @@ class NVRpcQueue:
     self.gsp, self.va, self.queue_va, self.seq = gsp, va, va + self.tx.entryOff, 0
     self.queue_mv = to_mv(self.queue_va, self.tx.msgSize * self.tx.msgCount)
 
-  def _checksum(self, data):
+  def _checksum(self, data:bytes):
     if (pad_len:=(-len(data)) % 8): data += b'\x00' * pad_len
     checksum = 0
     for offset in range(0, len(data), 8): checksum ^= struct.unpack_from('Q', data, offset)[0]
     return hi32(checksum) ^ lo32(checksum)
 
-  def send_rpc(self, func, msg, wait=False):
+  def send_rpc(self, func:int, msg:bytes, wait=False):
     header = nv.rpc_message_header_v(signature=nv.NV_VGPU_MSG_SIGNATURE_VALID, rpc_result=nv.NV_VGPU_MSG_RESULT_RPC_PENDING,
       rpc_result_private=nv.NV_VGPU_MSG_RESULT_RPC_PENDING, header_version=(3<<24), function=func, length=len(msg) + 0x20)
 
@@ -49,7 +49,7 @@ class NVRpcQueue:
     self.seq += 1
     self.gsp.nvdev.NV_PGSP_QUEUE_HEAD[0].write(0x0)
 
-  def wait_resp(self, cmd) -> memoryview:
+  def wait_resp(self, cmd:int) -> memoryview:
     while True:
       System.memory_barrier()
       if self.rx.readPtr == self.tx.writePtr: continue
@@ -177,7 +177,7 @@ class NV_FLCN(NV_IP):
     self.nvdev.NV_PFALCON_FALCON_OS.with_base(self.falcon).write(0x0)
     assert self.nvdev.NV_PRISCV_RISCV_CPUCTL.with_base(self.falcon).read_bitfields()['active_stat'] == 1, "GSP Core is not active"
 
-  def execute_dma(self, base, cmd, dest, mem_off, sysmem, size):
+  def execute_dma(self, base:int, cmd:int, dest:int, mem_off:int, sysmem:int, size:int):
     wait_cond(lambda: self.nvdev.NV_PFALCON_FALCON_DMATRFCMD.with_base(base).read_bitfields()['full'], value=0, msg="DMA does not progress")
 
     self.nvdev.NV_PFALCON_FALCON_DMATRFBASE.with_base(base).write(lo32(sysmem >> 8))
@@ -194,7 +194,7 @@ class NV_FLCN(NV_IP):
 
     wait_cond(lambda: self.nvdev.NV_PFALCON_FALCON_DMATRFCMD.with_base(base).read_bitfields()['idle'], msg="DMA does not complete")
 
-  def start_cpu(self, base):
+  def start_cpu(self, base:int):
     if self.nvdev.NV_PFALCON_FALCON_CPUCTL.with_base(base).read_bitfields()['alias_en'] == 1:
       self.nvdev.wreg(base + self.nvdev.NV_PFALCON_FALCON_CPUCTL_ALIAS, 0x2)
     else: self.nvdev.NV_PFALCON_FALCON_CPUCTL.with_base(base).write(startcpu=1)
@@ -232,11 +232,11 @@ class NV_FLCN(NV_IP):
     if mailbox is not None:
       return self.nvdev.NV_PFALCON_FALCON_MAILBOX0.with_base(base).read(), self.nvdev.NV_PFALCON_FALCON_MAILBOX1.with_base(base).read()
 
-  def disable_ctx_req(self, base):
+  def disable_ctx_req(self, base:int):
     self.nvdev.NV_PFALCON_FBIF_CTL.with_base(base).update(allow_phys_no_ctx=1)
     self.nvdev.NV_PFALCON_FALCON_DMACTL.with_base(base).write(0x0)
 
-  def reset(self, base, riscv=False):
+  def reset(self, base:int, riscv=False):
     engine_reg = self.nvdev.NV_PGSP_FALCON_ENGINE if base == self.falcon else self.nvdev.NV_PSEC_FALCON_ENGINE
     engine_reg.write(reset=1)
     time.sleep(0.1)
@@ -408,10 +408,10 @@ class NV_GSP(NV_IP):
       assert self.nvdev.flcn.frts_offset == m.frtsOffset, f"FRTS mismatch: {self.nvdev.flcn.frts_offset} != {m.frtsOffset}"
     self.wpr_meta, self.wpr_meta_sysmem = self.nvdev._alloc_boot_struct(m)
 
-  def promote_ctx(self, client, subdevice, obj, ctxbufs, bufs=None, virt=None, phys=None):
+  def promote_ctx(self, client:int, subdevice:int, obj:int, ctxbufs:dict[int, GRBufDesc], bufs=None, virt=None, phys=None):
     res, prom = {}, nv_gpu.NV2080_CTRL_GPU_PROMOTE_CTX_PARAMS(entryCount=len(ctxbufs), engineType=0x1, hChanClient=client, hObject=obj)
     for i,(buf,desc) in enumerate(ctxbufs.items()):
-      use_v, use_p = (desc.v if virt is None else virt), (desc.p if phys is None else phys)
+      use_v, use_p = (desc.virt if virt is None else virt), (desc.phys if phys is None else phys)
       x = (bufs or {}).get(buf, self.nvdev.mm.valloc(desc.size, contiguous=True)) # allocate buffers
       prom.promoteEntry[i] = nv_gpu.NV2080_CTRL_GPU_PROMOTE_CTX_BUFFER_ENTRY(bufferId=buf, gpuVirtAddr=x.va_addr if use_v else 0, bInitialize=use_p,
         gpuPhysAddr=x.paddrs[0][0] if use_p else 0, size=desc.size if use_p else 0, physAttr=0x4 if use_p else 0, bNonmapped=(use_p and not use_v))
@@ -449,10 +449,11 @@ class NV_GSP(NV_IP):
     gr_size = _ctx_info(nv_gpu.NV0080_CTRL_FIFO_GET_ENGINE_CONTEXT_PROPERTIES_ENGINE_ID_GRAPHICS, add=0x40000)
     patch_size = _ctx_info(nv_gpu.NV0080_CTRL_FIFO_GET_ENGINE_CONTEXT_PROPERTIES_ENGINE_ID_GRAPHICS_PATCH)
     cfgs_sizes = {x: _ctx_info(x + 14, align=(2 << 20) if x == 5 else None) for x in range(3, 11)} # indices 3–10 are mapped to 17–24
-    self.grctx_bufs = {0: GRBufDesc(gr_size, p=1, v=1), 1: GRBufDesc(patch_size, p=1, v=1, lc=1), 2: GRBufDesc(patch_size, p=1, v=1),
-      **{x: GRBufDesc(cfgs_sizes[x], p=0, v=1) for x in range(3, 7)}, 9: GRBufDesc(cfgs_sizes[9], p=1, v=1),
-      10: GRBufDesc(cfgs_sizes[10], p=1, v=0), 11: GRBufDesc(cfgs_sizes[10], p=1, v=1)} # NOTE: 11 reuses cfgs_sizes[10]
-    self.promote_ctx(self.priv_root, subdev, ch_gpfifo, {k:v for k, v in self.grctx_bufs.items() if v.lc == 0})
+    self.grctx_bufs = {0: GRBufDesc(gr_size, phys=True, virt=True), 1: GRBufDesc(patch_size, phys=True, virt=True, local=True),
+      2: GRBufDesc(patch_size, phys=True, virt=True), **{x: GRBufDesc(cfgs_sizes[x], phys=False, virt=True) for x in range(3, 7)},
+      9: GRBufDesc(cfgs_sizes[9], phys=True, virt=True), 10: GRBufDesc(cfgs_sizes[10], phys=True, virt=False),
+      11: GRBufDesc(cfgs_sizes[10], phys=True, virt=True)} # NOTE: 11 reuses cfgs_sizes[10]
+    self.promote_ctx(self.priv_root, subdev, ch_gpfifo, {k:v for k, v in self.grctx_bufs.items() if not v.local})
 
     self.rpc_rm_alloc(hParent=ch_gpfifo, hClass=self.compute_class, params=None)
     self.rpc_rm_alloc(hParent=ch_gpfifo, hClass=self.dma_class, params=None)
@@ -473,7 +474,7 @@ class NV_GSP(NV_IP):
 
   ### RPCs
 
-  def rpc_rm_alloc(self, hParent, hClass, params, client=None) -> int:
+  def rpc_rm_alloc(self, hParent:int, hClass:int, params:Any, client=None) -> int:
     if hClass == self.gpfifo_class:
       ramfc_alloc = self.nvdev.mm.valloc(0x1000, contiguous=True)
       params.ramfcMem = nv_gpu.NV_MEMORY_DESC_PARAMS(base=ramfc_alloc.paddrs[0][0], size=0x200, addressSpace=2, cacheAttrib=0)
@@ -499,7 +500,7 @@ class NV_GSP(NV_IP):
       self.promote_ctx(client, self.subdevice, hParent, {k:v for k,v in self.grctx_bufs.items() if k in [0, 1, 2]}, phys_gr_ctx, phys=False)
     return obj if hClass != nv_gpu.NV1_ROOT else client
 
-  def rpc_rm_control(self, hObject, cmd, params, client=None):
+  def rpc_rm_control(self, hObject:int, cmd:int, params:Any, client=None):
     control_args = nv.rpc_gsp_rm_control_v(hClient=(client:=client or self.priv_root), hObject=hObject, cmd=cmd, flags=0x0,
       paramsSize=ctypes.sizeof(params) if params is not None else 0x0)
     self.cmd_q.send_rpc(nv.NV_VGPU_MSG_FUNCTION_GSP_RM_CONTROL, bytes(control_args) + (bytes(params) if params is not None else b''))
@@ -511,7 +512,7 @@ class NV_GSP(NV_IP):
       cast(nv_gpu.NVC36F_CTRL_CMD_GPFIFO_GET_WORK_SUBMIT_TOKEN_PARAMS, st).workSubmitToken |= (1 << 30)
     return st
 
-  def rpc_set_page_directory(self, device, hVASpace, pdir_paddr, client=None, pasid=0xffffffff):
+  def rpc_set_page_directory(self, device:int, hVASpace:int, pdir_paddr:int, client=None, pasid=0xffffffff):
     params = nv.struct_NV0080_CTRL_DMA_SET_PAGE_DIRECTORY_PARAMS_v1E_05(physAddress=pdir_paddr,
       numEntries=self.nvdev.mm.pte_cnt[0], flags=0x8, hVASpace=hVASpace, pasid=pasid, subDeviceId=1, chId=0) # flags field is all channels.
     alloc_args = nv.rpc_set_page_directory_v(hClient=client or self.priv_root, hDevice=device, pasid=pasid, params=params)
@@ -544,7 +545,7 @@ class NV_GSP(NV_IP):
     header = nv.PACKED_REGISTRY_TABLE(size=hdr_size + len(entries_bytes) + len(data_bytes), numEntries=len(table))
     self.cmd_q.send_rpc(nv.NV_VGPU_MSG_FUNCTION_SET_REGISTRY, bytes(header) + entries_bytes + data_bytes)
 
-  def run_cpu_seq(self, seq_buf):
+  def run_cpu_seq(self, seq_buf:memoryview):
     hdr = nv.rpc_run_cpu_sequencer_v17_00.from_address(mv_address(seq_buf))
     cmd_iter = iter(seq_buf[ctypes.sizeof(nv.rpc_run_cpu_sequencer_v17_00):].cast('I')[:hdr.cmdIndex])
 

tinygrad/runtime/support/nv/nvdev.py

@@ -71,7 +71,7 @@ class NVMemoryManager(MemoryManager):
   def on_range_mapped(self): self.dev.NV_VIRTUAL_FUNCTION_PRIV_MMU_INVALIDATE.write((1 << 0) | (1 << 1) | (1 << 6) | (1 << 31))
 
 class NVDev(PCIDevImplBase):
-  def __init__(self, devfmt, mmio:MMIOInterface, vram:MMIOInterface, venid:int, subvenid:int, rev:int, bars:dict):
+  def __init__(self, devfmt:str, mmio:MMIOInterface, vram:MMIOInterface, venid:int, subvenid:int, rev:int, bars:dict):
     self.devfmt, self.mmio, self.vram, self.venid, self.subvenid, self.rev, self.bars = devfmt, mmio, vram, venid, subvenid, rev, bars
     self.lock_fd = System.flock_acquire(f"nv_{self.devfmt}.lock")
 
@@ -101,10 +101,10 @@ class NVDev(PCIDevImplBase):
     for ip in [self.gsp, self.flcn]: ip.fini_hw()
 
   def reg(self, reg:str) -> NVReg: return self.__dict__[reg]
-  def wreg(self, addr, value):
+  def wreg(self, addr:int, value:int):
     self.mmio[addr // 4] = value
     if NV_DEBUG >= 4: print(f"wreg: {hex(addr)} = {hex(value)}")
-  def rreg(self, addr): return self.mmio[addr // 4]
+  def rreg(self, addr:int) -> int: return self.mmio[addr // 4]
 
   def _early_init(self):
     self.reg_names:set[str] = set()
@@ -134,12 +134,12 @@ class NVDev(PCIDevImplBase):
 
     self.vram_size = self.reg("NV_PGC6_AON_SECURE_SCRATCH_GROUP_42").read() << 20
 
-  def _alloc_boot_struct(self, struct):
+  def _alloc_boot_struct(self, struct:ctypes.Structure) -> tuple[ctypes.Structure, int]:
     va, paddrs = System.alloc_sysmem(sz:=ctypes.sizeof(type(struct)), contiguous=True)
     to_mv(va, sz)[:] = bytes(struct)
     return type(struct).from_address(va), paddrs[0]
 
-  def _download(self, file) -> str:
+  def _download(self, file:str) -> str:
     url = f"https://raw.githubusercontent.com/NVIDIA/open-gpu-kernel-modules/8ec351aeb96a93a4bb69ccc12a542bf8a8df2b6f/{file}"
     return fetch(url, subdir="defines").read_text()
 

tinygrad/schedule/kernelize.py

@@ -3,7 +3,7 @@ from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewr
 from tinygrad.uop.ops import track_rewrites, _substitute
 from tinygrad.uop.spec import type_verify, tensor_uop_spec
 from tinygrad.uop.symbolic import symbolic_simple
-from tinygrad.helpers import Metadata, all_int, all_same, colored, prod, dedup, unwrap, getenv, pluralize, FUSE_ARANGE, DEBUG, SPLIT_REDUCEOP
+from tinygrad.helpers import Metadata, all_int, all_same, colored, prod, dedup, unwrap, getenv, pluralize, FUSE_ARANGE, DEBUG, SPLIT_REDUCEOP, argsort
 from tinygrad.dtype import ImageDType, dtypes
 from tinygrad.schedule.multi import multi_pm
 from tinygrad.shape.shapetracker import ShapeTracker
@@ -417,6 +417,121 @@ finalize_contiguous = PatternMatcher([
 
 remove_tags = PatternMatcher([(UPat(GroupOp.All, name="x"), lambda x: x.replace(tag=None) if x.tag is not None else None)])
 
+def contiguous_create_ranges(ctx:list[int], x:UOp):
+  if len(x.src) != 1: return None
+  ranges = []
+  for s in x.shape:
+    if resolve(s!=1):
+      ranges.append(UOp.range(dtypes.int, s, ctx[0]))
+      ctx[0] += 1
+    else:
+      ranges.append(UOp.const(dtypes.int, 0))
+  mm = UOp(Ops.MAP, dtype=x.src[0].dtype, src=(x.src[0],)+tuple(ranges))
+  buf = UOp.new_buffer(x.device, prod(x.shape), x.dtype).reshape(x.shape)
+  mm2 = UOp(Ops.MAP, dtype=x.src[0].dtype, src=(buf,)+tuple(ranges))
+  return UOp(Ops.STORE, src=(mm2, mm)+tuple(ranges))
+  #return x.replace(src=(mm,)+tuple(ranges))
+
+def map_reshape(x:UOp):
+  # don't push on the final buffer reshape for readable graph
+  #if x.src[0].src[0].op is Ops.BUFFER: return None
+  acc = 1
+  to_sum = []
+  for s,src in list(zip(x.shape, x.src[1:]))[::-1]:
+    to_sum.append(acc*src)
+    acc *= s
+  mish = sum(to_sum)
+  ret = []
+  for s in x.src[0].src[0].shape[::-1]:
+    if resolve(s!=1):
+      ret.append(mish % s)
+      mish //= s
+    else:
+      ret.append(UOp.const(dtypes.int, 0))
+  ret = UOp.sink(*ret).simplify().src[::-1] if len(ret) else ()
+  #return UOp(Ops.MAP, dtype=x.src[0].src[0].dtype, src=(x.src[0].src[0],)+ret)
+  # generic
+  return x.src[0].replace(src=tuple([UOp(Ops.MAP, dtype=s.dtype, src=(s,)+ret) for s in x.src[0].src]))
+
+def map_reduce(ctx:list[int], x:UOp):
+  rngs = list(x.src[1:])
+  r = x.src[0]
+  new_ranges = []
+  for i,s in enumerate(r.src[0].shape):
+    if i in r.arg[1]:
+      assert rngs[i].op == Ops.CONST
+      rngs[i] = UOp.range(dtypes.int, s, ctx[0])
+      new_ranges.append(rngs[i])
+      ctx[0] += 1
+  mm = UOp(Ops.MAP, r.src[0].dtype, src=(r.src[0],)+tuple(rngs))
+  return UOp(Ops.REDUCE_AXIS, r.dtype, src=(mm,)+tuple(new_ranges), arg=r.arg)
+
+def map_permute(x:UOp):
+  ret = x.src[1:]
+  # argsort or not?
+  perm = argsort(x.src[0].arg)
+  print(perm, x.src[0].arg)
+  ret = tuple([ret[p] for p in perm])
+  print(x.src[0].src[0].shape, ret)
+  #return UOp(Ops.MAP, dtype=x.src[0].src[0].dtype, src=(x.src[0].src[0],)+ret)
+  return x.src[0].replace(src=tuple([UOp(Ops.MAP, dtype=s.dtype, src=(s,)+ret) for s in x.src[0].src]))
+
+def map_expand(x:UOp):
+  r = x.src[0]
+  inp_shape, exp_shape = x.src[0].src[0].shape, x.src[0].shape
+  ret = list(x.src[1:])
+  exp_ranges = []
+  for i,(x,y) in enumerate(zip(inp_shape, exp_shape)):
+    if x != y:
+      exp_ranges.append(ret[i])
+      ret[i] = UOp.const(dtypes.int, 0)
+  mm = UOp(Ops.MAP, r.dtype, src=(r.src[0],)+tuple(ret))
+  return UOp(Ops.EXPAND, r.dtype, src=(mm,)+tuple(exp_ranges), arg=r.arg)
+
+def map_shrink(ctx:list[int], x:UOp):
+  r = x.src[0]
+  ret = list(x.src[1:])
+  for i,(s,(ss,se)) in enumerate(zip(r.src[0].shape, r.arg)):
+    assert ss == 0, "add to range?"
+    if se-ss != s and False:
+      new_ret_i = [ret[i]]
+      if ss != 0:
+        new_ret_i = [UOp.range(dtypes.int, ss, ctx[0])] + new_ret_i
+        ctx[0] += 1
+      if se != s:
+        new_ret_i = new_ret_i + [UOp.range(dtypes.int, s-se, ctx[0])]
+        ctx[0] += 1
+      ret[i] = UOp(Ops.CATRANGE, src=tuple(new_ret_i))
+  mm = UOp(Ops.MAP, r.dtype, src=(r.src[0],)+tuple(ret))
+  #return mm
+  # TODO: put the ranges on the shrink?
+  return UOp(Ops.SHRINK, r.dtype, src=(mm,), arg=r.arg)
+
+index_pushing = PatternMatcher([
+  (UPat(Ops.CONTIGUOUS, name="x"), contiguous_create_ranges),
+  (UPat(Ops.MAP, src=(UPat(Ops.RESHAPE),), allow_any_len=True, name="x"), map_reshape),
+  (UPat(Ops.MAP, src=(UPat(Ops.PERMUTE),), allow_any_len=True, name="x"), map_permute),
+  (UPat(Ops.MAP, src=(UPat(Ops.EXPAND),), allow_any_len=True, name="x"), map_expand),
+  (UPat(Ops.MAP, src=(UPat(Ops.SHRINK),), allow_any_len=True, name="x"), map_shrink),
+  (UPat(Ops.MAP, src=(UPat(Ops.REDUCE_AXIS),), allow_any_len=True, name="x"), map_reduce),
+  # move MAP through elementwise ALU
+  (UPat(Ops.MAP, src=(UPat(GroupOp.Elementwise),), allow_any_len=True, name="x"),
+   lambda x: x.src[0].replace(src=tuple([UOp(Ops.MAP, dtype=s.dtype, src=(s,)+x.src[1:]) for s in x.src[0].src]))),
+  # MAP on STORE is NOOP
+  (UPat(Ops.MAP, src=(UPat(Ops.STORE),), allow_any_len=True, name="x"), lambda x: x.src[0]),
+])
+
+fix_buffers = PatternMatcher([
+  (UPat(Ops.BUFFER, name="x"), lambda x: UOp(Ops.DEFINE_GLOBAL, dtype=x.dtype.ptr(x.arg), arg=x.src[0].arg)),
+  (UPat(Ops.MAP, name="x"), lambda x: x.replace(op=Ops.INDEX, dtype=x.src[0].dtype).load()),
+  (UPat(Ops.STORE, src=(UPat(Ops.LOAD),), name="x", allow_any_len=True), lambda x: x.replace(src=(x.src[0].src[0],)+x.src[1:])),
+  (UPat((Ops.RESHAPE, Ops.SHRINK, Ops.PERMUTE), name="x"), lambda x: x.src[0]),
+  # do EXPANDs need to track the ranges they end?
+  (UPat(Ops.EXPAND, name="x"), lambda x: x.src[0]),
+  #(UPat(Ops.EXPAND, name="x"), lambda x: x.replace(arg=None, op=Ops.NOOP)),
+  (UPat(Ops.REDUCE_AXIS, name="x"), lambda x: x.replace(op=Ops.REDUCE, arg=x.arg[0])),
+])
+
 @track_rewrites(name=lambda sink,ret: f"Schedule {pluralize('Kernel',len([u for u in ret[sink].toposort() if u.op is Ops.KERNEL]))}")
 def get_kernelize_map(sink:UOp) -> dict[UOp, UOp]:
   """
@@ -428,6 +543,20 @@ def get_kernelize_map(sink:UOp) -> dict[UOp, UOp]:
   Returns:
     Map transforming each UOp in the sink to the Ops.KERNEL graph.
   """
+  pushed = graph_rewrite(sink, index_pushing, ctx=[0], name="index pushing", bottom_up=True)
+  pushed = graph_rewrite(pushed, fix_buffers, name="fix buffers")
+  from tinygrad.codegen.devectorizer import pm_reduce, ReduceContext
+  pushed = graph_rewrite(pushed, pm_reduce, ctx=ReduceContext(), name="remove reduce")
+  from tinygrad.codegen.linearize import block_create, BlockContext, pm_blockend_merge, block_merge, pm_finalize
+  pushed = graph_rewrite(pushed, block_create, ctx=BlockContext.from_sink(pushed), name="block create", bottom_up=True)
+  pushed = graph_rewrite(pushed, pm_blockend_merge, name="blockend merge")
+  pushed = graph_rewrite(pushed, block_merge, name="block merge")
+  pushed = graph_rewrite(pushed, pm_finalize, name="finalize")
+  from tinygrad.device import Device
+  try:
+    print(Device['CPU'].renderer.render(pushed.arg.lst))
+  except Exception as e:
+    print("render fail", e)
 
   # multi + merge_views + simplify
   tensor_map = graph_rewrite_map(sink, multi_pm+do_fuse+merge_views+sym+replace_contiguous, ctx={}, name="merge_views")

tinygrad/tensor.py

@@ -282,7 +282,7 @@ class Tensor(MathTrait):
     # TODO: this is a hack for writing to DISK. remove with working assign
     if isinstance(self.device, str) and self.device.startswith("DISK"):
       if x.__class__ is not Tensor: x = Tensor(x, device="CPU", dtype=self.dtype)
-      cast(Buffer, self.contiguous().realize().uop.base.buffer).ensure_allocated().copyin(x._data())
+      self._buffer().copyin(x._data())
       return self
     if x.__class__ is not Tensor: x = Tensor(x, device=self.device, dtype=self.dtype)
     if self.uop is x.uop: return self  # a self assign is a NOOP
@@ -299,7 +299,10 @@ class Tensor(MathTrait):
     """
     return Tensor(self.uop.detach(), device=self.device, requires_grad=False)
 
-  def _buffer(self) -> Buffer: return cast(Buffer, self.cast(self.dtype.base).contiguous().to("CPU").realize().uop.base.buffer)
+  def _buffer(self) -> Buffer:
+    x = self.cast(self.dtype.base).contiguous()
+    if isinstance(self.device, tuple): x = x.to("CPU")
+    return cast(Buffer, x.realize().uop.base.buffer).ensure_allocated()
   def _data(self) -> memoryview: return self._buffer().as_buffer()
 
   def data(self) -> memoryview:

tinygrad/uop/__init__.py

@@ -10,6 +10,7 @@ class FastEnum(IntEnum):
 class Ops(FastEnum):
   # uops that aren't rendered
   NOOP = auto(); SINK = auto(); UNIQUE = auto(); DEVICE = auto(); KERNEL = auto(); PRECAST = auto()  # noqa: E702
+  MAP = auto(); CATRANGE = auto()
 
   # buffer ops
   COPY = auto(); BUFFER = auto(); BUFFER_VIEW = auto(); MSELECT = auto(); MSTACK = auto() # noqa: E702
@@ -82,6 +83,7 @@ class GroupOp:
             Ops.XOR, Ops.SHL, Ops.SHR, Ops.OR, Ops.AND, Ops.THREEFRY, Ops.SUB, Ops.FDIV, Ops.POW}
   Ternary = {Ops.WHERE, Ops.MULACC}
   ALU = set.union(Unary, Binary, Ternary)
+  Elementwise = set.union(ALU, {Ops.CAST, Ops.BITCAST})
 
   Defines = {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}
 

tinygrad/viz/index.html

@@ -223,6 +223,7 @@
     pointer-events: none;
     display: none;
     font-size: 10px;
+    white-space: pre;
   }
   #device-list > div {
     min-height: 32px;
@@ -239,6 +240,8 @@
     padding: 0 8px;
     width: 100%;
     height: 100%;
+    max-height: 100vh;
+    overflow-x: auto;
   }
   .raw-text code {
     max-height: none !important;
@@ -247,8 +250,6 @@
     width: 100%;
     border-collapse: separate;
     border-spacing: 0;
-    border-radius: 8px;
-    overflow: hidden;
     background-color: #1a1b26;
     color: #f0f0f5;
     font-size: 0.95em;
@@ -269,6 +270,40 @@
   tr.main-row > td, tr.sub-row > td {
     padding: 8px 12px;
   }
+  tr.code-row > td:first-child {
+    font-family: monospace;
+  }
+  td.pct-row > div {
+    height: 12px;
+    width: 100%;
+    display: flex;
+  }
+  td.pct-row > div > div {
+    height: 100%;
+  }
+  thead {
+    position: sticky;
+    top: 0;
+    z-index: 10;
+    background-color: #20222e;
+  }
+  thead th {
+    text-align: left;
+    padding: 10px 12px;
+    font-weight: 600;
+    border-bottom: 1px solid #3a3d52;
+    font-size: 0.95em;
+    letter-spacing: 0.03em;
+  }
+  .legend {
+    display: flex;
+    align-items: center;
+  }
+  .legend > div {
+    width: 0.95em;
+    height: 0.95em;
+    margin-right: 4px;
+  }
   </style>
 </head>
 <body>

tinygrad/viz/js/index.js

@@ -109,11 +109,11 @@ function formatTime(ts, dur=ts) {
 }
 const formatUnit = (d, unit="") => d3.format(".3~s")(d)+unit;
 
-const devColors = {"TINY":["rgb(27 87 69)", "rgb(53 79 82)", "rgb(53 79 82)", "rgb(70 172 194)", "rgb(29, 46, 98)"],
-                   "DEFAULT":["rgb(29,31,42)","rgb(42,45,61)","rgb(55,59,79)","rgb(68,72,98)","rgb(18,19,26)","rgb(47,50,68)","rgb(59,63,84)","rgb(74,78,101)","rgb(24,26,35)","rgb(35,37,50)","rgb(49,53,72)","rgb(64,68,89)"],}
-const bufColors = ["#3A57B7","#5066C1","#6277CD","#7488D8","#8A9BE3","#A3B4F2"];
-
-const lighten = (rgb, depth, step=0.08) => rgb.replace(/\d+/g, n => Math.round(parseInt(n)+(255-parseInt(n)) * Math.min(1, depth*step)));
+const colorScheme = {TINY:["#1b5745", "#354f52", "#354f52", "#46acc2", "#1d2e62"],
+  DEFAULT:["#2b2e39", "#2c2f3a", "#31343f", "#323544", "#2d303a", "#2e313c", "#343746", "#353847", "#3c4050", "#404459", "#444862", "#4a4e65"],
+  BUFFER:["#3A57B7","#5066C1","#6277CD","#7488D8","#8A9BE3","#A3B4F2"],
+  CATEGORICAL:["#ff8080", "#F4A261", "#C8F9D4", "#8D99AE", "#F4A261", "#ffffa2", "#ffffc0", "#87CEEB"],}
+const cycleColors = (lst, i) => lst[i%lst.length];
 
 var profileRet, focusedDevice, canvasZoom, zoomLevel = d3.zoomIdentity;
 async function renderProfiler() {
@@ -153,10 +153,10 @@ async function renderProfiler() {
     for (const e of timeline.shapes) {
       if (e.depth === 0) colorKey = e.cat ?? e.name;
       if (!colorMap.has(colorKey)) {
-        const colors = devColors[k] ?? devColors.DEFAULT;
+        const colors = colorScheme[k] ?? colorScheme.DEFAULT;
         colorMap.set(colorKey, colors[colorMap.size%colors.length]);
       }
-      const fillColor = lighten(colorMap.get(colorKey), e.depth);
+      const fillColor = d3.color(colorMap.get(colorKey)).brighter(e.depth).toString();
       const label = parseColors(e.name).map(({ color, st }) => ({ color, st, width:ctx.measureText(st).width }));
       if (e.ref != null) ref = {ctx:e.ref, step:0};
       else if (ref != null) {
@@ -164,7 +164,7 @@ async function renderProfiler() {
         const stepIdx = ctxs[ref.ctx+1].steps.findIndex((s, i) => i >= start && s.name == e.name);
         ref = stepIdx === -1 ? null : {ctx:ref.ctx, step:stepIdx};
       }
-      const arg = { tooltipText:formatTime(e.dur), ...ref };
+      const arg = { tooltipText:formatTime(e.dur)+(e.info != null ? "\n"+e.info : ""), ...ref };
       // offset y by depth
       data.shapes.push({x:e.st-st, y:offsetY+levelHeight*e.depth, width:e.dur, height:levelHeight, arg, label, fillColor });
     }
@@ -184,7 +184,7 @@ async function renderProfiler() {
       const y0 = e.y.map(yscale);
       const y1 = e.y.map(y => yscale(y+e.arg.nbytes));
       const arg = { tooltipText:`${e.arg.dtype} len:${formatUnit(e.arg.sz)}\n${formatUnit(e.arg.nbytes, "B")}` };
-      data.shapes.push({ x, y0, y1, arg, fillColor:bufColors[i%bufColors.length] });
+      data.shapes.push({ x, y0, y1, arg, fillColor:cycleColors(colorScheme.BUFFER, i) });
     }
     // lastly, adjust device rect by number of levels
     div.style.height = `${Math.max(levelHeight*timeline.maxDepth, baseHeight)+area+padding}px`;
@@ -377,11 +377,16 @@ function codeBlock(st, language, { loc, wrap }={}) {
   return ret;
 }
 
-function appendRow(table, name, value, unit, cls) {
+function appendTd(tr, value, unit=null) {
+  const fmt = (typeof value === "number" && !Number.isInteger(value)) ? value.toFixed(2) : value;
+  tr.appendChild(document.createElement("td")).innerText = unit == "us" ? formatTime(value) : fmt+(unit ?? "");
+}
+
+function appendRow(table, name, value, unit=null, cls="main-row") {
   const tr = table.appendChild(document.createElement("tr"));
   tr.className = cls;
   tr.appendChild(document.createElement("td")).innerText = name;
-  tr.appendChild(document.createElement("td")).innerText = unit === "us" ? formatTime(value) : value.toFixed(2)+(unit != null ? " "+unit : "%");
+  appendTd(tr, value, unit);
   return tr;
 }
 
@@ -495,10 +500,44 @@ async function main() {
   if (ckey.startsWith("/disasm")) {
     if (!(ckey in cache)) cache[ckey] = ret = await (await fetch(ckey)).json();
     displayGraph("profiler");
-    document.querySelector(".metadata").innerHTML = "";
     const root = document.createElement("div");
     root.className = "raw-text";
-    root.appendChild(codeBlock(ret.src, "x86asm"));
+    const metadata = document.querySelector(".metadata");
+    metadata.innerHTML = "";
+    // detailed assembly view
+    if (ret.cols != null) {
+      const asm = root.appendChild(document.createElement("table"));
+      const thead = asm.appendChild(document.createElement("thead"));
+      const usage = {};
+      for (const c of ret.cols) thead.appendChild(document.createElement("th")).innerText = c;
+      for (const r of ret.rows) {
+        const tr = asm.appendChild(document.createElement("tr"));
+        tr.className = "main-row code-row";
+        for (const d of Object.values(r.data)) appendTd(tr, d);
+        const segmentsTd = tr.appendChild(document.createElement("td"));
+        segmentsTd.className = "pct-row";
+        const usageBar = segmentsTd.appendChild(document.createElement("div"));
+        for (const [k, {width, value}] of Object.entries(r.segs)) {
+          const seg = usageBar.appendChild(document.createElement("div"));
+          seg.style.width = width+"%";
+          seg.title = `${ret.segments[k]} ${value}`;
+          seg.style.background = cycleColors(colorScheme.CATEGORICAL, parseInt(k));
+          if (!(k in usage)) usage[k] = 0;
+          usage[k] += value;
+        }
+      }
+      const summary = metadata.appendChild(document.createElement("table"));
+      for (const [i,s] of ret.segments.entries()) {
+        const tr = summary.appendChild(document.createElement("tr"));
+        tr.className = "main-row";
+        const td = tr.appendChild(document.createElement("td"));
+        const div = td.appendChild(document.createElement("div"));
+        div.className = "legend";
+        div.appendChild(document.createElement("div")).style.background = cycleColors(colorScheme.CATEGORICAL, i);
+        div.appendChild(document.createElement("p")).textContent = s;
+        appendTd(tr, usage[i] ?? 0);
+      }
+    } else root.appendChild(codeBlock(ret.src, "x86asm"));
     return document.querySelector(".profiler").replaceChildren(root);
   }
   // ** UOp view (default)

tinygrad/viz/serve.py

@@ -1,5 +1,6 @@
 #!/usr/bin/env python3
 import multiprocessing, pickle, difflib, os, threading, json, time, sys, webbrowser, socket, argparse, socketserver, functools, codecs, io
+import subprocess, ctypes
 from contextlib import redirect_stdout
 from decimal import Decimal
 from http.server import BaseHTTPRequestHandler
@@ -54,7 +55,7 @@ def uop_to_json(x:UOp) -> dict[int, dict]:
   excluded: set[UOp] = set()
   for u in (toposort:=x.toposort()):
     # always exclude DEVICE/CONST/UNIQUE
-    if u.op in {Ops.DEVICE, Ops.CONST, Ops.UNIQUE}: excluded.add(u)
+    if u.op in {Ops.DEVICE, Ops.CONST, Ops.UNIQUE} and u is not x: excluded.add(u)
     # only exclude CONST VIEW source if it has no other children in the graph
     if u.op is Ops.CONST and len(u.src) != 0 and all(cr.op is Ops.CONST for c in u.src[0].children if (cr:=c()) is not None and cr in toposort):
       excluded.update(u.src)
@@ -125,12 +126,15 @@ def timeline_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
     depth = next((i for i,level_et in enumerate(levels) if st>=level_et), len(levels))
     if depth < len(levels): levels[depth] = et
     else: levels.append(et)
-    name, cat = e.name, None
-    if (ref:=ref_map.get(name)) is not None: name = ctxs[ref]["name"]
+    name, cat, info = e.name, None, None
+    if (ref:=ref_map.get(name)) is not None:
+      name = ctxs[ref]["name"]
+      if isinstance(p:=contexts[0][ref].ret, ProgramSpec):
+        info = f"{p.estimates.ops/(t:=dur*1e3):.2f} GFLOPS {p.estimates.mem/t:4.1f}|{p.estimates.lds/t:.1f} GB/s"
     elif isinstance(e.name, TracingKey):
       name, cat = e.name.display_name, e.name.cat
       ref = next((v for k in e.name.keys if (v:=ref_map.get(k)) is not None), None)
-    shapes.append({"name":name, "ref":ref, "st":st, "dur":dur, "depth":depth, "cat":cat})
+    shapes.append({"name":name, "ref":ref, "st":st, "dur":dur, "depth":depth, "cat":cat, "info":info})
   return {"shapes":shapes, "maxDepth":len(levels)}
 
 def mem_layout(events:list[tuple[int, int, float, DevEvent]]) -> dict:
@@ -187,9 +191,29 @@ def get_runtime_stats(key) -> list[dict]:
 
 def get_disassembly(ctx:list[str]):
   if not isinstance(prg:=contexts[0][int(ctx[0])].ret, ProgramSpec): return
-  lib = Device[prg.device].compiler.compile(prg.src)
-  with redirect_stdout(buf:=io.StringIO()): Device[prg.device].compiler.disassemble(lib)
-  return json.dumps({"src":buf.getvalue()}).encode()
+  lib = (compiler:=Device[prg.device].compiler).compile(prg.src)
+  with redirect_stdout(buf:=io.StringIO()): compiler.disassemble(lib)
+  disasm_str = buf.getvalue()
+  from tinygrad.runtime.ops_llvm import llvm, LLVMCompiler
+  if isinstance(compiler, LLVMCompiler):
+    mtriple = ctypes.string_at(llvm.LLVMGetTargetMachineTriple(tm:=compiler.target_machine)).decode()
+    mcpu = ctypes.string_at(llvm.LLVMGetTargetMachineCPU(tm)).decode()
+    # NOTE: llvm-objdump may contain headers, skip if llvm-mca can't parse those lines
+    data = json.loads(subprocess.check_output(["llvm-mca", f"-mtriple={mtriple}", f"-mcpu={mcpu}", "-skip-unsupported-instructions=parse-failure",
+                                               "--json", "-"], input=disasm_str.encode()))
+    cr = data["CodeRegions"][0]
+    instrs:list = [{"data":[rep], "segs":{}} for rep in cr["Instructions"]]
+    for i,info in enumerate(cr["InstructionInfoView"]["InstructionList"]): instrs[i]["data"].append(info["Latency"])
+    for d in cr["ResourcePressureView"]["ResourcePressureInfo"]:
+      i, r = d["InstructionIndex"], d["ResourceIndex"]
+      if i>len(instrs)-1: continue
+      instrs[i]["segs"][r] = instrs[i]["segs"].get(r, 0)+d["ResourceUsage"]
+    # rescale segment width to 0-100
+    if instrs:
+      hi = max([sum(ins["segs"].values()) for ins in instrs])
+      for n in instrs: n["segs"] = {k:{"width":v/hi*100, "value":v} for k,v in n["segs"].items()}
+    return json.dumps({"rows":instrs, "cols":["Opcode", "Latency", "HW Resources"], "segments":data["TargetInfo"]["Resources"]}).encode()
+  return json.dumps({"src":disasm_str}).encode()
 
 # ** HTTP server