in_loop

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

@@ -112,16 +112,7 @@ runs:
         fi
 
     # ******************* apt *******************
-    - name: Setup apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
-      shell: bash
-      run: |
-        sudo chown -R $USER:$USER /var/cache/apt/archives
 
-        echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
-        echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
-        echo 'Binary::apt::APT::Keep-Downloaded-Packages "true";' | sudo tee -a /etc/apt/apt.conf.d/99keep-debs
-    
     - name: Add OpenCL Repo
       if: inputs.opencl == 'true' && runner.os == 'Linux'
       shell: bash
@@ -144,11 +135,14 @@ runs:
         wget -qO- https://apt.llvm.org/llvm-snapshot.gpg.key | sudo tee /etc/apt/trusted.gpg.d/apt.llvm.org.asc
         echo "deb http://apt.llvm.org/$(lsb_release -cs)/ llvm-toolchain-$(lsb_release -cs)-20 main" | sudo tee /etc/apt/sources.list.d/llvm.list
 
-    - name: Compute Package List + Hash
+    - name: apt-get update + install
       if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
-      id: apt-pkgs
       shell: bash
       run: |
+        echo 'Acquire::GzipIndexes "true";' | sudo tee /etc/apt/apt.conf.d/gzip
+        echo 'Acquire::http::Pipeline-Depth "5";' | sudo tee -a /etc/apt/apt.conf.d/99parallel
+        sudo apt -qq update || true
+
         pkgs=""
         # **** OpenCL ****
         if [[ "${{ inputs.opencl }}" == "true" ]]; then
@@ -159,7 +153,7 @@ runs:
         fi
         # **** AMD ****
         if [[ "${{ inputs.amd }}" == "true" ]]; then
-          pkgs+=" hsa-rocr comgr hsa-rocr-dev liburing-dev libibverbs-dev libc6-dev"
+          pkgs+=" hsa-rocr comgr hsa-rocr-dev liburing-dev libc6-dev"
         fi
         # **** CUDA ****
         if [[ "${{ inputs.cuda }}" == "true" ]]; then
@@ -174,31 +168,14 @@ runs:
         if [[ "${{ inputs.llvm }}" == "true" ]]; then
           pkgs+=" libllvm20 clang-20 lld-20"
         fi
-        
-        echo "pkgs=$pkgs" >> "$GITHUB_OUTPUT"
-        echo "hash=$(echo -n "$pkgs" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
 
-    - name: Cache apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
-      uses: actions/cache@v4
-      with:
-        path: /var/cache/apt/archives/
-        key: ${{ runner.os }}-apt-${{ steps.apt-pkgs.outputs.hash }}
-
-    - name: Run apt Update + Install
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.cuda == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true')
-      shell: bash
-      run: |
-        sudo apt -qq update || true
-        
         # ******** do install ********
-        if [[ -n "${{ steps.apt-pkgs.outputs.pkgs }}" ]]; then
+        if [[ -n "$pkgs" ]]; then
-          sudo apt-get -y --allow-unauthenticated --no-install-recommends install ${{ steps.apt-pkgs.outputs.pkgs }}
+          sudo apt-get -y --allow-unauthenticated --no-install-recommends install $pkgs
         fi
-        
+
-        sudo chown -R $USER:$USER /var/cache/apt/archives/
-    
     # **** AMD ****
+
     - name: Setup AMD (Linux)
       if: inputs.amd == 'true' && runner.os == 'Linux'
       shell: bash
@@ -251,7 +228,7 @@ runs:
       shell: bash
       run: |
         cd ${{ github.workspace }}/gpuocelot/ocelot/build
-        sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || '' }}lib/
+        sudo cp libgpuocelot.${{ runner.os == 'macOS' && 'dylib' || 'so' }} /usr/${{ runner.os == 'macOS' && 'local/' || ''}}lib/
 
     # **** WebGPU ****
 

.github/workflows/benchmark.yml

@@ -617,10 +617,6 @@ 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
@@ -641,127 +637,3 @@ jobs:
           openpilot_0_9_7.txt
           openpilot_image_0_9_4.txt
           openpilot_image_0_9_7.txt
-
-  testreddriverbenchmark:
-    name: AM Benchmark
-    runs-on: [self-hosted, Linux, tinyboxrandom]
-    timeout-minutes: 15
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - 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
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Test driver cold start time
-      run: time DEBUG=3 AMD=1 AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
-    - name: Test driver warm start time
-      run: time DEBUG=3 AMD=1 python3 test/test_tiny.py TestTiny.test_plus
-    # Fails on 9070
-    # - name: Test tensor cores
-    #   run: |
-    #     AMD=1 AMD_LLVM=0 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
-    #     AMD=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded_amd TestLinearizer.test_tensor_cores_padded_uops
-    #     AMD=1 SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-    - name: Run Tensor Core GEMM (AMD)
-      run: AMD=1 SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py | tee am_matmul_amd.txt
-    - name: Test AMD=1
-      run: DEBUG=2 AMD=1 python -m pytest -rA test/test_tiny.py
-    - name: Test DISK copy time
-      run: AMD=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps AMD=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee am_train_resnet_one_gpu.txt
-    - name: Run 10 MLPerf Bert training steps (1 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps AMD=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee am_train_bert_one_gpu.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (AM Driver)
-        path: |
-          am_matmul_amd.txt
-          am_train_cifar_one_gpu.txt
-          am_train_resnet_one_gpu.txt
-          am_train_bert_one_gpu.txt
-    - name: Run process replay tests
-      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py
-
-  testgreendriverbenchmark:
-    name: NV Benchmark
-    runs-on: [self-hosted, Linux, tinyboxrandom]
-    timeout-minutes: 15
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Remove nv modules
-      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
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Test driver start time
-      run: time DEBUG=3 NV=1 python3 test/test_tiny.py TestTiny.test_plus
-    - name: Test tensor cores
-      run: NV=1 ALLOW_TF32=1 python3 test/test_linearizer.py TestLinearizer.test_tensor_cores TestLinearizer.test_tensor_cores_padded TestLinearizer.test_tensor_cores_padded_uops
-    - name: Test DISK copy time
-      run: NV=1 TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
-    - name: Test LLAMA-3
-      run: BENCHMARK_LOG=llama3_beam NV=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0 | tee nv_llama3_beam.txt
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee nv_train_cifar_one_gpu.txt
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps NV=1 MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py | tee nv_train_resnet_one_gpu.txt
-    - name: Run 10 MLPerf Bert training steps (1 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps NV=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 FUSE_ARANGE=1 FUSE_ARANGE_UINT=0 MODEL=bert python3 examples/mlperf/model_train.py | tee nv_train_bert_one_gpu.txt
-    - uses: actions/upload-artifact@v4
-      with:
-        name: Speed (NV Driver)
-        path: |
-          nv_llama3_beam.txt
-          nv_train_cifar_one_gpu.txt
-          nv_train_resnet_one_gpu.txt
-          nv_train_bert_one_gpu.txt
-    - name: Run process replay tests
-      run: cp test/external/process_replay/process_replay.py ./process_replay.py && git fetch origin master && git -c advice.detachedHead=false checkout origin/master && PYTHONPATH=. python3 process_replay.py

.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: 360
+    timeout-minutes: 240
 
     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"

.github/workflows/test.yml

@@ -132,13 +132,10 @@ jobs:
       run: |
         cp tinygrad/runtime/autogen/libc.py /tmp/libc.py.bak
         cp tinygrad/runtime/autogen/io_uring.py /tmp/io_uring.py.bak
-        cp tinygrad/runtime/autogen/ib.py /tmp/ib.py.bak
         ./autogen_stubs.sh libc
         ./autogen_stubs.sh io_uring
-        ./autogen_stubs.sh ib
         diff /tmp/libc.py.bak tinygrad/runtime/autogen/libc.py
         diff /tmp/io_uring.py.bak tinygrad/runtime/autogen/io_uring.py
-        diff /tmp/ib.py.bak tinygrad/runtime/autogen/ib.py
     - name: Verify WebGPU autogen
       run: |
         cp tinygrad/runtime/autogen/webgpu.py /tmp/webgpu.py.bak
@@ -870,29 +867,29 @@ jobs:
     - name: Test ONNX Runner (WEBGPU)
       run: WEBGPU=1 PYTHONPATH=. python3 test/external/external_test_onnx_runner.py
 
-  #osxremote:
+  osxremote:
-  #  name: MacOS (remote metal)
+    name: MacOS (remote metal)
-  #  runs-on: macos-15
+    runs-on: macos-15
-  #  timeout-minutes: 10
+    timeout-minutes: 10
-  #  env:
+    env:
-  #    REMOTE: 1
+      REMOTE: 1
-  #    REMOTEDEV: METAL
+      REMOTEDEV: METAL
-  #  steps:
+    steps:
-  #    - name: Checkout Code
+      - name: Checkout Code
-  #      uses: actions/checkout@v4
+        uses: actions/checkout@v4
-  #    - name: Setup Environment
+      - name: Setup Environment
-  #      uses: ./.github/actions/setup-tinygrad
+        uses: ./.github/actions/setup-tinygrad
-  #      with:
+        with:
-  #        key: macos-remote
+          key: macos-remote
-  #        deps: testing_minimal
+          deps: testing_minimal
-  #    - name: Check Device.DEFAULT and print some source
+      - name: Check Device.DEFAULT and print some source
-  #      run: |
+        run: |
-  #        python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.DEFAULT == 'REMOTE', Device.DEFAULT"
-  #        python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
+          python -c "from tinygrad import Device; assert Device.default.properties.real_device == 'METAL', Device.default.properties.real_device"
-  #        DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
+          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
-  #    - name: Run REMOTE=1 Test
+      - name: Run REMOTE=1 Test
-  #      run: |
+        run: |
-  #        python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
+          python3 -m pytest test/test_tiny.py test/test_jit.py test/test_subbuffer.py test/test_graph.py test/test_multitensor.py test/test_tensor_variable.py
 
   amdremote:
     name: Linux (remote)

autogen_stubs.sh

@@ -240,21 +240,6 @@ generate_io_uring() {
   fixup $BASE/io_uring.py
 }
 
-generate_ib() {
-  clang2py -k cdefstum \
-    /usr/include/infiniband/verbs.h \
-    /usr/include/infiniband/verbs_api.h \
-    /usr/include/infiniband/ib_user_ioctl_verbs.h \
-    /usr/include/rdma/ib_user_verbs.h \
-    -o $BASE/ib.py
-
-  sed -i "s\import ctypes\import ctypes, ctypes.util\g" "$BASE/ib.py"
-  sed -i "s\FIXME_STUB\libibverbs\g" "$BASE/ib.py"
-  sed -i "s\FunctionFactoryStub()\ctypes.CDLL(ctypes.util.find_library('ibverbs'), use_errno=True)\g" "$BASE/ib.py"
-
-  fixup $BASE/ib.py
-}
-
 generate_libc() {
   clang2py -k cdefstum \
     $(dpkg -L libc6-dev | grep sys/mman.h) \
@@ -480,7 +465,6 @@ elif [ "$1" == "nvdrv" ]; then generate_nvdrv
 elif [ "$1" == "sqtt" ]; then generate_sqtt
 elif [ "$1" == "qcom" ]; then generate_qcom
 elif [ "$1" == "io_uring" ]; then generate_io_uring
-elif [ "$1" == "ib" ]; then generate_ib
 elif [ "$1" == "libc" ]; then generate_libc
 elif [ "$1" == "llvm" ]; then generate_llvm
 elif [ "$1" == "kgsl" ]; then generate_kgsl

docs/ramp.py

@@ -126,7 +126,7 @@ print(t_log_grad.uop)
 """
 void E_(float* restrict data0, float* restrict data1) {
   float val0 = *(data1+0);
-  *(data0+0) = (1/val0);
+  *(data0+0) = (0.6931471805599453f*(1/(val0*0.6931471805599453f)));
 }
 """
 # the derivative is close to 1/3

examples/mlperf/dataloader.py

@@ -1,6 +1,4 @@
-import functools
+import os, random, pickle, queue
-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
@@ -8,7 +6,6 @@ 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
 
@@ -513,202 +510,6 @@ 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"
@@ -737,18 +538,6 @@ 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, math
+import time
 start = time.perf_counter()
 from pathlib import Path
 import numpy as np
@@ -241,34 +241,6 @@ 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

@@ -1290,16 +1290,9 @@ def train_llama3():
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
 
   config = {}
-  BS                 = config["BS"]                     = getenv("BS", 16)
+  BS                 = config["BS"]                     = getenv("BS", 4)
   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
@@ -1307,6 +1300,7 @@ 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
@@ -1314,33 +1308,7 @@ def train_llama3():
 
   # TODO: confirm weights are in bf16
   # vocab_size from the mixtral tokenizer
-  params = MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}
+  model = Transformer(**(MODEL_PARAMS[getenv("LLAMA3_SIZE", "8B")]["args"]|{"vocab_size": 32000}), max_context=sequence_length, jit=False, disable_kv_cache=True)
-  if (llama_layers:=getenv("LLAMA_LAYERS")) != 0: params['n_layers'] = llama_layers
-  model = Transformer(**params, max_context=SEQLEN, jit=False, disable_kv_cache=True)
-
-  if (DP := getenv("DP", 1)) > 1:
-    device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
-    for v in get_parameters(model):
-      v.shard_(device, axis=None)
-
-  # TODO: MP
-  # if (GPUS := getenv("GPUS", 1)) > 1:
-  #   device = tuple(f"{Device.DEFAULT}:{i}" for i in range(GPUS))
-  #   for k,v in get_state_dict(model).items():
-  #     if 'scale' in k: v.shard_(device, axis=None)  # from quantized
-  #     # elif '.attention.wq' in k: v.shard_(device, axis=0)
-  #     # elif '.attention.wk' in k: v.shard_(device, axis=0)
-  #     # elif '.attention.wv' in k: v.shard_(device, axis=0)
-  #     # elif '.attention.wo' in k: v.shard_(device, axis=1)
-  #     # elif '.feed_forward.w1.' in k: v.shard_(device, axis=0)
-  #     # elif '.feed_forward.w2.' in k: v.shard_(device, axis=1)
-  #     # elif '.feed_forward.w3.' in k: v.shard_(device, axis=0)
-  #     # elif 'tok_embeddings.weight' in k: v.shard_(device, axis=0)
-  #     elif 'output.weight' in k: v.shard_(device, axis=0)  # 243.32
-  #     else:
-  #       # print(k)
-  #       # attention_norm, ffn_norm, norm
-  #       v.shard_(device, axis=None)
 
   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)
@@ -1348,17 +1316,12 @@ def train_llama3():
 
   @TinyJit
   @Tensor.train()
-  def train_step(model, tokens:Tensor, grad_acc:int):
+  def train_step(model, x, y):
     optim.zero_grad()
-    # grad acc
+    logits:Tensor = model(x, start_pos=0, temperature=math.nan)
-    for batch in tokens.split(tokens.shape[0]//grad_acc):
+    loss = logits.cross_entropy(y)
-      if (DP := getenv("DP", 1)) > 1:
+    loss.backward()
-        device = tuple(f"{Device.DEFAULT}:{i}" for i in range(DP))
+
-        batch = batch.shard(device, 0)
-      logits:Tensor = model(batch[:, :-1], start_pos=0, temperature=math.nan)
-      loss = logits.sparse_categorical_crossentropy(batch[:, 1:])
-      loss.backward()
-      Tensor.realize(*[p.grad for p in optim.params])
     # L2 norm grad clip
     # https://github.com/NVIDIA/NeMo/blob/3368c3fc0b4a186ab33a1d68a504315100c0b2a6/nemo/collections/nlp/modules/common/megatron/clip_grads.py#L57
     # https://docs.pytorch.org/docs/stable/generated/torch.nn.utils.clip_grad_norm_.html
@@ -1377,32 +1340,19 @@ def train_llama3():
     loss.realize(lr)
     return loss, lr
 
-  if getenv("FAKEDATA", 0):
+  # overfitting this example should give cross_entropy log(BS)
-    def fake_data():
+  fake_input = Tensor([list(range(getenv("SEQLEN", 10)))], dtype="int16").expand(BS, -1)
-      for _ in range(SAMPLES // GBS):
+  fake_label = Tensor(list(range(BS)), dtype="int16")
-        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))
 
-  i = 0
+  for _ in range(100):
-  for tokens in tqdm(iter, total=SAMPLES//GBS):
-    t = time.perf_counter()
     GlobalCounters.reset()
-    loss, lr = train_step(model, tokens, grad_acc)
+    loss, lr = train_step(model, fake_input, fake_label)
-    # above as tqdm.write f-string
+    # 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
-    tqdm.write(f"{loss.item():.4f} loss, {lr.item():.12f} LR, {GlobalCounters.mem_used / 1e9:.2f} GB used, {time.perf_counter()-t:.2f} s")
+    # uses 43% ~= 83GB
-    if (fname:=getenv("LOSS_FILE", "")):
+    # 8B bf16 = 16GB. model + grad + optim m and v = 64GB
-      with open(fname, "a") as f:
+    # TODO: this OOM
-        f.write(f"{i} {loss.item():.4f} {lr.item():.12f} {GlobalCounters.mem_used / 1e9:.2f}\n")
+    # 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=}")
-    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/gemm/amd_matmul.py

@@ -19,9 +19,6 @@ if __name__ == "__main__":
   elif getenv("ASM") == -1:
     src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel3_registers.cpp").read_text()
     prgfast = replace(prg, name="kernel3_registers", src=src, global_size=[N//128, N//128, 1], local_size=[256, 1, 1])
-  elif getenv("ASM") == -2:
-    src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel4_gmem_df.cpp").read_text()
-    prgfast = replace(prg, name="kernel4_gmem_db", src=src, global_size=[N//128, N//128, 1], local_size=[256, 1, 1])
   else:
     src = (pathlib.Path(__file__).parent / "amd_seb" / "kernel5_lds_optim.cpp").read_text()
     prgfast = replace(prg, name="kernel5_lds_optim", src=src, global_size=[N//128, N//128, 1], local_size=[128, 1, 1])

extra/gemm/amd_seb/kernel3_registers.cpp

@@ -10,8 +10,7 @@ __attribute__((device)) inline void __syncthreads() {
 }
 
 #define BLOCK_SIZE 256
-extern "C" __attribute__((global)) void __attribute__((amdgpu_flat_work_group_size(1, BLOCK_SIZE)))
+extern "C" __attribute__((global)) void kernel3_registers(float *a, float *b, float *c)
-kernel3_registers(float *a, float *b, float *c)
 {
   constexpr int N = 4096;
   constexpr float alpha = 1.0;

extra/gemm/amd_seb/kernel4_gmem_df.cpp

@@ -1,172 +0,0 @@
-typedef long unsigned int size_t;
-extern "C" __attribute__((device, const)) size_t __ockl_get_local_id(unsigned int);
-extern "C" __attribute__((device, const)) size_t __ockl_get_group_id(unsigned int);
-struct Dim3 { size_t x, y, z; };
-#define __shared__ __attribute__((shared, aligned(16)))
-__attribute__((device)) inline void __syncthreads() {
-  __builtin_amdgcn_fence(__ATOMIC_RELEASE, "workgroup");
-  __builtin_amdgcn_s_barrier();
-  __builtin_amdgcn_fence(__ATOMIC_ACQUIRE, "workgroup");
-}
-
-#define BLOCK_SIZE 256
-extern "C" __attribute__((global)) void __attribute__((amdgpu_flat_work_group_size(1, BLOCK_SIZE)))
-kernel4_gmem_db(float *a, float *b, float *c)
-{
-  constexpr int N = 4096;
-  constexpr float alpha = 1.0;
-  constexpr float beta = 0.0;
-
-  const Dim3 blockIdx{ __ockl_get_group_id(0), __ockl_get_group_id(1), __ockl_get_group_id(2) };
-  const Dim3 threadIdx{ __ockl_get_local_id(0), __ockl_get_local_id(1), __ockl_get_local_id(2) };
-
-  // Block Tile size
-  constexpr int BN = 128;
-  constexpr int BM = 128;
-  // Number of Row or column we read per batch
-  constexpr int BK = 8;
-
-  // Thread Tile size
-  constexpr int TN = 4;
-  constexpr int TM = 4;
-
-  constexpr int nbWaves = BLOCK_SIZE / 32;
-  // Wave Tile size
-  constexpr int WN = 64;
-  constexpr int WM = BN * BM / nbWaves / WN;
-
-  // Number of wave on X & Y axis in the Block tile
-  constexpr int nbWaveX = BN / WN;
-  constexpr int nbWaveY = BM / WM;
-
-  const int waveIndex = threadIdx.x / 32;
-  const int waveIdx = waveIndex % nbWaveX;
-  const int waveIdy = waveIndex / nbWaveX;
-  const int indexInWave = threadIdx.x % 32;
-
-  // A wave is a block of 8x4 of the output matrix
-  constexpr int nbThreadXPerWave = 8;
-  constexpr int nbThreadYPerWave = 4;
-
-  // Thread coordinates in Wave
-  const int idxInWave = indexInWave % nbThreadXPerWave;
-  const int idyInWave = indexInWave / nbThreadXPerWave;
-
-  constexpr int nbIterWaveN = WN / (nbThreadXPerWave * TN);
-  constexpr int nbIterWaveM = WM / (nbThreadYPerWave * TM);
-
-  // Wave Sub-tile size
-  constexpr int SUBWN = WN / nbIterWaveN;
-  constexpr int SUBWM = WM / nbIterWaveM;
-
-  // Thread mapping to read BKxBN block from A
-  int rAIdx = threadIdx.x % BK;
-  int rAIdy = threadIdx.x / BK;
-  // Thread mapping to read BNxBK block from B
-  int rBIdx = threadIdx.x % BN;
-  int rBIdy = threadIdx.x / BN;
-
-  constexpr int strideReadB = BLOCK_SIZE / BN;
-  constexpr int strideReadA = BLOCK_SIZE / BK;
-  constexpr int nbReadsB = BN * BK / BLOCK_SIZE;
-  constexpr int nbReadsA = BM * BK / BLOCK_SIZE;
-
-  float A_col[nbIterWaveM * TM];
-  float B_row[nbIterWaveN * TN];
-
-  __shared__ float As[BK][BM];
-  __shared__ float Bs[BK][BN];
-
-  float c_regs[TM * nbIterWaveM * TN * nbIterWaveN] = {0.0f};
-
-  for (int i = 0; i < nbReadsB; i++) {
-    int index_x = BN * blockIdx.x + rBIdx;
-    int index_y = rBIdy + i * strideReadB;
-    Bs[index_y % BK][index_x % BN] = b[N * index_y + index_x];
-  }
-
-  for (int i = 0; i < nbReadsA; i++) {
-    int index_x = rAIdx;
-    int index_y = BM * blockIdx.y + rAIdy + i * strideReadA;
-    As[(index_x % BK)][(index_y % BM)] = a[N * index_y + index_x];
-  }
-
-  __syncthreads();
-  // Iteration over BK blocks.
-  for (int kId = 0; kId < N; kId += BK) {
-    float regA[nbReadsA];
-    float regB[nbReadsB];
-    if (kId < N - BK) {
-      // We populate the Shared Memory with Ks row and columns
-      for (int i = 0; i < nbReadsB; i++) {
-        int index_x = BN * blockIdx.x + rBIdx;
-        int index_y = rBIdy + i * strideReadB + kId + BK;
-        regB[i] = b[N * index_y + index_x];
-      }
-
-      for (int i = 0; i < nbReadsA; i++) {
-        int index_x = rAIdx + kId + BK;
-        int index_y = BM * blockIdx.y + rAIdy + i * strideReadA;
-        regA[i] = a[N * index_y + index_x];
-      }
-    }
-
-    for (int k = 0; k < BK; k++) {
-      // we cache A & B for the entire Wave tile
-      for (int iterWave = 0; iterWave < nbIterWaveN; iterWave++) {
-        for (int i = 0; i < TN; i++) {
-          int index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i;
-          B_row[iterWave * TN + i] = Bs[k][index];
-        }
-      }
-
-      for (int iterWave = 0; iterWave < nbIterWaveM; iterWave++) {
-        for (int i = 0; i < TM; i++) {
-          int index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i;
-          A_col[iterWave * TM + i] = As[k][index];
-        }
-      }
-
-      // we accumulate to C_regs
-      for (int iterWaveM = 0; iterWaveM < nbIterWaveM; iterWaveM++) {
-        for (int iterWaveN = 0; iterWaveN < nbIterWaveN; iterWaveN++) {
-          for (int yt = 0; yt < TM; yt++) {
-            for (int xt = 0; xt < TN; xt++) {
-              const int x = iterWaveN * TN + xt;
-              const int y = iterWaveM * TM + yt;
-              c_regs[y * TN * nbIterWaveN + x] += A_col[y] * B_row[x];
-            }
-          }
-        }
-      }
-    }
-    __syncthreads();
-    if (kId < N - BK) {
-      for (int i = 0; i < nbReadsB; i++) {
-        int index_x = BN * blockIdx.x + rBIdx;
-        int index_y = rBIdy + i * strideReadB + kId + BK;
-        Bs[index_y % BK][index_x % BN] = regB[i]; // row
-      }
-
-      for (int i = 0; i < nbReadsA; i++) {
-        int index_x = rAIdx + kId + BK;
-        int index_y = BM * blockIdx.y + rAIdy + i * strideReadA;
-        As[(index_x % BK)][(index_y % BM)] = regA[i];
-      }
-      __syncthreads();
-    }
-  }
-
-  for (int iterWaveM = 0; iterWaveM < nbIterWaveM; iterWaveM++) {
-    for (int iterWaveN = 0; iterWaveN < nbIterWaveN; iterWaveN++) {
-      int xOut = blockIdx.x * BN + waveIdx * WN + iterWaveN * SUBWN + TN * idxInWave;
-      int yOut = blockIdx.y * BM + waveIdy * WM + iterWaveM * SUBWM + TM * idyInWave;
-      for (int yt = 0; yt < TM; yt++) {
-        for (int xt = 0; xt < TN; xt++) {
-          int indexC = N * (yOut + yt) + xOut + xt;
-          c[indexC] = beta * c[indexC] + alpha * c_regs[TN * nbIterWaveN * (iterWaveM * TM + yt) + (iterWaveN * TN + xt)];
-        }
-      }
-    }
-  }
-}

extra/gemm/amd_seb/kernel5_lds_optim.cpp

@@ -26,7 +26,7 @@ kernel5_lds_optim(float *a, float *b, float *c)
   // Number of Row or column we read per batch
   constexpr int BK = 8;
 
-  // Thread Tile size
+  // Thread Tile size . 4x4
   constexpr int TN = 4;
   constexpr int TM = 4;
 

extra/gemm/amd_uop_matmul.py

@@ -1,14 +1,9 @@
 from tinygrad import Tensor, Device, Context, GlobalCounters, dtypes
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, graph_rewrite, AxisType, PatternMatcher, UPat
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, graph_rewrite
 from tinygrad.engine.realize import CompiledRunner, ExecItem, get_program
 from tinygrad.dtype import AddrSpace
-from tinygrad.schedule.kernelize import merge_views, view_left
+from tinygrad.schedule.kernelize import merge_views
-from tinygrad.helpers import getenv, colored, prod, unwrap
+from tinygrad.helpers import getenv
-from tinygrad.shape.shapetracker import ShapeTracker, View
-from tinygrad.shape.view import strides_for_shape
-from tinygrad.opt.kernel import axis_colors
-
-def to_colored(full_shape, axis_types): return '_'.join([colored(str(s), axis_colors[at]) for s,at in zip(full_shape, axis_types)])
 
 N = 4096
 run_count = 5
@@ -20,54 +15,19 @@ BK = 8
 TN = 4
 TM = 4
 
-# NOTE: this is from testgrad
-# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
-# src->r->view  -->   src->view->r
-def swizzle_reduceop(src:UOp, r:UOp, view:UOp):
-  if r.tag is not None: return None
-  # confirm the input is in order
-  # TODO: replace this with a UOp that allows for nothing else then remove this
-  permute = tuple(i for i in range(len(src.shape)) if i not in r.axis_arg)+r.axis_arg
-  assert permute == tuple(range(len(permute))), f"reduce axis must already be in order, {permute} isn't"
-
-  # append the reduce shape to each of the views
-  prshape = prod(rshape:=src.shape[-len(r.axis_arg):])
-  rstrides = strides_for_shape(rshape)
-  nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+rstrides, v.offset*prshape,
-                    v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
-
-  # no reshape required with shrinking REDUCE_AXIS
-  return UOp(Ops.REDUCE_AXIS, r.dtype, (src.view(ShapeTracker(tuple(nv))),),
-             (r.arg[0], tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))))
-
-pm = PatternMatcher([
-  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
-])
-
-def top_spec_kernel3():
-  a = Tensor.empty(N,N)
-  b = Tensor.empty(N,N)
-  c = a@b
-  sink = c.schedule()[-1].ast
-  L = 16
-  sink = sink.reshape((N//L, L, N//L, L)) #.lift({0:UOp.range(dtypes.int, N//BM, 0), 2:UOp.range(dtypes.int, N//BN, 1)})
-  sink = graph_rewrite(sink, view_left+pm)
-  axis_types = (AxisType.GLOBAL, AxisType.LOCAL, AxisType.GLOBAL, AxisType.LOCAL, AxisType.REDUCE)
-  return sink.replace(arg=KernelInfo(name="top_"+to_colored(sink.full_shape, axis_types), axis_types=axis_types))
-
 def hl_spec_kernel3():
   nbIterWaveM = 2
   nbIterWaveN = 2
 
   # define buffers
-  # TODO: remove these views once the defines have a shape
+  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0)
-  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1).view(ShapeTracker.from_shape((N,N)))
+  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1)
-  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2).view(ShapeTracker.from_shape((N,N))).permute((1,0))
+  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2)
-  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0).view(ShapeTracker.from_shape((N,N)))
+  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM, AddrSpace.LOCAL), arg=0)
-  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM, AddrSpace.LOCAL), arg=0).view(ShapeTracker.from_shape((BK, BM))).permute((1,0))
+  Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1)
-  Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1).view(ShapeTracker.from_shape((BK, BN))).permute((1,0))
+  junk = UOp.const(dtypes.float, 0)
-  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((nbIterWaveM * TM,)))
+  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), src=(junk,), arg=0)
-  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1).view(ShapeTracker.from_shape((nbIterWaveN * TN,)))
+  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), src=(junk,), arg=1)
 
   # shape buffers. TODO: permutes
   full_shape = (N//BM, nbIterWaveM, BM//(nbIterWaveM * TM), TM, N//BN, nbIterWaveN, BN//(nbIterWaveN * TN), TN, N//BK, BK)
@@ -79,47 +39,16 @@ def hl_spec_kernel3():
   A_col = A_col.reshape((1, nbIterWaveM, 1, TM, 1, 1, 1, 1, 1, 1)).expand(full_shape)
   B_row = B_row.reshape((1, 1, 1, 1, 1, nbIterWaveN, 1, TN, 1, 1)).expand(full_shape)
 
-  #                     U1   L2 L3 L4 L5   U6 U7      U9   L10 L11 L12 L13   U14 U15      U17  U18  U19
+  out = (A_col.store(As.store(a.load()).load()).load() * B_row.store(Bs.store(b.load()).load()).load()).r(Ops.ADD, (8, 9))
-  expanded_shape = (32, 2,   2, 2, 2, 2,   2, 2,  32, 2,   2,  2,  2,  2,    2,  2,  512, 2,   2,   2)
+  sink = c.store(out).sink(arg=KernelInfo(name="tinygemm"))
-  assert len(expanded_shape) == 20
-  permute_a = list(range(len(expanded_shape)))
-  permute_b = permute_a[:]
-
-  # this makes all the global loads match
-  # this can also be more simply done by rebinding the RANGEs
-  # but sadly, rebinding the RANGEs doesn't work to change the order of the local axes
-  permute_a[17:20] = [11,12,13]
-  permute_a[11:14] = [17,18,19]
-  permute_a[7], permute_a[10] = permute_a[10], permute_a[7]
-  permute_a[2:7] = [3,4,5,6,2]
-
-  permute_b[2:16] = [19,9,10,11,17,18,8,2,12,13,14,15,3,4]
-  permute_b[17:20] = [5,6,7]
-
-  a_permute   = a.reshape(expanded_shape).permute(tuple(permute_a)).reshape(full_shape)
-  As_permute = As.reshape(expanded_shape).permute(tuple(permute_a)).reshape(full_shape)
-
-  b_permute   = b.reshape(expanded_shape).permute(tuple(permute_b)).reshape(full_shape)
-  Bs_permute = Bs.reshape(expanded_shape).permute(tuple(permute_b)).reshape(full_shape)
-
-  #out = (a.load() * b.load()).r(Ops.ADD, (8, 9))
-  out = (As.load(As_permute.store(a_permute.load())) * Bs.load(Bs_permute.store(b_permute.load()))).r(Ops.ADD, (8, 9))
-  #out = (A_col.load(A_col.store(As.load(As.store(a.load())))) * B_row.load(B_row.store(Bs.load(Bs.store(b.load()))))).r(Ops.ADD, (8, 9))
-
-  axis_types = (
-    AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
-    AxisType.GLOBAL, AxisType.UPCAST, AxisType.LOCAL, AxisType.UPCAST,
-    AxisType.REDUCE, AxisType.REDUCE)
-
-  sink = c.store(out).sink(arg=KernelInfo(name="tg_"+to_colored(full_shape, axis_types), axis_types=axis_types))
   sink = graph_rewrite(sink, merge_views)
   return sink
 
-def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
+def hand_spec_kernel3():
-  BLOCK_SIZE = 128 if kernel5 else 256
+  BLOCK_SIZE = 256
 
   nbWaves = BLOCK_SIZE // 32
-  WN = 128 if kernel5 else 64
+  WN = 64
   WM = BN * BM // nbWaves // WN
 
   nbWaveX = BN // WN
@@ -158,163 +87,75 @@ def hand_spec_kernel3(kernel4=getenv("K4", 0), kernel5=getenv("K5", 0)):
   blockIdx_x = UOp(Ops.SPECIAL, dtypes.int, arg=("gidx0", N//BN))
   blockIdx_y = UOp(Ops.SPECIAL, dtypes.int, arg=("gidx1", N//BM))
 
-  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1)
+  a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0)
-  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2)
+  b = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1)
-  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0)
+  c = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=2)
 
-  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), arg=0)
+  junk = UOp.const(dtypes.float, 0) # TODO: remove this
-  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), arg=1)
+  A_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveM * TM, AddrSpace.REG), src=(junk,), arg=0)
+  B_row = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbIterWaveN * TN, AddrSpace.REG), src=(junk,), arg=1)
 
-  BM_As_stride = (BM+4) if kernel5 else BM
+  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM, AddrSpace.LOCAL), arg=0)
-  As = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BM_As_stride, AddrSpace.LOCAL), arg=0)
   Bs = UOp(Ops.DEFINE_LOCAL, dtypes.float.ptr(BK*BN, AddrSpace.LOCAL), arg=1)
 
-  c_regs = UOp(Ops.DEFINE_REG, dtypes.float.ptr(TM * nbIterWaveM * TN * nbIterWaveN), arg=2)
+  c_regs = UOp(Ops.DEFINE_REG, dtypes.float.ptr(TM * nbIterWaveM * TN * nbIterWaveN), src=(junk,), arg=2)
 
-  i = UOp.range(dtypes.int, c_regs.dtype.size, 16)
+  kId_range = UOp.range(dtypes.int, N//BK, 0)
-  init_store = c_regs[i].store(UOp.const(dtypes.float, 0.0), i)
+  kId = kId_range*BK
 
-  if kernel4:
+  # load from globals into locals
-    regA = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbReadsA, AddrSpace.REG), arg=3)
+  i = UOp.range(dtypes.int, nbReadsB, 1)
-    regB = UOp(Ops.DEFINE_REG, dtypes.float.ptr(nbReadsB, AddrSpace.REG), arg=4)
+  index_x = BN * blockIdx_x + rBIdx
+  index_y = rBIdy + i * strideReadB + kId
+  Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
 
-    # initial load from globals into locals (0)
+  i = UOp.range(dtypes.int, nbReadsA, 2)
-    kId = 0
+  index_x = rAIdx + kId
+  index_y = BM * blockIdx_y + rAIdy + i * strideReadA
+  As_store = As[(index_x % BK) * BM + index_y % BM].store(a[N * index_y + index_x].load(), i)
 
-    # load from globals into locals
+  barrier = UOp(Ops.BARRIER, src=(As_store, Bs_store))
-    i = UOp.range(dtypes.int, nbReadsB, 0)
-    index_x = BN * blockIdx_x + rBIdx
-    index_y = rBIdy + i * strideReadB + kId
-    Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
 
-    i = UOp.range(dtypes.int, nbReadsA, 1)
+  k = UOp.range(dtypes.int, BK, 3)
-    index_x = rAIdx + kId
-    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
-    As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(a[N * index_y + index_x].load(), i)
 
-    # iterate over the middle chunk
+  # load from locals into registers
-    kId_range = UOp.range(dtypes.int, N//BK-1, 2)
+  iterWave = UOp.range(dtypes.int, nbIterWaveN, 4)
-    kId = kId_range*BK
+  i = UOp.range(dtypes.int, TN, 5)
+  index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
+  B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(barrier), iterWave, i)
 
-    barrier = UOp.barrier(As_store, Bs_store)
+  iterWave = UOp.range(dtypes.int, nbIterWaveM, 6)
+  i = UOp.range(dtypes.int, TM, 7)
+  index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
+  A_col_store = A_col[iterWave*TM + i].store(As[k*BM + index].load(barrier), iterWave, i)
 
-    # load from globals into registers (next round)
+  # do the GEMM math
-    i = UOp.range(dtypes.int, nbReadsB, 3)
+  iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 8)
-    index_x = BN * blockIdx_x + rBIdx
+  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 9)
-    index_y = rBIdy + i * strideReadB + kId + BK
+  yt = UOp.range(dtypes.int, TM, 10)
-    regB_store = regB[i].store(b[N * index_y + index_x].load(), i)
+  xt = UOp.range(dtypes.int, TN, 11)
-
+  x = iterWaveN * TN + xt
-    i = UOp.range(dtypes.int, nbReadsA, 4)
+  y = iterWaveM * TM + yt
-    index_x = rAIdx + kId + BK
+  c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
-    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
+  sink = c_regs_idx.store(c_regs_idx.load() + A_col[y].load(A_col_store) * B_row[x].load(B_row_store),
-    regA_store = regA[i].store(a[N * index_y + index_x].load(), i)
+                          iterWaveM, iterWaveN, yt, xt, k, kId_range)
-
-    def inner_loop(first_range, inp_dep=()):
-      # inner unroll
-      k = UOp.range(dtypes.int, BK, first_range+0)
-
-      # load from locals into registers
-      iterWave = UOp.range(dtypes.int, nbIterWaveN, first_range+1)
-      i = UOp.range(dtypes.int, TN, first_range+2)
-      index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
-      B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(*inp_dep), iterWave, i)
-
-      iterWave = UOp.range(dtypes.int, nbIterWaveM, first_range+3)
-      i = UOp.range(dtypes.int, TM, first_range+4)
-      index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
-      A_col_store = A_col[iterWave*TM + i].store(As[k*BM_As_stride + index].load(*inp_dep), iterWave, i)
-
-      # do the GEMM math
-      iterWaveM = UOp.range(dtypes.int, nbIterWaveM, first_range+5)
-      yt = UOp.range(dtypes.int, TM, first_range+6)
-      iterWaveN = UOp.range(dtypes.int, nbIterWaveN, first_range+7)
-      xt = UOp.range(dtypes.int, TN, first_range+8)
-      x = iterWaveN * TN + xt
-      y = iterWaveM * TM + yt
-      c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
-      # sketchy, this should end the kId_range but it doesn't
-      sink = c_regs_idx.store(c_regs_idx.load(init_store) + A_col[y].load(A_col_store) * B_row[x].load(B_row_store),
-                              iterWaveM, iterWaveN, yt, xt, k)
-      return sink
-
-    # TODO: kId_range should endrange after a barrier
-    sink = inner_loop(5, (barrier, regB_store, regA_store)).barrier()
-
-    # load from registers into locals
-    i = UOp.range(dtypes.int, nbReadsB, 14)
-    index_x = BN * blockIdx_x + rBIdx
-    index_y = rBIdy + i * strideReadB + kId + BK
-    Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(regB[i].load(sink), i, kId_range)
-
-    i = UOp.range(dtypes.int, nbReadsA, 15)
-    index_x = rAIdx + kId + BK
-    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
-    As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(regA[i].load(sink), i, kId_range)
-
-    # final iteration without the copy
-    sink = inner_loop(16, (UOp.barrier(Bs_store, As_store),))
-  else:
-    kId_range = UOp.range(dtypes.int, N//BK, 0)
-    kId = kId_range*BK
-
-    # load from globals into locals
-    i = UOp.range(dtypes.int, nbReadsB, 1)
-    index_x = BN * blockIdx_x + rBIdx
-    index_y = rBIdy + i * strideReadB + kId
-    Bs_store = Bs[(index_y % BK) * BN + index_x % BN].store(b[N * index_y + index_x].load(), i)
-
-    i = UOp.range(dtypes.int, nbReadsA, 2)
-    index_x = rAIdx + kId
-    index_y = BM * blockIdx_y + rAIdy + i * strideReadA
-    As_store = As[(index_x % BK) * BM_As_stride + index_y % BM].store(a[N * index_y + index_x].load(), i)
-
-    barrier = UOp.barrier(As_store, Bs_store)
-
-    k = UOp.range(dtypes.int, BK, 3)
-
-    # load from locals into registers
-    iterWave = UOp.range(dtypes.int, nbIterWaveN, 4)
-    i = UOp.range(dtypes.int, TN, 5)
-    index = waveIdx * WN + iterWave * SUBWN + TN * idxInWave + i
-    B_row_store = B_row[iterWave*TN + i].store(Bs[k*BN + index].load(barrier), iterWave, i)
-
-    iterWave = UOp.range(dtypes.int, nbIterWaveM, 6)
-    i = UOp.range(dtypes.int, TM, 7)
-    index = waveIdy * WM + iterWave * SUBWM + TM * idyInWave + i
-    A_col_store = A_col[iterWave*TM + i].store(As[k*BM_As_stride + index].load(barrier), iterWave, i)
-
-    # do the GEMM math
-    iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 8)
-    yt = UOp.range(dtypes.int, TM, 9)
-    iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 10)
-    xt = UOp.range(dtypes.int, TN, 12)
-    x = iterWaveN * TN + xt
-    y = iterWaveM * TM + yt
-    c_regs_idx = c_regs[y * TN * nbIterWaveN + x]
-    sink = c_regs_idx.store(c_regs_idx.load(init_store) + A_col[y].load(A_col_store) * B_row[x].load(B_row_store),
-                            iterWaveM, iterWaveN, yt, xt, k, kId_range)
 
   # store c_regs into c
-  iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 1000)
+  iterWaveM = UOp.range(dtypes.int, nbIterWaveM, 12)
-  yt = UOp.range(dtypes.int, TM, 1001)
+  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 13)
-  iterWaveN = UOp.range(dtypes.int, nbIterWaveN, 1002)
+  yt = UOp.range(dtypes.int, TM, 14)
-  xt = UOp.range(dtypes.int, TN, 1003)
+  xt = UOp.range(dtypes.int, TN, 15)
   xOut = blockIdx_x * BN + waveIdx * WN + iterWaveN * SUBWN + TN * idxInWave
   yOut = blockIdx_y * BM + waveIdy * WM + iterWaveM * SUBWM + TM * idyInWave
   indexC = N * (yOut + yt) + xOut + xt
-  sink = c[indexC].store(c_regs[TN * nbIterWaveN * (iterWaveM * TM + yt) + (iterWaveN * TN + xt)].load(sink),
+  sink = c[indexC].store(c_regs[TN * nbIterWaveN * (iterWaveM * TM + yt) + (iterWaveN * TN + xt)].load(sink), iterWaveM, iterWaveN, yt, xt)
-                         iterWaveM, iterWaveN, yt, xt)
 
   return sink.sink(arg=KernelInfo(name="tinygemm"))
 
 if __name__ == "__main__":
-  HL = getenv("HL")
+  hprg = hl_spec_kernel3() if getenv("HL") else hand_spec_kernel3()
-  if HL == 2: hprg = top_spec_kernel3()
-  elif HL == 1: hprg = hl_spec_kernel3()
-  else: hprg = hand_spec_kernel3()
   prg = get_program(hprg, Device.default.renderer)
   print(prg.src)
-  if getenv("SRC"): exit(0)
   hrunner = CompiledRunner(prg)
 
   a = Tensor.randn(N, N).realize()
@@ -326,8 +167,7 @@ if __name__ == "__main__":
     for _ in range(run_count): tc = (a@b).realize()
 
   GlobalCounters.reset()
-  buffers = [hc.uop.buffer, a.uop.buffer, b.uop.buffer]
+  ei = ExecItem(hrunner, [a.uop.buffer, b.uop.buffer, hc.uop.buffer])
-  ei = ExecItem(hrunner, buffers)
   with Context(DEBUG=2):
     for _ in range(run_count): ei.run(wait=True)
   err = (hc-tc).square().mean().item()

extra/hcq/hcq_smi.py

@@ -1,122 +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, 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", "ast"] 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

@@ -99,9 +99,7 @@ class FeedForward:
     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()
+    return self.w2(self.w1(x).silu() * self.w3(x)) # SwiGLU [arxiv/2002.05202, eq (5)]
-    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,
@@ -113,7 +111,7 @@ 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().contiguous_backward()
+    return (h + self.feed_forward(self.ffn_norm(h))).contiguous()
 
 # standard openai sampling
 def sample(logits: Tensor, temp: float, k: int, p: float, af: float, ap: float):
@@ -187,10 +185,10 @@ 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()
+    logits = self.output(self.norm(h)).float()[:, -1, :]
     if math.isnan(temperature): return logits
 
-    return sample(logits[:, -1, :].flatten(), temperature, top_k, top_p, alpha_f, alpha_p)
+    return sample(logits.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

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

extra/nvpci/nv_smi.py

@@ -0,0 +1,65 @@
+#!/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/onnx.py

@@ -1,4 +1,4 @@
-# mypy: disable-error-code="misc, list-item, assignment, operator, index, arg-type"
+# mypy: disable-error-code="misc, list-item, assignment, attr-defined, operator, index, arg-type"
 from types import SimpleNamespace
 from typing import Any, Sequence, cast, Literal, Callable, get_args, NamedTuple
 import dataclasses, functools, io, math, types, warnings, pathlib, sys, enum
@@ -798,11 +798,12 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
 
   def Gather(x:Tensor, indices:Tensor, axis:int=0):
     if indices.numel() < 9: # NOTE lessor kernels for smaller indices but kernel number increases depending on size of indices
-      ret_shape = x.shape[:axis] + indices.shape + x.shape[axis+1:]
+      x_sh = list(x.shape)
+      ret_shape = x_sh[:axis] + list(indices.shape) + x_sh[axis+1:]
       if indices.ndim > 1: indices = indices.flatten()
-      index_consts = [_cached_to_python_const(indices)] if indices.shape == () else _cached_to_python_const(indices)
+      indices = [_cached_to_python_const(indices)] if indices.shape == () else _cached_to_python_const(indices)
-      index_consts = [x.shape[axis]+i if i<0 else i for i in index_consts]
+      indices = [x_sh[axis]+x if x<0 else x for x in indices]
-      args = [[(0,x) if j != axis else (i,i+1) for j, x in enumerate(x.shape)] for i in index_consts]
+      args = [[(0,x) if j != axis else (i,i+1) for j, x in enumerate(x_sh)] for i in indices] # type: ignore
       return x.shrink(arg=tuple(args[0])).cat(*[x.shrink(arg=tuple(arg)) for arg in args[1:]], dim=axis).reshape(ret_shape)
     # NOTE faster gather, fixed number of kernels, but exceeds limited kernels for openpilot
     return x[tuple([slice(None) if i != axis else indices for i in range(x.ndim)])]

extra/torch_backend/backend.py

@@ -128,12 +128,6 @@ 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

@@ -198,11 +198,6 @@ 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

setup.py

@@ -9,7 +9,7 @@ with open(directory / 'README.md', encoding='utf-8') as f:
 
 testing_minimal = [
   "numpy",
-  "torch==2.7.1",
+  "torch",
   "pytest",
   "pytest-xdist",
   "hypothesis",

test/external/external_benchmark_disk_raw.py

@@ -1,8 +1,7 @@
 import pathlib
 from tinygrad import Tensor, Device, Context
-from tinygrad.helpers import getenv
 
 if __name__ == "__main__":
   with Context(DEBUG=2):
-    disk_llama = Tensor(pathlib.Path(getenv("TESTFILE", "/raid/weights/LLaMA-3/8B/consolidated.00.pth")))
+    disk_llama = Tensor(pathlib.Path("/raid/weights/LLaMA-3/8B/consolidated.00.pth"))
     device_llama = disk_llama.to(Device.DEFAULT).realize()

test/external/external_llm_eval.py

@@ -10,7 +10,7 @@ if __name__ == "__main__":
 
   model, kv = Transformer.from_gguf(Tensor.from_url(models["1B"]), max_context=4096)
 
-  tok = SimpleTokenizer.from_gguf_kv(kv)
+  tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
   bos_id: int = kv['tokenizer.ggml.bos_token_id']
   eos_id: int = kv['tokenizer.ggml.eos_token_id']
 

test/external/external_test_simple_tokenizer.py

@@ -1,19 +1,17 @@
 from transformers import AutoTokenizer
 from datasets import load_dataset
-from tinygrad.apps.llm import SimpleTokenizer, gpt2_decode_vocab, get_llama_re
+from tinygrad.apps.llm import SimpleTokenizer
-from tinygrad.helpers import tqdm, getenv, partition
+from tinygrad.helpers import tqdm, getenv
 
 # use ALLOW_FAILED=-1 to go over the entire dataset without printing.
 if __name__ == "__main__":
   base_tokenizer = AutoTokenizer.from_pretrained("NousResearch/Meta-Llama-3-8B-Instruct")
-  special_tokens, normal_tokens = partition(((t, tid) for t, tid in base_tokenizer.vocab.items()),
+  vocab_words = [ word for word, _ in sorted(base_tokenizer.get_vocab().items(), key=lambda t: t[1]) ]
-    lambda e: e[1] in base_tokenizer.all_special_ids)
   inv_vocab = { tid: word for word, tid in base_tokenizer.get_vocab().items() }
-  simple_tokenizer = SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
+  simple_tokenizer = SimpleTokenizer(vocab_words)
 
   color_codes = [ 91, 92, 94, 93, 95 ]
-  def color_tokens(tids):
+  def color_tokens(tids): return "".join(f"\033[{color_codes[i%len(color_codes)]}m{inv_vocab[t]}" for i, t in enumerate(tids)) + "\033[0m"
-    return "".join(f"\033[{color_codes[i%len(color_codes)]}m{base_tokenizer.decode([t])}" for i, t in enumerate(tids)) + "\033[0m"
 
   ds = load_dataset("OpenAssistant/oasst1")
   allow_failed = getenv("ALLOW_FAILED", 10)

test/external/process_replay/process_replay.py

@@ -74,7 +74,6 @@ def diff(offset:int, fxns:dict[str, Callable[..., tuple|None]]) -> None:
       warnings.warn(f"detected changes in over {MAX_DIFF_PCT}%. skipping further diff generation.", ProcessReplayWarning)
       early_stop.set()
       break
-    name, loc = "", ""
     try:
       name, args, kwargs, ctx_vals, loc, ret = pickle.loads(row[0])
       ctx_vars = {k:v.value for k,v in ctx_vals.items() if k != "DEBUG" and (var:=ContextVar._cache.get(k)) is not None and var.value != v.value}
@@ -91,7 +90,7 @@ def diff(offset:int, fxns:dict[str, Callable[..., tuple|None]]) -> None:
         warnings.warn("PROCESS REPLAY DETECTED CHANGE", ProcessReplayWarning)
     except Exception as e:
       changed += 1
-      warnings.warn(f"{name=} {loc=} {e=}", ProcessReplayWarning)
+      warnings.warn(e, ProcessReplayWarning)
   conn.commit()
   cur.close()
 
@@ -124,5 +123,5 @@ if __name__ == "__main__":
   logging.info(f"running process replay with {ASSERT_DIFF=}")
   try: _pmap(replayers)
   except Exception as e:
-    logging.info(f"process replay err: {e}")
+    logging.info("process replay err", e)
     exit(int(ASSERT_DIFF))

test/test_define_reg.py

@@ -1,32 +0,0 @@
-import unittest
-from tinygrad import dtypes, Device, Tensor, Context
-from tinygrad.dtype import AddrSpace
-from tinygrad.helpers import getenv
-from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.engine.realize import get_program, ExecItem, CompiledRunner
-
-class TestDefineReg(unittest.TestCase):
-  def test_simple(self, at=AxisType.UPCAST):
-    N = 16
-    bout = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=0).view(ShapeTracker.from_shape((N,N)))
-    a = UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(N*N), arg=1).view(ShapeTracker.from_shape((N,N)))
-    a_col = UOp(Ops.DEFINE_REG, dtypes.float.ptr(N, AddrSpace.REG), arg=0).view(ShapeTracker.from_shape((N,N), (0,1)))
-
-    out = a_col.load(a_col.store(a.load()))
-    sink = bout.store(out).sink(arg=KernelInfo(name="regcopy", axis_types=(AxisType.LOOP, at)))
-    prg = get_program(sink, Device.default.renderer)
-
-    with Context(DEBUG=0):
-      a = Tensor.randn(N, N).realize()
-      b = Tensor.empty(N, N).realize()
-    hrunner = CompiledRunner(prg)
-    ExecItem(hrunner, [b.uop.buffer, a.uop.buffer]).run(wait=True)
-    with Context(DEBUG=0):
-      self.assertEqual((b-a).mean().item(), 0.0)
-
-  @unittest.skipIf(getenv("PTX"), "ptx needs regs to be unrolled")
-  def test_simple_loop(self): self.test_simple(AxisType.LOOP)
-
-if __name__ == '__main__':
-  unittest.main()

test/test_dtype.py

@@ -4,7 +4,7 @@ import torch
 from typing import Any, List
 from tinygrad.device import is_dtype_supported
 from tinygrad.helpers import getenv, DEBUG, CI
-from tinygrad.dtype import DType, DTYPES_DICT, least_upper_dtype, fp8_to_float, float_to_fp8
+from tinygrad.dtype import DType, DTYPES_DICT, ImageDType, PtrDType, least_upper_dtype, to_dtype, fp8_to_float, float_to_fp8
 from tinygrad import Device, Tensor, dtypes
 from tinygrad.tensor import _to_np_dtype
 from hypothesis import assume, given, settings, strategies as strat
@@ -384,6 +384,30 @@ class TestPtrDType(unittest.TestCase):
     self.assertEqual(dt.v, 4)
     self.assertEqual(dt.count, 4)
 
+class TestImageDType(unittest.TestCase):
+  def test_image_scalar(self):
+    assert dtypes.imagef((10,10)).base.scalar() == dtypes.float32
+    assert dtypes.imageh((10,10)).base.scalar() == dtypes.float32
+  def test_image_vec(self):
+    assert dtypes.imagef((10,10)).base.vec(4) == dtypes.float32.vec(4)
+    assert dtypes.imageh((10,10)).base.vec(4) == dtypes.float32.vec(4)
+
+class TestEqStrDType(unittest.TestCase):
+  def test_image_ne(self):
+    if ImageDType is None: raise unittest.SkipTest("no ImageDType support")
+    assert dtypes.float == dtypes.float32, "float doesn't match?"
+    assert dtypes.imagef((1,2,4)) != dtypes.imageh((1,2,4)), "different image dtype doesn't match"
+    assert dtypes.imageh((1,2,4)) != dtypes.imageh((1,4,2)), "different shape doesn't match"
+    assert dtypes.imageh((1,2,4)) == dtypes.imageh((1,2,4)), "same shape matches"
+    assert isinstance(dtypes.imageh((1,2,4)), ImageDType)
+  def test_ptr_eq(self):
+    assert dtypes.float32.ptr() == dtypes.float32.ptr()
+    assert not (dtypes.float32.ptr() != dtypes.float32.ptr())
+  def test_strs(self):
+    if PtrDType is None: raise unittest.SkipTest("no PtrDType support")
+    self.assertEqual(str(dtypes.imagef((1,2,4))), "dtypes.imagef((1, 2, 4))")
+    self.assertEqual(str(dtypes.float32.ptr(16)), "dtypes.float.ptr(16)")
+
 class TestImplicitFunctionTypeChange(unittest.TestCase):
   def test_functions(self):
     result = []
@@ -414,6 +438,19 @@ class TestDtypeUsage(unittest.TestCase):
         t = Tensor([[1, 2], [3, 4]], dtype=d)
         (t*t).max().item()
 
+class TestToDtype(unittest.TestCase):
+  def test_dtype_to_dtype(self):
+    dtype = dtypes.int32
+    res = to_dtype(dtype)
+    self.assertIsInstance(res, DType)
+    self.assertEqual(res, dtypes.int32)
+
+  def test_str_to_dtype(self):
+    dtype = "int32"
+    res = to_dtype(dtype)
+    self.assertIsInstance(res, DType)
+    self.assertEqual(res, dtypes.int32)
+
 @unittest.skipUnless(is_dtype_supported(dtypes.bfloat16), f"no bfloat16 on {Device.DEFAULT}")
 class TestOpsBFloat16(unittest.TestCase):
   def test_cast(self):

test/test_graph.py

@@ -107,9 +107,8 @@ class TestGraph(unittest.TestCase):
     helper_test_graphs(Device[d0].graph, graphs)
 
   def skip_if_not_multigraph(self):
-    graph = g.func if isinstance(g:=(d:=Device[Device.DEFAULT]).graph, functools.partial) else g
+    graph = g.func if isinstance(g:=Device[Device.DEFAULT].graph, functools.partial) else g
     if not issubclass(graph, MultiGraphRunner): self.skipTest("graph is not supported (not MultiGraphRunner)")
-    if not hasattr(d.allocator, '_transfer'): self.skipTest("device is not supported (no transfers)")
 
   def test_order_copy_writed(self):
     self.skip_if_not_multigraph()

test/test_jit.py

@@ -5,10 +5,9 @@ import numpy as np
 from hypothesis import given, settings, strategies as strat
 from test.helpers import assert_jit_cache_len, not_support_multi_device, REAL_DEV
 from tinygrad.tensor import Tensor
-from tinygrad.engine.jit import TinyJit, GraphRunner, MultiGraphRunner, graph_class
+from tinygrad.engine.jit import TinyJit
-from tinygrad.engine.realize import CompiledRunner, BufferCopy, BufferXfer
 from tinygrad.device import Device
-from tinygrad.helpers import Context, JIT, GlobalCounters, getenv
+from tinygrad.helpers import Context, JIT, GlobalCounters
 from tinygrad.dtype import dtypes
 from extra.models.unet import ResBlock
 
@@ -670,169 +669,5 @@ class TestJitFree(unittest.TestCase):
     out = fxn(Tensor([11,1,2,3,4]))
     self.assertEqual(out.item(), 13600)
 
-class TestJitGraphSplit(unittest.TestCase):
-  def compute(self, device, inp):
-    assert inp.device == device, f"Input device {inp.device} does not match expected {device}"
-    return (inp + 1.0).contiguous().realize()
-
-  def copy(self, device, to_device, inp):
-    assert inp.device == device, f"Input device {inp.device} does not match expected {device}"
-    return inp.to(to_device).realize()
-
-  def expect(self, f, *args, graph=None, multigraph=None, hcqgraph=None):
-    def _numpies(tpl): return tpl.numpy() if tpl.__class__ is Tensor else tuple([t.numpy() for t in tpl])
-
-    expected = _numpies(f(*args))
-    for i in range(4):
-      res = _numpies(f(*args))
-      np.testing.assert_allclose(res, expected, atol=1e-4, rtol=1e-5)
-
-    dev = Device[Device.DEFAULT]
-    graph_t = graph_class(dev)
-    if graph_t is None: return
-
-    got = f.jit_cache
-    from tinygrad.runtime.graph.hcq import HCQGraph
-    if graph_t is HCQGraph:
-      validate = hcqgraph
-    elif issubclass(graph_t, MultiGraphRunner):
-      validate = multigraph
-    else:
-      validate = graph
-
-    assert len(got) == len(validate), f"Expected {len(validate)} operations, got {len(got)}"
-    for expected, got in zip(validate, got):
-      if expected["type"] == "graph":
-        assert isinstance(got.prg, GraphRunner), f"Expected GraphRunner, got {type(got.prg)}"
-        assert len(got.prg.jit_cache) == expected["cnt"], f"Expected {expected['cnt']} operations in graph, got {len(got.prg.jit_cache)}"
-      elif expected["type"] == "comp":
-        assert isinstance(got.prg, CompiledRunner), f"Expected CompiledRunner, got {type(got.prg)}"
-      elif expected["type"] == "copy":
-        assert isinstance(got.prg, BufferCopy), f"Expected BufferCopy, got {type(got.prg)}"
-      elif expected["type"] == "xfer":
-        assert isinstance(got.prg, BufferXfer), f"Expected BufferXfer, got {type(got.prg)}"
-
-  def ji_graph(self, cnt): return {"type": "graph", "cnt": cnt}
-  def ji_comp(self): return {"type": "comp"}
-  def ji_copy(self): return {"type": "copy"}
-  def ji_xfer(self): return {"type": "xfer"}
-
-  def test_jit_split_simple(self):
-    if Device.DEFAULT == "REMOTE": raise unittest.SkipTest("REMOTE gpu is broken")
-
-    @TinyJit
-    def f(inp):
-      op0 = self.compute(Device.DEFAULT, inp)
-      op1 = self.compute(Device.DEFAULT, op0)
-      op2 = self.compute(Device.DEFAULT, op1)
-      return op2
-
-    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
-    self.expect(f, inp,
-      graph=[self.ji_graph(3)],
-      multigraph=[self.ji_graph(3)],
-      hcqgraph=[self.ji_graph(3)])
-
-  def test_jit_cpu_simple(self):
-    if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
-
-    @TinyJit
-    def f(inp, inp_cpu):
-      op0 = self.compute(Device.DEFAULT, inp)
-      op1 = self.compute(Device.DEFAULT, op0)
-      op2 = self.compute("CPU", inp_cpu)
-      op3 = self.compute(Device.DEFAULT, op1)
-      return op2, op3
-
-    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
-    inp_cpu = Tensor.randn(10, 10, device="CPU").realize()
-    self.expect(f, inp, inp_cpu,
-      graph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()],
-      multigraph=[self.ji_graph(2), self.ji_comp(), self.ji_comp()],
-      hcqgraph=[self.ji_graph(4)])
-
-  def test_jit_cpu_several(self):
-    if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
-
-    @TinyJit
-    def f(inp, inp_cpu):
-      op0 = self.compute(Device.DEFAULT, inp)
-      op1 = self.compute(Device.DEFAULT, op0)
-      op2 = self.compute("CPU", inp_cpu)
-      op3 = self.compute("CPU", op2)
-      op4 = self.compute(Device.DEFAULT, op1)
-      return op3, op4
-
-    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
-    inp_cpu = Tensor.randn(10, 10, device="CPU").realize()
-    self.expect(f, inp, inp_cpu,
-      graph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
-      multigraph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
-      hcqgraph=[self.ji_graph(5)])
-
-  def test_jit_multidev(self):
-    if Device.DEFAULT == "CPU": raise unittest.SkipTest("CPU is not a valid default device for this test")
-
-    try: Device[f"{Device.DEFAULT}:1"]
-    except Exception: raise unittest.SkipTest("no multidevice")
-
-    @TinyJit
-    def f(inp, inp_d1):
-      op0 = self.compute(Device.DEFAULT, inp)
-      op1 = self.compute(Device.DEFAULT, op0)
-      op2 = self.compute(f"{Device.DEFAULT}:1", inp_d1)
-      op3 = self.compute(f"{Device.DEFAULT}:1", op2)
-      op4 = self.compute(Device.DEFAULT, op1)
-      return op3, op4
-
-    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
-    inp_d1 = Tensor.randn(10, 10, device=f"{Device.DEFAULT}:1").realize()
-    self.expect(f, inp, inp_d1,
-      graph=[self.ji_graph(2), self.ji_graph(2), self.ji_comp()],
-      multigraph=[self.ji_graph(5)],
-      hcqgraph=[self.ji_graph(5)])
-
-  def test_jit_multidev_xfer(self):
-    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
-
-    try: Device[f"{Device.DEFAULT}:1"]
-    except Exception: raise unittest.SkipTest("no multidevice")
-
-    @TinyJit
-    def f(inp, inp_d1):
-      op0 = self.compute(Device.DEFAULT, inp)
-      op1 = self.compute(Device.DEFAULT, op0)
-      op2 = self.compute(f"{Device.DEFAULT}:1", inp_d1)
-      op3 = self.copy(f"{Device.DEFAULT}:1", Device.DEFAULT, op2)
-      op4 = self.compute(f"{Device.DEFAULT}:1", op2)
-      op5 = self.compute(Device.DEFAULT, op3)
-      return op1, op4, op5
-
-    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
-    inp_d1 = Tensor.randn(10, 10, device=f"{Device.DEFAULT}:1").realize()
-    self.expect(f, inp, inp_d1,
-      graph=[self.ji_graph(2), self.ji_comp(), self.ji_xfer(), self.ji_comp(), self.ji_comp()],
-      multigraph=[self.ji_graph(6)],
-      hcqgraph=[self.ji_graph(6)])
-
-  @unittest.skipIf(getenv("MOCKGPU"), "MockGPU does not support parallel copies")
-  def test_jit_multidev_copy(self):
-    if Device.DEFAULT in {"CPU", "LLVM"}: raise unittest.SkipTest("CPU/LLVM is not a valid default device for this test (zero-copies)")
-    if Device.DEFAULT == "REMOTE": raise unittest.SkipTest("REMOTE gpu is broken")
-
-    @TinyJit
-    def f(inp):
-      op0 = self.compute(Device.DEFAULT, inp)
-      op1 = self.compute(Device.DEFAULT, op0)
-      op2 = self.copy(Device.DEFAULT, "CPU", op1)
-      op3 = self.compute("CPU", op2)
-      return op3
-
-    inp = Tensor.randn(10, 10, device=Device.DEFAULT).realize()
-    self.expect(f, inp,
-      graph=[self.ji_graph(2), self.ji_copy(), self.ji_comp()],
-      multigraph=[self.ji_graph(2), self.ji_copy(), self.ji_comp()],
-      hcqgraph=[self.ji_graph(4)])
-
 if __name__ == '__main__':
   unittest.main()

test/test_linalg.py

@@ -16,7 +16,7 @@ def reconstruction_helper(A:List[Tensor],B:Tensor, tolerance=1.0e-5):
 class TestLinAlg(unittest.TestCase):
 
   def test_svd_general(self):
-    sizes = [(2,2),(5,3),(3,5),(3,4,4),(2,2,2,2,3)]
+    sizes = [(2,2),(5,3),(3,5),(2,2,2,2,3)]
     for size in sizes:
       a = Tensor.randn(size).realize()
       U,S,V = Tensor.svd(a)

test/test_linearizer.py

@@ -114,6 +114,27 @@ class TestLinearizer(unittest.TestCase):
         if skip and i in skip: continue
         assert ranges[i-1] != u, f"multireduce nested the ranges! {ranges[i-1], {u}}"
 
+  @unittest.expectedFailure
+  def test_const_alu_indexing(self):
+    st = ShapeTracker.from_shape((4,)).to_uop()
+    load = UOp.load(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=1, src=()), st, dtype=dtypes.float)
+    op = load+UOp.const(dtypes.float, 1.0)*UOp.const(dtypes.float, -1)
+    store = UOp.store(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0, src=()), st, op)
+    Tensor.manual_seed(0)
+    x = Tensor.randn(4,).realize()
+    helper_linearizer_ast(store.sink(), [x], wanna_output=[x.numpy()+1*-1], opts=[])
+
+  # shapeless CONST in AST is not supported
+  @unittest.expectedFailure
+  def test_const_alu_indexing_one_const_fine(self):
+    st = ShapeTracker.from_shape((4,)).to_uop()
+    load = UOp.load(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=1, src=()), st, dtype=dtypes.float)
+    op = load+UOp.const(dtypes.float, 1.0)
+    store = UOp.store(UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=0, src=()), st, op)
+    Tensor.manual_seed(0)
+    x = Tensor.randn(4,).realize()
+    helper_linearizer_ast(store.sink(), [x], wanna_output=[x.numpy()+1], opts=[])
+
   @unittest.skipIf(CI and Device.DEFAULT in {"PTX", "AMD", "NV"}, "very slow")
   def test_indexing_multireduce(self):
     dataset = Tensor.rand(16384, 256).realize()
@@ -270,7 +291,7 @@ class TestLinearizer(unittest.TestCase):
     realized_ast = realized_ast.replace(arg=KernelInfo(opts_to_apply=tuple(opts_to_apply)))
     program = get_program(realized_ast, Device[Device.DEFAULT].renderer)
 
-    stores = [u for u in program.uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
+    stores = [u for u in program.uops if u.op is Ops.STORE and u.dtype.addrspace != AddrSpace.REG]
 
     # the first store is to lds and can be upcasted
     assert stores[0].src[1].dtype == dtypes.float.vec(4)
@@ -612,7 +633,6 @@ class TestLinearizer(unittest.TestCase):
     helper(Tensor.arange(255), max_ops=2)
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_grouped_store_phis(self):
     """
     float4 acc0 = float4(0.0,0.0,0.0,0.0);
@@ -628,7 +648,7 @@ class TestLinearizer(unittest.TestCase):
     k = helper_linearizer_opt(out)[-1]
     uops = get_program(k.get_optimized_ast(), k.opts).uops
     # check that the float4 cast collapses
-    store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
+    store_vals = [u.src[1] for u in uops if u.op is Ops.STORE and u.dtype.addrspace != AddrSpace.REG]
     for val in store_vals:
       assert val.dtype == dtypes.float.vec(4) # and val.op is not Ops.VECTORIZE
 
@@ -679,13 +699,12 @@ class TestLinearizer(unittest.TestCase):
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "test requires locals")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_shared, "test requires shared")
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.supports_float4, "test requires float4")
-  @unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
   def test_grouped_store_local_only(self):
     x, y = Tensor.rand(1,128), Tensor.rand(128, 128)
     r = (x@y).relu()
     k = helper_linearizer_opt(r)[-1]
     uops = get_program(k.get_optimized_ast(), k.opts).uops
-    stores = [u for u in uops if u.op is Ops.STORE and u.src[0].dtype.addrspace != AddrSpace.REG]
+    stores = [u for u in uops if u.op is Ops.STORE and u.dtype.addrspace != AddrSpace.REG]
 
     # the float4 value stores directly in lds and we skip upcast
     self.assertEqual(stores[0].src[1].dtype, dtypes.float.vec(4))

test/test_linearizer_dumb.py

@@ -82,7 +82,6 @@ class TestLinearizerDumb(unittest.TestCase):
     assert prg.uops is not None and not any(uop.op is Ops.MAX for uop in prg.uops), "leftover MAX"
 
   @unittest.skipUnless(Device[Device.DEFAULT].renderer.has_local, "need local")
-  @unittest.skip("not applicable")
   def test_expander_new_srcs(self):
     ast = UOp(Ops.SINK, dtypes.void, arg=None, src=(
       UOp(Ops.STORE, dtypes.void, arg=None, src=(

test/test_linearizer_overflows.py

@@ -1,6 +1,7 @@
 # ruff: noqa: E501
 import unittest
-from tinygrad import dtypes
+from tinygrad import dtypes, Device
+from tinygrad.helpers import CI
 from tinygrad.opt.kernel import Kernel
 from tinygrad.opt.search import Opt, OptOps, bufs_from_lin
 from extra.optimization.helpers import time_linearizer
@@ -161,5 +162,33 @@ class TestLinearizerOverflow(unittest.TestCase):
     opts = [Opt(op=OptOps.UPCAST, axis=3, arg=4), Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=2, arg=8), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=2, arg=4)]
     _test_overflow(ast, opts)
 
+@unittest.skipIf(Device.DEFAULT not in {"GPU", "HSA", "CUDA", "METAL"}, "only backends with locals")
+@unittest.skipIf(CI, "slow")
+class TestLinearizerOverflowAlt(unittest.TestCase):
+  def test_overflow_1(self):
+    BS = 2
+    g0, g1, g2 = [UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=i) for i in range(3)]
+    in_st_1 = ShapeTracker(views=(View(shape=(1, BS, 1, 3, 8, 230, 8, 230), strides=(0, 150528, 0, 50176, 0, 224, 0, 1), offset=-675, mask=((0, 1), (0, BS), (0, 1), (0, 3), (0, 8), (3, 227), (0, 8), (3, 227)), contiguous=False),
+                                  View(shape=(BS, 1, 64, 112, 112, 3, 7, 7), strides=(10156800, 0, 0, 3680, 2, 3385600, 425040, 231), offset=0, mask=None, contiguous=False))).to_uop()
+    in_st_2 = ShapeTracker(views=(View(shape=(BS, 1, 64, 112, 112, 3, 7, 7), strides=(0, 0, 147, 0, 0, 49, 7, 1), offset=0, mask=None, contiguous=False),)).to_uop()
+    ot_st = ShapeTracker(views=(View(shape=(BS, 1, 64, 112, 112, 1, 1, 1), strides=(802816, 0, 12544, 112, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)).to_uop()
+    prod = UOp(Ops.LOAD, dtypes.float, (g1.view(in_st_1.arg),)) * UOp(Ops.LOAD, dtypes.float, (g2.view(in_st_2.arg),))
+    store = UOp(Ops.STORE, src=(g0.view(ot_st.arg), UOp(Ops.REDUCE_AXIS, dtypes.float, (prod,), (Ops.ADD, (7, 6, 5)))))
+    ast = UOp(Ops.SINK, src=(store,))
+    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.LOCAL, axis=2, arg=2), Opt(op=OptOps.UPCAST, axis=0, arg=2)]
+    _test_overflow(ast, opts)
+  def test_overflow_2(self):
+    BS = 2
+    g0, g1, g2 = [UOp(Ops.DEFINE_GLOBAL, dtypes.float.ptr(), arg=i) for i in range(3)]
+    in_st_1 = ShapeTracker(views=(View(shape=(1, BS, 1, 3, 8, 230, 8, 230), strides=(0, 150528, 0, 50176, 0, 224, 0, 1), offset=-675, mask=((0, 1), (0, BS), (0, 1), (0, 3), (0, 8), (3, 227), (0, 8), (3, 227)), contiguous=False),
+                                  View(shape=(BS, 1, 64, 112, 112, 3, 7, 7), strides=(10156800, 0, 0, 3680, 2, 3385600, 425040, 231), offset=0, mask=None, contiguous=False))).to_uop()
+    in_st_2 = ShapeTracker(views=(View(shape=(BS, 1, 64, 112, 112, 3, 7, 7), strides=(0, 0, 147, 0, 0, 49, 7, 1), offset=0, mask=None, contiguous=False),)).to_uop()
+    ot_st = ShapeTracker(views=(View(shape=(BS, 1, 64, 112, 112, 1, 1, 1), strides=(802816, 0, 12544, 112, 1, 0, 0, 0), offset=0, mask=None, contiguous=True),)).to_uop()
+    prod = UOp(Ops.LOAD, dtypes.float, (g1.view(in_st_1.arg),)) * UOp(Ops.LOAD, dtypes.float, (g2.view(in_st_2.arg),))
+    store = UOp(Ops.STORE, src=(g0.view(ot_st.arg), UOp(Ops.REDUCE_AXIS, dtypes.float, (prod,), (Ops.ADD, (7, 6, 5)))))
+    ast = UOp(Ops.SINK, src=(store,))
+    opts = [Opt(op=OptOps.LOCAL, axis=3, arg=16), Opt(op=OptOps.UPCAST, axis=1, arg=4), Opt(op=OptOps.LOCAL, axis=2, arg=16), Opt(op=OptOps.UPCAST, axis=4, arg=4), Opt(op=OptOps.UPCAST, axis=1, arg=2), Opt(op=OptOps.UPCAST, axis=5, arg=2)]
+    _test_overflow(ast, opts)
+
 if __name__ == '__main__':
   unittest.main()

test/test_multitensor.py

@@ -2,7 +2,7 @@ import unittest, functools, random
 from tinygrad import Tensor, Device, nn, GlobalCounters, TinyJit, dtypes, Variable
 from tinygrad.device import is_dtype_supported
 from tinygrad.uop.ops import Ops, UOp
-from tinygrad.helpers import CI, getenv, prod, Context
+from tinygrad.helpers import CI, getenv, prod, Context, OSX
 from tinygrad.nn.state import get_parameters, get_state_dict
 from tinygrad.engine.realize import lower_schedule, BufferCopy, CompiledRunner, run_schedule
 import numpy as np
@@ -374,6 +374,7 @@ class TestMultiTensor(unittest.TestCase):
 
   # NOTE: this is failing on LLVM CI, no idea why. Works locally.
   @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU"), "slow, and flaky on LLVM/CPU")
+  @unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_data_parallel_resnet(self):
     from extra.models.resnet import ResNet18
 
@@ -410,6 +411,7 @@ class TestMultiTensor(unittest.TestCase):
     np.testing.assert_allclose(grad, shard_grad, atol=1e-5, rtol=1e-5)
 
   @unittest.skipIf(CI and REAL_DEV in ("CUDA", "NV", "LLVM", "CPU"), "slow, and flaky on LLVM/CPU")
+  @unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_data_parallel_resnet_train_step(self):
     from extra.models.resnet import ResNet18
     fake_image = Tensor.rand((2, 3, 224//8, 224//8))
@@ -936,6 +938,7 @@ class TestShrinkMultiTensorShardedAxis(unittest.TestCase):
     np.testing.assert_allclose(output.numpy(), expected)
 
 @unittest.skipIf(not_support_multi_device(), "no multi")
+@unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
 class TestBatchNorm(unittest.TestCase):
   def test_unsynced_backprop_conv_bn(self):
     with Tensor.train():
@@ -963,6 +966,7 @@ class TestBatchNorm(unittest.TestCase):
       optim.step()
       out.numpy()
 
+  @unittest.skipIf(REAL_DEV == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_unsynced_backprop_standalone_bn(self):
     from extra.lr_scheduler import OneCycleLR
     GPUS = (d1, d2)
@@ -1122,7 +1126,6 @@ class TestMultiRamUsage(unittest.TestCase):
     # NOTE: the first one on the DEFAULT device should be freed
     self.assertUsed(self.N*self.N*4*2)
 
-  @unittest.skip("flaky")
   def test_zeros_shard(self, devices=(d1, d2)):
     _ = Tensor.zeros(self.N, self.N).contiguous().shard(devices, axis=0).realize()
     self.assertUsed(self.N*self.N*4) # sharding should not increase total ram usage

test/test_nn.py

@@ -4,7 +4,7 @@ import numpy as np
 import torch
 from tinygrad import Tensor, Device, TinyJit
 from tinygrad.uop.ops import Ops
-from tinygrad.helpers import GlobalCounters, CI, Context
+from tinygrad.helpers import GlobalCounters, CI, Context, OSX
 from tinygrad.nn import Conv1d, ConvTranspose1d, Conv2d, ConvTranspose2d, Linear, Embedding
 from tinygrad.nn import BatchNorm, LayerNorm, LayerNorm2d, GroupNorm, InstanceNorm, RMSNorm, LSTMCell
 from tinygrad.nn.state import load_state_dict
@@ -108,39 +108,105 @@ class TestNN(unittest.TestCase):
     _test_linear(Tensor.randn(BS, in_dim), in_dim, out_dim)
     _test_linear(Tensor.randn(BS, T, in_dim), in_dim, out_dim) # test with more dims
 
-  def _test_conv(self, tiny_conv, torch_conv, BS, C1, DIMS, C2, K, S, P, D=1):
+  def test_conv1d(self):
+    BS, C1, W = 4, 16, 224//4
+    C2, K, S, P = 64, 7, 2, 1
+
     # create in tinygrad
-    layer = tiny_conv(C1, C2, kernel_size=K, stride=S, padding=P, dilation=D)
+    layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)
 
     # create in torch
     with torch.no_grad():
-      torch_layer = torch_conv(C1, C2, kernel_size=K, stride=S, padding=P, dilation=D).eval()
+      torch_layer = torch.nn.Conv1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
       torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
       torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
 
     # test
-    x = Tensor.uniform(BS, C1, *DIMS)
+    x = Tensor.uniform(BS, C1, W)
     z = layer(x)
     torch_x = torch.tensor(x.numpy())
     torch_z = torch_layer(torch_x)
     np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
 
-  def test_conv1d(self): self._test_conv(Conv1d, torch.nn.Conv1d, BS=4, C1=16, DIMS=[224//4], C2=64, K=7, S=2, P=1)
+  def test_conv2d(self):
-  def test_conv2d(self): self._test_conv(Conv2d, torch.nn.Conv2d, BS=4, C1=16, DIMS=[224//4, 224//4], C2=64, K=7, S=2, P=1)
+    BS, C1, H, W = 4, 16, 224//4, 224//4
+    C2, K, S, P = 64, 7, 2, 1
+
+    # create in tinygrad
+    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=P)
+
+    # create in torch
+    with torch.no_grad():
+      torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
+      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
+      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
+
+    # test
+    x = Tensor.uniform(BS, C1, H, W)
+    z = layer(x)
+    torch_x = torch.tensor(x.numpy())
+    torch_z = torch_layer(torch_x)
+    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
 
   def test_conv1d_same_padding(self):
-    self._test_conv(Conv1d, torch.nn.Conv1d, BS=8, C1=3, DIMS=[32], C2=16, K=3, S=1, P='same')
+    BS, C1, W = 8, 3, 32
+    C2, K, S, P = 16, 3, 1, 'same'
+
+    # create in tinygrad
+    layer = Conv1d(C1, C2, kernel_size=K, stride=S, padding=P)
+
+    # create in torch
+    with torch.no_grad():
+      torch_layer = torch.nn.Conv1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
+      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
+      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
+
+    # test
+    x = Tensor.uniform(BS, C1, W)
+    z = layer(x)
+    torch_x = torch.tensor(x.numpy())
+    torch_z = torch_layer(torch_x)
+    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
+
+  def _run_conv2d_same_padding_test(self, BS, C1, C2, H, W, K, S, padding='same', D=1):
+    # create in tinygrad
+    layer = Conv2d(C1, C2, kernel_size=K, stride=S, padding=padding, dilation=D)
+
+    # create in torch
+    with torch.no_grad():
+      torch_layer = torch.nn.Conv2d(C1, C2, kernel_size=K, stride=S, padding=padding, dilation=D).eval()
+      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
+      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
+
+    # test
+    x = Tensor.uniform(BS, C1, H, W)
+    z = layer(x)
+    torch_x = torch.tensor(x.numpy())
+    torch_z = torch_layer(torch_x)
+    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
+
   def test_conv2d_same_padding_odd_input(self):
-    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=16, DIMS=[29, 31], C2=32, K=5, S=1, P='same')
+    BS, C1, H, W = 16, 16, 29, 31
+    C2, K, S, P = 32, 5, 1, 'same'
+    self._run_conv2d_same_padding_test(BS, C1, C2, H, W, K, S, P)
+
   def test_conv2d_same_padding_large_kernel(self):
-    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=16, DIMS=[28, 33], C2=32, K=9, S=1, P='same')
+    BS, C1, H, W = 16, 16, 28, 33
+    C2, K, S, P = 32, 9, 1, 'same'
+    self._run_conv2d_same_padding_test(BS, C1, C2, H, W, K, S, P)
+
   def test_conv2d_same_padding_with_dilation(self):
-    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=3, DIMS=[28, 28], C2=32, K=3, S=1, P='same', D=3)
+    BS, C1, H, W = 16, 3, 28, 28
+    C2, K, S, P, D = 32, 3, 1, 'same', 3
+    self._run_conv2d_same_padding_test(BS, C1, C2, H, W, K, S, P, D)
 
   def test_conv2d_same_padding_invalid_stride(self):
-    self.assertRaises(ValueError, Conv2d, in_channels=16, out_channels=32, kernel_size=2, stride=2, padding='same')
+    C1, C2, K, S, P = 16, 32, 2, 2, 'same'
+    self.assertRaises(ValueError, Conv2d, C1, C2, kernel_size=K, stride=S, padding=P)
+
   def test_conv2d_same_padding_invalid_padding_str(self):
-    self.assertRaises(ValueError, Conv2d, in_channels=16, out_channels=32, kernel_size=2, stride=1, padding='not_same')
+    C1, C2, K, S, P = 16, 32, 2, 1, 'not_same'
+    self.assertRaises(ValueError, Conv2d, C1, C2, kernel_size=K, stride=S, padding=P)
 
   @unittest.skip("Takes too long to compile for Compiled backends")
   def test_conv2d_winograd(self):
@@ -163,13 +229,12 @@ class TestNN(unittest.TestCase):
     with Context(WINO=1):
       z = layer(x)
 
-    m = z.mean()
-    m.backward()
-
     torch_x = torch.tensor(x.numpy(), requires_grad=True)
     torch_z = torch_layer(torch_x)
     np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
 
+    m = z.mean()
+    m.backward()
     gw = layer.weight.grad.realize()
     gb = layer.bias.grad.realize()
     gx = x.grad.realize()
@@ -180,10 +245,46 @@ class TestNN(unittest.TestCase):
     np.testing.assert_allclose(gx.numpy(), torch_x.grad.numpy(), atol=5e-4, rtol=1e-5)
 
   def test_conv_transpose1d(self):
-    self._test_conv(ConvTranspose1d, torch.nn.ConvTranspose1d, BS=4, C1=16, DIMS=[224//4], C2=64, K=7, S=2, P=1)
+    BS, C1, W = 4, 16, 224//4
-  def test_conv_transpose2d(self):
+    C2, K, S, P = 64, 7, 2, 1
-    self._test_conv(ConvTranspose2d, torch.nn.ConvTranspose2d, BS=4, C1=16, DIMS=[224//4, 224//4], C2=64, K=7, S=2, P=1)
 
+    # create in tinygrad
+    layer = ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P)
+
+    # create in torch
+    with torch.no_grad():
+      torch_layer = torch.nn.ConvTranspose1d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
+      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
+      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
+
+    # test
+    x = Tensor.uniform(BS, C1, W)
+    z = layer(x)
+    torch_x = torch.tensor(x.numpy())
+    torch_z = torch_layer(torch_x)
+    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
+
+  def test_conv_transpose2d(self):
+    BS, C1, H, W = 4, 16, 224//4, 224//4
+    C2, K, S, P = 64, 7, 2, 1
+
+    # create in tinygrad
+    layer = ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P)
+
+    # create in torch
+    with torch.no_grad():
+      torch_layer = torch.nn.ConvTranspose2d(C1, C2, kernel_size=K, stride=S, padding=P).eval()
+      torch_layer.weight[:] = torch.tensor(layer.weight.numpy(), dtype=torch.float32)
+      torch_layer.bias[:] = torch.tensor(layer.bias.numpy(), dtype=torch.float32)
+
+    # test
+    x = Tensor.uniform(BS, C1, H, W)
+    z = layer(x)
+    torch_x = torch.tensor(x.numpy())
+    torch_z = torch_layer(torch_x)
+    np.testing.assert_allclose(z.numpy(), torch_z.detach().numpy(), atol=5e-4, rtol=1e-5)
+
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_groupnorm(self):
     BS, H, W, C, G = 20, 10, 10, 6, 3
 
@@ -210,6 +311,7 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_layernorm(self):
     N, C, H, W = 20, 5, 10, 10
 
@@ -236,6 +338,7 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_layernorm_2d(self):
     N, C, H, W = 20, 5, 10, 10
 
@@ -262,6 +365,7 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=5e-4, rtol=5e-4)
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_instancenorm_2d(self):
     N, C, H, W = 20, 10, 10, 10
 
@@ -288,6 +392,7 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_instancenorm_3d(self):
     N, C, D, H, W = 20, 10, 10, 10, 10
 
@@ -314,6 +419,7 @@ class TestNN(unittest.TestCase):
       np.testing.assert_allclose(layer.weight.grad.numpy(), torch_layer.weight.grad.detach().numpy(), atol=2e-3, rtol=1e-3)
       np.testing.assert_allclose(layer.bias.grad.numpy(), torch_layer.bias.grad.detach().numpy(), atol=1e-3, rtol=1e-3)
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_rmsnorm(self):
     class TorchRMSNorm(torch.nn.Module):
       # https://github.com/meta-llama/llama/blob/be327c427cc5e89cc1d3ab3d3fec4484df771245/llama/model.py#L34C1-L77C36

test/test_ops.py

@@ -2,7 +2,7 @@ import time, math, unittest, functools, platform, warnings
 import numpy as np
 from typing import List, Callable
 import torch
-from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, AMD_LLVM
+from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, OSX, AMD_LLVM
 from tinygrad import Tensor, Device, dtypes
 from tinygrad.tensor import _to_np_dtype
 from tinygrad.device import is_dtype_supported
@@ -2682,6 +2682,7 @@ class TestOps(unittest.TestCase):
     i, j, k, o, p = [Tensor(tor.detach().cpu().numpy().astype(np.int32), requires_grad=False) for tor in [a,b,c,d,e]]
     return a,b,c,d,e,i,j,k,o,p
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU can only run kernels with up to 10 buffers")
   def test_slice_fancy_indexing_no_dim_collapse(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     # no dim collapse from int or dim injection from None
@@ -2733,6 +2734,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,3)], lambda x: x[torch.tensor([[0,1,-1],[-1,-2,0]]), torch.tensor([2,1,-1])],
                             lambda x: x[Tensor([[0,1,-1],[-1,-2,0]]), Tensor([2,1,-1])])
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "WEBGPU can only run kernels with up to 10 buffers")
   def test_slice_fancy_indexing_list_indices(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[[[0]]], lambda x: x[[[0]]])
@@ -2752,6 +2754,7 @@ class TestOps(unittest.TestCase):
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[a,((2,),(1,),(0,)),c,(2,1,0)], lambda x: x[i,((2,),(1,),(0,)),k,(2,1,0)])
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[1,(2,1,0),None,c,(2,1,0),e], lambda x: x[1,(2,1,0),None,k,(2,1,0),p])
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
   def test_slice_fancy_indexing_list_with_tensors(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()
     helper_test_op([(2,5,6,5,3,4)], lambda x: x[[a]], lambda x: x[[i]])

test/test_sample.py

@@ -3,7 +3,6 @@ import numpy as np
 from tinygrad import Tensor, Variable, Device
 from tinygrad.helpers import OSX
 
-# TODO: still fails with MAX_KERNEL_BUFFERS
 @unittest.skipIf(Device.DEFAULT == "WEBGPU" and not OSX, "WEBGPU Vulkan can only run kernels with up to 10 buffers")
 class TestSample(unittest.TestCase):
   def test_sample(self):

test/test_search.py

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

test/test_setitem.py

@@ -158,13 +158,6 @@ 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_softmax_fusion.py

@@ -86,18 +86,7 @@ class TestFuse(unittest.TestCase):
       return (arange == idx).mul(vals).sum(-2, dtype=vals.dtype)
     self._test_fuse(embedding, a, atol=1e-5)
 
-  def test_attention_kernel_count(self):
+  @unittest.skip("still broken")
-    wq = Tensor.empty(32, 32)
-    wk = Tensor.empty(32, 32)
-    wv = Tensor.empty(32, 32)
-    x = Tensor.empty(2, 100, 32)
-    q = (x @ wq).contiguous()
-    k = (x @ wk).contiguous()
-    v = (x @ wv).contiguous()
-    attn = q.scaled_dot_product_attention(k, v).fuse()
-    s = attn.schedule()
-    self.assertEqual(len(s), 4) # 3 matmul and 1 attention
-
   def test_flash_attention(self):
     BS = 4
     HEADS = 2
@@ -109,7 +98,7 @@ class TestFuse(unittest.TestCase):
       v = Tensor.randn(BS, HEADS, MATDIM, EMB).realize()
     # TODO: OPT is breaking things. NOOPT isn't linearizing
     with Context(NOOPT=1):
-      self._test_fuse(Tensor.scaled_dot_product_attention, q, k, v, atol=1e-5)
+      self._test_fuse(Tensor.scaled_dot_product_attention, q, k, v)
 
 class TestSoftmaxFusion(unittest.TestCase):
   @classmethod
@@ -133,7 +122,7 @@ class TestSoftmaxFusion(unittest.TestCase):
       out = (inp / div).reshape(32, 10)
       out.realize()
 
-    np.testing.assert_allclose(sout.numpy(), out.numpy(), atol=3e-7)
+    np.testing.assert_allclose(sout.numpy(), out.numpy())
 
   def test_softmax(self):
     # this is the softmax from scaled_dot_product_attention

test/test_tensor.py

@@ -892,13 +892,13 @@ class TestIdxUpcast(unittest.TestCase):
 
   @unittest.skipUnless(is_dtype_supported(dtypes.long), "int64 is supported")
   def test_overflow_sym(self):
-    self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 1, 2048).bind(32))
+    self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 0, 2048).bind(32))
 
   def test_regular(self):
     self.do_op_then_assert(dtypes.int, 64, 64, 64)
 
   def test_regular_sym(self):
-    self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 1, 64).bind(32))
+    self.do_op_then_assert(dtypes.int, 2048, 2048, UOp.variable("dim3", 0, 64).bind(32))
 
   @unittest.skipIf(PTX, "PTX always convert Ops.INDEX to int64")
   def test_symfold(self):
@@ -910,7 +910,7 @@ class TestIdxUpcast(unittest.TestCase):
   @unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
   def test_int64_unsupported_overflow_sym(self):
     with self.assertRaises(KeyError):
-      self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 1, 2048).bind(32))
+      self.do_op_then_assert(dtypes.long, 2048, 2048, UOp.variable("dim3", 0, 2048).bind(32))
 
   @unittest.skipIf(is_dtype_supported(dtypes.long), "int64 is supported")
   def test_int64_unsupported_overflow(self):

test/test_uop_graph.py

@@ -317,7 +317,6 @@ class TestUOpGraph(unittest.TestCase):
       for uop, const in zip(uops, consts):
         self.assertEqual(uop, const)
 
-  @unittest.skip("no longer testable standalone")
   def test_wmma_vectorize_fold(self):
     for i in [2, 4, 8]:
       vec = UOp(Ops.VECTORIZE, dtypes.half.vec(i), tuple(UOp.const(dtypes.half, 0.0) for _ in range(i)))

test/unit/test_dtype.py

@@ -1,57 +0,0 @@
-import unittest
-from tinygrad.tensor import Tensor
-from tinygrad.dtype import dtypes, DType, ImageDType, PtrDType, to_dtype
-
-class TestImageDType(unittest.TestCase):
-  def test_image_scalar(self):
-    assert dtypes.imagef((10,10)).base.scalar() == dtypes.float32
-    assert dtypes.imageh((10,10)).base.scalar() == dtypes.float32
-  def test_image_vec(self):
-    assert dtypes.imagef((10,10)).base.vec(4) == dtypes.float32.vec(4)
-    assert dtypes.imageh((10,10)).base.vec(4) == dtypes.float32.vec(4)
-
-class TestEqStrDType(unittest.TestCase):
-  def test_image_ne(self):
-    if ImageDType is None: raise unittest.SkipTest("no ImageDType support")
-    assert dtypes.float == dtypes.float32, "float doesn't match?"
-    assert dtypes.imagef((1,2,4)) != dtypes.imageh((1,2,4)), "different image dtype doesn't match"
-    assert dtypes.imageh((1,2,4)) != dtypes.imageh((1,4,2)), "different shape doesn't match"
-    assert dtypes.imageh((1,2,4)) == dtypes.imageh((1,2,4)), "same shape matches"
-    assert isinstance(dtypes.imageh((1,2,4)), ImageDType)
-  def test_ptr_eq(self):
-    assert dtypes.float32.ptr() == dtypes.float32.ptr()
-    assert not (dtypes.float32.ptr() != dtypes.float32.ptr())
-  def test_strs(self):
-    if PtrDType is None: raise unittest.SkipTest("no PtrDType support")
-    self.assertEqual(str(dtypes.imagef((1,2,4))), "dtypes.imagef((1, 2, 4))")
-    self.assertEqual(str(dtypes.float32.ptr(16)), "dtypes.float.ptr(16)")
-
-class TestToDtype(unittest.TestCase):
-  def test_dtype_to_dtype(self):
-    dtype = dtypes.int32
-    res = to_dtype(dtype)
-    self.assertIsInstance(res, DType)
-    self.assertEqual(res, dtypes.int32)
-
-  def test_str_to_dtype(self):
-    dtype = "int32"
-    res = to_dtype(dtype)
-    self.assertIsInstance(res, DType)
-    self.assertEqual(res, dtypes.int32)
-
-class TestCastConvenienceMethod(unittest.TestCase):
-  def test_method(self):
-    for input_dtype in (dtypes.float, dtypes.int):
-      t = Tensor([1, 2], dtype=input_dtype)
-      self.assertEqual(t.dtype, input_dtype)
-      self.assertEqual(t.bool().dtype, dtypes.bool)
-      self.assertEqual(t.short().dtype, dtypes.short)
-      self.assertEqual(t.int().dtype, dtypes.int)
-      self.assertEqual(t.long().dtype, dtypes.long)
-      self.assertEqual(t.half().dtype, dtypes.half)
-      self.assertEqual(t.bfloat16().dtype, dtypes.bfloat16)
-      self.assertEqual(t.float().dtype, dtypes.float)
-      self.assertEqual(t.double().dtype, dtypes.double)
-
-if __name__ == "__main__":
-  unittest.main()

test/unit/test_keccak.py

@@ -2,21 +2,6 @@ from typing_extensions import Callable
 import hashlib, random, unittest
 from tinygrad import Tensor, Device, getenv, dtypes
 from tinygrad.device import is_dtype_supported
-from tinygrad.helpers import CI
-
-@unittest.skipUnless(is_dtype_supported(dtypes.uint8) and is_dtype_supported(dtypes.uint64), "Device must support uint8 and uint64")
-@unittest.skipIf(getenv("MOCKGPU") and Device.DEFAULT == "NV", "crashes in NV CI")
-class TestHashing(unittest.TestCase):
-  def _python_hash_1mb(self, data:bytes):
-    chunks = [data[i:i+4096] for i in range(0, len(data), 4096)]
-    chunk_hashes = [hashlib.shake_128(chunk).digest(16) for chunk in chunks]
-    return hashlib.shake_128(b''.join(chunk_hashes)).digest(16)
-
-  @unittest.skipIf(CI, "very slow")
-  def test_abc(self):
-    expected = self._python_hash_1mb(b"abc" + b"\x00" * (2**20 - 3))
-    out = Tensor(b"abc").hash()
-    self.assertEqual(bytes(out.data()), expected)
 
 @unittest.skipUnless(is_dtype_supported(dtypes.uint8) and is_dtype_supported(dtypes.uint64), "Device must support uint8 and uint64")
 @unittest.skipIf(getenv("MOCKGPU") and Device.DEFAULT == "NV", "crashes in NV CI")
@@ -65,8 +50,10 @@ class TestKeccak(unittest.TestCase):
     data = b"\x00" * 4
     self.assertEqual(bytes(Tensor(data).keccak("shake_128").tolist()), hashlib.shake_128(data).digest(16))
 
-    data = b"\x00" * (1000 if CI else 4096)
+    data = b"\x00" * 4096
-    self.assertEqual(bytes(Tensor(data).keccak("shake_128").tolist()), hashlib.shake_128(data).digest(16))
+    with self.assertRaises(RecursionError):
+      # TODO: fix
+      self.assertEqual(bytes(Tensor(data).keccak("shake_128").tolist()), hashlib.shake_128(data).digest(16))
 
 if __name__ == "__main__":
   unittest.main()

test/unit/test_llm_tokenizer.py

@@ -1,57 +0,0 @@
-import unittest, base64, functools
-from tinygrad.apps.llm import SimpleTokenizer, get_llama_re
-from tinygrad.helpers import fetch
-
-class TestLLMTokenizer(unittest.TestCase):
-  @functools.cached_property
-  def basic_tok(self): return SimpleTokenizer(".*", { b"a": 0, b"b": 1, b"c": 2, b"ab": 3, b"bc": 4 }, { "<x>": 5, "<y>": 6, "<z>": 7 })
-
-  @functools.cached_property
-  def llama_tok(self):
-    # from https://github.com/tinygrad/tinygrad/blob/e0106b6b257ebc003eb3694144e3e198f7d8cc37/examples/llama3.py#L14
-    model_file = fetch("https://huggingface.co/bofenghuang/Meta-Llama-3-8B/resolve/main/original/tokenizer.model")
-    with open(model_file, "rt") as fd:
-      str_vocab = [ line.split(maxsplit=1) for line in fd.read().splitlines() if line ]
-      normal_tokens = { base64.b64decode(stok): int(srank) for stok, srank in str_vocab }
-
-    special_tokens = [
-      "<|begin_of_text|>",
-      "<|end_of_text|>",
-      "<|reserved_special_token_0|>",
-      "<|reserved_special_token_1|>",
-      "<|reserved_special_token_2|>",
-      "<|reserved_special_token_3|>",
-      "<|start_header_id|>",
-      "<|end_header_id|>",
-      "<|reserved_special_token_4|>",
-      "<|eot_id|>",
-    ] + [ f"<|reserved_special_token_{i}|>" for i in range(5, 256 - 5) ]
-    return SimpleTokenizer(get_llama_re(), normal_tokens, { token: len(normal_tokens) + i for i, token in enumerate(special_tokens) })
-
-  def _test_coding(self, tok: SimpleTokenizer, text: str, expected_tokens: list[int]):
-    self.assertEqual(tok.encode(text), expected_tokens)
-    self.assertEqual(tok.decode(expected_tokens), text)
-
-  def test_abc(self): self._test_coding(self.basic_tok, "abc", [ 3, 2 ])
-  def test_abbc(self): self._test_coding(self.basic_tok, "abbc", [ 3, 4 ])
-  def test_aabbbcc(self): self._test_coding(self.basic_tok, "aabbbcc", [ 0, 3, 1, 4, 2 ])
-  def test_specials1(self): self._test_coding(self.basic_tok, "a<x>a<y>a<z>a", [ 0, 5, 0, 6, 0, 7, 0 ])
-  def test_specials2(self): self._test_coding(self.basic_tok, "<x>a<y>a<z>", [ 5, 0, 6, 0, 7 ])
-  def test_invalid_token(self):
-    with self.assertRaises(RuntimeError): self._test_coding(self.basic_tok, "L", [])
-
-  def test_no_specials(self): self._test_coding(SimpleTokenizer(".*", { bytes([i]): i for i in range(256) }, {}), "abc", [97, 98, 99])
-
-  # NOTE: the correct tokenization for this can only be found by looking up the text chunk in the vocab, not by applying merges
-  def test_llama_early_tokenize(self): self._test_coding(self.llama_tok, " например", [ 111797 ])
-
-  def test_llama_basic(self): self._test_coding(self.llama_tok, "hello world", [ 15339, 1917 ])
-  def test_llama_control_char(self): self._test_coding(self.llama_tok, " \x850", [ 220, 116360, 15 ])
-  def test_llama_bytes(self): self._test_coding(self.llama_tok, " \xec\x8b\xa4\xed", [ 1717, 105, 116174, 82638, 2483 ])
-  def test_llama_special1(self): self._test_coding(self.llama_tok, "hello <|end_of_text|>", [ 15339, 220, 128001 ])
-  def test_llama_special2(self): self._test_coding(self.llama_tok, "<|start_header_id|>user<|end_header_id|>\n\n", [ 128006, 882, 128007, 271 ])
-  def test_llama_repeat(self): self._test_coding(self.llama_tok, "00000000000000000", [ 931, 931, 931, 931, 931, 410 ])
-  def test_llama_pat(self): self._test_coding(self.llama_tok, "today\n  \n", [ 31213, 14211 ])
-
-if __name__ == '__main__':
-  unittest.main()

test/unit/test_shapetracker.py

@@ -827,6 +827,39 @@ class TestShapeTrackerSize(unittest.TestCase):
     st = ShapeTracker.from_shape((10,10)).pad(((2,4), (3,1))).flip((True, True))
     self.assertEqual(st.real_size(), 100)
 
+class TestConsecutive(unittest.TestCase):
+  @classmethod
+  def setUpClass(self):
+    from tinygrad.tensor import Tensor  # easier test setup
+    self.t = Tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
+    self.const = Tensor(2)
+    self.ones = Tensor.ones(2, 4)
+
+  def test_unmodified(self):
+    assert self.t.uop.st.consecutive
+    assert self.t.reshape(4, 2).uop.st.consecutive
+    assert self.t.reshape(1, 8).uop.st.consecutive
+
+  def test_sliced(self):
+    assert self.t[0].uop.st.consecutive
+    assert self.t[0, 1:2].uop.st.consecutive
+    assert self.t[1].uop.st.consecutive
+    assert not self.t[:, 0].uop.st.consecutive
+    assert not self.t[:, 1].uop.st.consecutive
+
+  def test_padded(self):
+    assert not self.t.pad(((1, 1), None)).uop.st.consecutive
+    assert not self.t.pad((None, (1, 1))).uop.st.consecutive
+
+  def test_const(self):
+    assert self.const.uop.st.consecutive
+
+  def test_ones(self):
+    assert not self.ones.uop.st.consecutive
+    assert not self.ones[0, :].uop.st.consecutive
+    # consecutive if sliced into size 1
+    assert self.ones[0, 0].uop.st.consecutive
+
 class TestRender(unittest.TestCase):
   def test_render(self):
     st = ShapeTracker.from_shape((2, 3))

test/unit/test_uop_symbolic.py

@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-import unittest, pickle, functools, math
+import unittest, pickle, functools
 import z3
 
 from tinygrad.dtype import dtypes, ConstType
@@ -29,17 +29,16 @@ class TestSymbolicPickle(unittest.TestCase):
   def test_pickle_variable_times_2(self): self._test_pickle_unpickle(Variable("a", 3, 8)*2)
 
 class TestSymbolic(unittest.TestCase):
-  def helper_test_variable(self, v, n, m, s, test_z3:bool=True):
+  def helper_test_variable(self, v, n, m, s):
     rendered, nmin, nmax = render(v)
     if isinstance(s, tuple): self.assertIn(rendered, s)
     else: self.assertEqual(rendered, s)
     self.assertEqual(nmin, n)
     self.assertEqual(nmax, m)
-    if test_z3:
+    solver = z3.Solver()
-      solver = z3.Solver()
+    z3_sink = graph_rewrite(v.sink(v.simplify()), z3_renderer, ctx=(solver, {}))
-      z3_sink = graph_rewrite(v.sink(v.simplify()), z3_renderer, ctx=(solver, {}))
+    expr, epxr_simplified = z3_sink.src[0].arg, z3_sink.src[1].arg
-      expr, epxr_simplified = z3_sink.src[0].arg, z3_sink.src[1].arg
+    self.assertEqual(solver.check(expr != epxr_simplified), z3.unsat, "simplified expression not equal to original")
-      self.assertEqual(solver.check(expr != epxr_simplified), z3.unsat, "simplified expression not equal to original")
 
   def test_cmp_simple(self):
     self.helper_test_variable(Variable("a", 3, 8) < 4, 0, 1, "(a<4)")
@@ -673,12 +672,6 @@ class TestSymbolic(unittest.TestCase):
     self.helper_test_variable(numerator, 3, 390, "(a*((a*4)+-1))")
     self.helper_test_variable((numerator//denominator)<=0, 1, 1, "True")
 
-  def test_const_reciprocal(self):
-    a = Variable("a", 1, 10, dtypes.float)
-    # TODO: bounds for reciprocal
-    # TODO: should z3 work?
-    self.helper_test_variable(2*(2*a).reciprocal(), -math.inf, math.inf, "(1/a)", test_z3=False)
-
 class TestSymbolicNumeric(unittest.TestCase):
   def helper_test_numeric(self, f):
     MIN, MAX = 0, 10

test/unit/test_viz.py

@@ -106,12 +106,13 @@ class TestViz(BaseTestViz):
 
   # name can also come from a function that returns a TracingKey
   def test_tracing_key(self):
-    @track_rewrites(name=lambda inp,ret: TracingKey("custom_name", (inp,)))
+    @track_rewrites(name=lambda inp,ret: TracingKey("custom_name", (inp,), fmt=f"input={inp.render()}"))
     def test(s:UOp): return graph_rewrite(s, PatternMatcher([]))
     test(UOp.variable("a", 1, 10)+1)
     lst = get_viz_list()
     # NOTE: names from TracingKey do not get deduped
     self.assertEqual(lst[0]["name"], "custom_name")
+    self.assertEqual(lst[0]["fmt"], "input=(a+1)")
 
   def test_colored_label(self):
     # NOTE: dataclass repr prints literal escape codes instead of unicode chars
@@ -137,16 +138,6 @@ 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/apps/llm.py

@@ -1,57 +1,33 @@
 from __future__ import annotations
-import sys, argparse, typing, re, itertools, unicodedata
+import sys, argparse
-from tinygrad import Tensor, nn, UOp, TinyJit, getenv, helpers
+from tinygrad import Tensor, nn, UOp, TinyJit, getenv
-
-def gpt2_decode_vocab(voc: dict[str, int]): # https://github.com/openai/gpt-2/blob/9b63575ef42771a015060c964af2c3da4cf7c8ab/src/encoder.py#L9
-  c2b = { chr(cp): cp for cp in itertools.chain(range(ord("!"), ord("~")+1), range(ord("¡"), ord("¬")+1), range(ord("®"), ord("ÿ")+1)) }
-  c2b.update({ chr(256+off): cp for off, cp in enumerate(cp for cp in range(256) if chr(cp) not in c2b) })
-  return { bytes(c2b[c] for c in tok): tid for tok, tid in voc.items() }
-
-def get_llama_re():
-  def ucat_range(pre: str): return "".join(re.escape(chr(cp)) for cp in range(sys.maxunicode + 1) if unicodedata.category(chr(cp)).startswith(pre))
-  r_ws, r_p_N, r_p_L = r"\t\n\x0b\x0c\r\x85" + ucat_range("Z"), ucat_range("N"), ucat_range("L")
-  # https://github.com/ggml-org/llama.cpp/blob/94933c8c2eeaa9a7983e3f6c08af76bd86724094/src/llama-vocab.cpp#L286
-  return "(?i:'s|'t|'re|'ve|'m|'ll|'d)|" + \
-    f"[^\\r\\n{r_p_N}{r_p_L}]?[{r_p_L}]+|[{r_p_N}]{{1,3}}| ?[^{r_ws}{r_p_N}{r_p_L}]+[\\r\\n]*|[{r_ws}]*[\\r\\n]+|[{r_ws}]+(?![^{r_ws}])|[{r_ws}]+"
 
 class SimpleTokenizer:
-  def __init__(self, pat: str, normal_tokens: dict[bytes, int], special_tokens: dict[str, int]):
+  def __init__(self, vocab: list[str]):
-    self._normal_tokens, self._special_tokens, self._pat = normal_tokens, special_tokens, re.compile(pat)
+    self.vocab: list[str] = vocab
-    self._tok2str = { tid: tok.encode() for tok, tid in special_tokens.items() } | { tid: tok for tok, tid in normal_tokens.items()  }
+    self.biggest_token: int = max(map(len, vocab))
-    self._special_re = re.compile("|".join(re.escape(tok) for tok in self._special_tokens.keys()) if special_tokens else r"(?!)")
+    self.token_to_id: dict[str, int] = {tok: i for i, tok in enumerate(vocab)}
+    self.replace_space = "Ġ"
+    self.replace_newline = "Ċ"
 
-  @staticmethod
+  def encode(self, text:str) -> list[int]:
-  def from_gguf_kv(kv: dict):
+    s = text.replace(" ", self.replace_space).replace("\n", self.replace_newline)
-    # https://github.com/ggml-org/llama.cpp/blob/94933c8c2eeaa9a7983e3f6c08af76bd86724094/src/llama-vocab.cpp#L1818-L1820
+    out: list[int] = []
-    if kv["tokenizer.ggml.pre"] not in ("llama3","llama-v3","llama-bpe"): raise ValueError(f"Invalid tokenizer preset '{kv['tokenizer.ggml.pre']}'")
+    i = 0
-    vocab: typing.Iterable[tuple[str, int]] = ((tok, idx) for idx, tok in enumerate(kv["tokenizer.ggml.tokens"]))
+    while i < len(s):
-    normal_tokens, special_tokens = helpers.partition(vocab, lambda e: kv["tokenizer.ggml.token_type"][e[1]] == 1)
+      j = min(i+self.biggest_token, len(s))
-    return SimpleTokenizer(get_llama_re(), gpt2_decode_vocab(dict(normal_tokens)), dict(special_tokens))
+      while i < j and (tid:=self.token_to_id.get(s[i:j])) is None: j -= 1
+      if tid is None: raise RuntimeError(f"token not found in {s}")
+      assert tid is not None, f"token not found in {s}"
+      out.append(tid)
+      i = j
+    return out
 
-  def encode(self, text: str):
+  def decode(self, ids: list[int]) -> str:
-    tokens: list[int] = []
+    return ''.join(self.vocab[tid] for tid in ids).replace(self.replace_space, " ").replace(self.replace_newline, "\n")
-    pos = 0
-    for match in self._special_re.finditer(text):
-      tokens.extend(self._encode_sentence(text[pos:match.start(0)]) + [self._special_tokens[text[match.start(0):match.end(0)]]])
-      pos = match.end(0)
-    return tokens + self._encode_sentence(text[pos:])
 
-  def decode(self, ids: list[int]) -> str: return b''.join(self._tok2str[tid] for tid in ids).decode()
+  def role(self, role:str):
-  def role(self, role:str): return self.encode("<|start_header_id|>" + role + "<|end_header_id|>\n\n")
+    return [t for x in ["<|start_header_id|>", role, "<|end_header_id|>\n\n"] for t in self.encode(x)]  # llama style
-
-  def _encode_sentence(self, chunk: str): return [ tok for word in self._pat.findall(chunk) for tok in self._encode_word(word.encode()) ]
-  def _encode_word(self, word: bytes):
-    if (early_token:=self._normal_tokens.get(word)) is not None: return [early_token]
-    parts = [word[i:i+1] for i in range(len(word))]
-    while True:
-      min_tid, min_idx = 2**32, -1
-      for idx, (p1, p2) in enumerate(zip(parts[:-1], parts[1:])):
-        tid = self._normal_tokens.get(p1 + p2, min_tid)
-        if tid < min_tid: min_tid, min_idx = tid, idx
-      if min_idx == -1: break
-      parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx+1]] + parts[min_idx+2:]
-    try: return [ self._normal_tokens[p] for p in parts ]
-    except KeyError: raise RuntimeError("token not found")
 
 def apply_rope(x:Tensor, start_pos:int|UOp, base:int=10000):
   B, H, T, Hd = x.shape
@@ -189,7 +165,7 @@ if __name__ == "__main__":
   model, kv = Transformer.from_gguf(Tensor.from_url(models[args.size]), args.max_context)
 
   # extract some metadata
-  tok = SimpleTokenizer.from_gguf_kv(kv)
+  tok = SimpleTokenizer(kv["tokenizer.ggml.tokens"])
   bos_id: int = kv['tokenizer.ggml.bos_token_id']
   eos_id: int = kv['tokenizer.ggml.eos_token_id']
 

tinygrad/codegen/__init__.py

@@ -16,7 +16,6 @@ from tinygrad.codegen.devectorizer import load_store_folding, load_store_indexin
   ReduceContext, correct_load_store, pm_render
 from tinygrad.codegen.optional import get_late_rewrite_patterns
 from tinygrad.codegen.linearize import block_create, pm_blockend_merge, block_merge, pm_finalize, BlockContext
-from tinygrad.opt import pm_optimize
 
 @dataclass
 class RewriteStep:
@@ -43,10 +42,6 @@ def get_rewrites_for_renderer(opts:Renderer, linearizer:bool=True) -> list[Rewri
 def _get_rewrites_for_renderer(opts:Renderer, linearizer:bool, _QUANTIZE, _DEVECTORIZE, _TRANSCENDENTAL) -> list[RewriteStep]:
   # ** lowerer (rewrite_shapetracker_with_index) **
   ret: list[RewriteStep] = []
-
-  # this is kernel.py
-  ret.append(RewriteStep(pm_optimize, ctx=lambda _: opts, name="optimize ast"))
-
   if _QUANTIZE and opts.device in {"CPU", "DSP"}: ret.append(RewriteStep(pm_quant, name="quantize"))
   ret.append(RewriteStep(pm_lowerer, get_index, name="lowerer", bottom_up=True))
 

tinygrad/codegen/devectorizer.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass
 from tinygrad.dtype import dtypes, ImageDType, PtrDType, DType, AddrSpace
 from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, graph_rewrite, GroupOp, identity_element
 from tinygrad.uop.symbolic import split_uop, uop_given_valid, parse_valid, simplify_valid, sym, symbolic_flat
-from tinygrad.helpers import getenv, flatten, AMX, prod, partition
+from tinygrad.helpers import getenv, flatten, AMX, prod, partition, all_same
 from tinygrad.renderer import Renderer
 
 # ***** image load valid simplification *****
@@ -111,7 +111,11 @@ def cat_after_store(cat:UOp, data:UOp, sto:UOp):
   for s in cat.src:
     ret.append(s.store(data.gep(tuple(range(offset, offset+s.dtype.count))), *sto.src[2:]))
     offset += s.dtype.count
-  return UOp(Ops.NOOP, src=tuple(ret))
+  # dtype CAT
+  dtypes: list[PtrDType] = [x.dtype for x in ret if isinstance(x.dtype, PtrDType)]
+  assert len(dtypes) == len(ret) and all_same([(x.size, x.addrspace) for x in dtypes])
+  out_dtype = dtypes[0].base.scalar().vec(sum([x.count for x in dtypes])).ptr(dtypes[0].size, dtypes[0].addrspace)
+  return UOp(Ops.PTRCAT, dtype=out_dtype, src=tuple(ret))
 
 def gep_on_store(gep:UOp, st:UOp, sto:UOp):
   # NOTE: we need to invert the gep here, but it may be an expanding gep
@@ -122,8 +126,8 @@ def gep_on_store(gep:UOp, st:UOp, sto:UOp):
   return gep.src[0].store(st.gep(new_arg), *sto.src[2:])
 
 load_store_folding = PatternMatcher([
-  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat(GroupOp.Defines, name="buf")), UPat.var("vec"))), expand_index),
+  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL), name="buf")), UPat.var("vec"))), expand_index),
-  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat(GroupOp.Defines, name="buf")), UPat.var("vec"),
+  (UPat(Ops.INDEX, src=(UPat(Ops.VECTORIZE, src=UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL), name="buf")), UPat.var("vec"),
                         UPat.var("mask"))), expand_index),
   # GEP after LOAD
   (UPat(Ops.LOAD, src=(UPat(Ops.GEP, name="gep"),), name="ld", allow_any_len=True),
@@ -154,8 +158,6 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
     must_divide = False
   elif buf.dtype.base != dtypes.float and buf.dtype.base != dtypes.half and not isinstance(buf.dtype, ImageDType):
     pass
-  elif cast(PtrDType, buf.dtype).addrspace == AddrSpace.REG:
-    pass
   elif isinstance(buf.dtype, ImageDType):
     lengths = [4]
   elif ctx is not None and ctx.supports_float4:
@@ -182,8 +184,7 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
       break
 
   # if it wasn't split, we return None. otherwise we CAT them
-  if len(ret) <= 1: return None
+  return UOp(Ops.CAT, ls.dtype, tuple(ret)) if len(ret) > 1 else None
-  return UOp(Ops.CAT, ls.dtype, tuple(ret)) if ls.op is Ops.LOAD else UOp(Ops.NOOP, src=tuple(ret))
 
 def image_fixup(ls:UOp):
   # normal image load or store, with the CAST from expand_index
@@ -235,8 +236,9 @@ def no_vectorized_alu(alu:UOp):
 def no_vectorized_acc(acc:UOp, c:UOp):
   if acc.dtype.count == 1: return None
   assert c.arg == 0, "this only supports index 0"
-  new_acc = acc.replace(dtype=acc.dtype.base.scalar().ptr(acc.dtype.count, cast(PtrDType, acc.dtype).addrspace))
+  alus = tuple(UOp(acc.op, acc.dtype.base.scalar().ptr(1, cast(PtrDType, acc.dtype).addrspace),
-  return UOp(Ops.PTRCAT, acc.dtype, tuple([new_acc.index(UOp.const(dtypes.int, i)) for i in range(acc.dtype.count)]))
+    tuple(s.gep(i) if j == 0 else s for j,s in enumerate(acc.src)), acc.arg+(i,)).index(UOp.const(dtypes.int, 0)) for i in range(acc.dtype.count))
+  return UOp(Ops.PTRCAT, acc.dtype, alus)
 
 devectorize = PatternMatcher([
   # no ALU on vectorized dtypes
@@ -282,16 +284,13 @@ def reduce_to_acc(ctx:ReduceContext, red:UOp):
   assert all(x.dtype == red.dtype for x in lst), f"horizontal reduction mismatch {lst[0].dtype} != {red.dtype}"
   # if we have a range
   if len(reduce_range) != 0:
-    topo = inp.toposort()
-    stored_ranges = flatten([x.src[2:] for x in topo if x.op is Ops.STORE])
-    input_ranges = tuple([x for x in topo if x.op is Ops.RANGE and x not in reduce_range and x not in stored_ranges])
     identity = red.const_like(identity_element(red.arg, red.dtype.scalar()))
-    acc = UOp(Ops.DEFINE_REG, red.dtype.ptr(size=1, addrspace=AddrSpace.REG), arg=(ctx.acc_num,)).index(UOp.const(dtypes.int, 0))
+    in_loop = functools.reduce(operator.or_, [x.ne(x.const_like(0)) for x in reduce_range]).broadcast(red.dtype.count)
-    do_store = acc.store(identity, UOp(Ops.NOOP, src=input_ranges)) if len(input_ranges) else acc.store(identity)
+    acc = UOp(Ops.DEFINE_REG, red.dtype.ptr(size=1, addrspace=AddrSpace.REG), src=(identity,), arg=(ctx.acc_num,)).index(UOp.const(dtypes.int, 0))
-    lst = [acc.load(do_store, *reduce_range)] + lst  # put acc as the first element
+    lst = [in_loop.where(acc.load(*reduce_range), identity)] + lst  # put acc as the first element
     ctx.acc_num += 1
   ret = functools.reduce(lambda x,y: x.alu(red.arg, y), lst)
-  return acc.load(acc.store(ret, *reduce_range)) if len(reduce_range) != 0 else ret
+  return acc.store(ret, *reduce_range).load() if len(reduce_range) != 0 else ret
 
 def no_vectorized_reduce(inp:UOp, red:UOp):
   if inp.dtype != red.dtype:

tinygrad/codegen/expander.py

@@ -86,6 +86,9 @@ expander = PatternMatcher([
   (UPat((*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.GEP, Ops.WMMA, Ops.LOAD, Ops.STORE, Ops.INDEX,
          Ops.VECTORIZE, Ops.IF, Ops.REDUCE), name="root", custom_early_reject=set([Ops.UNROLL])), do_expand),
   (UPat(Ops.CONTRACT, name="con"), do_contract),
+  # vectorize DEFINE_ACC
+  (UPat(Ops.VECTORIZE, src=UPat(Ops.DEFINE_REG, name="acc"), name="v"),
+    lambda acc,v: acc.replace(dtype=v.dtype, src=(acc.src[0].broadcast(v.dtype.count),)+acc.src[1:])),
   # BARRIERs aren't actually expanded
   (UPat(Ops.BARRIER, src=(UPat(Ops.UNROLL, name="ex"),)),
    lambda ex: UOp(Ops.UNROLL, src=(UOp(Ops.BARRIER, src=ex.src),)*len(ex.src), arg=ex.arg)),

tinygrad/codegen/gpudims.py

@@ -1,5 +1,5 @@
 import math
-from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify, AxisType
+from tinygrad.uop.ops import UOp, Ops, sint, PatternMatcher, UPat, KernelInfo, ssimplify
 from tinygrad.helpers import all_int
 from tinygrad.dtype import dtypes
 from tinygrad.shape.view import get_contraction
@@ -53,36 +53,20 @@ def get_grouped_dims(prefix, dims:tuple[sint, ...], max_sizes:tuple[int, ...]|No
 def add_gpudims(ctx:Renderer, s:UOp):
   if s.arg is None: return None
   ki: KernelInfo = s.arg
-  global_dims = [i for i,x in enumerate(ki.axis_types) if x is AxisType.GLOBAL]
+  if not ki.global_dims and not ki.local_dims: return None
-  local_dims = [i for i,x in enumerate(ki.axis_types) if x in (AxisType.LOCAL, AxisType.GROUP_REDUCE)]
-  if not global_dims and not local_dims: return None
   s_topo = list(s.toposort())
   if any(x.op is Ops.SPECIAL for x in s_topo): return None
-
+  ranges = sorted([x for x in s_topo if x.op is Ops.RANGE and x.arg in (ki.global_dims+ki.local_dims)], key=lambda x: x.arg)
-  # get global and local shape
+  if not len(ranges): return None
-  all_ranges = {x.arg%1000:x for x in s_topo if x.op is Ops.RANGE}
+  global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg in ki.global_dims])
-  ranges = [all_ranges[r] for r in global_dims+local_dims if r in all_ranges]
+  local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg in ki.local_dims])
-  global_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg%1000 in global_dims])
-  local_shape = tuple([ssimplify(r.src[0]) for r in ranges if r.arg%1000 in local_dims])
-
-  # get the idxs
   if ki.dont_use_locals:
-    assert not local_dims, "can't use locals if there's no local dims"
+    assert not ki.local_dims, "can't use locals if there's no local dims"
     idxs = get_grouped_dims("idx", global_shape, ctx.global_max, reverse=True)
   else:
     # define indexes for GPU-like execution
     idxs = get_grouped_dims("gidx", global_shape, ctx.global_max, reverse=True) + get_grouped_dims("lidx", local_shape, ctx.local_max)
-
+  return s.substitute(dict(zip(ranges, idxs)))
-  # apply to multiple ranges
-  subs = {}
-  for r in s_topo:
-    if r.op is not Ops.RANGE: continue
-    try:
-      ii = (global_dims+local_dims).index(r.arg%1000)
-      if r.arg < 2000 and ki.axis_types[r.arg%1000] == AxisType.GROUP_REDUCE: continue
-      subs[r] = idxs[ii]
-    except ValueError: continue
-  return s.substitute(subs)
 
 pm_add_gpudims = PatternMatcher([
   (UPat(Ops.SINK, name="s"), add_gpudims),

tinygrad/codegen/linearize.py

@@ -3,7 +3,7 @@ import heapq
 from collections import defaultdict
 from dataclasses import dataclass, replace
 from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp
-from tinygrad.helpers import dedup, all_same, flatten, getenv
+from tinygrad.helpers import dedup, partition, all_same, flatten, getenv
 
 # NOTE: any toposort should be valid here, unlike last time this isn't required, it's just for speed
 def block_reorder(lst:list[UOp]) -> list[UOp]:
@@ -97,7 +97,7 @@ class BlockContext:
 
 # ***** make blocks *****
 
-DONT_PLACE_IN_BLOCK = {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.CONST}
+DONT_PLACE_IN_BLOCK = {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.CONST}
 
 def add_blockends(base_block:UOp, new_ctx:tuple[UOp, ...], current_ctx:tuple[UOp, ...], cnt:int=1) -> UOp:
   ends_to_add = [z for z in new_ctx if z not in current_ctx]
@@ -207,15 +207,12 @@ def remove_blockend(x:UOp):
     assert all_same(parent_blocks), f"should never have two parent blocks (has {len(parent_blocks)})"
     parent_block = parent_blocks[0]
     assert len(parent_blocks) == parent_block.arg.cnt
-    # NOTE: DEFINE_ACC doesn't have to be handled in any special way
+    # range needs DEFINE_ACC to be before the range (never in DEFINE_ACC for if)
-    late_ops = list(x.arg.lst)
+    early_ops, late_ops = partition(x.arg.lst, lambda y: y.op is Ops.DEFINE_REG and x.arg.end in y.src)
     # NOTE: we have to add a barrier at the start if barrier is used in the range
     if x.op is Ops.BLOCKEND and any(y.op is Ops.BARRIER for y in late_ops) and late_ops[-1].op is Ops.ENDRANGE:
       late_ops = [UOp(Ops.BARRIER)] + late_ops
-    # peephole opt, remove any BARRIERs next to each other
+    arg = BasicBlock(tuple(early_ops)+parent_block.arg.lst+tuple(late_ops), tuple([y for y in x.arg.ctx if y is not x.arg.end]), cnt=x.arg.cnt)
-    for i in range(len(late_ops)-1):
-      if late_ops[i].op is Ops.BARRIER and late_ops[i+1].op is Ops.BARRIER: late_ops[i+1] = UOp(Ops.NOOP)
-    arg = BasicBlock(parent_block.arg.lst+tuple(late_ops), tuple([y for y in x.arg.ctx if y is not x.arg.end]), cnt=x.arg.cnt)
     return UOp(Ops.BLOCK, src=tuple(y for y in x.src if y is not parent_block)+parent_block.src, arg=arg)
 
 block_merge = PatternMatcher([

tinygrad/codegen/lowerer.py

@@ -1,94 +1,68 @@
 # the job of the lowerer is to do indexing
-import functools, operator
-from typing import cast
 from dataclasses import dataclass
-from tinygrad.dtype import dtypes, AddrSpace, PtrDType
+from typing import cast
-from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType, graph_rewrite
+from tinygrad.dtype import dtypes, PtrDType, AddrSpace
+from tinygrad.uop.ops import KernelInfo, UOp, Ops, PatternMatcher, UPat, sint_to_uop, AxisType
 from tinygrad.helpers import prod, partition, flatten
 
 # ***** indexing *****
 
 @dataclass
 class IndexContext:
-  axis_types: tuple[AxisType, ...]
   idxs: list[UOp]
-  start: int = 0
+  ridxs: list[UOp]
-
-def shape_to_idx(s, axis_types, start=0):
-  # indexes
-  idxs = []
-  for i, (s, at) in enumerate(zip(s, axis_types)):
-    if at in (AxisType.UPCAST, AxisType.UNROLL):
-      assert isinstance(s, int), "needs to be int to upcast/unroll"
-      idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s), tuple(range(s))),), ((i,s),), tag=1))
-    else:
-      # all others are RANGES
-      idxs.append(UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), start+i))
-  return idxs
 
 def get_index(ast:UOp) -> IndexContext:
   axis_types = ast.arg.axis_types if isinstance(ast.arg, KernelInfo) else ()
   if len(ast.full_shape) != len(axis_types): axis_types = (AxisType.LOOP,)*len(ast.full_shape)
-  return IndexContext(axis_types, [], 0)
+
+  # indexes
+  idxs = []
+  for i, (s, at) in enumerate(zip(ast.full_shape, axis_types)):
+    if at in (AxisType.UPCAST, AxisType.UNROLL):
+      assert isinstance(s, int), "needs to be int to upcast/unroll"
+      idxs.append(UOp(Ops.UNROLL, dtypes.int, (UOp.const(dtypes.int.vec(s), tuple(range(s))),), ((i,s),)))
+    else:
+      # all others are RANGES
+      idxs.append(UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), i))
+
+  # late indexes (group for reduce)
+  ridxs = idxs[:]
+  for i, (s, at) in enumerate(zip(ast.full_shape, axis_types)):
+    if at == AxisType.GROUP_REDUCE:
+      ridxs[i] = UOp(Ops.RANGE, dtypes.int, (sint_to_uop(s),), 1000+i)
+
+  return IndexContext(idxs, ridxs)
 
 # ***** lowering (given index) *****
 
-def subblock(ctx: IndexContext, full_new_idx: list[UOp], src: UOp):
-  lc = IndexContext(ctx.axis_types, full_new_idx, ctx.start+1000)
-  ctx.start = lc.start
-  return graph_rewrite(src, pm_lowerer, lc, name="subblock", bottom_up=True)
-
 def lower_reduce_axis(ctx: IndexContext, x: UOp):
-  new_idxs = shape_to_idx(x.src[0].shape, ctx.axis_types, ctx.start)
-  full_new_idx = list(ctx.idxs)
-  for a in x.axis_arg: full_new_idx[a] = new_idxs[a]
-
-  ret = subblock(ctx, full_new_idx, x.src[0])
-
   # NOTE: always using ridxs is fine here
-  reduce_range, reduce_expand = partition([full_new_idx[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
+  reduce_range, reduce_expand = partition([ctx.ridxs[i] for i in x.axis_arg], lambda y: y.op is Ops.RANGE)
   assert all(x.op is Ops.UNROLL for x in reduce_expand), f"not all UNROLLS in {reduce_expand} for {x.axis_arg}"
+  ret = x.src[0]
   if len(contract_axis:=flatten(x.arg for x in reduce_expand)):
-    ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis), tag=1)
+    ret = UOp(Ops.CONTRACT, x.dtype.vec(prod(x[1] for x in contract_axis)), (ret,), tuple(contract_axis))
   # REDUCE supports both "horizontal" reduction and range reduction. the horizontal elements are taken in the nearest group
   return UOp(Ops.REDUCE, x.dtype, (ret,)+tuple(reduce_range), x.arg[0])
 
+def lower_load(ctx: IndexContext, x: UOp, buf: UOp):
+  idx, valid = x.st_arg.to_indexed_uops(ctx.ridxs if buf.op is Ops.DEFINE_LOCAL else ctx.idxs)
+  barrier = (UOp(Ops.BARRIER, dtypes.void, (x.src[1],)),) if buf.op is Ops.DEFINE_LOCAL else ()
+  return UOp(Ops.LOAD, x.dtype, (buf.index(idx, valid),) + barrier)
+
 def lower_store(ctx: IndexContext, x: UOp, buf: UOp):
-  # TODO: reenable after REDUCE_AXIS is fixed
+  idx, valid = x.st_arg.to_indexed_uops(ctx.idxs)
-  #assert x.src[1].shape == x.src[0].shape, f"shape mismatch on store {x.src[1].shape} != {x.src[0].shape}"
+  if cast(PtrDType, buf.dtype).addrspace == AddrSpace.GLOBAL:
+    # NOTE: only store the local reduceop in the threads that are actually doing the reduce
+    for oidx, ridx in zip(ctx.idxs, ctx.ridxs):
+      if oidx is not ridx: valid = valid * oidx.eq(0)
+  return buf.index(idx, valid).store(x.src[1], *[x for x in UOp.sink(idx, valid).toposort() if x.op is Ops.RANGE])
 
-  new_idxs = shape_to_idx(x.src[0].shape, ctx.axis_types, ctx.start)
+def lower_const(ctx:IndexContext, view:UOp, c:UOp):
-  idx, valid = x.st_arg.to_indexed_uops(new_idxs)
+  if all(x.mask is None for x in view.arg.views): return c
-  used_idxs = [x for x in UOp.sink(idx, valid).toposort() if x in new_idxs]
+  _, valid = view.arg.to_indexed_uops(ctx.idxs)
-  real_new_idxs = []
+  return valid.where(c, c.const_like(0))
-  for i in range(len(x.src[0].shape)):
-    if new_idxs[i] in used_idxs or len(ctx.idxs) <= i: real_new_idxs.append(new_idxs[i])
-    else: real_new_idxs.append(ctx.idxs[i])
-
-  stored = subblock(ctx, real_new_idxs, x.src[1])
-  used_ranges = [x for x in used_idxs if x.op is Ops.RANGE]
-  ret = buf.index(idx, valid).store(stored, *used_ranges)
-
-  # insert BARRIER if we are ending a LOCAL, IF if we are ending a GROUP_REDUCE
-  if cast(PtrDType, buf.dtype).addrspace == AddrSpace.LOCAL and \
-      any(ctx.axis_types[x.arg%1000] in {AxisType.GROUP_REDUCE, AxisType.LOCAL} for x in used_ranges):
-    ret = ret.barrier()
-    range_gates = [x.eq(0) for x in used_ranges if ctx.axis_types[x.arg%1000] == AxisType.GROUP_REDUCE]
-    if len(range_gates): ret = UOp(Ops.IF, src=(functools.reduce(operator.and_, range_gates), ret))
-  return ret
-
-def fixup_wmma(ctx:IndexContext, x:UOp):
-  if x.tag is not None: return None
-  new_idxs = shape_to_idx(x.src[0].shape, ctx.axis_types, ctx.start)
-  full_new_idx = list(ctx.idxs)
-  for a in x.arg[-1]: full_new_idx[a] = new_idxs[a]
-
-  srcs = subblock(ctx, full_new_idx, UOp.sink(*x.src)).src
-
-  # NOTE: this assumes these are expanded. which now shouldn't change anything
-  new_x_arg_m2 = tuple([tuple([(full_new_idx[a].arg[0][0], sz) for a,sz in v]) for v in x.arg[-2]])
-  new_x_arg_m1 = tuple([full_new_idx[a].arg[0][0] for a in x.arg[-1]])
-  return x.replace(src=srcs, arg=x.arg[:-2]+(new_x_arg_m2, new_x_arg_m1), tag=1)
 
 pm_lowerer = PatternMatcher([
   # TODO: remove these hacks
@@ -97,18 +71,10 @@ pm_lowerer = PatternMatcher([
   # hack for old style VALID (now it's just VIEW(CONST))
   (UPat(Ops.VALID, src=(UPat(Ops.VIEW, name="v"),)).where(UPat.cvar("c"), UPat(Ops.CONST, arg=0)), lambda c,v: c.replace(src=()).view(v.arg)),
 
-  # consts and loads
-  (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="c"),), name="view"),
-   lambda ctx,view,c: c if all(x.mask is None for x in view.arg.views) else view.arg.to_indexed_uops(ctx.idxs)[1].where(c, c.const_like(0))),
-  (UPat(Ops.LOAD, src=(UPat.var("buf").view(),), allow_any_len=True, name="x"),
-   lambda ctx,buf,x: UOp(Ops.LOAD, x.dtype, (buf.index(*x.st_arg.to_indexed_uops(ctx.idxs)),)+x.src[1:])),
-
   # reduce/view_const
   (UPat(Ops.REDUCE_AXIS, name="x"), lower_reduce_axis),
+  (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="c"),), name="view"), lower_const),
+  # rewrite LOAD/STORE VIEW to LOAD/STORE with indexed
+  (UPat(Ops.LOAD, src=(UPat.var("buf").view(),), allow_any_len=True, name="x"), lower_load),
   (UPat(Ops.STORE, src=(UPat.var("buf").view(),), allow_any_len=True, name="x"), lower_store),
-  (UPat(Ops.WMMA, name="x"), fixup_wmma),
-
-  # axis fixups for WMMA
-  (UPat((Ops.CONTRACT, Ops.UNROLL), name="x"),
-   lambda ctx,x: x.replace(tag=1, arg=tuple([(ctx.idxs[a].arg[0][0], sz) for a,sz in x.arg])) if x.tag is None else None),
 ])

tinygrad/device.py

@@ -336,7 +336,7 @@ if PROFILE:
 
     if not getenv("SQTT", 0):
       from tinygrad.uop.ops import launch_viz
-      launch_viz(PROFILE, fn)
+      launch_viz("PROFILE", fn)
 
 if __name__ == "__main__":
   for device in ALL_DEVICES:

tinygrad/dtype.py

@@ -193,21 +193,6 @@ def least_upper_float(dt:DType) -> DType: return dt if dtypes.is_float(dt) else
 DTYPES_DICT = {k: v for k, v in dtypes.__dict__.items() if isinstance(v, DType) and not k.startswith(("default", "void"))}
 INVERSE_DTYPES_DICT = {**{v.name:k for k,v in DTYPES_DICT.items()}, "void": "void"}
 
-@functools.cache
-def can_safe_cast(dt0:DType, dt1:DType) -> bool:
-  # return if dt1 preserves value of dt0
-  # https://numpy.org/doc/stable/reference/generated/numpy.can_cast.html
-  if dt0 == dt1 or dt0 == dtypes.bool: return True
-  match dt1:
-    case dtypes.double: return dt0 in (dtypes.float, dtypes.half, dtypes.bfloat16)
-    case dtypes.float: return dt0 in (dtypes.half, dtypes.bfloat16)
-    case dtypes.uint64: return dt0 in (dtypes.uint32, dtypes.uint16, dtypes.uint8)
-    case dtypes.uint32: return dt0 in (dtypes.uint16, dtypes.uint8)
-    case dtypes.int64: return dt0 in (dtypes.uint32, dtypes.uint16, dtypes.uint8, dtypes.int32, dtypes.int16, dtypes.int8)
-    case dtypes.int32: return dt0 in (dtypes.uint16, dtypes.uint8, dtypes.int16, dtypes.int8)
-    case dtypes.int16: return dt0 in (dtypes.uint8, dtypes.int8)
-    case _: return False
-
 def sum_acc_dtype(dt:DType):
   # default acc dtype for sum
   if dtypes.is_unsigned(dt): return least_upper_dtype(dt, dtypes.uint)

tinygrad/engine/jit.py

@@ -21,24 +21,24 @@ def apply_graph_to_jit(jit_cache: list[ExecItem], input_rawbuffers: list[Buffer]
   # This allows the accelerator to run some batches while subsequent graphs are still being updated.
   graphed_jit_cache: list[ExecItem] = []
   current_batch: list[ExecItem] = []
-  current_batch_devs: list[Compiled] = []
+  current_device: Compiled|None = None
 
   def flush_batch():
-    nonlocal current_batch, current_batch_devs, max_batch_size
+    nonlocal current_batch, current_device, max_batch_size
     try:
-      if len(current_batch_devs) == 0: raise GraphException("no device for graph")
+      if current_device is None: raise GraphException("no device for graph")
       if len(current_batch) <= 1 and not getenv("GRAPH_ONE_KERNEL"): raise GraphException("only one kernel doesn't graph")
-      graph_runner = current_batch_devs[0].graph(current_batch, input_rawbuffers, var_vals)
+      graph_runner = current_device.graph(current_batch, input_rawbuffers, var_vals)
       # clear jit inputs to allow their memory to be freed/reused
       for (j,i) in graph_runner.input_replace.keys(): graph_runner.jit_cache[j].bufs[i] = None
       graphed_jit_cache.append(ExecItem(graph_runner, cast(list[Buffer|None], input_rawbuffers)))
       max_batch_size *= 2
-      if DEBUG >= 2: print(f"JIT GRAPHing batch with {len(current_batch)} kernels on device {current_batch_devs[0]}")
+      if DEBUG >= 2: print(f"JIT GRAPHing batch with {len(current_batch)} kernels on device {current_device}")
     except GraphException as e:
       graphed_jit_cache.extend(current_batch)
-      if DEBUG >= 2: print(f"JIT GRAPHing failed batch with {len(current_batch)} kernels on device {current_batch_devs[0]}: {e}")
+      if DEBUG >= 2: print(f"JIT GRAPHing failed batch with {len(current_batch)} kernels on device {current_device}: {e}")
     current_batch = []
-    current_batch_devs = []
+    current_device = None
 
   for ji in jit_cache:
     match ji.prg:
@@ -48,18 +48,13 @@ def apply_graph_to_jit(jit_cache: list[ExecItem], input_rawbuffers: list[Buffer]
       case ViewOp(): continue # ViewOps are just ignored
       case _: ji_graph_dev = None # Everything else is not graphed and flushes existing graph if it's being constructed
 
-    # Check if this jit item can be graphed at all, so check if a new graph supports the current item.
+    can_be_graphed = ji_graph_dev is not None and ji_graph_dev.graph is not None and graph_class(ji_graph_dev).supports_exec_item(ji_graph_dev, ji)
-    can_be_graphed = ji_graph_dev is not None and ji_graph_dev.graph is not None and graph_class(ji_graph_dev).supports_exec_item([ji_graph_dev], ji)
+    is_multigraph = can_be_graphed and issubclass(graph_class(ji_graph_dev), MultiGraphRunner)
-
+    can_share_graph = can_be_graphed and (type(ji_graph_dev) is type(current_device) if is_multigraph else ji_graph_dev == current_device)
-    # Check if the current batch can be extended with this item.
-    can_share_graph = can_be_graphed and len(current_batch_devs) > 0 and \
-                      graph_class(current_batch_devs[0]).supports_exec_item(dedup(current_batch_devs + [ji_graph_dev]), ji)
     can_extend_graph_batch = can_share_graph and (max_batch_size == 0 or len(current_batch) < max_batch_size)
-
-    # Flush the current batch if any, since it can't be extended or is full.
     if not can_extend_graph_batch and len(current_batch) > 0: flush_batch()
     (current_batch if can_be_graphed else graphed_jit_cache).append(ji)
-    current_batch_devs = dedup(current_batch_devs + [ji_graph_dev]) if can_be_graphed else []
+    current_device = ji_graph_dev if can_be_graphed else None
 
   if len(current_batch) > 0: flush_batch()
   return graphed_jit_cache
@@ -132,14 +127,12 @@ class GraphRunner(Runner):
     return list({id(x):x for x in wait_nodes}.values())
 
   @staticmethod
-  def supports_exec_item(devs:list[Compiled], ei:ExecItem) -> bool: return isinstance(ei.prg, CompiledRunner) and len(dedup(devs)) == 1
+  def supports_exec_item(dev, ei:ExecItem) -> bool: return isinstance(ei.prg, CompiledRunner)
 
 # a marker for your graph supporting multiple devices of the same type
 class MultiGraphRunner(GraphRunner):
   @staticmethod
-  def supports_exec_item(devs:list[Compiled], ei:ExecItem) -> bool:
+  def supports_exec_item(dev, ei:ExecItem) -> bool: return isinstance(ei.prg, (CompiledRunner, BufferXfer))
-    # Devices must be the same type
-    return isinstance(ei.prg, (CompiledRunner, BufferXfer)) and len(dedup([type(Device[b.device]) for b in ei.bufs if b]+[type(d) for d in devs]))==1
 
 def get_out_buffers_for_ei(ei:ExecItem) -> list[Buffer]:
   if isinstance(ei.prg, CompiledRunner): return [cast(Buffer, ei.bufs[out]) for out in ei.prg.p.outs if out not in ei.prg.p.ins]

tinygrad/engine/realize.py

@@ -2,16 +2,18 @@ from typing import cast, Generator
 import time, pprint
 from dataclasses import dataclass, replace, field
 from tinygrad.helpers import all_same, colored, DEBUG, GlobalCounters, ansilen, BEAM, NOOPT, all_int, CAPTURING, Metadata, TRACEMETA, TracingKey
-from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv, cpu_profile
+from tinygrad.helpers import DEVECTORIZE, time_to_str, VALIDATE_WITH_CPU, getenv
 from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, Variable, sym_infer, graph_rewrite, print_uops, track_rewrites
 from tinygrad.device import Device, Buffer
 from tinygrad.renderer import Renderer, ProgramSpec, Estimates
 from tinygrad.engine.schedule import ScheduleItem
+from tinygrad.opt import get_optimized_ast
 from tinygrad.codegen import full_rewrite
+from tinygrad.uop.spec import type_verify
 
 # **************** Program Creation ****************
 
-@track_rewrites(name=lambda _ast,_renderer,ret: TracingKey(ret.name, (ret.function_name, ret.ast), ret=ret))
+@track_rewrites(name=lambda _ast,_renderer,ret: TracingKey(ret.name, (ret.function_name, ret.ast), ret.src))
 def get_program(ast:UOp, renderer:Renderer) -> ProgramSpec:
   """
   Transform an AST into a ProgramSpec. May trigger BEAM search.
@@ -25,13 +27,16 @@ def get_program(ast:UOp, renderer:Renderer) -> ProgramSpec:
   """
 
   if getenv("VIZ"): graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
+  modified_ast = get_optimized_ast(ast, renderer) if ast.arg is None or ast.arg.opts_to_apply is not None else ast
+  if __debug__: type_verify(list(modified_ast.toposort()))
 
   # linearize
   try:
-    uops = full_rewrite(ast, renderer)
+    uops = full_rewrite(modified_ast, renderer)
   except RuntimeError:
     print("***** LINEARIZE FAILURE *****")
     print(f"ast = {ast}")
+    print(f"opts = {modified_ast.arg.applied_opts}")
     raise
   assert uops[-1].op is Ops.SINK, "last uop must be sink"
 
@@ -58,10 +63,7 @@ class CompiledRunner(Runner):
   def __init__(self, p:ProgramSpec, precompiled:bytes|None=None, prg=None):
     if DEBUG >= 4: print(p.src)
     self.p:ProgramSpec = p
-    if precompiled is not None: self.lib = precompiled
+    self.lib:bytes = precompiled if precompiled is not None else Device[p.device].compiler.compile_cached(p.src)
-    else:
-      with cpu_profile(TracingKey(f"compile {p.name}", (p.function_name,), cat="compiler"), "TINY"):
-        self.lib = Device[p.device].compiler.compile_cached(p.src)
     if DEBUG >= 7: Device[p.device].compiler.disassemble(self.lib)
     self._prg = Device[p.device].runtime(p.function_name, self.lib) if prg is None else prg
     super().__init__(p.name, p.device, p.estimates)
@@ -154,7 +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+' '*(44-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+' '*(41-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/gradient.py

@@ -1,5 +1,6 @@
 from typing import cast
 import math, dataclasses
+from tinygrad.dtype import dtypes, sum_acc_dtype
 from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops, all_metadata
 from tinygrad.helpers import argsort
 
@@ -7,7 +8,7 @@ def reduce_gradient(ctx:UOp, ret:UOp):
   def to_inp_shape(x): return x.reshape(x.shape+(1,)*(len(ret.src[0].shape)-len(x.shape))).expand(ret.src[0].shape)
   if ret.arg[0] == Ops.ADD: return (to_inp_shape(ctx),)
   if ret.arg[0] == Ops.MAX:
-    max_is_1s = ret.src[0].eq(to_inp_shape(ret)).cast(ctx.dtype)
+    max_is_1s = ret.src[0].ne(to_inp_shape(ret)).ne(ret.src[0].const_like(1).cast(dtypes.bool)).cast(ctx.dtype)
     div = to_inp_shape(max_is_1s.r(Ops.ADD, ret.arg[1]))
     return ((max_is_1s/div) * to_inp_shape(ctx),)
   if ret.arg[0] == Ops.MUL: return (to_inp_shape(ctx * ret) / ret.src[0],)
@@ -37,7 +38,9 @@ pm_gradient = PatternMatcher([
   (UPat(Ops.PAD, name="ret"), lambda ctx, ret: (ctx.shrink(tuple([(p[0], s+p[0]) for s,p in zip(ret.src[0].shape, ret.arg)])),)),
   (UPat(Ops.SHRINK, name="ret"), lambda ctx, ret: (ctx.pad(tuple([(p[0], s-p[1]) for s,p in zip(ret.src[0].shape, ret.arg)])),)),
   (UPat(Ops.FLIP, name="ret"), lambda ctx, ret: (ctx.flip(ret.arg),)),
-  (UPat(Ops.EXPAND, name="ret"), lambda ctx, ret: (ctx.r(Ops.ADD, tuple(i for i,(si,so) in enumerate(zip(ret.src[0].shape, ret.arg)) if si!=so)),)),
+  # TODO: this cast can be removed by putting the casts around the EXPAND
+  (UPat(Ops.EXPAND, name="ret"), lambda ctx, ret:
+    (ctx.cast(sum_acc_dtype(ctx.dtype)).r(Ops.ADD, tuple(i for i,(si,so) in enumerate(zip(ret.src[0].shape, ret.arg)) if si!=so)).cast(ctx.dtype),)),
   (UPat(Ops.MULTI, name="ret"), lambda ctx, ret: ctx.shard(ret.device, ret.axis).src),
   # there's no gradient for bitcast
   (UPat(Ops.BITCAST), lambda ctx: (None,)),

tinygrad/helpers.py

@@ -81,13 +81,6 @@ 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__
@@ -196,8 +189,8 @@ class Profiling(contextlib.ContextDecorator):
 class TracingKey:
   display_name:str                       # display name of this trace event
   keys:tuple[str, ...]=()                # optional keys to search for related traces
+  fmt:str|None=None                      # optional detailed formatting
   cat:str|None=None                      # optional category to color this by
-  ret:Any=None
 
 class ProfileEvent: pass
 

tinygrad/nn/__init__.py

@@ -10,6 +10,7 @@ class BatchNorm:
   """
   Applies Batch Normalization over a 2D or 3D input.
 
+  - Described: https://paperswithcode.com/method/batch-normalization
   - Paper: https://arxiv.org/abs/1502.03167v3
 
   See: `Tensor.batchnorm`
@@ -181,6 +182,7 @@ class GroupNorm:
   """
   Applies Group Normalization over a mini-batch of inputs.
 
+  - Described: https://paperswithcode.com/method/group-normalization
   - Paper: https://arxiv.org/abs/1803.08494v3
 
   ```python exec="true" source="above" session="tensor" result="python"
@@ -211,6 +213,7 @@ class InstanceNorm:
   """
   Applies Instance Normalization over a mini-batch of inputs.
 
+  - Described: https://paperswithcode.com/method/instance-normalization
   - Paper: https://arxiv.org/abs/1607.08022v3
 
   ```python exec="true" source="above" session="tensor" result="python"
@@ -237,6 +240,7 @@ class LayerNorm:
   """
   Applies Layer Normalization over a mini-batch of inputs.
 
+  - Described: https://paperswithcode.com/method/layer-normalization
   - Paper: https://arxiv.org/abs/1607.06450v1
 
   ```python exec="true" source="above" session="tensor" result="python"
@@ -283,6 +287,7 @@ class RMSNorm:
   """
   Applies Root Mean Square Normalization to input.
 
+  - Described: https://paperswithcode.com/method/rmsnorm
   - Paper: https://arxiv.org/abs/1910.07467
 
   ```python exec="true" source="above" session="tensor" result="python"

tinygrad/nn/optim.py

@@ -76,6 +76,8 @@ def SGD(params: list[Tensor], lr=0.001, momentum=0.0, weight_decay=0.0, nesterov
   Stochastic Gradient Descent (SGD) optimizer with optional momentum and weight decay.
 
   `classic` is a boolean flag that determines whether to use the popular momentum update rule or the classic momentum update rule.
+
+  - Described: https://paperswithcode.com/method/sgd
   """
   return LARS(params, lr, momentum, weight_decay, nesterov, classic, tcoef=0.0, fused=fused)
 
@@ -83,6 +85,7 @@ class LARS(Optimizer):
   """
   Layer-wise Adaptive Rate Scaling (LARS) optimizer with optional momentum and weight decay.
 
+  - Described: https://paperswithcode.com/method/lars
   - Paper: https://arxiv.org/abs/1708.03888v3
   """
   def __init__(self, params:list[Tensor], lr=0.001, momentum=0.9, weight_decay=1e-4, nesterov=False, classic=True, tcoef=0.001, fused=FUSE_OPTIM):
@@ -116,6 +119,7 @@ def AdamW(params: list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-8, weight_dec
   """
   AdamW optimizer with optional weight decay.
 
+  - Described: https://paperswithcode.com/method/adamw
   - Paper: https://arxiv.org/abs/1711.05101v3
   """
   return LAMB(params, lr, b1, b2, eps, weight_decay, adam=True, fused=fused)
@@ -123,6 +127,7 @@ def Adam(params: list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-8, fused=FUSE_
   """
   Adam optimizer.
 
+  - Described: https://paperswithcode.com/method/adam
   - Paper: https://arxiv.org/abs/1412.6980
   """
   return LAMB(params, lr, b1, b2, eps, 0.0, adam=True, fused=fused)
@@ -131,6 +136,7 @@ class LAMB(Optimizer):
   """
   LAMB optimizer with optional weight decay.
 
+  - Described: https://paperswithcode.com/method/lamb
   - Paper: https://arxiv.org/abs/1904.00962
   """
   def __init__(self, params: list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-6, weight_decay=0.0, adam=False, fused=FUSE_OPTIM):

tinygrad/opt/__init__.py

@@ -2,10 +2,9 @@
 
 from tinygrad.opt.kernel import Kernel
 from tinygrad.opt.heuristic import hand_coded_optimizations
-from tinygrad.uop.ops import UOp, PatternMatcher, UPat, Ops
+from tinygrad.uop.ops import UOp
 from tinygrad.helpers import NOOPT, BEAM, USE_TC, getenv
 from tinygrad.renderer import Renderer
-from tinygrad.uop.spec import type_verify
 
 def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp:
   """
@@ -28,11 +27,4 @@ def get_optimized_ast(ast:UOp, renderer:Renderer) -> UOp:
       kb = Kernel(ast, opts=renderer)
       rawbufs = bufs_from_lin(kb, allocate=False)
       k = beam_search(kb, rawbufs, BEAM.value, bool(getenv("BEAM_ESTIMATE", 1)))
-  ret = k.get_optimized_ast()
+  return k.get_optimized_ast()
-  if __debug__: type_verify(list(ret.toposort()))
-  return ret
-
-pm_optimize = PatternMatcher([
-  (UPat(Ops.SINK, name="ast"), lambda ctx,ast:
-   get_optimized_ast(ast, ctx) if (ast.arg is None or ast.arg.opts_to_apply is not None) and ast.src[0].st is not None else None),
-])

tinygrad/opt/kernel.py

@@ -5,7 +5,7 @@ from collections import defaultdict
 from typing import cast, Final, Callable, Sequence
 from enum import Enum, auto
 
-from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, AxisType
+from tinygrad.uop.ops import GroupOp, KernelInfo, UOp, Ops, can_pad, resolve, Variable, sint, graph_rewrite, smax, AxisType
 from tinygrad.uop.spec import type_verify, ast_spec
 from tinygrad.device import Device
 from tinygrad.opt.tc import TensorCore
@@ -14,7 +14,7 @@ from tinygrad.dtype import ImageDType, AddrSpace
 from tinygrad.helpers import all_same, colored, ansilen, dedup, prod, round_up, to_function_name, unwrap, argfix, DEBUG, TC_SELECT, TC_OPT, AMX
 from tinygrad.shape.shapetracker import ShapeTracker
 from tinygrad.shape.view import strides_for_shape, get_contraction
-from tinygrad.opt.swizzler import view_left
+from tinygrad.schedule.kernelize import view_left
 
 class OptOps(Enum):
   TC = auto(); UPCAST = auto(); UNROLL = auto(); LOCAL = auto() # noqa: E702
@@ -73,8 +73,7 @@ class Kernel:
       self.sts.append(unwrap(x.src[0].st))
 
     # add a shapetracker to the end to track the full shape, with 0 strides so it can merge
-    full_shape = ast.full_shape
+    self.sts.append(ShapeTracker.from_shape(tuple([smax(*s) for s in zip(*[x.shape for x in self.sts])]), (0,)*len(self.sts[0].shape)))
-    self.sts.append(ShapeTracker.from_shape(full_shape, (0,)*len(full_shape)))
 
     # parameters for optimization
     self.tensor_core: TensorCore|None = None
@@ -90,10 +89,11 @@ class Kernel:
 
     # axis types
     global_loops = AxisType.GLOBAL if self.opts.has_local else AxisType.LOOP
-    self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.output_shape, self.full_shape)]
+    self.axis_types: list[AxisType] = [AxisType.REDUCE if resolve(x!=y) else global_loops for x,y in zip(self.sts[0].shape, self.sts[-1].shape)]
 
     # confirm all reduce axes are at the end
-    if (final_reduces := [x for x in self.axis_types if x == AxisType.REDUCE]) and final_reduces != self.axis_types[-len(final_reduces):]:
+    final_reduces = [i for i,(s,n) in enumerate(zip(self.full_shape, self.output_shape)) if resolve(s != n)]
+    if final_reduces != list(range(len(self.full_shape)-len(final_reduces), len(self.full_shape))):
       raise RuntimeError(f"reduces are not at the end of the shape {self.full_shape} -> {self.output_shape}")
 
   def copy(self):
@@ -200,7 +200,7 @@ class Kernel:
     if self.shape_len == 0: return
     shapes, strides = [x.shape for x in self.sts], [x.real_strides() for x in self.sts]
     # NOTE: we can't use self.first_reduce yet
-    first_reduce = [resolve(x!=y) for x,y in zip(self.output_shape+(0,), self.full_shape+(1,))].index(True)
+    first_reduce = [resolve(x!=y) for x,y in zip(self.sts[0].shape+(0,), self.full_shape+(1,))].index(True)
 
     # if it's an image, insert fake strides such that this fusion doesn't happen across image axes
     # TODO: remove membufs
@@ -447,7 +447,9 @@ class Kernel:
       ret = op.replace(src=tuple(fixup_ast(x) for x in op.src)) # noqa: F821
       if op.op in GroupOp.Buffer and op in self.bufs:
         st = self.sts[self.bufs.index(op)]
-        # replace the VIEW source
+        # NOTE: if CONST got masked after applying opts, we create a new VALID
+        if op.op is Ops.CONST and any(v.mask is not None for v in st.views): return op.view(st).valid()
+        # otherwise we just replace the VIEW source
         return ret.replace(src=(ret.src[0].replace(arg=st),)+ret.src[1:])
       if op.op is Ops.SINK:
         # NOTE: should group_for_reduces be added to the local_dims?
@@ -461,7 +463,8 @@ class Kernel:
         if (tc := self.tensor_core) and self.use_tensor_cores == 1:
           # get reduce/upcast axes for the tensor cores
           tc_reduce_axes = self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))])
-          base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis(tc.base_upcast_axes())])
+          base_upcast_axes = tuple([(s,2) for s in self.shape_str_to_axis([f"r{i}" for i in range(len(tc.get_reduce_axes()))] + \
+                                                                          [f"u{i}" for i in range(len(tc.get_upcast_axes()))])])[::-1]
           tc_upcast_axes = tuple([base_upcast_axes[:int(math.log2(tc.elements_per_thread[i]))] for i in range(3)])
 
           # permute the srcs

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, AxisType
+from tinygrad.uop.ops import UOp, Ops, Variable, sym_infer
 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, TRACK_MATCH_STATS=0).__enter__()
+  Context(ALLOW_DEVICE_USAGE=0, VIZ=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, candidates:list[Opt]|None=None) -> dict[int, Kernel]:
+def get_kernel_actions(lin:Kernel, include_0=True) -> 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 if candidates is None else candidates).copy()
+  kernel_actions = actions.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, candidates:list[Opt]|None=Non
     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, a.axis, 0) in kernel_actions): continue
+      if (ax >= lin.shape_len) or (lin.full_shape[ax] == a.arg and Opt(a.op, ax, 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.axis_types):
+      for s,c in zip(lin2.full_shape, lin2.colors()):
-        if c in (AxisType.UPCAST, AxisType.UNROLL): up *= s
+        if c in {"magenta", "yellow"}: up *= s
-        elif c in (AxisType.LOCAL, AxisType.GROUP_REDUCE): lcl *= s
+        elif c in {"cyan", "green", "white"}: 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/swizzler.py

@@ -1,133 +0,0 @@
-from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, resolve, sint
-from tinygrad.helpers import all_same, prod, unwrap, colored
-from tinygrad.shape.shapetracker import ShapeTracker
-from tinygrad.shape.view import View, strides_for_shape, get_contraction_with_reduce
-from tinygrad.schedule.grouper import ALWAYS_CONTIGUOUS
-from tinygrad.dtype import ImageDType, dtypes
-
-merge_views = PatternMatcher([
-  # merge adjacent views
-  (UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
-  # replace MovementOps with VIEW
-  (UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
-  # remove NOOP views
-  (UPat.var("x").view(name="view"),
-   lambda x,view: x if x.st is not None and x.op not in GroupOp.Defines and view.st.contiguous and view.shape == x.shape else None),
-  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
-   lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
-  # only unmaksed VIEW on CONST replaces the ShapeTracker
-  (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
-   lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
-])
-
-def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
-  # contiguous, expand, and the same with ones removed
-  if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
-      tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
-    new_shape: list[sint] = []
-    new_reduce_axis = []
-    if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
-    for i,pairs in enumerate(contraction):
-      new_shape_chunk = [view.shape[p] for p in pairs]
-      if i in r.arg[1]:
-        # if this is a reduce axis, we need a 1 in the view here to put it
-        assert len(new_shape_chunk) > 0
-        new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
-        new_reduce_axis.append(len(new_shape)-1)
-      else:
-        # otherwise, pass through the new_shape_chunk
-        new_shape += new_shape_chunk
-    ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
-    assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
-    return ret
-  return None
-
-view_left = merge_views+PatternMatcher([
-  # view before elementwise and buffer ops
-  (UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID, Ops.SINK}, name="e"),), name="view"),
-   lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
-  # if there's ones added after reduce, put this before the reduce
-  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
-])
-
-def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
-
-# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
-def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
-  # contiguous and same size can push to children
-  # if there's a reduce child, shapes match with ones removed
-  if unwrap(view.st).contiguous and view.size == r.size and \
-      (not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
-       tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
-    return None
-  # swizzle the input
-  input_st = ShapeTracker.from_shape(src.shape)
-  tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
-  prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
-  strides = strides_for_shape(rshape)
-  nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
-                    v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
-  new_view = tmp + ShapeTracker(tuple(nv))
-  swizzled_input = apply_swizzle(src.view(new_view))
-  # create a new reduceop
-  new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
-  if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
-  else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
-  return red.reshape(view.shape)
-
-def reduceop_view_right(src:UOp, v:UOp, r:UOp):
-  assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
-  new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
-  return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
-
-def elementwise_view_right(root:UOp):
-  if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
-  assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
-  # place view after applying the elementwise op
-  new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
-  new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
-  # reshape to match downstream shapes
-  return root.replace(src=tuple(new_src)).reshape(root.shape)
-
-# push VIEW to children
-view_right = merge_views+PatternMatcher([
-  # push a non contiguous ShapeTracker through reduceop
-  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
-  # apply view after reduceops
-  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
-  # apply view after elementwise ops
-  (UPat(GroupOp.All-{Ops.SINK, Ops.REDUCE_AXIS, Ops.LOAD, Ops.STORE}, name="root"), elementwise_view_right),
-  # merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
-  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
-   lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
-])
-
-def check_load_st(glbl:UOp, view:UOp):
-  if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
-  # if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
-  if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
-  # if it has a single view and it's equal when you shrink a contig, it's fine
-  if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
-  # otherwise, it's not fine
-  raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
-                     +colored("   - a += a.T\n", "red")+colored("   + a += a.T.contiguous()", "green"))
-
-fix_kernel_ops = PatternMatcher([
-  # add the LOAD
-  (UPat(Ops.DEFINE_GLOBAL, name="x"), lambda x: x.replace(tag=None).view(x.st).load() if x.tag is not None else None),
-  # STORE (except for meta ops)
-  (UPat(Ops.SINK, src=UPat(GroupOp.All-{Ops.STORE}), name="sink"), lambda sink:
-   UOp.sink(*[UOp.store(UOp(Ops.DEFINE_GLOBAL, (s:=x.base).dtype.ptr(s.st.real_size()), (), i).view(s.st), s) for i,x in enumerate(sink.src)])),
-  # passthrough ASSIGN
-  (UPat(Ops.ASSIGN, name="x"), lambda x: x.src[1]),
-  # VALID
-  (UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"),
-   lambda self: UOp.where(UOp(Ops.VALID, dtypes.bool, (UOp(Ops.VIEW, arg=self.st),)), self.const_like(self.base.arg), 0)),
-  # remove CONTIGUOUS/DEVICE from kernel AST
-  (UPat((Ops.CONTIGUOUS, Ops.MSELECT), src=(UPat.var("x"),)), lambda x: x),
-  (UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),), name="view"), lambda view: view.replace(src=())),
-  # no ImageDType after index
-  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
-  # if this kernel also assigns to the loaded buffer, ensure we can index it correctly
-  (UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
-])

tinygrad/opt/tc.py

@@ -25,9 +25,6 @@ class TensorCore: # D = A * B + C, A is (M x K), B is (K x N), C and D are (M x
   def get_reduce_axes(self): return [(i, 2) for i in range(int(math.log2(self.dims[2])))]
   def get_upcast_axes(self): return [opt for opt in self.opts if opt[0] == "u"]
   def get_local_axes(self): return [opt for opt in self.opts if opt[0] == "l"]
-  def base_upcast_axes(self):
-    # this is defined in the swizzle. first we use the upcast axes, then the reduce
-    return ([f"r{i}" for i in range(len(self.get_reduce_axes()))] + [f"u{i}" for i in range(len(self.get_upcast_axes()))])[::-1]
   def __str__(self): return "_".join(["WMMA"] + list(map(str, self.dims)) + [self.dtype_in.name, self.dtype_out.name])
   def __post_init__(self):
     # all axes have size 2, <local> <reduce> <upcast> is the order
@@ -37,30 +34,12 @@ 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"
     assert len(self.swizzle[0][1]) == len(self.swizzle[1][1]) == upcast_axes, "upcast swizzle size is wrong"
     assert len(self.swizzle[0][2]) == len(self.swizzle[1][2]) == reduce_axes, "reduce swizzle size is wrong"
     assert all(len(s) == local_axes+upcast_axes+reduce_axes for s in self._remaps()), "remaps are the wrong size"
-    # check elements_per_thread
-    un, ln = 0, 0
-    zero_stride_0 = []
-    zero_stride_1 = []
-    for o in self.opts:
-      if o[1] == '0': zero_stride_0.append(o[0] + str(un if o[0] == 'u' else ln))
-      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]}"
-    assert 2**len(upcasted_1) == self.elements_per_thread[1], f"mismatch in elements_per_thread[1], {upcasted_1} vs {self.elements_per_thread[1]}"
 
 # ***** NVIDIA *****
 
@@ -86,14 +65,12 @@ cuda_sm75: list[TensorCore] = cuda_8168_f16
 
 # https://gpuopen.com/learn/wmma_on_rdna3/
 amd_rdna3 = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(16,16,8), dtype_in=di, dtype_out=do,
-  opts=("l0","l0","l0","l0","l1","u1","u1","u1"),
+  opts=("l0","l0","l0","l0","l1","u1","u1","u1"), swizzle=((('l4', 'u0', 'u1', 'u2', 'l0'), ('r1', 'r2', 'r3'), ('l1', 'l2', 'l3', 'r0')),
-  swizzle=((('l4', 'u0', 'u1', 'u2', 'l0'), ('r1', 'r2', 'r3'), ('l1', 'l2', 'l3', 'r0')),
+                                                           (('l0', 'l1', 'l2', 'l3', 'l4'), ('r1', 'r2', 'r3'), ('u0', 'u1', 'u2', 'r0'))))
-           (('l0', 'l1', 'l2', 'l3', 'l4'), ('r1', 'r2', 'r3'), ('u0', 'u1', 'u2', 'r0'))))
   for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float)]]
 amd_rdna4 = [TensorCore(dims=(16,16,16), threads=32, elements_per_thread=(8,8,8), dtype_in=di, dtype_out=do,
-  opts=("l0","l0","l0","l0","u1","u1","u1","l1"),
+  opts=("l0","l0","l0","l0","u1","u1","u1","l1"), swizzle=((('u0', 'u1', 'u2', 'l4', 'r2'), ('r0', 'r1', 'r3'), ('l0', 'l1', 'l2', 'l3')),
-  swizzle=((('u0', 'u1', 'u2', 'l4', 'r2'), ('r0', 'r1', 'r3'), ('l0', 'l1', 'l2', 'l3')),
+                                                           (('l0', 'l1', 'l2', 'l3', 'r2'), ('r0', 'r1', 'r3'), ('l4', 'u0', 'u1', 'u2'))))
-           (('l0', 'l1', 'l2', 'l3', 'r2'), ('r0', 'r1', 'r3'), ('l4', 'u0', 'u1', 'u2'))))
   for di,do in [(dtypes.half,dtypes.float),(dtypes.half,dtypes.half),(dtypes.bfloat16,dtypes.float),(dtypes.bfloat16,dtypes.bfloat16)]]
 
 # https://gpuopen.com/learn/amd-lab-notes/amd-lab-notes-matrix-cores-readme

tinygrad/renderer/cstyle.py

@@ -9,7 +9,7 @@ from tinygrad.renderer import Renderer
 from tinygrad.codegen.devectorizer import no_vectorized_alu
 
 base_rewrite = PatternMatcher([
-  (UPat(Ops.DEFINE_REG, name="x"), lambda ctx,x: f"{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}];"),
+  (UPat(Ops.DEFINE_REG, name="x"), lambda ctx,x: f"{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}] = {{{ctx[x.src[0]]}}};"),
   (UPat(Ops.IF, name="x"), lambda ctx,x: f"if ({ctx[x.src[0]]}) {{"),
   (UPat((Ops.ENDIF, Ops.ENDRANGE)), lambda ctx: "}"),
   (UPat(Ops.WMMA, name="x"), lambda ctx,x: f"__{x.arg[0]}({ctx[x.src[0]]}, {ctx[x.src[1]]}, {ctx[x.src[2]]})"),
@@ -25,7 +25,7 @@ base_rewrite = PatternMatcher([
   (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"(*(({ctx.buffer_prefix}{ctx.render_dtype(x.dtype)}*)&{ctx[x.src[0]]}))"),
   (UPat(Ops.DEFINE_LOCAL, name="x"), lambda ctx,x: f"{ctx.smem_align}{ctx.smem_prefix}{ctx.render_dtype(x.dtype.base)} {ctx[x]}[{x.dtype.size}];"),
   (UPat(Ops.BARRIER), lambda ctx: ctx.barrier),
-  (UPat(Ops.PRECAST, name="x"), lambda ctx,x: ctx[x.src[0]]),
+  (UPat(Ops.NOOP, name="x"), lambda ctx,x: ctx[x.src[0]]),
   (UPat(Ops.SPECIAL, name="x"), lambda ctx,x: f"{ctx.code_for_workitem[x.arg[0][0]](x.arg[0][-1])}; /* {x.arg[1]} */"),
   # const
   (UPat(Ops.CONST, arg=math.inf, name="x"), lambda ctx, x: f"({ctx.render_cast(x.dtype, ctx.infinity)})"),
@@ -47,7 +47,7 @@ base_rewrite = PatternMatcher([
    lambda ctx,buf,idx: f"({ctx[buf]}+{strip_parens(ctx[idx]) if idx.arg == Ops.ADD else ctx[idx]})"),
   (UPat(Ops.LOAD, src=(UPat(Ops.INDEX, src=(UPat(), UPat(), UPat.var("gate"))).or_casted("bidx"), UPat.var("var")), allow_any_len=True),
    lambda ctx,bidx,var,gate: f"({ctx[gate]}?*{ctx[bidx]}:{ctx[var]})"),
-  (UPat(Ops.LOAD, src=(UPat.var('bidx'),), allow_any_len=True), lambda ctx,bidx: f"(*{ctx[bidx]})"),
+  (UPat(Ops.LOAD, src=(UPat.var('bidx'),), allow_any_len=True), lambda ctx,bidx: f"*{ctx[bidx]}"),
   (UPat(Ops.STORE, src=(UPat.var('bidx'), UPat.var("var")), allow_any_len=True), lambda ctx,bidx,var: f"*{ctx[bidx]} = {ctx[var]};"),
   # alu/gep
   # TODO: look for left-associative
@@ -60,9 +60,9 @@ base_rewrite = PatternMatcher([
 ])
 
 extra_pm = PatternMatcher([
-  # insert a PRECAST before BITCAST to force it to be rendered. not needed on all backends?
+  # insert a NOOP before BITCAST to force it to be rendered. not needed on all backends?
-  (UPat(Ops.BITCAST, name="x"), lambda x: UOp(Ops.BITCAST, x.dtype, (UOp(Ops.PRECAST, x.src[0].dtype, x.src),))
+  (UPat(Ops.BITCAST, name="x"),
-   if x.src[0].op not in {Ops.PRECAST, Ops.LOAD, Ops.CUSTOM} else None),
+   lambda x: UOp(Ops.BITCAST, x.dtype, (UOp(Ops.NOOP, x.src[0].dtype, x.src),)) if x.src[0].op not in {Ops.NOOP, Ops.LOAD, Ops.CUSTOM} else None),
   # rewrite MAX to CMPLT + WHERE (max function is annoying on many cstyle backends)
   (UPat(Ops.MAX, name="m"), lambda m: (m.src[0] < m.src[1]).where(m.src[1], m.src[0])),
   # devectorize any bools
@@ -75,10 +75,6 @@ extra_pm = PatternMatcher([
 
 def uops_to_dtypes(uops:list[UOp]) -> list[DType]: return dedup(u.dtype for u in uops if not isinstance(u.dtype, (ImageDType, PtrDType)))
 
-# (name, dims, dtype_in, dtype_out, device, threads, upcast_axes, reduce_axes)
-def wmma_args(uops:list[UOp]):
-  return dedup((uop.arg[0], uop.arg[1], uop.src[0].dtype.scalar(), uop.dtype.scalar(), *(uop.arg[4:8])) for uop in uops if uop.op is Ops.WMMA)
-
 class CStyleLanguage(Renderer):
   kernel_typedef: str = "void"
   buffer_prefix: str = ""
@@ -122,9 +118,7 @@ class CStyleLanguage(Renderer):
   def render_dtype(self, dt:DType, mutable=True) -> str:
     if isinstance(dt, ImageDType): return f"{'write_only' if mutable else 'read_only'} image2d_t"
     if isinstance(dt, PtrDType):
-      prefix = ""
+      prefix = self.smem_prefix if dt.addrspace == AddrSpace.LOCAL and self.smem_prefix_for_cast else self.buffer_prefix
-      if dt.addrspace == AddrSpace.LOCAL and self.smem_prefix_for_cast: prefix = self.smem_prefix
-      if dt.addrspace == AddrSpace.GLOBAL: prefix = self.buffer_prefix
       return prefix + self.render_dtype(dt.base) + "*"
     if dt.count > 1: return self.type_map.get(scalar:=dt.scalar(), scalar.name).replace(" ", "_") + str(dt.count)
     return self.type_map.get(scalar:=dt.scalar(), scalar.name)
@@ -141,7 +135,6 @@ 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.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue
@@ -161,7 +154,7 @@ class CStyleLanguage(Renderer):
       elif u.op is Ops.RANGE: r[u] = f"ridx{u.arg}"
       else:
         prefix = {Ops.WMMA: "wmma", Ops.DEFINE_LOCAL: "temp", Ops.CONST: "const",
-                  Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.GEP: "gep", Ops.VECTORIZE: "cast", Ops.PRECAST: "precast",
+                  Ops.CAST: "cast", Ops.BITCAST: "cast", Ops.GEP: "gep", Ops.VECTORIZE: "cast", Ops.NOOP: "precast",
                   Ops.INDEX: "bidx", Ops.DEFINE_REG: "acc", Ops.LOAD: "val"}.get(u.op, "alu")
         r[u] = f"{prefix}{c[prefix]}"
 
@@ -170,13 +163,13 @@ class CStyleLanguage(Renderer):
 
       if u.op in {Ops.ENDIF, Ops.ENDRANGE}: depth -= 1
       if (u.op is not Ops.CAST or u.dtype.vcount == 1) and (u.op in {Ops.CONST, Ops.GEP, Ops.INDEX, Ops.CUSTOMI} or \
-        (u.op is Ops.LOAD and cast(PtrDType, u.src[0].dtype).addrspace == AddrSpace.REG) or \
-        (u.op is Ops.CAST and isinstance(u.dtype, PtrDType)) or \
         (u.op in {Ops.VECTORIZE, *(GroupOp.ALU-{Ops.WHERE}), Ops.CAST, Ops.BITCAST} and child_count[u] == 1 and not getenv("EXPAND_SSA"))):
         r[u] = l
       else:
-        if u.op in {Ops.RANGE, Ops.DEFINE_LOCAL, Ops.STORE, Ops.DEFINE_REG} or u.dtype == dtypes.void: pass
+        if u.op in {Ops.RANGE, Ops.DEFINE_LOCAL, Ops.STORE, Ops.DEFINE_REG} or u.dtype == dtypes.void:
-        else: l = f"{self.render_dtype(u.dtype)} {r[u]} = {l}" + (";" if u.op is not Ops.SPECIAL else "")
+          if u.op is Ops.STORE: r[u] = r[u.src[0]]
+        else:
+          l = f"{self.render_dtype(u.dtype)} {r[u]} = {l}" + (";" if u.op is not Ops.SPECIAL else "")
         kernel.append("  "*depth + l)
         if prefix: c[prefix] += 1  # if it was used, increment
       if u.op in {Ops.IF, Ops.RANGE}: depth += 1
@@ -216,7 +209,7 @@ class ClangRenderer(CStyleLanguage):
   def _render_defines(self, uops) -> list[str]:
     prefix = [self.render_vector_prefix(dt) for dt in uops_to_dtypes(uops) if dt.count > 1]
     # https://github.com/corsix/amx
-    for name, (N, M, _), dtype_in, _, _, _, _, _ in wmma_args(uops):
+    for name, (N, M, _), dtype_in, _, _, _, _, _ in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
       prefix += [
         '#define AMX_SET(imm5) __asm("nop\\nnop\\nnop\\n.word (0x201000+(%0<<5)+%1)" : : "i"(17), "i"(imm5) : "memory")',
         '#define AMX(op, gpr, btf) __asm(".word (0x201000+(%0 << 5)+0%1-((0%1>>4)*6))" : : "i"(op), "r"((unsigned long long)(gpr)+(btf)) : "memory")',
@@ -276,9 +269,9 @@ class IntelRenderer(OpenCLRenderer):
 
   def render_kernel(self, function_name, kernel, bufs, uops, prefix=None) -> str:
     prefix = []
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops):
+    for arg in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
-      dt_in = ("ushort", "bf16") if dtype_in == dtypes.bfloat16 else (dtype_in.name, "f16")
+      dt_in = ("ushort", "bf16") if arg[2] == dtypes.bfloat16 else (arg[2].name, "f16")
-      prefix.append(f"""{dtype_out.name}8 __{name}({dt_in[0]}16 a, {dt_in[0]}16 b, {dtype_out.name}8 c) {{
+      prefix.append(f"""{arg[3].name}8 __{arg[0]}({dt_in[0]}16 a, {dt_in[0]}16 b, {arg[3].name}8 c) {{
     return intel_sub_group_{dt_in[1]}_{dt_in[1]}_matrix_mad_k16(as_int8(a), as_int8(b), c);\n}}""")
     return super().render_kernel(function_name, kernel, bufs, uops, prefix or None)
 
@@ -314,13 +307,13 @@ class MetalRenderer(CStyleLanguage):
   ]) + base_rewrite
 
   def render_kernel(self, function_name, kernel, bufs, uops, prefix=None):
-    prefix = ["#include <metal_stdlib>","using namespace metal;"]
+    prefix, wmma_args = ["#include <metal_stdlib>","using namespace metal;"], set([uop.arg for uop in uops if uop.op is Ops.WMMA])
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops): prefix.append(
+    for arg in wmma_args: prefix.append(
-  f"""{(dstr_out:=self.render_dtype(dtype_out.vec(2)))} __{name}({(dstr_in:=self.render_dtype(dtype_in.vec(2)))} a, {dstr_in} b, {dstr_out} c){{
+  f"""{(dtype_out:=self.render_dtype(arg[3].vec(2)))} __{arg[0]}({(dtype_in:=self.render_dtype(arg[2].vec(2)))} a, {dtype_in} b, {dtype_out} c){{
-  simdgroup_{self.render_dtype(dtype_in)}8x8 mat_a, mat_b; simdgroup_{self.render_dtype(dtype_out)}8x8 mat_c;
+  simdgroup_{self.render_dtype(arg[2])}8x8 mat_a, mat_b; simdgroup_{self.render_dtype(arg[3])}8x8 mat_c;
   mat_a.thread_elements()[0] = a[0]; mat_b.thread_elements()[0] = b[0]; mat_c.thread_elements()[0] = c[0];
   mat_a.thread_elements()[1] = a[1]; mat_b.thread_elements()[1] = b[1]; mat_c.thread_elements()[1] = c[1];
-  simdgroup_multiply_accumulate(mat_c, mat_a, mat_b, mat_c);\n  return {dstr_out}(mat_c.thread_elements()[0], mat_c.thread_elements()[1]);\n}}""")
+  simdgroup_multiply_accumulate(mat_c, mat_a, mat_b, mat_c);\n  return {dtype_out}(mat_c.thread_elements()[0], mat_c.thread_elements()[1]);\n}}""")
     return super().render_kernel(function_name, kernel, bufs, uops, prefix)
 
 _nms = "xyzwabcdefghijkl"
@@ -369,7 +362,7 @@ class CUDARenderer(CStyleLanguage):
 
     dt_map_in = { dtypes.float: "tf32", dtypes.half: "f16", dtypes.bfloat16: "bf16" }
     dt_map_out = { dtypes.float: "f32", dtypes.half: "f16" }
-    for name, (N, M, K), dtype_in, dtype_out, _, _, upcast_axes, _ in wmma_args(uops):
+    for name, (N, M, K), dtype_in, dtype_out, _, _, upcast_axes, _ in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]):
       upcast_sizes = [prod(size for _, size in upcast) for upcast in upcast_axes]
       wmma_dtypes = [self.render_dtype(dtype.vec(size)) for dtype, size in zip([dtype_in, dtype_in, dtype_out], upcast_sizes)]
       n_operands = [size*dtype.itemsize//4 for dtype, size in zip([dtype_in, dtype_in, dtype_out], upcast_sizes)] # 4 => CUDA reg size in bytes
@@ -464,15 +457,15 @@ class AMDRenderer(CStyleLanguage):
     if any(dt.scalar() == dtypes.bfloat16 for dt in used_dtypes): prefix.append("typedef unsigned short hip_bfloat16;")
     prefix += [self.render_vector_prefix(dt) for dt in used_dtypes if dt.count > 1]
 
-    for name, _, dtype_in, dtype_out, _, _, _, _ in wmma_args(uops): # TODO: handle TCs f32_bf16 and bf16_bf16 w/ wrapper
+    for arg in dedup([uop.arg for uop in uops if uop.op is Ops.WMMA]): # TODO: handle TCs f32_bf16 and bf16_bf16 w/ wrapper
       if self.tensor_cores == tc.amd_cdna:
-        prefix.append(f"#define __{name} __builtin_amdgcn_mfma_f32_16x16x16{'f16' if dtype_in == dtypes.half else 'bf16_1k'}")
+        prefix.append(f"#define __{arg[0]} __builtin_amdgcn_mfma_f32_16x16x16{'f16' if arg[2] == dtypes.half else 'bf16_1k'}")
       # #define __WMMA_16_16_16_half_half __builtin_amdgcn_wmma_f16_16x16x16_f16_w32_gfx12
       elif self.tensor_cores == tc.amd_rdna4:
-        prefix.append(f"#define __{name} __builtin_amdgcn_wmma_{type_map[dtype_out]}_16x16x16_{type_map[dtype_in]}_w32_gfx12")
+        prefix.append(f"#define __{arg[0]} __builtin_amdgcn_wmma_{type_map[arg[3]]}_16x16x16_{type_map[arg[2]]}_w32_gfx12")
-      elif dtype_out == dtypes.float:
+      elif arg[3] == dtypes.float:
-        prefix.append(f"#define __{name} __builtin_amdgcn_wmma_f32_16x16x16_{'f16' if dtype_in == dtypes.half else 'bf16'}_w32")
+        prefix.append(f"#define __{arg[0]} __builtin_amdgcn_wmma_f32_16x16x16_{'f16' if arg[2] == dtypes.half else 'bf16'}_w32")
-      else: prefix.append(f"static inline __attribute__((device)) half8 __{name}"+"""(half16 a, half16 b, half8 c) {
+      else: prefix.append(f"static inline __attribute__((device)) half8 __{arg[0]}"+"""(half16 a, half16 b, half8 c) {
   half16 c_frag = {}; half8 d; for (int n = 0; n < 8; n++) { c_frag[n*2] = c[n]; }
   c_frag = __builtin_amdgcn_wmma_f16_16x16x16_f16_w32(a, b, c_frag, false);
   for (int n = 0; n < 8; n++) { d[n] = c_frag[n*2]; } return d;\n}""")

tinygrad/renderer/llvmir.py

@@ -48,9 +48,8 @@ def render_wmma_amx(ctx, wmma: UOp) -> str:
 def render_wmma_amd(ctx, wmma: UOp, arch: str) -> str:
   dt_map = {dtypes.half: "f16", dtypes.float: "f32", dtypes.bfloat16: "bf16", dtypes.ushort: "bf16"}
   # https://github.com/llvm/llvm-project/blob/main/clang/test/CodeGenOpenCL/builtins-amdgcn-mfma.cl
-  if arch.split(":")[0] in {"gfx942", "gfx950"}:
+  if arch.split(":")[0] == "gfx942": return f"  {ctx[wmma]} = call {ldt(wmma.dtype)} @llvm.amdgcn.mfma.{dt_map[wmma.src[-1].dtype.scalar()]}" + \
-    return f"  {ctx[wmma]} = call {ldt(wmma.dtype)} @llvm.amdgcn.mfma.{dt_map[wmma.src[-1].dtype.scalar()]}" + \
+    f".16x16x16{dt_map[wmma.src[0].dtype.scalar()]}(" + ", ".join([f"{ldt(w.dtype)} {ctx[w]}" for w in wmma.src]) + ", i32 0, i32 0, i32 0)"
-           f".16x16x16{dt_map[wmma.src[0].dtype.scalar()]}(" + ", ".join([f"{ldt(w.dtype)} {ctx[w]}" for w in wmma.src]) + ", i32 0, i32 0, i32 0)"
   # https://github.com/llvm/llvm-project/blob/main/llvm/test/CodeGen/AMDGPU/GlobalISel/llvm.amdgcn.wmma_32.ll
   # example: %wmma0 = call <8 x float> @llvm.amdgcn.wmma.f32.16x16x16.f16(<16 x half> %v99,<16 x half> %v100,<8 x float> %v101)
   return f"  {ctx[wmma]} = call {ldt(wmma.dtype)} @llvm.amdgcn.wmma.{dt_map[wmma.src[-1].dtype.scalar()]}.16x16x16." + \
@@ -161,7 +160,6 @@ class LLVMRenderer(Renderer):
 
     name = "test"
     for u in uops:
-      if u.op is Ops.NOOP: continue
       if u.op is Ops.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue
@@ -172,7 +170,13 @@ class LLVMRenderer(Renderer):
         r[u] = f"%{'local' if u.op is Ops.DEFINE_LOCAL else 'reg'}_{str(u.arg).replace('(', '').replace(')', '').replace(',', '_').replace(' ', '')}"
         assert isinstance(u.dtype, PtrDType)
         if self.device == "LLVM" or u.op is Ops.DEFINE_REG:
-          kernel.append(f"  {r[u]} = alloca [{u.dtype.size} x {ldt(u.dtype.base)}]")
+          # put alloca in the beginning of the function always
+          kernel = [f"  {r[u]} = alloca [{u.dtype.size} x {ldt(u.dtype.base)}]"] + kernel
+          if u.op is Ops.DEFINE_REG:
+            # store the const here. TODO: this should be INDEX and STORE and shouldn't be handcoded here
+            for i in range(u.dtype.size):
+              kernel.append(f"  {r[u]}_idx_{i} = getelementptr inbounds {ldt(u.dtype.base)}, {ldt(u.dtype)} {r[u]}, i32 {i}")
+              kernel.append(f"  store {ldt(u.src[0].dtype)} {r[u.src[0]]}, {ldt(u.dtype)} {r[u]}_idx_{i}")
         else:
           local_args.append(f"@{r[u][1:]} = internal unnamed_addr addrspace(3) global [{u.dtype.size} x {ldt(u.dtype)}] undef, align 16")
           kernel.append(f"  {r[u]} = addrspacecast [{u.dtype.size} x {ldt(u.dtype)}] addrspace(3)* @{r[u][1:]} to [{u.dtype.size} x {ldt(u.dtype)}]*")
@@ -189,6 +193,9 @@ class LLVMRenderer(Renderer):
         if (l:=self.string_rewrite.rewrite(u, ctx=r)) is None:
           raise RuntimeError(f"failed to render {u.op} with {u.dtype} srcs {[x.dtype for x in u.src]}")
         kernel.append(cast(str, l))
+
+        # stores pass the first arg through
+        if u.op is Ops.STORE: r[u] = r[u.src[0]]
     return tuple(local_args), self._render_fn(name, args, kernel, prefix)
 
 barrier = 'fence syncscope("workgroup") release\ntail call void @llvm.amdgcn.s.barrier()\nfence syncscope("workgroup") acquire\n'

tinygrad/renderer/ptx.py

@@ -110,7 +110,10 @@ string_rewrite = PatternMatcher([
   (UPat(Ops.LOAD, name="x", src=(UPat.var('loc'),), allow_any_len=True),
    lambda ctx, x, loc: f"ld.{mem_type(x)}.v{x.dtype.count}.{ctx.mem_types[x.dtype.scalar()]} {{{', '.join(ctx.r[x])}}}, [{ctx.r[loc]}+0];" \
      if x.dtype.count > 1 else f"ld.{mem_type(x)}.{ctx.mem_types[x.dtype]} {ctx.r[x]}, [{ctx.r[loc]}+0];"),
-  (UPat(Ops.DEFINE_REG, src=()), lambda ctx: []),
+  (UPat(Ops.DEFINE_REG, name="x", src=(UPat.cvar("pred", dtype=dtypes.bool),), allow_any_len=True), lambda ctx, x, pred: [
+    f"setp.ne.s16 {ctx.r[pred]}, {render_val(pred.arg, pred.dtype)}, 0;", f"mov.pred {ctx.r[x]}, {ctx.r[pred]};"]),
+  (UPat(Ops.DEFINE_REG, name="x", src=(UPat.cvar("pred"),), allow_any_len=True),
+   lambda ctx, x, pred: f"mov.b{ctx.types[x.dtype.base][1:]} {ctx.r[x]}, {render_val(pred.arg, x.dtype.base)};"),
   (UPat(Ops.RANGE, name="x"), lambda ctx, x: [f"mov.u32 {ctx.r[x]}, 0;", "LOOP_" + f"{ctx.r[x][1:]}:"]),
   (UPat(Ops.ENDRANGE, name="x", src=(UPat.var("src0"),)), lambda ctx, x, src0: [
     ctx.code_for_op[Ops.ADD](ctx.r[src0], ctx.r[src0], "1", dtypes.int, ctx.types[dtypes.int]),
@@ -173,7 +176,6 @@ class PTXRenderer(Renderer):
 
     name = "test"
     for u in uops:
-      if u.op is Ops.NOOP: continue
       if u.op is Ops.SINK:
         if u.arg is not None: name = u.arg.function_name
         continue
@@ -186,18 +188,11 @@ class PTXRenderer(Renderer):
       if u.op in {Ops.CAST, Ops.BITCAST} and (u.src[0].dtype == u.dtype or isinstance(u.src[0].dtype, PtrDType)):
         r[u] = r[u.src[0]]
         continue
-      if u.op is Ops.DEFINE_REG:
-        r[u] = [ssa("reg", u, self.types[u.dtype.base.scalar()]) for _ in range(cast(PtrDType, u.dtype).size)]
-        continue
       if u.op in {Ops.INDEX, Ops.LOAD, Ops.STORE} and isinstance(u.src[0].dtype, PtrDType) and u.src[0].dtype.addrspace == AddrSpace.REG:
-        if u.op is Ops.INDEX:
+        r[u] = r[u.src[0]]
-          assert u.src[1].op == Ops.CONST, f"index on REG in ptx only supported on CONST, not {u.src[1].op}"
+        if u.op is Ops.STORE:
-          r[u] = r[u.src[0]][u.src[1].arg]
+          typ = "pred" if u.src[1].dtype == dtypes.bool else ("b"+self.types[u.src[1].dtype][1:])
-        else:
+          kernel.append(f"mov.{typ} {self.r[u.src[0]]}, {self.r[u.src[1]]};")
-          r[u] = r[u.src[0]]
-          if u.op is Ops.STORE:
-            typ = "pred" if u.src[1].dtype == dtypes.bool else ("b"+self.types[u.src[1].dtype][1:])
-            kernel.append(f"mov.{typ} {self.r[u.src[0]]}, {self.r[u.src[1]]};")
         continue
       if u.op is Ops.SPECIAL: r[u] = "%" + u.arg[0]
       elif u.op is Ops.DEFINE_VAR: bufs.append((u.arg[0], u.dtype))
@@ -207,12 +202,12 @@ class PTXRenderer(Renderer):
       elif u.op is Ops.DEFINE_GLOBAL: bufs.append((f"data{u.arg}", u.dtype))
       elif u.op is Ops.WMMA:
         # registers for packing/unpacking input and acc
-        self.wmma_r = [[ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[0]]), 4 // u.src[0].dtype.scalar().itemsize)],
+        self.wmma_r = [[ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[0]]), 4 // u.arg[2].itemsize)],
-                       [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.src[0].dtype.scalar().itemsize)],
+                       [ssa("wmma_in", dtype="b32") for _ in range(0, len(r[u.src[1]]), 4 // u.arg[2].itemsize)],
-                       [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.dtype.scalar().itemsize)]]
+                       [ssa("wmma_acc", dtype="b32") for _ in range(0, len(r[u.src[2]]), 4 // u.arg[3].itemsize)]]
         r[u] = [ssa("wmma", dtype=self.types[u.dtype.scalar()]) for _ in range(u.dtype.count)]
       prefix, dtype = {Ops.CAST: ("cast", None), Ops.BITCAST: ("cast", None), Ops.ENDRANGE: ("pred", "pred"), Ops.RANGE: ("ridx", None),
-        Ops.DEFINE_VAR: ("dat", None), Ops.CONST: ("const", None), Ops.DEFINE_LOCAL:("local",self.types[dtypes.ulong]),
+        Ops.DEFINE_REG: ("acc", None), Ops.DEFINE_VAR: ("dat", None), Ops.CONST: ("const", None), Ops.DEFINE_LOCAL:("local",self.types[dtypes.ulong]),
         Ops.DEFINE_GLOBAL: ("dat", self.types[dtypes.ulong]), **{op: ("alu", None) for op in GroupOp.ALU}}.get(u.op, (None, None))
       if prefix: r[u] = ssa(prefix, u, dtype)
 

tinygrad/renderer/wgsl.py

@@ -40,6 +40,11 @@ wgsl_matcher = PatternMatcher([
   (UPat.var("x") >> UPat.var("y"), lambda x,y: UOp(Ops.SHR, x.dtype, (x,y.cast(dtypes.uint))) if y.dtype != dtypes.uint else None),
   ]) + extra_pm
 
+def webgpu_define_reg(ctx, x):
+  ret = [f"var {ctx[x]}: array<{ctx.buf_map(x.dtype)},{x.dtype.size//(4//x.dtype.itemsize) if is_packed(x.dtype) else x.dtype.size}>;"]
+  for i in range(x.dtype.size): ret.append(f"{ctx[x]}[{i}] = {ctx[x.src[0]]};")
+  return ' '.join(ret)
+
 class WGSLRenderer(CStyleLanguage):
   device = "WEBGPU"
   global_max = (65535, 65535, 65535)
@@ -59,8 +64,7 @@ class WGSLRenderer(CStyleLanguage):
      lambda x: f"bitcast<u32>({x.arg})" if x.arg < 0 else f"{x.arg&0xFFFFFFFF}u"),
     (UPat(Ops.DEFINE_LOCAL, name="x"), lambda ctx,x:
       f"var<workgroup> {ctx[x]}: array<{ctx.buf_map(x.dtype.base)},{x.dtype.size//(4//x.dtype.itemsize) if is_packed(x.dtype) else x.dtype.size}>;"),
-    (UPat(Ops.DEFINE_REG, name="x"), lambda ctx,x:
+    (UPat(Ops.DEFINE_REG, name="x"), webgpu_define_reg),
-     f"var {ctx[x]}: array<{ctx.buf_map(x.dtype)},{x.dtype.size//(4//x.dtype.itemsize) if is_packed(x.dtype) else x.dtype.size}>;"),
     (UPat(Ops.BITCAST, dtype=dtypes.half, name="x", src=(UPat(dtype=(dtypes.short, dtypes.ushort, dtypes.uint32),),)),
      lambda ctx,x: f"bitcast<vec2<f16>>({ctx[x.src[0]]})[0]"),
     (UPat(Ops.BITCAST, dtype=(dtypes.char, dtypes.uchar), name="x"), lambda ctx,x: f"bitcast<{ctx.type_map[x.dtype]}>({ctx[x.src[0]]}&0xFF)"),

tinygrad/runtime/graph/hcq.py

@@ -1,7 +1,7 @@
 import collections, time
 from typing import Any, cast
-from tinygrad.helpers import round_up, PROFILE, merge_dicts, getenv, dedup
+from tinygrad.helpers import round_up, PROFILE, merge_dicts, getenv
-from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQSignal, HCQBuffer, HWQueue, HCQArgsState, BumpAllocator, MMIOInterface
+from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocator, HCQSignal, HCQBuffer, HWQueue, HCQArgsState, BumpAllocator
 from tinygrad.device import Buffer, BufferSpec, Compiled, Device, ProfileGraphEntry, ProfileGraphEvent
 from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, Variable
@@ -29,7 +29,7 @@ class HCQGraph(MultiGraphRunner):
     for ji in jit_cache:
       if not isinstance(ji.prg, CompiledRunner): continue
       kernargs_size[ji.prg.dev] += round_up(ji.prg._prg.kernargs_alloc_size, 16)
-    self.kernargs_bufs: dict[Compiled, HCQBuffer] = {d:d.allocator._alloc(max(sz, 1), BufferSpec(cpu_access=True)) for d,sz in kernargs_size.items()}
+    self.kernargs_bufs: dict[Compiled, HCQBuffer] = {dev:dev.allocator._alloc(sz, BufferSpec(cpu_access=True)) for dev,sz in kernargs_size.items()}
 
     # Fill initial arguments.
     self.ji_args: dict[int, HCQArgsState] = {}
@@ -51,8 +51,8 @@ class HCQGraph(MultiGraphRunner):
     self.comp_queues: dict[HCQCompiled, HWQueue] = {dev: dev.hw_compute_queue_t() for dev in self.devices}
     self.copy_queues: dict[HCQCompiled, HWQueue] = {} # lazy allocation
 
-    self.signals: dict[Any, HCQSignal] = {**{dev: dev.new_signal(value=0) for dev in self.devices if not dev._is_cpu()},
+    self.signals: dict[Any, HCQSignal] = {**{dev: dev.new_signal(value=0) for dev in self.devices if dev.device != "CPU"},
-      **{"KICK": self.devices[0].new_signal(value=0)}, **{dev: self.devices[0].new_signal(value=0) for dev in self.devices if dev._is_cpu()}}
+      **{"KICK": self.devices[0].new_signal(value=0)}, **{dev: self.devices[0].new_signal(value=0) for dev in self.devices if dev.device == "CPU"}}
     self.kickoff_value: int = 0
     self.kickoff_var = UOp.variable("kickoff_var", 0, 0xffffffff, dtype=dtypes.uint32)
 
@@ -87,7 +87,7 @@ class HCQGraph(MultiGraphRunner):
         assert (enqueue_dev.hw_copy_queue_t is not None), "device must implement a copy queue"
         enqueue_queue = self.copy_queues.setdefault(enqueue_dev, enqueue_dev.hw_copy_queue_t())
 
-      out_signal = self.signals.setdefault(enqueue_queue, self.devices[0].new_signal(value=0))
+      out_signal = self.signals.setdefault(enqueue_queue, enqueue_dev.new_signal(value=0))
 
       # Get dependencies based on input and output buffers.
       rdeps = self._access_resources(ji.bufs, ji.prg.p.outs if is_exec_prg else [0], (enqueue_queue, j + 1)) #type:ignore
@@ -225,17 +225,7 @@ class HCQGraph(MultiGraphRunner):
     for fdev, buf in self.kernargs_bufs.items(): fdev.allocator._free(buf, BufferSpec(cpu_access=True))
 
   @staticmethod
-  def supports_exec_item(devs:list[Compiled], ei:ExecItem) -> bool:
+  def supports_exec_item(dev, ei:ExecItem) -> bool:
-    # Check if all devices are HCQ
-    all_devs = cast(list[HCQCompiled], dedup(devs + [Device[b.device] for b in ei.bufs if b]))
-    if not all(issubclass(type(d), HCQCompiled) for d in all_devs): return False
-
-    # If all of devices are mapped into CPU address space, can use CPU inside the peer group.
-    cpu_support = all(isinstance(d.timeline_signal.base_buf.view, MMIOInterface) for d in all_devs)
-
-    # Check if all devices are within the same peer group. If CPU is supported, don't count it as a separate peer group.
-    if len(set(d.peer_group for d in all_devs if cpu_support and not d._is_cpu())) > 1: return False
-
     # MOCKGPU is not supported, since it can't execute commands in parallel
-    copy = (isinstance(ei.prg, BufferCopy) and cast(HCQCompiled, devs[0]).hw_copy_queue_t is not None) and not getenv("MOCKGPU")
+    copy = (isinstance(ei.prg, BufferCopy) and cast(HCQCompiled, dev).hw_copy_queue_t is not None) and not getenv("MOCKGPU")
-    return isinstance(ei.prg, (CompiledRunner, BufferXfer)) or copy
+    return all(issubclass(type(Device[b.device]), HCQCompiled) for b in ei.bufs if b) and (isinstance(ei.prg, (CompiledRunner, BufferXfer)) or copy)

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, suppress_finalizing
+from tinygrad.helpers import getenv, to_mv, round_up, data64_le, all_same, flatten, DEBUG, AMD_LLVM, PROFILE, ProfileEvent
 from tinygrad.renderer.cstyle import AMDRenderer
 from tinygrad.renderer.llvmir import AMDLLVMRenderer
 from tinygrad.runtime.autogen import kfd, hsa, pci, sqtt
@@ -473,10 +473,11 @@ 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):
-    self.dev.synchronize()
+    try:
-    self.dev.iface.free(opaque)
+      self.dev.synchronize()
+      self.dev.iface.free(opaque)
+    except AttributeError: pass
 
   def _map(self, buf:HCQBuffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
 
@@ -592,7 +593,7 @@ class KFDIface:
 
   def free(self, mem):
     if len(mem.mapped_devs) > 0:
-      gpus = (ctypes.c_int32 * len(mem.mapped_devs))(*[x.iface.gpu_id for x in mem.mapped_devs])
+      gpus = (ctypes.c_int32 * len(mem.mapped_devs))(*[x.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)
@@ -673,8 +674,8 @@ class PCIIface(PCIIfaceBase):
       self.dev_impl.gfx.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr, wptr_addr=gart.va_addr+0x10,
         eop_addr=eop_buffer.va_addr, eop_size=eop_buffer.size, doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_MEC_RING0), pipe=0, queue=0)
 
-    return AMDQueueDesc(ring=ring.cpu_view().view(fmt='I'), doorbells=[self.dev_impl.doorbell64.view(doorbell_index * 8, 8, fmt='Q')],
+    return AMDQueueDesc(ring=MMIOInterface(ring.va_addr, ring.size, fmt='I'), read_ptrs=[MMIOInterface(gart.va_addr, 8, fmt='Q')],
-      read_ptrs=[gart.cpu_view().view(size=8, fmt='Q')], write_ptrs=[gart.cpu_view().view(offset=0x10, size=8, fmt='Q')])
+      write_ptrs=[MMIOInterface(gart.va_addr+0x10, 8, fmt='Q')], doorbells=[MMIOInterface(self.doorbell_cpu_addr + doorbell_index * 8, 8, fmt='Q')])
 
   def sleep(self, timeout):
     if self.pci_dev.irq_poller is not None and (events_cnt:=len(self.pci_dev.irq_poller.poll(timeout))):
@@ -717,8 +718,16 @@ class USBIface(PCIIface):
       view=USBMMIOInterface(self.usb, self.bars[0][0] + am_mapping.paddrs[0][0], size, fmt='B') if cpu_access else None, owner=self.dev)
 
   def create_queue(self, queue_type, ring, gart, eop_buffer=None, cwsr_buffer=None, ctl_stack_size=0, ctx_save_restore_size=0, xcc_id=0):
-    if queue_type == kfd.KFD_IOC_QUEUE_TYPE_COMPUTE: self.usb._pci_cacheable += [(ring.cpu_view().addr, ring.size)]
+    if queue_type == kfd.KFD_IOC_QUEUE_TYPE_SDMA:
-    return super().create_queue(queue_type, ring, gart, eop_buffer, cwsr_buffer, ctl_stack_size, ctx_save_restore_size, xcc_id)
+      self.dev_impl.sdma.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr, wptr_addr=gart.va_addr+0x10,
+                                    doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_sDMA_ENGINE0), pipe=0, queue=0)
+    else:
+      self.dev_impl.gfx.setup_ring(ring_addr=ring.va_addr, ring_size=ring.size, rptr_addr=gart.va_addr, wptr_addr=gart.va_addr+0x10,
+        eop_addr=eop_buffer.va_addr, eop_size=eop_buffer.size, doorbell=(doorbell_index:=am.AMDGPU_NAVI10_DOORBELL_MEC_RING0), pipe=0, queue=0)
+      self.usb._pci_cacheable += [(ring.cpu_view().addr, ring.size)]
+
+    return AMDQueueDesc(ring=ring.cpu_view().view(fmt='I'), doorbells=[self.dev_impl.doorbell64.view(doorbell_index * 8, 8, fmt='Q')],
+      read_ptrs=[gart.cpu_view().view(size=8, fmt='Q')], write_ptrs=[gart.cpu_view().view(offset=0x10, size=8, fmt='Q')])
 
   def sleep(self, timeout): pass
 
@@ -739,7 +748,7 @@ class AMDDevice(HCQCompiled):
                        (min((self.max_cu_id+1)*40, self.iface.props['array_count'] // self.iface.props['simd_arrays_per_engine'] * 512) - 1)
     self.xccs = self.iface.props.get('num_xcc', 1) if getenv("XCCS", 1) else 1
     # this is what llvm refers to as "architected flat scratch"
-    self.has_scratch_base_registers = self.target >= (11,0,0) or self.target in {(9,4,2), (9,5,0)}
+    self.has_scratch_base_registers = self.target >= (11,0,0) or self.target in {(9,4,2),(9,5)}
 
     # https://gitlab.freedesktop.org/agd5f/linux/-/blob/a1fc9f584c4aaf8bc1ebfa459fc57a3f26a290d8/drivers/gpu/drm/amd/amdkfd/kfd_queue.c#L391
     sgrp_size_per_cu, lds_size_per_cu, hwreg_size_per_cu = 0x4000, 0x10000, 0x1000

tinygrad/runtime/ops_cpu.py

@@ -1,16 +1,12 @@
 from __future__ import annotations
-import platform, subprocess, sys, ctypes, functools, time, mmap, threading, queue
+import platform, subprocess, sys, ctypes, functools, time, mmap
-from tinygrad.helpers import capstone_flatdump, getenv, from_mv, to_mv, OSX, mv_address, wait_cond, cpu_profile
+from tinygrad.helpers import capstone_flatdump, getenv, from_mv, to_mv, OSX, mv_address, wait_cond
 from tinygrad.device import Compiler, BufferSpec, DMACPURef
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQAllocatorBase, HCQBuffer, HWQueue, HCQArgsState, HCQSignal, HCQProgram, MMIOInterface
 from tinygrad.runtime.support.elf import jit_loader
 from tinygrad.renderer.cstyle import ClangRenderer
 from tinygrad.uop.ops import sint
 
-class CPUSignal(HCQSignal):
-  def _sleep(self, time_spent_waiting_ms:int):
-    if self.is_timeline and self.owner is not None: self.owner.tasks.join()
-
 class ClangJITCompiler(Compiler):
   def __init__(self, cachekey="compile_clang_jit"): super().__init__(cachekey)
 
@@ -25,19 +21,6 @@ class ClangJITCompiler(Compiler):
 
   def disassemble(self, lib:bytes): return capstone_flatdump(lib)
 
-class CPUWorker(threading.Thread):
-  def __init__(self, dev):
-    super().__init__()
-    self.dev, self.tasks, self.daemon = dev, dev.tasks, True
-
-  def run(self):
-    while True:
-      cmd_iter = iter(self.tasks.get())
-      for cmd in cmd_iter:
-        args_cnt = next(cmd_iter)
-        cmd(*[next(cmd_iter) for _ in range(args_cnt)])
-      self.tasks.task_done()
-
 class CPUComputeQueue(HWQueue):
   def _exec(self, prg, bufs, *args):
     prg.fxn(*map(ctypes.c_uint64, args[:bufs]), *map(ctypes.c_int64 if platform.machine() == "arm64" else ctypes.c_int32, args[bufs:]))
@@ -54,7 +37,13 @@ class CPUComputeQueue(HWQueue):
   def wait(self, signal, value=0): return self.cmd(self._wait, signal.value_addr, value)
   def timestamp(self, signal): return self.cmd(self._timestamp, signal.timestamp_addr)
   def signal(self, signal, value:sint=0): return self.cmd(self._signal, signal.value_addr, value)
-  def _submit(self, dev): dev.tasks.put(self._q[:])
+
+  def _submit(self, dev):
+    # Execute the commands in the queue: fn, argc, args...
+    off = 0
+    while off < len(self._q):
+      self._q[off](*self._q[off + 2:off + 2 + self._q[off + 1]])
+      off += self._q[off + 1] + 2
 
 # NOTE: MAP_JIT is added to mmap module in python 3.13
 MAP_JIT = 0x0800
@@ -101,23 +90,14 @@ class CPUAllocator(HCQAllocatorBase):
     elif sys.platform == "win32": addr = mv_address(buf:=mmap.mmap(-1, size, access=mmap.ACCESS_WRITE))
     else: addr = mv_address(buf:=mmap.mmap(-1, size, mmap.MAP_ANON | mmap.MAP_PRIVATE, mmap.PROT_READ | mmap.PROT_WRITE))
     return HCQBuffer(va:=addr, sz:=size, meta=buf, view=MMIOInterface(va, sz, fmt='B'), owner=self.dev)
-  def _as_buffer(self, src) -> memoryview:
+  def _as_buffer(self, src) -> memoryview: return to_mv(src.va_addr, src.size)
-   self.dev.synchronize()
+  def _as_dmaref(self, buf): return DMACPURef(buf.va_addr, buf.size)
-   return to_mv(src.va_addr, src.size)
+  def _copyin(self, dest, src:memoryview): ctypes.memmove(dest.va_addr, from_mv(src), len(src))
-  def _as_dmaref(self, buf):
+  def _copyout(self, dest:memoryview, src): ctypes.memmove(from_mv(dest), src.va_addr, len(dest))
-    self.dev.synchronize()
-    return DMACPURef(buf.va_addr, buf.size)
-  def _copyin(self, dest, src:memoryview):
-    self.dev.synchronize()
-    with cpu_profile('TINY -> CPU', self.dev.device, is_copy=True): ctypes.memmove(dest.va_addr, from_mv(src), len(src))
-  def _copyout(self, dest:memoryview, src):
-    self.dev.synchronize()
-    with cpu_profile('CPU -> TINY', self.dev.device, is_copy=True): ctypes.memmove(from_mv(dest), src.va_addr, len(dest))
   def _map(self, buf:HCQBuffer):
     if buf.view is None or not isinstance(buf.view, MMIOInterface): raise RuntimeError("Cannot map buffer without view to cpu")
 
 class CPUDevice(HCQCompiled):
   def __init__(self, device:str=""):
-    self.tasks:queue.Queue = queue.Queue()
+    super().__init__(device, CPUAllocator(self), ClangRenderer(), ClangJITCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue,
-    CPUWorker(self).start()
+                     supports_graph=False)
-    super().__init__(device, CPUAllocator(self), ClangRenderer(), ClangJITCompiler(), functools.partial(CPUProgram, self), CPUSignal, CPUComputeQueue)

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, suppress_finalizing
+from tinygrad.helpers import DEBUG, getenv, mv_address, init_c_var, init_c_struct_t
 from tinygrad.device import Compiled, BufferSpec, LRUAllocator
 from tinygrad.renderer.cstyle import CUDARenderer
 from tinygrad.renderer.ptx import PTXRenderer
@@ -45,8 +45,9 @@ 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))
 
-  @suppress_finalizing
+  def __del__(self):
-  def __del__(self): check(cuda.cuModuleUnload(self.module))
+    try: check(cuda.cuModuleUnload(self.module))
+    except AttributeError: pass
 
   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, suppress_finalizing
+from tinygrad.helpers import init_c_var, to_char_p_p, from_mv, OSX, DEBUG, getenv, mv_address
 from tinygrad.renderer.cstyle import OpenCLRenderer, IntelRenderer
 from tinygrad.device import BufferSpec, LRUAllocator, Compiled, Compiler, CompileError
 
@@ -69,8 +69,9 @@ 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)
-  @suppress_finalizing
+  def _free(self, opaque:tuple[ctypes._CData, BufferSpec], options:BufferSpec):
-  def _free(self, opaque:tuple[ctypes._CData, BufferSpec], options:BufferSpec): check(cl.clReleaseMemObject(opaque[0]))
+    try: check(cl.clReleaseMemObject(opaque[0]))
+    except AttributeError: pass
   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_llvm.py

@@ -1,7 +1,7 @@
-import ctypes, platform, functools, queue
+import ctypes, platform, functools
 from tinygrad.device import Compiler
 from tinygrad.runtime.support.hcq import HCQCompiled, HCQSignal
-from tinygrad.runtime.ops_cpu import CPUAllocator, CPUProgram, CPUComputeQueue, CPUWorker
+from tinygrad.runtime.ops_cpu import CPUAllocator, CPUProgram, CPUComputeQueue
 from tinygrad.helpers import OSX, getenv, capstone_flatdump, DEBUG
 from tinygrad.renderer.llvmir import LLVMRenderer
 import tinygrad.runtime.autogen.llvm as llvm
@@ -73,6 +73,5 @@ class HostLLVMCompiler(LLVMCompiler):
 
 class LLVMDevice(HCQCompiled):
   def __init__(self, device:str=""):
-    self.tasks:queue.Queue = queue.Queue()
+    super().__init__(device, CPUAllocator(self), LLVMRenderer(), HostLLVMCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue,
-    CPUWorker(self).start()
+                     supports_graph=False)
-    super().__init__(device, CPUAllocator(self), LLVMRenderer(), HostLLVMCompiler(), functools.partial(CPUProgram, self), HCQSignal, CPUComputeQueue)

tinygrad/runtime/ops_metal.py

@@ -1,6 +1,6 @@
-import subprocess, pathlib, struct, ctypes, tempfile, functools, contextlib, decimal, platform
+import os, pathlib, struct, ctypes, tempfile, functools, contextlib, decimal, platform
 from typing import Any, cast
-from tinygrad.helpers import prod, to_mv, getenv, round_up, cache_dir, T, init_c_struct_t, PROFILE, ProfileRangeEvent, cpu_profile, unwrap
+from tinygrad.helpers import prod, to_mv, getenv, round_up, cache_dir, T, init_c_struct_t, PROFILE, ProfileRangeEvent, cpu_profile
 from tinygrad.device import Compiled, Compiler, CompileError, LRUAllocator, ProfileDeviceEvent
 from tinygrad.renderer.cstyle import MetalRenderer
 
@@ -144,10 +144,7 @@ class MetalCompiler(Compiler):
     with tempfile.NamedTemporaryFile(delete=True) as shader:
       shader.write(lib)
       shader.flush()
-      proc = subprocess.Popen(f"cd {pathlib.Path(__file__).parents[2]}/extra/disassemblers/applegpu && python3 compiler_explorer.py {shader.name}",
+      ret = os.system(f"cd {pathlib.Path(__file__).parents[2]}/extra/disassemblers/applegpu && python3 compiler_explorer.py {shader.name}")
-                              stdout=subprocess.PIPE, shell=True, text=True, bufsize=1)
-      for line in unwrap(proc.stdout): print(line, end="")
-      ret = proc.wait()
       if ret: print("Disassembler Error: Make sure you have https://github.com/dougallj/applegpu cloned to tinygrad/extra/disassemblers/applegpu")
 
 class MetalProgram:
@@ -226,6 +223,6 @@ class MetalAllocator(LRUAllocator[MetalDevice]):
   def _as_buffer(self, src:MetalBuffer) -> memoryview:
     self.dev.synchronize()
     return to_mv(cast(int, msg("contents", objc_id)(src.buf).value), src.size + src.offset)[src.offset:]
-  def _copyin(self, dest:MetalBuffer, src:memoryview): self._cp_mv(self._as_buffer(dest), src, "TINY -> METAL")
+  def _copyin(self, dest:MetalBuffer, src:memoryview): self._cp_mv(self._as_buffer(dest), src, "CPU -> METAL")
-  def _copyout(self, dest:memoryview, src:MetalBuffer): self._cp_mv(dest, self._as_buffer(src), "METAL -> TINY")
+  def _copyout(self, dest:memoryview, src:MetalBuffer): self._cp_mv(dest, self._as_buffer(src), "METAL -> CPU")
   def _offset(self, buf:MetalBuffer, size:int, offset:int): return MetalBuffer(buf.buf, size, offset)

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, suppress_finalizing
+from tinygrad.helpers import getenv, mv_address, round_up, data64, data64_le, prod, OSX, to_mv, hi32, lo32
 from tinygrad.renderer.ptx import PTXRenderer
 from tinygrad.renderer.cstyle import NVRenderer
 from tinygrad.runtime.support.compiler_cuda import CUDACompiler, PTXCompiler, PTX, NVPTXCompiler, NVCompiler
@@ -276,10 +276,11 @@ 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):
-    self.dev.synchronize()
+    try:
-    self.dev.iface.free(opaque)
+      self.dev.synchronize()
+      self.dev.iface.free(opaque)
+    except AttributeError: pass
 
   def _map(self, buf:HCQBuffer): return self.dev.iface.map(buf._base if buf._base is not None else buf)
 

tinygrad/runtime/ops_python.py

@@ -40,7 +40,8 @@ class PythonProgram:
       loop_ends: dict[int, int] = {}
       while i < len(self.uops):
         uop, dtype, idp, arg = self.uops[i]
-        void_ops = {Ops.ENDRANGE, Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK, Ops.NOOP, Ops.STORE}
+        void_ops = {Ops.ENDRANGE, Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK}
+        if uop is Ops.DEFINE_REG: idp = [idp[0]]
         inp = [ul[v] for v in idp if self.uops[v][0] not in void_ops]
         dtp = [dl[v] for v in idp if self.uops[v][0] not in void_ops]
         if getenv("TRACE"): print(i, uop, dtype, arg, inp, dtp)
@@ -48,7 +49,7 @@ class PythonProgram:
           loop_ends[idp[0]] = i
           i = idp[0]
           continue
-        if uop in (Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK, Ops.NOOP):
+        if uop in (Ops.BARRIER, Ops.IF, Ops.ENDIF, Ops.SINK):
           # in the python emulator, the warp is always in sync
           i += 1
           continue
@@ -58,6 +59,7 @@ class PythonProgram:
           for j,val in enumerate(inp[1] if dtp[1].count > 1 else [inp[1]]):
             for (m,o,g),v in zip(inp[0], val):
               if g: _store(m, o+j, v)
+          ul[i] = inp[0]
           i += 1
           continue
         if uop in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
@@ -66,6 +68,8 @@ class PythonProgram:
           if uop is Ops.DEFINE_REG:
             # REGs are per thread
             ul[i] = [memoryview(bytearray(dtype.size*dtype.itemsize)).cast(dtype.fmt) for _ in range(warp_size)]
+            for buf, val in zip(ul[i], inp[0]):
+              for x in range(dtype.size): buf[x] = val
           else:
             buf = memoryview(bytearray(dtype.size*dtype.itemsize)) if uop is not Ops.DEFINE_GLOBAL else pbufs.pop(0)
             ul[i] = [buf.cast(dtype.fmt)] * warp_size
@@ -123,27 +127,24 @@ class PythonProgram:
                   out[elem_idx][goff+lane_id] += sum(a_elem(inp[0], _k, c_j, goff) * b_elem(inp[1], c_i, _k, goff) for _k in range(K))
             return out
 
-          first_src_dtype = self.uops[idp[0]][1]
-          assert isinstance(first_src_dtype, DType) # mypy
-          dims, dtype_in, device, threads = arg[1], first_src_dtype.scalar(), arg[4], arg[5]
           # TODO: refactor these to a shared TensorCoreLayout in kernel.py
-          if device == "METAL":
+          if arg[4] == "METAL":
             # A (2 elements on 32 threads): row major
             def a_b_elem(x, i, j, goff): return x[(i%2)][goff+(i//2)%2+(j%4)*2+(i//4)*8+(j//4)*16]
             # (i, j), C, D (2 elements on 32 threads): row major same as A/B
             def c_map(lane, elem): return (elem + ((lane%2)*2) + ((lane//8)%2)*4, ((lane//2)%4) + (lane//16)*4)
             ul[i] = wmma_helper(32, 8, 2, 2, 2, a_b_elem, a_b_elem, c_map)
-          elif device == "AMD" and threads == 64:
+          elif arg[4] == "AMD" and arg[5] == 64:
             def a_elem(x, k, row, goff): return x[k%4][goff + (k//4)*16 + row]
             def b_elem(x, col, k, goff): return a_elem(x, k, col, goff) # pylint: disable=arguments-out-of-order
             def c_map(lane, elem): return (lane%16, (lane//16)*4 + elem)
             ul[i] = wmma_helper(64, 16, 4, 4, 4, a_elem, b_elem, c_map)
-          elif device == "AMD" and len(inp[0]) == 8: # RDNA4
+          elif arg[4] == "AMD" and len(inp[0]) == 8: # RDNA4
             def a_elem(x, k, row, goff): return x[k - [0, 4, 4, 8][k//4]][goff + row + [0, 16, 0, 16][k//4]]
             def b_elem(x, col, k, goff): return a_elem(x, k, col, goff)
             def c_map(lane, elem): return (lane%16, (lane//16)*8 + elem)
             ul[i] = wmma_helper(32, 16, 8, 8, 8, a_elem, b_elem, c_map)
-          elif device == "AMD":
+          elif arg[4] == "AMD":
             # A (16 elements on 32 threads): col major, lane 16-32 == lane 0-15
             def a_elem(x, k, row, goff):
               assert x[k][goff+row] == x[k][goff+row+16], "warp elements not duplicated properly across lanes"
@@ -152,27 +153,27 @@ class PythonProgram:
             def b_elem(x, col, k, goff): return a_elem(x, k, col, goff)  # pylint: disable=arguments-out-of-order
             def c_map(lane, elem): return (lane%16, lane//16+elem*2) # (i, j), C, D (8 elements on 32 threads): row major
             ul[i] = wmma_helper(32, 16, 16, 16, 8, a_elem, b_elem, c_map)
-          elif device == "CUDA":
+          elif arg[4] == "CUDA":
             # (col, row) given (lane, elem) for C & D (4 elements on 32 threads); shared by all tc shapes with M=16 N=8
             def c_map(lane, elem): return (elem%2 + (lane%4)*2, lane//4 + (elem//2)*8)
 
-            if dims == (8,16,16):
+            if arg[1] == (8,16,16):
               def a_elem(x, k, row, goff): return x[k%2 + (row//8)*2 + (k//8)*4][goff + (k//2)%4 + (row%8)*4]
               def b_elem(x, col, k, goff): return x[k%2 + (k//8)*2][goff + (k//2)%4 + col*4]
               ul[i] = wmma_helper(32, 16, 8, 4, 4, a_elem, b_elem, c_map)
 
-            elif dims == (8,16,8) and dtype_in == dtypes.half:
+            elif arg[1] == (8,16,8) and arg[2] == dtypes.half:
               def a_elem(x, k, row, goff): return x[k%2 + (row//8)*2][goff + k//2 + (row%8)*4]
               def b_elem(x, col, k, goff): return x[k%2][goff + k//2 + col*4]
               ul[i] = wmma_helper(32, 8, 4, 2, 4, a_elem, b_elem, c_map)
 
-            elif dims == (8,16,8) and dtype_in == dtypes.float:
+            elif arg[1] == (8,16,8) and arg[2] == dtypes.float:
               def a_elem(x, k, row, goff): return x[(k//4)*2 + row//8][goff + k%4 + (row%8)*4]
               def b_elem(x, col, k, goff): return x[k//4][goff + k%4 + col*4]
               ul[i] = wmma_helper(32, 8, 4, 2, 4, a_elem, b_elem, c_map)
 
             else: raise NotImplementedError(f"unimplemented tensor core {arg}")
-          elif device == "INTEL":
+          elif arg[4] == "INTEL":
             # A (16 elements on 8 threads)
             def a_elem(x, k, row, goff): return x[k%2+row*2][goff+k//2]
             # B (16 elements on 8 threads)
@@ -180,7 +181,7 @@ class PythonProgram:
             # C, D (8 elements on 8 threads)
             def c_map(lane, elem): return (lane, elem)
             ul[i] = wmma_helper(8, 16, 16, 16, 8, a_elem, b_elem, c_map)
-          elif device == "CPU":
+          elif arg[4] == "CPU":
             def elem(x, col, row, _): return x[col+row][0] # k is always 0
             def c_map(_, elem): return (elem%16, elem//16)
             ul[i] = wmma_helper(1, 1, 16, 16, 256, elem, elem, c_map)

tinygrad/runtime/ops_remote.py

@@ -16,7 +16,6 @@ from tinygrad.helpers import getenv, DEBUG, fromimport, unwrap, LazySeq, Timing
 from tinygrad.engine.jit import GraphRunner, MultiGraphRunner, ExecItem, graph_class
 from tinygrad.engine.realize import CompiledRunner, BufferXfer
 from tinygrad.device import Compiled, Buffer, Allocator, Compiler, Device, BufferSpec
-from tinygrad.runtime.support.ib import IBCtx, IBConn, SGE
 
 # ***** API *****
 
@@ -36,7 +35,6 @@ class RemoteProperties:
   offset_supported: bool
   graph_supported: bool
   graph_supports_multi: bool
-  ib_gid: bytes|None
 
 @dataclass(frozen=True)
 class GetProperties(RemoteRequest): pass
@@ -47,18 +45,12 @@ class Event(RemoteRequest): event_session: SessionKey; event: int # noqa: E702
 @dataclass(frozen=True)
 class Wait(RemoteRequest): event: int
 
-@dataclass(frozen=True)
-class IBConnect(RemoteRequest): host: str; gid: bytes; qp_num: int # noqa: E702
-
 @dataclass(frozen=True)
 class BufferAlloc(RemoteRequest): buffer_num: int; size: int; options: BufferSpec # noqa: E702
 
 @dataclass(frozen=True)
 class BufferOffset(RemoteRequest): buffer_num: int; size: int; offset: int; sbuffer_num: int # noqa: E702
 
-@dataclass(frozen=True)
-class BufferIOVAS(RemoteRequest): buffer_nums: list[tuple[SessionKey, int]] # noqa: E702
-
 @dataclass(frozen=True)
 class BufferFree(RemoteRequest): buffer_num: int # noqa: E702
 
@@ -119,9 +111,9 @@ class GraphExec(RemoteRequest):
   wait: bool
 
 # for safe deserialization
-eval_globals = {x.__name__:x for x in [SessionKey, SessionFree, RemoteProperties, GetProperties, Event, Wait, BufferAlloc, BufferOffset, BufferIOVAS,
+eval_globals = {x.__name__:x for x in [SessionKey, SessionFree, RemoteProperties, GetProperties, Event, Wait, BufferAlloc, BufferOffset, BufferFree,
-                                       BufferFree, CopyIn, CopyOut, Transfer, BatchTransfer, IBConnect, ProgramAlloc, ProgramFree, ProgramExec,
+                                       CopyIn, CopyOut, Transfer, BatchTransfer, ProgramAlloc, ProgramFree, ProgramExec, GraphComputeItem, GraphAlloc,
-                                       GraphComputeItem, GraphAlloc, GraphFree, GraphExec, BufferSpec, UOp, Ops, dtypes]}
+                                       GraphFree, GraphExec, BufferSpec, UOp, Ops, dtypes]}
 attribute_whitelist: dict[Any, set[str]] = {dtypes: {*DTYPES_DICT.keys(), 'imagef', 'imageh'}, Ops: {x.name for x in Ops}}
 eval_fxns = {ast.Constant: lambda x: x.value, ast.Tuple: lambda x: tuple(map(safe_eval, x.elts)), ast.List: lambda x: list(map(safe_eval, x.elts)),
   ast.Dict: lambda x: {safe_eval(k):safe_eval(v) for k,v in zip(x.keys, x.values)},
@@ -168,12 +160,6 @@ class RemoteHandler:
     self.base_device = base_device
     self.sessions: defaultdict[SessionKey, RemoteSession] = defaultdict(RemoteSession)
 
-    try: self.ib_ctx: IBCtx|None = IBCtx(getenv("IB_DEV", 0))
-    except (IndexError, AttributeError): self.ib_ctx = None
-    self.ib_lock = asyncio.Lock()
-    self.ib_conns: dict[str, IBConn|None] = {}
-    self.iova_cache: dict[tuple[SessionKey, int], tuple[int, int, int]] = {}
-
   async def __call__(self, reader:asyncio.StreamReader, writer:asyncio.StreamWriter):
     while (req_hdr:=(await reader.readline()).decode().strip()):
       req_method, req_path, _ = req_hdr.split(' ')
@@ -185,30 +171,6 @@ class RemoteHandler:
       res_status, res_body = await self.handle(req_method, req_path, req_body)
       writer.write(f"HTTP/1.1 {res_status.value} {res_status.phrase}\r\nContent-Length: {len(res_body)}\r\n\r\n".encode() + res_body)
 
-  async def ib_connect(self, ssession:SessionKey, dsession:SessionKey) -> IBConn|None:
-    if self.ib_ctx is None: return None
-    await self.ib_lock.acquire()
-    conn = RemoteConnection(dsession.host)
-    if dsession.host not in self.ib_conns:
-      props = safe_eval(ast.parse(conn.q(GetProperties(session=dsession), wait=True), mode="eval").body)
-      if props.ib_gid is not None:
-        self.ib_conns[dsession.host] = ib_conn = IBConn(self.ib_ctx)
-        ibxc_ret = conn.q(IBConnect(ssession.host, ib_conn.gid, ib_conn.qp_num, session=dsession), wait=True)
-        ib_conn.connect(*struct.unpack('<16sQ', ibxc_ret))
-      else:
-        self.ib_conns[dsession.host] = None
-    self.ib_lock.release()
-    return self.ib_conns[dsession.host]
-
-  async def get_iovas(self, bufs:list[tuple[SessionKey, int]]) -> list[tuple[int, int, int]]:
-    await self.ib_lock.acquire()
-    if (rbufs:=[buf for buf in bufs if buf not in self.iova_cache]):
-      conn = RemoteConnection(rbufs[0][0].host)
-      resp = await conn.aq(BufferIOVAS(rbufs, session=rbufs[0][0]), wait=True)
-      self.iova_cache.update({rbuf: struct.unpack('<QQQ', resp[i*24:(i+1)*24]) for i,rbuf in enumerate(rbufs)})
-    self.ib_lock.release()
-    return [self.iova_cache[buf] for buf in bufs]
-
   async def handle(self, method:str, path:str, body:bytes) -> tuple[http.HTTPStatus, bytes]:
     status, ret = http.HTTPStatus.OK, b""
     if path == "/batch" and method == "POST":
@@ -225,9 +187,7 @@ class RemoteHandler:
             graph_cls = graph_class(Device[self.base_device])
             rp = RemoteProperties(
               real_device=dev.device, renderer=(cls.__module__, cls.__name__, args), offset_supported=hasattr(dev.allocator, '_offset'),
-              graph_supported=graph_cls is not None,
+              graph_supported=graph_cls is not None, graph_supports_multi=graph_cls is not None and issubclass(graph_cls, MultiGraphRunner),
-              graph_supports_multi=graph_cls is not None and issubclass(graph_cls, MultiGraphRunner) and hasattr(dev.allocator, '_transfer'),
-              ib_gid=bytes(self.ib_ctx.gid_attr.raw) if self.ib_ctx is not None else None,
             )
             ret = repr(rp).encode()
           case Event():
@@ -240,19 +200,9 @@ class RemoteHandler:
           case Wait():
             assert await session.events[c.event].wait()
             del session.events[c.event] # do not leak memory
-          case IBConnect():
-            self.ib_conns[c.host] = ibc = IBConn(unwrap(self.ib_ctx))
-            ibc.connect(c.gid, c.qp_num)
-            ret = struct.pack('<16sQ', ibc.gid, ibc.qp_num)
           case BufferAlloc():
             assert c.buffer_num not in session.buffers, f"buffer {c.buffer_num} already allocated"
             session.buffers[c.buffer_num] = Buffer(dev.device, c.size, dtypes.uint8, options=c.options, preallocate=True)
-          case BufferIOVAS():
-            rets = []
-            for buffer_session,buffer_num in c.buffer_nums:
-              iova, mr = unwrap(self.ib_ctx).reg(buf:=self.sessions[buffer_session].buffers[buffer_num])
-              rets.append(struct.pack("<QQQ", iova, mr.contents.rkey, buf.nbytes))
-            ret = b"".join(rets)
           case BufferOffset():
             assert c.buffer_num not in session.buffers, f"buffer {c.buffer_num} already exists"
             session.buffers[c.buffer_num] = session.buffers[c.sbuffer_num].view(c.size, dtypes.uint8, c.offset).allocate()
@@ -270,29 +220,16 @@ class RemoteHandler:
                 sbuf.copyout(data:=memoryview(bytearray(sbuf.nbytes)))
                 dbuf.copyin(data)
             else:
-              conn, ib_conn = RemoteConnection(c.dsession.host), await self.ib_connect(unwrap(c.session), c.dsession)
+              conn = RemoteConnection(c.dsession.host)
               sbuf = session.buffers[c.buffer_num]
-              if ib_conn is not None:
+              sbuf.copyout(data:=memoryview(bytearray(sbuf.nbytes)))
-                src_iova, src_mr = unwrap(self.ib_ctx).reg(sbuf)
+              conn.q(CopyIn(c.dbuffer_num, conn.req.h(data), session=c.dsession), wait=True)
-                dst_iova, dst_key, dst_size = (await self.get_iovas([(c.dsession, c.dbuffer_num)]))[0]
-                assert sbuf.nbytes == dst_size, f"{sbuf.nbytes} != {dst_size}"
-                for d in Device._opened_devices: Device[d].synchronize()
-                ib_conn.rdma_write([SGE(dst_iova, dst_key, src_iova, src_mr.contents.lkey, dst_size)])
-              else:
-                sbuf.copyout(data:=memoryview(bytearray(sbuf.nbytes)))
-                await conn.aq(CopyIn(c.dbuffer_num, conn.req.h(data), session=c.dsession), wait=True)
           case BatchTransfer():
-            conn, ib_conn = RemoteConnection(c.dbuffer_nums[0][0].host), await self.ib_connect(c.sbuffer_nums[0][0], c.dbuffer_nums[0][0])
+            conn = RemoteConnection(c.dbuffer_nums[0][0].host)
-            if ib_conn is not None:
+            for (sbuf_session,sbuf_num),(dbuf_session,dbuf_num) in zip(c.sbuffer_nums, c.dbuffer_nums):
-              sbufs = [unwrap(self.ib_ctx).reg(self.sessions[s].buffers[bi]) for s,bi in c.sbuffer_nums]
+              sbuf = self.sessions[sbuf_session].buffers[sbuf_num]
-              dbufs = await self.get_iovas(c.dbuffer_nums)
+              sbuf.copyout(data:=memoryview(bytearray(sbuf.nbytes)))
-              for d in Device._opened_devices: Device[d].synchronize()
+              await conn.aq(CopyIn(dbuf_num, conn.req.h(data), session=dbuf_session), wait=True)
-              ib_conn.rdma_write([SGE(di, dk, si, sm.contents.lkey, ds) for (di,dk,ds),(si,sm) in zip(dbufs, sbufs)])
-            else:
-              for (sbuf_session,sbuf_num),(dbuf_session,dbuf_num) in zip(c.sbuffer_nums, c.dbuffer_nums):
-                sbuf = self.sessions[sbuf_session].buffers[sbuf_num]
-                sbuf.copyout(data:=memoryview(bytearray(sbuf.nbytes)))
-                await conn.aq(CopyIn(dbuf_num, conn.req.h(data), session=dbuf_session), wait=True)
           case ProgramAlloc():
             lib = dev.compiler.compile_cached(req._h[c.datahash].decode())
             session.programs[(c.name, c.datahash)] = dev.runtime(c.name, lib)

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, suppress_finalizing
+from tinygrad.helpers import round_up
 from tinygrad.runtime.autogen import webgpu
 from typing import List, Any, TypeAlias
 import ctypes
@@ -188,8 +188,9 @@ 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
-  @suppress_finalizing
+  def _free(self, opaque:WGPUBufPtr, options:BufferSpec):
-  def _free(self, opaque:WGPUBufPtr, options:BufferSpec): webgpu.wgpuBufferDestroy(opaque)
+    try: webgpu.wgpuBufferDestroy(opaque)
+    except AttributeError: pass
 
 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:int, param=0, debug=False):
+  def _smu_cmn_send_msg(self, msg, 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:int, param:int, read_back_arg=False, timeout=10000, debug=False): # default timeout is 10 seconds
+  def _send_msg(self, msg, param, read_back_arg=False, timeout=10000, debug=False): # 10s
     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:memoryview):
+  def _prep_msg1(self, data):
     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:int, compid:int):
+  def _bootloader_load_component(self, fw, compid):
     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:am.struct_psp_gfx_cmd_resp) -> am.struct_psp_gfx_cmd_resp:
+  def _ring_submit(self, cmd):
     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:list[int], fw_bytes:memoryview):
+  def _load_ip_fw_cmd(self, fw_types, fw_bytes):
     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) -> am.struct_psp_gfx_cmd_resp:
+  def _tmr_load_cmd(self):
     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:int) -> am.struct_psp_gfx_cmd_resp:
+  def _load_toc_cmd(self, toc_size):
     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/hcq.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 from typing import cast, Callable, Type, TypeVar, Generic, Any
 import contextlib, decimal, statistics, time, ctypes, array, os, struct, traceback, collections
-try: import fcntl # windows misses that
-except ImportError: fcntl = None #type:ignore[assignment]
 from tinygrad.helpers import PROFILE, getenv, to_mv, round_up, ProfileRangeEvent
 from tinygrad.renderer import Renderer
 from tinygrad.device import BufferSpec, Compiler, Compiled, LRUAllocator, ProfileDeviceEvent, ProfileProgramEvent
@@ -27,7 +25,9 @@ class FileIOInterface:
     self.fd:int = fd or os.open(path, flags)
   def __del__(self):
     if hasattr(self, 'fd'): os.close(self.fd)
-  def ioctl(self, request, arg): return fcntl.ioctl(self.fd, request, arg)
+  def ioctl(self, request, arg):
+    import fcntl # to support windows
+    return fcntl.ioctl(self.fd, request, arg)
   def mmap(self, start, sz, prot, flags, offset):
     x = libc.mmap(start, sz, prot, flags, self.fd, offset)
     if x == 0xffffffffffffffff: raise OSError(f"Failed to mmap {sz} bytes at {hex(start)}: {os.strerror(ctypes.get_errno())}")
@@ -358,14 +358,14 @@ class HCQCompiled(Compiled, Generic[SignalType]):
   peer_groups: dict[str, list[HCQCompiled]] = collections.defaultdict(list)
   signal_pages: dict[str, list[HCQBuffer]] = collections.defaultdict(list) # per peer group
   signal_pool: dict[str, list[HCQBuffer]] = collections.defaultdict(list) # per peer group
-  cpu_devices: list[HCQCompiled] = []
 
   def __init__(self, device:str, allocator:HCQAllocatorBase, renderer:Renderer, compiler:Compiler, runtime, signal_t:Type[SignalType],
-               comp_queue_t:Callable[[], HWQueue], copy_queue_t:Callable[[], HWQueue]|None=None, kernargs_size=(16 << 20), sigalloc_size=0x1000):
+               comp_queue_t:Callable[[], HWQueue], copy_queue_t:Callable[[], HWQueue]|None=None, kernargs_size=(16 << 20), sigalloc_size=0x1000,
+               supports_graph=True):
     self.device_id:int = int(device.split(":")[1]) if ":" in device else 0
 
     from tinygrad.runtime.graph.hcq import HCQGraph
-    super().__init__(device, allocator, renderer, compiler, runtime, HCQGraph)
+    super().__init__(device, allocator, renderer, compiler, runtime, HCQGraph if supports_graph else None)
 
     # TODO: peer logic is determined based on device name.
     self.peer_group = device.split(":")[0]
@@ -383,13 +383,7 @@ class HCQCompiled(Compiled, Generic[SignalType]):
     self.kernargs_buf:HCQBuffer = self.allocator.alloc(kernargs_size, BufferSpec(cpu_access=True))
     self.kernargs_offset_allocator:BumpAllocator = BumpAllocator(self.kernargs_buf.size, wrap=True)
 
-    if self._is_cpu(): HCQCompiled.cpu_devices.append(self)
-
   def synchronize(self):
-    # If we have any work on CPU devices, need to synchronize them. This is just an optimization to release GIL allowing to finish faster.
-    if not self._is_cpu():
-      for dev in HCQCompiled.cpu_devices: dev.synchronize()
-
     try: self.timeline_signal.wait(self.timeline_value - 1)
     except RuntimeError as e:
       if hasattr(self, 'on_device_hang'): self.on_device_hang()
@@ -412,8 +406,6 @@ class HCQCompiled(Compiled, Generic[SignalType]):
     return self.signal_t(base_buf=HCQCompiled.signal_pool[pg].pop(), owner=self, **kwargs)
 
   def _at_profile_finalize(self):
-    self.synchronize() # Expect device to be synchronizes
-
     def _sync(d:HCQCompiled, q_t:Callable[[], HWQueue]):
       q_t().timestamp(d.timeline_signal).signal(d.timeline_signal, d.next_timeline()).submit(d)
       st = time.perf_counter_ns()
@@ -502,7 +494,7 @@ class HCQAllocatorBase(LRUAllocator[HCQDeviceType], Generic[HCQDeviceType]):
 class HCQAllocator(HCQAllocatorBase, Generic[HCQDeviceType]):
   def _copyin(self, dest:HCQBuffer, src:memoryview):
     assert self.dev.hw_copy_queue_t is not None
-    with hcq_profile(self.dev, queue_type=self.dev.hw_copy_queue_t, desc=f"TINY -> {self.dev.device}", enabled=PROFILE):
+    with hcq_profile(self.dev, queue_type=self.dev.hw_copy_queue_t, desc=f"CPU -> {self.dev.device}", enabled=PROFILE):
       for i in range(0, src.nbytes, self.b[0].size):
         self.b_next = (self.b_next + 1) % len(self.b)
         self.dev.timeline_signal.wait(self.b_timeline[self.b_next])
@@ -534,7 +526,7 @@ class HCQAllocator(HCQAllocatorBase, Generic[HCQDeviceType]):
     self.dev.synchronize()
 
     assert self.dev.hw_copy_queue_t is not None
-    with hcq_profile(self.dev, queue_type=self.dev.hw_copy_queue_t, desc=f"{self.dev.device} -> TINY", enabled=PROFILE):
+    with hcq_profile(self.dev, queue_type=self.dev.hw_copy_queue_t, desc=f"{self.dev.device} -> CPU", enabled=PROFILE):
       for i in range(0, dest.nbytes, cp_size:=(self.max_copyout_size or self.b[0].size)):
         self.dev.hw_copy_queue_t().wait(self.dev.timeline_signal, self.dev.timeline_value - 1) \
                                   .copy(self.b[0].va_addr, src.va_addr+i, lsize:=min(cp_size, dest.nbytes-i)) \

tinygrad/runtime/support/ib.py

@@ -1,172 +0,0 @@
-from __future__ import annotations
-import resource, ctypes, weakref, functools, itertools, tinygrad.runtime.autogen.ib as ib
-from typing import Iterator
-from dataclasses import dataclass
-from weakref import WeakKeyDictionary
-from tinygrad.device import Buffer, DMACPURef, DMAFdRef
-from tinygrad.helpers import getenv, round_up, DEBUG
-
-DEFAULT_PORT, DEFAULT_GID = getenv("DEFAULT_PORT", 1), getenv("DEFAULT_GID", 3) # DEFAULT_GID=0 for RXE
-IOVA_ALIGN = resource.getpagesize()
-
-def checkz(x, ret=None):
-  assert x == 0, f'{x} != 0 (errno {ctypes.get_errno()})'
-  return ret
-
-@dataclass(frozen=True)
-class SGE:
-  dst_iova: int
-  dst_key: int
-  src_iova: int
-  src_key: int
-  size: int
-
-class IBCtx:
-  def __init__(self, idx:int):
-    # Open the device (aka Host Channel Adapter in ib-speak)
-    devs = ib.ibv_get_device_list(ctypes.byref(ndevs:=ctypes.c_int32()))
-    if idx >= ndevs.value: raise IndexError(f"{idx} > {ndevs.value}")
-    self.ctx = ib.ibv_open_device(devs[idx])
-    ib.ibv_free_device_list(devs)
-
-    # HACK: remove this (and all usage of `ctx.contents.ops`) when clang2py can deal with `static inline` wrapper-functions
-    self.vctx = ctypes.cast(ctypes.addressof(self.ctx.contents) - ib.struct_verbs_context.context.offset, ctypes.POINTER(ib.struct_verbs_context))
-
-    # Get attributes. Something like port_attr.max_msg_sz sound like it might requre taking the min of host's and remote's attributes if they differ
-    self.device_attr = checkz(ib.ibv_query_device(self.ctx, ctypes.byref(da:=ib.struct_ibv_device_attr())), da)
-    self.port_attr = checkz(self.vctx.contents.query_port(self.ctx, DEFAULT_PORT, ctypes.byref(pa:=ib.struct_ibv_port_attr()), ctypes.sizeof(pa)), pa)
-    self.gid_attr = checkz(ib.ibv_query_gid(self.ctx, DEFAULT_PORT, DEFAULT_GID, ctypes.byref(ga:=ib.union_ibv_gid())), ga)
-
-    # Allocate protection domain
-    self.pd = ib.ibv_alloc_pd(self.ctx)
-    self.next_iova: int = IOVA_ALIGN # don't start at zero (nullptr)
-
-    # weakref(buf) => (iova, mr, mr_dealloc). mr_dealloc is kept here to avoid double freeing mrs that are deallocated in __del__
-    self.mrs: WeakKeyDictionary[Buffer, tuple[int, ctypes._Pointer[ib.struct_ibv_mr], weakref.finalize]] = WeakKeyDictionary()
-
-    # Default soft fd limit is 1024, which is not enough, set soft to hard (maximum allowed by the os)
-    IBCtx.rlimit_fix()
-
-  def __del__(self):
-    # must deallocate all mrs in protection domain before deallocating the protection domain
-    if hasattr(self, "mrs"): [fin() for _,_,fin in self.mrs.values()]
-    if hasattr(self, "pd"): ib.ibv_dealloc_pd(self.pd)
-    if hasattr(self, "ctx"): ib.ibv_close_device(self.ctx)
-
-  @functools.cache # run once
-  @staticmethod
-  def rlimit_fix():
-    soft, hard = resource.getrlimit(resource.RLIMIT_NOFILE)
-    resource.setrlimit(resource.RLIMIT_NOFILE, (hard, hard))
-    if DEBUG>=2: print(f"IB: Increased fd limit from {soft} to {hard}")
-
-  def alloc_iova(self, size:int, required_offset:int):
-    iova = round_up(self.next_iova - required_offset, IOVA_ALIGN) + required_offset
-    self.next_iova = iova + size
-    return iova
-
-  def reg(self, buf:Buffer) -> tuple[int, ctypes._Pointer[ib.struct_ibv_mr]]:
-    buf = buf.base
-    if buf not in self.mrs:
-      if buf.nbytes > self.device_attr.max_mr_size: raise RuntimeError(f"Buffer too big: {buf.nbytes:#x} > {self.device_attr.max_mr_size:#x}")
-      if len(self.mrs) >= self.device_attr.max_mr: raise RuntimeError(f"Out of memory region cap: {len(self.mrs)} >= {self.device_attr.max_mr}")
-      # Local read is implied (but still have to create the memory region, except for short sends/writes with IBV_SEND_INLINE that are inlined by cpu)
-      mr_flags = ib.IBV_ACCESS_LOCAL_WRITE | ib.IBV_ACCESS_REMOTE_READ | ib.IBV_ACCESS_REMOTE_WRITE
-      match (dmaref:=buf.as_dmaref()):
-        case DMACPURef():
-          iova = self.alloc_iova(dmaref.size, dmaref.addr % IOVA_ALIGN)
-          mr = ib.ibv_reg_mr_iova2(self.pd, ctypes.c_void_p(dmaref.addr), dmaref.size, iova, mr_flags)
-        case DMAFdRef():
-          iova = self.alloc_iova(dmaref.size, dmaref.offset % IOVA_ALIGN)
-          mr = ib.ibv_reg_dmabuf_mr(self.pd, dmaref.offset, dmaref.size, iova, dmaref.fd, mr_flags)
-        case _: raise RuntimeError(f"Unknown type of dma ref: {dmaref}")
-      if not mr: raise RuntimeError(f"Couldn't register memory region for {buf} {dmaref} (errno={ctypes.get_errno()})")
-      self.mrs[buf] = (iova, mr, weakref.finalize(buf, ib.ibv_dereg_mr, mr))
-    return self.mrs[buf][0:2]
-
-class IBConn:
-  def __init__(self, ctx:IBCtx):
-    self.ctx = ctx
-
-    # Create Completion Channel. It is a file descriptor that kernel sends notifications through, not a thing in infiniband spec, just linux-ism
-    self.comp_channel = ib.ibv_create_comp_channel(self.ctx.ctx)
-    # Create Completion Queue. When a Work Request with signaled flag is completed a Completion Queue Entry is pushed onto this queue
-    self.cq = ib.ibv_create_cq(self.ctx.ctx, _capacity:=256, _cq_context:=None, self.comp_channel, _comp_vector:=0)
-    self.pending_wrids: set[int] = set()
-    self.wrid_num: Iterator[int] = itertools.count(0) # wc_id is uint64, this will never overflow
-
-    # Create Queue Pair. It's the closest thing to a socket in infiniband with QP num being the closest thing to a port, except it's allocated by hca
-    qp_init_attrs_cap = ib.struct_ibv_qp_cap(max_send_wr=1024, max_recv_wr=64, max_send_sge=8, max_recv_sge=8, max_inline_data=64)
-    qp_init_attrs = ib.struct_ibv_qp_init_attr(send_cq=self.cq, recv_cq=self.cq, cap=qp_init_attrs_cap, qp_type=ib.IBV_QPT_RC) # Reliable Connection
-    self.qp = ib.ibv_create_qp(self.ctx.pd, ctypes.byref(qp_init_attrs))
-    self.qp_cap = qp_init_attrs.cap
-
-    # The most important thing about QPs is their state, when a new QP is created it's in the RESET state, before it can be properly used it has to go
-    # through Init, Ready To Receive, Ready To Send. A good docs on QP state machine: https://www.rdmamojo.com/2012/05/05/qp-state-machine/
-
-    # INIT
-    qp_access_flags = ib.IBV_ACCESS_REMOTE_WRITE | ib.IBV_ACCESS_REMOTE_READ
-    qpa = ib.struct_ibv_qp_attr(qp_state=ib.IBV_QPS_INIT, port_num=DEFAULT_PORT, qp_access_flags=qp_access_flags)
-    checkz(ib.ibv_modify_qp(self.qp, qpa, ib.IBV_QP_STATE | ib.IBV_QP_PORT | ib.IBV_QP_ACCESS_FLAGS | ib.IBV_QP_PKEY_INDEX))
-
-    self.gid, self.qp_num = bytes(self.ctx.gid_attr.raw), self.qp.contents.qp_num
-
-  # Exchange GID and QP num with remote. At least in RoCEv2 gid can be guessed from remote's ip, QP num can't.
-
-  def connect(self, remote_gid:bytes, remote_qp_num:int):
-    # RTR
-    qp_ah_attr_grh = ib.struct_ibv_global_route(hop_limit=1, dgid=ib.union_ibv_gid(raw=(ctypes.c_ubyte * 16)(*remote_gid)), sgid_index=DEFAULT_GID)
-    qp_ah_attr = ib.struct_ibv_ah_attr(is_global=1, port_num=DEFAULT_PORT, grh=qp_ah_attr_grh)
-    qpa = ib.struct_ibv_qp_attr(qp_state=ib.IBV_QPS_RTR, path_mtu=ib.IBV_MTU_4096, dest_qp_num=remote_qp_num, rq_psn=0, max_dest_rd_atomic=1,
-                                min_rnr_timer=12, ah_attr=qp_ah_attr)
-    checkz(ib.ibv_modify_qp(self.qp, qpa, ib.IBV_QP_STATE | ib.IBV_QP_PATH_MTU | ib.IBV_QP_DEST_QPN | ib.IBV_QP_RQ_PSN | \
-                                          ib.IBV_QP_MAX_DEST_RD_ATOMIC | ib.IBV_QP_MIN_RNR_TIMER | ib.IBV_QP_AV))
-
-    # RTS
-    qpa = ib.struct_ibv_qp_attr(qp_state=ib.IBV_QPS_RTS, timeout=14, retry_cnt=7, rnr_retry=7, sq_psn=0, max_rd_atomic=1)
-    checkz(ib.ibv_modify_qp(self.qp, qpa, ib.IBV_QP_STATE | ib.IBV_QP_TIMEOUT | ib.IBV_QP_RETRY_CNT | ib.IBV_QP_RNR_RETRY | ib.IBV_QP_SQ_PSN | \
-                                          ib.IBV_QP_MAX_QP_RD_ATOMIC))
-
-  def __del__(self):
-    self.wait_cq() # need to wait for **everything** to complete before it's safe to dealloc queues and stuff
-    ib.ibv_destroy_qp(self.qp)
-    ib.ibv_destroy_cq(self.cq)
-    ib.ibv_destroy_comp_channel(self.comp_channel)
-
-  def next_wrid(self):
-    self.pending_wrids.add(wrid:=next(self.wrid_num))
-    return wrid
-
-  def wait_cq(self, wr_id: int|None=None):
-    while (wr_id in self.pending_wrids) if wr_id is not None else self.pending_wrids:
-      if self.ctx.ctx.contents.ops.poll_cq(self.cq, _num_entries:=1, ctypes.byref(wc:=ib.struct_ibv_wc())):
-        if wc.status != ib.IBV_WC_SUCCESS:
-          raise RuntimeError(f'Work Request completed with error: wr_id={wc.wr_id} status={ib.ibv_wc_status__enumvalues.get(wc.status, wc.status)}')
-        self.pending_wrids.remove(wc.wr_id)
-
-  def rdma_write(self, sgl:list[SGE]):
-    swr: ctypes._Pointer[ib.struct_ibv_send_wr]|None = None
-    swr_cnt, wr_id = 0, self.next_wrid()
-    def _post():
-      nonlocal swr, swr_cnt, wr_id
-      if swr is not None:
-        # The swr can be freed when this returns, the memory that sge points to can be unmapped after work completion is retrieved from cq
-        checkz(self.ctx.ctx.contents.ops.post_send(self.qp, swr, ctypes.byref(_bad_wr:=ctypes.POINTER(ib.struct_ibv_send_wr)())))
-        # TODO: async
-        self.wait_cq(wr_id)
-        swr, swr_cnt, wr_id = None, 0, self.next_wrid()
-    # Everything is in reverse for elegant chaining
-    for sg in reversed(sgl):
-      # Message size limit (max 2GB per ib spec, 1GB on tinybox mellanoxes) applies to both scatter-gather entries and entire wrs
-      for off in reversed(range(0, sg.size, self.ctx.port_attr.max_msg_sz)):
-        # Scatter-Gather Entry for local memory
-        sge = ctypes.pointer(ib.struct_ibv_sge(addr=sg.src_iova+off, length=min(sg.size-off, self.ctx.port_attr.max_msg_sz), lkey=sg.src_key))
-        # RDMA struct for remote memory
-        wr = ib.union_ibv_send_wr_wr(rdma=ib.struct_ibv_send_wr_1_rdma(remote_addr=sg.dst_iova+off, rkey=sg.dst_key))
-        # Signal (with chosen work request id) if it's the last wr (first in the loop since it's reversed)
-        wid, flags = (wr_id, ib.IBV_SEND_SIGNALED) if swr is None else (0, 0)
-        # Create Send Request
-        swr = ctypes.pointer(ib.struct_ibv_send_wr(opcode=ib.IBV_WR_RDMA_WRITE, sg_list=sge, num_sge=1, wr=wr, wr_id=wid, send_flags=flags, next=swr))
-        # Flush if queue is being overrun
-        if (swr_cnt:=swr_cnt + 1) >= self.qp_cap.max_send_wr: _post()
-    _post()

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, Any
+from typing import cast
 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; virt:bool; phys:bool; local:bool=False # noqa: E702
+class GRBufDesc: size:int; v:int; p:int; lc:int=0 # 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:bytes):
+  def _checksum(self, data):
     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:int, msg:bytes, wait=False):
+  def send_rpc(self, func, msg, 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:int) -> memoryview:
+  def wait_resp(self, cmd) -> memoryview:
     while True:
       System.memory_barrier()
       if self.rx.readPtr == self.tx.writePtr: continue
@@ -60,8 +60,7 @@ class NVRpcQueue:
 
       # Handling special functions
       if hdr.function == nv.NV_VGPU_MSG_EVENT_GSP_RUN_CPU_SEQUENCER: self.gsp.run_cpu_seq(msg)
-      elif hdr.function == nv.NV_VGPU_MSG_EVENT_OS_ERROR_LOG:
+      elif hdr.function == nv.NV_VGPU_MSG_EVENT_OS_ERROR_LOG: print(f"GSP LOG: {msg[12:].tobytes().rstrip(bytes([0])).decode('utf-8')}")
-        print(f"nv {self.gsp.nvdev.devfmt}: GSP LOG: {msg[12:].tobytes().rstrip(bytes([0])).decode('utf-8')}")
 
       # Update the read pointer
       self.rx.readPtr = (self.rx.readPtr + round_up(hdr.length, self.tx.msgSize) // self.tx.msgSize) % self.tx.msgCount
@@ -178,7 +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:int, cmd:int, dest:int, mem_off:int, sysmem:int, size:int):
+  def execute_dma(self, base, cmd, dest, mem_off, sysmem, size):
     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))
@@ -195,7 +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:int):
+  def start_cpu(self, base):
     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)
@@ -233,11 +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:int):
+  def disable_ctx_req(self, base):
     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:int, riscv=False):
+  def reset(self, base, 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)
@@ -409,10 +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:int, subdevice:int, obj:int, ctxbufs:dict[int, GRBufDesc], bufs=None, virt=None, phys=None):
+  def promote_ctx(self, client, subdevice, obj, ctxbufs, 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.virt if virt is None else virt), (desc.phys if phys is None else phys)
+      use_v, use_p = (desc.v if virt is None else virt), (desc.p 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))
@@ -450,11 +449,10 @@ 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, phys=True, virt=True), 1: GRBufDesc(patch_size, phys=True, virt=True, local=True),
+    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),
-      2: GRBufDesc(patch_size, phys=True, virt=True), **{x: GRBufDesc(cfgs_sizes[x], phys=False, virt=True) for x in range(3, 7)},
+      **{x: GRBufDesc(cfgs_sizes[x], p=0, v=1) for x in range(3, 7)}, 9: GRBufDesc(cfgs_sizes[9], p=1, v=1),
-      9: GRBufDesc(cfgs_sizes[9], phys=True, virt=True), 10: GRBufDesc(cfgs_sizes[10], phys=True, virt=False),
+      10: GRBufDesc(cfgs_sizes[10], p=1, v=0), 11: GRBufDesc(cfgs_sizes[10], p=1, v=1)} # NOTE: 11 reuses cfgs_sizes[10]
-      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 v.lc == 0})
-    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)
@@ -475,7 +473,7 @@ class NV_GSP(NV_IP):
 
   ### RPCs
 
-  def rpc_rm_alloc(self, hParent:int, hClass:int, params:Any, client=None) -> int:
+  def rpc_rm_alloc(self, hParent, hClass, params, 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)
@@ -501,7 +499,7 @@ 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:int, cmd:int, params:Any, client=None):
+  def rpc_rm_control(self, hObject, cmd, params, 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''))
@@ -513,7 +511,7 @@ 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:int, hVASpace:int, pdir_paddr:int, client=None, pasid=0xffffffff):
+  def rpc_set_page_directory(self, device, hVASpace, pdir_paddr, 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)
@@ -546,7 +544,7 @@ 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:memoryview):
+  def run_cpu_seq(self, seq_buf):
     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

@@ -66,12 +66,12 @@ class NVPageTableEntry:
     return self.read_fields(entry_id)[f'address{small}{sys}'] << 12
 
 class NVMemoryManager(MemoryManager):
-  va_allocator = TLSFAllocator((1 << 44), base=0x1000000000) # global for all devices.
+  va_allocator = TLSFAllocator((1 << 44), base=1 << 30) # global for all devices.
 
   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:str, mmio:MMIOInterface, vram:MMIOInterface, venid:int, subvenid:int, rev:int, bars:dict):
+  def __init__(self, devfmt, 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:int, value:int):
+  def wreg(self, addr, value):
     self.mmio[addr // 4] = value
     if NV_DEBUG >= 4: print(f"wreg: {hex(addr)} = {hex(value)}")
-  def rreg(self, addr:int) -> int: return self.mmio[addr // 4]
+  def rreg(self, addr): 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:ctypes.Structure) -> tuple[ctypes.Structure, int]:
+  def _alloc_boot_struct(self, struct):
     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) -> str:
+  def _download(self, file) -> str:
     url = f"https://raw.githubusercontent.com/NVIDIA/open-gpu-kernel-modules/8ec351aeb96a93a4bb69ccc12a542bf8a8df2b6f/{file}"
     return fetch(url, subdir="defines").read_text()
 

tinygrad/schedule/grouper.py

@@ -3,7 +3,7 @@ from tinygrad.helpers import all_int, prod, unwrap, dedup, DONT_REALIZE_EXPAND,
 from tinygrad.shape.shapetracker import ShapeTracker
 
 ALWAYS_CONTIGUOUS = {Ops.CONTIGUOUS, Ops.ASSIGN, Ops.COPY, Ops.BUFFER, Ops.BUFFER_VIEW,
-                     Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK, Ops.DEFINE_GLOBAL}
+                     Ops.CONST, Ops.BIND, Ops.DEVICE, Ops.MSELECT, Ops.MSTACK}
 
 # **** Grouper decides which of the UOps realize
 

tinygrad/schedule/kernelize.py

@@ -1,13 +1,14 @@
 from dataclasses import dataclass
-from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve
+from tinygrad.uop.ops import UOp, Ops, GroupOp, PatternMatcher, UPat, graph_rewrite, graph_rewrite_map, identity_element, resolve, sint
 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, 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
 from tinygrad.dtype import ImageDType
 from tinygrad.schedule.multi import multi_pm
+from tinygrad.shape.shapetracker import ShapeTracker
+from tinygrad.shape.view import View, strides_for_shape, get_contraction_with_reduce
 from tinygrad.schedule.grouper import group_realizes, ALWAYS_CONTIGUOUS
-from tinygrad.opt.swizzler import merge_views, view_left, view_right, fix_kernel_ops, apply_swizzle, swizzle_reduceop
 
 # creation can recurse a lot
 import sys
@@ -147,13 +148,137 @@ create_kernels = PatternMatcher([
    lambda ms: UOp(Ops.MSTACK, ms.dtype, tuple(x.src[0] for x in ms.src)).reshape(ms.src[0].arg)),
 ])
 
+# **** swizzler
+
+merge_views = PatternMatcher([
+  # merge adjacent views
+  (UPat(Ops.VIEW, src=(UPat(Ops.VIEW, name="v1"),), name="v2"), lambda v1,v2: v1.replace(arg=v1.arg+v2.arg)),
+  # replace MovementOps with VIEW
+  (UPat(GroupOp.Movement, src=(UPat.var("x"),), name="mop"), lambda mop,x: x.base.view(mop.st)),
+  # remove NOOP views
+  (UPat.var("x").view(name="view"), lambda x,view: x if x.st is not None and view.st.contiguous and view.shape == x.shape else None),
+  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL}).view(name="view"),
+   lambda view: view.const_like(0) if (mask:=view.st.views[-1].mask) is not None and any((x[1]-x[0]) == 0 for x in mask) else None),
+  # only unmaksed VIEW on CONST replaces the ShapeTracker
+  (UPat(Ops.VIEW, src=(UPat((Ops.CONST, Ops.DEFINE_VAR), name="x"),), name="view"),
+   lambda x,view: x.replace(src=(x.src[0].replace(arg=x.st+view.st),)) if all(v.mask is None for v in (x.st+view.st).views) else None),
+])
+
+def reduce_push_add_ones(src:UOp, r:UOp, view:UOp):
+  # contiguous, expand, and the same with ones removed
+  if unwrap(view.st).contiguous and len(r.shape) < len(view.shape) and \
+      tuple(x for x in r.shape if resolve(x != 1)) == tuple(x for x in view.shape if resolve(x != 1)):
+    new_shape: list[sint] = []
+    new_reduce_axis = []
+    if (contraction:=get_contraction_with_reduce(view.shape, r.shape, r.arg[1])) is None: return None
+    for i,pairs in enumerate(contraction):
+      new_shape_chunk = [view.shape[p] for p in pairs]
+      if i in r.arg[1]:
+        # if this is a reduce axis, we need a 1 in the view here to put it
+        assert len(new_shape_chunk) > 0
+        new_shape += [1]*(len(pairs)-1) + [src.shape[i]]
+        new_reduce_axis.append(len(new_shape)-1)
+      else:
+        # otherwise, pass through the new_shape_chunk
+        new_shape += new_shape_chunk
+    ret = r.replace(src=(src.reshape(tuple(new_shape)),), arg=(r.arg[0], tuple(new_reduce_axis))+r.arg[2:])
+    assert ret.shape == view.shape, f"shape mismatch on reduce_push_add_ones, {ret.shape} != {view.shape}"
+    return ret
+  return None
+
+view_left = merge_views+PatternMatcher([
+  # view before elementwise and buffer ops
+  (UPat(Ops.VIEW, src=(UPat({*GroupOp.ALU, Ops.CAST, Ops.BITCAST, Ops.BIND, Ops.LOAD, Ops.STORE, Ops.VALID}, name="e"),), name="view"),
+   lambda e,view: e.replace(src=tuple(s.view(view.st) for s in e.src))),
+  # if there's ones added after reduce, put this before the reduce
+  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), reduce_push_add_ones),
+])
+
+def apply_swizzle(u:UOp) -> UOp: return graph_rewrite(u, view_left, name="Sub View Left")
+
+# change reduceop axes and input ShapeTrackers, view gets replaced with a reshape.
+def swizzle_reduceop(r:UOp, src:UOp, view:UOp, fuse=False):
+  # contiguous and same size can push to children
+  # if there's a reduce child, shapes match with ones removed
+  if unwrap(view.st).contiguous and view.size == r.size and \
+      (not (len(r.arg) == 3 and r.arg[2]) or # arg[2] = True is fuse marker
+       tuple((i,x) for i,x in enumerate(r.shape) if resolve(x != 1)) == tuple((i,x) for i,x in enumerate(view.shape) if resolve(x != 1))):
+    return None
+  # swizzle the input
+  input_st = ShapeTracker.from_shape(src.shape)
+  tmp = input_st.permute(tuple(i for i in range(len(input_st.shape)) if i not in r.axis_arg)+r.axis_arg)
+  prshape = prod(rshape:=tmp.shape[-len(r.axis_arg):])
+  strides = strides_for_shape(rshape)
+  nv = [View.create(v.shape+rshape, tuple(x*prshape for x in v.strides)+strides,
+                    v.offset*prshape, v.mask+tuple((0,s) for s in rshape) if v.mask is not None else None) for v in unwrap(view.st).views]
+  new_view = tmp + ShapeTracker(tuple(nv))
+  swizzled_input = apply_swizzle(src.view(new_view))
+  # create a new reduceop
+  new_axis = tuple(range(len(view.shape), len(view.shape) + len(r.axis_arg)))
+  if fuse: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input.fuse(),), (r.arg[0], new_axis, True))
+  else: red = UOp(Ops.REDUCE_AXIS, r.dtype, (swizzled_input,), (r.arg[0], new_axis))
+  return red.reshape(view.shape)
+
+def reduceop_view_right(src:UOp, v:UOp, r:UOp):
+  assert unwrap(v.st).contiguous and v.size == src.size, f"can't compute new axis for {src.shape} -> {r.shape}"
+  new_axis = [i for i,(s,u) in enumerate(zip(src.shape, r.shape)) if s != u]
+  return src.r(r.arg[0], tuple(new_axis)).reshape(r.shape)
+
+def elementwise_view_right(root:UOp):
+  if not (swizzles:=[x for x in root.src if x.op is Ops.VIEW and x.base.op not in ALWAYS_CONTIGUOUS]): return None
+  assert all_same([x.base.size for x in swizzles]), f"swizzle inputs must have the same size {swizzles}"
+  # place view after applying the elementwise op
+  new_st = ShapeTracker.from_shape(swizzles[0].base.shape)
+  new_src = [x.base if x.base.shape==new_st.shape else apply_swizzle(x.view(new_st)) for x in root.src]
+  # reshape to match downstream shapes
+  return root.replace(src=tuple(new_src)).reshape(root.shape)
+
+# push VIEW to children
+view_right = merge_views+PatternMatcher([
+  # push a non contiguous ShapeTracker through reduceop
+  (UPat(Ops.VIEW, src=(UPat(Ops.REDUCE_AXIS, src=(UPat.var("src"),), name="r"),), name="view"), swizzle_reduceop),
+  # apply view after reduceops
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.All-ALWAYS_CONTIGUOUS, name="src"),), name="v"),), name="r"), reduceop_view_right),
+  # apply view after elementwise ops
+  (UPat(GroupOp.All-{Ops.SINK, Ops.REDUCE_AXIS}, name="root"), elementwise_view_right),
+  # merge axes for double reduce (invert of SPLIT_REDUCEOP=1)
+  (UPat(Ops.REDUCE_AXIS, src=(UPat(Ops.REDUCE_AXIS, name="r1"),), name="r2"),
+   lambda r1,r2: r1.replace(arg=(r1.arg[0], r2.arg[1]+r1.arg[1])) if r1.arg[0] is r2.arg[0] else None),
+])
+
 # **** fix kernel AST
 
-early_buffer_ops = PatternMatcher([
+add_buffer_ops = PatternMatcher([
   # LOAD
-  (UPat(Ops.BUFFER, name="x"), lambda ctx,x: UOp(Ops.DEFINE_GLOBAL, x.dtype.ptr(x.size), (), ctx.index(x), tag=1)),
+  (UPat(Ops.BUFFER, name="x"), lambda ctx,x: UOp.load(UOp(Ops.DEFINE_GLOBAL, x.dtype.ptr(x.size), (), ctx.index(x)).view(x.st),)),
-  # no SINK for meta ops
+  # STORE (except for meta ops)
   (UPat(Ops.SINK, src=(UPat(Ops.CONTIGUOUS, src=(UPat(GroupOp.Meta, name="x"),),))), lambda x:x),
+  (UPat(Ops.SINK, src=UPat(GroupOp.All-{Ops.STORE}), name="sink"), lambda ctx,sink:
+   UOp.sink(*[UOp.store(UOp(Ops.DEFINE_GLOBAL, (s:=x.base).dtype.ptr(ctx[i].size), (), i).view(s.st), s) for i,x in enumerate(sink.src)])),
+  # passthrough ASSIGN
+  (UPat(Ops.ASSIGN, name="x"), lambda x: x.src[1]),
+  # VALID
+  (UPat(Ops.VIEW, src=(UPat.cvar(),), name="self"), UOp.valid),
+])
+
+def check_load_st(glbl:UOp, view:UOp):
+  if glbl.arg != 0 or (st:=unwrap(view.st)).contiguous: return
+  # if it has a single view and it becomes contiguous when you shrink expanded axes, it's fine
+  if len(st.views) == 1 and st.shrink(tuple((0,1) if st == 0 else (0,s) for s,st in zip(st.shape, st.views[0].strides))).contiguous: return
+  # if it has a single view and it's equal when you shrink a contig, it's fine
+  if len(st.views) == 1 and (mask:=st.views[0].mask) is not None and ShapeTracker.from_shape(st.shape).shrink(mask) == st.shrink(mask): return
+  # otherwise, it's not fine
+  raise RuntimeError("self operand of augmented assign must be contiguous.\nhelp: consider using .contiguous():\n"
+                     +colored("   - a += a.T\n", "red")+colored("   + a += a.T.contiguous()", "green"))
+
+fix_kernel_ops = PatternMatcher([
+  # remove CONTIGUOUS/DEVICE from kernel AST
+  (UPat((Ops.CONTIGUOUS, Ops.MSELECT), src=(UPat.var("x"),)), lambda x: x),
+  (UPat(Ops.VIEW, src=(UPat(Ops.DEVICE),), name="view"), lambda view: view.replace(src=())),
+  # no ImageDType after index
+  (UPat(GroupOp.All-{Ops.DEFINE_GLOBAL, Ops.VIEW}, name="x"), lambda x: x.replace(dtype=x.dtype.base) if isinstance(x.dtype, ImageDType) else None),
+  # if this kernel also assigns to the loaded buffer, ensure we can index it correctly
+  (UPat(Ops.LOAD, src=(UPat.var("glbl").view(name="view"),)), check_load_st),
 ])
 
 replace_globals = PatternMatcher([
@@ -165,6 +290,10 @@ replace_globals = PatternMatcher([
 
 def fix_kernel_ast(k:UOp) -> UOp|None:
   if k.arg.ast.op in GroupOp.Meta or all(s.op is Ops.STORE for s in k.arg.ast.src): return None
+  # replace global memory ops with the BUFFER they write to
+  ast = graph_rewrite(k.arg.ast, replace_globals, bottom_up=True, name="replace globals")
+  # push views to edges
+  ast = graph_rewrite(graph_rewrite(ast, view_left, name="Main View Left"), view_right, name="Main View Right")
   # replace buffer with define_global + add load/store last
   bufs = []
   for s in k.src:
@@ -172,15 +301,9 @@ def fix_kernel_ast(k:UOp) -> UOp|None:
     # traverse back through MSELECT and MSTACK. HACK: 0 branch of MSTACK only
     while s.op in {Ops.MSELECT, Ops.MSTACK}: s = s.src[0]
     bufs.append(s)
-  # replace global memory ops with the BUFFER they write to
+  ast = graph_rewrite(ast, view_left+add_buffer_ops+fix_kernel_ops, bufs, bottom_up=True, name="replace buffer")
-  ast = graph_rewrite(k.arg.ast, replace_globals, bottom_up=True, name="replace globals")
-  ast = graph_rewrite(ast, early_buffer_ops, bufs, bottom_up=True, name="replace buffer early")
   if ast.op is Ops.SINK and not all_same([x.device for x in k.src]):
     raise RuntimeError(f"all buffers must be on the same device: {tuple(b.buf_uop.buffer for b in k.src)}")
-  # TODO: move these to codegen
-  ast = graph_rewrite(ast, view_left, name="Main View Left")
-  ast = graph_rewrite(ast, view_right, name="Main View Right")
-  ast = graph_rewrite(ast, view_left+fix_kernel_ops, bottom_up=True, name="replace buffer")
   return k.replace(arg=Kernel(ast, k.arg.metadata))
 
 create_ast = PatternMatcher([(UPat(Ops.KERNEL, name="k"), fix_kernel_ast),])
@@ -220,7 +343,7 @@ pm_fuse = PatternMatcher([
 def do_fusion(x:UOp):
   found_contiguous = {}
   def gate_contiguous(x):
-    if is_contiguous:=(x.op is Ops.CONTIGUOUS): found_contiguous[x] = x.replace(src=(UOp(Ops.VIEW, arg=x.st), UOp.unique()))
+    if is_contiguous:=(x.op is Ops.CONTIGUOUS): found_contiguous[x] = x.replace(src=(UOp(Ops.VIEW, arg=x.st),))
     return not is_contiguous
   x.toposort(gate=gate_contiguous)
   del gate_contiguous
@@ -316,6 +439,8 @@ def get_kernelize_map(sink:UOp) -> dict[UOp, UOp]:
   tensor_map = graph_rewrite_map(tensor_map[sink], add_contiguous, ctx=realize_map, bottom_up=True, input_map=tensor_map, name="add_contiguous")
   tensor_map = graph_rewrite_map(tensor_map[sink], finalize_contiguous+remove_tags, input_map=tensor_map, name="finalize_contiguous")
 
+  # TODO: move view_left/view_right here
+
   # group into kernels (this is context-free)
   tensor_map = graph_rewrite_map(tensor_map[sink], create_kernels, input_map=tensor_map, name="create_kernels")
 

tinygrad/shape/shapetracker.py

@@ -4,7 +4,7 @@ from dataclasses import dataclass
 import functools
 from typing import Callable
 from tinygrad.helpers import merge_dicts, getenv
-from tinygrad.shape.view import View, unravel
+from tinygrad.shape.view import View, strides_for_shape, unravel
 from tinygrad.dtype import dtypes
 from tinygrad.uop.ops import UOp, Ops, graph_rewrite, Variable, sint, sint_to_uop, Context, PatternMatcher, UPat, GroupOp
 from tinygrad.uop.symbolic import split_uop, symbolic_flat, uop_given_valid, simplify_valid
@@ -75,6 +75,9 @@ class ShapeTracker:
   @property
   def contiguous(self) -> bool: return len(self.views) == 1 and self.views[0].contiguous
 
+  @property
+  def consecutive(self) -> bool: return len(self.views) == 1 and (v:=self.views[0]).mask is None and v.strides == strides_for_shape(v.shape)
+
   @property
   def shape(self) -> tuple[sint, ...]: return self.views[-1].shape
 
@@ -83,6 +86,7 @@ class ShapeTracker:
 
   def reduce(self, axis:tuple[int, ...]) -> tuple[sint, ...]: return tuple(1 if i in axis else s for i,s in enumerate(self.shape))
 
+  def to_uop(self) -> UOp: return UOp(Ops.VIEW, dtypes.void, (), self)
   def to_indexed_uops(self, _idxs:list[UOp]|tuple[UOp, ...]|None=None) -> tuple[UOp, UOp]:
     return views_to_indexed_uops(self.views, tuple(_idxs) if _idxs is not None else None)
 

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)
-      self._buffer().copyin(x._data())
+      cast(Buffer, self.contiguous().realize().uop.base.buffer).ensure_allocated().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
@@ -299,10 +299,7 @@ class Tensor(MathTrait):
     """
     return Tensor(self.uop.detach(), device=self.device, requires_grad=False)
 
-  def _buffer(self) -> Buffer:
+  def _buffer(self) -> Buffer: return cast(Buffer, self.cast(self.dtype.base).contiguous().to("CPU").realize().uop.base.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:
@@ -1979,9 +1976,7 @@ class Tensor(MathTrait):
 
     # https://keccak.team/keccak_specs_summary.html
 
-    def ctensor(l: Sequence[ConstType], dtype: DType = dtypes.uint64):
+    def ctensor(l: Sequence[ConstType], dtype: DType = dtypes.uint64): return Tensor.stack(*(Tensor(v, dtype=dtype, device=self.device) for v in l))
-      # TODO: contiguous is here for compile speed
-      return Tensor.stack(*(Tensor(v, dtype=dtype, device=self.device) for v in l)).contiguous()
     rot_offsets = [44, 43, 21, 14, 28, 20, 3, 45, 61, 1, 6, 25, 8, 18, 27, 36, 10, 15, 56, 62, 55, 39, 41, 2]
     rot_offsets_v0, rot_offsets_v1 =  ctensor([0] + [1 << v for v in rot_offsets]), ctensor([1] + [1 << (64 - v) for v in rot_offsets])
 
@@ -1997,9 +1992,9 @@ class Tensor(MathTrait):
     data = data.pad((None, (0, data_pad))).reshape(bs := data.shape[0], -1, rate).pad((None, None, (0, 200 - rate)))
 
     # create pad mask
-    lbe = prod(data.shape[1:]) + rate - data_pad - 200
+    lbe = (blen := prod(data.shape[1:])) + rate - data_pad - 200
-    if data_pad == 1: mb = [(lbe, 0), (1, dsbyte ^ 0x80), (200 - rate, 0)]
+    if data_pad == 1: mb = [(lbe, 0), (1, dsbyte ^ 0x80), (blen - lbe - 1, 0)]
-    else: mb = [(lbe, 0), (1, dsbyte), (data_pad - 2, 0), (1, 0x80), (200 - rate, 0)]
+    else: mb = [(lbe, 0), (1, dsbyte), (blen + rate - lbe - 202, 0), (1, 0x80), (200 - rate, 0)]
     pad_mask = Tensor.cat(*(Tensor(v, dtype=dtypes.uint8, device=data.device).expand(l) for l, v in mb if l > 0)).unsqueeze(0)
 
     data = (data.flatten(1) ^ pad_mask).reshape(*data.shape[:2], 200).bitcast(dtypes.uint64)
@@ -2018,42 +2013,8 @@ class Tensor(MathTrait):
         # χ and ι step
         state = state.bitwise_xor(~state.roll(shifts=-1, dims=2) & state.roll(shifts=-2, dims=2))
         state = state.flatten(1) ^ rnd_const_masks[i]
-      # NOTE: kernelize here to prevent internal stack from growing propotional to data size
-      state = state.kernelize()
     return state.bitcast(dtypes.uint8)[:,:(obytes:=(200 - rate) // 2)].reshape(*self.shape[:-1], obytes)
 
-  def _hash_1mb(self) -> Tensor:
-    assert self.dtype == dtypes.uint8, "only support uint8 tensors for hashing"
-    assert self.ndim == 2, "only support batched 1d tensors"
-    assert self.shape[1] == 1024 * 1024, "only support messages of 1mb"
-
-    blocks = self.shape[0] * self.shape[1] // 4096
-    data = self.reshape(blocks, 4096)
-    block_hashes = data.keccak("shake_128").reshape(self.shape[0], 4096)
-    return block_hashes.keccak("shake_128").reshape(self.shape[0], 16)
-
-  def hash(self) -> Tensor:
-    """
-    Calculates a 16-byte hash of the tensor.
-    ```python exec="false source="above" session="tensor" result="python"
-    t = Tensor(b"Hello World!").hash()
-    print(t.data().hex())
-    ```
-    """
-
-    data = self.flatten().bitcast(dtypes.uint8)
-    if (tsize := data.shape[0]) % 2**20 != 0: data = data.pad((0, 2**20 - tsize % 2**20))
-    base_chunks = ceildiv(data.shape[0], 2**20)
-    tree_depth = math.ceil(math.log(base_chunks, 65536)) if base_chunks > 1 else 0
-
-    level_chunks = base_chunks
-    for _ in range(tree_depth + 1):
-      data = data.reshape(level_chunks, 2**20)._hash_1mb().flatten()
-      if (tsize := data.shape[0]) % 2**20 != 0: data = data.pad((0, 2**20 - tsize % 2**20))
-      level_chunks = ceildiv(data.shape[0], 2**20)
-
-    return data[:16]
-
   def _softmax(self, axis, dtype:DTypeLike|None=None) -> tuple[Tensor, Tensor, Tensor]:
     m = self - self.max(axis=axis, keepdim=True).detach()
     if dtype is not None: m = m.cast(dtype)
@@ -2234,7 +2195,7 @@ class Tensor(MathTrait):
     """
     def parse_formula(formula:str, *operands:Tensor):
       if "..." in (formula := formula.replace(" ", "")):
-        ell_chars, ell_longest = "".join(c for c in string.ascii_letters if c not in formula), 0
+        ell_chars, ell_longest = "".join(set(string.ascii_letters) - set(formula)), 0
         for i, inp in enumerate(filter(lambda x: "..." in x, inputs := formula.split("->")[0].split(","))):
           if (ell_count := max(operands[i].ndim, 1) - (len(inp) - len("..."))) > ell_longest: ell_longest = ell_count
           inputs[i] = inp.replace("...", ell_chars[-ell_count:])
@@ -2332,6 +2293,8 @@ class Tensor(MathTrait):
 
     NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
 
+    See: https://paperswithcode.com/method/average-pooling
+
     ```python exec="true" source="above" session="tensor" result="python"
     t = Tensor.arange(25).reshape(1, 1, 5, 5)
     print(t.avg_pool2d().numpy())
@@ -2378,6 +2341,8 @@ class Tensor(MathTrait):
 
     NOTE: unlike PyTorch, this implementation is not limited to only 2d pooling and instead works for any number of dimensions.
 
+    See: https://paperswithcode.com/method/max-pooling
+
     ```python exec="true" source="above" session="tensor" result="python"
     t = Tensor.arange(25).reshape(1, 1, 5, 5)
     print(t.max_pool2d().numpy())
@@ -3006,6 +2971,8 @@ class Tensor(MathTrait):
     """
     Applies the Rectified Linear Unit (ReLU) function element-wise.
 
+    - Described: https://paperswithcode.com/method/relu
+
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).relu().numpy())
     ```
@@ -3042,6 +3009,7 @@ class Tensor(MathTrait):
     Applies the Hardsigmoid function element-wise.
     NOTE: default `alpha` and `beta` values are taken from torch
 
+    - Described: https://paperswithcode.com/method/hard-sigmoid
     - See: https://pytorch.org/docs/stable/generated/torch.nn.functional.hardsigmoid.html
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3284,6 +3252,7 @@ class Tensor(MathTrait):
     """
     Applies the Exponential Linear Unit (ELU) function element-wise.
 
+    - Described: https://paperswithcode.com/method/elu
     - Paper: https://arxiv.org/abs/1511.07289v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3296,6 +3265,7 @@ class Tensor(MathTrait):
     """
     Applies the Continuously differentiable Exponential Linear Unit (CELU) function element-wise.
 
+    - Described: https://paperswithcode.com/method/celu
     - Paper: https://arxiv.org/abs/1704.07483
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3308,6 +3278,7 @@ class Tensor(MathTrait):
     """
     Applies the Scaled Exponential Linear Unit (SELU) function element-wise.
 
+    - Described: https://paperswithcode.com/method/selu
     - Paper: https://arxiv.org/abs/1706.02515v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3332,6 +3303,7 @@ class Tensor(MathTrait):
     """
     Applies the Sigmoid Linear Unit (SiLU) function element-wise.
 
+    - Described: https://paperswithcode.com/method/silu
     - Paper: https://arxiv.org/abs/1606.08415
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3344,6 +3316,7 @@ class Tensor(MathTrait):
     """
     Applies the ReLU6 function element-wise.
 
+    - Described: https://paperswithcode.com/method/relu6
     - Paper: https://arxiv.org/abs/1704.04861v1
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3356,6 +3329,7 @@ class Tensor(MathTrait):
     """
     Applies the Hardswish function element-wise.
 
+    - Described: https://paperswithcode.com/method/hard-swish
     - Paper: https://arxiv.org/abs/1905.02244v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3440,6 +3414,8 @@ class Tensor(MathTrait):
     """
     Applies the Hardtanh function element-wise.
 
+    - Described: https://paperswithcode.com/method/hardtanh-activation
+
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-1.5, -1.0, -0.5, 0., 0.5, 1.0, 1.5]).hardtanh().numpy())
     ```
@@ -3464,6 +3440,7 @@ class Tensor(MathTrait):
     """
     Applies the Gaussian Error Linear Unit (GELU) function element-wise.
 
+    - Described: https://paperswithcode.com/method/gelu
     - Paper: https://arxiv.org/abs/1606.08415v5
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3476,6 +3453,8 @@ class Tensor(MathTrait):
     """
     Applies the Sigmoid GELU approximation element-wise.
 
+    - Described: https://paperswithcode.com/method/gelu
+
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).quick_gelu().numpy())
     ```
@@ -3486,6 +3465,8 @@ class Tensor(MathTrait):
     """
     Applies the Leaky ReLU function element-wise.
 
+    - Described: https://paperswithcode.com/method/leaky-relu
+
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).leaky_relu().numpy())
     ```
@@ -3499,6 +3480,7 @@ class Tensor(MathTrait):
     """
     Applies the Mish function element-wise.
 
+    - Described: https://paperswithcode.com/method/mish
     - Paper: https://arxiv.org/abs/1908.08681v3
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3511,6 +3493,8 @@ class Tensor(MathTrait):
     """
     Applies the Softplus function element-wise.
 
+    - Described: https://paperswithcode.com/method/softplus
+
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softplus().numpy())
     ```
@@ -3521,6 +3505,8 @@ class Tensor(MathTrait):
     """
     Applies the Softsign function element-wise.
 
+    - Described: https://paperswithcode.com/method/softsign
+
     ```python exec="true" source="above" session="tensor" result="python"
     print(Tensor([-3., -2., -1., 0., 1., 2., 3.]).softsign().numpy())
     ```
@@ -3536,8 +3522,7 @@ class Tensor(MathTrait):
     # for each dimension, check either dim is 1, or it does not change
     if not all(resolve(s == ns) or resolve(s == 1) for s,ns in zip(shape, new_shape)):
       raise ValueError(f"cannot broadcast {self.shape} to {new_shape=}")
-    # NOTE: this cast is no-op in forward and uses sum_acc_dtype in the backward sum
+    return self.reshape(shape)._apply_uop(UOp.expand, arg=new_shape)
-    return self.reshape(shape).cast(sum_acc_dtype(self.dtype))._apply_uop(UOp.expand, arg=new_shape).cast(self.dtype)
 
   def _broadcasted(self, y:Tensor|ConstType|UOp, reverse:bool=False, match_dtype:bool=True) -> tuple[Tensor, Tensor]:
     x: Tensor = self
@@ -3814,6 +3799,7 @@ class Tensor(MathTrait):
     """
     Applies Layer Normalization over a mini-batch of inputs.
 
+    - Described: https://paperswithcode.com/method/layer-normalization
     - Paper: https://arxiv.org/abs/1607.06450v1
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3832,6 +3818,7 @@ class Tensor(MathTrait):
     """
     Applies Batch Normalization over a mini-batch of inputs.
 
+    - Described: https://paperswithcode.com/method/batch-normalization
     - Paper: https://arxiv.org/abs/1502.03167
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3856,6 +3843,7 @@ class Tensor(MathTrait):
 
     NOTE: dropout is only applied when `Tensor.training` is `True`.
 
+    - Described: https://paperswithcode.com/method/dropout
     - Paper: https://jmlr.org/papers/v15/srivastava14a.html
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -3897,6 +3885,7 @@ class Tensor(MathTrait):
     Computes scaled dot-product attention.
     `self` is the query tensor, `key` is the key tensor, and `value` is the value tensor.
 
+    - Described: https://paperswithcode.com/method/scaled
     - Paper: https://arxiv.org/abs/1706.03762v7
 
     ```python exec="true" source="above" session="tensor" result="python"
@@ -4089,8 +4078,8 @@ class Tensor(MathTrait):
     #extract singular values and sort. construct U from Q
     S, indices = U.square().sum(-2).sqrt().sort(dim = -1, descending=True)
     new_indices = Tensor.arange(num).reshape((1,) * (self.ndim - 1) + (num,)).expand(b_shape + 2 * (num,)).contiguous()
-    new_indices[..., :num] = indices.reshape(b_shape + (1,) + (num,)).expand(b_shape + 2 * (num,))
+    new_indices[..., :num] = indices.reshape(b_shape + (1,) + (U.shape[0],)).expand(b_shape + 2 * (num,))
-    U,V = U.gather(-1, new_indices[...,0:num,0:num]) / S.unsqueeze(-2), V.gather(-1, new_indices[..., 0:num, 0:num]).realize()
+    U,V = U.gather(-1, new_indices[...,0:num,0:num]) / S.unsqueeze(-2), V.gather(-1, new_indices[..., 0:num, 0:num])
 
     padded_u = Tensor.eye(q_num, dtype = U.dtype).reshape((1,) * (self.ndim - 2) + 2 * (q_num,)).expand(b_shape + 2 * (q_num,)).contiguous()
     padded_u[..., 0:num, 0:num] = U
@@ -4288,11 +4277,6 @@ class Tensor(MathTrait):
     """
     return self.cast(dtypes.bool)
 
-  def bfloat16(self) -> Tensor: return self.cast(dtypes.bfloat16)
-  def double(self) -> Tensor: return self.cast(dtypes.double)
-  def long(self) -> Tensor: return self.cast(dtypes.long)
-  def short(self) -> Tensor: return self.cast(dtypes.short)
-
   # *** image Tensor function replacements ***
 
   def image_dot(self, w:Tensor, dtype:DTypeLike|None=None) -> Tensor:

tinygrad/uop/__init__.py

@@ -9,7 +9,7 @@ class FastEnum(IntEnum):
 # the order of these Ops controls the order of the toposort
 class Ops(FastEnum):
   # uops that aren't rendered
-  NOOP = auto(); SINK = auto(); UNIQUE = auto(); DEVICE = auto(); KERNEL = auto(); PRECAST = auto()  # noqa: E702
+  NOOP = auto(); SINK = auto(); UNIQUE = auto(); DEVICE = auto(); KERNEL = auto()  # noqa: E702
 
   # buffer ops
   COPY = auto(); BUFFER = auto(); BUFFER_VIEW = auto(); MSELECT = auto(); MSTACK = auto() # noqa: E702
@@ -83,8 +83,6 @@ class GroupOp:
   Ternary = {Ops.WHERE, Ops.MULACC}
   ALU = set.union(Unary, Binary, Ternary)
 
-  Defines = {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}
-
   Irreducible = {Ops.CONST, Ops.DEFINE_VAR, Ops.SPECIAL, Ops.RANGE}
   Movement = {Ops.RESHAPE, Ops.EXPAND, Ops.PERMUTE, Ops.PAD, Ops.SHRINK, Ops.FLIP}
 

tinygrad/uop/ops.py

@@ -5,7 +5,7 @@ from dataclasses import dataclass, field
 from enum import Enum, auto
 from tinygrad.uop import Ops, GroupOp
 from tinygrad.uop.mathtraits import MathTrait
-from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate, PtrDType
+from tinygrad.dtype import ConstType, ImageDType, dtypes, DType, truncate
 from tinygrad.helpers import ContextVar, all_int, prod, getenv, all_same, Context, partition, temp, unwrap, T, argfix, Metadata, flatten
 from tinygrad.helpers import PICKLE_BUFFERS, PROFILE, dedup, cdiv, cmod, diskcache_put, to_function_name, cpu_profile, TracingKey
 if TYPE_CHECKING:
@@ -150,12 +150,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     # BUFFER/BUFFER_VIEW and KERNEL only have a size
     if self.op in {Ops.BUFFER, Ops.BUFFER_VIEW}: return ShapeTracker.from_shape((self.size,))
     if self.op is Ops.KERNEL: return ShapeTracker.from_shape((self.arg.ast.size,))
-    if self.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}:
+    #if self.op in {Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG}: return ShapeTracker.from_shape((self.dtype.size,))
-      sz = cast(PtrDType, self.dtype).size
-      return ShapeTracker.from_shape((sz,)) if sz > 0 else None
-
-    # hack for PTX, CASTing the ptr loses the shape. even worse hack with tag
-    if self.op is Ops.CAST and self.src[0].op is Ops.DEFINE_GLOBAL and self.src[0].tag is None: return None
 
     # otherwise we get the shape from sources
     if not (src_sts := [x.st for x in self.src if x.st is not None]): return None
@@ -174,11 +169,9 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     if self.op is Ops.VIEW: return self.shape
     # NOTE: if a parent doesn't have st its full_shape is empty
     parent_shapes = [x.full_shape for x in self.src]
-    return tuple(smax(x) for x in itertools.zip_longest(*parent_shapes, fillvalue=1))
+    return tuple(smax(x) for x in zip(*[x for x in parent_shapes if x != ()]))
   @property
-  def shape(self) -> tuple[sint, ...]:
+  def shape(self) -> tuple[sint, ...]: return unwrap(self.st).shape
-    assert self.st is not None, f"{self.op} doesn't have a shape"
-    return unwrap(self.st).shape
   @property
   def size(self) -> int: return self.arg[0] if self.op is Ops.BUFFER_VIEW else self.arg if self.op is Ops.BUFFER else unwrap(self.st).size
 
@@ -243,9 +236,8 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       i = (i,)
     return UOp(Ops.GEP, self.dtype.scalar().vec(len(i)) if len(i) > 1 else self.dtype.scalar(), (self,), i)
   def load(self, *src:UOp, **kwargs): return UOp(Ops.LOAD, dtype=kwargs.pop("dtype", self.dtype.base), src=(self,)+src, **kwargs)
-  def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, dtypes.void, (self,)+src, **kwargs)
+  def store(self, *src:UOp, **kwargs): return UOp(Ops.STORE, self.dtype, (self,)+src, **kwargs)
   def assign(self, x:UOp): return UOp(Ops.ASSIGN, self.dtype, (self, x))
-  def barrier(self, *src:UOp): return UOp(Ops.BARRIER, src=(self,)+src)
   def alu(self, op, *src:UOp, **kwargs):
     out_dtype = (self, *src)[-1].dtype
     if op in {Ops.CMPLT, Ops.CMPNE}: out_dtype = dtypes.bool.vec(out_dtype.count) if out_dtype.count > 1 else dtypes.bool
@@ -257,21 +249,25 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     ret = UOp(Ops.VCONST if isinstance(b, tuple) else Ops.CONST, dtype, arg=dtypes.as_const(b, dtype))
     if shape is not None:
       from tinygrad.shape.shapetracker import ShapeTracker
-      ret = ret.replace(src=(UOp(Ops.VIEW, dtypes.void, (), ShapeTracker.from_shape(shape, (0,)*len(shape))),))
+      ret = ret.replace(src=(ShapeTracker.from_shape(()).reshape((1,)*len(shape)).expand(shape).to_uop(),))
     if device is not None:
       ret = ret.replace(src=(UOp(Ops.DEVICE, arg=device).view(unwrap(ret.st)),))
     return ret
+  def valid(self): return UOp.where(UOp(Ops.VALID, dtypes.bool, (UOp(Ops.VIEW, arg=self.st),)), self.const_like(self.base.arg), 0)
   @staticmethod
   def range(dtype:DType, end:sint, idx:int): return UOp(Ops.RANGE, dtype=dtype, src=(sint_to_uop(end),), arg=idx)
-  def r(self, op:Ops, axis:tuple[int, ...]):
+  def r(self, op:Ops, axis:tuple[int, ...], permute=True):
     axis = tuple(sorted([x for x in axis if resolve(self.shape[x] != 1)]))
     if len(axis) == 0: return self
     # move any non reduce axis before the first reduce axis
     move_early, rest = partition(range(axis[0], len(self.shape)), lambda i: i not in axis and resolve(self.shape[i] != 1))
-    permaxis = tuple(range(axis[0])) + tuple(move_early) + tuple(rest)
+    if move_early and permute:
-    ret = self.permute(permaxis)
+      permaxis = tuple(range(axis[0])) + tuple(move_early) + tuple(rest)
-    new_axis = tuple([x for x in range(axis[0]+len(move_early), len(self.shape)) if resolve(ret.shape[x] != 1)])
+      ret = self.permute(permaxis)
-    assert len(axis) == len(new_axis)
+      new_axis = tuple([x for x in range(axis[0]+len(move_early), len(self.shape)) if resolve(ret.shape[x] != 1)])
+      assert len(axis) == len(new_axis)
+    else:
+      ret, new_axis = self, axis
     ret = UOp(Ops.REDUCE_AXIS, self.dtype, (ret,), (op, new_axis))
     return ret.reshape(tuple([x if i not in axis else 1 for i,x in enumerate(self.shape)]))
   def reduce(self, *src:UOp, **kwargs): return UOp(Ops.REDUCE, kwargs.pop('dtype', self.dtype), src=(self,)+src, **kwargs)
@@ -376,7 +372,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
       return self.src[0].device[self.arg]
     if self.op is Ops.MSTACK: return tuple(cast(str, x.device) for x in self.src)
     if self.op in {Ops.COPY, Ops.BUFFER, Ops.ALLREDUCE}: return self.src[1].device
-    return next((x._device for x in self.src if x._device is not None), None)
+    return dsrcs[0]._device if len(dsrcs:=[x for x in self.src if x._device is not None]) != 0 else None
   @property
   def buf_uop(self) -> UOp:
     if self.op is Ops.BUFFER: return self
@@ -440,7 +436,7 @@ class UOp(MathTrait, metaclass=UOpMetaClass):
     all_vars = set([x for x in self.toposort() if x.op is Ops.DEFINE_VAR])
     return bound_vars.union(set([x for x in all_vars if x not in bound_var_base]))
   def variables(self) -> list[Variable]:
-    st_vars: list[set[Variable]] = [x.arg.vars() for x in self.toposort() if x.op is Ops.VIEW]
+    st_vars: list[set[Variable]] = [x.st_arg.vars() for x in self.toposort() if x.op in GroupOp.Buffer]
     return sorted(set.union(*st_vars, set([x.unbind()[0] if x.op is not Ops.DEFINE_VAR else x for x in self.vars()])), key=lambda v: v.arg)
 
   # *** uop symbolic stuff ***
@@ -544,6 +540,10 @@ class KernelInfo:
   opts_to_apply: tuple|None = None
   @property
   def function_name(self): return to_function_name(self.name)
+  @property
+  def global_dims(self) -> list[int]: return [i for i,x in enumerate(self.axis_types) if x is AxisType.GLOBAL]
+  @property
+  def local_dims(self) -> list[int]: return [i for i,x in enumerate(self.axis_types) if x in (AxisType.LOCAL, AxisType.GROUP_REDUCE)]
 
 # ******** ops in python ********
 
@@ -642,7 +642,6 @@ class UPat(MathTrait):
   def const(dtype:DType|tuple[DType, ...]|None, b:ConstType): return UPat(Ops.CONST, dtype=dtype, arg=b)
 
   # copied from UOp
-  def sink(self, *srcs:UPat|None, **kwargs): return UPat(Ops.SINK, dtypes.void, (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
   def index(self, idx:UPat, valid:UPat|None=None): return UPat(Ops.INDEX, self.dtype, (self,idx,valid) if valid is not None else (self,idx))
   def view(self, st=None, **kwargs): return UPat(Ops.VIEW, self.dtype, (self,), st, **kwargs)
   def cast(self, dtype=None, **kwargs): return UPat(Ops.CAST, dtype, (self,), **kwargs)
@@ -859,7 +858,7 @@ if TRACK_MATCH_STATS or PROFILE:
       with open(fn:=temp("rewrites.pkl", append_user=True), "wb") as f:
         print(f"rewrote {len(tracked_ctxs)} graphs and matched {sum(len(r.matches) for x in tracked_ctxs for r in x)} times, saved to {fn}")
         pickle.dump((tracked_keys, tracked_ctxs, uop_fields), f)
-    if VIZ: launch_viz(VIZ, temp("rewrites.pkl", append_user=True))
+    if VIZ: launch_viz("VIZ", temp("rewrites.pkl", append_user=True))
     if getenv("PRINT_MATCH_STATS", TRACK_MATCH_STATS.value):
       ret = [0,0,0.0,0.0]
       for k,v in sorted(list(match_stats.items()), key=lambda x: x[1][2]+x[1][3]):
@@ -869,10 +868,9 @@ if TRACK_MATCH_STATS or PROFILE:
       print(f"{ret[0]:6d} / {ret[1]:7d} -- {ret[3]*1000.:9.2f} / {(ret[2]+ret[3])*1000.:9.2f} ms -- TOTAL")
       print(f"{len(match_stats)} rules, {sum(v[0] > 0 for v in match_stats.values())} matched once")
 
-  def launch_viz(var:ContextVar, data:str):
+  def launch_viz(env_str:str, data:str):
-    os.environ[(env_str:=var.key)] = "0"
+    os.environ[env_str] = "0"
     os.environ[f"{env_str}_DATA"] = data
-    os.environ[f"{env_str}_VALUE"] = str(var.value)
     if not int(os.getenv("VIZ", "0")) and not int(os.getenv("PROFILE", "0")):
       args = ['--kernels', getenv("VIZ_DATA", "")] if getenv("VIZ_DATA", "") else []
       args += ['--profile', getenv("PROFILE_DATA", "")] if getenv("PROFILE_DATA", "") else []
@@ -949,7 +947,6 @@ renderer = PatternMatcher([
   (UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: x.src[0]),
   #(UPat(Ops.BIND, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"{x.src[0].arg}[={x.src[1].arg}]")),
   (UPat(Ops.NEG, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(-{x.src[0].arg})")),
-  (UPat(Ops.RECIP, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"(1/{x.src[0].arg})")),
   (UPat(Ops.MAX, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"max({x.src[0].arg}, {x.src[1].arg})")),
   (UPat(Ops.MULACC, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[0].arg}*{x.src[1].arg}+{x.src[2].arg})")),
   (UPat(Ops.WHERE, src=UPat(Ops.NOOP), name="x"), lambda x: UOp(Ops.NOOP, arg=f"({x.src[1].arg} if {x.src[0].arg} else {x.src[2].arg})")),

tinygrad/uop/spec.py

@@ -2,7 +2,6 @@ from typing import cast, Callable
 from tinygrad.uop.ops import PatternMatcher, UPat, GroupOp, Ops, UOp, print_uops, python_alu, graph_rewrite, resolve
 from tinygrad.dtype import DType, ImageDType, dtypes, PtrDType, AddrSpace
 from tinygrad.helpers import all_same, prod, DEBUG, ContextVar, Context
-from tinygrad.shape.shapetracker import ShapeTracker
 try:
   import z3
 
@@ -131,43 +130,44 @@ index_pat = UPat(Ops.INDEX, name="idx").or_casted()
 spec = PatternMatcher([
   (UPat(Ops.DEFINE_GLOBAL, name="x"), lambda x: isinstance(x.dtype, (PtrDType, ImageDType)) and x.dtype.addrspace == AddrSpace.GLOBAL),
   (UPat(Ops.DEFINE_LOCAL, name="x"), lambda x: isinstance(x.dtype, PtrDType) and x.dtype.addrspace == AddrSpace.LOCAL),
-  (UPat(Ops.DEFINE_REG, src=()), lambda: True),
+  (UPat(Ops.DEFINE_REG, src=(UPat.var("c"),), name="x", allow_any_len=True),
+   lambda x,c: all(y.op is Ops.RANGE for y in x.src[1:]) and c.dtype.base == x.dtype.base),
   (UPat(Ops.DEFINE_VAR, name="x"), lambda x: isinstance(x.arg[1], int) and isinstance(x.arg[2], int)),
 
   (UPat(Ops.RANGE, src=(UPat.var("x"),), name="rng"), lambda rng,x: rng.dtype == x.dtype and isinstance(rng.arg, int)),
   (UPat(Ops.SPECIAL, src=()), lambda: True),
 
-  (UPat(Ops.VIEW, dtypes.void, src=(), name="x"), lambda x: isinstance(x.arg, ShapeTracker)),
+  # TODO: confirm the args of both of these are shapetrackers
-  (UPat(Ops.VIEW, src=(UPat.var("src"),), name="x"),
+  (UPat(Ops.VIEW, dtypes.void, src=()), lambda: True),
-   lambda x,src: isinstance(x.arg, ShapeTracker) and src.op is not Ops.STORE and x.dtype.base == src.dtype.base),
+  (UPat(Ops.VIEW, src=(UPat.var("src"),), name="x"), lambda x,src: src.op is not Ops.STORE and x.dtype.base == src.dtype.base),
 
   (UPat(Ops.VALID, dtypes.bool, (UPat(Ops.VIEW),)), lambda: True),
   (UPat(Ops.CONST, name="x"), lambda x: type(x.arg) is type(dtypes.as_const(x.arg, x.dtype))),
 
   # early LOAD has a <bufview, store?>
-  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.Defines),)),)), lambda: True),
+  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL)),)),)), lambda: True),
-  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.Defines),)), UPat(Ops.STORE))), lambda: True),
+  (UPat(Ops.LOAD, src=(UPat(Ops.VIEW, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL)),)), UPat(Ops.STORE))), lambda: True),
 
   # early STORE has a <bufview, val>
-  (UPat(Ops.STORE, src=(UPat(Ops.VIEW, src=(UPat(GroupOp.Defines),)), UPat())), lambda: True),
+  (UPat(Ops.STORE, src=(UPat(Ops.VIEW, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL)),)), UPat())), lambda: True),
 
   # **** new style load/store ****
 
   # INDEX is used in new style load/store
   # INDEX takes a <buf, alu, gate?>
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat())), lambda: True),
+  (UPat(Ops.INDEX, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG)), UPat())), lambda: True),
-  (UPat(Ops.INDEX, src=(UPat(GroupOp.Defines), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
+  (UPat(Ops.INDEX, src=(UPat((Ops.DEFINE_GLOBAL, Ops.DEFINE_LOCAL, Ops.DEFINE_REG)), UPat(), UPat(dtype=dtypes.bool))), lambda: True),
 
   # LOAD on STORE
-  (UPat(Ops.LOAD, src=(UPat(Ops.STORE),), allow_any_len=True), lambda: True),
+  (UPat(Ops.LOAD, src=(UPat(Ops.STORE),)), lambda: True),
 
   # LOAD takes a <bufidx, alt?, barrier?>
   (UPat(Ops.LOAD, src=(index_pat, UPat(Ops.IF, name="cond")), allow_any_len=True), lambda idx,cond: validate_index(idx,cond.src[0])),
   (UPat(Ops.LOAD, src=(index_pat,), allow_any_len=True), validate_index),
 
   # STORE takes a <bufidx, val, gate?>
-  (UPat(Ops.STORE, dtype=dtypes.void, src=(index_pat, UPat(name="val"), UPat(Ops.IF, name="gate")), allow_any_len=True), validate_store),
+  (UPat(Ops.STORE, src=(index_pat, UPat(name="val"), UPat(Ops.IF, name="gate")), allow_any_len=True), validate_store),
-  (UPat(Ops.STORE, dtype=dtypes.void, src=(index_pat, UPat(name="val")), allow_any_len=True), validate_store),
+  (UPat(Ops.STORE, src=(index_pat, UPat(name="val")), allow_any_len=True), validate_store),
 
   # most ALUs have all matching dtypes, except CMPLT, CMPNE, and WHERE
   (UPat(Ops.WHERE, name="w", src=(UPat(dtype=dtypes.bool), UPat.var("x"), UPat.var("y"))), lambda w,x,y: w.dtype == x.dtype == y.dtype),
@@ -199,7 +199,7 @@ spec = PatternMatcher([
   # NOTE: for testing, we let sinks be anything
   #(UPat(Ops.SINK, src=UPat(Ops.STORE)), lambda: True),
   (UPat(Ops.SINK, dtypes.void), lambda: True),
-  (UPat((Ops.NOOP, Ops.CUSTOMI, Ops.CUSTOM, Ops.PRECAST)), lambda: True),
+  (UPat((Ops.NOOP, Ops.CUSTOMI, Ops.CUSTOM)), lambda: True),
 
   # PTX LOAD/STORE
   (UPat((Ops.LOAD, Ops.STORE), src=(UPat(dtype=dtypes.int64),), allow_any_len=True), lambda: True),

tinygrad/uop/symbolic.py

@@ -3,7 +3,7 @@ from typing import Any, Literal, cast
 import math, operator, struct, functools
 from collections import defaultdict
 from tinygrad.uop.ops import Ops, PatternMatcher, UPat, UOp, GroupOp, exec_alu
-from tinygrad.dtype import ConstType, dtypes, PtrDType, AddrSpace, can_safe_cast
+from tinygrad.dtype import ConstType, dtypes, PtrDType, AddrSpace
 from tinygrad.helpers import partition, all_same, prod, flatten, get_single_element, cdiv, cmod, CORRECT_DIVMOD_FOLDING
 from tinygrad.uop.transcendental import xpow
 
@@ -65,8 +65,6 @@ symbolic_simple = PatternMatcher([
   (UPat(Ops.CAST, name="root", src=(UPat.cvar("c"),)), lambda root, c: root.const_like(c.arg)),
   (UPat((Ops.CAST, Ops.BITCAST), name="root"), lambda root: root.src[0] if root.dtype == root.src[0].dtype else None),
   (UPat(Ops.BITCAST, name="root", src=(UPat.cvar("c"),)), fold_bitcast),
-  # b.cast(a).cast(b) -> b if a preserves all values in b
-  (UPat.var('x').cast().named('a').cast().named('b'), lambda x,a,b: x if x.dtype == b.dtype and can_safe_cast(b.dtype, a.dtype) else None),
   # ** pow **
   (UPat.var("x").alu(Ops.POW, UPat.cvar("c", vec=False)), simplify_pow),
   # positive const ** x
@@ -407,8 +405,8 @@ def reduce_mul_chain(r:UOp):
   return r.replace(src=(prod(inside) if len(inside) else r.src[0].const_like(1),)+r.src[1:])*prod(outside)
 
 # this is symbolic 2.0
-REMOVE_FROM_SINK = {Ops.SINK, Ops.UNROLL, Ops.PTRCAT, Ops.CAT, Ops.NOOP}
+REMOVE_FROM_SINK = {Ops.SINK, Ops.UNROLL, Ops.PTRCAT, Ops.CAT}
-REMOVE_FROM_BARRIER = {Ops.VECTORIZE, Ops.SINK, Ops.CAT, Ops.PTRCAT, Ops.NOOP}
+REMOVE_FROM_BARRIER = {Ops.VECTORIZE, Ops.SINK, Ops.CAT, Ops.PTRCAT}
 sym = symbolic_flat+PatternMatcher([
   # LOAD/STORE -> NOOP
   (UPat.var('x').store(UPat.var('x').load(), allow_any_len=True), lambda x: None if x.dtype.addrspace != AddrSpace.REG else x.src[0].src[0]),
@@ -429,6 +427,7 @@ sym = symbolic_flat+PatternMatcher([
   (UPat(Ops.WMMA, src=(UPat.var(), UPat.const(None, 0.0), UPat.var("acc"))), lambda acc: acc),
   # threefry + remove longs
   (UPat(Ops.THREEFRY, dtype=dtypes.uint64, src=(UPat.var("x"), UPat.var("key"))), threefry2x32),
+  (UPat.var('x', dtypes.uint32).cast(dtypes.uint64).cast(dtypes.uint32), lambda x: x),   # cast there and back is noop (TODO: genericize)
   ((UPat.var('x', dtypes.uint64)&0xFFFFFFFF).cast(dtypes.uint32), lambda x: x.cast(dtypes.uint32)),  # cast does truncation
   (((UPat.var(None, dtypes.uint64)*(1<<32)) | UPat.var('y',  dtypes.uint32).cast(dtypes.uint64)).cast(dtypes.uint32), lambda y: y),
   (((UPat.var('x',  dtypes.uint64)*(1<<32)) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))//(1<<32), lambda x: x),
@@ -458,6 +457,9 @@ sym = symbolic_flat+PatternMatcher([
   (UPat().index(UPat(), UPat.const(dtypes.bool, True)).named("idx"), lambda idx: idx.replace(src=idx.src[0:2])), # remove True
   (UPat((Ops.LOAD, Ops.STORE), src=(UPat().index(UPat(), UPat.const(dtypes.bool, False)).or_casted(),), allow_any_len=True, name="x"),
     lambda x: UOp(Ops.NOOP) if x.op is Ops.STORE else x.const_like(0)), # NULL pointer store does nothing. NULL pointer load produces 0
+  # remove NOOPs from SINK
+  (UPat(Ops.SINK, name="root"),
+    lambda root: UOp(Ops.SINK, root.dtype, a, root.arg) if len(a:=tuple(x for x in root.src if x.op is not Ops.NOOP)) != len(root.src) else None),
   # remove VECTORIZE from SINK/BARRIER. TODO: SINK/BARRIER are really the same thing at GLOBAL/LOCAL levels
   (UPat(Ops.BARRIER, name="root"),
     lambda root: UOp(Ops.BARRIER, root.dtype, tuple(flatten(x.src if x.op in REMOVE_FROM_BARRIER else (x,) for x in root.src)), root.arg)
@@ -467,7 +469,6 @@ sym = symbolic_flat+PatternMatcher([
       if any(x.op in REMOVE_FROM_SINK for x in root.src) else None),
   ((UPat.var("x") * UPat.var("x")).reciprocal(), lambda x: x.reciprocal()*x.reciprocal()),  # 1/(x^c) -> (1/x)^c
   ((UPat.var("x") * UPat.var("x") * UPat.var("x")).reciprocal(), lambda x: x.reciprocal()*x.reciprocal()*x.reciprocal()),
-  ((UPat.var("x") * UPat.cvar("c")).reciprocal(), lambda x,c: x.reciprocal()*c.reciprocal()), # 1/(x*c) -> (1/c)*(1/x)
   (UPat.var("x") * ((1+UPat.var("x")).reciprocal().named("d")), lambda x,d: 1-d), # x*/(1+x) -> 1-1/(1+x)
   (UPat.var("x") * ((1+UPat.var("x")).reciprocal().named("d")*UPat.var("y")), lambda x,y,d: y*(1-d)),
   (UPat.var("x") * ((1+UPat.var("x")).reciprocal().named("d")+UPat.var("y")), lambda x,y,d: (1-d)+x*y),

tinygrad/viz/fetch_assets.sh

@@ -10,6 +10,5 @@ fetch "dagrejs.github.io/project/dagre/latest/dagre.min.js"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/styles/default.min.css"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/highlight.min.js"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/python.min.js"
-fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/x86asm.min.js"
 fetch "cdnjs.cloudflare.com/ajax/libs/highlight.js/11.10.0/languages/cpp.min.js"
 fetch "unpkg.com/@highlightjs/[email protected]/styles/tokyo-night-dark.min.css"