experiments with multi being range

fix assert in test_schedule (#12745 )
* fix assert in test_schedule updated kernel counts and some old tests * fix
2025-10-17 14:11:55 +08:00 · 2025-10-16 15:39:50 -04:00 · 2025-10-16 14:11:33 -04:00 · 2025-10-16 12:43:27 -04:00 · 2025-10-16 11:40:36 -04:00 · 2025-10-16 11:17:47 -04:00
447 changed files with 65245 additions and 6384 deletions
@@ -41,6 +41,10 @@ inputs:
    description: "Install LLVM?"
    required: false
    default: 'false'
+  mesa:
+    description: "Install mesa"
+    required: false
+    default: 'false'
 runs:
  using: "composite"
  steps:
@@ -289,3 +293,13 @@ runs:
      if: inputs.llvm == 'true' && runner.os == 'macOS'
      shell: bash
      run: brew install llvm@20
+
+    # **** mesa ****
+    - name: Install mesa (linux)
+      if: inputs.mesa == 'true' && runner.os == 'Linux'
+      shell: bash
+      run: sudo curl -L https://github.com/sirhcm/tinymesa/releases/download/tinymesa-32dc66c/libtinymesa_cpu-mesa-25.2.4-linux-amd64.so -o /usr/lib/libtinymesa_cpu.so
+    - name: Install mesa (macOS)
+      if: inputs.mesa == 'true' && runner.os == 'macOS'
+      shell: bash
+      run: brew install sirhcm/tinymesa/tinymesa
@@ -36,8 +36,9 @@ jobs:
        cuda: 'true'
        webgpu: 'true'
        llvm: 'true'
+        pydeps: 'pyyaml mako'
    - name: Install autogen support packages
-      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev
+      run: sudo apt-get install -y --no-install-recommends llvm-14-dev libclang-14-dev llvm-20-dev
    - name: Verify OpenCL autogen
      run: |
        cp tinygrad/runtime/autogen/opencl.py /tmp/opencl.py.bak
@@ -89,3 +90,8 @@ jobs:
        cp tinygrad/runtime/autogen/llvm.py /tmp/llvm.py.bak
        ./autogen_stubs.sh llvm
        diff /tmp/llvm.py.bak tinygrad/runtime/autogen/llvm.py
+    - name: Verify mesa autogen
+      run: |
+        cp tinygrad/runtime/autogen/mesa.py /tmp/mesa.py.bak
+        ./autogen_stubs.sh mesa
+        diff /tmp/mesa.py.bak tinygrad/runtime/autogen/mesa.py
@@ -51,15 +51,18 @@ jobs:
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
    - name: reset process replay
      run: python3.11 test/external/process_replay/reset.py
+    - name: Print macOS version
+      run: sw_vers
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=500 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+      run: BENCHMARK_LOG=stable_diffusion JIT=1 ASSERT_MIN_STEP_TIME=720 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
    - name: Run Stable Diffusion without fp16
-      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=700 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
+      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=800 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing | tee sd_no_fp16.txt
    - name: Run Stable Diffusion v2
-      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=1600 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
+      # TODO: very slow step time
+      run: BENCHMARK_LOG=stable_diffusion_v2 JIT=1 ASSERT_MIN_STEP_TIME=4500 python3.11 examples/sdv2.py --fp16 --seed 0 --noshow --timing | tee sdv2.txt
    # process replay can't capture this, the graph is too large
    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=5000 CAPTURE_PROCESS_REPLAY=0 JIT=1 python3.11 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run model inference benchmark
      run: METAL=1 python3.11 test/external/external_model_benchmark.py
    - name: Test speed vs torch
@@ -99,7 +102,7 @@ jobs:
    - name: Run GPT2
      run: |
        BENCHMARK_LOG=gpt2_nojit JIT=0 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=8 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2 JIT=1 ASSERT_MIN_STEP_TIME=13 python3.11 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
    - name: Run GPT2 w HALF
      run: BENCHMARK_LOG=gpt2_half HALF=1 python3.11 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
    - name: Run GPT2 w HALF/BEAM
@@ -108,14 +111,19 @@ jobs:
      run: BENCHMARK_LOG=olmoe python3.11 examples/olmoe.py
    - name: Train MNIST
      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py | tee beautiful_mnist.txt
-    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=320 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=385 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
+
+    # NOTE: this is failing in CI. it is not failing on my machine and I don't really have a way to debug it
+    # the error is "RuntimeError: Internal Error (0000000e:Internal Error)"
+    #- name: Run 10 CIFAR training steps
+    #  run: BENCHMARK_LOG=cifar_10steps JIT=1 ASSERT_MIN_STEP_TIME=3000 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar.txt
+    #- name: Run 10 CIFAR training steps w HALF
+    #  run: BENCHMARK_LOG=cifar_10steps_half JIT=2 ASSERT_MIN_STEP_TIME=3000 STEPS=10 DEFAULT_FLOAT=HALF python3.11 examples/hlb_cifar10.py | tee train_cifar_half.txt
+
    #- name: Run 10 CIFAR training steps w BF16
    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+    # TODO: too slow
+    # - name: Run 10 CIFAR training steps w winograd
+    #   run: BENCHMARK_LOG=cifar_10steps_wino JIT=1 ASSERT_MIN_STEP_TIME=150 WINO=1 STEPS=10 python3.11 examples/hlb_cifar10.py | tee train_cifar_wino.txt
    - name: UsbGPU boot time
      run: sudo -E PYTHONPATH=. DEBUG=2 AM_RESET=1 AMD=1 AMD_IFACE=USB time python3.11 test/test_tiny.py TestTiny.test_plus
    - name: UsbGPU tiny tests
@@ -213,8 +221,9 @@ jobs:
      run: DEBUG=2 CUDA=1 python -m pytest -rA test/test_tiny.py
    - name: Run Stable Diffusion
      run: BENCHMARK_LOG=stable_diffusion NV=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
-    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+    # TODO: too slow
+    # - name: Run SDXL
+    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 NV=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run LLaMA
      run: |
        BENCHMARK_LOG=llama_nojit NV=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
@@ -238,9 +247,9 @@ jobs:
    - name: Run GPT2
      run: |
        BENCHMARK_LOG=gpt2_nojit NV=1 JIT=0 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_unjitted.txt
-        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
+        BENCHMARK_LOG=gpt2 NV=1 JIT=1 ASSERT_MIN_STEP_TIME=4 python3 examples/gpt2.py --prompt "Hello." --count 10 --temperature 0 --timing | tee gpt2_jitted.txt
    - name: Run GPT2 w HALF
-      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=5 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
+      run: BENCHMARK_LOG=gpt2_half NV=1 HALF=1 ASSERT_MIN_STEP_TIME=6 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half.txt
    - name: Run GPT2 w HALF/BEAM
      run: BENCHMARK_LOG=gpt2_half_beam NV=1 HALF=1 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/gpt2.py --count 10 --temperature 0 --timing | tee gpt2_half_beam.txt
    - uses: actions/upload-artifact@v4
@@ -299,24 +308,27 @@ jobs:
        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: Fuzz Padded Tensor Core GEMM (NV)
-      run: NV=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
-    - name: Fuzz Padded Tensor Core GEMM (PTX)
-      run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
+    # TODO: too slow
+    # - name: Fuzz Padded Tensor Core GEMM (NV)
+    #   run: NV=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
+    # TODO: too slow
+    # - name: Fuzz Padded Tensor Core GEMM (PTX)
+    #   run: NV=1 NV_PTX=1 M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
    - name: Train MNIST
      run: time PYTHONPATH=. NV=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=85 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=270 NV=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=68 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=310 NV=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    - name: Run 10 CIFAR training steps w BF16
-      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=75 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=35 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=310 NV=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
+    # TODO: too slow
+    # - name: Run 10 CIFAR training steps w winograd
+    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 NV=1 CAPTURE_PROCESS_REPLAY=0 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF LATEWINO=1 STEPS=1000 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
+      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
    - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
+      run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 NV=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
    - name: Run MLPerf resnet eval on training data
      run: time BENCHMARK_LOG=resnet_eval NV=1 MODEL=resnet python3 examples/mlperf/model_eval.py
    #- name: Run 10 MLPerf ResNet50 training steps (1 gpu)
@@ -415,9 +427,10 @@ jobs:
    - name: Test AM warm start time
      run: time AMD=1 python3 test/test_tiny.py TestTiny.test_plus
    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=450 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
-    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=1400 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
+      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=550 AMD=1 python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing | tee sd.txt
+    # TODO: too slow
+    # - name: Run SDXL
+    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3200 CAPTURE_PROCESS_REPLAY=0 AMD=1 python3 examples/sdxl.py --seed 0 --noshow --timing | tee sdxl.txt
    - name: Run LLaMA 7B
      run: |
        BENCHMARK_LOG=llama_nojit AMD=1 JIT=0 python3 examples/llama.py --gen 1 --prompt "Hello." --count 10 --temperature 0 --timing | tee llama_unjitted.txt
@@ -508,19 +521,20 @@ jobs:
    - name: Train MNIST
      run: time PYTHONPATH=. AMD=1 TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py | tee beautiful_mnist.txt
    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=85 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
+      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=330 AMD=1 STEPS=10 python3 examples/hlb_cifar10.py | tee train_cifar.txt
    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=188 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
+      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=330 AMD=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_half.txt
    # - name: Run 10 CIFAR training steps w BF16
    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 AMD=1 STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py | tee train_cifar_bf16.txt
-    - name: Run 10 CIFAR training steps w winograd
-      run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.txt
+    # TODO: too slow
+    # - name: Run 10 CIFAR training steps w winograd
+    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 AMD=1 WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py | tee train_cifar_wino.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 train_cifar_one_gpu.txt
+      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_one_gpu.txt
    #- name: Run full CIFAR training steps w 6 GPUS
-    #  run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
+    #  run: time BENCHMARK_LOG=cifar_6gpu AMD=1 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu.txt
    #- name: Run full CIFAR training steps w 6 GPUS (REMOTE)
-    #  run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.2 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
+    #  run: time BENCHMARK_LOG=cifar_6gpu_remote REMOTE=1 REMOTEDEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee train_cifar_six_gpu_remote.txt
    - uses: actions/upload-artifact@v4
      with:
        name: Speed (AMD Training)
@@ -606,21 +620,21 @@ jobs:
    - name: reset process replay
      run: test/external/process_replay/reset.py
    - name: benchmark openpilot 0.9.9 driving_vision
-      run: BENCHMARK_LOG=openpilot_0_9_9_vision ASSERT_MIN_STEP_TIME=30 PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
+      run: BENCHMARK_LOG=openpilot_0_9_9_vision PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
    - name: benchmark openpilot 0.9.9 driving_policy
-      run: BENCHMARK_LOG=openpilot_0_9_9_policy ASSERT_MIN_STEP_TIME=45 PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
+      run: BENCHMARK_LOG=openpilot_0_9_9_policy PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
    - name: benchmark openpilot 0.9.9 dmonitoring
-      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring ASSERT_MIN_STEP_TIME=70 PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
+      run: BENCHMARK_LOG=openpilot_0_9_9_dmonitoring PYTHONPATH=. NOLOCALS=1 FLOAT16=1 IMAGE=2 QCOM=1 taskset -c 4-7 python3 test/external/external_benchmark_openpilot.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
    - name: openpilot compile3 0.9.9 driving_vision
-      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
+      run: PYTHONPATH="." ASSERT_MIN_STEP_TIME=18 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_vision.onnx
    - name: openpilot compile3 0.9.9 driving_policy
-      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
+      run: PYTHONPATH="." ASSERT_MIN_STEP_TIME=7 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/driving_policy.onnx
    - name: openpilot compile3 0.9.9 dmonitoring
-      run: PYTHONPATH="." QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/selfdrive/modeld/models/dmonitoring_model.onnx
+      run: PYTHONPATH="." ASSERT_MIN_STEP_TIME=12 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.9/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
+        PYTHONPATH="." ASSERT_MIN_STEP_TIME=5 QCOM=1 taskset -c 4-7 python3 examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/22aec22a10ce09384d4a4af2a0bbff08d54af7e0c888503508f356fae4ff0e29
+        PYTHONPATH="." ASSERT_MIN_STEP_TIME=26 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
@@ -628,7 +642,7 @@ jobs:
        ln -s /data/home/tiny/tinygrad/testsig-*.so .
        PYTHONPATH=. CC=clang-19 CPU=1 CPU_LLVM=0 QUANT=1 CNT=0 python3 examples/test_onnx_imagenet.py https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx /tmp/model.quant.onnx
        # benchmark on DSP with NOOPT=1, the devectorizer has issues
-        PYTHONPATH=. CC=clang-19 DSP=1 DONT_REALIZE_EXPAND=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+        PYTHONPATH=. CC=clang-19 DSP=1 NOOPT=1 CNT=2 DEBUG=2 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
    - 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
    - uses: actions/upload-artifact@v4
@@ -695,7 +709,7 @@ jobs:
        AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
        AMD=1 GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
    - 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
+      run: time BENCHMARK_LOG=cifar AMD=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py | tee am_train_cifar_one_gpu.txt
    # TODO: enable
    # - 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
@@ -758,7 +772,7 @@ jobs:
    - 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 NV=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
+      run: time BENCHMARK_LOG=cifar NV=1 DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 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)
@@ -12,7 +12,7 @@ jobs:
  run_script_job:
    runs-on: [self-hosted, Linux, tinybox]
    if: github.repository_owner == 'tinygrad'
-    timeout-minutes: 360
+    timeout-minutes: 720

    steps:
    - name: Checkout Code
@@ -89,64 +89,65 @@ jobs:
        clang -O2 recognize.c -lm -o recognize
        cat test/models/efficientnet/Chicken.jpg | ./recognize | grep cock

-  torchbackend:
-    name: Torch Backend Tests
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: torch-backend-pillow-torchvision-et-pt
-        deps: testing_minimal
-        pydeps: "pillow torchvision expecttest"
-        llvm: 'true'
-    - name: Install ninja
-      run: |
-        sudo apt update || true
-        sudo apt install -y --no-install-recommends ninja-build
-    - name: Lint with ruff
-      run: |
-        pip3 install --upgrade --force-reinstall ruff==0.11.0
-        python3 -m ruff check extra/torch_backend/backend.py
-    - name: Test one op
-      run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
-    - name: Test ResNet-18
-      run: DEBUG=2 python3 extra/torch_backend/example.py
-    - name: My (custom) tests
-      run: python3 extra/torch_backend/test.py
-    - name: Test one op in torch tests
-      run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
-    - name: Test Ops with TINY_BACKEND
-      run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
-    - name: Test in-place operations on views
-      run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
-    - name: Test multi-gpu
-      run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py
+  # TODO: fix the torch backend and reenable
+  # torchbackend:
+  #   name: Torch Backend Tests
+  #   runs-on: ubuntu-latest
+  #   timeout-minutes: 15
+  #   steps:
+  #   - name: Checkout Code
+  #     uses: actions/checkout@v4
+  #   - name: Setup Environment
+  #     uses: ./.github/actions/setup-tinygrad
+  #     with:
+  #       key: torch-backend-pillow-torchvision-et-pt
+  #       deps: testing_minimal
+  #       pydeps: "pillow torchvision expecttest"
+  #       llvm: 'true'
+  #   - name: Install ninja
+  #     run: |
+  #       sudo apt update || true
+  #       sudo apt install -y --no-install-recommends ninja-build
+  #   - name: Lint with ruff
+  #     run: |
+  #       pip3 install --upgrade --force-reinstall ruff==0.11.0
+  #       python3 -m ruff check extra/torch_backend/backend.py
+  #   - name: Test one op
+  #     run: FORWARD_ONLY=1 TINY_BACKEND=1 python3 test/test_ops.py TestOps.test_add
+  #   - name: Test ResNet-18
+  #     run: DEBUG=2 python3 extra/torch_backend/example.py
+  #   - name: My (custom) tests
+  #     run: python3 extra/torch_backend/test.py
+  #   - name: Test one op in torch tests
+  #     run: DEBUG=2 python3 extra/torch_backend/torch_tests.py TestTinyBackendPRIVATEUSE1.test_unary_log_tiny_float32
+  #   - name: Test Ops with TINY_BACKEND
+  #     run: CPU=1 CPU_LLVM=1 LLVMOPT=0 TINY_BACKEND=1 python3 -m pytest -n auto test/test_ops.py --durations=20
+  #   - name: Test in-place operations on views
+  #     run: TORCH_DEBUG=1 python3 extra/torch_backend/test_inplace.py
+  #   - name: Test multi-gpu
+  #     run: CPU=1 CPU_LLVM=1 GPUS=4 TORCH_DEBUG=1 python3 extra/torch_backend/test_multigpu.py

-  torchbackendmore:
-    name: Torch Backend Tests More
-    runs-on: ubuntu-latest
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: torch-backend-pillow-torchvision-et-pt
-        deps: testing_minimal
-        llvm: 'true'
-    - name: Install ninja
-      run: |
-        sudo apt update || true
-        sudo apt install -y --no-install-recommends ninja-build
-    - name: Test beautiful_mnist in torch with TINY_BACKEND
-      run: SPLIT_REDUCEOP=0 FUSE_ARANGE=1 CPU=1 CPU_LLVM=1 TARGET_EVAL_ACC_PCT=96.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
-    - name: Test some torch tests (expect failure)
-      run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true
+  # torchbackendmore:
+  #   name: Torch Backend Tests More
+  #   runs-on: ubuntu-latest
+  #   timeout-minutes: 15
+  #   steps:
+  #   - name: Checkout Code
+  #     uses: actions/checkout@v4
+  #   - name: Setup Environment
+  #     uses: ./.github/actions/setup-tinygrad
+  #     with:
+  #       key: torch-backend-pillow-torchvision-et-pt
+  #       deps: testing_minimal
+  #       llvm: 'true'
+  #   - name: Install ninja
+  #     run: |
+  #       sudo apt update || true
+  #       sudo apt install -y --no-install-recommends ninja-build
+  #   - name: Test beautiful_mnist in torch with TINY_BACKEND
+  #     run: STEPS=20 CPU=1 TARGET_EVAL_ACC_PCT=90.0 TINY_BACKEND=1 python3 examples/other_mnist/beautiful_mnist_torch.py
+  #   - name: Test some torch tests (expect failure)
+  #     run: python3 -m pytest extra/torch_backend/torch_tests.py -v --tb=no || true

  bepython:
    name: Python Backend
@@ -160,10 +161,8 @@ jobs:
      with:
        key: be-minimal
        deps: testing_minimal
-    - name: Test dtype with Python emulator (with RANGEIFY)
-      run: |
-        RANGEIFY=0 DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
-        RANGEIFY=1 DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
+    - name: Test dtype with Python emulator
+      run: DEBUG=1 PYTHON=1 python3 -m pytest -n=auto test/test_dtype.py test/test_dtype_alu.py
    - name: Test ops with Python emulator
      run: DEBUG=2 SKIP_SLOW_TEST=1 PYTHON=1 python3 -m pytest -n=auto test/test_ops.py --durations=20
    - name: Test uops with Python emulator
@@ -240,8 +239,6 @@ jobs:
        pip3 install --upgrade --force-reinstall ruff==0.11.0
        python3 -m ruff check .
        python3 -m ruff check examples/mlperf/ --ignore E501
-    - name: Lint tinygrad with pylint
-      run: python -m pylint tinygrad/
    - name: Run mypy
      run: |
        python -m mypy --strict-equality --lineprecision-report .
@@ -267,12 +264,17 @@ jobs:
      run: python -c "from tinygrad import Device; assert Device.DEFAULT == 'CPU', Device.DEFAULT"
    - name: Run unit tests
      run: CPU=1 python -m pytest -n=auto test/unit/ --durations=20
+    - name: Check SPEC=1
+      run: SPEC=1 python3 test/test_tiny.py
    - name: Run targetted tests on NULL backend
      run: NULL=1 python3 -m unittest test.test_multitensor.TestMultiTensor.test_data_parallel_resnet_train_step test/device/test_null.py
-    - name: Run SDXL on NULL backend
-      run: MAX_BUFFER_SIZE=0 NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
+    # TODO: too slow
+    # - name: Run SDXL on NULL backend
+    #   run: NULL=1 DEBUG=1 python3 examples/sdxl.py --seed 0 --noshow --timing --fakeweights
    - name: Run Clip tests for SD MLPerf on NULL backend
-      run: MAX_BUFFER_SIZE=0 NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
+      run: NULL=1 python -m pytest -n=auto test/external/mlperf_stable_diffusion/external_test_models.py::TestOpenClip --durations=20
+    - name: Run AMD emulated BERT training on NULL backend
+      run: EMULATE=AMD_RDNA4 NULL=1 CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
    # TODO: support fake weights
    #- name: Run LLaMA 7B on 4 fake devices
    #  run: NULL=1 python3 examples/llama.py --gen 1 --size 7B --shard 4 --prompt "Hello." --count 3 --temperature 0 --timing
@@ -308,10 +310,6 @@ jobs:
      run: python test/external/fuzz_symbolic.py
    - name: Fuzz Test fast idiv
      run: python test/external/fuzz_fast_idiv.py
-    - name: Fuzz Test shapetracker
-      run: |
-        python test/external/fuzz_shapetracker.py
-        python test/external/fuzz_shapetracker_math.py
    - name: Fuzz Test shape ops
      run: python test/external/fuzz_shape_ops.py

@@ -332,10 +330,6 @@ jobs:
        run: |
          CL=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
          CL=1 IMAGE=2 python test/models/test_end2end.py TestEnd2End.test_linear_mnist
-      - name: Test CL IMAGE=2 ops + training (rangeify)
-        run: |
-          RANGEIFY=1 CL=1 IMAGE=2 python -m pytest -n=auto test/test_ops.py --durations=20
-          RANGEIFY=1 CL=1 IMAGE=2 python test/models/test_end2end.py TestEnd2End.test_linear_mnist
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -380,10 +374,7 @@ jobs:
          llvm: 'true'
      - name: Test openpilot model kernel count and gate usage
        run: |
-          ALLOWED_KERNEL_COUNT=208 ALLOWED_READ_IMAGE=2160 ALLOWED_GATED_READ_IMAGE=16 RANGEIFY=0 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
-      - name: Test openpilot model with rangeify
-        run: |
-          ALLOWED_KERNEL_COUNT=190 ALLOWED_READ_IMAGE=2041 ALLOWED_GATED_READ_IMAGE=33 RANGEIFY=1 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
+          ALLOWED_KERNEL_COUNT=190 ALLOWED_READ_IMAGE=2081 ALLOWED_GATED_READ_IMAGE=28 FLOAT16=0 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/v0.9.4/selfdrive/modeld/models/supercombo.onnx
      - name: Test openpilot alt model correctness (float32)
        run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/3799fe46b3a629e491d4b8498b8ae83e4c88c304/selfdrive/modeld/models/supercombo.onnx
      - name: Test openpilot fastvits model correctness (float32)
@@ -392,8 +383,6 @@ jobs:
      #   run: FLOAT16=0 DEBUGCL=1 CL=1 IMAGE=2 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/35ff4f4577002f2685e50c8346addae33fe8da27a41dd4d6a0f14d1f4b1af81b
      - name: Test openpilot LLVM compile
        run: CPU=1 CPU_LLVM=1 LLVMOPT=1 JIT=2 BEAM=0 IMAGE=0 python examples/openpilot/compile3.py https://github.com/commaai/openpilot/raw/9118973ed03c1ae1d40cf69a29507ec2cc78efd7/selfdrive/modeld/models/supercombo.onnx
-      - name: Test openpilot compile4
-        run: NOLOCALS=1 CL=1 IMAGE=2 FLOAT16=1 DEBUG=2 python3 examples/openpilot/compile4.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -452,10 +441,12 @@ jobs:
        run: CL=1 IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
      - name: Test MLPerf stuff
        run: CL=1 python -m pytest -n=auto test/external/external_test_optim.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
+      - name: NULL=1 beautiful_mnist_multigpu
+        run: NULL=1 python examples/beautiful_mnist_multigpu.py
      - name: Test Bert training
-        run: MAX_BUFFER_SIZE=0 NULL=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=24 GPUS=4 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+        run: NULL=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=24 GPUS=4 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
      - name: Test llama 3 training
-        run: MAX_BUFFER_SIZE=0 NULL=1 SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
+        run: NULL=1 SAMPLES=300 BS=8 SEQLEN=512 GRADIENT_ACC_STEPS=8 FAKEDATA=1 DEFAULT_FLOAT=bfloat16 OPTIM_DTYPE=bfloat16 LLAMA3_SIZE=1B MODEL=llama3 python3 examples/mlperf/model_train.py
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -518,88 +509,6 @@ jobs:

 # ****** Feature Tests ******

-  testrangeifycpu:
-    name: Linux (rangeify) CPU
-    runs-on: ubuntu-24.04
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: rangeify-minimal-llvm
-        deps: testing_minimal
-        opencl: 'true'
-        llvm: "true"
-    - name: Test CPU=1 RANGEIFY=1
-      # TODO: add more passing tests here
-      run: |
-        CPU=1 CPU_LLVM=0 RANGEIFY=1 python3 -m pytest -n auto --durations 20 \
-          test/test_tiny.py test/test_rangeify.py test/test_ops.py test/test_symbolic_ops.py test/test_symbolic_jit.py test/test_tensor_variable.py \
-          test/test_outerworld_range.py test/test_randomness.py test/test_nn.py test/test_arange.py test/test_tensor.py test/test_optim.py \
-          test/test_setitem.py test/test_assign.py test/test_multitensor.py test/test_const_folding.py
-    - name: Test CPU=1 DEVECTORIZE=0 (RANGEIFY=1)
-      run: CPU=1 CPU_LLVM=0 RANGEIFY=1 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
-    - name: Test CPU=1 CPU_LLVM=1 RANGEIFY=1
-      run: |
-        CPU=1 CPU_LLVM=1 RANGEIFY=1 python3 -m pytest -n auto --durations 20 test/test_edgecases.py
-    - name: Test Docs RANGEIFY=1
-      run: |
-        RANGEIFY=1 python docs/abstractions2.py
-    # RANGEIFY=2 isn't supported
-    #- name: Test CPU=1 RANGEIFY=2
-    #  run: CPU=1 CPU_LLVM=0 RANGEIFY=2 python3 -m pytest -n auto test/test_tiny.py test/test_rangeify.py test/test_ops.py --durations 20
-    # slow (and still wrong on beautiful_mnist)
-    #- name: Test LLVM RANGEIFY=1 (slow tests)
-    #  run: CPU=1 CPU_LLVM=1 RANGEIFY=1 python3 -m pytest -n auto test/models/test_mnist.py --durations 20
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testrangeifycl:
-    name: Linux (rangeify) CL
-    runs-on: ubuntu-24.04
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: rangeify-cl
-        deps: testing
-        opencl: 'true'
-        llvm: "true"
-    - name: Test CL=1 RANGEIFY=1
-      run: CL=1 RANGEIFY=1 pytest -n auto test/test_ops.py test/test_schedule.py test/test_symbolic_ops.py test/test_jit.py test/unit/test_disk_tensor.py test/models/test_mnist.py test/unit/test_mnist_dataset.py test/test_optim.py --durations 20
-    - name: Test Fuse
-      run: CL=1 RANGEIFY=2 python3 -m pytest --durations 20 test/test_softmax_fusion.py -k "not test_auto_softmax"
-    - name: Test ONNX
-      run: CL=1 RANGEIFY=1 python -m pytest -n=auto test/external/external_test_onnx_backend.py --durations=20
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testrangeifymacos:
-    name: MacOS (rangeify)
-    runs-on: macos-14
-    timeout-minutes: 15
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v4
-    - name: Setup Environment
-      uses: ./.github/actions/setup-tinygrad
-      with:
-        key: metal
-        deps: testing
-    - name: some unit tests
-      run: METAL=1 RANGEIFY=1 python -m pytest -n=auto test/unit/test_winograd.py test/unit/test_linalg.py --durations=20
-    - name: Test METAL=1 RANGEIFY=1
-      run: |
-        METAL=1 RANGEIFY=1 python -m pytest -n=auto test/test_ops.py test/test_multitensor.py --durations=20
-        METAL=1 MAX_KERNEL_BUFFERS=6 RANGEIFY=1 PYTHONPATH=. python test/test_multitensor.py TestBatchNorm.test_batchnorm
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
  testdevectorize:
    name: Linux (devectorize)
    runs-on: ubuntu-24.04
@@ -619,7 +528,7 @@ jobs:
    - name: Test LLVM=1 DEVECTORIZE=0 for model
      run: CPU=1 CPU_LLVM=1 DEVECTORIZE=0 python3 test/models/test_efficientnet.py
    - name: Test CPU=1 DEVECTORIZE=0
-      run: CPU=1 CPU_LLVM=0 DEVECTORIZE=0 FUSE_ARANGE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"
+      run: CPU=1 CPU_LLVM=0 DEVECTORIZE=0 python3 -m pytest -n auto test/test_tiny.py test/test_ops.py -k "not test_avg_pool3d_failure"

  testdsp:
    name: Linux (DSP)
@@ -722,8 +631,6 @@ jobs:
        run: |
          VIZ=1 SQTT=1 DEBUG=5 python3 test/test_ops.py TestOps.test_add
          extra/sqtt/rgptool.py create "/tmp/profile.pkl.$USER" -o /tmp/gpu0.rgp
-      - name: Run pytest (amd) with RANGEIFY
-        run: RANGEIFY=1 python -m pytest test/test_linearizer.py::TestLinearizer::test_where_fold
      - name: Run process replay tests
        uses: ./.github/actions/process-replay

@@ -765,7 +672,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        backend: [llvm, cpu, opencl]
+        backend: [llvm, cpu, opencl, lvp]

    name: Linux (${{ matrix.backend }})
    runs-on: ubuntu-22.04
@@ -779,9 +686,10 @@ jobs:
          key: ${{ matrix.backend }}-minimal
          deps: testing_minimal
          opencl: ${{ matrix.backend == 'opencl' && 'true' }}
-          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
+          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
+          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'opencl' && 'CL=1' }}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'opencl' && 'CL=1' || matrix.backend == 'lvp' && 'CPU=1\nCPU_LVP=1' }}" >> $GITHUB_ENV
      - name: Check Device.DEFAULT and print some source
        run: |
          python3 -c "from tinygrad import Device; assert Device.DEFAULT in ['CPU','CL'], Device.DEFAULT"
@@ -983,7 +891,7 @@ jobs:
    strategy:
      fail-fast: false
      matrix:
-        backend: [metal, llvm, cpu]
+        backend: [metal, llvm, cpu, lvp]
    name: MacOS (${{ matrix.backend }})
    runs-on: macos-15
    timeout-minutes: 20
@@ -996,12 +904,13 @@ jobs:
          key: macos-${{ matrix.backend }}-minimal
          deps: testing_minimal
          pydeps: "capstone"
-          llvm: ${{ matrix.backend == 'llvm' && 'true' }}
+          llvm: ${{ matrix.backend == 'llvm' || matrix.backend == 'lvp' }}
+          mesa: ${{ matrix.backend == 'lvp' && 'true' }}
      - name: Set env
-        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1'}}" >> $GITHUB_ENV
+        run: printf "${{ matrix.backend == 'llvm' && 'CPU=1\nCPU_LLVM=1' || matrix.backend == 'cpu' && 'CPU=1\nCPU_LLVM=0\nCPU_COUNT=2' || matrix.backend == 'metal' && 'METAL=1' || matrix.backend == 'lvp' && 'CPU=1\nCPU_LVP=1' }}" >> $GITHUB_ENV
      - name: Check Device.DEFAULT and print some source
        run: |
-          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
+          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU','LVP':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
          DEBUG=4 python3 test/test_tiny.py TestTiny.test_plus
      - name: Run pytest (${{ matrix.backend }})
        run: python3 -m pytest -n=auto test/ --ignore=test/models --ignore=test/unit --durations=20
@@ -1043,9 +952,3 @@ jobs:
        run: |
          python -c "from tinygrad import Device; assert Device.DEFAULT == {'LLVM':'CPU'}.get(x:='${{ matrix.backend }}'.upper(), x), Device.DEFAULT"
          python -m pytest -n=auto test/test_tiny.py test/test_ops.py --durations=20
-      - name: Run pytest (${{ matrix.backend }}) with RANGEIFY
-        if: matrix.backend=='webgpu'
-        env:
-          RANGEIFY: 1
-        shell: bash
-        run: python -m pytest -n=auto test/test_tiny.py test/test_ops.py --durations=20
@@ -38,6 +38,7 @@ extra/huggingface_onnx/models/*
 extra/huggingface_onnx/*.yaml
 extra/weights
 venv
+venv_sd_mlperf
 examples/**/net.*[js,json]
 examples/**/*.safetensors
 node_modules
@@ -20,21 +20,15 @@ repos:
        language: system
        always_run: true
        pass_filenames: false
-      - id: tests
-        name: subset of tests
-        entry: env PYTHONPATH="." python3 -m pytest -n=4 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
-        language: system
-        always_run: true
-        pass_filenames: false
      - id: example
-        name: multi device tests
+        name: test all devices
        entry: python3 test/external/external_test_example.py
        language: system
        always_run: true
        pass_filenames: false
-      - id: pylint
-        name: pylint
-        entry: python3 -m pylint tinygrad/
+      - id: tests
+        name: subset of tests
+        entry: env PYTHONPATH="." python3 -m pytest -n=8 test/test_ops.py test/test_dtype.py test/test_schedule.py test/test_assign.py
        language: system
        always_run: true
        pass_filenames: false
@@ -30,10 +30,6 @@ persistent=yes
 # Specify a configuration file.
 #rcfile=

-# When enabled, pylint would attempt to guess common misconfiguration and emit
-# user-friendly hints instead of false-positive error messages
-suggestion-mode=yes
-
 # Allow loading of arbitrary C extensions. Extensions are imported into the
 # active Python interpreter and may run arbitrary code.
 unsafe-load-any-extension=no
@@ -414,10 +414,29 @@ generate_sqtt() {
  clang2py -k cdefstum \
    extra/sqtt/sqtt.h \
    -o $BASE/sqtt.py
-
  fixup $BASE/sqtt.py
  sed -i "s\import ctypes\import ctypes, os\g" $BASE/sqtt.py
  python3 -c "import tinygrad.runtime.autogen.sqtt"
+
+  ROCPROF_COMMIT_HASH=dd0485100971522cc4cd8ae136bdda431061a04d
+  ROCPROF_SRC=/tmp/rocprof-trace-decoder-$ROCPROF_COMMIT_HASH
+  if [ ! -d "$ROCPROF_SRC" ]; then
+    git clone https://github.com/ROCm/rocprof-trace-decoder $ROCPROF_SRC
+    pushd .
+    cd $ROCPROF_SRC
+    git reset --hard $ROCPROF_COMMIT_HASH
+    popd
+  fi
+
+  clang2py -k cdefstum \
+    $ROCPROF_SRC/include/rocprof_trace_decoder.h \
+    $ROCPROF_SRC/include/trace_decoder_instrument.h \
+    $ROCPROF_SRC/include/trace_decoder_types.h \
+    -o extra/sqtt/rocprof/rocprof.py
+  fixup extra/sqtt/rocprof/rocprof.py
+  sed -i '1s/^/# pylint: skip-file\n/' extra/sqtt/rocprof/rocprof.py
+  sed -i "s/import ctypes/import ctypes, ctypes.util/g" extra/sqtt/rocprof/rocprof.py
+  sed -i "s|FunctionFactoryStub()|ctypes.CDLL(ctypes.util.find_library('rocprof-trace-decoder'))|g" extra/sqtt/rocprof/rocprof.py
 }

 generate_webgpu() {
@@ -442,6 +461,85 @@ generate_libusb() {
  python3 -c "import tinygrad.runtime.autogen.libusb"
 }

+generate_mesa() {
+  MESA_TAG="mesa-25.2.4"
+  MESA_SRC=/tmp/mesa-$MESA_TAG
+  TINYMESA_TAG=tinymesa-32dc66c
+  TINYMESA_DIR=/tmp/tinymesa-$MESA_TAG-$TINYMESA_TAG/
+  TINYMESA_SO=$TINYMESA_DIR/libtinymesa_cpu.so
+  if [ ! -d "$MESA_SRC" ]; then
+    git clone --depth 1 --branch $MESA_TAG https://gitlab.freedesktop.org/mesa/mesa.git $MESA_SRC
+    pushd .
+    cd $MESA_SRC
+    git reset --hard $MESA_COMMIT_HASH
+    # clang 14 doesn't support packed enums
+    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/headers/nv_device_info.h
+    sed -i "s/enum \w\+ \(\w\+\);$/uint8_t \1;/" $MESA_SRC/src/nouveau/compiler/nak.h
+    sed -i "s/nir_instr_type \(\w\+\);/uint8_t \1;/" $MESA_SRC/src/compiler/nir/nir.h
+    mkdir -p gen/util/format
+    python3 src/util/format/u_format_table.py src/util/format/u_format.yaml --enums > gen/util/format/u_format_gen.h
+    python3 src/compiler/nir/nir_opcodes_h.py > gen/nir_opcodes.h
+    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
+    python3 src/compiler/nir/nir_intrinsics_indices_h.py --outdir gen
+    python3 src/compiler/nir/nir_builder_opcodes_h.py > gen/nir_builder_opcodes.h
+    python3 src/compiler/nir/nir_intrinsics_h.py --outdir gen
+    python3 src/compiler/builtin_types_h.py gen/builtin_types.h
+    popd
+  fi
+
+  if [ ! -d "$TINYMESA_DIR" ]; then
+    mkdir $TINYMESA_DIR
+    curl -L https://github.com/sirhcm/tinymesa/releases/download/$TINYMESA_TAG/libtinymesa_cpu-$MESA_TAG-linux-amd64.so -o $TINYMESA_SO
+  fi
+
+  clang2py -k cdefstu \
+    $MESA_SRC/src/compiler/nir/nir.h \
+    $MESA_SRC/src/compiler/nir/nir_builder.h \
+    $MESA_SRC/src/compiler/nir/nir_shader_compiler_options.h \
+    $MESA_SRC/src/compiler/nir/nir_serialize.h \
+    $MESA_SRC/gen/nir_intrinsics.h \
+    $MESA_SRC/src/nouveau/headers/nv_device_info.h \
+    $MESA_SRC/src/nouveau/compiler/nak.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_passmgr.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_misc.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_type.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_init.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_nir.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_struct.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_jit_types.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_flow.h \
+    $MESA_SRC/src/gallium/auxiliary/gallivm/lp_bld_const.h \
+    $MESA_SRC/src/compiler/glsl_types.h \
+    $MESA_SRC/src/util/blob.h \
+    $MESA_SRC/src/util/ralloc.h \
+    --clang-args="-DHAVE_ENDIAN_H -DHAVE_STRUCT_TIMESPEC -DHAVE_PTHREAD -I$MESA_SRC/src -I$MESA_SRC/include -I$MESA_SRC/gen -I$MESA_SRC/src/compiler/nir -I$MESA_SRC/src/gallium/auxiliary -I$MESA_SRC/src/gallium/include -I$(llvm-config-20 --includedir)" \
+    -l $TINYMESA_SO \
+    -o $BASE/mesa.py
+
+  LVP_NIR_OPTIONS=$(./extra/mesa/lvp_nir_options.sh $MESA_SRC)
+
+  fixup $BASE/mesa.py
+  patch_dlopen $BASE/mesa.py tinymesa_cpu "(BASE:=os.getenv('MESA_PATH', f\"/usr{'/local/' if helpers.OSX else '/'}lib\"))+'/libtinymesa_cpu'+(EXT:='.dylib' if helpers.OSX else '.so')" "f'{BASE}/libtinymesa{EXT}'" "f'{brew_prefix()}/lib/libtinymesa_cpu.dylib'"
+  echo "lvp_nir_options = gzip.decompress(base64.b64decode('$LVP_NIR_OPTIONS'))" >> $BASE/mesa.py
+  cat <<EOF | sed -i "/import ctypes.*/r /dev/stdin" $BASE/mesa.py
+def brew_prefix():
+  try: return subprocess.check_output(['brew', '--prefix', 'tinymesa']).decode().strip()
+  except Exception: return ''
+EOF
+  sed -i "/in_dll/s/.*/try: &\nexcept AttributeError: pass/" $BASE/mesa.py
+  sed -i "s/import ctypes/import ctypes, ctypes.util, os, gzip, base64, subprocess, tinygrad.helpers as helpers/" $BASE/mesa.py
+  sed -i "s/ctypes.CDLL('.\+')/(dll := _try_dlopen_tinymesa_cpu())/" $BASE/mesa.py
+  echo "def __getattr__(nm): raise AttributeError() if dll else FileNotFoundError(f'libtinymesa not found (MESA_PATH={BASE}). See https://github.com/sirhcm/tinymesa ($TINYMESA_TAG, $MESA_TAG)')" >> $BASE/mesa.py
+  sed -i "s/ctypes.glsl_base_type/glsl_base_type/" $BASE/mesa.py
+  # bitfield bug in clang2py
+  sed -i "s/('fp_fast_math', ctypes.c_bool, 9)/('fp_fast_math', ctypes.c_uint32, 9)/" $BASE/mesa.py
+  sed -i "s/('\(\w\+\)', pipe_shader_type, 8)/('\1', ctypes.c_ubyte)/" $BASE/mesa.py
+  sed -i "s/\([0-9]\+\)()/\1/" $BASE/mesa.py
+  sed -i "s/\(struct_nir_builder._pack_\) = 1/\1 = 0/" $BASE/mesa.py
+  python3 -c "import tinygrad.runtime.autogen.mesa"
+}
+
 if [ "$1" == "opencl" ]; then generate_opencl
 elif [ "$1" == "hip" ]; then generate_hip
 elif [ "$1" == "comgr" ]; then generate_comgr
@@ -465,6 +563,7 @@ elif [ "$1" == "pci" ]; then generate_pci
 elif [ "$1" == "vfio" ]; then generate_vfio
 elif [ "$1" == "webgpu" ]; then generate_webgpu
 elif [ "$1" == "libusb" ]; then generate_libusb
-elif [ "$1" == "all" ]; then generate_opencl; generate_hip; generate_comgr; generate_cuda; generate_nvrtc; generate_hsa; generate_kfd; generate_nv; generate_amd; generate_io_uring; generate_libc; generate_am; generate_webgpu
+elif [ "$1" == "mesa" ]; then generate_mesa
+elif [ "$1" == "all" ]; then generate_opencl; generate_hip; generate_comgr; generate_cuda; generate_nvrtc; generate_hsa; generate_kfd; generate_nv; generate_amd; generate_io_uring; generate_libc; generate_am; generate_webgpu; generate_mesa
 else echo "usage: $0 <type>"
 fi
@@ -42,7 +42,6 @@ import struct
 from tinygrad.dtype import dtypes
 from tinygrad.device import Buffer, Device
 from tinygrad.uop.ops import UOp, Ops
-from tinygrad.shape.shapetracker import ShapeTracker

 # allocate some buffers + load in values
 out = Buffer(DEVICE, 1, dtypes.int32).allocate()
@@ -51,13 +50,14 @@ b = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struc
 # NOTE: a._buf is the same as the return from cpu.allocator.alloc

 # describe the computation
+idx = UOp.const(dtypes.index, 0)
 buf_1 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 1)
 buf_2 = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 2)
-ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1.view(ShapeTracker.from_shape((1,))),))
-ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2.view(ShapeTracker.from_shape((1,))),))
+ld_1 = UOp(Ops.LOAD, dtypes.int32, (buf_1.index(idx),))
+ld_2 = UOp(Ops.LOAD, dtypes.int32, (buf_2.index(idx),))
 alu = ld_1 + ld_2
 output_buf = UOp(Ops.DEFINE_GLOBAL, dtypes.int32.ptr(), (), 0)
-st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.view(ShapeTracker.from_shape((1,))), alu))
+st_0 = UOp(Ops.STORE, dtypes.void, (output_buf.index(idx), alu))
 s = UOp(Ops.SINK, dtypes.void, (st_0,))

 # convert the computation to a "linearized" format (print the format)
@@ -80,8 +80,6 @@ print("******** third, the UOp ***********")

 from tinygrad.engine.realize import run_schedule
 from tinygrad.engine.schedule import create_schedule_with_vars
-from tinygrad.helpers import RANGEIFY
-from tinygrad.schedule.kernelize import get_kernelize_map
 from tinygrad.schedule.rangeify import get_rangeify_map

 # allocate some values + load in values
@@ -95,7 +93,7 @@ out = a + b
 s = UOp(Ops.SINK, dtypes.void, (out,))

 # group the computation into kernels
-becomes_map = get_rangeify_map(s) if RANGEIFY else get_kernelize_map(s)
+becomes_map = get_rangeify_map(s)

 # the compute maps to an assign
 assign = becomes_map[a+b].base
@@ -10,7 +10,7 @@ Directories are listed in order of how they are processed.

 Group UOps into kernels.

-::: tinygrad.schedule.kernelize.get_kernelize_map
+::: tinygrad.schedule.rangeify.get_rangeify_map
    options:
        members: false
        show_labels: false
@@ -10,7 +10,7 @@ GPUS = [f'{Device.DEFAULT}:{i}' for i in range(getenv("GPUS", 1))]

 # override tinygrad defaults
 dtypes.default_float = dtypes.half
-Context(FUSE_ARANGE=1, FUSE_OPTIM=1).__enter__()
+Context(FUSE_OPTIM=1).__enter__()

 # from https://github.com/tysam-code/hlb-CIFAR10/blob/main/main.py
 batchsize = getenv("BS", 1024)
@@ -1,6 +1,6 @@
 import sys, time
 from tinygrad import TinyJit, GlobalCounters, fetch, getenv
-from tinygrad.frontend.onnx import OnnxRunner
+from tinygrad.nn.onnx import OnnxRunner
 from extra.onnx_helpers import get_example_inputs, validate

 def load_onnx_model(onnx_file):
@@ -8,7 +8,7 @@ import numpy as np
 import subprocess
 import tensorflow as tf
 import tf2onnx
-from tinygrad.frontend.onnx import OnnxRunner
+from tinygrad.nn.onnx import OnnxRunner
 from tinygrad.tensor import Tensor
 from tinygrad.helpers import to_mv
 from extra.export_model import export_model_clang, compile_net, jit_model
@@ -232,7 +232,7 @@ if __name__ == "__main__":
  gpt2 = GPT2.build_gguf(args.model_size) if args.model_size.startswith("gpt2_gguf_") else GPT2.build(args.model_size)

  if args.benchmark != -1:
-    gpt2.model(Tensor.rand(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
+    gpt2.model(Tensor.randint(args.batch_size, args.benchmark), Variable("a", 0, MAX_CONTEXT).bind(0)).realize()
  else:
    texts = gpt2.generate(args.prompt, args.count, args.temperature, timing=args.timing, batch_size=args.batch_size)
    if not args.noshow:
@@ -145,7 +145,6 @@ hyp = {
  },
 }

-@Context(FUSE_ARANGE=getenv("FUSE_ARANGE", 1))
 def train_cifar():

  def set_seed(seed):
@@ -3,7 +3,7 @@ from pathlib import Path
 import multiprocessing

 from tinygrad import Device, GlobalCounters, Tensor, TinyJit, dtypes
-from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, FUSE_CONV_BW, Profiling
+from tinygrad.helpers import getenv, BEAM, WINO, round_up, diskcache_clear, Profiling
 from tinygrad.nn.state import get_parameters, get_state_dict, load_state_dict, safe_load, safe_save
 from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, Adam, AdamW

@@ -707,7 +707,7 @@ def train_unet3d():
  ```BASEDIR=<folder_path> ./examples/mlperf/scripts/setup_kits19_dataset.sh```

  2) To start training the model, run the following:
-  ```time PYTHONPATH=. WANDB=1 TRAIN_BEAM=3 FUSE_CONV_BW=1 GPUS=6 BS=6 MODEL=unet3d python3 examples/mlperf/model_train.py```
+  ```time PYTHONPATH=. WANDB=1 TRAIN_BEAM=3 GPUS=6 BS=6 MODEL=unet3d python3 examples/mlperf/model_train.py```
  """
  from examples.mlperf.losses import dice_ce_loss
  from examples.mlperf.metrics import dice_score
@@ -749,7 +749,6 @@ def train_unet3d():
    "train_beam": TRAIN_BEAM,
    "eval_beam": EVAL_BEAM,
    "wino": WINO.value,
-    "fuse_conv_bw": FUSE_CONV_BW.value,
    "gpus": GPUS,
    "default_float": dtypes.default_float.name
  }
@@ -1189,7 +1188,9 @@ def train_bert():
      if MLLOGGER and RUNMLPERF:
        MLLOGGER.start(key=mllog_constants.EVAL_START, value=None, metadata={"epoch_num": i*GBS, "step_num": i})
      if getenv("RESET_STEP"): train_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 1) and train_step_bert.captured is not None: train_step_bert.captured.free_intermediates()
+      elif getenv("FREE_INTERMEDIATE", 0) and train_step_bert.captured is not None:
+        # TODO: FREE_INTERMEDIATE nan'ed after jit step 2
+        train_step_bert.captured.free_intermediates()
      eval_lm_losses = []
      eval_clsf_losses = []
      eval_lm_accs = []
@@ -1223,7 +1224,7 @@ def train_bert():
          return

      if getenv("RESET_STEP"): eval_step_bert.reset()
-      elif getenv("FREE_INTERMEDIATE", 1) and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()
+      elif getenv("FREE_INTERMEDIATE", 0) and eval_step_bert.captured is not None: eval_step_bert.captured.free_intermediates()

      del eval_data
      avg_lm_loss = sum(eval_lm_losses) / len(eval_lm_losses)
@@ -1309,7 +1310,7 @@ def train_llama3():
  EVAL_BS            = config["EVAL_BS"]                = getenv("EVAL_BS", 16)
  EVAL_TARGET        = config["EVAL_TARGET"]            = getenv("EVAL_TARGET", 5.6)

-  # 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
+  # LR=1e-4 TRAIN_ON_VAL=1 DEFAULT_FLOAT=bfloat16 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
@@ -0,0 +1,72 @@
+#!/usr/bin/env bash
+
+DATETIME=${2:-$(date "+%m%d%H%M")}
+LOGFILE="${HOME}/logs/sd_mi300x_${DATETIME}.log"
+# UNET_CKPTDIR must be set: training saves checkpoints to this path, then a separate eval process scans this path to know which checkpoints to eval
+export UNET_CKPTDIR="${HOME}/stable_diffusion/training_checkpoints/${DATETIME}"
+mkdir -p "${HOME}/logs" "$UNET_CKPTDIR"
+
+# run this script in isolation when using the --bg flag
+if [[ "${1:-}" == "--bg" ]]; then
+  echo "logging output to $LOGFILE"
+  echo "saving UNet checkpoints to $UNET_CKPTDIR"
+  script_path="$(readlink -f "${BASH_SOURCE[0]}")"
+  nohup bash "$script_path" run "$DATETIME" >"$LOGFILE" 2>&1 & disown $!
+  exit 0
+fi
+
+# venv management
+if [[ -d .venv-sd-mlperf ]]; then
+  . .venv-sd-mlperf/bin/activate
+else
+  python3 -m venv .venv-sd-mlperf && . .venv-sd-mlperf/bin/activate
+  pip install --index-url https://download.pytorch.org/whl/cpu torch && pip install tqdm numpy ftfy regex pillow scipy wandb webdataset
+fi
+pip list
+apt list --installed | grep amdgpu
+rocm-smi --version
+modinfo amdgpu | grep version
+
+export BEAM=2 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 IGNORE_JIT_FIRST_BEAM=1 HCQDEV_WAIT_TIMEOUT_MS=300000
+export AMD_LLVM=0 # bf16 seems to require this
+export DATADIR="/raid/datasets/stable_diffusion"
+export CKPTDIR="/raid/weights/stable_diffusion"
+export EVAL_CKPT_DIR=$UNET_CKPTDIR
+export MODEL="stable_diffusion" PYTHONPATH="."
+export GPUS=8 BS=304
+export CONTEXT_BS=816 DENOISE_BS=600 DECODE_BS=384 INCEPTION_BS=560 CLIP_BS=240
+export WANDB=1
+export PARALLEL=4
+export PYTHONUNBUFFERED=1
+sudo rocm-smi -d 0 1 2 3 4 5 6 7 --setperfdeterminism 1500 || exit 1
+
+# Retry BEAM search if script fails before BEAM COMPLETE is printed, but don't retry after that
+run_retry(){ local try=0 max=5 code tmp py pgid kids
+  while :; do
+    tmp=$(mktemp)
+    setsid bash -c 'exec env "$@"' _ "$@" > >(tee -a "$LOGFILE" | tee "$tmp") 2>&1 &
+    py=$!; pgid=$(ps -o pgid= -p "$py" | tr -d ' ')
+    wait "$py"; code=$?
+    [[ -n "$pgid" ]] && { kill -TERM -"$pgid" 2>/dev/null; sleep 1; kill -KILL -"$pgid" 2>/dev/null; }
+    kids=$(pgrep -P "$py" || true)
+    while [[ -n "$kids" ]]; do
+      kill -TERM $kids 2>/dev/null; sleep 0.5
+      kids=$(for k in $kids; do pgrep -P "$k" || true; done)
+    done
+    grep -q 'BEAM COMPLETE' "$tmp" && { rm -f "$tmp"; return 1; }
+    rm -f "$tmp"
+    ((code==0)) && return 0
+    ((try>=max)) && return 2
+    ((try++)); sleep 90; echo "try = ${try}"
+  done
+}
+
+# Power limiting to 400W is only needed if GPUs fall out of sync (causing 2.2x increased train time) at higher power, which has been observed at 450W
+sudo rocm-smi -d 0 1 2 3 4 5 6 7 --setpoweroverdrive 750 && \
+run_retry TOTAL_CKPTS=7 python3 examples/mlperf/model_train.py; (( $? == 2 )) && { echo "training failed before BEAM completion"; exit 2; }
+sleep 90
+
+run_retry EVAL_SAMPLES=600 python3 examples/mlperf/model_eval.py; (( $? == 2 )) && { echo "eval failed before BEAM completion"; exit 2; }
+# Checkpoints will be evaluated in reverse chronological order, even if above training crashed early
+# STOP_IF_CONVERGED=1: Stop the eval after the first time convergence is detected; no more checkpoints will be evaluated after that.
+STOP_IF_CONVERGED=1 python3 examples/mlperf/model_eval.py
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" GPUS=1 BS=128 EVAL_BS=128
+
+export IGNORE_OOB=1
+
+export BEAM=3 BEAM_UOPS_MAX=4000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+# export BEAM_LOG_SURPASS_MAX=1
+# export BASEDIR="/raid/datasets/wiki"
+
+export RESET_STEP=1
+export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,69 @@
+# 1. Problem
+
+This problem uses BERT for NLP.
+
+## Requirements
+
+Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
+```
+git clone https://github.com/tinygrad/tinygrad.git
+python3 -m pip install -e ".[mlperf]"
+```
+Also install gdown (for dataset), numpy, tqdm and tensorflow.
+```
+pip install gdown numpy tqdm tensorflow
+```
+
+### tinybox_green
+Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
+This is the default on production tinybox green.
+
+# 2. Directions
+
+## Steps to download and verify data
+
+### 1. Download raw data
+
+```
+BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
+```
+
+### 2. Preprocess train and validation data
+
+Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended. 
+
+#### Training:
+```
+BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
+```
+
+Generating a specific topic (Between 0 and 499)
+```
+BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
+```
+
+#### Validation:
+```
+BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
+```
+## Running
+
+### tinybox_green
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
+```
+
+### tinybox_red
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
+```
+### tinybox_8xMI300X
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
+```
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
+export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
+export BASEDIR="/raid/datasets/wiki"
+
+export BENCHMARK=10 BERT_LAYERS=2
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,20 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
+
+# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
+export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
+export TRAIN_STEPS=3900
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
+export BASEDIR="/raid/datasets/wiki"
+
+export WANDB=1 PARALLEL=0
+
+RUNMLPERF=1 python3 examples/mlperf/model_train.py
@@ -0,0 +1,31 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export SUBMISSION_PLATFORM="tinybox_8xMI300X"
+export DEFAULT_FLOAT="HALF" GPUS=8 BS=1024 EVAL_BS=1024
+
+# similar to https://github.com/mlcommons/training_results_v3.1/blob/d06288b2bd675a9d88e0e6181f5bb5626b71ec19/Quanta_Cloud_Technology/results/D54U-3U/bert/result_1.txt#L54
+export OPT_BASE_LEARNING_RATE=0.0011 OPT_LAMB_BETA_1=0.60466 OPT_LAMB_BETA_2=0.85437 DECAY=0.1
+export TRAIN_STEPS=3900
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=3 BEAM_UOPS_MAX=6000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1 FREE_INTERMEDIATE=0
+export BASEDIR="/raid/datasets/wiki"
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=1
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="bert_8xMI300x_${DATETIME}_${SEED}.log"
+
+BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -0,0 +1,69 @@
+# 1. Problem
+
+This problem uses BERT for NLP.
+
+## Requirements
+
+Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
+```
+git clone https://github.com/tinygrad/tinygrad.git
+python3 -m pip install -e ".[mlperf]"
+```
+Also install gdown (for dataset), numpy, tqdm and tensorflow.
+```
+pip install gdown numpy tqdm tensorflow
+```
+
+### tinybox_green
+Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
+This is the default on production tinybox green.
+
+# 2. Directions
+
+## Steps to download and verify data
+
+### 1. Download raw data
+
+```
+BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
+```
+
+### 2. Preprocess train and validation data
+
+Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended. 
+
+#### Training:
+```
+BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
+```
+
+Generating a specific topic (Between 0 and 499)
+```
+BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
+```
+
+#### Validation:
+```
+BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
+```
+## Running
+
+### tinybox_green
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
+```
+
+### tinybox_red
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
+```
+### tinybox_8xMI300X
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
+```
@@ -0,0 +1,17 @@
+#!/bin/bash
+
+export PYTHONPATH="." NV=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+export BEAM_LOG_SURPASS_MAX=1
+export BASEDIR="/raid/datasets/wiki"
+
+export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+export PYTHONPATH="." NV=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+export BASEDIR="/raid/datasets/wiki"
+
+export WANDB=1 PARALLEL=0
+
+RUNMLPERF=1 python3 examples/mlperf/model_train.py
@@ -0,0 +1,28 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." NV=1
+export MODEL="bert"
+export SUBMISSION_PLATFORM="tinybox_green"
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=8 BEAM_UOPS_MAX=10000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+export BASEDIR="/raid/datasets/wiki"
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=1
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="bert_green_${DATETIME}_${SEED}.log"
+
+# init
+BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -0,0 +1,69 @@
+# 1. Problem
+
+This problem uses BERT for NLP.
+
+## Requirements
+
+Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
+```
+git clone https://github.com/tinygrad/tinygrad.git
+python3 -m pip install -e ".[mlperf]"
+```
+Also install gdown (for dataset), numpy, tqdm and tensorflow.
+```
+pip install gdown numpy tqdm tensorflow
+```
+
+### tinybox_green
+Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
+This is the default on production tinybox green.
+
+# 2. Directions
+
+## Steps to download and verify data
+
+### 1. Download raw data
+
+```
+BASEDIR="/raid/datasets/wiki" WIKI_TRAIN=1 VERIFY_CHECKSUM=1 python3 extra/datasets/wikipedia_download.py
+```
+
+### 2. Preprocess train and validation data
+
+Note: The number of threads used for preprocessing is limited by available memory. With 128GB of RAM, a maximum of 16 threads is recommended. 
+
+#### Training:
+```
+BASEDIR="/raid/datasets/wiki" NUM_WORKERS=16 python3 extra/datasets/wikipedia.py pre-train all
+```
+
+Generating a specific topic (Between 0 and 499)
+```
+BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-train 42
+```
+
+#### Validation:
+```
+BASEDIR="/raid/datasets/wiki" python3 extra/datasets/wikipedia.py pre-eval
+```
+## Running
+
+### tinybox_green
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_green/run_and_time.sh
+```
+
+### tinybox_red
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_red/run_and_time.sh
+```
+### tinybox_8xMI300X
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/bert/implementations/tinybox_8xMI300X/run_and_time.sh
+```
@@ -0,0 +1,18 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+export BEAM_LOG_SURPASS_MAX=1
+export BASEDIR="/raid/datasets/wiki"
+
+export RESET_STEP=1
+export BENCHMARK=10 BERT_LAYERS=2 DEBUG=2
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,16 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+export BASEDIR="/raid/datasets/wiki"
+
+export WANDB=1 PARALLEL=0
+
+RUNMLPERF=1 python3 examples/mlperf/model_train.py
@@ -0,0 +1,31 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." AMD=1
+export MODEL="bert"
+export SUBMISSION_PLATFORM="tinybox_red"
+export DEFAULT_FLOAT="HALF" SUM_DTYPE="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export IGNORE_OOB=1
+export REWRITE_STACK_LIMIT=500000
+
+export BEAM=5 BEAM_UOPS_MAX=8000 BEAM_UPCAST_MAX=256 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5
+export IGNORE_JIT_FIRST_BEAM=1
+export BASEDIR="/raid/datasets/wiki"
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=1
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="bert_red_${DATETIME}_${SEED}.log"
+
+export HCQDEV_WAIT_TIMEOUT_MS=100000  # prevents hang?
+
+# init
+sleep 5 && sudo rmmod amdgpu || true
+BENCHMARK=10 INITMLPERF=1 BERT_LAYERS=2 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -0,0 +1,50 @@
+# 1. Problem
+
+This problem uses the ResNet-50 CNN to do image classification.
+
+## Requirements
+
+Install tinygrad and mlperf-logging from master.
+```
+git clone https://github.com/tinygrad/tinygrad.git
+python3 -m pip install -e ".[mlperf]"
+```
+
+### tinybox_green
+Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
+This is the default on production tinybox green.
+
+### tinybox_red
+Disable cwsr
+This is the default on production tinybox red.
+```
+sudo vi /etc/modprobe.d/amdgpu.conf
+cat <<EOF > /etc/modprobe.d/amdgpu.conf
+options amdgpu cwsr_enable=0
+EOF
+sudo update-initramfs -u
+sudo reboot
+
+# validate
+sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
+```
+
+# 2. Directions
+
+## Steps to download and verify data
+
+```
+IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
+```
+
+## Steps for one time setup
+
+### tinybox_red
+```
+examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
+```
+
+## Steps to run benchmark
+```
+examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
+```
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+export PYTHONPATH="." NV=1
+export MODEL="resnet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
+
+export RESET_STEP=0
+
+export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
+
+export BENCHMARK=10 DEBUG=2
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+export PYTHONPATH="." NV=1
+export MODEL="resnet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
+
+export RESET_STEP=0
+
+export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
+
+export EVAL_START_EPOCH=3 EVAL_FREQ=4
+
+export WANDB=1 PARALLEL=0
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,25 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." NV=1
+export MODEL="resnet"
+export SUBMISSION_PLATFORM="tinybox_green"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
+
+export RESET_STEP=0
+
+export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=10 BEAM_PADTO=0
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=${LOGMLPERF:-1}
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="resnet_green_${DATETIME}_${SEED}.log"
+
+# init
+BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -0,0 +1,50 @@
+# 1. Problem
+
+This problem uses the ResNet-50 CNN to do image classification.
+
+## Requirements
+
+Install tinygrad and mlperf-logging from master.
+```
+git clone https://github.com/tinygrad/tinygrad.git
+python3 -m pip install -e ".[mlperf]"
+```
+
+### tinybox_green
+Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
+This is the default on production tinybox green.
+
+### tinybox_red
+Disable cwsr
+This is the default on production tinybox red.
+```
+sudo vi /etc/modprobe.d/amdgpu.conf
+cat <<EOF > /etc/modprobe.d/amdgpu.conf
+options amdgpu cwsr_enable=0
+EOF
+sudo update-initramfs -u
+sudo reboot
+
+# validate
+sudo cat /sys/module/amdgpu/parameters/cwsr_enable #= 0
+```
+
+# 2. Directions
+
+## Steps to download and verify data
+
+```
+IMGNET_TRAIN=1 python3 extra/datasets/imagenet_download.py
+```
+
+## Steps for one time setup
+
+### tinybox_red
+```
+examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/setup.sh
+```
+
+## Steps to run benchmark
+```
+examples/mlperf/training_submission_v4.0/tinycorp/benchmarks/resnet/implementations/tinybox_red/run_and_time.sh
+```
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="resnet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
+
+export RESET_STEP=0
+
+export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+export BENCHMARK=10 DEBUG=${DEBUG:-2}
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="resnet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
+
+export RESET_STEP=0
+
+export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+export EVAL_START_EPOCH=3 EVAL_FREQ=4
+
+export WANDB=1 PARALLEL=0
+
+python3 examples/mlperf/model_train.py
@@ -0,0 +1,26 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." AMD=1
+export MODEL="resnet"
+export SUBMISSION_PLATFORM="tinybox_red"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=1536 EVAL_BS=192
+
+export RESET_STEP=0
+
+export TRAIN_BEAM=4 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=2000 BEAM_UPCAST_MAX=96 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=${LOGMLPERF:-1}
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="resnet_red_${DATETIME}_${SEED}.log"
+
+# init
+sleep 5 && sudo rmmod amdgpu || true
+BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 EVAL_START_EPOCH=3 EVAL_FREQ=4 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -0,0 +1,8 @@
+#!/bin/bash
+
+rocm-smi --setprofile compute
+rocm-smi --setmclk 3
+rocm-smi --setperflevel high
+
+# power cap to 350W
+echo "350000000" | sudo tee /sys/class/drm/card{1..6}/device/hwmon/hwmon*/power1_cap
@@ -0,0 +1,38 @@
+# 1. Problem
+
+This problem uses RetinaNet for SSD.
+
+## Requirements
+
+Install tinygrad and mlperf-logging (uncomment mlperf from setup.py) from branch mlperf_training_v5.0.
+```
+git clone https://github.com/tinygrad/tinygrad.git
+python3 -m pip install -e ".[mlperf]"
+```
+
+Also install the following dependencies:
+```
+pip install tqdm numpy pycocotools boto3 pandas torch torchvision
+```
+
+### tinybox_green
+Install the p2p driver per [README](https://github.com/tinygrad/open-gpu-kernel-modules/blob/550.54.15-p2p/README.md)
+This is the default on production tinybox green.
+
+# 2. Directions
+
+## Steps to download data
+
+Run the following:
+```
+BASEDIR=/raid/datasets/openimages python3 extra/datasets/openimages.py
+```
+
+## Running
+
+### tinybox_green
+
+#### Steps to run benchmark
+```
+examples/mlperf/training_submission_v5.0/tinycorp/benchmarks/retinanet/implementations/tinybox_green/run_and_time.sh
+```
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+export PYTHONPATH="." NV=1
+export MODEL="retinanet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
+export BASEDIR="/raid/datasets/openimages"
+
+# export RESET_STEP=0
+
+export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+export BENCHMARK=5 DEBUG=2
+
+python examples/mlperf/model_train.py
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+export PYTHONPATH="." NV=1
+export MODEL="retinanet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
+export BASEDIR="/raid/datasets/openimages"
+
+# export RESET_STEP=0
+
+export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+export WANDB=1 PARALLEL=0
+export RUNMLPERF=1
+
+python examples/mlperf/model_train.py
@@ -0,0 +1,25 @@
+#!/bin/bash
+set -e  # Exit on any error
+set -o pipefail  # Make pipeline fail if any command fails
+
+export PYTHONPATH="." NV=1
+export MODEL="retinanet"
+export SUBMISSION_PLATFORM="tinybox_green"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
+
+export TRAIN_BEAM=2 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+export IGNORE_JIT_FIRST_BEAM=1
+export BASEDIR="/raid/datasets/openimages"
+
+# pip install -e ".[mlperf]"
+export LOGMLPERF=1
+
+export SEED=$RANDOM
+DATETIME=$(date "+%m%d%H%M")
+LOGFILE="retinanet_green_${DATETIME}_${SEED}.log"
+
+# init
+BENCHMARK=10 INITMLPERF=1 python3 examples/mlperf/model_train.py | tee $LOGFILE
+
+# run
+PARALLEL=0 RUNMLPERF=1 python3 examples/mlperf/model_train.py | tee -a $LOGFILE
@@ -0,0 +1,14 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="retinanet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
+export BASEDIR="/raid/datasets/openimages"
+
+# export RESET_STEP=0
+
+export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+export BENCHMARK=5 DEBUG=2
+
+python examples/mlperf/model_train.py
@@ -0,0 +1,15 @@
+#!/bin/bash
+
+export PYTHONPATH="." AMD=1
+export MODEL="retinanet"
+export DEFAULT_FLOAT="HALF" GPUS=6 BS=96 EVAL_BS=96
+export BASEDIR="/raid/datasets/openimages"
+
+# export RESET_STEP=0
+
+export TRAIN_BEAM=2 IGNORE_JIT_FIRST_BEAM=1 BEAM_UOPS_MAX=1500 BEAM_UPCAST_MAX=64 BEAM_LOCAL_MAX=1024 BEAM_MIN_PROGRESS=5 BEAM_PADTO=0
+
+export WANDB=1 PARALLEL=0
+export RUNMLPERF=1
+
+python examples/mlperf/model_train.py
@@ -0,0 +1,38 @@
+{
+    "submitter": "tinycorp",
+    "division": "closed",
+    "status": "Available on-premise",
+    "system_name": "tinybox 8xMI300X",
+    "number_of_nodes": "1",
+    "host_processors_per_node": "2",
+    "host_processor_model_name": "AMD EPYC 9354",
+    "host_processor_core_count": "32",
+    "host_processor_vcpu_count": "64",
+    "host_processor_frequency": "",
+    "host_processor_caches": "",
+    "host_processor_interconnect": "",
+    "host_memory_capacity": "2304GB",
+    "host_storage_type": "NVMe SSD",
+    "host_storage_capacity": "3x 4TB raid array",
+    "host_networking": "",
+    "host_networking_topology": "",
+    "host_memory_configuration": "24x 96GB DDR5",
+    "accelerators_per_node": "8",
+    "accelerator_model_name": "AMD Instinct MI300X 192GB HBM3",
+    "accelerator_host_interconnect": "PCIe 5.0 x16",
+    "accelerator_frequency": "",
+    "accelerator_on-chip_memories": "",
+    "accelerator_memory_configuration": "HBM3",
+    "accelerator_memory_capacity": "192GB",
+    "accelerator_interconnect": "",
+    "accelerator_interconnect_topology": "",
+    "cooling": "air",
+    "hw_notes": "",
+    "framework": "tinygrad, branch mlperf_training_v5.0",
+    "other_software_stack": {
+        "python": "3.10.16",
+        "ROCm": "3.0.0+94441cb"
+    },
+    "operating_system": "Ubuntu 24.04.1 LTS",
+    "sw_notes": ""
+  }
@@ -0,0 +1,38 @@
+{
+  "submitter": "tinycorp",
+  "division": "closed",
+  "status": "Available on-premise",
+  "system_name": "tinybox green",
+  "number_of_nodes": "1",
+  "host_processors_per_node": "1",
+  "host_processor_model_name": "AMD EPYC 7532",
+  "host_processor_core_count": "32",
+  "host_processor_vcpu_count": "64",
+  "host_processor_frequency": "",
+  "host_processor_caches": "",
+  "host_processor_interconnect": "",
+  "host_memory_capacity": "128GB",
+  "host_storage_type": "NVMe SSD",
+  "host_storage_capacity": "4 TB raid array + 1 TB boot",
+  "host_networking": "",
+  "host_networking_topology": "",
+  "host_memory_configuration": "8x 16GB DDR4",
+  "accelerators_per_node": "6",
+  "accelerator_model_name": "NVIDIA GeForce RTX 4090",
+  "accelerator_host_interconnect": "PCIe 4.0 x16",
+  "accelerator_frequency": "",
+  "accelerator_on-chip_memories": "",
+  "accelerator_memory_configuration": "GDDR6X",
+  "accelerator_memory_capacity": "24GB",
+  "accelerator_interconnect": "",
+  "accelerator_interconnect_topology": "",
+  "cooling": "air",
+  "hw_notes": "",
+  "framework": "tinygrad, branch mlperf_training_v5.0",
+  "other_software_stack": {
+    "python": "3.10.12",
+    "CUDA": "12.4"
+  },
+  "operating_system": "Ubuntu 22.04.4",
+  "sw_notes": ""
+}
@@ -0,0 +1,37 @@
+{
+  "submitter": "tinycorp",
+  "division": "closed",
+  "status": "Available on-premise",
+  "system_name": "tinybox red",
+  "number_of_nodes": "1",
+  "host_processors_per_node": "1",
+  "host_processor_model_name": "AMD EPYC 7532",
+  "host_processor_core_count": "32",
+  "host_processor_vcpu_count": "64",
+  "host_processor_frequency": "",
+  "host_processor_caches": "",
+  "host_processor_interconnect": "",
+  "host_memory_capacity": "128GB",
+  "host_storage_type": "NVMe SSD",
+  "host_storage_capacity": "4 TB raid array + 1 TB boot",
+  "host_networking": "",
+  "host_networking_topology": "",
+  "host_memory_configuration": "8x 16GB DDR4",
+  "accelerators_per_node": "6",
+  "accelerator_model_name": "AMD Radeon RX 7900 XTX",
+  "accelerator_host_interconnect": "PCIe 4.0 x16",
+  "accelerator_frequency": "",
+  "accelerator_on-chip_memories": "",
+  "accelerator_memory_configuration": "GDDR6",
+  "accelerator_memory_capacity": "24GB",
+  "accelerator_interconnect": "",
+  "accelerator_interconnect_topology": "",
+  "cooling": "air",
+  "hw_notes": "",
+  "framework": "tinygrad, branch mlperf_training_v5.0",
+  "other_software_stack": {
+    "python": "3.10.12"
+  },
+  "operating_system": "Ubuntu 22.04.4",
+  "sw_notes": ""
+}
@@ -1,4 +1,4 @@
-import os, sys, pickle, time
+import os, sys, pickle, time, re
 import numpy as np
 if "FLOAT16" not in os.environ: os.environ["FLOAT16"] = "1"
 if "IMAGE" not in os.environ: os.environ["IMAGE"] = "2"
@@ -10,7 +10,7 @@ from tinygrad.helpers import DEBUG, getenv
 from tinygrad.engine.realize import CompiledRunner

 import onnx
-from tinygrad.frontend.onnx import OnnxRunner
+from tinygrad.nn.onnx import OnnxRunner

 OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
 OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
@@ -52,6 +52,8 @@ def compile(onnx_file):
      kernel_count += 1
      read_image_count += ei.prg.p.src.count("read_image")
      gated_read_image_count += ei.prg.p.src.count("?read_image")
+      for v in [m.group(1) for m in re.finditer(r'(val\d+)\s*=\s*read_imagef\(', ei.prg.p.src)]:
+        if len(re.findall(fr'[\?\:]{v}\.[xyzw]', ei.prg.p.src)) > 0: gated_read_image_count += 1
  print(f"{kernel_count=},  {read_image_count=}, {gated_read_image_count=}")
  if (allowed_kernel_count:=getenv("ALLOWED_KERNEL_COUNT", -1)) != -1:
    assert kernel_count == allowed_kernel_count, f"different kernels! {kernel_count=}, {allowed_kernel_count=}"
@@ -77,13 +79,20 @@ def test_vs_compile(run, new_inputs, test_val=None):
            **{k:Tensor(v, device="NPY").realize() for k,v in new_inputs_numpy.items() if 'img' not in k}}

  # run 20 times
+  step_times = []
  for _ in range(20):
    st = time.perf_counter()
    out = run(**inputs)
    mt = time.perf_counter()
    val = out.numpy()
    et = time.perf_counter()
-    print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {(et-st)*1e3:6.2f} ms")
+    step_times.append((et-st)*1e3)
+    print(f"enqueue {(mt-st)*1e3:6.2f} ms -- total run {step_times[-1]:6.2f} ms")
+
+  if (assert_time:=getenv("ASSERT_MIN_STEP_TIME")):
+    min_time = min(step_times)
+    assert min_time < assert_time, f"Speed regression, expected min step time of < {assert_time} ms but took: {min_time} ms"
+
  print(out, val.shape, val.dtype)
  if test_val is not None: np.testing.assert_equal(test_val, val)
  print("**** test done ****")
@@ -1,47 +0,0 @@
-import sys
-from tinygrad import Tensor, fetch, GlobalCounters, dtypes
-from tinygrad.uop.ops import UOp
-from tinygrad.frontend.onnx import OnnxRunner
-from tinygrad.schedule.kernelize import get_kernelize_map
-from tinygrad.engine.schedule import create_schedule_with_vars
-from tinygrad.engine.realize import run_schedule
-
-# NOLOCALS=1 CL=1 IMAGE=2 FLOAT16=1 VIZ=1 DEBUG=2 python3 examples/openpilot/compile4.py
-
-OPENPILOT_MODEL = sys.argv[1] if len(sys.argv) > 1 else "https://github.com/commaai/openpilot/raw/v0.9.7/selfdrive/modeld/models/supercombo.onnx"
-OUTPUT = sys.argv[2] if len(sys.argv) > 2 else "/tmp/openpilot.pkl"
-
-if __name__ == "__main__":
-  onnx_file = fetch(OPENPILOT_MODEL)
-  run_onnx = OnnxRunner(onnx_file)
-
-  inputs = run_onnx.get_empty_input_data("npy", dtypes.float32)
-  out: Tensor = next(iter(run_onnx({k:v.to(None) for k,v in inputs.items()}).values())).to('cpu')
-  root = out.uop
-  targets = [x.uop for x in inputs.values()]
-  print(targets)
-
-  # TODO: abstract this from gradient?
-
-  # compute the target path (top down)
-  in_target_path: dict[UOp, bool] = {}
-  for u in root.toposort(): in_target_path[u] = any(x in targets or in_target_path[x] for x in u.src)
-  independent_set = {}
-  for u in root.toposort():
-    if in_target_path[u]:
-      for s in u.src:
-        if not in_target_path[s]:
-          independent_set[s] = None
-  independent = UOp.sink(*independent_set.keys())
-  kernelized = get_kernelize_map(independent)
-  independent = independent.substitute(kernelized)
-  schedule, var_vals = create_schedule_with_vars(independent)
-  run_schedule(schedule)
-
-  print("**** real ****")
-  GlobalCounters.reset()
-  out.uop = root.substitute(kernelized)
-  out.kernelize()
-
-  # realize
-  out.realize()
@@ -27,7 +27,7 @@ class Model(nn.Module):

 if __name__ == "__main__":
  if getenv("TINY_BACKEND"):
-    import tinygrad.frontend.torch  # noqa: F401
+    import tinygrad.nn.torch  # noqa: F401
    device = torch.device("tiny")
  else:
    device = torch.device({"METAL":"mps","NV":"cuda"}.get(Device.DEFAULT, "cpu"))
@@ -269,12 +269,15 @@ if __name__ == "__main__":

  # load in weights
  with WallTimeEvent(BenchEvent.LOAD_WEIGHTS):
-    load_state_dict(model, torch_load(fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt'))['state_dict'], strict=False)
+    model_bin = fetch('https://huggingface.co/CompVis/stable-diffusion-v-1-4-original/resolve/main/sd-v1-4.ckpt', 'sd-v1-4.ckpt')
+    load_state_dict(model, torch_load(model_bin)['state_dict'], verbose=False, strict=False, realize=False)

    if args.fp16:
      for k,v in get_state_dict(model).items():
        if k.startswith("model"):
-          v.replace(v.cast(dtypes.float16).realize())
+          v.replace(v.cast(dtypes.float16))
+
+    Tensor.realize(*get_state_dict(model).values())

  # run through CLIP to get context
  tokenizer = Tokenizer.ClipTokenizer()
@@ -19,8 +19,8 @@ from tinygrad.helpers import fetch, getenv

 # QUANT=1 python3 examples/test_onnx_imagenet.py
 #   https://github.com/xamcat/mobcat-samples/raw/refs/heads/master/onnx_runtime/InferencingSample/InferencingSample/mobilenetv2-7.onnx
-# DONT_REALIZE_EXPAND=1 python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
-# VIZ=1 DONT_REALIZE_EXPAND=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx
+# python3 examples/test_onnx_imagenet.py /tmp/model.quant.onnx
+# VIZ=1 python3 examples/benchmark_onnx.py /tmp/model.quant.onnx

 def imagenet_dataloader(cnt=0):
  input_mean = Tensor([0.485, 0.456, 0.406]).reshape(1, -1, 1, 1)
@@ -32,7 +32,7 @@ if __name__ == "__main__":

  lr = 5e-3
  transform = ComposeTransforms([
-    lambda x: [Image.fromarray(xx, mode='L').resize((64, 64)) for xx in x],
+    lambda x: [Image.fromarray(xx).resize((64, 64)) for xx in x],
    lambda x: np.stack([np.asarray(xx) for xx in x], 0),
    lambda x: x / 255.0,
    lambda x: np.tile(np.expand_dims(x, 1), (1, 3, 1, 1)).astype(np.float32),
@@ -2,7 +2,7 @@
 import os
 from ultralytics import YOLO
 from pathlib import Path
-from tinygrad.frontend.onnx import OnnxRunner
+from tinygrad.nn.onnx import OnnxRunner
 from extra.onnx_helpers import get_example_inputs

 os.chdir("/tmp")
@@ -49,8 +49,7 @@ def rangeify_kernel3():
  b = Tensor.empty(N,N)
  c = a@b
  #c = c.reshape((32,2,16,4,32,2,16,4)).contiguous()
-  with Context(RANGEIFY=1):
-    sink = c.schedule()[-1].ast
+  sink = c.schedule()[-1].ast
  #print(sink)

  opts  = [Opt(OptOps.UPCAST, 0, 4), Opt(OptOps.LOCAL, 0, 16), Opt(OptOps.UPCAST, 0, 2)]
@@ -329,7 +328,7 @@ if __name__ == "__main__":
  elif HL == 1: hprg = hl_spec_kernel3()
  else: hprg = hand_spec_kernel3()
  if HL == 3:
-    with Context(RANGEIFY=1, BLOCK_REORDER=0):
+    with Context(BLOCK_REORDER=0):
      prg = get_program(hprg, Device.default.renderer)
  else:
    prg = get_program(hprg, Device.default.renderer)
@@ -7,7 +7,6 @@ bert_train_params = {
  "GPUS": 6,
  "BS": 96,
  "EVAL_BS": 96,
-  "FUSE_ARANGE": 1,
  "BASEDIR": "/raid/datasets/wiki",
 }

@@ -50,7 +50,7 @@ def ioctls_from_header():
  hdr = (pathlib.Path(__file__).parent / "kfd_ioctl.h").read_text().replace("\\\n", "")
  pattern = r'#define\s+(AMDKFD_IOC_[A-Z0-9_]+)\s+AMDKFD_IOW?R?\((0x[0-9a-fA-F]+),\s+struct\s([A-Za-z0-9_]+)\)'
  matches = re.findall(pattern, hdr, re.MULTILINE)
-  return {int(nr, 0x10):(name, getattr(kfd_ioctl, "struct_"+sname)) for name, nr, sname in matches}
+  return {int(nr, 0x10):(name, getattr(kfd_ioctl, "struct_"+sname, None)) for name, nr, sname in matches}
 nrs = ioctls_from_header()

@ctypes.CFUNCTYPE(ctypes.c_int, ctypes.c_int, ctypes.c_ulong, ctypes.c_void_p)
@@ -1,7 +1,7 @@
 import onnx, yaml, tempfile, time, argparse, json
 from pathlib import Path
 from typing import Any
-from tinygrad.frontend.onnx import OnnxRunner
+from tinygrad.nn.onnx import OnnxRunner
 from extra.onnx_helpers import validate, get_example_inputs
 from extra.huggingface_onnx.huggingface_manager import DOWNLOADS_DIR, snapshot_download_with_retry

@@ -0,0 +1,23 @@
+#!/bin/sh
+
+if [ "$#" -ne 1 ] || ! [ -d $1 ]; then
+  echo "usage: $0 MESA_PREFIX"
+  exit 1
+fi
+
+TMP=$(mktemp)
+trap 'rm -f "$TMP"' EXIT
+
+(
+  cat <<EOF
+#define HAVE_ENDIAN_H
+#define HAVE_STRUCT_TIMESPEC
+#define HAVE_PTHREAD
+#include <unistd.h>
+#include "nir_shader_compiler_options.h"
+#include "compiler/shader_enums.h"
+EOF
+  sed -n '/struct nir_shader_compiler_options/,/^}/{p;/^}/q}' $1/src/gallium/drivers/llvmpipe/lp_screen.c
+  echo "int main(void) { write(1, &gallivm_nir_options, sizeof(gallivm_nir_options)); }"
+) | cc -x c -o $TMP - -I$1/src/compiler/nir -I$1/src -I$1/include && $TMP | gzip | base64 -w0
+
@@ -1,10 +1,23 @@
 from tinygrad import Tensor
 from tinygrad.tensor import _to_np_dtype
-from tinygrad.frontend.onnx import OnnxRunner, OnnxValue
+from tinygrad.nn.onnx import OnnxRunner, OnnxValue
 import numpy as np
 import onnxruntime as ort
+ort_options = ort.SessionOptions()
+ort_options.log_severity_level = 3

 def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
+  """
+  Generate example input tensors based on the provided ONNX graph input specifications.
+
+  NOTE: This is not guaranteed to be reliable. It's a best-effort helper
+  that uses heuristics to guess input shapes and values.
+
+  Example:
+    from tinygrad.nn.onnx import OnnxRunner
+    from extra.onnx_helpers import get_example_inputs
+    inputs = get_example_inputs(OnnxRunner(model_path).graph_inputs)
+  """
  def _get_shape(onnx_shape: tuple[str|int]):
    shape = []
    for onnx_dim in onnx_shape:
@@ -44,11 +57,9 @@ def get_example_inputs(graph_inputs:dict[str, OnnxValue], config={}):
    ret.update({name:value})
  return ret

-def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
+def _get_tinygrad_and_ort_np_outputs(onnx_file, inputs):
  run_onnx = OnnxRunner(onnx_file)

-  ort_options = ort.SessionOptions()
-  ort_options.log_severity_level = 3
  ort_sess = ort.InferenceSession(onnx_file, ort_options, ["CPUExecutionProvider"])
  np_inputs = {k:v.numpy() if isinstance(v, Tensor) else v for k,v in inputs.items()}
  out_names = list(run_onnx.graph_outputs)
@@ -56,9 +67,101 @@ def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
  ort_out = dict(zip(out_names, out_values))

  tinygrad_out = run_onnx(inputs)
+  Tensor.realize(*(x for x in tinygrad_out.values() if x is not None))
+  tinygrad_out = {k:v.numpy() if v is not None else None for k,v in tinygrad_out.items()}
+  return tinygrad_out, ort_out
+
+def validate(onnx_file, inputs, rtol=1e-5, atol=1e-5):
+  """
+  Compares the final output tensors of an onnx model run in tinygrad and onnxruntime.
+  """
+  tinygrad_out, ort_out = _get_tinygrad_and_ort_np_outputs(onnx_file, inputs)

  assert tinygrad_out.keys() == ort_out.keys()
  for k in tinygrad_out.keys():
    tiny_v, onnx_v = tinygrad_out[k], ort_out[k]
    if tiny_v is None: assert onnx_v is None, f"{k}: {tiny_v=}, {onnx_v=}"
-    else: np.testing.assert_allclose(tiny_v.numpy(), onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}")
+    else: np.testing.assert_allclose(tiny_v, onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tinygrad_out.keys()}")
+
+def validate_all_intermediates(onnx_file, inputs, rtol=1e-5, atol=1e-5):
+  """
+  Compares all intermediate node output of an onnx model run in tinygrad and onnxruntime.
+  """
+  report = generate_node_output_report(onnx_file, inputs)
+  for i, node in enumerate(report):
+    node_name = node["node"]
+    op = node["op"]
+    outputs = node["outputs"]
+    for output in outputs:
+      output_name = output["name"]
+      tinygrad_out = output["tinygrad"]
+      ort_out = output["onnxruntime"]
+      try:
+        if tinygrad_out is None: assert ort_out is None, f"None outputs are not equal {tinygrad_out=} {ort_out=}"
+        else: np.testing.assert_allclose(tinygrad_out, ort_out, rtol=rtol, atol=atol)
+        print(f"Validated {i}: {op=} {node_name=} {output_name=}")
+      except AssertionError as e:
+        print(f"FAILED {i}: {op=} {node_name=} {output_name=}")
+        print(str(e).strip() + "\n")
+
+def generate_node_output_report(onnx_file, inputs):
+  """
+  Build a report of all ONNX node outputs from tinygrad and onnxruntime
+
+  Returns:
+    A list of dictionaries, where each entry corresponds to one
+    node in the ONNX graph. The structure is as follows:
+    [
+      {
+        "node": str,  # The name of the ONNX node.
+        "op": str,    # The operation type of the ONNX node.
+        "outputs": [
+          {
+            "name": str,                       # The name of the output tensor.
+            "tinygrad": np.ndarray | None,     # The output value from tinygrad.
+            "onnxruntime": np.ndarray | None,  # The output value from onnxruntime.
+          },
+          ...
+        ]
+      },
+      ...
+    ]
+  """
+  import onnx_graphsurgeon as gs
+  import onnx
+  import tempfile
+
+  # rewrite the model to output all the node outputs
+  # `infer_shapes` here tries to fill the shapes and dtypes of intermediate values which graphsurgeon requires when assigning them as outputs
+  inferred_model = onnx.shape_inference.infer_shapes(onnx.load(onnx_file))
+  model = gs.import_onnx(inferred_model)
+  model_nodes = model.nodes
+  node_outputs = [n.outputs for n in model.nodes]
+  model.outputs = [
+      each_output for outputs in node_outputs for each_output in outputs
+      if not (each_output.dtype is None and each_output.shape is None)  # output with None dtype and None shape is likely a `None` value
+  ]
+  rewritten_model = gs.export_onnx(model)
+
+  # TODO: remove this once ORT supports 1.18.0
+  if getattr(rewritten_model, "ir_version", 0) > 10:
+    rewritten_model.ir_version = 10
+
+  with tempfile.NamedTemporaryFile(suffix=".onnx") as f:
+    onnx.save(rewritten_model, f.name)
+    rewritten_model_path = f.name
+    tinygrad_out, ort_out = _get_tinygrad_and_ort_np_outputs(rewritten_model_path, inputs)
+
+  report = []
+  for node in model_nodes:
+    outputs = []
+    for each_output in node.outputs:
+      if each_output.dtype is None and each_output.shape is None:
+        continue
+      name = each_output.name
+      tinygrad_output = tinygrad_out[name]
+      ort_output = ort_out[name]
+      outputs.append({"name": name, "tinygrad": tinygrad_output, "onnxruntime": ort_output})
+    report.append({"node": node.name, "op": node.op, "outputs": outputs})
+
+  return report
@@ -81,7 +81,7 @@ def lin_to_feats(lin:Kernel, use_sts=True):
  ret = [float(x) for x in ret]

  if use_sts:
-    my_sts = dedup([(x.shape == lin.full_shape, x.real_strides(), any(v.mask is not None for v in x.views), len(x.views)) for x in lin.sts])
+    my_sts = dedup([(x.shape == lin.full_shape, x.is_expanded(), any(v.mask is not None for v in x.views), len(x.views)) for x in lin.sts])
    assert len(my_sts) < MAX_BUFS
    sts_len = 3 + 5*MAX_DIMS
    for s in my_sts:
@@ -0,0 +1,68 @@
+import ctypes
+from dataclasses import dataclass
+import tinygrad.runtime.autogen.comgr as comgr
+from tinygrad.runtime.support.compiler_amd import check
+
+@dataclass
+class InstrCtx:
+  pc:int=0
+  inst:str=""
+
+@comgr.amd_comgr_create_disassembly_info.argtypes[2]
+def instr_cb(text, user_data):
+  c = ctypes.cast(user_data, ctypes.POINTER(ctypes.py_object)).contents.value
+  c.inst = ctypes.string_at(text).decode("utf-8","replace").strip()
+  return comgr.AMD_COMGR_STATUS_SUCCESS
+
+# nop callback
+@comgr.amd_comgr_create_disassembly_info.argtypes[3]
+def addr_cb(*args): return comgr.AMD_COMGR_STATUS_SUCCESS
+
+def comgr_get_address_table(lib:bytes) -> dict[int, tuple[str, int]]:
+  check(comgr.amd_comgr_create_data(comgr.AMD_COMGR_DATA_KIND_EXECUTABLE, ctypes.byref(data_src:=comgr.amd_comgr_data_t())))
+  lib_buf = ctypes.create_string_buffer(lib, len(lib))
+  check(comgr.amd_comgr_set_data(data_src, len(lib), lib_buf))
+  check(comgr.amd_comgr_get_data_isa_name(data_src, isa_sz:=ctypes.c_size_t(128), isa:=(ctypes.c_char*isa_sz.value)()))
+
+  @comgr.amd_comgr_create_disassembly_info.argtypes[1]
+  def memory_cb(from_addr, to, size, _):
+    base, buf_len = ctypes.addressof(lib_buf), len(lib_buf)
+    start = int(from_addr) - base
+    if start < 0 or start >= buf_len: return 0
+    ctypes.memmove(to, base + start, n:=min(int(size), buf_len - start))
+    return n
+
+  info_src = comgr.amd_comgr_disassembly_info_t()
+  check(comgr.amd_comgr_create_disassembly_info(ctypes.cast(isa, ctypes.POINTER(ctypes.c_char)), memory_cb, instr_cb, addr_cb, info_src))
+
+  @comgr.amd_comgr_iterate_symbols.argtypes[1]
+  def sym_callback(sym, udata):
+    check(comgr.amd_comgr_symbol_get_info(sym, comgr.AMD_COMGR_SYMBOL_INFO_TYPE, ctypes.byref(sym_type:=ctypes.c_int())))
+    if sym_type.value != comgr.AMD_COMGR_SYMBOL_TYPE_FUNC: return comgr.AMD_COMGR_STATUS_SUCCESS
+    check(comgr.amd_comgr_symbol_get_info(sym, comgr.AMD_COMGR_SYMBOL_INFO_VALUE, ctypes.byref(vaddr:=ctypes.c_uint64())))
+    check(comgr.amd_comgr_symbol_get_info(sym, comgr.AMD_COMGR_SYMBOL_INFO_SIZE, ctypes.byref(size:=ctypes.c_uint64())))
+    check(comgr.amd_comgr_map_elf_virtual_address_to_code_object_offset(data_src, vaddr.value, ctypes.byref(offset:=ctypes.c_uint64()),
+                                                                        ctypes.byref(ctypes.c_uint64()), ctypes.byref(nobits:=ctypes.c_bool())))
+    check(nobits.value)
+    base = ctypes.addressof(lib_buf)
+    pc = base + offset.value
+    end = pc + size.value
+    addr_table = ctypes.cast(udata, ctypes.POINTER(ctypes.py_object)).contents.value
+    instr_ref = ctypes.py_object(ctx:=InstrCtx())
+    instr_ptr = ctypes.cast(ctypes.pointer(instr_ref), ctypes.c_void_p)
+    while pc < end:
+      size_read = ctypes.c_uint64(0)
+      ctx.pc = pc
+      st = comgr.amd_comgr_disassemble_instruction(info_src, ctypes.c_uint64(pc), instr_ptr, ctypes.byref(size_read))
+      if st == comgr.AMD_COMGR_STATUS_SUCCESS and size_read.value:
+        rel = (pc - base) - offset.value
+        addr_table[vaddr.value + rel] = (ctx.inst, int(size_read.value))
+        pc += size_read.value
+      else: # don't inf loop if comgr fails
+        b = ctypes.c_ubyte.from_buffer(lib_buf, pc - base).value
+        addr_table[vaddr.value + (pc - base - offset.value)] = (f"DISASSEMBLER ISSUE 0x{b:02x}", 1)
+        pc += 1
+    return comgr.AMD_COMGR_STATUS_SUCCESS
+  addr_table:dict[int, tuple[str, int]] = {}
+  check(comgr.amd_comgr_iterate_symbols(data_src, sym_callback, ctypes.cast(ctypes.pointer(ctypes.py_object(addr_table)), ctypes.c_void_p)))
+  return addr_table
@@ -155,6 +155,10 @@ class RGP:
      device_event = device_events[device]
    sqtt_events = [x for x in profile if isinstance(x, ProfileSQTTEvent) and x.device == device_event.device]
    if len(sqtt_events) == 0: raise RuntimeError(f"Device {device_event.device} doesn't contain SQTT data")
+    device_props = sqtt_events[0].props
+    gfx_ver = device_props['gfx_target_version'] // 10000
+    gfx_iplvl = getattr(sqtt, f"SQTT_GFXIP_LEVEL_GFXIP_{device_props['gfx_target_version']//10000}_{(device_props['gfx_target_version']//100)%100}",
+                        getattr(sqtt, f"SQTT_GFXIP_LEVEL_GFXIP_{device_props['gfx_target_version']//10000}", None))
    sqtt_itrace_enabled = any([event.itrace for event in sqtt_events])
    sqtt_itrace_masked = not all_same([event.itrace for event in sqtt_events])
    sqtt_itrace_se_mask = functools.reduce(lambda a,b: a|b, [int(event.itrace) << event.se for event in sqtt_events], 0) if sqtt_itrace_masked else 0
@@ -192,21 +196,21 @@ class RGP:
        flags=0,
        trace_shader_core_clock=0x93f05080,
        trace_memory_clock=0x4a723a40,
-        device_id=0x744c,
+        device_id={110000: 0x744c, 110003: 0x7480, 120001: 0x7550}[device_props['gfx_target_version']],
        device_revision_id=0xc8,
        vgprs_per_simd=1536,
        sgprs_per_simd=128*16,
-        shader_engines=6,
-        compute_unit_per_shader_engine=16,
-        simd_per_compute_unit=2,
-        wavefronts_per_simd=16,
+        shader_engines=device_props['array_count'] // device_props['simd_arrays_per_engine'],
+        compute_unit_per_shader_engine=device_props['simd_count'] // device_props['simd_per_cu'] // (device_props['array_count'] // device_props['simd_arrays_per_engine']),
+        simd_per_compute_unit=device_props['simd_per_cu'],
+        wavefronts_per_simd=device_props['max_waves_per_simd'],
        minimum_vgpr_alloc=4,
        vgpr_alloc_granularity=8,
        minimum_sgpr_alloc=128,
        sgpr_alloc_granularity=128,
        hardware_contexts=8,
        gpu_type=sqtt.SQTT_GPU_TYPE_DISCRETE,
-        gfxip_level=sqtt.SQTT_GFXIP_LEVEL_GFXIP_11_0,
+        gfxip_level=gfx_iplvl,
        gpu_index=0,
        gds_size=0,
        gds_per_shader_engine=0,
@@ -218,7 +222,7 @@ class RGP:
        vram_bus_width=384, # 384-bit
        l2_cache_size=6 * 1024 * 1024, # 6 MB
        l1_cache_size=32 * 1024, # 32 KB per SIMD (?)
-        lds_size=65536, # 64 KB per CU
+        lds_size=device_props['lds_size_in_kb'] * 1024,
        gpu_name=b'NAVI31',
        alu_per_clock=0,
        texture_per_clock=0,
@@ -257,7 +261,7 @@ class RGP:
            major_version=0, minor_version=2,
          ),
          shader_engine_index=sqtt_event.se,
-          sqtt_version=sqtt.SQTT_VERSION_3_2,
+          sqtt_version={11: sqtt.SQTT_VERSION_3_2, 12: sqtt.SQTT_VERSION_3_3}.get(gfx_ver),
          _0=sqtt.union_sqtt_file_chunk_sqtt_desc_0(
            v1=sqtt.struct_sqtt_file_chunk_sqtt_desc_0_v1(
              instrumentation_spec_version=1,
@@ -0,0 +1,96 @@
+import ctypes, pathlib, argparse, pickle, re, functools, dataclasses
+from extra.sqtt.rocprof import rocprof
+from extra.sqtt.disasm import comgr_get_address_table
+from tinygrad.helpers import temp, DEBUG
+from tinygrad.device import ProfileEvent, ProfileProgramEvent
+from tinygrad.runtime.ops_amd import ProfileSQTTEvent
+
+@dataclasses.dataclass
+class InstInfo:
+  typ:str=""
+  inst:str=""
+  hit:int=0
+  lat:int=0
+  stall:int=0
+  def __str__(self): return f"{self.inst:>20} hits:{self.typ:>6} hits:{self.hit:>6} latency:{self.lat:>6} stall:{self.stall:>6}"
+
+  def on_ev(self, ev):
+    self.hit, self.lat, self.stall = self.hit + 1, self.lat + ev.duration, self.stall + ev.stall
+
+class _ROCParseCtx:
+  def __init__(self, sqtt_evs:list[ProfileSQTTEvent], prog_evs:list[ProfileProgramEvent]):
+    self.sqtt_evs, self.prog_evs = iter(sqtt_evs), prog_evs
+    self.wave_events, self.disasms, self.addr2prg = {}, {}, {}
+
+    for prog in prog_evs:
+      for addr, info in comgr_get_address_table(prog.lib).items():
+        self.disasms[prog.base + addr] = info
+        self.addr2prg[prog.base + addr] = prog
+
+  def next_sqtt(self): return next(self.sqtt_evs, None)
+  def find_program(self, addr): return self.addr2prg[addr]
+
+  def on_occupancy_ev(self, ev):
+    if DEBUG >= 4: print("OCC", ev.time, ev.cu, ev.simd, ev.wave_id, ev.start)
+
+  def on_wave_ev(self, ev):
+    if DEBUG >= 4: print("WAVE", ev.wave_id, ev.cu, ev.simd, ev.contexts, ev.begin_time, ev.end_time)
+
+    asm = {}
+    for j in range(ev.instructions_size):
+      inst_ev = ev.instructions_array[j]
+      inst_typ = rocprof.rocprofiler_thread_trace_decoder_inst_category_t__enumvalues[inst_ev.category]
+      asm.setdefault(inst_ev.pc.address, InstInfo(typ=inst_typ, inst=self.disasms[inst_ev.pc.address][0]))
+      asm[inst_ev.pc.address].on_ev(inst_ev)
+
+    self.wave_events[(self.find_program(ev.instructions_array[0].pc.address).name, ev.wave_id, ev.cu, ev.simd)] = asm
+
+if __name__ == "__main__":
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--profile', type=pathlib.Path, help='Path to profile', default=pathlib.Path(temp("profile.pkl", append_user=True)))
+  args = parser.parse_args()
+
+  with args.profile.open("rb") as f: profile = pickle.load(f)
+  sqtt_events:list[ProfileSQTTEvent] = []
+  prog_events:list[ProfileProgramEvent] = []
+  for e in profile:
+    if isinstance(e, ProfileSQTTEvent): sqtt_events.append(e)
+    if isinstance(e, ProfileProgramEvent) and e.device.startswith("AMD"): prog_events.append(e)
+
+  ROCParseCtx = _ROCParseCtx(sqtt_events, prog_events)
+
+  @rocprof.rocprof_trace_decoder_se_data_callback_t
+  def copy_cb(buf, buf_size, data_ptr):
+    if (prof:=ROCParseCtx.next_sqtt()) is None: return 0
+    buf[0] = ctypes.cast((ctypes.c_ubyte * len(prof.blob)).from_buffer_copy(prof.blob), ctypes.POINTER(ctypes.c_ubyte))
+    buf_size[0] = len(prof.blob)
+    return len(prof.blob)
+
+  @rocprof.rocprof_trace_decoder_trace_callback_t
+  def trace_cb(record_type, events_ptr, n, data_ptr):
+    match record_type:
+      case rocprof.ROCPROFILER_THREAD_TRACE_DECODER_RECORD_OCCUPANCY:
+        for ev in (rocprof.rocprofiler_thread_trace_decoder_occupancy_t * n).from_address(events_ptr): ROCParseCtx.on_occupancy_ev(ev)
+      case rocprof.ROCPROFILER_THREAD_TRACE_DECODER_RECORD_WAVE:
+        for ev in (rocprof.rocprofiler_thread_trace_decoder_wave_t * n).from_address(events_ptr): ROCParseCtx.on_wave_ev(ev)
+      case _:
+        if DEBUG >= 2: print(rocprof.rocprofiler_thread_trace_decoder_record_type_t__enumvalues[record_type], events_ptr, n)
+    return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS
+
+  @rocprof.rocprof_trace_decoder_isa_callback_t
+  def isa_cb(instr_ptr, mem_size_ptr, size_ptr, pc, data_ptr):
+    instr, mem_size_ptr[0] = ROCParseCtx.disasms[pc.address]
+
+    # this is the number of bytes to next instruction, set to 0 for end_pgm
+    if instr == "s_endpgm": mem_size_ptr[0] = 0
+    if (max_sz:=size_ptr[0]) == 0: return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_OUT_OF_RESOURCES
+
+    # truncate the instr if it doesn't fit
+    if (str_sz:=len(instr_bytes:=instr.encode()))+1 > max_sz: str_sz = max_sz
+    ctypes.memmove(instr_ptr, instr_bytes, str_sz)
+    size_ptr[0] = str_sz
+
+    return rocprof.ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS
+
+  rocprof.rocprof_trace_decoder_parse_data(copy_cb, trace_cb, isa_cb, None)
+  print(ROCParseCtx.wave_events.keys())
@@ -0,0 +1,18 @@
+#!/usr/bin/env python3
+import os, shutil
+from pathlib import Path
+from tinygrad.helpers import fetch, OSX
+
+DEST = Path("/usr/local/lib")
+DEST.mkdir(exist_ok=True)
+
+if __name__ == "__main__":
+  if OSX:
+    fp = fetch("https://github.com/ROCm/rocprof-trace-decoder/releases/download/0.1.4/rocprof-trace-decoder-macos-arm64-0.1.4-Darwin.sh")
+    lib = fp.parent/"rocprof-trace-decoder-macos-arm64-0.1.4-Darwin"/"lib"/"librocprof-trace-decoder.dylib"
+    os.chmod(fp, 0o755)
+    os.system(f"sudo {fp} --prefix={fp.parent} --include-subdir")
+  else:
+    lib = fetch("https://github.com/ROCm/rocprof-trace-decoder/raw/5420409ad0963b2d76450add067b9058493ccbd0/releases/linux_glibc_2_28_x86_64/librocprof-trace-decoder.so", name="librocprof-trace-decoder.so")
+  shutil.copy2(lib, DEST)
+  print(f"Installed {lib.name} to", DEST)
@@ -0,0 +1,656 @@
+# pylint: skip-file
+# mypy: ignore-errors
+# -*- coding: utf-8 -*-
+#
+# TARGET arch is: []
+# WORD_SIZE is: 8
+# POINTER_SIZE is: 8
+# LONGDOUBLE_SIZE is: 16
+#
+import ctypes, ctypes.util
+
+
+class AsDictMixin:
+    @classmethod
+    def as_dict(cls, self):
+        result = {}
+        if not isinstance(self, AsDictMixin):
+            # not a structure, assume it's already a python object
+            return self
+        if not hasattr(cls, "_fields_"):
+            return result
+        # sys.version_info >= (3, 5)
+        # for (field, *_) in cls._fields_:  # noqa
+        for field_tuple in cls._fields_:  # noqa
+            field = field_tuple[0]
+            if field.startswith('PADDING_'):
+                continue
+            value = getattr(self, field)
+            type_ = type(value)
+            if hasattr(value, "_length_") and hasattr(value, "_type_"):
+                # array
+                if not hasattr(type_, "as_dict"):
+                    value = [v for v in value]
+                else:
+                    type_ = type_._type_
+                    value = [type_.as_dict(v) for v in value]
+            elif hasattr(value, "contents") and hasattr(value, "_type_"):
+                # pointer
+                try:
+                    if not hasattr(type_, "as_dict"):
+                        value = value.contents
+                    else:
+                        type_ = type_._type_
+                        value = type_.as_dict(value.contents)
+                except ValueError:
+                    # nullptr
+                    value = None
+            elif isinstance(value, AsDictMixin):
+                # other structure
+                value = type_.as_dict(value)
+            result[field] = value
+        return result
+
+
+class Structure(ctypes.Structure, AsDictMixin):
+
+    def __init__(self, *args, **kwds):
+        # We don't want to use positional arguments fill PADDING_* fields
+
+        args = dict(zip(self.__class__._field_names_(), args))
+        args.update(kwds)
+        super(Structure, self).__init__(**args)
+
+    @classmethod
+    def _field_names_(cls):
+        if hasattr(cls, '_fields_'):
+            return (f[0] for f in cls._fields_ if not f[0].startswith('PADDING'))
+        else:
+            return ()
+
+    @classmethod
+    def get_type(cls, field):
+        for f in cls._fields_:
+            if f[0] == field:
+                return f[1]
+        return None
+
+    @classmethod
+    def bind(cls, bound_fields):
+        fields = {}
+        for name, type_ in cls._fields_:
+            if hasattr(type_, "restype"):
+                if name in bound_fields:
+                    if bound_fields[name] is None:
+                        fields[name] = type_()
+                    else:
+                        # use a closure to capture the callback from the loop scope
+                        fields[name] = (
+                            type_((lambda callback: lambda *args: callback(*args))(
+                                bound_fields[name]))
+                        )
+                    del bound_fields[name]
+                else:
+                    # default callback implementation (does nothing)
+                    try:
+                        default_ = type_(0).restype().value
+                    except TypeError:
+                        default_ = None
+                    fields[name] = type_((
+                        lambda default_: lambda *args: default_)(default_))
+            else:
+                # not a callback function, use default initialization
+                if name in bound_fields:
+                    fields[name] = bound_fields[name]
+                    del bound_fields[name]
+                else:
+                    fields[name] = type_()
+        if len(bound_fields) != 0:
+            raise ValueError(
+                "Cannot bind the following unknown callback(s) {}.{}".format(
+                    cls.__name__, bound_fields.keys()
+            ))
+        return cls(**fields)
+
+
+class Union(ctypes.Union, AsDictMixin):
+    pass
+
+
+
+c_int128 = ctypes.c_ubyte*16
+c_uint128 = c_int128
+void = None
+if ctypes.sizeof(ctypes.c_longdouble) == 16:
+    c_long_double_t = ctypes.c_longdouble
+else:
+    c_long_double_t = ctypes.c_ubyte*16
+
+def string_cast(char_pointer, encoding='utf-8', errors='strict'):
+    value = ctypes.cast(char_pointer, ctypes.c_char_p).value
+    if value is not None and encoding is not None:
+        value = value.decode(encoding, errors=errors)
+    return value
+
+
+def char_pointer_cast(string, encoding='utf-8'):
+    if encoding is not None:
+        try:
+            string = string.encode(encoding)
+        except AttributeError:
+            # In Python3, bytes has no encode attribute
+            pass
+    string = ctypes.c_char_p(string)
+    return ctypes.cast(string, ctypes.POINTER(ctypes.c_char))
+
+
+
+class FunctionFactoryStub:
+    def __getattr__(self, _):
+      return ctypes.CFUNCTYPE(lambda y:y)
+
+# libraries['FIXME_STUB'] explanation
+# As you did not list (-l libraryname.so) a library that exports this function
+# This is a non-working stub instead.
+# You can either re-run clan2py with -l /path/to/library.so
+# Or manually fix this by comment the ctypes.CDLL loading
+_libraries = {}
+_libraries['FIXME_STUB'] = ctypes.CDLL(ctypes.util.find_library('rocprof-trace-decoder')) #  ctypes.CDLL('FIXME_STUB')
+
+
+
+# values for enumeration 'rocprofiler_thread_trace_decoder_info_t'
+rocprofiler_thread_trace_decoder_info_t__enumvalues = {
+    0: 'ROCPROFILER_THREAD_TRACE_DECODER_INFO_NONE',
+    1: 'ROCPROFILER_THREAD_TRACE_DECODER_INFO_DATA_LOST',
+    2: 'ROCPROFILER_THREAD_TRACE_DECODER_INFO_STITCH_INCOMPLETE',
+    3: 'ROCPROFILER_THREAD_TRACE_DECODER_INFO_WAVE_INCOMPLETE',
+    4: 'ROCPROFILER_THREAD_TRACE_DECODER_INFO_LAST',
+}
+ROCPROFILER_THREAD_TRACE_DECODER_INFO_NONE = 0
+ROCPROFILER_THREAD_TRACE_DECODER_INFO_DATA_LOST = 1
+ROCPROFILER_THREAD_TRACE_DECODER_INFO_STITCH_INCOMPLETE = 2
+ROCPROFILER_THREAD_TRACE_DECODER_INFO_WAVE_INCOMPLETE = 3
+ROCPROFILER_THREAD_TRACE_DECODER_INFO_LAST = 4
+rocprofiler_thread_trace_decoder_info_t = ctypes.c_uint32 # enum
+class struct_rocprofiler_thread_trace_decoder_pc_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_pc_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_pc_t._fields_ = [
+    ('address', ctypes.c_uint64),
+    ('code_object_id', ctypes.c_uint64),
+]
+
+rocprofiler_thread_trace_decoder_pc_t = struct_rocprofiler_thread_trace_decoder_pc_t
+class struct_rocprofiler_thread_trace_decoder_perfevent_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_perfevent_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_perfevent_t._fields_ = [
+    ('time', ctypes.c_int64),
+    ('events0', ctypes.c_uint16),
+    ('events1', ctypes.c_uint16),
+    ('events2', ctypes.c_uint16),
+    ('events3', ctypes.c_uint16),
+    ('CU', ctypes.c_ubyte),
+    ('bank', ctypes.c_ubyte),
+    ('PADDING_0', ctypes.c_ubyte * 6),
+]
+
+rocprofiler_thread_trace_decoder_perfevent_t = struct_rocprofiler_thread_trace_decoder_perfevent_t
+class struct_rocprofiler_thread_trace_decoder_occupancy_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_occupancy_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_occupancy_t._fields_ = [
+    ('pc', rocprofiler_thread_trace_decoder_pc_t),
+    ('time', ctypes.c_uint64),
+    ('reserved', ctypes.c_ubyte),
+    ('cu', ctypes.c_ubyte),
+    ('simd', ctypes.c_ubyte),
+    ('wave_id', ctypes.c_ubyte),
+    ('start', ctypes.c_uint32, 1),
+    ('_rsvd', ctypes.c_uint32, 31),
+]
+
+rocprofiler_thread_trace_decoder_occupancy_t = struct_rocprofiler_thread_trace_decoder_occupancy_t
+
+# values for enumeration 'rocprofiler_thread_trace_decoder_wstate_type_t'
+rocprofiler_thread_trace_decoder_wstate_type_t__enumvalues = {
+    0: 'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_EMPTY',
+    1: 'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_IDLE',
+    2: 'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_EXEC',
+    3: 'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_WAIT',
+    4: 'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_STALL',
+    5: 'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_LAST',
+}
+ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_EMPTY = 0
+ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_IDLE = 1
+ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_EXEC = 2
+ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_WAIT = 3
+ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_STALL = 4
+ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_LAST = 5
+rocprofiler_thread_trace_decoder_wstate_type_t = ctypes.c_uint32 # enum
+class struct_rocprofiler_thread_trace_decoder_wave_state_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_wave_state_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_wave_state_t._fields_ = [
+    ('type', ctypes.c_int32),
+    ('duration', ctypes.c_int32),
+]
+
+rocprofiler_thread_trace_decoder_wave_state_t = struct_rocprofiler_thread_trace_decoder_wave_state_t
+
+# values for enumeration 'rocprofiler_thread_trace_decoder_inst_category_t'
+rocprofiler_thread_trace_decoder_inst_category_t__enumvalues = {
+    0: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_NONE',
+    1: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_SMEM',
+    2: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_SALU',
+    3: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_VMEM',
+    4: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_FLAT',
+    5: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_LDS',
+    6: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_VALU',
+    7: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_JUMP',
+    8: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_NEXT',
+    9: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_IMMED',
+    10: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_CONTEXT',
+    11: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_MESSAGE',
+    12: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_BVH',
+    13: 'ROCPROFILER_THREAD_TRACE_DECODER_INST_LAST',
+}
+ROCPROFILER_THREAD_TRACE_DECODER_INST_NONE = 0
+ROCPROFILER_THREAD_TRACE_DECODER_INST_SMEM = 1
+ROCPROFILER_THREAD_TRACE_DECODER_INST_SALU = 2
+ROCPROFILER_THREAD_TRACE_DECODER_INST_VMEM = 3
+ROCPROFILER_THREAD_TRACE_DECODER_INST_FLAT = 4
+ROCPROFILER_THREAD_TRACE_DECODER_INST_LDS = 5
+ROCPROFILER_THREAD_TRACE_DECODER_INST_VALU = 6
+ROCPROFILER_THREAD_TRACE_DECODER_INST_JUMP = 7
+ROCPROFILER_THREAD_TRACE_DECODER_INST_NEXT = 8
+ROCPROFILER_THREAD_TRACE_DECODER_INST_IMMED = 9
+ROCPROFILER_THREAD_TRACE_DECODER_INST_CONTEXT = 10
+ROCPROFILER_THREAD_TRACE_DECODER_INST_MESSAGE = 11
+ROCPROFILER_THREAD_TRACE_DECODER_INST_BVH = 12
+ROCPROFILER_THREAD_TRACE_DECODER_INST_LAST = 13
+rocprofiler_thread_trace_decoder_inst_category_t = ctypes.c_uint32 # enum
+class struct_rocprofiler_thread_trace_decoder_inst_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_inst_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_inst_t._fields_ = [
+    ('category', ctypes.c_uint32, 8),
+    ('stall', ctypes.c_uint32, 24),
+    ('duration', ctypes.c_int32),
+    ('time', ctypes.c_int64),
+    ('pc', rocprofiler_thread_trace_decoder_pc_t),
+]
+
+rocprofiler_thread_trace_decoder_inst_t = struct_rocprofiler_thread_trace_decoder_inst_t
+class struct_rocprofiler_thread_trace_decoder_wave_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_wave_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_wave_t._fields_ = [
+    ('cu', ctypes.c_ubyte),
+    ('simd', ctypes.c_ubyte),
+    ('wave_id', ctypes.c_ubyte),
+    ('contexts', ctypes.c_ubyte),
+    ('_rsvd1', ctypes.c_uint32),
+    ('_rsvd2', ctypes.c_uint32),
+    ('_rsvd3', ctypes.c_uint32),
+    ('begin_time', ctypes.c_int64),
+    ('end_time', ctypes.c_int64),
+    ('timeline_size', ctypes.c_uint64),
+    ('instructions_size', ctypes.c_uint64),
+    ('timeline_array', ctypes.POINTER(struct_rocprofiler_thread_trace_decoder_wave_state_t)),
+    ('instructions_array', ctypes.POINTER(struct_rocprofiler_thread_trace_decoder_inst_t)),
+]
+
+rocprofiler_thread_trace_decoder_wave_t = struct_rocprofiler_thread_trace_decoder_wave_t
+class struct_rocprofiler_thread_trace_decoder_realtime_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_realtime_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_realtime_t._fields_ = [
+    ('shader_clock', ctypes.c_int64),
+    ('realtime_clock', ctypes.c_uint64),
+    ('reserved', ctypes.c_uint64),
+]
+
+rocprofiler_thread_trace_decoder_realtime_t = struct_rocprofiler_thread_trace_decoder_realtime_t
+
+# values for enumeration 'rocprofiler_thread_trace_decoder_shaderdata_flags_t'
+rocprofiler_thread_trace_decoder_shaderdata_flags_t__enumvalues = {
+    0: 'ROCPROFILER_THREAD_TRACE_DECODER_SHADERDATA_FLAGS_IMM',
+    1: 'ROCPROFILER_THREAD_TRACE_DECODER_SHADERDATA_FLAGS_PRIV',
+}
+ROCPROFILER_THREAD_TRACE_DECODER_SHADERDATA_FLAGS_IMM = 0
+ROCPROFILER_THREAD_TRACE_DECODER_SHADERDATA_FLAGS_PRIV = 1
+rocprofiler_thread_trace_decoder_shaderdata_flags_t = ctypes.c_uint32 # enum
+class struct_rocprofiler_thread_trace_decoder_shaderdata_t(Structure):
+    pass
+
+struct_rocprofiler_thread_trace_decoder_shaderdata_t._pack_ = 1 # source:False
+struct_rocprofiler_thread_trace_decoder_shaderdata_t._fields_ = [
+    ('time', ctypes.c_int64),
+    ('value', ctypes.c_uint64),
+    ('cu', ctypes.c_ubyte),
+    ('simd', ctypes.c_ubyte),
+    ('wave_id', ctypes.c_ubyte),
+    ('flags', ctypes.c_ubyte),
+    ('reserved', ctypes.c_uint32),
+]
+
+rocprofiler_thread_trace_decoder_shaderdata_t = struct_rocprofiler_thread_trace_decoder_shaderdata_t
+
+# values for enumeration 'rocprofiler_thread_trace_decoder_record_type_t'
+rocprofiler_thread_trace_decoder_record_type_t__enumvalues = {
+    0: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_GFXIP',
+    1: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_OCCUPANCY',
+    2: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_PERFEVENT',
+    3: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_WAVE',
+    4: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_INFO',
+    5: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_DEBUG',
+    6: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_SHADERDATA',
+    7: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_REALTIME',
+    8: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_RT_FREQUENCY',
+    9: 'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_LAST',
+}
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_GFXIP = 0
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_OCCUPANCY = 1
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_PERFEVENT = 2
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_WAVE = 3
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_INFO = 4
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_DEBUG = 5
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_SHADERDATA = 6
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_REALTIME = 7
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_RT_FREQUENCY = 8
+ROCPROFILER_THREAD_TRACE_DECODER_RECORD_LAST = 9
+rocprofiler_thread_trace_decoder_record_type_t = ctypes.c_uint32 # enum
+
+# values for enumeration 'c__EA_rocprofiler_thread_trace_decoder_status_t'
+c__EA_rocprofiler_thread_trace_decoder_status_t__enumvalues = {
+    0: 'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS',
+    1: 'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR',
+    2: 'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_OUT_OF_RESOURCES',
+    3: 'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_INVALID_ARGUMENT',
+    4: 'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_INVALID_SHADER_DATA',
+    5: 'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_LAST',
+}
+ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS = 0
+ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR = 1
+ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_OUT_OF_RESOURCES = 2
+ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_INVALID_ARGUMENT = 3
+ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_INVALID_SHADER_DATA = 4
+ROCPROFILER_THREAD_TRACE_DECODER_STATUS_LAST = 5
+c__EA_rocprofiler_thread_trace_decoder_status_t = ctypes.c_uint32 # enum
+rocprofiler_thread_trace_decoder_status_t = c__EA_rocprofiler_thread_trace_decoder_status_t
+rocprofiler_thread_trace_decoder_status_t__enumvalues = c__EA_rocprofiler_thread_trace_decoder_status_t__enumvalues
+rocprof_trace_decoder_trace_callback_t = ctypes.CFUNCTYPE(c__EA_rocprofiler_thread_trace_decoder_status_t, rocprofiler_thread_trace_decoder_record_type_t, ctypes.POINTER(None), ctypes.c_uint64, ctypes.POINTER(None))
+rocprof_trace_decoder_isa_callback_t = ctypes.CFUNCTYPE(c__EA_rocprofiler_thread_trace_decoder_status_t, ctypes.POINTER(ctypes.c_char), ctypes.POINTER(ctypes.c_uint64), ctypes.POINTER(ctypes.c_uint64), struct_rocprofiler_thread_trace_decoder_pc_t, ctypes.POINTER(None))
+rocprof_trace_decoder_se_data_callback_t = ctypes.CFUNCTYPE(ctypes.c_uint64, ctypes.POINTER(ctypes.POINTER(ctypes.c_ubyte)), ctypes.POINTER(ctypes.c_uint64), ctypes.POINTER(None))
+try:
+    rocprof_trace_decoder_parse_data = _libraries['FIXME_STUB'].rocprof_trace_decoder_parse_data
+    rocprof_trace_decoder_parse_data.restype = rocprofiler_thread_trace_decoder_status_t
+    rocprof_trace_decoder_parse_data.argtypes = [rocprof_trace_decoder_se_data_callback_t, rocprof_trace_decoder_trace_callback_t, rocprof_trace_decoder_isa_callback_t, ctypes.POINTER(None)]
+except AttributeError:
+    pass
+try:
+    rocprof_trace_decoder_get_info_string = _libraries['FIXME_STUB'].rocprof_trace_decoder_get_info_string
+    rocprof_trace_decoder_get_info_string.restype = ctypes.POINTER(ctypes.c_char)
+    rocprof_trace_decoder_get_info_string.argtypes = [rocprofiler_thread_trace_decoder_info_t]
+except AttributeError:
+    pass
+try:
+    rocprof_trace_decoder_get_status_string = _libraries['FIXME_STUB'].rocprof_trace_decoder_get_status_string
+    rocprof_trace_decoder_get_status_string.restype = ctypes.POINTER(ctypes.c_char)
+    rocprof_trace_decoder_get_status_string.argtypes = [rocprofiler_thread_trace_decoder_status_t]
+except AttributeError:
+    pass
+rocprofiler_thread_trace_decoder_debug_callback_t = ctypes.CFUNCTYPE(None, ctypes.c_int64, ctypes.POINTER(ctypes.c_char), ctypes.POINTER(ctypes.c_char), ctypes.POINTER(None))
+uint64_t = ctypes.c_uint64
+try:
+    rocprof_trace_decoder_dump_data = _libraries['FIXME_STUB'].rocprof_trace_decoder_dump_data
+    rocprof_trace_decoder_dump_data.restype = rocprofiler_thread_trace_decoder_status_t
+    rocprof_trace_decoder_dump_data.argtypes = [ctypes.POINTER(ctypes.c_char), uint64_t, rocprofiler_thread_trace_decoder_debug_callback_t, ctypes.POINTER(None)]
+except AttributeError:
+    pass
+class union_rocprof_trace_decoder_gfx9_header_t(Union):
+    pass
+
+class struct_rocprof_trace_decoder_gfx9_header_t_0(Structure):
+    pass
+
+struct_rocprof_trace_decoder_gfx9_header_t_0._pack_ = 1 # source:False
+struct_rocprof_trace_decoder_gfx9_header_t_0._fields_ = [
+    ('legacy_version', ctypes.c_uint64, 13),
+    ('gfx9_version2', ctypes.c_uint64, 3),
+    ('DSIMDM', ctypes.c_uint64, 4),
+    ('DCU', ctypes.c_uint64, 5),
+    ('reserved1', ctypes.c_uint64, 1),
+    ('SEID', ctypes.c_uint64, 6),
+    ('reserved2', ctypes.c_uint64, 32),
+]
+
+union_rocprof_trace_decoder_gfx9_header_t._pack_ = 1 # source:False
+union_rocprof_trace_decoder_gfx9_header_t._anonymous_ = ('_0',)
+union_rocprof_trace_decoder_gfx9_header_t._fields_ = [
+    ('_0', struct_rocprof_trace_decoder_gfx9_header_t_0),
+    ('raw', ctypes.c_uint64),
+]
+
+rocprof_trace_decoder_gfx9_header_t = union_rocprof_trace_decoder_gfx9_header_t
+class union_rocprof_trace_decoder_instrument_enable_t(Union):
+    pass
+
+class struct_rocprof_trace_decoder_instrument_enable_t_0(Structure):
+    pass
+
+struct_rocprof_trace_decoder_instrument_enable_t_0._pack_ = 1 # source:False
+struct_rocprof_trace_decoder_instrument_enable_t_0._fields_ = [
+    ('char1', ctypes.c_uint32, 8),
+    ('char2', ctypes.c_uint32, 8),
+    ('char3', ctypes.c_uint32, 8),
+    ('char4', ctypes.c_uint32, 8),
+]
+
+union_rocprof_trace_decoder_instrument_enable_t._pack_ = 1 # source:False
+union_rocprof_trace_decoder_instrument_enable_t._anonymous_ = ('_0',)
+union_rocprof_trace_decoder_instrument_enable_t._fields_ = [
+    ('_0', struct_rocprof_trace_decoder_instrument_enable_t_0),
+    ('u32All', ctypes.c_uint32),
+]
+
+rocprof_trace_decoder_instrument_enable_t = union_rocprof_trace_decoder_instrument_enable_t
+class union_rocprof_trace_decoder_packet_header_t(Union):
+    pass
+
+class struct_rocprof_trace_decoder_packet_header_t_0(Structure):
+    pass
+
+struct_rocprof_trace_decoder_packet_header_t_0._pack_ = 1 # source:False
+struct_rocprof_trace_decoder_packet_header_t_0._fields_ = [
+    ('opcode', ctypes.c_uint32, 8),
+    ('type', ctypes.c_uint32, 4),
+    ('data20', ctypes.c_uint32, 20),
+]
+
+union_rocprof_trace_decoder_packet_header_t._pack_ = 1 # source:False
+union_rocprof_trace_decoder_packet_header_t._anonymous_ = ('_0',)
+union_rocprof_trace_decoder_packet_header_t._fields_ = [
+    ('_0', struct_rocprof_trace_decoder_packet_header_t_0),
+    ('u32All', ctypes.c_uint32),
+]
+
+rocprof_trace_decoder_packet_header_t = union_rocprof_trace_decoder_packet_header_t
+
+# values for enumeration 'rocprof_trace_decoder_packet_opcode_t'
+rocprof_trace_decoder_packet_opcode_t__enumvalues = {
+    4: 'ROCPROF_TRACE_DECODER_PACKET_OPCODE_CODEOBJ',
+    5: 'ROCPROF_TRACE_DECODER_PACKET_OPCODE_RT_TIMESTAMP',
+    6: 'ROCPROF_TRACE_DECODER_PACKET_OPCODE_AGENT_INFO',
+}
+ROCPROF_TRACE_DECODER_PACKET_OPCODE_CODEOBJ = 4
+ROCPROF_TRACE_DECODER_PACKET_OPCODE_RT_TIMESTAMP = 5
+ROCPROF_TRACE_DECODER_PACKET_OPCODE_AGENT_INFO = 6
+rocprof_trace_decoder_packet_opcode_t = ctypes.c_uint32 # enum
+
+# values for enumeration 'rocprof_trace_decoder_agent_info_type_t'
+rocprof_trace_decoder_agent_info_type_t__enumvalues = {
+    0: 'ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_RT_FREQUENCY_KHZ',
+    1: 'ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_COUNTER_INTERVAL',
+    2: 'ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_LAST',
+}
+ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_RT_FREQUENCY_KHZ = 0
+ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_COUNTER_INTERVAL = 1
+ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_LAST = 2
+rocprof_trace_decoder_agent_info_type_t = ctypes.c_uint32 # enum
+class union_rocprof_trace_decoder_codeobj_marker_tail_t(Union):
+    pass
+
+class struct_rocprof_trace_decoder_codeobj_marker_tail_t_0(Structure):
+    pass
+
+struct_rocprof_trace_decoder_codeobj_marker_tail_t_0._pack_ = 1 # source:False
+struct_rocprof_trace_decoder_codeobj_marker_tail_t_0._fields_ = [
+    ('isUnload', ctypes.c_uint32, 1),
+    ('bFromStart', ctypes.c_uint32, 1),
+    ('legacy_id', ctypes.c_uint32, 30),
+]
+
+union_rocprof_trace_decoder_codeobj_marker_tail_t._pack_ = 1 # source:False
+union_rocprof_trace_decoder_codeobj_marker_tail_t._anonymous_ = ('_0',)
+union_rocprof_trace_decoder_codeobj_marker_tail_t._fields_ = [
+    ('_0', struct_rocprof_trace_decoder_codeobj_marker_tail_t_0),
+    ('raw', ctypes.c_uint32),
+]
+
+rocprof_trace_decoder_codeobj_marker_tail_t = union_rocprof_trace_decoder_codeobj_marker_tail_t
+
+# values for enumeration 'rocprof_trace_decoder_codeobj_marker_type_t'
+rocprof_trace_decoder_codeobj_marker_type_t__enumvalues = {
+    0: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_TAIL',
+    1: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_SIZE_LO',
+    2: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ADDR_LO',
+    3: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ADDR_HI',
+    4: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_SIZE_HI',
+    5: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ID_LO',
+    6: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ID_HI',
+    7: 'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_LAST',
+}
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_TAIL = 0
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_SIZE_LO = 1
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ADDR_LO = 2
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ADDR_HI = 3
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_SIZE_HI = 4
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ID_LO = 5
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ID_HI = 6
+ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_LAST = 7
+rocprof_trace_decoder_codeobj_marker_type_t = ctypes.c_uint32 # enum
+__all__ = \
+    ['ROCPROFILER_THREAD_TRACE_DECODER_INFO_DATA_LOST',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INFO_LAST',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INFO_NONE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INFO_STITCH_INCOMPLETE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INFO_WAVE_INCOMPLETE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_BVH',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_CONTEXT',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_FLAT',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_IMMED',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_JUMP',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_LAST',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_LDS',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_MESSAGE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_NEXT',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_NONE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_SALU',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_SMEM',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_VALU',
+    'ROCPROFILER_THREAD_TRACE_DECODER_INST_VMEM',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_DEBUG',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_GFXIP',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_INFO',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_LAST',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_OCCUPANCY',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_PERFEVENT',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_REALTIME',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_RT_FREQUENCY',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_SHADERDATA',
+    'ROCPROFILER_THREAD_TRACE_DECODER_RECORD_WAVE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_SHADERDATA_FLAGS_IMM',
+    'ROCPROFILER_THREAD_TRACE_DECODER_SHADERDATA_FLAGS_PRIV',
+    'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR',
+    'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_INVALID_ARGUMENT',
+    'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_INVALID_SHADER_DATA',
+    'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_ERROR_OUT_OF_RESOURCES',
+    'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_LAST',
+    'ROCPROFILER_THREAD_TRACE_DECODER_STATUS_SUCCESS',
+    'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_EMPTY',
+    'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_EXEC',
+    'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_IDLE',
+    'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_LAST',
+    'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_STALL',
+    'ROCPROFILER_THREAD_TRACE_DECODER_WSTATE_WAIT',
+    'ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_COUNTER_INTERVAL',
+    'ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_LAST',
+    'ROCPROF_TRACE_DECODER_AGENT_INFO_TYPE_RT_FREQUENCY_KHZ',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ADDR_HI',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ADDR_LO',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ID_HI',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_ID_LO',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_LAST',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_SIZE_HI',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_SIZE_LO',
+    'ROCPROF_TRACE_DECODER_CODEOBJ_MARKER_TYPE_TAIL',
+    'ROCPROF_TRACE_DECODER_PACKET_OPCODE_AGENT_INFO',
+    'ROCPROF_TRACE_DECODER_PACKET_OPCODE_CODEOBJ',
+    'ROCPROF_TRACE_DECODER_PACKET_OPCODE_RT_TIMESTAMP',
+    'c__EA_rocprofiler_thread_trace_decoder_status_t',
+    'rocprof_trace_decoder_agent_info_type_t',
+    'rocprof_trace_decoder_codeobj_marker_tail_t',
+    'rocprof_trace_decoder_codeobj_marker_type_t',
+    'rocprof_trace_decoder_dump_data',
+    'rocprof_trace_decoder_get_info_string',
+    'rocprof_trace_decoder_get_status_string',
+    'rocprof_trace_decoder_gfx9_header_t',
+    'rocprof_trace_decoder_instrument_enable_t',
+    'rocprof_trace_decoder_isa_callback_t',
+    'rocprof_trace_decoder_packet_header_t',
+    'rocprof_trace_decoder_packet_opcode_t',
+    'rocprof_trace_decoder_parse_data',
+    'rocprof_trace_decoder_se_data_callback_t',
+    'rocprof_trace_decoder_trace_callback_t',
+    'rocprofiler_thread_trace_decoder_debug_callback_t',
+    'rocprofiler_thread_trace_decoder_info_t',
+    'rocprofiler_thread_trace_decoder_inst_category_t',
+    'rocprofiler_thread_trace_decoder_inst_t',
+    'rocprofiler_thread_trace_decoder_occupancy_t',
+    'rocprofiler_thread_trace_decoder_pc_t',
+    'rocprofiler_thread_trace_decoder_perfevent_t',
+    'rocprofiler_thread_trace_decoder_realtime_t',
+    'rocprofiler_thread_trace_decoder_record_type_t',
+    'rocprofiler_thread_trace_decoder_shaderdata_flags_t',
+    'rocprofiler_thread_trace_decoder_shaderdata_t',
+    'rocprofiler_thread_trace_decoder_status_t',
+    'rocprofiler_thread_trace_decoder_status_t__enumvalues',
+    'rocprofiler_thread_trace_decoder_wave_state_t',
+    'rocprofiler_thread_trace_decoder_wave_t',
+    'rocprofiler_thread_trace_decoder_wstate_type_t',
+    'struct_rocprof_trace_decoder_codeobj_marker_tail_t_0',
+    'struct_rocprof_trace_decoder_gfx9_header_t_0',
+    'struct_rocprof_trace_decoder_instrument_enable_t_0',
+    'struct_rocprof_trace_decoder_packet_header_t_0',
+    'struct_rocprofiler_thread_trace_decoder_inst_t',
+    'struct_rocprofiler_thread_trace_decoder_occupancy_t',
+    'struct_rocprofiler_thread_trace_decoder_pc_t',
+    'struct_rocprofiler_thread_trace_decoder_perfevent_t',
+    'struct_rocprofiler_thread_trace_decoder_realtime_t',
+    'struct_rocprofiler_thread_trace_decoder_shaderdata_t',
+    'struct_rocprofiler_thread_trace_decoder_wave_state_t',
+    'struct_rocprofiler_thread_trace_decoder_wave_t', 'uint64_t',
+    'union_rocprof_trace_decoder_codeobj_marker_tail_t',
+    'union_rocprof_trace_decoder_gfx9_header_t',
+    'union_rocprof_trace_decoder_instrument_enable_t',
+    'union_rocprof_trace_decoder_packet_header_t']
@@ -43,6 +43,7 @@ enum sqtt_version
   SQTT_VERSION_2_3 = 0x6, /* GFX9 */
   SQTT_VERSION_2_4 = 0x7, /* GFX10+ */
   SQTT_VERSION_3_2 = 0xb, /* GFX11+ */
+   SQTT_VERSION_3_3 = 0xc, /* GFX12+ */
 };

 enum sqtt_file_chunk_type
@@ -144,6 +145,8 @@ enum sqtt_gfxip_level
   SQTT_GFXIP_LEVEL_GFXIP_10_1 = 0x7,
   SQTT_GFXIP_LEVEL_GFXIP_10_3 = 0x9,
   SQTT_GFXIP_LEVEL_GFXIP_11_0 = 0xc,
+   SQTT_GFXIP_LEVEL_GFXIP_11_5 = 0xd,
+   SQTT_GFXIP_LEVEL_GFXIP_12 = 0x10,
 };

 enum sqtt_memory_type
@@ -427,6 +430,8 @@ enum elf_gfxip_level
   EF_AMDGPU_MACH_AMDGCN_GFX1010 = 0x033,
   EF_AMDGPU_MACH_AMDGCN_GFX1030 = 0x036,
   EF_AMDGPU_MACH_AMDGCN_GFX1100 = 0x041,
+   EF_AMDGPU_MACH_AMDGCN_GFX1150 = 0x043,
+   EF_AMDGPU_MACH_AMDGCN_GFX1200 = 0x04e,
 };

 struct sqtt_file_chunk_spm_db {
@@ -1,40 +0,0 @@
-import time
-from extra.optimization.helpers import load_worlds, ast_str_to_ast
-from tinygrad import Device
-from tinygrad.codegen.lowerer import pm_lowerer, get_index
-from tinygrad.uop.ops import graph_rewrite
-from tinygrad.codegen.opt.kernel import Kernel
-from tinygrad.codegen.opt.postrange import Scheduler
-from tinygrad.codegen.opt.heuristic import hand_coded_optimizations
-from tinygrad.helpers import getenv
-
-if __name__ == "__main__":
-  renderer = Device.default.renderer
-  ast_strs = load_worlds()
-  if (n:=getenv("N", -1)) != -1: ast_strs = ast_strs[n:n+1]
-  good = 0
-  for i, ast_str in enumerate(ast_strs):
-    ast = ast_str_to_ast(ast_str)
-
-    st = time.perf_counter()
-    lin = Kernel(ast, renderer)
-    opt1 = hand_coded_optimizations(lin)
-    et_lin = time.perf_counter() - st
-
-    lowered = graph_rewrite(ast, pm_lowerer, ctx=get_index(ast), bottom_up=True)
-    st = time.perf_counter()
-    sch = Scheduler(lowered, renderer)
-    sch.convert_loop_to_global()
-    sch.simplify_merge_adjacent()
-    opt2 = hand_coded_optimizations(sch)
-    et_sch = time.perf_counter() - st
-
-    if opt1 != opt2:
-      print(f"******* {i:6d}")
-      print("Kernel:    ", lin.colored_shape(), "->", lin.apply_opts(opt1).colored_shape())
-      print("Scheduler: ", sch.colored_shape(), "->", sch.apply_opts(opt2).colored_shape())
-      print(opt1)
-      print(opt2)
-    else:
-      good += 1
-      print(f"******* {i:6d} MATCH {good/(i+1)*100:.2f}% -- {et_lin/et_sch:4.2f}x speedup")
@@ -0,0 +1,400 @@
+/**
+ * @file
+ * @brief Basic operations on generic types.
+ */
+
+#pragma once
+
+#include <cuda_bf16.h>
+#include <limits>
+#include "base_types.cuh"
+
+namespace kittens {
+
+/**
+ * @namespace base_ops
+ *
+ * @brief A namespace for operations on basic data types.
+ */
+namespace base_ops {
+
+/* ----------  CONST OPS  ---------- */
+
+/**
+ * @brief Represents the zero constant operation.
+ *
+ * This operation returns the zero value of the specified type.
+ *
+ * @tparam T The data type for which to return the zero value.
+ * @return The zero value of type T.
+ */
+struct zero {
+    template<typename T, typename... args> __device__ static inline constexpr T op(args... _) { return base_types::constants<T>::zero();      }
+};
+/**
+ * @brief Represents the one constant operation.
+ *
+ * This operation returns the one value of the specified type.
+ *
+ * @tparam T The data type for which to return the one value.
+ * @return The one value of type T.
+ */
+struct one {
+    template<typename T, typename... args> __device__ static inline constexpr T op(args... _) { return base_types::constants<T>::one();       }
+};
+/**
+ * @brief Represents the positive infinity constant operation.
+ *
+ * This operation returns the positive infinity value of the specified type.
+ *
+ * @tparam T The data type for which to return the positive infinity value.
+ * @return The positive infinity value of type T.
+ */
+struct pos_infty {
+    template<typename T, typename... args> __device__ static inline constexpr T op(args... _) { return base_types::constants<T>::pos_infty(); }
+};
+/**
+ * @brief Represents the negative infinity constant operation.
+ *
+ * This operation returns the negative infinity value of the specified type.
+ *
+ * @tparam T The data type for which to return the negative infinity value.
+ * @return The negative infinity value of type T.
+ */
+struct neg_infty {
+    template<typename T, typename... args> __device__ static inline constexpr T op(args... _) { return base_types::constants<T>::neg_infty(); }
+};
+
+
+/* ----------  UNARY OPS  ---------- */
+
+/**
+ * @brief Exponential function operation.
+ *
+ * This operation calculates the exponential of the input value.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param x[in] The input value.
+ * @return The exponential of the input value.
+ */
+struct exp {
+    template<typename T> static __device__ inline T op(const T &x) { return exp(x); }
+};
+template<> __device__ inline float  exp::op<float> (const float &x ) { return __expf(x);                        }
+template<> __device__ inline float2 exp::op<float2>(const float2 &x) { return float2{__expf(x.x), __expf(x.y)}; }
+template<> __device__ inline bf16   exp::op<bf16>  (const bf16 &x  ) { return hexp(x);                          }
+template<> __device__ inline bf16_2 exp::op<bf16_2>(const bf16_2 &x) { return h2exp(x);                         }
+template<> __device__ inline half   exp::op<half>  (const half &x  ) { return hexp(x);                          }
+template<> __device__ inline half_2 exp::op<half_2>(const half_2 &x) { return h2exp(x);                         }
+
+/**
+ * @brief Exponential function operation, in base 2
+ *
+ * This operation calculates the exponential of the input value, in base 2.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param x[in] The input value.
+ * @return The exponential of the input value.
+ */
+struct exp2 {
+    template<typename T> static __device__ inline T op(const T &x) { return exp2f(x); }
+};
+template<> __device__ inline float  exp2::op<float> (const float &x ) { return exp2f(x);                        }
+template<> __device__ inline float2 exp2::op<float2>(const float2 &x) { return float2{exp2f(x.x), exp2f(x.y)}; }
+template<> __device__ inline bf16   exp2::op<bf16>  (const bf16 &x  ) { return hexp2(x);                          }
+template<> __device__ inline bf16_2 exp2::op<bf16_2>(const bf16_2 &x) { return h2exp2(x);                         }
+template<> __device__ inline half   exp2::op<half>  (const half &x  ) { return hexp2(x);                          }
+template<> __device__ inline half_2 exp2::op<half_2>(const half_2 &x) { return h2exp2(x);                         }
+/**
+ * @brief Natural log function operation.
+ *
+ * This operation calculates the natural logarithm of the input value.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param x[in] The input value.
+ * @return The natural logarithm of the input value.
+ */
+struct log {
+    template<typename T> static __device__ inline T op(const T &x) { return log(x); }
+};
+template<> __device__ inline float  log::op<float> (const float &x ) { return __logf(x);                        }
+template<> __device__ inline float2 log::op<float2>(const float2 &x) { return float2{__logf(x.x), __logf(x.y)}; }
+template<> __device__ inline bf16   log::op<bf16>  (const bf16 &x  ) { return hlog(x);                          }
+template<> __device__ inline bf16_2 log::op<bf16_2>(const bf16_2 &x) { return h2log(x);                         }
+template<> __device__ inline half   log::op<half>  (const half &x  ) { return hlog(x);                          }
+template<> __device__ inline half_2 log::op<half_2>(const half_2 &x) { return h2log(x);                         }
+/**
+ * @brief Logarithm base 2 operation.
+ *
+ * This operation calculates the logarithm base 2 of the input value.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param x[in] The input value.
+ * @return The logarithm base 2 of the input value.
+ */
+struct log2 {
+    template<typename T> static __device__ inline T op(const T &x) { return log2(x); }
+};
+template<> __device__ inline float  log2::op<float> (const float &x ) { return __log2f(x);                        }
+template<> __device__ inline float2 log2::op<float2>(const float2 &x) { return float2{__log2f(x.x), __log2f(x.y)}; }
+template<> __device__ inline bf16   log2::op<bf16>  (const bf16 &x  ) { return hlog2(x);                          }
+template<> __device__ inline bf16_2 log2::op<bf16_2>(const bf16_2 &x) { return h2log2(x);                         }
+template<> __device__ inline half   log2::op<half>  (const half &x  ) { return hlog2(x);                          }
+template<> __device__ inline half_2 log2::op<half_2>(const half_2 &x) { return h2log2(x);                         }
+/**
+ * @brief Absolute value operation.
+ *
+ * This operation calculates the absolute value of the input.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param x[in] The input value.
+ * @return The absolute value of the input.
+ */
+struct abs {
+    template<typename T> static __device__ inline T op(const T &x) { return abs(x); }
+};
+template<> __device__ inline float  abs::op<float> (const float &x ) { return fabsf(x);                       }
+template<> __device__ inline float2 abs::op<float2>(const float2 &x) { return float2{fabsf(x.x), fabsf(x.y)}; }
+template<> __device__ inline bf16   abs::op<bf16>  (const bf16 &x  ) { return __habs(x);                      }
+template<> __device__ inline bf16_2 abs::op<bf16_2>(const bf16_2 &x) { return __habs2(x);                     }
+template<> __device__ inline half   abs::op<half>  (const half &x  ) { return __habs(x);                      }
+template<> __device__ inline half_2 abs::op<half_2>(const half_2 &x) { return __habs2(x);                     }
+/**
+ * @brief Rectified Linear Unit (ReLU) operation.
+ *
+ * This operation applies the ReLU function to the input, which is the
+ * maximum of zero and the input value.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param x[in] The input value.
+ * @return The result of ReLU function applied to the input.
+ */
+struct relu {
+    template<typename T> static __device__ inline T op(const T &x) { return max(x, base_types::constants<T>::zero()); }
+};
+template<> __device__ inline float  relu::op<float> (const float &x ) { return max(x, 0.f);                                  }
+template<> __device__ inline float2 relu::op<float2>(const float2 &x) { return float2{max(x.x, 0.f), max(x.y, 0.f)};         }
+template<> __device__ inline bf16   relu::op<bf16>  (const bf16 &x  ) { return __hmax(x, base_types::constants<bf16>::zero());    }
+template<> __device__ inline bf16_2 relu::op<bf16_2>(const bf16_2 &x) { return __hmax2(x, base_types::constants<bf16_2>::zero()); }
+template<> __device__ inline half   relu::op<half>  (const half &x  ) { return __hmax(x, base_types::constants<half>::zero());    }
+template<> __device__ inline half_2 relu::op<half_2>(const half_2 &x) { return __hmax2(x, base_types::constants<half_2>::zero()); }
+/**
+ * @brief Copy operation.
+ *
+ * This operation returns the input value unchanged.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The input value.
+ * @return The same value as the input.
+ */
+struct copy { // for non-compile-time setters.
+    template<typename T> static __device__ inline T op(const T &a) { return a; }
+};
+
+
+/* ----------  BINARY OPS  ---------- */
+
+/**
+ * @brief Copy2 operation.
+ *
+ * This operation returns the second input value unchanged.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value (ignored).
+ * @param b[in] The second input value.
+ * @return The same value as the second input.
+ */
+struct copy2 { // this turns out to be a slightly hacky op that makes some code cleaner :/
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return b; }
+};
+/**
+ * @brief Sum operation.
+ *
+ * This operation calculates the sum of two input values.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @return The sum of the input values.
+ */
+struct sum {
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return a+b; }
+};
+template<> __device__ inline float2 sum::op<float2>(const float2 &a, const float2 &b) {
+#ifdef KITTENS_BLACKWELL
+    float2 c;
+    asm volatile("add.f32x2 %0, %1, %2;" : "=l"(*(uint64_t*)&c) : "l"(*(uint64_t*)&a), "l"(*(uint64_t*)&b));
+    return c;
+#else
+    return float2{a.x+b.x, a.y+b.y};
+#endif
+}
+template<> __device__ inline bf16   sum::op<bf16>  (const bf16   &a, const bf16   &b) { return __hadd(a, b);             }
+template<> __device__ inline bf16_2 sum::op<bf16_2>(const bf16_2 &a, const bf16_2 &b) { return __hadd2(a, b);            }
+template<> __device__ inline half   sum::op<half>  (const half   &a, const half   &b) { return __hadd(a, b);             }
+template<> __device__ inline half_2 sum::op<half_2>(const half_2 &a, const half_2 &b) { return __hadd2(a, b);            }
+/**
+ * @brief Subtraction operation.
+ *
+ * This operation calculates the difference between two input values.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @return The difference between the input values.
+ */
+struct sub {
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return a-b; }
+};
+template<> __device__ inline float2 sub::op<float2>(const float2 &a, const float2 &b) { 
+#ifdef KITTENS_BLACKWELL
+    float2 c;
+    asm volatile("sub.f32x2 %0, %1, %2;" : "=l"(*(uint64_t*)&c) : "l"(*(uint64_t*)&a), "l"(*(uint64_t*)&b));
+    return c;
+#else
+    return float2{a.x-b.x, a.y-b.y}; 
+#endif
+}
+template<> __device__ inline bf16   sub::op<bf16>  (const bf16   &a, const bf16   &b) { return __hsub(a, b);             }
+template<> __device__ inline bf16_2 sub::op<bf16_2>(const bf16_2 &a, const bf16_2 &b) { return __hsub2(a, b);            }
+template<> __device__ inline half   sub::op<half>  (const half   &a, const half   &b) { return __hsub(a, b);             }
+template<> __device__ inline half_2 sub::op<half_2>(const half_2 &a, const half_2 &b) { return __hsub2(a, b);            }
+/**
+ * @brief Multiplication operation.
+ *
+ * This operation calculates the product of two input values.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @return The product of the input values.
+ */
+struct mul {
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return a*b; }
+};
+template<> __device__ inline float2 mul::op<float2>(const float2 &a, const float2 &b) { 
+#ifdef KITTENS_BLACKWELL
+    float2 c;
+    asm volatile("mul.f32x2 %0, %1, %2;" : "=l"(*(uint64_t*)&c) : "l"(*(uint64_t*)&a), "l"(*(uint64_t*)&b));
+    return c;
+#else
+    return float2{a.x*b.x, a.y*b.y}; 
+#endif
+}
+template<> __device__ inline bf16   mul::op<bf16>  (const bf16   &a, const bf16   &b) { return __hmul(a, b);             }
+template<> __device__ inline bf16_2 mul::op<bf16_2>(const bf16_2 &a, const bf16_2 &b) { return __hmul2(a, b);            }
+template<> __device__ inline half   mul::op<half>  (const half   &a, const half   &b) { return __hmul(a, b);             }
+template<> __device__ inline half_2 mul::op<half_2>(const half_2 &a, const half_2 &b) { return __hmul2(a, b);            }
+/**
+ * @brief Division operation.
+ *
+ * This operation calculates the quotient of two input values.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @return The quotient of the input values.
+ */
+struct div {
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return a/b; }
+};
+template<> __device__ inline float2 div::op<float2>(const float2 &a, const float2 &b) { return float2{a.x/b.x, a.y/b.y}; }
+template<> __device__ inline bf16   div::op<bf16>  (const bf16   &a, const bf16   &b) { return __hdiv(a, b);             }
+template<> __device__ inline bf16_2 div::op<bf16_2>(const bf16_2 &a, const bf16_2 &b) { return __h2div(a, b);            } // this op is a special snowflake
+template<> __device__ inline half   div::op<half>  (const half   &a, const half   &b) { return __hdiv(a, b);             }
+template<> __device__ inline half_2 div::op<half_2>(const half_2 &a, const half_2 &b) { return __h2div(a, b);            }
+/**
+ * @brief Maximum operation.
+ *
+ * This operation calculates the maximum of two input values.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @return The maximum of the input values.
+ */
+ struct max {
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return ::max(a, b); }
+};
+template<>  __device__ inline float2 max::op<float2>(const float2 &a, const float2 &b) { return float2{::max(a.x, b.x), ::max(a.y, b.y)}; }
+template<>  __device__ inline bf16   max::op<bf16>  (const bf16   &a, const bf16   &b) { return __hmax(a, b);                             }
+template<>  __device__ inline bf16_2 max::op<bf16_2>(const bf16_2 &a, const bf16_2 &b) { return __hmax2(a, b);                            }
+template<>  __device__ inline half   max::op<half>  (const half   &a, const half   &b) { return __hmax(a, b);                             }
+template<>  __device__ inline half_2 max::op<half_2>(const half_2 &a, const half_2 &b) { return __hmax2(a, b);                            }
+/**
+ * @brief Minimum operation.
+ *
+ * This operation calculates the minimum of two input values.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @return The minimum of the input values.
+ */
+struct min {
+    template<typename T> static __device__ inline T op(const T &a, const T &b) { return ::min(a, b); }
+};
+template<>  __device__ inline float2 min::op<float2>(const float2 &a, const float2 &b) { return float2{::min(a.x, b.x), ::min(a.y, b.y)}; }
+template<>  __device__ inline bf16   min::op<bf16>  (const bf16   &a, const bf16   &b) { return __hmin(a, b);                         }
+template<>  __device__ inline bf16_2 min::op<bf16_2>(const bf16_2 &a, const bf16_2 &b) { return __hmin2(a, b);                        }
+template<>  __device__ inline half   min::op<half>  (const half   &a, const half   &b) { return __hmin(a, b);                         }
+template<>  __device__ inline half_2 min::op<half_2>(const half_2 &a, const half_2 &b) { return __hmin2(a, b);                        }
+
+
+/* ----------  TERNARY OPS  ---------- */
+
+/**
+ * @brief Fused multiply-add operation A * B + C.
+ *
+ * This operation performs a fused multiply-add, computing (A * B) + C with only one rounding.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The second input value.
+ * @param c[in] The third input value to be added.
+ * @return The result of the fused multiply-add operation.
+ */
+struct fma_AxBtC {
+    template<typename T> static __device__ inline T op(const T &a, const T &b, const T &c) {
+        return sum::op<T>(mul::op<T>(a, b), c);
+    }
+};
+template<> __device__ inline float2 fma_AxBtC::op<float2>(const float2 &a, const float2 &b, const float2 &c) {
+#ifdef KITTENS_BLACKWELL
+    float2 d;
+    asm volatile("fma.rn.f32x2 %0, %1, %2, %3;" : "=l"(*(uint64_t*)&d) : "l"(*(uint64_t*)&a), "l"(*(uint64_t*)&b), "l"(*(uint64_t*)&c));
+    return d;
+#else
+    return float2{a.x*b.x+c.x, a.y*b.y+c.y};
+#endif
+}
+/**
+ * @brief Fused multiply-add operation A * C + B.
+ *
+ * This operation performs a fused multiply-add, computing (A * C) + B with only one rounding.
+ * This is particularly useful for attention mechanisms in neural networks.
+ *
+ * @tparam T The data type of the input and output values.
+ * @param a[in] The first input value.
+ * @param b[in] The third input value to be added.
+ * @param c[in] The second input value.
+ * @return The result of the fused multiply-add operation.
+ */
+struct fma_AxCtB { // this is the one needed for attention
+    template<typename T> static __device__ inline T op(const T &a, const T &b, const T &c) {
+        return sum::op<T>(mul::op<T>(a, c), b);
+    }
+};
+template<> __device__ inline float2 fma_AxCtB::op<float2>(const float2 &a, const float2 &b, const float2 &c) {
+#ifdef KITTENS_BLACKWELL
+    float2 d;
+    asm volatile("fma.rn.f32x2 %0, %1, %2, %3;" : "=l"(*(uint64_t*)&d) : "l"(*(uint64_t*)&a), "l"(*(uint64_t*)&c), "l"(*(uint64_t*)&b));
+    return d;
+#else
+    return float2{a.x*c.x+b.x, a.y*c.y+b.y};
+#endif
+}
+
+} // namespace base_ops
+
+} // namespace kittens
@@ -0,0 +1,519 @@
+/**
+ * @file
+ * @brief Declarations, manipulations, and wrappers for basic types.
+ * 
+ * This file is a bunch of utilities for going back and forth between different types.
+ * 
+ * Many of them are for the compiler, so as to clean up the code. It unfortunately
+ * seems necessary when we have types we really care about that are less than word width.
+ */
+
+#pragma once
+
+#ifdef KITTENS_HOPPER
+#include <cuda_fp8.h>
+#endif
+
+#include <cuda_bf16.h>
+#include <cuda_fp16.h>
+#include <string>
+#include <bit>
+
+
+namespace kittens {
+
+/**
+ * @brief Bfloat16 floating-point type.
+ */
+using bf16 = __nv_bfloat16;
+/**
+ * @brief Half-precision floating-point type.
+ */
+using half = __half;
+/**
+ * @brief Packed word of two bfloat16 floating-point values.
+ */
+using bf16_2 = __nv_bfloat162;
+/**
+ * @brief Packed word of two half-precision floating-point values.
+ */
+using half_2 = __half2;
+#ifdef KITTENS_HOPPER
+/**
+ * @brief float8 floating-point type.
+ */
+using fp8e4m3 = __nv_fp8_e4m3;
+using fp8e5m2 = __nv_fp8_e5m2;
+#ifdef KITTENS_BLACKWELL
+using fp8e8m0 = __nv_fp8_e8m0;
+#endif
+/**
+ * @brief 2-packed float8 floating-point type.
+ */
+using fp8e4m3_2 = __nv_fp8x2_e4m3;
+using fp8e5m2_2 = __nv_fp8x2_e5m2;
+#ifdef KITTENS_BLACKWELL
+using fp8e8m0_2 = __nv_fp8x2_e8m0;
+#endif
+/**
+ * @brief 4-packed float8 floating-point type.
+ */
+using fp8e4m3_4 = __nv_fp8x4_e4m3;
+using fp8e5m2_4 = __nv_fp8x4_e5m2;
+#ifdef KITTENS_BLACKWELL
+using fp8e8m0_4 = __nv_fp8x4_e8m0;
+#endif
+#endif
+
+namespace ducks {
+/**
+ * @namespace base_types
+ *
+ * @brief A namespace for concepts for basic data types.
+ */
+namespace base_types {
+
+#ifdef KITTENS_HOPPER
+#ifdef KITTENS_BLACKWELL
+template<typename T>
+concept T2 = std::is_same_v<T, float2> || std::is_same_v<T, bf16_2> || std::is_same_v<T, half_2> || std::is_same_v<T, fp8e4m3_4> || std::is_same_v<T, fp8e5m2_4> || std::is_same_v<T, fp8e8m0_4>; // could add half_2 later if implemented.
+template<typename T>
+concept T1 = std::is_same_v<T, float>  || std::is_same_v<T, bf16  > || std::is_same_v<T, half> || std::is_same_v<T, fp8e4m3> || std::is_same_v<T, fp8e5m2> || std::is_same_v<T, fp8e8m0>; // could add half_2 later if implemented.
+#else
+template<typename T>
+concept T2 = std::is_same_v<T, float2> || std::is_same_v<T, bf16_2> || std::is_same_v<T, half_2> || std::is_same_v<T, fp8e4m3_4> || std::is_same_v<T, fp8e5m2_4>;
+template<typename T>
+concept T1 = std::is_same_v<T, float>  || std::is_same_v<T, bf16  > || std::is_same_v<T, half> || std::is_same_v<T, fp8e4m3> || std::is_same_v<T, fp8e5m2>;
+#endif
+#else
+template<typename T>
+concept T2 = std::is_same_v<T, float2> || std::is_same_v<T, bf16_2> || std::is_same_v<T, half_2>;
+template<typename T>
+concept T1 = std::is_same_v<T, float>  || std::is_same_v<T, bf16  > || std::is_same_v<T, half>;
+#endif
+
+} // namespace base_types
+} // namespace ducks
+
+/**
+ * @namespace base_types
+ *
+ * @brief A namespace for ThunderKittens basic data types.
+ */
+namespace base_types {
+
+/**
+ * @brief Provides compile-time constants for different types.
+ *
+ * @tparam T The type for which to provide constants.
+ */
+template<typename T> struct constants {
+    /**
+     * @brief Zero
+     * @return Constexpr zero with type T
+     */
+    static __device__ inline constexpr T zero()      { return T{0}; }
+    /**
+     * @brief One
+     * @return Constexpr one with type T
+     */
+    static __device__ inline constexpr T one()       { return T{1}; }
+    /**
+     * @brief Positive infinity. Particularly useful for initializing before a min op.
+     * @return Constexpr positive infinity with type T
+     */
+    static __device__ inline constexpr T pos_infty() { return T{INFINITY}; } // I'll find a better way at some point but this appears to work.
+    /**
+     * @brief Negative infinity. Particularly useful for initializing before a max op.
+     * @return Constexpr negative infinity with type T
+     */
+    static __device__ inline constexpr T neg_infty() { return T{-INFINITY}; }
+};
+template<> struct constants<float2> {
+    static __device__ inline constexpr float2 zero()      { return float2{0.f, 0.f}; }
+    static __device__ inline constexpr float2 one()       { return float2{1.f, 1.f}; }
+    static __device__ inline constexpr float2 pos_infty() { return float2{constants<float>::pos_infty(), constants<float>::pos_infty()}; }
+    static __device__ inline constexpr float2 neg_infty() { return float2{constants<float>::neg_infty(), constants<float>::neg_infty()}; }
+};
+template<> struct constants<bf16> {
+    static __device__ inline constexpr bf16 zero()      { return std::bit_cast<__nv_bfloat16>(uint16_t(0x0000)); } // unfortunately __float2bf16_rn is not constexpr
+    static __device__ inline constexpr bf16 one()       { return std::bit_cast<__nv_bfloat16>(uint16_t(0x3F80)); }
+    static __device__ inline constexpr bf16 pos_infty() { return std::bit_cast<__nv_bfloat16>(uint16_t(0x7F80)); }
+    static __device__ inline constexpr bf16 neg_infty() { return std::bit_cast<__nv_bfloat16>(uint16_t(0xFF80)); }
+};
+template<> struct constants<bf16_2> {
+    static __device__ inline constexpr bf16_2 zero()      { return bf16_2{constants<bf16>::zero(),      constants<bf16>::zero()};      }
+    static __device__ inline constexpr bf16_2 one()       { return bf16_2{constants<bf16>::one(),       constants<bf16>::one()};       }
+    static __device__ inline constexpr bf16_2 pos_infty() { return bf16_2{constants<bf16>::pos_infty(), constants<bf16>::pos_infty()}; }
+    static __device__ inline constexpr bf16_2 neg_infty() { return bf16_2{constants<bf16>::neg_infty(), constants<bf16>::neg_infty()}; }
+};
+template<> struct constants<half> {
+    static __device__ inline constexpr half zero()      { return std::bit_cast<__half>(uint16_t(0x0000)); }
+    static __device__ inline constexpr half one()       { return std::bit_cast<__half>(uint16_t(0x3C00)); }
+    static __device__ inline constexpr half pos_infty() { return std::bit_cast<__half>(uint16_t(0x7C00)); }
+    static __device__ inline constexpr half neg_infty() { return std::bit_cast<__half>(uint16_t(0xFC00)); }
+};
+template<> struct constants<half_2> {
+    static __device__ inline constexpr half_2 zero()      { return half_2{constants<half>::zero(),      constants<half>::zero()};      }
+    static __device__ inline constexpr half_2 one()       { return half_2{constants<half>::one(),       constants<half>::one()};       }
+    static __device__ inline constexpr half_2 pos_infty() { return half_2{constants<half>::pos_infty(), constants<half>::pos_infty()}; }
+    static __device__ inline constexpr half_2 neg_infty() { return half_2{constants<half>::neg_infty(), constants<half>::neg_infty()}; }
+};
+#ifdef KITTENS_HOPPER
+template<> struct constants<fp8e4m3> {
+    static __device__ inline constexpr fp8e4m3 zero() { return std::bit_cast<__nv_fp8_e4m3>(uint8_t(0x00)); }
+    static __device__ inline constexpr fp8e4m3 one() { return std::bit_cast<__nv_fp8_e4m3>(uint8_t(0x38)); }
+};
+template<> struct constants<fp8e4m3_2> {
+    static __device__ inline constexpr fp8e4m3_2 zero() { return std::bit_cast<fp8e4m3_2>(uint16_t(0x0000)); }
+    static __device__ inline constexpr fp8e4m3_2 one() { return std::bit_cast<fp8e4m3_2>(uint16_t(0x3838)); }
+};
+template<> struct constants<fp8e4m3_4> {
+    static __device__ inline constexpr fp8e4m3_4 zero() { return std::bit_cast<fp8e4m3_4>(uint32_t(0x00000000)); }
+    static __device__ inline constexpr fp8e4m3_4 one() { return std::bit_cast<fp8e4m3_4>(uint32_t(0x38383838)); }
+};
+template<> struct constants<fp8e5m2> {
+    static __device__ inline constexpr fp8e5m2 zero() { return std::bit_cast<__nv_fp8_e5m2>(uint8_t(0x00)); }
+    static __device__ inline constexpr fp8e5m2 one() { return std::bit_cast<__nv_fp8_e5m2>(uint8_t(0x3C)); }
+};
+template<> struct constants<fp8e5m2_2> {
+    static __device__ inline constexpr fp8e5m2_2 zero() { return std::bit_cast<fp8e5m2_2>(uint16_t(0x0000)); }
+    static __device__ inline constexpr fp8e5m2_2 one() { return std::bit_cast<fp8e5m2_2>(uint16_t(0x3C3C)); }
+};
+template<> struct constants<fp8e5m2_4> {
+    static __device__ inline constexpr fp8e5m2_4 zero() { return std::bit_cast<fp8e5m2_4>(uint32_t(0x00000000)); }
+    static __device__ inline constexpr fp8e5m2_4 one() { return std::bit_cast<fp8e5m2_4>(uint32_t(0x3C3C3C3C)); }
+};
+#endif
+
+template<> struct constants<int> {
+    static __device__ inline constexpr int zero()      { return 0; }
+    static __device__ inline constexpr int one()       { return 1; }
+};
+template<> struct constants<int2> {
+    static __device__ inline constexpr int2 zero()      { return int2{0, 0}; }
+    static __device__ inline constexpr int2 one()       { return int2{1, 1}; }
+};
+
+/**
+ * @brief Provides information about packing of elements for a given type.
+ *
+ * @tparam T The type for which to provide packing information.
+ */
+template<typename T> struct packing {
+    /**
+     * @brief The number of elements packed together.
+     *
+     * @return constexpr int representing number of elements within the type.
+     */
+    static __device__ inline constexpr int num() { return 1; }
+    /**
+     * @brief Packs a single T element twice (replicated) into its packed type.
+     *
+     * @param i[in] The element to pack.
+     * @return The packed type.
+     */
+    static __device__ inline constexpr T pack(const bf16 &i);
+};
+template<> struct packing<bf16> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = bf16;
+    using packed_type = bf16_2;
+    static __device__ inline constexpr bf16_2 pack(const bf16 &i) { return bf16_2{i, i}; }
+};
+template<> struct packing<bf16_2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = bf16;
+    using packed_type = bf16_2;
+    static __device__ inline constexpr bf16_2 pack(const bf16 &i) { return bf16_2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<half> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = half;
+    using packed_type = half_2;
+    static __device__ inline constexpr half_2 pack(const half &i) { return half_2{i, i}; }
+};
+template<> struct packing<half_2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = half;
+    using packed_type = half_2;
+    static __device__ inline constexpr half_2 pack(const half &i) { return half_2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<float> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = float;
+    using packed_type = float2;
+    static __device__ inline constexpr float2 pack(const float &i) { return float2{i, i}; }
+};
+template<> struct packing<float2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = float;
+    using packed_type = float2;
+    static __device__ inline constexpr float2 pack(const float &i) { return float2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<char> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = char;
+    using packed_type = char2;
+    static __device__ inline constexpr char2 pack(const char &i) { return char2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<char2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = char;
+    using packed_type = char2;
+    static __device__ inline constexpr char2 pack(const char &i) { return char2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<int> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = int;
+    using packed_type = int2;
+    static __device__ inline constexpr int2 pack(const int &i) { return int2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<int2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = int;
+    using packed_type = int2;
+    static __device__ inline constexpr int2 pack(const int &i) { return int2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<uint> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = uint;
+    using packed_type = uint2;
+    static __device__ inline constexpr uint2 pack(const uint &i) { return uint2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<uint2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = uint;
+    using packed_type = uint2;
+    static __device__ inline constexpr uint2 pack(const uint &i) { return uint2{i, i}; } // this replication makes code cleaner later.
+};
+struct uint64_2 { uint64_t x, y; };
+template<> struct packing<uint64_t> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = uint64_t;
+    using packed_type = uint64_2;
+    static __device__ inline constexpr uint64_2 pack(const uint64_t &i) { return uint64_2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<uint64_2> {
+    static __device__ inline constexpr int num() { return 2; }
+    using unpacked_type = uint64_t;
+    using packed_type = uint64_2;
+    static __device__ inline constexpr uint64_2 pack(const uint64_t &i) { return uint64_2{i, i}; } // this replication makes code cleaner later.
+};
+template<> struct packing<float4> {
+    static __device__ inline constexpr int num() { return 4; }
+};
+template<> struct packing<int4> {
+    static __device__ inline constexpr int num() { return 4; }
+};
+#ifdef KITTENS_HOPPER
+template<> struct packing<fp8e4m3> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = fp8e4m3;
+    using packed_type = fp8e4m3_4;
+};
+template<> struct packing<fp8e4m3_4> {
+    static __device__ inline constexpr int num() { return 4; }
+    using unpacked_type = fp8e4m3;
+    using packed_type = fp8e4m3_4;
+};
+template<> struct packing<fp8e5m2> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = fp8e5m2;
+    using packed_type = fp8e5m2_4;
+};
+template<> struct packing<fp8e5m2_4> {
+    static __device__ inline constexpr int num() { return 4; }
+    using unpacked_type = fp8e5m2;
+    using packed_type = fp8e5m2_4;
+};
+#ifdef KITTENS_BLACKWELL
+template<> struct packing<fp8e8m0> {
+    static __device__ inline constexpr int num() { return 1; }
+    using unpacked_type = fp8e8m0;
+    using packed_type = fp8e8m0_4;
+};
+template<> struct packing<fp8e8m0_4> {
+    static __device__ inline constexpr int num() { return 4; }
+    using unpacked_type = fp8e8m0;
+    using packed_type = fp8e8m0_4;
+};
+#endif
+#endif
+
+
+/**
+ * @brief Provides templated functionality to convert between different types.
+ *
+ * @tparam T The target type for conversion.
+ * @tparam U The source type for conversion.
+ */
+template<typename T, typename U> struct convertor {
+    /**
+     * @brief Converts a value of type U to type T.
+     *
+     * @param u[in] The value of type U to convert.
+     * @return T The converted value of type T.
+     */
+    static __host__ __device__ inline T convert(const U & u) {
+        return (T)u;
+    }
+};
+template<> struct convertor<float, bf16> {
+    static __host__ __device__ inline float convert(const bf16 & u) {
+        return 	__bfloat162float(u);
+    }
+};
+template<> struct convertor<bf16, float> {
+    static __host__ __device__ inline bf16 convert(const float & u) {
+        return 	__float2bfloat16_rn(u);
+    }
+};
+template<> struct convertor<float2, bf16_2> {
+    static __host__ __device__ inline float2 convert(const bf16_2 & u) {
+        return 	__bfloat1622float2(u);
+    }
+};
+template<> struct convertor<bf16_2, float2> {
+    static __host__ __device__ inline bf16_2 convert(const float2 & u) {
+        return 	__float22bfloat162_rn(u);
+    }
+};
+template<> struct convertor<float, half> {
+    static __host__ __device__ inline float convert(const half & u) {
+        return __half2float(u);
+    }
+};
+template<> struct convertor<half, float> {
+    static __host__ __device__ inline half convert(const float & u) {
+        return __float2half(u);
+    }
+};
+template<> struct convertor<float2, half_2> {
+    static __host__ __device__ inline float2 convert(const half_2 & u) {
+        return __half22float2(u);
+    }
+};
+template<> struct convertor<half_2, float2> {
+    static __host__ __device__ inline half_2 convert(const float2 & u) {
+        return __float22half2_rn(u);
+    }
+};
+template<> struct convertor<bf16, half> {
+    static __host__ __device__ inline bf16 convert(const half & u) {
+        return __float2bfloat16_rn(__half2float(u));
+    }
+};
+template<> struct convertor<half, bf16> {
+    static __host__ __device__ inline half convert(const bf16 & u) {
+        return __float2half(__bfloat162float(u));
+    }
+};
+template<> struct convertor<bf16_2, half_2> {
+    static __host__ __device__ inline bf16_2 convert(const half_2 & u) {
+        return __float22bfloat162_rn(__half22float2(u));
+    }
+};
+template<> struct convertor<half_2, bf16_2> {
+    static __host__ __device__ inline half_2 convert(const bf16_2 & u) {
+        return __float22half2_rn(__bfloat1622float2(u));
+    }
+};
+#ifdef KITTENS_HOPPER
+// fp8e4m3
+template<> struct convertor<fp8e4m3_4, float4> {
+    static __host__ __device__ inline fp8e4m3_4 convert(const float4& u) {
+        return __nv_fp8x4_e4m3(u); 
+    }
+};
+template<> struct convertor<float4, fp8e4m3_4> {
+    static __host__ __device__ inline float4 convert(const fp8e4m3_4& u) {
+        __nv_fp8_e4m3 *vals = reinterpret_cast<__nv_fp8_e4m3*>(const_cast<__nv_fp8x4_e4m3*>(&u));
+        return make_float4(float(vals[0]), float(vals[1]), float(vals[2]), float(vals[3]));
+    }
+};
+template<> struct convertor<fp8e4m3_2, float2> {
+    static __host__ __device__ inline fp8e4m3_2 convert(const float2& u) {
+        return __nv_fp8x2_e4m3(u); 
+    }
+};
+template<> struct convertor<float2, fp8e4m3_2> {
+    static __host__ __device__ inline float2 convert(const fp8e4m3_2& u) {
+        __nv_fp8_e4m3 *vals = reinterpret_cast<__nv_fp8_e4m3*>(const_cast<__nv_fp8x2_e4m3*>(&u));
+        return make_float2(float(vals[0]), float(vals[1]));
+    }
+};
+template<> struct convertor<fp8e4m3, float> {
+    static __host__ __device__ inline fp8e4m3 convert(const float & u) {
+        return __nv_fp8_e4m3(u);
+    }
+};
+template<> struct convertor<float, fp8e4m3> {
+    static __host__ __device__ inline float convert(const fp8e4m3 & u) {
+        return float(u);
+    }
+};
+template<> struct convertor<bf16_2, fp8e4m3_4> {
+    static __host__ __device__ inline bf16_2 convert(const fp8e4m3_4 & u) {
+        float4 f4 = convertor<float4, fp8e4m3_4>::convert(u);
+        float2 f2 = make_float2(f4.x, f4.y);
+        return __float22bfloat162_rn(f2);
+    }
+};
+template<> struct convertor<fp8e4m3_4, bf16_2> {
+    static __host__ __device__ inline fp8e4m3_4 convert(const bf16_2 & u) {
+        float2 f2 = __bfloat1622float2(u);
+        float4 f4 = make_float4(f2.x, f2.y, 0.0f, 0.0f);
+        return __nv_fp8x4_e4m3(f4);
+    }
+};
+// fp8e5m2
+template<> struct convertor<fp8e5m2_4, float4> {
+    static __host__ __device__ inline fp8e5m2_4 convert(const float4& u) {
+        return __nv_fp8x4_e5m2(u); 
+    }
+};
+template<> struct convertor<float4, fp8e5m2_4> {
+    static __host__ __device__ inline float4 convert(const fp8e5m2_4& u) {
+        __nv_fp8_e5m2 *vals = reinterpret_cast<__nv_fp8_e5m2*>(const_cast<__nv_fp8x4_e5m2*>(&u));
+        return make_float4(float(vals[0]), float(vals[1]), float(vals[2]), float(vals[3]));
+    }
+};
+template<> struct convertor<fp8e5m2_2, float2> {
+    static __host__ __device__ inline fp8e5m2_2 convert(const float2& u) {
+        return __nv_fp8x2_e5m2(u); 
+    }
+};
+template<> struct convertor<float2, fp8e5m2_2> {
+    static __host__ __device__ inline float2 convert(const fp8e5m2_2& u) {
+        __nv_fp8_e5m2 *vals = reinterpret_cast<__nv_fp8_e5m2*>(const_cast<__nv_fp8x2_e5m2*>(&u));
+        return make_float2(float(vals[0]), float(vals[1]));
+    }
+};
+template<> struct convertor<fp8e5m2, float> {
+    static __host__ __device__ inline fp8e5m2 convert(const float & u) {
+        return __nv_fp8_e5m2(u);
+    }
+};
+template<> struct convertor<float, fp8e5m2> {
+    static __host__ __device__ inline float convert(const fp8e5m2 & u) {
+        return float(u);
+    }
+};
+template<> struct convertor<bf16_2, fp8e5m2_4> {
+    static __host__ __device__ inline bf16_2 convert(const fp8e5m2_4 & u) {
+        float4 f4 = convertor<float4, fp8e5m2_4>::convert(u);
+        float2 f2 = make_float2(f4.x, f4.y);
+        return __float22bfloat162_rn(f2);
+    }
+};
+template<> struct convertor<fp8e5m2_4, bf16_2> {
+    static __host__ __device__ inline fp8e5m2_4 convert(const bf16_2 & u) {
+        float2 f2 = __bfloat1622float2(u);
+        float4 f4 = make_float4(f2.x, f2.y, 0.0f, 0.0f);
+        return __nv_fp8x4_e5m2(f4);
+    }
+};
+#endif
+}
+}
@@ -0,0 +1,11 @@
+/**
+ * @file
+ * @brief A collection of common resources on which ThunderKittens depends.
+ */
+ 
+
+#pragma once
+
+#include "util.cuh"
+#include "base_types.cuh"
+#include "base_ops.cuh"
@@ -0,0 +1,56 @@
+#pragma once
+
+// Reset
+#define TK_RESET "\033[0m"
+
+// Foreground colors
+#define TK_FG_BLACK "\033[30m"
+#define TK_FG_RED "\033[31m"
+#define TK_FG_GREEN "\033[32m"
+#define TK_FG_YELLOW "\033[33m"
+#define TK_FG_BLUE "\033[34m"
+#define TK_FG_MAGENTA "\033[35m"
+#define TK_FG_CYAN "\033[36m"
+#define TK_FG_WHITE "\033[37m"
+
+// Background colors
+#define TK_BG_BLACK "\033[40m"
+#define TK_BG_RED "\033[41m"
+#define TK_BG_GREEN "\033[42m"
+#define TK_BG_YELLOW "\033[43m"
+#define TK_BG_BLUE "\033[44m"
+#define TK_BG_MAGENTA "\033[45m"
+#define TK_BG_CYAN "\033[46m"
+#define TK_BG_WHITE "\033[47m"
+
+// Bright foreground colors
+#define TK_FG_BRIGHT_BLACK "\033[90m"
+#define TK_FG_BRIGHT_RED "\033[91m"
+#define TK_FG_BRIGHT_GREEN "\033[92m"
+#define TK_FG_BRIGHT_YELLOW "\033[93m"
+#define TK_FG_BRIGHT_BLUE "\033[94m"
+#define TK_FG_BRIGHT_MAGENTA "\033[95m"
+#define TK_FG_BRIGHT_CYAN "\033[96m"
+#define TK_FG_BRIGHT_WHITE "\033[97m"
+
+// Bright background colors
+#define TK_BG_BRIGHT_BLACK "\033[100m"
+#define TK_BG_BRIGHT_RED "\033[101m"
+#define TK_BG_BRIGHT_GREEN "\033[102m"
+#define TK_BG_BRIGHT_YELLOW "\033[103m"
+#define TK_BG_BRIGHT_BLUE "\033[104m"
+#define TK_BG_BRIGHT_MAGENTA "\033[105m"
+#define TK_BG_BRIGHT_CYAN "\033[106m"
+#define TK_BG_BRIGHT_WHITE "\033[107m"
+
+// Text styles
+#define TK_BOLD "\033[1m"
+#define TK_DIM "\033[2m"
+#define TK_ITALIC "\033[3m"
+#define TK_UNDERLINE "\033[4m"
+#define TK_BLINK "\033[5m"
+#define TK_REVERSE "\033[7m"
+#define TK_HIDDEN "\033[8m"
+
+// Macro to combine styles
+#define TK_STYLE(...)  "\033[" #__VA_ARGS__ "m"
@@ -0,0 +1,314 @@
+/**
+ * @file
+ * @brief General utilities for ThunderKittens.
+ */
+
+#pragma once
+
+#include <stdint.h>
+#include <type_traits>
+#include <concepts>
+#include <memory>
+
+// CUDA driver API
+#define CUCHECK(cmd) do {                                     \
+    CUresult err = cmd;                                       \
+    if (err != CUDA_SUCCESS) {                                \
+        const char *errStr;                                   \
+        cuGetErrorString(err, &errStr);                       \
+        fprintf(stderr, "Failed: CUDA error %s:%d '%s'\n",    \
+            __FILE__, __LINE__, errStr);                      \
+        exit(EXIT_FAILURE);                                   \
+    }                                                         \
+} while(0)
+
+// CUDA runtime API
+#define CUDACHECK(cmd) do {                                   \
+    cudaError_t err = cmd;                                    \
+    if (err != cudaSuccess) {                                 \
+        fprintf(stderr, "Failed: CUDA error %s:%d '%s'\n",    \
+            __FILE__, __LINE__, cudaGetErrorString(err));     \
+        exit(EXIT_FAILURE);                                   \
+    }                                                         \
+} while(0)
+
+/**
+ * @namespace kittens
+ *
+ * @brief The main namespace of ThunderKittens.
+ */
+namespace kittens {
+
+/* ----------  GENERAL CONSTANTS FOR KITTENS  ---------- */
+
+/**
+ * @brief Tile dimension constant.
+ */
+template<typename T> constexpr int TILE_COL_DIM = sizeof(T) == 1 ? 32 : 16;
+template<typename T> constexpr int TILE_ROW_DIM = 16;
+/**
+ * @brief Tile num elements constant calculated as TILE_DIM squared.
+ */
+template<typename T> constexpr int TILE_ELEMENTS{TILE_COL_DIM<T>*TILE_ROW_DIM<T>};
+/**
+ * @brief Constant representing number of threads in a warp.
+ */
+constexpr int WARP_THREADS{32};
+/**
+ * @brief Constant representing number of threads in a warpgroup of four warps.
+ */
+constexpr int WARPGROUP_THREADS{128};
+/**
+
+ * @brief Constant representing number of warps in a warpgroup of four warps.
+ */
+constexpr int WARPGROUP_WARPS{4};
+/**
+
+ * @brief Get the warp ID of the current thread.
+ * @return The warp ID.
+ */
+__device__ static __forceinline__ int warpid() {
+    // uint32_t wid;
+    // asm volatile("mov.u32 %0, %warpid;" : "=r"(wid));
+    // return wid;
+    return threadIdx.x >> 5;
+}
+/**
+ * @brief Get the warpgroup ID of the current thread.
+ * @return The warpgroup ID.
+ */
+__device__ static __forceinline__ int warpgroupid() { return warpid() >> 2; }
+/**
+ * @brief Get the lane ID of the current thread within its warp.
+ * @return The lane ID.
+ */
+__device__ static __forceinline__ int laneid() {
+    // uint32_t lid;
+    // asm volatile("mov.u32 %0, %laneid;" : "=r"(lid));
+    // return lid;
+    return threadIdx.x & 31;
+}
+
+#if defined(KITTENS_HOPPER)
+constexpr int MAX_SHARED_MEMORY = 227000;
+#elif defined(KITTENS_A100)
+constexpr int MAX_SHARED_MEMORY = 164000;
+#elif defined(KITTENS_4090)
+constexpr int MAX_SHARED_MEMORY = 100000;
+#endif
+
+struct transpose {
+    static constexpr int N = 0; // not transposed
+    static constexpr int T = 1; // transposed
+};
+struct axis {
+    static constexpr int ROW = 0; // row axis of a tile
+    static constexpr int COL = 1; // column axis of a tile
+};
+
+/* ----------  TYPE HELPERS  ---------- */
+
+/**
+ * @namespace ducks
+ *
+ * @brief ThunderKittens' namespace for template metaprogramming..
+ * 
+ * This includes primarily dummy types and concept wrappers, along
+ * with a few additional utilities.
+ */
+namespace ducks {
+
+/**
+ * @brief A type representing an empty default for a template.
+ */
+struct default_type {};
+
+// This macro can't be done as a template, so it doesn't really have a location in kittens.
+#define typeof(A) typename std::remove_const<typename std::remove_reference<decltype(A)>::type>::type
+
+}
+
+/* ----------  SHUFFLE UTILS  ---------- */
+
+/**
+ * @brief Mask constant for all active threads in a warp.
+ */
+static constexpr uint32_t MASK_ALL = 0xFFFFFFFF;
+
+/**
+ * @brief Perform a shuffle down operation on a packed type synchronously across a warp.
+ * @tparam T The type of the value to be shuffled.
+ * @param mask[in] The mask of active threads.
+ * @param f[in] The value to be shuffled.
+ * @param delta[in] The number of positions to shuffle down.
+ * @return The result of the shuffle operation.
+ */
+template<typename T>
+__device__ static inline T packed_shfl_down_sync(uint32_t mask, const T &f, int delta) {
+    return __shfl_down_sync(mask, f, delta);
+}
+template<>
+__device__ inline float2 packed_shfl_down_sync<float2>(uint32_t mask, const float2 &f, int delta) {
+    float2 r;
+    r.x = __shfl_down_sync(mask, f.x, delta);
+    r.y = __shfl_down_sync(mask, f.y, delta);
+    return r;
+}
+/**
+ * @brief Perform a packed shuffle operation synchronously across a warp.
+ * @tparam T The type of the value to be shuffled.
+ * @param mask[in] The mask of active threads.
+ * @param f[in] The value to be shuffled.
+ * @param src[in] The source lane from which to shuffle.
+ * @return The result of the shuffle operation.
+ */
+template<typename T>
+__device__ static inline T packed_shfl_sync(uint32_t mask, const T &f, int src) {
+    return __shfl_sync(mask, f, src);
+}
+template<>
+__device__ inline float2 packed_shfl_sync<float2>(uint32_t mask, const float2 &f, int src) {
+    float2 r;
+    r.x = __shfl_sync(mask, f.x, src);
+    r.y = __shfl_sync(mask, f.y, src);
+    return r;
+}
+
+/* ----------  SHARED MEMORY UTILS  ---------- */
+
+// namespace ducks {
+// namespace sb {
+// struct identifier {};
+// }
+// }
+
+// template<typename Args...>
+// struct sb {
+//     using identifier = ducks::sb::identifier;
+//     Args... args;
+// };
+
+// namespace ducks {
+// namespace sb {
+// template<typename T> concept all = requires {
+//     typename T::identifier;
+// } && std::is_same_v<T::identifier, identifier>;
+// }
+// }
+
+// Joyously stolen from https://github.com/NVIDIA/cutlass/blob/5c447dd84f8ae0e1d48ff9a2eae26ce8c4958101/include/cute/container/alignment.hpp#L51
+#if defined(__CUDACC__)
+#define KITTENS_ALIGN_AS(n) __align__(n)
+#else
+#define KITTENS_ALIGN_AS(n) alignas(n)
+#endif
+
+#ifdef KITTENS_HOPPER
+#define KITTENS_DEFAULT_ALIGN KITTENS_ALIGN_AS(128)
+#else
+#define KITTENS_DEFAULT_ALIGN KITTENS_ALIGN_AS(16)
+#endif
+
+/**
+ * @brief Dummy structure for alignment purposes. Needed for WGMMA and TMA calls.
+ */
+struct KITTENS_DEFAULT_ALIGN alignment_dummy { int dummy; };
+/**
+ * @brief Very simple allocator for dynamic shared memory. Advances pointer and tracks alignments.
+ * @tparam default_alignment The default alignment this allocator will enforce. If <=0 (default -1) it will not align.
+ */
+#ifdef KITTENS_HOPPER
+template<int default_alignment=1024> 
+#else
+template<int default_alignment=16> 
+#endif
+struct shared_allocator {
+    int *ptr;
+
+    private:
+        // Recursive template to generate N-dimensional array type
+        template<typename A, size_t... dims>
+        struct variadic_array;
+        template<typename A, size_t first_dim, size_t... rest_dims>
+        struct variadic_array<A, first_dim, rest_dims...> {
+            using type = typename variadic_array<A, rest_dims...>::type[first_dim];
+        };
+        template<typename A>
+        struct variadic_array<A> {
+            using type = A;
+        };
+        template<typename A, size_t... dims> 
+        using variadic_array_t = typename variadic_array<A, dims...>::type;
+
+        template<int alignment>
+        __device__ inline void align_ptr() {
+            if constexpr (alignment > 0) {
+                uint64_t p = reinterpret_cast<uint64_t>(ptr);
+                if(p % alignment != 0) {
+                    ptr = (int*)(p + (alignment-(p%alignment)));
+                }
+            }
+        }
+
+    public:
+        /**
+        * @brief Construct a new shared allocator using a pointer to extern shared memory.
+        * @param[in] _ptr Pointer to the start of the extern shared memory.
+        */
+        __device__ shared_allocator(int *_ptr): ptr(_ptr) {}
+        /**
+        * @brief Allocate shared memory for a single instance or N-dimensional array of type A.
+        * @tparam A The type of the object to allocate.
+        * @tparam dims... A list of dimensions for the N-dimensional array.
+        * @return Reference to the allocated object.
+        */
+        template<typename A, size_t... dims> 
+        __device__ inline variadic_array_t<A, dims...>& allocate() {
+            // static_assert(sizeof(A) % default_alignment == 0, "Type is not aligned properly for array allocation");
+            align_ptr<default_alignment>();
+            using at = variadic_array_t<A, dims...>;
+            at*p = reinterpret_cast<at*>(ptr);
+            ptr += sizeof(at)/sizeof(int);
+            return *p;
+        }
+        /**
+        * @brief Allocate shared memory for a single instance or N-dimensional array of type A.
+        * @tparam alignment An alignment to enforce for this particular object.
+        * @tparam A The type of the object to allocate.
+        * @tparam dims... A list of dimensions for the N-dimensional array.
+        * @return Reference to the allocated object.
+        */
+        template<int alignment, typename A, size_t... dims> 
+        __device__ inline variadic_array_t<A, dims...>& allocate() {
+            // static_assert(sizeof(A) % alignment == 0, "Type is not aligned properly for array allocation");
+            align_ptr<alignment>();
+            using at = variadic_array_t<A, dims...>;
+            at*p = reinterpret_cast<at*>(ptr);
+            ptr += sizeof(at)/sizeof(int);
+            return *p;
+        }
+};
+#if (defined(KITTENS_HOPPER) || defined(KITTENS_BLACKWELL))
+/**
+ * @brief A wrapper for an allocator that enforces sufficient alignment to be used for TMA loads and stores.
+ */
+using tma_allocator = shared_allocator<1024>;
+using tma_swizzle_allocator = tma_allocator; // swizzled TMA modes require up to 1024 byte alignments :/
+
+/* Get CTA ID within a cluster */
+__device__ static inline int3 clusterIdx() {
+    int3 cluster_idx;
+    asm volatile("mov.u32 %0, %clusterid.x;\n" : "=r"(cluster_idx.x));
+    asm volatile("mov.u32 %0, %clusterid.y;\n" : "=r"(cluster_idx.y));
+    asm volatile("mov.u32 %0, %clusterid.z;\n" : "=r"(cluster_idx.z));
+    return cluster_idx;
+}
+__device__ static inline int cluster_ctarank() {
+    uint32_t ctarank;
+    asm volatile("mov.u32 %0, %cluster_ctarank;\n" : "=r"(ctarank));
+    return ctarank;
+}
+#endif
+
+} // namespace kittens
@@ -0,0 +1,12 @@
+/**
+ * @file
+ * @brief The master header file of ThunderKittens. This file includes everything you need!
+ */
+
+#pragma once
+
+#include "common/common.cuh"
+#include "types/types.cuh"
+#include "ops/ops.cuh"
+#include "pyutils/util.cuh"
+// #include "pyutils/pyutils.cuh" // for simple binding without including torch
@@ -0,0 +1,51 @@
+/**
+ * @file
+ * @brief An aggregate header of all device (multi-GPU) operations defined by ThunderKittens
+ */
+
+#pragma once
+
+#include "../../types/types.cuh"
+
+namespace kittens {
+
+template<int _NUM_DEVICES>
+struct device {
+
+static_assert(_NUM_DEVICES >= 0 && _NUM_DEVICES <= 72, "Invalid number of devices");
+static constexpr int NUM_DEVICES = _NUM_DEVICES;
+
+#ifdef KITTENS_HOPPER
+
+using barrier_t = pgl<gl<int, 1, 1, 1, -1>, NUM_DEVICES, true>;
+
+/**
+ * @brief Multi-GPU synchronization barrier for coordinated kernel exit
+ * 
+ * Performs a synchronization across all devices to ensure all GPUs complete 
+ * their work before any kernel exits. Does not synchronize intra-node threads
+ * or threadblocks.
+ * 
+ * @param barrier Pre-allocated barrier structure, must be initialized to 0
+ * @param dev_idx Current device index (0 to NUM_DEVICES - 1)
+ * @param id Synchronization point identifier (default: 0). 0 is fine for most cases
+ * 
+ */
+__device__ static inline void sync_on_exit(const barrier_t &barrier, const int dev_idx, const int id = 0) {
+    if (blockIdx.x == 0 && blockIdx.y == 0 && blockIdx.z == 0 &&
+        threadIdx.x == 0 && threadIdx.y == 0 && threadIdx.z == 0) {
+        cuda::atomic_ref<int, cuda::thread_scope_system> barrier_uc(barrier[dev_idx][{id}]);
+
+        // Inter-note check-in
+        multimem<int>::red<reduce_op::ADD>(barrier.mc_ptr_at({id}), 1);
+        asm volatile ("{fence.proxy.alias;}" ::: "memory");
+        while (barrier_uc.load(cuda::memory_order_acquire) < NUM_DEVICES);
+        barrier_uc.fetch_sub(NUM_DEVICES, cuda::memory_order_release);
+    }
+}
+
+#endif
+
+};
+
+} // namespace kittens
@@ -0,0 +1,96 @@
+/**
+ * @file
+ * @brief An aggregate header of all group (multi-warp) operations defined by ThunderKittens
+ */
+
+#pragma once
+
+#include <cuda/pipeline>
+
+#include "../../common/common.cuh"
+#include "../../types/types.cuh"
+#include "../thread/thread.cuh" // several group memory ops rely on underlying warp-scope ops
+
+#define KITTENS_CHECK_WARP static_assert(GROUP_WARPS==1, "Warp (GROUP_WARPS=1) function called from a non-warp group.");
+// A "warpgroup" is a special group of 4 consecutive warps defined by NVIDIA for certain SM_90+ operations.
+#define KITTENS_CHECK_WARPGROUP static_assert(GROUP_WARPS==4, "Warpgroup (GROUP_WARPS=4) function called from a non-warpgroup group.");
+
+// WGMMA relies on some template structures that cannot be specialized within the group struct, so we declare them in advance.
+#ifdef KITTENS_HOPPER
+#include "mma/warpgroup/base/base.cuh"
+#endif
+
+namespace kittens {
+/*
+This is meant to be used with a `using group_N = kittens::group<NUM_WORKERS>;` at the start of every kernel.
+*/
+template<int _GROUP_WARPS>
+struct group {
+static constexpr int GROUP_WARPS = _GROUP_WARPS; // This alias produces nice parallelism.
+static constexpr int GROUP_THREADS = GROUP_WARPS * kittens::WARP_THREADS; // This alias produces nice parallelism.
+__device__ static inline int laneid() { return threadIdx.x % GROUP_THREADS; }
+__device__ static inline int warpid() { return laneid() / kittens::WARP_THREADS; }
+__device__ static inline int groupid() { return threadIdx.x / GROUP_THREADS; }
+
+__device__ static inline void sync(int id) {
+    asm volatile("bar.sync %0, %1;\n" :: "r"(id), "n"(GROUP_THREADS));
+}
+template<uint32_t MASK=0xFFFFFFFF> __device__ static inline void sync() {
+    static_assert(GROUP_WARPS==1, "barrier-less sync() can only be called by a single warp!");
+    asm volatile("bar.warp.sync %0;\n" :: "n"(MASK));
+}
+__device__ static inline void arrive(int id) {
+    asm volatile("bar.arrive %0, %1;\n" :: "r"(id), "n"(GROUP_THREADS));
+}
+
+#include "memory/memory.cuh"
+#include "shared/shared.cuh"
+#include "register/register.cuh"
+
+#ifdef KITTENS_HOPPER
+#include "mma/mma.cuh"
+
+template<int n_reg> __device__ static inline void increase_registers() {
+    static_assert(n_reg % 8 == 0, "n_reg must be a multiple of 8");
+    asm volatile("setmaxnreg.inc.sync.aligned.u32 %0;\n" :: "n"(n_reg));
+}
+template<int n_reg> __device__ static inline void decrease_registers() {
+    static_assert(n_reg % 8 == 0, "n_reg must be a multiple of 8");
+    asm volatile("setmaxnreg.dec.sync.aligned.u32 %0;\n" :: "n"(n_reg));
+}
+__device__ static inline void producer_registers() { decrease_registers<24>(); }
+template<int NCWG> __device__ static inline void consumer_registers() { increase_registers<480/NCWG - 8*(NCWG>3) - 224*(NCWG==1)>(); }
+
+#endif
+
+};
+
+namespace everyone {
+
+// Block-level synchronization
+__device__ static inline void sync(int id) {
+    asm volatile("bar.sync %0;\n" :: "r"(id));
+}
+
+// Cluster-level synchronization functions
+namespace tma {
+namespace cluster {
+__device__ static inline void arrive_aligned() { // All threads in the cluster must call this
+    asm volatile ("barrier.cluster.arrive.release.aligned;\n");
+}
+__device__ static inline void wait_aligned() {
+    asm volatile ("barrier.cluster.wait.acquire.aligned;\n");
+}
+__device__ static inline void sync() {
+    arrive_aligned();
+    wait_aligned();
+}
+}
+}
+
+};
+
+using warp = group<1>;      // scope used by most pre-Hopper GPUs, and also for most register operations.
+using warpgroup = group<4>; // special scope commonly used by Hopper and later.
+
+}
@@ -0,0 +1,21 @@
+/**
+ * @file
+ * @brief An aggregate header of colaborative group memory movement operations
+ */
+
+#include "util/util.cuh"
+#include "tile/tile.cuh"
+#include "vec/vec.cuh"
+
+#ifdef KITTENS_HOPPER
+struct tma {
+#include "util/tma.cuh"
+#include "tile/tma.cuh"
+#include "vec/tma.cuh"
+struct cluster {
+#include "util/tma_cluster.cuh"
+#include "tile/tma_cluster.cuh"
+#include "vec/tma_cluster.cuh"
+};
+};
+#endif
@@ -0,0 +1,42 @@
+/**
+ * @file
+ * @brief Functions for a group to collaboratively transfer data directly between global memory and registers and back.
+ */
+
+/**
+ * @brief Collaboratively loads data from a source array into register tiles.
+ *
+ * @tparam RT The register tile type.
+ * @tparam U The data type of the source array.
+ * @param dst[out] The destination tile to load data into.
+ * @param src[in] The source array to load data from.
+ * @param row_stride[in] The stride in elements between rows in the source array.
+ */
+template<int axis, ducks::crt::all CRT, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<crt<typename CRT::T, GROUP_WARPS*CRT::rows, CRT::cols, typename CRT::layout>>>
+__device__ inline static void load(CRT &dst, const CGL &src, const COORD &idx) {
+    load<axis, CRT::component, CGL::component, COORD>(dst.real, src.real, idx);
+    load<axis, CRT::component, CGL::component, COORD>(dst.imag, src.imag, idx);
+}
+template<ducks::crt::all CRT, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<crt<typename CRT::T, GROUP_WARPS*CRT::rows, CRT::cols, typename CRT::layout>>>
+__device__ inline static void load(CRT &dst, const CGL &src, const COORD &idx) {
+    load<2, CRT, CGL>(dst, src, idx);
+}
+
+/**
+ * @brief Collaboratively stores data from register tiles to a destination array in global memory.
+ *
+ * @tparam RT The register tile type.
+ * @tparam U The data type of the destination array.
+ * @param[out] dst The destination array in global memory to store data into.
+ * @param[in] src The source register tile to store data from.
+ * @param row_stride[in] The stride in elements between rows in the destination array.
+ */
+template<int axis, ducks::crt::all CRT, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<crt<typename CRT::T, GROUP_WARPS*CRT::rows, CRT::cols, typename CRT::layout>>>
+__device__ inline static void store(CGL &dst, const CRT &src, const COORD &idx) {
+    store<axis, typename CRT::component, typename CGL::component>(dst.real, src.real, idx);
+    store<axis, typename CRT::component, typename CGL::component>(dst.imag, src.imag, idx);
+}
+template<ducks::crt::all CRT, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<crt<typename CRT::T, GROUP_WARPS*CRT::rows, CRT::cols, typename CRT::layout>>>
+__device__ inline static void store(CGL &dst, const CRT &src, const COORD &idx) {
+    store<2, CRT, CGL>(dst, src, idx);
+}
@@ -0,0 +1,37 @@
+/**
+ * @file
+ * @brief Group (collaborative warp) ops for loading shared tiles from and storing to global memory. 
+ */
+
+template<int axis, bool assume_aligned, ducks::cst::all CST, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<CST>>
+__device__ static inline void load(CST &dst, const CGL &src, const COORD &idx) {
+    load<axis, assume_aligned, typename CST::component, typename CGL::component, COORD>(dst.real, src.real, idx);
+    load<axis, assume_aligned, typename CST::component, typename CGL::component, COORD>(dst.imag, src.imag, idx);
+}
+template<ducks::cst::all CST, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<CST>>
+__device__ static inline void load(CST &dst, const CGL &src, const COORD &idx) {
+    load<2, false, typename CST::component, typename CGL::component, COORD>(dst.real, src.real, idx);
+    load<2, false, typename CST::component, typename CGL::component, COORD>(dst.imag, src.imag, idx);
+}
+
+template<int axis, bool assume_aligned, ducks::cst::all CST, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<CST>>
+__device__ static inline void store(CGL &dst, const CST &src, const COORD &idx) {
+    store<axis, assume_aligned, typename CST::component, typename CGL::component, COORD>(dst.real, src.real, idx);
+    store<axis, assume_aligned, typename CST::component, typename CGL::component, COORD>(dst.imag, src.imag, idx);
+}
+template<ducks::cst::all CST, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<CST>>
+__device__ static inline void store(CGL &dst, const CST &src, const COORD &idx) {
+    store<2, false, typename CST::component, typename CGL::component, COORD>(dst.real, src.real, idx);
+    store<2, false, typename CST::component, typename CGL::component, COORD>(dst.imag, src.imag, idx);
+}
+
+template<int axis, bool assume_aligned, ducks::cst::all CST, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<CST>>
+__device__ static inline void load_async(CST &dst, const CGL &src, const COORD &idx) {
+    load_async<axis, assume_aligned, typename CST::component, typename CGL::component, COORD>(dst.real, src.real, idx);
+    load_async<axis, assume_aligned, typename CST::component, typename CGL::component, COORD>(dst.imag, src.imag, idx);
+}
+template<ducks::cst::all CST, ducks::cgl::all CGL, ducks::coord::tile COORD=coord<CST>>
+__device__ static inline void load_async(CST &dst, const CGL &src, const COORD &idx) {
+    load_async<2, false, typename CST::component, typename CGL::component, COORD>(dst.real, src.real, idx);
+    load_async<2, false, typename CST::component, typename CGL::component, COORD>(dst.imag, src.imag, idx);
+}
@@ -0,0 +1,34 @@
+/**
+ * @file
+ * @brief Functions for a warpgroup to collaboratively transfer data directly between shared memory and registers and back.
+ */
+
+/**
+ * @brief Collaboratively load data from a shared tile into register tiles split across a warpgroup.
+ *
+ * @tparam RT The register tile type
+ * @tparam ST The shared tile type
+ * @param dst[out] The destination register tile.
+ * @param src[in]  The source shared tile.
+ */
+template<ducks::crt::all RT, ducks::cst::all ST>
+__device__ inline static void load(RT &dst, const ST &src) {
+    load(dst.real, src.real);
+    load(dst.imag, src.imag);
+}
+
+
+/**
+ * @brief Collaboratively store data into a shared tile from register tiles split across a warpgroup.
+ *
+ * @tparam RT The register tile type
+ * @tparam ST The shared tile type
+ * @param dst[out] The destination shared tile.
+ * @param src[in]  The source register tile.
+ */
+template<ducks::cst::all ST, ducks::crt::all RT>
+__device__ inline static void store(ST &dst, const RT &src) {
+    store(dst.real, src.real);
+    store(dst.imag, src.imag);
+}
+
@@ -0,0 +1,207 @@
+/**
+ * @file
+ * @brief Functions for a group to collaboratively transfer data directly between global memory and registers and back.
+ */
+
+/**
+ * @brief Collaboratively loads data from a source array into row-major layout tiles.
+ *
+ * @tparam RT The row-major layout tile type.
+ * @tparam U The data type of the source array.
+ * @param dst[out] The destination tile to load data into.
+ * @param src[in] The source array to load data from.
+ * @param row_stride[in] The stride in elements between rows in the source array.
+ */
+template<int axis, ducks::rt::row_layout RT, ducks::gl::all GL, ducks::coord::tile COORD=coord<rt<typename RT::T, GROUP_WARPS*RT::rows, RT::cols, typename RT::layout>>>
+__device__ inline static void load(RT &dst, const GL &src, const COORD &idx) {
+    using T2 = RT::dtype;
+    using U = typename GL::dtype;
+
+    #ifdef KITTENS_HOPPER
+    static_assert(!std::is_same_v<T2, fp8e4m3_4> && !std::is_same_v<T2, fp8e5m2_4>, "Unsupported type for load/store");
+    #endif
+
+    U *src_ptr = (U*)&src[(idx.template unit_coord<axis, 3>())];
+    const int row_stride = src.template stride<axis>();
+    using U2 = base_types::packing<U>::packed_type;
+    int warp_laneid = threadIdx.x % WARP_THREADS;
+    int local_warpid;
+    if constexpr(GROUP_WARPS % 4 == 0) local_warpid = (warpid()/4+(warpid()%4)*(GROUP_WARPS/4));
+    else local_warpid = warpid();
+    const int row_offset = dst.rows*local_warpid;
+    #pragma unroll
+    for(int i = 0; i < dst.height; i++) {
+        int row = row_offset + i*dst.tile_size_row + (warp_laneid / 4);
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            int col = j*dst.tile_size_col + 2*(warp_laneid % 4);
+            dst.tiles[i][j].data[0] = base_types::convertor<T2, U2>::convert(*(U2*)(&src_ptr[(row+0)*row_stride + (col+0)]));
+            dst.tiles[i][j].data[2] = base_types::convertor<T2, U2>::convert(*(U2*)(&src_ptr[(row+0)*row_stride + (col+8)]));
+        }
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            int col = j*dst.tile_size_col + 2*(warp_laneid % 4);
+            dst.tiles[i][j].data[1] = base_types::convertor<T2, U2>::convert(*(U2*)(&src_ptr[(row+8)*row_stride + (col+0)]));
+            dst.tiles[i][j].data[3] = base_types::convertor<T2, U2>::convert(*(U2*)(&src_ptr[(row+8)*row_stride + (col+8)]));
+        }
+    }
+}
+/**
+ * @brief Collaboratively loads data from a source array into column-major layout tiles.
+ *
+ * @tparam RT The column-major layout tile type.
+ * @tparam U The data type of the source array.
+ * @param dst[out] The destination tile to load data into.
+ * @param src[in] The source array to load data from.
+ * @param row_stride[in] The stride in elements between rows in the source array.
+ */
+template<int axis, ducks::rt::col_layout RT, ducks::gl::all GL, ducks::coord::tile COORD=coord<rt<typename RT::T, GROUP_WARPS*RT::rows, RT::cols, typename RT::layout>>>
+__device__ inline static void load(RT &dst, const GL &src, const COORD &idx) {
+    using T = typename RT::T;
+    using U = typename GL::dtype;
+
+    #ifdef KITTENS_HOPPER
+    static_assert(!std::is_same_v<T, fp8e4m3> && !std::is_same_v<T, fp8e5m2>, "Unsupported type for load/store");
+    #endif
+
+    U *src_ptr = (U*)&src[(idx.template unit_coord<axis, 3>())];
+    const int row_stride = src.template stride<axis>();
+    int warp_laneid = threadIdx.x % WARP_THREADS;
+    int local_warpid;
+    if constexpr(GROUP_WARPS % 4 == 0) local_warpid = (warpid()/4+(warpid()%4)*(GROUP_WARPS/4));
+    else local_warpid = warpid();
+    const int row_offset = dst.rows*local_warpid;
+    #pragma unroll
+    for(int i = 0; i < dst.height; i++) {
+        int row = row_offset + i*dst.tile_size_row + 2*(warp_laneid % 4);
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            int col = j*dst.tile_size_col + (warp_laneid / 4);
+            dst.tiles[i][j].data[0].x = base_types::convertor<T, U>::convert(src_ptr[(row+0)*row_stride + (col+0)]);
+            dst.tiles[i][j].data[1].x = base_types::convertor<T, U>::convert(src_ptr[(row+0)*row_stride + (col+8)]);
+        }
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            int col = j*dst.tile_size_col + (warp_laneid / 4);
+            dst.tiles[i][j].data[0].y = base_types::convertor<T, U>::convert(src_ptr[(row+1)*row_stride + (col+0)]);
+            dst.tiles[i][j].data[1].y = base_types::convertor<T, U>::convert(src_ptr[(row+1)*row_stride + (col+8)]);
+        }
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            int col = j*dst.tile_size_col + (warp_laneid / 4);
+            dst.tiles[i][j].data[2].x = base_types::convertor<T, U>::convert(src_ptr[(row+8)*row_stride + (col+0)]);
+            dst.tiles[i][j].data[3].x = base_types::convertor<T, U>::convert(src_ptr[(row+8)*row_stride + (col+8)]);
+        }
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            int col = j*dst.tile_size_col + (warp_laneid / 4);
+            dst.tiles[i][j].data[2].y = base_types::convertor<T, U>::convert(src_ptr[(row+9)*row_stride + (col+0)]);
+            dst.tiles[i][j].data[3].y = base_types::convertor<T, U>::convert(src_ptr[(row+9)*row_stride + (col+8)]);
+        }
+    }
+}
+template<ducks::rt::all RT, ducks::gl::all GL, ducks::coord::tile COORD=coord<rt<typename RT::T, GROUP_WARPS*RT::rows, RT::cols, typename RT::layout>>>
+__device__ inline static void load(RT &dst, const GL &src, const COORD &idx) {
+    load<2>(dst, src, idx);
+}
+/**
+ * @brief Collaboratively stores data from register tiles to a destination array in global memory with a row-major layout.
+ *
+ * @tparam RT The register tile type with a row-major layout.
+ * @tparam U The data type of the destination array.
+ * @param[out] dst The destination array in global memory to store data into.
+ * @param[in] src The source register tile to store data from.
+ * @param row_stride[in] The stride in elements between rows in the destination array.
+ */
+template<int axis, ducks::rt::row_layout RT, ducks::gl::all GL, ducks::coord::tile COORD=coord<rt<typename RT::T, GROUP_WARPS*RT::rows, RT::cols, typename RT::layout>>>
+__device__ inline static void store(const GL &dst, const RT &src, const COORD &idx) {
+    using T2 = RT::dtype;
+    using U = typename GL::dtype;
+
+    #ifdef KITTENS_HOPPER
+    static_assert(!std::is_same_v<T2, fp8e4m3_4> && !std::is_same_v<T2, fp8e5m2_4>, "Unsupported type for load/store");
+    #endif
+
+    U *dst_ptr = (U*)&dst[(idx.template unit_coord<axis, 3>())];
+    const int row_stride = dst.template stride<axis>();
+    using U2 = base_types::packing<U>::packed_type;
+    int warp_laneid = threadIdx.x % WARP_THREADS;
+    int local_warpid;
+    if constexpr(GROUP_WARPS % 4 == 0) local_warpid = (warpid()/4+(warpid()%4)*(GROUP_WARPS/4));
+    else local_warpid = warpid();
+    const int row_offset = src.rows*local_warpid;
+    #pragma unroll
+    for(int i = 0; i < src.height; i++) {
+        int row = row_offset + i*src.tile_size_row + (warp_laneid / 4);
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            int col = j*src.tile_size_col + 2*(warp_laneid % 4);
+            *(U2*)(&dst_ptr[(row+0)*row_stride + (col+0)]) = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[0]);
+            *(U2*)(&dst_ptr[(row+0)*row_stride + (col+8)]) = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[2]);
+        }
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            int col = j*src.tile_size_col + 2*(warp_laneid % 4);
+            *(U2*)(&dst_ptr[(row+8)*row_stride + (col+0)]) = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[1]);
+            *(U2*)(&dst_ptr[(row+8)*row_stride + (col+8)]) = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[3]);
+        }
+    }
+}
+/**
+ * @brief Collaboratively stores data from register tiles to a destination array in global memory with a column-major layout.
+ *
+ * @tparam RT The register tile type with a column-major layout.
+ * @tparam U The data type of the destination array.
+ * @param[out] dst The destination array in global memory to store data into.
+ * @param[in] src The source register tile to store data from.
+ * @param row_stride[in] The stride in elements between rows in the destination array.
+ */
+template<int axis, ducks::rt::col_layout RT, ducks::gl::all GL, ducks::coord::tile COORD=coord<rt<typename RT::T, GROUP_WARPS*RT::rows, RT::cols, typename RT::layout>>>
+__device__ inline static void store(const GL &dst, const RT &src, const COORD &idx) {
+    using T = base_types::packing<typename RT::dtype>::unpacked_type;
+    using U = typename GL::dtype;
+
+    #ifdef KITTENS_HOPPER
+    static_assert(!std::is_same_v<T, fp8e4m3_4> && !std::is_same_v<T, fp8e5m2_4>, "Unsupported type for load/store");
+    #endif
+    
+    U *dst_ptr = (U*)&dst[(idx.template unit_coord<axis, 3>())];
+    const int row_stride = dst.template stride<axis>();
+    int warp_laneid = threadIdx.x % WARP_THREADS;
+    int local_warpid;
+    if constexpr(GROUP_WARPS % 4 == 0) local_warpid = (warpid()/4+(warpid()%4)*(GROUP_WARPS/4));
+    else local_warpid = warpid();
+    const int row_offset = src.rows*local_warpid;
+    #pragma unroll
+    for(int i = 0; i < src.height; i++) {
+        int row = row_offset + i*src.tile_size_row + 2*(warp_laneid % 4);
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            int col = j*src.tile_size_col + (warp_laneid / 4);
+            dst_ptr[(row+0)*row_stride + (col+0)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[0].x);
+            dst_ptr[(row+0)*row_stride + (col+8)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[1].x);
+        }
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            int col = j*src.tile_size_col + (warp_laneid / 4);
+            dst_ptr[(row+1)*row_stride + (col+0)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[0].y);
+            dst_ptr[(row+1)*row_stride + (col+8)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[1].y);
+        }
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            int col = j*src.tile_size_col + (warp_laneid / 4);
+            dst_ptr[(row+8)*row_stride + (col+0)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[2].x);
+            dst_ptr[(row+8)*row_stride + (col+8)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[3].x);
+        }
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            int col = j*src.tile_size_col + (warp_laneid / 4);
+            dst_ptr[(row+9)*row_stride + (col+0)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[2].y);
+            dst_ptr[(row+9)*row_stride + (col+8)] = base_types::convertor<U, T>::convert(src.tiles[i][j].data[3].y);
+        }
+    }
+}
+template<ducks::rt::all RT, ducks::gl::all GL, ducks::coord::tile COORD=coord<rt<typename RT::T, GROUP_WARPS*RT::rows, RT::cols, typename RT::layout>>>
+__device__ inline static void store(const GL &dst, const RT &src, const COORD &idx) {
+    store<2>(dst, src, idx);
+}
@@ -0,0 +1,168 @@
+/**
+ * @file
+ * @brief Group (collaborative warp) ops for loading shared tiles from and storing to global memory. 
+ */
+ 
+
+/**
+ * @brief Loads data from global memory into a shared memory tile.
+ *
+ * @tparam ST The type of the shared tile.
+ * @param[out] dst The destination shared memory tile.
+ * @param[in] src The source global memory array.
+ * @param[in] idx The coordinate of the tile in the global memory array.
+ */
+template<int axis, bool assume_aligned, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load(ST &dst, const GL &src, const COORD &idx) {
+    using T = typename ST::dtype;
+    const int row_stride = src.template stride<axis>();
+    // we can handle this many rows each time we run a memcpy_async
+    constexpr int elem_per_memcpy = sizeof(float4)/sizeof(typename ST::dtype);
+    constexpr int memcpy_per_row = dst.cols / elem_per_memcpy;
+    constexpr int total_calls = (dst.height*dst.width * kittens::TILE_ROW_DIM<T>*kittens::TILE_COL_DIM<T> + GROUP_THREADS*elem_per_memcpy-1) / (GROUP_THREADS*elem_per_memcpy); // round up
+    constexpr int total_rows = dst.height*dst.width;
+
+    coord<> unit_coord = idx.template unit_coord<axis, 3>();
+    typename GL::dtype *src_ptr = (typename GL::dtype*)&src[unit_coord];
+    uint32_t dst_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+    int laneid = threadIdx.x % GROUP_THREADS;
+
+    #pragma unroll
+    for(int i = 0; i < total_calls; i++) {
+
+        int load_idx = i * GROUP_THREADS + laneid;
+        
+        int row = load_idx / memcpy_per_row;
+        int col = (load_idx*elem_per_memcpy) % dst.cols;
+
+        if constexpr (assume_aligned) {
+            float4 tmp;
+            move<float4>::ldg(tmp, (float4*)&src_ptr[row*row_stride + col]);
+            move<float4>::sts(dst.idx(dst_ptr, {row, col}), tmp);
+        }
+        else {
+            if (row + unit_coord.template dim<axis>() < src.template shape<axis>()) {
+                float4 tmp;
+                move<float4>::ldg(tmp, (float4*)&src_ptr[row*row_stride + col]);
+                move<float4>::sts(dst.idx(dst_ptr, {row, col}), tmp);
+            }
+            else {
+                float4 zeros = {0.f,0.f,0.f,0.f};
+                move<float4>::sts(dst.idx(dst_ptr, {row, col}), zeros); // use the default value
+            }
+        }
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load(ST &dst, const GL &src, const COORD &idx) {
+    load<2, false, ST, GL, COORD>(dst, src, idx);
+}
+
+/**
+ * @brief Stores data from a shared memory tile into global memory.
+ *
+ * @tparam ST The type of the shared tile.
+ * @param[out] dst The destination global memory array.
+ * @param[in] src The source shared memory tile.
+ * @param row_stride[in] The stride between rows in the destination array.
+ */
+template<int axis, bool assume_aligned, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store(const GL &dst, const ST &src, const COORD &idx) {
+    using T = typename ST::dtype;
+    const int row_stride = dst.template stride<axis>();
+    // we can handle this many rows each time we run a memcpy_async
+    constexpr int elem_per_memcpy = sizeof(float4)/sizeof(typename ST::dtype);
+    constexpr int memcpy_per_row = src.cols / elem_per_memcpy;
+    constexpr int total_calls = (src.height*src.width * kittens::TILE_ROW_DIM<T>*kittens::TILE_COL_DIM<T> + GROUP_THREADS*elem_per_memcpy-1) / (GROUP_THREADS*elem_per_memcpy); // round up
+
+    coord<> unit_coord = idx.template unit_coord<axis, 3>();
+    typename GL::dtype *dst_ptr = (typename GL::dtype*)&dst[unit_coord];
+    uint32_t src_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&src.data[0]));
+    int laneid = threadIdx.x % GROUP_THREADS;
+
+    #pragma unroll
+    for(int i = 0; i < total_calls; i++) {
+
+        int load_idx = i * GROUP_THREADS + laneid;
+        
+        int row = load_idx / memcpy_per_row;
+        int col = (load_idx*elem_per_memcpy) % src.cols;
+
+        if constexpr (assume_aligned) {
+            float4 tmp;
+            move<float4>::lds(tmp, src.idx(src_ptr, {row, col}));
+            move<float4>::stg((float4*)&dst_ptr[row*row_stride + col], tmp);
+        }
+        else {
+            if (row + unit_coord.template dim<axis>() < dst.template shape<axis>()) {
+                float4 tmp;
+                move<float4>::lds(tmp, src.idx(src_ptr, {row, col}));
+                move<float4>::stg((float4*)&dst_ptr[row*row_stride + col], tmp);
+            }
+        }
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store(const GL &dst, const ST &src, const COORD &idx) {
+    store<2, false, ST, GL, COORD>(dst, src, idx);
+}
+
+/**
+ * @brief Asynchronously loads data from global memory into a shared memory tile.
+ *
+ * @tparam ST The type of the shared tile.
+ * @param[out] dst The destination shared memory tile.
+ * @param[in] src The source global memory array.
+ *
+ * @note This function expects 16-byte alignments. Otherwise, behavior is undefined.
+ */
+template<int axis, bool assume_aligned, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx) {
+    using T = typename ST::dtype;
+    const int row_stride = src.template stride<axis>();
+    // we can handle this many rows each time we run a memcpy_async
+    constexpr int elem_per_memcpy = sizeof(float4)/sizeof(typename ST::dtype);
+    constexpr int memcpy_per_row = dst.cols / elem_per_memcpy;
+    constexpr int total_calls = (dst.height*dst.width * kittens::TILE_ROW_DIM<T>*kittens::TILE_COL_DIM<T> + GROUP_THREADS*elem_per_memcpy-1) / (GROUP_THREADS*elem_per_memcpy); // round up
+
+    coord<> unit_coord = idx.template unit_coord<axis, 3>();
+    typename GL::dtype *src_ptr = (typename GL::dtype*)&src[unit_coord];
+    uint32_t dst_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+    int laneid = threadIdx.x % GROUP_THREADS;
+
+    #pragma unroll
+    for(int i = 0; i < total_calls; i++) {
+
+        int load_idx = i * GROUP_THREADS + laneid;
+        
+        int row = load_idx / memcpy_per_row;
+        int col = (load_idx*elem_per_memcpy) % dst.cols;
+
+        if constexpr (assume_aligned) {
+            asm volatile(
+                "cp.async.cg.shared.global.L2::128B [%0], [%1], 16;\n"
+                :: "r"(dst.idx(dst_ptr, {row, col})), "l"(&src_ptr[row*row_stride + col])
+                : "memory"
+            );
+        }
+        else {
+            if (row + unit_coord.template dim<axis>() < src.template shape<axis>()) {
+                asm volatile(
+                    "cp.async.cg.shared.global.L2::128B [%0], [%1], 16;\n"
+                    :: "r"(dst.idx(dst_ptr, {row, col})), "l"(&src_ptr[row*row_stride + col])
+                    : "memory"
+                );
+            }
+            else {
+                // printf("thread %d skipping async load on row %d, col %d\n", threadIdx.x, row + unit_coord.template dim<axis>(), col);
+                float4 zeros = {0.f,0.f,0.f,0.f};
+                move<float4>::sts(dst.idx(dst_ptr, {row, col}), zeros); // use the default value
+            }
+        }
+    }
+    asm volatile("cp.async.commit_group;\n" ::: "memory");
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx) {
+    load_async<2, false, ST, GL, COORD>(dst, src, idx);
+}
@@ -0,0 +1,323 @@
+/**
+ * @file
+ * @brief Functions for a warpgroup to collaboratively transfer data directly between shared memory and registers and back.
+ */
+
+/**
+ * @brief Collaboratively load data from a shared tile into register tiles split across a warpgroup.
+ *
+ * @tparam RT The register tile type
+ * @tparam ST The shared tile type
+ * @param dst[out] The destination register tile.
+ * @param src[in]  The source shared tile.
+ */
+template<ducks::rt::all RT, ducks::st::all ST>
+__device__ inline static void load(RT &dst, const ST &src) {
+    constexpr int height = ST::height;
+    constexpr int warp_height = RT::height;
+    static_assert(height%GROUP_WARPS == 0, "Group load / store requires tile height to be a multiple of GROUP_WARPS.");
+    static_assert(height%warp_height == 0, "Group load / store requires tile height to be a multiple of the RT height.");
+    static_assert(ST::width==RT::width, "Group load / store requires tile widths to match.");
+    int local_warpid;
+    if constexpr(GROUP_WARPS % 4 == 0) local_warpid = (warpid()/4+(warpid()%4)*(GROUP_WARPS/4));
+    else local_warpid = warpid();
+    using T2 = RT::dtype;
+    using U  = ST::dtype;
+    using T  = base_types::packing<T2>::unpacked_type;
+    using U2 = base_types::packing<U>::packed_type;
+    int warp_laneid = ::kittens::laneid();
+
+    // convert to shared state space
+    uint32_t shared_addr = static_cast<uint32_t>(__cvta_generic_to_shared(&src.data[0]));
+
+    #pragma unroll
+    for(int i = 0; i < dst.height; i++) {
+        #pragma unroll
+        for(int j = 0; j < dst.width; j++) {
+            if constexpr (sizeof(typename ST::dtype) == 2) {
+                // handle the row-major layout for 16-bit types
+                U2 tmp[4];
+                int row = (local_warpid*warp_height + i)*dst.tile_size_row + (warp_laneid % 16);
+                int col = j*dst.tile_size_col + (warp_laneid / 16) * 8;
+                if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row>) {
+                    move<U2>::ldsm4(tmp[0], tmp[1], tmp[2], tmp[3], src.idx(shared_addr, {row, col}));
+                }
+                else {
+                    move<U2>::ldsm4t(tmp[0], tmp[2], tmp[1], tmp[3], src.idx(shared_addr, {row, col}));
+                }
+                dst.tiles[i][j].data[0] = base_types::convertor<T2, U2>::convert(tmp[0]);
+                dst.tiles[i][j].data[1] = base_types::convertor<T2, U2>::convert(tmp[1]);
+                dst.tiles[i][j].data[2] = base_types::convertor<T2, U2>::convert(tmp[2]);
+                dst.tiles[i][j].data[3] = base_types::convertor<T2, U2>::convert(tmp[3]);
+            }
+            else if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row> && sizeof(typename ST::dtype) == 1) {
+                // handle the row-major layout for 8-bit types
+                int warp_group_16 = (warp_laneid / 16);  // divide each warp into two groups of 16 threads
+                int lane_in_16 = warp_laneid % 16;       // position in group of 16 threads
+                int row = (local_warpid*warp_height + i)*dst.tile_size_row + (lane_in_16 % 16); // find base row for warp in warpgroup and then distribute the 16 threads in the warp across the rows
+                int col = j*dst.tile_size_col + warp_group_16 * 16; // find base column and then *16 for second half of the warp
+
+                U2 tmp[4];
+                if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row>) {
+                    move<U2>::ldsm4(tmp[0], tmp[1], tmp[2], tmp[3], src.idx(shared_addr, {row, col}));
+                }
+                else {
+                    move<U2>::ldsm4t(tmp[0], tmp[2], tmp[1], tmp[3], src.idx(shared_addr, {row, col}));
+                }
+                dst.tiles[i][j].data[0] = base_types::convertor<T2, U2>::convert(tmp[0]);
+                dst.tiles[i][j].data[1] = base_types::convertor<T2, U2>::convert(tmp[1]);
+                dst.tiles[i][j].data[2] = base_types::convertor<T2, U2>::convert(tmp[2]);
+                dst.tiles[i][j].data[3] = base_types::convertor<T2, U2>::convert(tmp[3]);
+            }
+            else if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row> && sizeof(typename ST::dtype) == 4) {
+                // handle the row-major layout for 32-bit types
+                int row = (local_warpid*warp_height + i)*dst.tile_size_row + (warp_laneid / 4);
+                int col = j*dst.tile_size_col + 2*(warp_laneid % 4);
+                if constexpr (ST::rows != ST::underlying_rows || ST::cols != ST::underlying_cols) { // subtile case
+                    row += src.row_offset;
+                    col += src.col_offset;
+                }
+                int blit = sizeof(typename ST::dtype) * ((warp_laneid%4) / 2);
+                U2 tmp[4];
+                static constexpr int swizzle_repeat = ST::swizzle_bytes * 8;
+                static constexpr int subtile_cols   = ST::swizzle_bytes / sizeof(U);
+                const int outer_idx = col/subtile_cols;
+                const uint32_t addr_1 = shared_addr + sizeof(U)*(outer_idx*ST::underlying_rows*subtile_cols + (row+0)*subtile_cols + col%subtile_cols);
+                const uint32_t addr_2 = shared_addr + sizeof(U)*(outer_idx*ST::underlying_rows*subtile_cols + (row+8)*subtile_cols + col%subtile_cols);
+                const int swizzle_1 = blit ^ ((addr_1 % swizzle_repeat) >> 7) << 4;
+                const int swizzle_2 = blit ^ ((addr_2 % swizzle_repeat) >> 7) << 4;
+                move<U>::lds(tmp[0].x, (addr_1+ 0)^swizzle_1);
+                move<U>::lds(tmp[0].y, (addr_1+ 4)^swizzle_1);
+                move<U>::lds(tmp[2].x, (addr_1+32)^swizzle_1);
+                move<U>::lds(tmp[2].y, (addr_1+36)^swizzle_1);
+                move<U>::lds(tmp[1].x, (addr_2+ 0)^swizzle_2);
+                move<U>::lds(tmp[1].y, (addr_2+ 4)^swizzle_2);
+                move<U>::lds(tmp[3].x, (addr_2+32)^swizzle_2);
+                move<U>::lds(tmp[3].y, (addr_2+36)^swizzle_2);
+                dst.tiles[i][j].data[0] = base_types::convertor<T2, U2>::convert(tmp[0]);
+                dst.tiles[i][j].data[1] = base_types::convertor<T2, U2>::convert(tmp[1]);
+                dst.tiles[i][j].data[2] = base_types::convertor<T2, U2>::convert(tmp[2]);
+                dst.tiles[i][j].data[3] = base_types::convertor<T2, U2>::convert(tmp[3]);
+                if(blit) {
+                    #pragma unroll
+                    for(int k = 0; k < 4; k++) {
+                        dst.tiles[i][j].data[k] = T2{dst.tiles[i][j].data[k].y, dst.tiles[i][j].data[k].x};
+                    }
+                }
+            }
+            else {
+                // handle the column-major layout
+                int row = (local_warpid*warp_height + i)*dst.tile_size_row + 2*(warp_laneid % 4);
+                int col = j*dst.tile_size_col + (warp_laneid / 4);
+                U2 tmp[4];
+                move<U>::lds(tmp[0].x, src.idx(shared_addr, {row+0, col+0}));
+                move<U>::lds(tmp[0].y, src.idx(shared_addr, {row+1, col+0}));
+                move<U>::lds(tmp[1].x, src.idx(shared_addr, {row+0, col+8}));
+                move<U>::lds(tmp[1].y, src.idx(shared_addr, {row+1, col+8}));
+                move<U>::lds(tmp[2].x, src.idx(shared_addr, {row+8, col+0}));
+                move<U>::lds(tmp[2].y, src.idx(shared_addr, {row+9, col+0}));
+                move<U>::lds(tmp[3].x, src.idx(shared_addr, {row+8, col+8}));
+                move<U>::lds(tmp[3].y, src.idx(shared_addr, {row+9, col+8}));
+                dst.tiles[i][j].data[0] = base_types::convertor<T2, U2>::convert(tmp[0]);
+                dst.tiles[i][j].data[1] = base_types::convertor<T2, U2>::convert(tmp[1]);
+                dst.tiles[i][j].data[2] = base_types::convertor<T2, U2>::convert(tmp[2]);
+                dst.tiles[i][j].data[3] = base_types::convertor<T2, U2>::convert(tmp[3]);
+            }
+        }
+    }
+}
+
+
+/**
+ * @brief Collaboratively store data into a shared tile from register tiles split across a warpgroup.
+ *
+ * @tparam RT The register tile type
+ * @tparam ST The shared tile type
+ * @param dst[out] The destination shared tile.
+ * @param src[in]  The source register tile.
+ */
+template<ducks::st::all ST, ducks::rt::all RT>
+__device__ inline static void store(ST &dst, const RT &src) {
+    constexpr int height = ST::height;
+    constexpr int warp_height = RT::height;
+    static_assert(height%GROUP_WARPS == 0, "Group load / store requires tile height to be a multiple of GROUP_WARPS.");
+    static_assert(height%warp_height == 0, "Group load / store requires tile height to be a multiple of the RT height.");
+    static_assert(ST::width==RT::width, "Group load / store requires tile widths to match.");
+    int local_warpid;
+    if constexpr(GROUP_WARPS % 4 == 0) local_warpid = (warpid()/4+(warpid()%4)*(GROUP_WARPS/4));
+    else local_warpid = warpid();
+    using T2 = RT::dtype;
+    using U  = ST::dtype;
+    using T  = base_types::packing<T2>::unpacked_type;
+    using U2 = base_types::packing<U>::packed_type;
+    int warp_laneid = ::kittens::laneid();
+
+    // convert to shared state space
+    uint32_t shared_addr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+
+    #pragma unroll
+    for(int i = 0; i < warp_height; i++) {
+        #pragma unroll
+        for(int j = 0; j < src.width; j++) {
+            if constexpr (sizeof(typename ST::dtype) == 2) {
+                // handle the row-major layout
+                U2 tmp[4];
+                tmp[0] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[0]);
+                tmp[1] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[1]);
+                tmp[2] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[2]);
+                tmp[3] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[3]);
+#ifdef KITTENS_HOPPER
+                int row = (local_warpid*warp_height + i)*src.tile_size_row + (warp_laneid % 16);
+                int col = j*src.tile_size_col + (warp_laneid / 16) * 8;
+                if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row>) {
+                    move<U2>::stsm4(dst.idx(shared_addr, {row, col}), tmp[0], tmp[1], tmp[2], tmp[3]);
+                }
+                else {
+                    move<U2>::stsm4t(dst.idx(shared_addr, {row, col}), tmp[0], tmp[2], tmp[1], tmp[3]);
+                }
+#else
+                if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row>) {
+                    int row = (local_warpid*warp_height + i)*src.tile_size_row + (warp_laneid / 4);
+                    int col = j*src.tile_size_col + 2*(warp_laneid % 4);
+                    move<U2>::sts(dst.idx(shared_addr, {row+0, col+0}), tmp[0]);
+                    move<U2>::sts(dst.idx(shared_addr, {row+8, col+0}), tmp[1]);
+                    move<U2>::sts(dst.idx(shared_addr, {row+0, col+8}), tmp[2]);
+                    move<U2>::sts(dst.idx(shared_addr, {row+8, col+8}), tmp[3]);
+                }
+                else {
+                    int row = (local_warpid*warp_height + i)*src.tile_size_row + 2*(warp_laneid % 4);
+                    int col = j*src.tile_size_col + (warp_laneid / 4);
+                    move<U>::sts(dst.idx(shared_addr, {row+0, col+0}), tmp[0].x);
+                    move<U>::sts(dst.idx(shared_addr, {row+1, col+0}), tmp[0].y);
+                    move<U>::sts(dst.idx(shared_addr, {row+0, col+8}), tmp[1].x);
+                    move<U>::sts(dst.idx(shared_addr, {row+1, col+8}), tmp[1].y);
+                    move<U>::sts(dst.idx(shared_addr, {row+8, col+0}), tmp[2].x);
+                    move<U>::sts(dst.idx(shared_addr, {row+9, col+0}), tmp[2].y);
+                    move<U>::sts(dst.idx(shared_addr, {row+8, col+8}), tmp[3].x);
+                    move<U>::sts(dst.idx(shared_addr, {row+9, col+8}), tmp[3].y);
+                }
+#endif
+            }
+            else if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row> && sizeof(typename ST::dtype) == 1) { 
+                // handle the row-major layout for 8-bit types
+
+                int warp_group_16 = (warp_laneid / 16);  // divide each warp into two groups of 16 threads
+                int lane_in_16 = warp_laneid % 16;       // position in group of 16 threads
+                int row = (local_warpid*warp_height + i)*src.tile_size_row + (lane_in_16 % 16); // find base row for warp in warpgroup and then distribute the 16 threads in the warp across the rows
+                int col = j*src.tile_size_col + warp_group_16 * 16; // find base column and then *16 for second half of the warp
+
+                U2 tmp[4];
+                tmp[0] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[0]);
+                tmp[1] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[1]);
+                tmp[2] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[2]);
+                tmp[3] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[3]);
+                if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row>) {
+                    move<U2>::stsm4(dst.idx(shared_addr, {row, col}), tmp[0], tmp[1], tmp[2], tmp[3]);
+                }
+                else {
+                    move<U2>::stsm4t(dst.idx(shared_addr, {row, col}), tmp[0], tmp[2], tmp[1], tmp[3]);
+                }
+            }
+            else if constexpr (std::is_same_v<typename RT::layout, ducks::rt_layout::row> && sizeof(typename ST::dtype) == 4) {
+                // handle the row-major layout for 32-bit types
+                int row = (local_warpid*warp_height + i)*src.tile_size_row + (warp_laneid / 4);
+                int col = j*src.tile_size_col + 2*(warp_laneid % 4);
+                if constexpr (ST::rows != ST::underlying_rows || ST::cols != ST::underlying_cols) { // subtile case
+                    row += dst.row_offset;
+                    col += dst.col_offset;
+                }
+                int blit = sizeof(typename ST::dtype) * ((warp_laneid%4) / 2);
+                T2 reg_tmp[4];
+                if(blit) {
+                    #pragma unroll
+                    for(int k = 0; k < 4; k++) {
+                        reg_tmp[k] = T2{src.tiles[i][j].data[k].y, src.tiles[i][j].data[k].x};
+                    }
+                }
+                else {
+                    #pragma unroll
+                    for(int k = 0; k < 4; k++) {
+                        reg_tmp[k] = src.tiles[i][j].data[k];
+                    }
+                }
+                U2 tmp[4];
+                tmp[0] = base_types::convertor<U2, T2>::convert(reg_tmp[0]);
+                tmp[1] = base_types::convertor<U2, T2>::convert(reg_tmp[1]);
+                tmp[2] = base_types::convertor<U2, T2>::convert(reg_tmp[2]);
+                tmp[3] = base_types::convertor<U2, T2>::convert(reg_tmp[3]);
+                static constexpr int swizzle_repeat = ST::swizzle_bytes * 8;
+                static constexpr int subtile_cols   = ST::swizzle_bytes / sizeof(U);
+                const int outer_idx = col/subtile_cols;
+                const uint32_t addr_1 = shared_addr + sizeof(U)*(outer_idx*ST::underlying_rows*subtile_cols + (row+0)*subtile_cols + col%subtile_cols);
+                const uint32_t addr_2 = shared_addr + sizeof(U)*(outer_idx*ST::underlying_rows*subtile_cols + (row+8)*subtile_cols + col%subtile_cols);
+                const int swizzle_1 = blit ^ ((addr_1 % swizzle_repeat) >> 7) << 4;
+                const int swizzle_2 = blit ^ ((addr_2 % swizzle_repeat) >> 7) << 4;
+                move<U>::sts((addr_1+ 0)^swizzle_1, tmp[0].x);
+                move<U>::sts((addr_1+ 4)^swizzle_1, tmp[0].y);
+                move<U>::sts((addr_1+32)^swizzle_1, tmp[2].x);
+                move<U>::sts((addr_1+36)^swizzle_1, tmp[2].y);
+                move<U>::sts((addr_2+ 0)^swizzle_2, tmp[1].x);
+                move<U>::sts((addr_2+ 4)^swizzle_2, tmp[1].y);
+                move<U>::sts((addr_2+32)^swizzle_2, tmp[3].x);
+                move<U>::sts((addr_2+36)^swizzle_2, tmp[3].y);
+            }
+            else {
+                // handle the column-major layout
+                int row = (local_warpid*warp_height + i)*src.tile_size_row + 2*(warp_laneid % 4);
+                int col = j*src.tile_size_col + (warp_laneid / 4);
+                U2 tmp[4];
+                tmp[0] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[0]);
+                tmp[1] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[1]);
+                tmp[2] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[2]);
+                tmp[3] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[3]);
+                move<U>::sts(dst.idx(shared_addr, {row+0, col+0}), tmp[0].x);
+                move<U>::sts(dst.idx(shared_addr, {row+1, col+0}), tmp[0].y);
+                move<U>::sts(dst.idx(shared_addr, {row+0, col+8}), tmp[1].x);
+                move<U>::sts(dst.idx(shared_addr, {row+1, col+8}), tmp[1].y);
+                move<U>::sts(dst.idx(shared_addr, {row+8, col+0}), tmp[2].x);
+                move<U>::sts(dst.idx(shared_addr, {row+9, col+0}), tmp[2].y);
+                move<U>::sts(dst.idx(shared_addr, {row+8, col+8}), tmp[3].x);
+                move<U>::sts(dst.idx(shared_addr, {row+9, col+8}), tmp[3].y);
+            }
+        }
+    }
+}
+
+// Load and store of vectors from/to shared tiles.
+
+template<ducks::rv::naive_layout RV, ducks::st::all ST>
+__device__ inline static auto load(RV &dst, const ST &src, int2 row_col) {
+    KITTENS_CHECK_WARP;
+    static_assert(ST::cols>=RV::length, "Shared tile must be at least as wide as the vector.");
+    using T = RV::T;
+    using U = ST::T;
+    int warp_laneid = ::kittens::laneid();
+
+    // convert to shared state space
+    uint32_t shared_addr = static_cast<uint32_t>(__cvta_generic_to_shared(&src.data[0]));
+
+    #pragma unroll
+    for(int col = warp_laneid; col < dst.length; col+=WARP_THREADS) {
+        U tmp;
+        move<U>::lds(tmp, src.idx(shared_addr, {row_col.x, row_col.y + col}));
+        dst.data[col/WARP_THREADS][0] = base_types::convertor<T, U>::convert(tmp);
+    }
+}
+
+template<ducks::rv::naive_layout RV, ducks::st::all ST>
+__device__ inline static auto store(ST &dst, const RV &src, int2 row_col) {
+    KITTENS_CHECK_WARP;
+    static_assert(ST::cols>=RV::length, "Shared tile must be at least as wide as the vector.");
+    using T = RV::T;
+    using U = ST::T;
+    int warp_laneid = ::kittens::laneid();
+
+    // convert to shared state space
+    uint32_t shared_addr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+
+    #pragma unroll
+    for(int col = warp_laneid; col < src.length; col+=WARP_THREADS) {
+        U tmp = base_types::convertor<U, T>::convert(src.data[col/WARP_THREADS][0]);
+        move<U>::sts(dst.idx(shared_addr, {row_col.x, row_col.y + col}), tmp);
+    }
+}
@@ -0,0 +1,325 @@
+/**
+ * @file
+ * @brief Group (collaborative warp) ops for loading tensor tiles into register tiles.
+ */
+
+/**
+ * @brief Load data from a tensor tile into a register tile.
+ *
+ * @tparam RT The register tile type
+ * @tparam TM The tensor memory tile type
+ * @param dst[out] The destination register tile.
+ * @param src[in]  The source tensor tile.
+ */
+template<ducks::rt::row_layout RT, ducks::tt::all TM>
+__device__ inline static void load_async(RT &dst, const TM &src) {
+    if constexpr (GROUP_WARPS == 1) {
+        static_assert(RT::height == TM::height, "register tile and tensor tile must match height");
+        static_assert(RT::width == TM::width, "register tile and tensor tile must match width");
+
+        using T2 = RT::dtype;
+        using U  = typename TM::dtype;
+        using U2 = base_types::packing<typename TM::dtype>::packed_type;
+
+        if constexpr (sizeof(typename TM::dtype) == 1) {
+            #pragma unroll
+            for(int i = 0; i < dst.height; i++) {
+                #pragma unroll
+                for(int j = 0; j < dst.width; j++) {
+                    asm volatile(
+                        "tcgen05.ld.sync.aligned.16x128b.x2.pack::16b.b32 {%0, %1, %2, %3}, [%4];\n"
+                        : "=r"(*(uint32_t*) &dst.tiles[i][j].data[0]),
+                            "=r"(*(uint32_t*) &dst.tiles[i][j].data[1]),
+                            "=r"(*(uint32_t*) &dst.tiles[i][j].data[2]),
+                            "=r"(*(uint32_t*) &dst.tiles[i][j].data[3])
+                        : "r"(src.addr + ((i * dst.tile_size_row) << 16) + (j * dst.tile_size_col)/(4/(uint32_t)sizeof(U)))
+                    );
+                }
+            }
+        } else if constexpr (sizeof(typename TM::dtype) == 2) {
+            #pragma unroll
+            for(int i = 0; i < dst.height; i++) {
+                #pragma unroll
+                for(int j = 0; j < dst.width; j++) {
+                    asm volatile(
+                        "tcgen05.ld.sync.aligned.16x128b.x2.pack::16b.b32 {%0, %1, %2, %3}, [%4];\n"
+                        : "=r"(*(uint32_t*) &dst.tiles[i][j].data[0]),
+                            "=r"(*(uint32_t*) &dst.tiles[i][j].data[1]),
+                            "=r"(*(uint32_t*) &dst.tiles[i][j].data[2]),
+                            "=r"(*(uint32_t*) &dst.tiles[i][j].data[3])
+                        : "r"(src.addr + ((i * dst.tile_size_row) << 16) + (j * dst.tile_size_col))
+                    );
+                }
+            }
+        }
+        else if constexpr (sizeof(typename TM::dtype) == 4) {
+            #pragma unroll
+            for(int i = 0; i < dst.height; i++) {
+                if constexpr (dst.width%4 == 0) {
+                    #pragma unroll
+                    for(int j = 0; j < dst.width; j+=4) {
+                        U2 data[16];
+                        asm volatile(
+                            "tcgen05.ld.sync.aligned.16x256b.x8.b32 {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31}, [%32];\n"
+                            : "=f"(data[0].x), "=f"(data[0].y),
+                            "=f"(data[1].x), "=f"(data[1].y),
+                            "=f"(data[2].x), "=f"(data[2].y),
+                            "=f"(data[3].x), "=f"(data[3].y),
+                            "=f"(data[4].x), "=f"(data[4].y),
+                            "=f"(data[5].x), "=f"(data[5].y),
+                            "=f"(data[6].x), "=f"(data[6].y),
+                            "=f"(data[7].x), "=f"(data[7].y),
+                            "=f"(data[8].x), "=f"(data[8].y),
+                            "=f"(data[9].x), "=f"(data[9].y),
+                            "=f"(data[10].x), "=f"(data[10].y),
+                            "=f"(data[11].x), "=f"(data[11].y),
+                            "=f"(data[12].x), "=f"(data[12].y),
+                            "=f"(data[13].x), "=f"(data[13].y),
+                            "=f"(data[14].x), "=f"(data[14].y),
+                            "=f"(data[15].x), "=f"(data[15].y)
+                            : "r"(src.addr + ((i * dst.tile_size_row) << 16) + (j * dst.tile_size_col)/(4/(uint32_t)sizeof(U)))
+                        );
+                        #pragma unroll
+                        for(int k = 0; k < 4; k++) {
+                            dst.tiles[i][j+0].data[k] = base_types::convertor<T2, U2>::convert(data[k]);
+                            dst.tiles[i][j+1].data[k] = base_types::convertor<T2, U2>::convert(data[k+4]);
+                            dst.tiles[i][j+2].data[k] = base_types::convertor<T2, U2>::convert(data[k+8]);
+                            dst.tiles[i][j+3].data[k] = base_types::convertor<T2, U2>::convert(data[k+12]);
+                        }
+                    }
+                }
+                else if constexpr (dst.width%2 == 0) {
+                    #pragma unroll
+                    for(int j = 0; j < dst.width; j+=2) {
+                        U2 data[8];
+                        asm volatile(
+                            "tcgen05.ld.sync.aligned.16x256b.x4.b32 {%0, %1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15}, [%16];\n"
+                            : "=f"(data[0].x), "=f"(data[0].y),
+                            "=f"(data[1].x), "=f"(data[1].y),
+                            "=f"(data[2].x), "=f"(data[2].y),
+                            "=f"(data[3].x), "=f"(data[3].y),
+                            "=f"(data[4].x), "=f"(data[4].y),
+                            "=f"(data[5].x), "=f"(data[5].y),
+                            "=f"(data[6].x), "=f"(data[6].y),
+                            "=f"(data[7].x), "=f"(data[7].y)
+                            : "r"(src.addr + ((i * dst.tile_size_row) << 16) + (j * dst.tile_size_col)/(4/(uint32_t)sizeof(U)))
+                        );
+                        #pragma unroll
+                        for(int k = 0; k < 4; k++) {
+                            dst.tiles[i][j+0].data[k] = base_types::convertor<T2, U2>::convert(data[k]);
+                            dst.tiles[i][j+1].data[k] = base_types::convertor<T2, U2>::convert(data[k+4]);
+                        }
+                    }
+                }
+                else {
+                    #pragma unroll
+                    for(int j = 0; j < dst.width; j++) {
+                        U2 data[4];
+                        asm volatile(
+                            "tcgen05.ld.sync.aligned.16x256b.x2.b32 {%0, %1, %2, %3, %4, %5, %6, %7}, [%8];\n"
+                            : "=f"(data[0].x), "=f"(data[0].y),
+                            "=f"(data[1].x), "=f"(data[1].y),
+                            "=f"(data[2].x), "=f"(data[2].y),
+                            "=f"(data[3].x), "=f"(data[3].y)
+                            : "r"(src.addr + ((i * dst.tile_size_row) << 16) + (j * dst.tile_size_col)/(4/(uint32_t)sizeof(U)))
+                        );
+                        #pragma unroll
+                        for(int k = 0; k < 4; k++) {
+                            dst.tiles[i][j].data[k] = base_types::convertor<T2, U2>::convert(data[k]);
+                        }
+                    }
+                }
+            }
+        }
+    }
+    else {
+        static_assert(GROUP_WARPS==4 || GROUP_WARPS==8);
+        constexpr int warp_rows = TM::rows/GROUP_WARPS;
+        static_assert(TM::cols==RT::cols);
+        static_assert(warp_rows==RT::rows);
+        if constexpr (GROUP_WARPS == 4) {
+            auto src_subtile = src.template subtile<tt<typename TM::dtype, warp_rows, TM::cols>>(32*warpid(), 0);
+            ::kittens::group<1>::load_async(dst, src_subtile);
+        }
+        else {
+            auto src_subtile = src.template subtile<tt<typename TM::dtype, warp_rows, TM::cols>>(32*(warpid()%4)+16*(warpid()/4), 0);
+            ::kittens::group<1>::load_async(dst, src_subtile);
+        }
+    }
+}
+
+
+/**
+ * @brief Store data into a tensor tile from a register tile.
+ *
+ * @tparam RT The register tile type
+ * @tparam TM The tensor memory tile type
+ * @param dst[out] The destination tensor tile.
+ * @param src[in]  The source register tile.
+ */
+template<ducks::rt::all RT, ducks::tt::all TM>
+__device__ inline static void store_async(TM &dst, const RT &src) {
+    if constexpr (GROUP_WARPS == 1) {
+        static_assert(RT::height == TM::height, "register tile and tensor tile must match height");
+        static_assert(RT::width == TM::width, "register tile and tensor tile must match width");
+
+        using T2 = RT::dtype;
+        using T = base_types::packing<T2>::unpacked_type;
+        using U = TM::dtype;
+        using U2 = base_types::packing<U>::packed_type;
+
+        if constexpr (sizeof(typename TM::dtype) == 2) {
+            #pragma unroll
+            for(int i = 0; i < src.height; i++) {
+                if constexpr (src.width%4 == 0) {
+                    #pragma unroll
+                    for(int j = 0; j < src.width; j+=4) {
+                        asm volatile(
+                            "tcgen05.st.sync.aligned.16x128b.x8.b32 [%0], {%1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16};\n"
+                            :: "r"(dst.addr + ((i * src.tile_size_row) << 16) + (j * src.tile_size_col)/(4/(uint32_t)sizeof(U))),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[3]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[3]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+2].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+2].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+2].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+2].data[3]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+3].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+3].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+3].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+3].data[3])
+                        );
+                    }
+                }
+                else if constexpr (src.width%2 == 0) {
+                    #pragma unroll
+                    for(int j = 0; j < src.width; j+=2) {
+                        asm volatile(
+                            "tcgen05.st.sync.aligned.16x128b.x4.b32 [%0], {%1, %2, %3, %4, %5, %6, %7, %8};\n"
+                            :: "r"(dst.addr + ((i * src.tile_size_row) << 16) + (j * src.tile_size_col)/(4/(uint32_t)sizeof(U))),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+0].data[3]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j+1].data[3])
+                        );
+                    }
+                }
+                else {
+                    #pragma unroll
+                    for(int j = 0; j < src.width; j++) {
+                        asm volatile(
+                            "tcgen05.st.sync.aligned.16x128b.x2.b32 [%0], {%1, %2, %3, %4};\n"
+                            :: "r"(dst.addr + ((i * src.tile_size_row) << 16) + (j * src.tile_size_col)/(4/(uint32_t)sizeof(U))),
+                            "r"(*(uint32_t*)&src.tiles[i][j].data[0]),
+                            "r"(*(uint32_t*)&src.tiles[i][j].data[1]),
+                            "r"(*(uint32_t*)&src.tiles[i][j].data[2]),
+                            "r"(*(uint32_t*)&src.tiles[i][j].data[3])
+                        );
+                    }
+                }
+            }
+        }
+        else if constexpr (sizeof(typename TM::dtype) == 4) {
+            #pragma unroll
+            for(int i = 0; i < src.height; i++) {
+                if constexpr(src.width%4 == 0) {
+                    #pragma unroll
+                    for(int j = 0; j < src.width; j+=4) {
+                        U2 data[16];
+                        #pragma unroll
+                        for(int k = 0; k < 4; k++) {
+                            data[k] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[k]);
+                            data[k+4] = base_types::convertor<U2, T2>::convert(src.tiles[i][j+1].data[k]);
+                            data[k+8] = base_types::convertor<U2, T2>::convert(src.tiles[i][j+2].data[k]);
+                            data[k+12] = base_types::convertor<U2, T2>::convert(src.tiles[i][j+3].data[k]);
+                        }
+                        asm volatile(
+                            "tcgen05.st.sync.aligned.16x256b.x8.b32 [%0], {%1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16, %17, %18, %19, %20, %21, %22, %23, %24, %25, %26, %27, %28, %29, %30, %31, %32};\n"
+                            :: "r"(dst.addr + ((i * src.tile_size_row) << 16) + (j * src.tile_size_col)/(4/(uint32_t)sizeof(U))),
+                            "f"(data[0].x), "f"(data[0].y),
+                            "f"(data[1].x), "f"(data[1].y),
+                            "f"(data[2].x), "f"(data[2].y),
+                            "f"(data[3].x), "f"(data[3].y),
+                            "f"(data[4].x), "f"(data[4].y),
+                            "f"(data[5].x), "f"(data[5].y),
+                            "f"(data[6].x), "f"(data[6].y),
+                            "f"(data[7].x), "f"(data[7].y),
+                            "f"(data[8].x), "f"(data[8].y),
+                            "f"(data[9].x), "f"(data[9].y),
+                            "f"(data[10].x), "f"(data[10].y),
+                            "f"(data[11].x), "f"(data[11].y),
+                            "f"(data[12].x), "f"(data[12].y),
+                            "f"(data[13].x), "f"(data[13].y),
+                            "f"(data[14].x), "f"(data[14].y),
+                            "f"(data[15].x), "f"(data[15].y)
+                        );
+                    }
+                }
+                else if constexpr(src.width%2 == 0) {
+                    #pragma unroll
+                    for(int j = 0; j < src.width; j+=2) {
+                        U2 data[8];
+                        #pragma unroll
+                        for(int k = 0; k < 4; k++) {
+                            data[k] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[k]);
+                            data[k+4] = base_types::convertor<U2, T2>::convert(src.tiles[i][j+1].data[k]);
+                        }
+                        asm volatile(
+                            "tcgen05.st.sync.aligned.16x256b.x4.b32 [%0], {%1, %2, %3, %4, %5, %6, %7, %8, %9, %10, %11, %12, %13, %14, %15, %16};\n"
+                            :: "r"(dst.addr + ((i * src.tile_size_row) << 16) + (j * src.tile_size_col)/(4/(uint32_t)sizeof(U))),
+                            "f"(data[0].x), "f"(data[0].y),
+                            "f"(data[1].x), "f"(data[1].y),
+                            "f"(data[2].x), "f"(data[2].y),
+                            "f"(data[3].x), "f"(data[3].y),
+                            "f"(data[4].x), "f"(data[4].y),
+                            "f"(data[5].x), "f"(data[5].y),
+                            "f"(data[6].x), "f"(data[6].y),
+                            "f"(data[7].x), "f"(data[7].y)
+                        );
+                    }
+                }
+                else {
+                    #pragma unroll
+                    for(int j = 0; j < src.width; j++) {
+                        U2 data[4];
+                        #pragma unroll
+                        for(int k = 0; k < 4; k++) {
+                            data[k] = base_types::convertor<U2, T2>::convert(src.tiles[i][j].data[k]);
+                        }
+                        asm volatile(
+                            "tcgen05.st.sync.aligned.16x256b.x2.b32 [%0], {%1, %2, %3, %4, %5, %6, %7, %8};\n"
+                            :: "r"(dst.addr + ((i * src.tile_size_row) << 16) + (j * src.tile_size_col)/(4/(uint32_t)sizeof(U))),
+                            "f"(data[0].x), "f"(data[0].y),
+                            "f"(data[1].x), "f"(data[1].y),
+                            "f"(data[2].x), "f"(data[2].y),
+                            "f"(data[3].x), "f"(data[3].y)
+                        );
+                    }
+                }
+            }
+        }
+    }
+    else {
+        static_assert(GROUP_WARPS==4 || GROUP_WARPS==8);
+        constexpr int warp_rows = TM::rows/GROUP_WARPS;
+        static_assert(TM::cols==RT::cols);
+        static_assert(warp_rows==RT::rows);
+        if constexpr (GROUP_WARPS == 4) {
+            auto dst_subtile = dst.template subtile<tt<typename TM::dtype, warp_rows, TM::cols>>(32*warpid(), 0);
+            ::kittens::group<1>::store_async(dst_subtile, src);
+        }
+        else {
+            auto dst_subtile = dst.template subtile<tt<typename TM::dtype, warp_rows, TM::cols>>(32*(warpid()%4)+16*(warpid()/4), 0);
+            ::kittens::group<1>::store_async(dst_subtile, src);
+        }
+    }
+}
@@ -0,0 +1,16 @@
+/**
+ * @file
+ * @brief An aggregate header of group memory operations on tiles.
+ */
+
+#include "shared_to_register.cuh"
+#include "global_to_register.cuh"
+#include "global_to_shared.cuh"
+#ifdef KITTENS_BLACKWELL
+#include "tensor_to_register.cuh"
+#endif
+
+#include "complex/complex_shared_to_register.cuh"
+#include "complex/complex_global_to_register.cuh"
+#include "complex/complex_global_to_shared.cuh"
+
@@ -0,0 +1,134 @@
+/**
+ * @file
+ * @brief Functions for a group scope to call tile TMA functions.
+ */
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void prefetch(ST &dst, const GL &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::prefetch<axis, policy, ST, GL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void prefetch(ST &dst, const GL &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::prefetch<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_async<axis, policy, ST, GL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_async<axis, policy, ST, PGL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_async<dim::ROW, cache_policy::NORMAL, ST, PGL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_add_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_add_async<axis, policy, ST, GL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_add_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_add_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_add_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_add_async<axis, policy, ST, PGL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_add_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_add_async<dim::ROW, cache_policy::NORMAL, ST, PGL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_min_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_min_async<axis, policy, ST, GL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_min_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_min_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_min_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_min_async<axis, policy, ST, PGL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_min_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_min_async<dim::ROW, cache_policy::NORMAL, ST, PGL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_max_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_max_async<axis, policy, ST, GL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_max_async(const GL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_max_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_max_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_max_async<axis, policy, ST, PGL, COORD>(dst, src, idx); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::pgl::all PGL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void store_max_async(const PGL &dst, const ST &src, const COORD &idx) {
+    if(laneid() == 0) {
+        ::kittens::tma::store_max_async<dim::ROW, cache_policy::NORMAL, ST, PGL, COORD>(dst, src, idx);
+    }
+}
+
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx, semaphore& bar) {
+    if(laneid() == 0) {
+        ::kittens::tma::load_async<axis, policy, ST, GL, COORD>(dst, src, idx, bar); // Don't do the mask
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx, semaphore& bar) {
+    if(laneid() == 0) {
+        ::kittens::tma::load_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx, bar);
+    }
+}
@@ -0,0 +1,33 @@
+/**
+ * @file
+ * @brief Functions for a group scope to call tile TMA cluster functions.
+ */
+
+
+#ifdef KITTENS_BLACKWELL
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx, semaphore& bar, uint16_t cluster_mask, int dst_mbar_cta=-1) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::load_async<axis, policy, ST, GL, COORD>(dst, src, idx, bar, cluster_mask, dst_mbar_cta);
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx, semaphore& bar, uint16_t cluster_mask, int dst_mbar_cta=-1) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::load_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx, bar, cluster_mask, dst_mbar_cta);
+    }
+}
+#else
+template<int axis, cache_policy policy, ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx, semaphore& bar, uint16_t cluster_mask) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::load_async<axis, policy, ST, GL, COORD>(dst, src, idx, bar, cluster_mask);
+    }
+}
+template<ducks::st::all ST, ducks::gl::all GL, ducks::coord::tile COORD=coord<ST>>
+__device__ static inline void load_async(ST &dst, const GL &src, const COORD &idx, semaphore& bar, uint16_t cluster_mask) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::load_async<dim::ROW, cache_policy::NORMAL, ST, GL, COORD>(dst, src, idx, bar, cluster_mask);
+    }
+}
+#endif
@@ -0,0 +1,68 @@
+/**
+ * @file
+ * @brief Various utilities for group TMA memory operations.
+ */
+
+/* ----------   Barrier functions for async load  ---------- */
+
+/**
+* @brief Sets the number of bytes expected at the semaphore.
+*
+* This function sets the number of bytes expected at the semaphore for the first thread in the warp.
+* It converts the semaphore pointer to a generic shared memory pointer and uses an inline assembly
+* instruction to set the expected number of bytes.
+*
+* @param semaphore Reference to the semaphore variable.
+* @param bytes The number of bytes expected at the semaphore.
+*/
+__device__ static inline void expect_bytes(semaphore& bar, uint32_t bytes) {
+    if(laneid() == 0) {
+        ::kittens::tma::expect_bytes(bar, bytes);
+    }
+}
+/**
+* @brief Sets the number of bytes expected at the semaphore.
+*
+* This function sets the number of bytes expected at the mbarrier before the transaction arrives.
+*/
+template<typename T, typename... args>
+__device__ static inline void expect(semaphore& bar, const T& _1, const args&... _2) {
+    expect_bytes(bar, size_bytes<T, args...>);
+}
+
+/* ----------   Synchronization functions for async store  ---------- */
+
+/**
+ * @brief Commits previous asynchronous TMA stores to a group and performs them.
+*/
+__device__ static inline void store_commit_group() {
+    asm volatile("cp.async.bulk.commit_group;");
+}
+/**
+ * @brief Waits for previous committed TMA store groups to complete.
+ *
+ * @tparam N The maximum number of remaining TMA store groups. Defaults to 0.
+*/
+template <int N=0>
+__device__ static inline void store_async_wait() {
+    asm volatile (
+        "cp.async.bulk.wait_group %0;"
+        :
+        : "n"(N)
+        : "memory"
+    );
+}
+/**
+ * @brief Waits for previous committed TMA store groups to finish reading from shared memory.
+ *
+ * @tparam N The maximum number of remaining TMA store groups. Defaults to 0.
+*/
+template <int N=0>
+__device__ static inline void store_async_read_wait() {
+    asm volatile (
+        "cp.async.bulk.wait_group.read %0;"
+        :
+        : "n"(N)
+        : "memory"
+    );
+}
@@ -0,0 +1,90 @@
+
+/**
+* @brief Waits for the requested semaphore phase, at cluster scope
+*
+* @param semaphore Reference to the semaphore variable.
+* @param kPhaseBit The phase bit used for the semaphore.
+*/
+__device__ static inline void wait(semaphore& bar, int kPhaseBit) {
+    void const* const ptr = &bar;
+    uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(ptr)); 
+
+    asm volatile (
+        "{\n"
+        ".reg .pred                P1;\n"
+        "LAB_WAIT:\n"
+        "mbarrier.try_wait.parity.acquire.cluster.shared::cta.b64 P1, [%0], %1;\n"
+        "@P1                       bra.uni DONE;\n"
+        "bra.uni                   LAB_WAIT;\n"
+        "DONE:\n"
+        "}\n"
+        :: "r"(mbar_ptr),
+        "r"(kPhaseBit)
+    );
+}
+
+/**
+* @brief Sets the number of bytes expected at the semaphore, assuming a multicast instruction.
+*
+* This function sets the number of bytes expected at the semaphore for the first thread in the warp.
+* It converts the semaphore pointer to a generic shared memory pointer and uses an inline assembly
+* instruction to set the expected number of bytes.
+* 
+* It's worth being aware that this function is particularly necessary for multicast loads, and
+* distributed shared memory can actually be done with a normal tma::expect followed by wait. See
+* the unit tests of dsmem for an example.
+*
+* @param semaphore Reference to the semaphore variable.
+* @param bytes The number of bytes expected at the semaphore.
+*/
+__device__ static inline void expect_bytes(semaphore& bar, uint32_t bytes, int dst_cta) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::expect_bytes(bar, bytes, dst_cta);
+    }
+}
+/**
+* @brief Sets the number of bytes expected at the semaphore.
+*
+* This function sets the number of bytes expected at the semaphore for the first thread in the warp.
+* It converts the semaphore pointer to a generic shared memory pointer and uses an inline assembly
+* instruction to set the expected number of bytes.
+*
+* @tparam T The type of the data to be stored at the semaphore.
+* @param semaphore Reference to the semaphore variable.
+*/
+/**
+* @brief Sets the number of bytes expected at the semaphore.
+*
+* This function sets the number of bytes expected at the mbarrier before the transaction arrives.
+*/
+template<typename T, typename... args>
+__device__ static inline void expect(semaphore& bar, int dst_cta, const T& _1, const args&... _2) {
+    expect_bytes(bar, size_bytes<T, args...>, dst_cta);
+}
+
+/**
+* @brief Arrives at a semaphore in cluster scope.
+*
+* Marks a thread arrival at an mbarrier
+*
+* @param semaphore Reference to the semaphore variable.
+* @param kPhaseBit The phase bit used for the semaphore.
+*/
+__device__ static inline void arrive(semaphore& bar, int dst_cta, uint32_t count=1) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::arrive(bar, dst_cta, count);
+    }
+}
+
+// Generic transfer
+__device__ static inline void store_async(void *dst, void *src, int dst_cta, uint32_t size_bytes, semaphore& bar) {
+    if(laneid() == 0) {
+        ::kittens::tma::cluster::store_async(dst, src, dst_cta, size_bytes, bar);
+    }
+}
+
+// Templated transfer for convenience
+template<typename T>
+__device__ static inline void store_async(T &dst_, T &src_, int dst_cta, semaphore& bar) {
+    store_async((void*)&dst_, (void*)&src_, dst_cta, size_bytes<T>, bar);
+}
@@ -0,0 +1,168 @@
+/**
+ * @file
+ * @brief Various utilities for group memory operations.
+ */
+
+
+template<int N=0> __device__ static inline void load_async_wait(int bar_id) { // for completing (non-TMA) async loads
+    asm volatile("cp.async.wait_group %0;\n" : : "n"(N) : "memory");
+    sync(bar_id);
+}
+template<int N=0> __device__ static inline void load_async_wait() { // for completing (non-TMA) async loads
+    KITTENS_CHECK_WARP
+    asm volatile("cp.async.wait_group %0;\n" : : "n"(N) : "memory");
+    __syncwarp();
+}
+
+__device__ static inline void arrive(barrier<GROUP_WARPS> bar) {
+    asm volatile("bar.arrive %0, %1;\n" :: "r"(bar.barrier_id), "n"(GROUP_WARPS*WARP_THREADS) : "memory");
+}
+__device__ static inline void arrive_and_wait(barrier<GROUP_WARPS> bar) {
+    asm volatile("bar.sync %0, %1;\n" :: "r"(bar.barrier_id), "n"(GROUP_WARPS*WARP_THREADS) : "memory");
+}
+
+/**
+ * @brief Initializes a synchronization semaphore with a transaction count and sets the expected number of bytes.
+ *
+ * This function sets up a semaphore that is used to synchronize threads within a block during asynchronous operations.
+ * It initializes the semaphore with a thread count semaphore.
+ *
+ * Additionally, if it is given a shared tile type, it will also call `set_bytes` to prepare for the memory transaction.
+ *
+ * @param[out] semaphore The semaphore variable to initialize.
+ * @param[in] tc The thread counter for the semaphore.
+ */
+__device__ static inline void init_semaphore(semaphore& bar, int thread_count, int transaction_count=0) {
+    if (laneid() == 0) {
+        void const* const ptr = &bar;
+        uint32_t bar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(ptr)); 
+
+        asm volatile (
+            "mbarrier.init.shared::cta.b64 [%0], %1;\n"
+            :: "r"(bar_ptr), "r"(thread_count+transaction_count)
+        );
+    }
+}
+/**
+ * @brief Invalidate an mbarrier
+ *
+ * @param[out] semaphore The semaphore variable to initialize.
+ * @param[in] tc The thread counter for the semaphore.
+ */
+__device__ static inline void invalidate_semaphore(semaphore& bar) {
+    if (laneid() == 0) {
+        void const* const ptr = &bar;
+        uint32_t bar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(ptr)); 
+        asm volatile (
+            "mbarrier.inval.shared::cta.b64 [%0];\n"
+            :: "r"(bar_ptr)
+        );
+    }
+}
+
+/**
+* @brief Arrives at a semaphore.
+*
+* Marks a warp arrival at an mbarrier
+*
+* @param semaphore Reference to the semaphore variable.
+* @param kPhaseBit The phase bit used for the semaphore.
+*/
+__device__ static inline void arrive(semaphore& sem) {
+    if(laneid() == 0) {
+            uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&sem)); 
+            asm volatile (
+                "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0];\n"
+            :
+            : "r"(mbar_ptr)
+            : "memory"
+        );
+    }
+}
+template<int num_warps> __device__ static inline void arrive(barrier<num_warps> bar) {
+    asm volatile("bar.arrive %0, %1;\n" :: "r"(bar.barrier_id), "n"(num_warps*WARP_THREADS) : "memory");
+}
+
+#ifdef KITTENS_HOPPER
+/**
+* @brief Arrives at a semaphore.
+*
+* Marks a warp arrival at an mbarrier
+*
+* @param semaphore Reference to the semaphore variable.
+* @param kPhaseBit The phase bit used for the semaphore.
+*/
+__device__ static inline void arrive(semaphore& sem, uint32_t count) {
+    if(laneid() == 0) {
+        uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&sem));
+        asm volatile (
+            "mbarrier.arrive.release.cta.shared::cta.b64 _, [%0], %1;\n"
+            :
+            : "r"(mbar_ptr), "r"(count)
+            : "memory"
+        );
+    }
+}
+#endif
+
+/**
+* @brief Waits for the requested semaphore phase.
+*
+* @param semaphore Reference to the semaphore variable.
+* @param kPhaseBit The phase bit used for the semaphore.
+*/
+__device__ static inline void wait(semaphore& sem, int kPhaseBit) {
+    void const* const ptr = &sem;
+    uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(ptr)); 
+
+#ifdef KITTENS_HOPPER
+    asm volatile (
+        "{\n"
+        ".reg .pred                P1;\n"
+        "LAB_WAIT:\n"
+        "mbarrier.try_wait.parity.shared::cta.b64 P1, [%0], %1;\n"
+        "@P1                       bra.uni DONE;\n"
+        "bra.uni                   LAB_WAIT;\n"
+        "DONE:\n"
+        "}\n"
+        :: "r"(mbar_ptr),
+        "r"(kPhaseBit)
+    );
+#else
+    asm volatile (
+        "{\n"
+        ".reg .pred                P1;\n"
+        "LAB_WAIT:\n"
+        "mbarrier.test_wait.parity.shared::cta.b64 P1, [%0], %1;\n"
+        "@P1                       bra.uni DONE;\n"
+        "nanosleep.u32 5;\n" // wait a few nanoseconds on pre-Hopper architectures to save instruction issue slots
+        "bra.uni                   LAB_WAIT;\n"
+        "DONE:\n"
+        "}\n"
+        :: "r"(mbar_ptr),
+        "r"(kPhaseBit)
+    );
+#endif
+}
+
+/**
+* @brief Checks if the requested semaphore phase is ready.
+*
+* @param semaphore Reference to the semaphore variable.
+* @param kPhaseBit The phase bit used for the semaphore.
+*/
+__device__ static inline int test_wait(semaphore& sem, int kPhaseBit) {
+    void const* const ptr = &sem;
+    uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(ptr));
+    int result;
+    asm volatile (
+        "{\n"
+        ".reg .pred P1;\n"
+        "mbarrier.test_wait.parity.shared::cta.b64 P1, [%1], %2;\n"
+        "selp.u32 %0,1,0,P1;"
+        "}\n"
+        : "=r"(result)
+        : "r"(mbar_ptr), "r"(kPhaseBit)
+    );
+    return result;
+}
@@ -0,0 +1,138 @@
+/**
+ * @file
+ * @brief Functions for a warpgroup to collaboratively transfer  data directly between global memory and registers and back.
+ */
+
+/**
+ * @brief Collaboratively loads data into register vectors from a source array in global memory.
+ *
+ * @tparam RV The register vector type.
+ * @tparam U The data type of the source array.
+ * @param[out] dst The destination register vector to load data into.
+ * @param[in] src The source array in global memory to load data from.
+ */
+template<ducks::rv::all RV, ducks::gl::all GL>
+__device__ inline static void load(RV &dst, const GL &src, const coord<rv<typename RV::T, GROUP_WARPS*RV::length, typename RV::layout>> &idx) {
+    if constexpr (GROUP_WARPS == 1) {
+        using T2 = RV::dtype;
+        using U = typename GL::dtype;
+        using U2 = base_types::packing<U>::packed_type;
+        using T = base_types::packing<T2>::unpacked_type;
+
+        U *src_ptr = (U*)&src[(idx.template unit_coord<-1, 3>())];
+        int laneid = ::kittens::laneid();
+        
+        if constexpr (std::is_same_v<typename RV::layout, align_l>) {
+            #pragma unroll
+            for(auto w = 0; w < (dst.outer_dim+3)/4; w++) {
+                int idx = w*64 + (laneid/4)*8 + 2*(laneid%4);
+                int o_dim = w*4 + (laneid/4) / 2;
+                int i_dim = (laneid/4) % 2;
+                // this should be a maximally coalesced load.
+                if(idx < dst.outer_dim*16)
+                    dst[o_dim][i_dim] = base_types::convertor<T2, U2>::convert(*(U2*)&src_ptr[idx]);
+            }
+            // now we need to do a bunch of shuffle_sync's to make sure everyone has everything they need.
+            #pragma unroll
+            for(auto w = 0; w < dst.outer_dim; w++) {
+                int leader = 8*(w%4) + (laneid%4); // repeats every 64 columns
+                dst[w][0] = packed_shfl_sync(MASK_ALL, dst[w][0], leader);
+                dst[w][1] = packed_shfl_sync(MASK_ALL, dst[w][1], leader+4);
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, ortho_l>) {
+            // really hoping https://stackoverflow.com/questions/15029765/is-coalescing-triggered-for-accessing-memory-in-reverse-order is still true
+            // otherwise there will be some pain :/
+            #pragma unroll
+            for(auto w = 0; w < (dst.outer_dim+1)/2; w++) {
+                int idx = w*32 + (laneid%4)*8 + (laneid/4);
+                int o_dim = w*2 + (laneid%4) / 2;
+                // this should be a maximally coalesced load.
+                if(idx < dst.outer_dim*16) {
+                    T tmp = base_types::convertor<T, U>::convert(src_ptr[idx]);
+                    if(laneid%2==0) dst[o_dim][0].x = tmp;
+                    else dst[o_dim][0].y = tmp;
+                }
+            }
+            // now we need to do a bunch of shuffle_sync's to make sure everyone has everything they need.
+            #pragma unroll
+            for(auto w = 0; w < dst.outer_dim; w++) {
+                int leader = (laneid/4)*4 + 2*(w%2); // repeats every 64 columns
+                dst[w][0].x = __shfl_sync(MASK_ALL, dst[w][0].x, leader);
+                dst[w][0].y = __shfl_sync(MASK_ALL, dst[w][0].y, leader+1);
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, naive_l>) {
+            #pragma unroll
+            for(auto w = 0; w < dst.outer_dim; w++) {
+                if(w < dst.outer_dim-1 || dst.length%32 == 0 || laneid<16) {
+                    dst[w][0] = base_types::convertor<T, U>::convert(src_ptr[w*32 + laneid]);
+                }
+            }
+        }
+    }
+    else {
+        // Call warp level load
+        ::kittens::group<1>::load(dst, src, coord<RV>(idx.b, idx.d, idx.r, idx.c*GROUP_WARPS+warpid()));
+    }
+}
+/**
+ * @brief Collaboratively stores data from register vectors to a destination array in global memory.
+ *
+ * @tparam RV The register vector type.
+ * @tparam U The data type of the destination array.
+ * @param[out] dst The destination array in global memory to store data into.
+ * @param[in] src The source register vector to store data from.
+ */
+template<ducks::rv::all RV, ducks::gl::all GL>
+__device__ inline static void store(GL &dst, const RV &src, const coord<rv<typename RV::T, GROUP_WARPS*RV::length, typename RV::layout>> &idx) {
+    if constexpr (GROUP_WARPS == 1) {
+        using T2 = RV::dtype;
+        using U = typename GL::dtype;
+        using U2 = base_types::packing<U>::packed_type;
+        using T = base_types::packing<T2>::unpacked_type;
+        
+        U *dst_ptr = (U*)&dst[(idx.template unit_coord<-1, 3>())];
+        int laneid = ::kittens::laneid();
+        
+        if constexpr (std::is_same_v<typename RV::layout, align_l>) {
+            #pragma unroll
+            for(auto w = 0; w < (src.outer_dim+3)/4; w++) {
+                int idx = w*64 + (laneid/4)*8 + 2*(laneid%4);
+                int o_dim = w*4 + (laneid/4) / 2;
+                int i_dim = (laneid/4) % 2;
+                // this should be a maximally coalesced store. I hope!
+                if(idx < src.outer_dim*16)
+                    *(U2*)&dst_ptr[idx] = base_types::convertor<U2, T2>::convert(src[o_dim][i_dim]);
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, ortho_l>) {
+            // really hoping https://stackoverflow.com/questions/15029765/is-coalescing-triggered-for-accessing-memory-in-reverse-order is still true
+            // otherwise there will be some pain :/
+            #pragma unroll
+            for(auto w = 0; w < (src.outer_dim+1)/2; w++) {
+                int idx = w*32 + (laneid%4)*8 + (laneid/4);
+                int o_dim = w*2 + (laneid%4) / 2;
+                // this should be a maximally coalesced load.
+                if(idx < src.outer_dim*16) {
+                    U tmp;
+                    if(laneid%2==0) tmp = base_types::convertor<U, T>::convert(src[o_dim][0].x);
+                    else tmp = base_types::convertor<U, T>::convert(src[o_dim][0].y);
+                    dst_ptr[idx] = tmp;
+                }
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, naive_l>) {
+            #pragma unroll
+            for(auto w = 0; w < src.outer_dim; w++) {
+                if(w < src.outer_dim-1 || src.length%32 == 0 || laneid<16) {
+                    dst_ptr[w*32 + laneid] = base_types::convertor<U, T>::convert(src[w][0]);
+                }
+            }
+        }
+    }
+    else {
+        // Call warp level store
+        ::kittens::group<1>::store(dst, src, coord<RV>(idx.b, idx.d, idx.r, idx.c*GROUP_WARPS+warpid()));
+    }
+}
@@ -0,0 +1,77 @@
+/**
+ * @file
+ * @brief Group (collaborative warp) ops for loading shared vectors from and storing to global memory. 
+ */
+
+/**
+ * @brief Loads data from global memory into shared memory vector.
+ * 
+ * This function loads data from a global memory location pointed to by `src` into a shared memory vector `dst`.
+ * It calculates the number of elements that can be transferred in one operation based on the size ratio of `float4` to the data type of `SV`.
+ * The function ensures coalesced memory access and efficient use of bandwidth by dividing the work among threads in a warp.
+ * 
+ * @tparam SV Shared vector type, must satisfy ducks::sv::all concept.
+ * @param dst Reference to the shared vector where the data will be loaded.
+ * @param src Pointer to the global memory location from where the data will be loaded.
+ */
+template<ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void load(SV &dst, const GL &src, const COORD &idx) {
+    constexpr uint32_t elem_per_transfer = sizeof(float4) / sizeof(typename SV::dtype);
+    constexpr uint32_t total_calls = SV::length / elem_per_transfer; // guaranteed to divide
+    typename GL::dtype *src_ptr = (typename GL::dtype*)&src[(idx.template unit_coord<-1, 3>())];
+    uint32_t dst_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+    #pragma unroll
+    for(uint32_t i = threadIdx.x%GROUP_THREADS; i < total_calls; i+=GROUP_THREADS) {
+        if(i * elem_per_transfer < dst.length) {
+            float4 tmp;
+            move<float4>::ldg(tmp, (float4*)&src_ptr[i*elem_per_transfer]);
+            move<float4>::sts(dst_ptr + sizeof(typename SV::dtype)*i*elem_per_transfer, tmp);
+        }
+    }
+}
+
+/**
+ * @brief Stores data from a shared memory vector to global memory.
+ * 
+ * This function stores data from a shared memory vector `src` to a global memory location pointed to by `dst`.
+ * Similar to the load function, it calculates the number of elements that can be transferred in one operation based on the size ratio of `float4` to the data type of `SV`.
+ * The function ensures coalesced memory access and efficient use of bandwidth by dividing the work among threads in a warp.
+ * 
+ * @tparam SV Shared vector type, must satisfy ducks::sv::all concept.
+ * @param dst Pointer to the global memory location where the data will be stored.
+ * @param src Reference to the shared vector from where the data will be stored.
+ */
+template<ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store(GL &dst, const SV &src, const COORD &idx) {
+    constexpr uint32_t elem_per_transfer = sizeof(float4) / sizeof(typename SV::dtype);
+    constexpr uint32_t total_calls = SV::length / elem_per_transfer; // guaranteed to divide
+    typename GL::dtype *dst_ptr = (typename GL::dtype*)&dst[(idx.template unit_coord<-1, 3>())];
+    uint32_t src_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&src.data[0]));
+    #pragma unroll
+    for(uint32_t i = threadIdx.x%GROUP_THREADS; i < total_calls; i+=GROUP_THREADS) {
+        if(i * elem_per_transfer < src.length) {
+            float4 tmp;
+            move<float4>::lds(tmp, src_ptr + sizeof(typename SV::dtype)*i*elem_per_transfer);
+            move<float4>::stg((float4*)&dst_ptr[i*elem_per_transfer], tmp);
+        }
+    }
+}
+
+template<ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void load_async(SV &dst, const GL &src, const COORD &idx) {
+    constexpr uint32_t elem_per_transfer = sizeof(float4) / sizeof(typename SV::dtype);
+    constexpr uint32_t total_calls = SV::length / elem_per_transfer; // guaranteed to divide
+    typename GL::dtype *src_ptr = (typename GL::dtype*)&src[(idx.template unit_coord<-1, 3>())];
+    uint32_t dst_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+    #pragma unroll
+    for(uint32_t i = threadIdx.x%GROUP_THREADS; i < total_calls; i+=GROUP_THREADS) {
+        if(i * elem_per_transfer < dst.length) {
+            asm volatile(
+                "cp.async.cg.shared.global.L2::128B [%0], [%1], 16;\n"
+                :: "r"(dst_ptr + (uint32_t)sizeof(typename SV::dtype)*i*elem_per_transfer), "l"((uint64_t)&src_ptr[i*elem_per_transfer])
+                : "memory"
+            );
+        }
+    }
+    asm volatile("cp.async.commit_group;\n" ::: "memory");
+}
@@ -0,0 +1,159 @@
+/**
+ * @file
+ * @brief Functions for a group to collaboratively transfer data directly between shared memory and registers and back.
+ */
+
+/**
+ * @brief Collaboratively load data from a shared vector into register vectors split across a warpgroup.
+ *
+ * @tparam RV The register vector type
+ * @tparam SV The shared vector type
+ * @param dst[out] The destination register vector.
+ * @param src[in]  The source shared vector.
+ */
+template<ducks::rv::all RV, ducks::sv::all SV>
+__device__ inline static void load(RV &dst, const SV &src) {
+    using T2 = RV::dtype;
+    using U = SV::dtype;
+    using U2 = base_types::packing<U>::packed_type;
+    using T = base_types::packing<T2>::unpacked_type;
+    if constexpr (GROUP_WARPS == 1) {
+        static_assert(SV::length == RV::length);
+        
+        int laneid = ::kittens::laneid();
+        uint32_t src_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&src.data[0]));
+        
+        __syncwarp();
+        if constexpr (std::is_same_v<typename RV::layout, align_l>) {
+            #pragma unroll
+            for(auto w = 0; w < (dst.outer_dim+3)/4; w++) {
+                int idx = w*64 + (laneid/4)*8 + 2*(laneid%4);
+                int o_dim = w*4 + (laneid/4) / 2;
+                int i_dim = (laneid/4) % 2;
+                // this should be a maximally coalesced load.
+                if(idx < dst.outer_dim*16) {
+                    U2 tmp;
+                    move<U2>::lds(tmp, src_ptr + sizeof(typename SV::dtype)*idx);
+                    dst[o_dim][i_dim] = base_types::convertor<T2, U2>::convert(tmp);
+                }
+            }
+            __syncwarp();
+            // now we need to do a bunch of shuffle_sync's to make sure everyone has everything they need.
+            #pragma unroll
+            for(auto w = 0; w < dst.outer_dim; w++) {
+                int leader = 8*(w%4) + (laneid%4); // repeats every 64 columns
+                dst[w][0] = packed_shfl_sync(MASK_ALL, dst[w][0], leader);
+                dst[w][1] = packed_shfl_sync(MASK_ALL, dst[w][1], leader+4);
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, ortho_l>) {
+            // really hoping https://stackoverflow.com/questions/15029765/is-coalescing-triggered-for-accessing-memory-in-reverse-order is still true
+            // otherwise there will be some pain :/
+            #pragma unroll
+            for(auto w = 0; w < (dst.outer_dim+1)/2; w++) {
+                int idx = w*32 + (laneid%4)*8 + (laneid/4);
+                int o_dim = w*2 + (laneid%4) / 2;
+                // this should be a maximally coalesced load.
+                if(idx < dst.outer_dim*16) {
+                    U tmp;
+                    move<U>::lds(tmp, src_ptr + sizeof(typename SV::dtype)*idx);
+                    if(laneid%2==0) dst[o_dim][0].x =  base_types::convertor<T, U>::convert(tmp);
+                    else dst[o_dim][0].y = base_types::convertor<T, U>::convert(tmp);
+                }
+            }
+            __syncwarp();
+            // now we need to do a bunch of shuffle_sync's to make sure everyone has everything they need.
+            #pragma unroll
+            for(auto w = 0; w < dst.outer_dim; w++) {
+                int leader = (laneid/4)*4 + 2*(w%2); // repeats every 64 columns
+                dst[w][0].x = __shfl_sync(MASK_ALL, dst[w][0].x, leader);
+                dst[w][0].y = __shfl_sync(MASK_ALL, dst[w][0].y, leader+1);
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, naive_l>) {
+            #pragma unroll
+            for(auto w = 0; w < dst.outer_dim; w++) {
+                if(w < dst.outer_dim-1 || RV::length%32 == 0 || laneid<16) {
+                    U tmp;
+                    move<U>::lds(tmp, src_ptr + sizeof(typename SV::dtype)*(w*32 + laneid));
+                    dst[w][0] = base_types::convertor<T, U>::convert(tmp);
+                }
+            }
+        }
+    }
+    else {
+        static_assert(SV::length == RV::length*GROUP_WARPS);// confirm size correct
+        auto &_src = src.template subvec<RV::length>(warpid()); // pretend it's smaller and do warp-level load
+
+        ::kittens::group<1>::load(dst, _src); // warp-level
+    }
+}
+
+/**
+ * @brief Collaboratively store data into a shared vector from register vectors split across a warpgroup.
+ *
+ * @tparam RV The register vector type
+ * @tparam SV The shared vector type
+ * @param dst[out] The destination shared vector.
+ * @param src[in]  The source register vector.
+ */
+template<ducks::sv::all SV, ducks::rv::all RV>
+__device__ inline static void store(SV &dst, const RV &src) {
+    using T2 = RV::dtype;
+    using U = SV::dtype;
+    using U2 = base_types::packing<U>::packed_type;
+    using T = base_types::packing<T2>::unpacked_type;
+
+    if constexpr (GROUP_WARPS == 1) {
+        static_assert(SV::length == RV::length);
+        
+        int laneid = ::kittens::laneid();
+        uint32_t dst_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&dst.data[0]));
+
+        __syncwarp();
+        if constexpr (std::is_same_v<typename RV::layout, align_l>) {
+            #pragma unroll
+            for(auto w = 0; w < (src.outer_dim+3)/4; w++) {
+                int idx = w*64 + (laneid/4)*8 + 2*(laneid%4);
+                int o_dim = w*4 + (laneid/4) / 2;
+                int i_dim = (laneid/4) % 2;
+                // this should be a maximally coalesced store. I hope!
+                if(idx < src.outer_dim*16) {
+                    U2 tmp = base_types::convertor<U2, T2>::convert(src[o_dim][i_dim]);
+                    move<U2>::sts(dst_ptr + sizeof(typename SV::dtype)*idx, tmp);
+                }
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, ortho_l>) {
+            // really hoping https://stackoverflow.com/questions/15029765/is-coalescing-triggered-for-accessing-memory-in-reverse-order is still true
+            // otherwise there will be some pain :/
+            #pragma unroll
+            for(auto w = 0; w < (src.outer_dim+1)/2; w++) {
+                int idx = w*32 + (laneid%4)*8 + (laneid/4);
+                int o_dim = w*2 + (laneid%4) / 2;
+                // this should be a maximally coalesced load.
+                if(idx < src.outer_dim*16) {
+                    U tmp;
+                    if(laneid%2==0) tmp = base_types::convertor<U, T>::convert(src[o_dim][0].x);
+                    else tmp = base_types::convertor<U, T>::convert(src[o_dim][0].y);
+                    move<U>::sts(dst_ptr + sizeof(typename SV::dtype)*idx, tmp);
+                }
+            }
+        }
+        else if constexpr (std::is_same_v<typename RV::layout, naive_l>) {
+            #pragma unroll
+            for(auto w = 0; w < src.outer_dim; w++) {
+                if(w < src.outer_dim-1 || RV::length%32 == 0 || laneid<16) {
+                    U tmp = base_types::convertor<U, T>::convert(src[w][0]);
+                    move<U>::sts(dst_ptr + sizeof(typename SV::dtype)*(w*32 + laneid), tmp);
+                }
+            }
+        }
+    }
+    else {
+        static_assert(SV::length == RV::length*GROUP_WARPS);// confirm size correct
+        auto &_dst = dst.template subvec<RV::length>(warpid()); // pretend it's smaller and do warp-level load
+
+        ::kittens::group<1>::store(_dst, src); // warp-level
+    }
+}
@@ -0,0 +1,221 @@
+/**
+ * @file
+ * @brief Functions for a group scope to call vec TMA functions.
+ */
+
+/* ----------   Prefetch Tensor Map  ---------- */
+
+/**
+ * @brief Prefetches data from global memory into a shared memory vector, along with the tensormap.
+ *
+ * @tparam SV A shared vector type with a TMA-compatible layout
+ * @param[out] dst The destination shared memory vector.
+ * @param[in] src_tma_map The source tensormap address in global memory
+ * @param[in] vec_idx The coord of the requested vector.
+ */
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void prefetch(SV &dst, const GL &src, const COORD &idx) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(src.template get_tma<SV, -1>());
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        ::kittens::detail::tma::vec_prefetch_tma_internal<policy>(tma_ptr, tma_coord);
+    }
+}
+__KITTENS_TMA_DEFINE_DEFAULT_LOAD_CACHE_VEC__(prefetch)
+
+
+/* ----------   Async load and store data from gmem/smem  ---------- */
+
+/**
+ * @brief Asynchronously stores data into global memory from a shared memory vector.
+ *
+ * This function performs an asynchronous copy operation using CUDA's cp.async.bulk.tensor instruction.
+ *
+ * @tparam SV A shared vector type with a TMA-compatible layout
+ * @param[out] dst_tma_map The destination tensormap address in global memory
+ * @param[in] src The source shared memory vector.
+ * @param[in] vec_idx The coord of the vector destination.
+ */
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_async(const GL &dst, const SV &src, const COORD &idx) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_DEFAULT_STORE_CACHE_VEC__(store_async)
+
+template<cache_policy policy, ducks::sv::all SV, ducks::pgl::all PGL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_async(const PGL &dst, const SV &src, const COORD &idx) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_PGL_DEFAULT_STORE_CACHE_VEC__(store_async)
+
+
+/**
+* @brief Asynchronously performs an add reduction and stores the result into global memory.
+*
+* This function performs an asynchronous add reduction operation using CUDA's cp.reduce.async.bulk.tensor instruction.
+*
+* @tparam SV A shared vector type with a TMA-compatible layout
+* @param[out] dst_tma_map The destination tensormap address in global memory
+* @param[in] src The source shared memory vector.
+* @param[in] vec_idx The coord of the vector destination.
+*/
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_add_async(const GL &dst, const SV &src, const COORD &idx) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_add_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_DEFAULT_STORE_CACHE_VEC__(store_add_async)
+
+template<cache_policy policy, ducks::sv::all SV, ducks::pgl::all PGL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_add_async(const PGL &dst, const SV &src, const COORD &idx) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_add_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_PGL_DEFAULT_STORE_CACHE_VEC__(store_add_async)
+
+
+/**
+* @brief Asynchronously performs an min reduction and stores the result into global memory.
+*
+* This function performs an asynchronous min reduction operation using CUDA's cp.reduce.async.bulk.tensor instruction.
+*
+* @tparam SV A shared vector type with a TMA-compatible layout
+* @param[out] dst_tma_map The destination tensormap address in global memory
+* @param[in] src The source shared memory vector.
+* @param[in] vec_idx The coord of the vector destination.
+*/
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_min_async(const GL &dst, const SV &src, const COORD &idx) {
+    static_assert(!std::is_same_v<typename SV::dtype, float>, "TMA does not support async min/max reductions for fp32 types.");
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_min_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_DEFAULT_STORE_CACHE_VEC__(store_min_async)
+
+template<cache_policy policy, ducks::sv::all SV, ducks::pgl::all PGL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_min_async(const PGL &dst, const SV &src, const COORD &idx) {
+    static_assert(!std::is_same_v<typename SV::dtype, float>, "TMA does not support async min/max reductions for fp32 types.");
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_min_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_PGL_DEFAULT_STORE_CACHE_VEC__(store_min_async)
+
+/**
+* @brief Asynchronously performs an max reduction and stores the result into global memory.
+*
+* This function performs an asynchronous max reduction operation using CUDA's cp.reduce.async.bulk.tensor instruction.
+*
+* @tparam SV A shared vector type with a TMA-compatible layout
+* @param[out] dst_tma_map The destination tensormap address in global memory
+* @param[in] src The source shared memory vector.
+* @param[in] vec_idx The coord of the vector destination.
+*/
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_max_async(const GL &dst, const SV &src, const COORD &idx) {
+    static_assert(!std::is_same_v<typename SV::dtype, float>, "TMA does not support async min/max reductions for fp32 types.");
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_max_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_DEFAULT_STORE_CACHE_VEC__(store_max_async)
+
+template<cache_policy policy, ducks::sv::all SV, ducks::pgl::all PGL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void store_max_async(const PGL &dst, const SV &src, const COORD &idx) {
+    static_assert(!std::is_same_v<typename SV::dtype, float>, "TMA does not support async min/max reductions for fp32 types.");
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(dst.template get_tma<SV, -1>());
+    uint32_t src_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&src));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t src_i_ptr = src_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_store_max_async_tma_internal<policy>(tma_ptr, src_i_ptr, tma_coord);
+    }
+    store_commit_group();
+}
+__KITTENS_TMA_DEFINE_PGL_DEFAULT_STORE_CACHE_VEC__(store_max_async)
+
+/**
+ * @brief Asynchronously loads data from global memory into a shared memory vector.
+ *
+ * This function performs an asynchronous copy operation using CUDA's cp.async.bulk.tensor instruction.
+ *
+ * @tparam SV A shared vector type with a TMA-compatible layout
+ * @param[out] dst The destination shared memory vector.
+ * @param[in] src_tma_map The source tensormap address in global memory
+ * @param[in] vec_idx The coord of the requested vector.
+ * @param[in,out] bar The semaphore used for synchronization of the asynchronous copy.
+ */
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void load_async(SV &dst, const GL &src, const COORD &idx, semaphore& bar) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(src.template get_tma<SV, -1>());
+    uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&bar));
+    uint32_t dst_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&dst));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t dst_i_ptr = dst_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::vec_load_async_tma_internal<policy>(tma_ptr, dst_i_ptr, mbar_ptr, tma_coord);
+    }
+}
+__KITTENS_TMA_DEFINE_SEMAPHORE_CACHE_VEC__(load_async)
@@ -0,0 +1,31 @@
+/**
+ * @file
+ * @brief Functions for a group scope to call vec TMA cluster functions.
+ */
+
+/**
+ * @brief Asynchronously loads data from global memory into a shared memory vector, broadcast across a cluster
+ *
+ * This function performs an asynchronous copy operation using CUDA's cp.async.bulk.tensor instruction.
+ *
+ * @tparam SV A shared vector type with a TMA-compatible layout
+ * @param[out] dst The destination shared memory vector.
+ * @param[in] src_tma_map The source tensormap address in global memory
+ * @param[in,out] bar The semaphore used for synchronization of the asynchronous copy.
+ * @param[in] vec_idx The coord of the requested vector.
+ * @param[in] cluster_mask The mask of the clusters to broadcast to.
+ */
+template<cache_policy policy, ducks::sv::all SV, ducks::gl::all GL, ducks::coord::vec COORD=coord<SV>>
+__device__ static inline void load_async(SV &dst, const GL &src, const COORD &idx, semaphore& bar, uint16_t cluster_mask, int dst_mbar_cta=-1) {
+    coord<> unit_coord = idx.template unit_coord<-1, 3>();
+    uint64_t tma_ptr  = reinterpret_cast<uint64_t>(src.template get_tma<SV, -1>());
+    uint32_t mbar_ptr = static_cast<uint32_t>(__cvta_generic_to_shared(&bar));
+    uint32_t dst_ptr  = static_cast<uint32_t>(__cvta_generic_to_shared(&dst));
+    for(int i = ::kittens::laneid(); i < ::kittens::detail::tma::sv_tma_dim2<SV>; i += WARP_THREADS) {
+        coord<> tma_coord = unit_coord;
+        tma_coord.c += i * ::kittens::detail::tma::sv_tma_dim1<SV>;
+        uint32_t dst_i_ptr = dst_ptr + i*::kittens::detail::tma::sv_tma_dim1<SV>*sizeof(typename SV::dtype);
+        ::kittens::detail::tma::cluster::vec_load_async_tma_internal<policy>(tma_ptr, dst_i_ptr, mbar_ptr, tma_coord, cluster_mask, dst_mbar_cta);
+    }
+}
+__KITTENS_TMA_DEFINE_CLUSTER_SEMAPHORE_CACHE_VEC__(load_async)
@@ -0,0 +1,8 @@
+/**
+ * @file
+ * @brief An aggregate header of group memory operations on vectors.
+ */
+
+#include "shared_to_register.cuh"
+#include "global_to_register.cuh"
+#include "global_to_shared.cuh"
@@ -0,0 +1,17 @@
+/**
+ * @file
+ * @brief An aggregate header for all group-scope MMA operations.
+ */
+
+// All compilation targets can use the warp-scope MMA operations.
+#include "warp/warp.cuh"
+
+// Hopper has its own warpgroup-scope MMA operations.
+#ifdef KITTENS_HOPPER
+#include "warpgroup/warpgroup.cuh"
+#endif
+
+// Blackwell has its own tensor-scope MMA operations.
+#ifdef KITTENS_BLACKWELL
+#include "tensor/tensor.cuh"
+#endif
--- a/Show More
+++ b/Show More