move down dtype decomps

move late decomp after new style (#16734 )
fix profile.sh BENCHMARK=3 exit status (#16736 )
2026-06-24 22:12:53 -07:00 · 2026-06-24 22:04:59 -07:00 · 2026-06-25 13:41:03 +09:00 · 2026-06-24 23:33:46 -04:00 · 2026-06-24 23:25:39 -04:00 · 2026-06-24 21:03:36 -04:00
32 changed files with 681 additions and 844 deletions
@@ -81,85 +81,180 @@ jobs:
  #      source /tmp/tinygrad_pytest_ci/bin/activate
  #      pytest -nauto --durations=20

-  testmacbenchmark:
-    name: Mac Benchmark
-    runs-on: [self-hosted, macOS]
+  sharedbenchmarks:
+    name: Benchmark (DEV=${{ matrix.dev }})
+    runs-on: [self-hosted, "${{ matrix.dev == 'METAL' && 'macOS' || matrix.dev == 'AMD' && 'tinybox' || 'tinyboxgreen' }}"]
+    strategy:
+      fail-fast: false
+      matrix:
+        dev: ['METAL', 'AMD', 'NV']
    timeout-minutes: 60
    defaults:
      run:
        shell: bash -e -o pipefail {0}
+    env:
+      DEV: ${{ matrix.dev }}
    if: github.repository_owner == 'tinygrad'
    steps:
    - name: Checkout Code
      uses: actions/checkout@v6
-    - name: Symlink models and datasets
+    - name: Setup (AMD)
+      if: ${{ matrix.dev == 'AMD' }}
      run: |
-        mkdir -p weights
-        mkdir -p extra/disassemblers
-        ln -s ~/tinygrad/extra/disassemblers/applegpu extra/disassemblers/applegpu
-        ln -s ~/tinygrad/weights/sd-v1-4.ckpt weights/sd-v1-4.ckpt
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
+        ./extra/amdpci/setup_python_cap.sh
+        ./extra/hcq/hcq_smi.py amd rmmod
+        ./extra/hcq/hcq_smi.py amd kill_pids
    - name: setup staging db
      if: github.ref == 'refs/heads/update_benchmark_staging'
      run: |
        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
    - name: reset process replay
-      run: 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=720 python3.11 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing
-    - name: Run Stable Diffusion without fp16
-      run: BENCHMARK_LOG=stable_diffusion_fp32 JIT=1 ASSERT_MIN_STEP_TIME=720 python3.11 examples/stable_diffusion.py --seed 0 --noshow --timing
-    - name: Run Stable Diffusion v2
-      # 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
-    # process replay can't capture this, the graph is too large
-    - name: Run SDXL
-      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
-    - name: Run model inference benchmark
-      run: DEV=METAL NOCLANG=1 python3.11 test/external/external_model_benchmark.py
-    - name: Test speed vs torch
-      run: BIG=2 MPS=1 python3.11 test/speed/external_test_speed_v_torch.py
+      run: python3 test/external/process_replay/reset.py
    - name: Test tensor cores
-      run: DEV=METAL python3.11 test/opt/test_tensor_cores.py
-    - name: Run Tensor Core GEMM (float)
-      run: DEBUG=2 SHOULD_USE_TC=1 python3.11 extra/gemm/simple_matmul.py
-    - name: Run Tensor Core GEMM (half)
-      run: DEBUG=2 SHOULD_USE_TC=1 HALF=1 python3.11 extra/gemm/simple_matmul.py
-    - name: Run Tensor Core GEMM (bfloat16)
-      run: DEBUG=2 SHOULD_USE_TC=1 BFLOAT16=1 python3.11 extra/gemm/simple_matmul.py
-    - name: Fuzz Padded Tensor Core GEMM
-      run: DEV=METAL M_START=6 M_STOP=10 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=6 K_STOP=24 K_STEP=1 TC_OPT=2 DEBUG=2 python3.11 ./extra/gemm/fuzz_matmul.py
+      run: |
+        if [[ "${{ matrix.dev }}" == "METAL" ]]; then
+          python3 test/opt/test_tensor_cores.py
+          DEBUG=2 SHOULD_USE_TC=1 python3 extra/gemm/simple_matmul.py
+          DEBUG=2 SHOULD_USE_TC=1 HALF=1 python3 extra/gemm/simple_matmul.py
+          DEBUG=2 SHOULD_USE_TC=1 BFLOAT16=1 python3 extra/gemm/simple_matmul.py
+          M_START=6 M_STOP=10 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=6 K_STOP=24 K_STEP=1 TC_OPT=2 DEBUG=2 python3 ./extra/gemm/fuzz_matmul.py
+        elif [[ "${{ matrix.dev }}" == "NV" ]]; then
+          ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
+          DEV=NV:PTX ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
+          DEV=CUDA SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          DEV=CUDA SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          DEV=CUDA SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
+          DEV=CUDA SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          DEV=NV:PTX SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          # TODO: too slow
+          # 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
+          # DEV=NV:PTX 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
+        else
+          python3 test/opt/test_tensor_cores.py
+          # TODO: this is flaky
+          # DEV=AMD:LLVM python3 test/opt/test_tensor_cores.py
+          SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
+          # TODO: AMD compiler bug causes this to fail
+          # HSA=1 M_START=12 M_STOP=20 M_STEP=1 N_START=12 N_STOP=20 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 DEBUG=2 python3 ./extra/gemm/fuzz_matmul.py
+        fi
+    - name: Run model inference benchmark
+      # TODO: unstable on AMD
+      if: ${{ matrix.dev != 'AMD' }}
+      run: CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
+    - name: Test speed vs torch
+      # TODO: unstable on AMD
+      if: ${{ matrix.dev != 'AMD' }}
+      env:
+        HALF: ${{ matrix.dev == 'NV' && '1' || '0' }}
+      run: CAPTURE_PROCESS_REPLAY=0 BIG=2 ${{ matrix.dev == 'METAL' && 'MPS=1' || 'TORCHCUDA=1' }} python3 test/speed/external_test_speed_v_torch.py
+    - name: Run Stable Diffusion
+      env:
+        ASSERT_MIN_STEP_TIME: ${{ matrix.dev == 'METAL' && '720' || matrix.dev == 'AMD' && '550' || '0' }}
+      run: BENCHMARK_LOG=stable_diffusion python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing
+    - name: Run SDXL
+      if: ${{ matrix.dev != 'NV' }}
+      env:
+        ASSERT_MIN_STEP_TIME: ${{ matrix.dev == 'METAL' && '5000' || matrix.dev == 'AMD' && '3200' || '2000' }}
+      run: BENCHMARK_LOG=stable_diffusion_xl CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing
    - name: Run llama3.2
-      run: BENCHMARK_LOG=llama32_3b-f16 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 -m tinygrad.llm -m llama3.2:3b-f16 --benchmark --warmup
+      run: BENCHMARK_LOG=llama32_3b-f16 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m llama3.2:3b-f16 --benchmark --warmup
+    - name: Run qwen3.5
+      # qwen3.5:35b-a3b doesn't fit on mac
+      if: ${{ matrix.dev != 'METAL' }}
+      run: BENCHMARK_LOG=qwen35_35b-a3b JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m qwen3.5:35b-a3b --benchmark --warmup
    - name: Run olmoe
-      run: BENCHMARK_LOG=olmoe JITBEAM=2 IGNORE_BEAM_CACHE=1 python3.11 -m tinygrad.llm -m olmoe --benchmark --warmup
+      # just metal for now
+      if: ${{ matrix.dev == 'METAL' }}
+      run: BENCHMARK_LOG=olmoe JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m olmoe --benchmark --warmup
    - name: Train MNIST
-      run: time PYTHONPATH=. TARGET_EVAL_ACC_PCT=96.0 python3.11 examples/beautiful_mnist.py
-
-    # 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
-    #- 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
-
-    #- name: Run 10 CIFAR training steps w BF16
-    #  run: STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3.11 examples/hlb_cifar10.py
-    # 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
+      run: time TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py
+    - name: Run 10 CIFAR training steps
+      env:
+        ASSERT_MIN_STEP_TIME: ${{ matrix.dev == 'NV' && '130' || matrix.dev == 'AMD' && '200' || '3000' }}
+      run: BENCHMARK_LOG=cifar_10steps STEPS=10 python3 examples/hlb_cifar10.py
+    - name: Run 10 CIFAR training steps w HALF
+      env:
+        ASSERT_MIN_STEP_TIME: ${{ matrix.dev == 'NV' && '120' || matrix.dev == 'AMD' && '230' || '3000' }}
+      run: BENCHMARK_LOG=cifar_10steps_half STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
    - uses: actions/upload-artifact@v7
+      if: ${{ matrix.dev != 'AMD' }}
      with:
-        name: Speed (Mac)
+        name: Speed (${{ matrix.dev }})
        path: |
          onnx_inference_speed.csv
    - name: Run process replay tests
      uses: ./.github/actions/process-replay

+  tinyboxbenchmark:
+    name: Tinybox Benchmark (${{ matrix.dev }})
+    runs-on: [self-hosted, Linux, "${{ matrix.dev == 'AMD' && 'tinybox' || 'tinyboxgreen' }}"]
+    strategy:
+      fail-fast: false
+      matrix:
+        dev: ['AMD', 'NV']
+    timeout-minutes: 60
+    defaults:
+      run:
+        shell: bash -e -o pipefail {0}
+    env:
+      DEV: ${{ matrix.dev }}
+    steps:
+    - name: Checkout Code
+      uses: actions/checkout@v6
+    - name: Setup (AMD)
+      if: ${{ matrix.dev == 'AMD' }}
+      run: |
+        ./extra/amdpci/setup_python_cap.sh
+        ./extra/hcq/hcq_smi.py amd rmmod
+        ./extra/hcq/hcq_smi.py amd kill_pids
+    - name: Symlink models and datasets
+      run: |
+        mkdir -p weights
+        mkdir -p extra/datasets
+        ln -s /raid/weights/LLaMA-3 weights/LLaMA-3
+        ln -s /raid/datasets/imagenet extra/datasets/imagenet
+    - name: setup staging db
+      if: github.ref == 'refs/heads/update_benchmark_staging'
+      run: |
+        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
+        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
+    - name: reset process replay
+      run: test/external/process_replay/reset.py
+    - name: Test tiny
+      run: |
+        DEBUG=2 python -m pytest -rA test/test_tiny.py
+        if [[ "${{ matrix.dev }}" == "NV" ]]; then
+          DEBUG=2 DEV=CUDA python -m pytest -rA test/test_tiny.py
+        fi
+    - name: Test speed vs theoretical
+      run: IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
+    - name: Test benchmark allreduce
+      if: ${{ matrix.dev == 'NV' }}
+      run: python test/external/external_benchmark_multitensor_allreduce.py
+    - name: HEVC Decode Benchmark
+      if: ${{ matrix.dev == 'NV' }}
+      run: VALIDATE=1 MAX_FRAMES=100 ASSERT_FPS=1400 JITBEAM=1 PYTHONPATH=. python3 extra/hevc/decode.py
+    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
+      run: BENCHMARK_LOG=llama3_beam_4gpu JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
+    - name: Run full CIFAR training w 1 GPU
+      run: time BENCHMARK_LOG=cifar DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
+    - name: Run full CIFAR training steps w 6 GPUS
+      run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
+    - name: Run MLPerf resnet eval on training data
+      run: time BENCHMARK_LOG=resnet_eval MODEL=resnet python3 examples/mlperf/model_eval.py
+    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+      run: BENCHMARK_LOG=resnet_10steps DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py
+    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
+      run: BENCHMARK_LOG=resnet_10steps_6gpu CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py
+    - name: Run 10 MLPerf Bert training steps (6 gpu)
+      # TODO: remove BERT_LAYERS once scheduler is fast
+      run: BENCHMARK_LOG=bert_10steps_6gpu CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+    - name: Run process replay tests
+      uses: ./.github/actions/process-replay
+
  testusbgpu:
    name: UsbGPU Benchmark
    runs-on: [self-hosted, macOS]
@@ -196,337 +291,6 @@ jobs:
    - name: UsbGPU (USB4/TB) tiny tests
      run: PYTHONPATH=. DEV=PCI+NV:NAK python3.11 test/test_tiny.py

-  testnvidiabenchmark:
-    name: tinybox green Benchmark
-    runs-on: [self-hosted, Linux, tinyboxgreen]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Print nvidia-smi
-      run: nvidia-smi
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s /raid/weights/LLaMA-3 weights/LLaMA-3
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Run model inference benchmark
-      run: DEV=NV CAPTURE_PROCESS_REPLAY=0 NOCLANG=1 python3 test/external/external_model_benchmark.py
-    - name: Test speed vs torch
-      run: DEV=NV CAPTURE_PROCESS_REPLAY=0 HALF=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py
-    - name: Test speed vs theoretical
-      run: DEV=NV IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
-    - name: Test benchmark allreduce
-      run: DEV=NV python test/external/external_benchmark_multitensor_allreduce.py
-    - name: Test tensor cores
-      run: |
-        DEV=NV ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
-        DEV=NV:PTX ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
-    - name: Run Tensor Core GEMM (CUDA)
-      run: |
-        DEV=CUDA SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-        DEV=CUDA SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-        DEV=CUDA SHOULD_USE_TC=1 ALLOW_TF32=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
-        DEV=CUDA SHOULD_USE_TC=1 FP8E4M3=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-    - name: Run Tensor Core GEMM (PTX)
-      run: DEV=NV:PTX SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-    - name: Run Tensor Core GEMM (NV)
-      run: DEV=NV SHOULD_USE_TC=1 HALF=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-    - name: Test DEV=NV
-      run: DEBUG=2 DEV=NV python -m pytest -rA test/test_tiny.py
-    - name: Test DEV=CUDA
-      run: DEBUG=2 DEV=CUDA python -m pytest -rA test/test_tiny.py
-    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion DEV=NV python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing
-    # TODO: too slow
-    # - name: Run SDXL
-    #   run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=2000 CAPTURE_PROCESS_REPLAY=0 DEV=NV CAPTURE_PROCESS_REPLAY=0 python3 examples/sdxl.py --seed 0 --noshow --timing
-    - name: Run llama3.2
-      run: DEV=NV BENCHMARK_LOG=llama32_3b-f16 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m llama3.2:3b-f16 --benchmark --warmup
-    - name: Run qwen3.5
-      run: DEV=NV BENCHMARK_LOG=qwen35_35b-a3b JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 -m tinygrad.llm -m qwen3.5:35b-a3b --benchmark --warmup
-    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu DEV=NV JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
-    # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: DEV=NV CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
-    # - name: Run LLaMA-2 70B
-    #   run: DEV=NV CAPTURE_PROCESS_REPLAY=0 MAX_CONTEXT=256 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing
-    - uses: actions/upload-artifact@v7
-      with:
-        name: Speed (NVIDIA)
-        path: |
-          onnx_inference_speed.csv
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testmorenvidiabenchmark:
-    name: tinybox green Training Benchmark
-    runs-on: [self-hosted, Linux, tinyboxgreen]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    # TODO: too slow
-    # - name: Fuzz Padded Tensor Core GEMM (NV)
-    #   run: DEV=NV 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: DEV=NV:PTX M_START=12 M_STOP=20 M_STEP=1 N_START=6 N_STOP=10 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 python3 ./extra/gemm/fuzz_matmul.py
-    - name: HEVC Decode Benchmark
-      run: VALIDATE=1 MAX_FRAMES=100 ASSERT_FPS=1400 JITBEAM=1 DEV=NV PYTHONPATH=. python3 extra/hevc/decode.py
-    - name: Train MNIST
-      run: time PYTHONPATH=. DEV=NV TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py
-    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=130 DEV=NV STEPS=10 python3 examples/hlb_cifar10.py
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=120 DEV=NV STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
-    - name: Run 10 CIFAR training steps w BF16
-      run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=120 DEV=NV STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py
-    # - name: Run 10 CIFAR training steps w winograd
-    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=350 DEV=NV WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar DEV=NV DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu CAPTURE_PROCESS_REPLAY=0 DEV=NV DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    - name: Run MLPerf resnet eval on training data
-      run: time BENCHMARK_LOG=resnet_eval DEV=NV MODEL=resnet python3 examples/mlperf/model_eval.py
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps DEV=NV DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py
-    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-      run: BENCHMARK_LOG=resnet_10steps_6gpu DEV=NV CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py
-    - name: Run 10 MLPerf Bert training steps (6 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu DEV=NV CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testamdbenchmark:
-    name: tinybox red Benchmark
-    runs-on: [self-hosted, Linux, tinybox]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-    #- name: Insert amdgpu
-    #  run: sudo modprobe amdgpu
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/LLaMA-3 weights/LLaMA-3
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    #- name: setup perflevel
-    #  run: |
-    #    examples/mlperf/training_submission_v4.1/tinycorp/benchmarks/bert/implementations/tinybox_red/setup.sh
-    #    rocm-smi
-    #- name: Show off tinybox
-    #  run: /opt/rocm/bin/rocm-bandwidth-test
-    # TODO: unstable on AMD
-    #- name: Run model inference benchmark
-    #  run: LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 NOCLANG=1 python3 test/external/external_model_benchmark.py
-    # TODO: unstable on AMD
-    #- name: Test speed vs torch
-    #  run: |
-    #    python3 -c "import torch; print(torch.__version__)"
-    #    LD_PRELOAD="/opt/rocm/lib/libhsa-runtime64.so" HSA=1 BIG=2 TORCHCUDA=1 python3 test/speed/external_test_speed_v_torch.py
-    - name: Test speed vs theoretical
-      run: DEV=AMD IGNORE_BEAM_CACHE=1 CCACHE=0 BEAM_DEBUG=1 DEBUG=1 python -m pytest -rA test/external/speed_v_theoretical.py --durations=20
-    - name: Test tensor cores (no LLVM)
-      run: DEV=AMD python3 test/opt/test_tensor_cores.py
-    # TODO: this is flaky
-    # - name: Test tensor cores AMD:LLVM
-    #   run: DEV=AMD:LLVM python3 test/opt/test_tensor_cores.py
-    - name: Run Tensor Core GEMM (AMD)
-      run: |
-        DEV=AMD SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-        DEV=AMD SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
-    - name: Test DEV=AMD
-      run: DEBUG=2 DEV=AMD python -m pytest -rA test/test_tiny.py
-    #- name: Test HIP=1
-    #  run: DEBUG=2 HIP=1 python -m pytest -rA test/test_tiny.py
-    # TODO: AMD compiler bug causes this to fail
-    #- name: Fuzz Padded Tensor Core GEMM
-    #  run: HSA=1 M_START=12 M_STOP=20 M_STEP=1 N_START=12 N_STOP=20 N_STEP=1 K_START=28 K_STOP=36 K_STEP=1 HALF=1 TC_OPT=2 DEBUG=2 python3 ./extra/gemm/fuzz_matmul.py
-    #- name: Remove amdgpu
-    #  run: sleep 10 && sudo rmmod amdgpu # sleep a bit to let the driver unload the prev pid.
-    - name: Test AM cold start time
-      run: time DEV=AMD AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
-    - name: Test AM warm start time
-      run: time DEV=AMD python3 test/test_tiny.py TestTiny.test_plus
-    - name: Run Stable Diffusion
-      run: BENCHMARK_LOG=stable_diffusion ASSERT_MIN_STEP_TIME=550 DEV=AMD python3 examples/stable_diffusion.py --fp16 --seed 0 --noshow --timing
-    - name: Run SDXL
-      run: BENCHMARK_LOG=stable_diffusion_xl ASSERT_MIN_STEP_TIME=3200 CAPTURE_PROCESS_REPLAY=0 DEV=AMD python3 examples/sdxl.py --seed 0 --noshow --timing
-    - name: Run llama3.2
-      run: DEV=AMD BENCHMARK_LOG=llama32_3b-f16 JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 -m tinygrad.llm -m llama3.2:3b-f16 --benchmark --warmup
-    - name: Run qwen3.5
-      run: DEV=AMD BENCHMARK_LOG=qwen35_35b-a3b JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 -m tinygrad.llm -m qwen3.5:35b-a3b --benchmark --warmup
-    - name: Run LLaMA-3 8B on 4 GPUs with BEAM
-      run: BENCHMARK_LOG=llama3_beam_4gpu DEV=AMD JITBEAM=2 IGNORE_BEAM_CACHE=1 CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 4 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
-    # - name: Run LLaMA-3 8B on 6 GPUs
-    #   run: DEV=AMD CAPTURE_PROCESS_REPLAY=0 python3 examples/llama3.py --size 8B --shard 6 --model weights/LLaMA-3/8B-SF-DPO/ --benchmark --temperature 0
-    #- name: Restore amdgpu
-    #  run: sudo modprobe amdgpu
-    # - name: Run LLaMA-2 70B
-    #   run: DEV=AMD CAPTURE_PROCESS_REPLAY=0 python3 examples/llama.py --gen 2 --size 70B --shard 6 --prompt "Hello." --count 10 --temperature 0  --timing
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testmoreamdbenchmark:
-    name: tinybox red Training Benchmark
-    runs-on: [self-hosted, Linux, tinybox]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Test GPU crash recovery
-      run: DEV=AMD python3 -m pytest -rA test/external/external_test_gpu_crash.py
-    - name: Train MNIST
-      run: time PYTHONPATH=. DEV=AMD TARGET_EVAL_ACC_PCT=96.0 python3 examples/beautiful_mnist.py
-    - name: Run 10 CIFAR training steps
-      run: BENCHMARK_LOG=cifar_10steps ASSERT_MIN_STEP_TIME=200 DEV=AMD STEPS=10 python3 examples/hlb_cifar10.py
-    - name: Run 10 CIFAR training steps w HALF
-      run: BENCHMARK_LOG=cifar_10steps_half ASSERT_MIN_STEP_TIME=230 DEV=AMD STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
-    # - name: Run 10 CIFAR training steps w BF16
-    #   run: BENCHMARK_LOG=cifar_10steps_bf16 ASSERT_MIN_STEP_TIME=288 DEV=AMD STEPS=10 DEFAULT_FLOAT=BFLOAT16 python3 examples/hlb_cifar10.py
-    # TODO: too slow
-    # - name: Run 10 CIFAR training steps w winograd
-    #   run: BENCHMARK_LOG=cifar_10steps_half_wino ASSERT_MIN_STEP_TIME=66 DEV=AMD WINO=1 STEPS=10 DEFAULT_FLOAT=HALF python3 examples/hlb_cifar10.py
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar DEV=AMD DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    - name: Run full CIFAR training steps w 6 GPUS
-      run: time BENCHMARK_LOG=cifar_6gpu DEV=AMD DEFAULT_FLOAT=HALF STEPS=350 BS=1536 GPUS=6 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    # TODO: broken on some of the machines
-    #- name: Test full tinyfs load
-    #  run: TINYFS_ENDPOINT=10.0.52.11:6767 PYTHONPATH=. python extra/tinyfs/fetch_file.py --hash d734f5e3be9f1e9d863bfaa4fc6c1ef2 --len 175866113 --dest mapping.json --check
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testmlperfamdbenchmark:
-    name: tinybox red MLPerf Benchmark
-    runs-on: [self-hosted, Linux, tinybox]
-    timeout-minutes: 60
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Run MLPerf resnet eval
-      run: time BENCHMARK_LOG=resnet_eval DEV=AMD MODEL=resnet python3 examples/mlperf/model_eval.py
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps DEV=AMD DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py
-    - name: Run 10 MLPerf ResNet50 training steps (6 gpu)
-      run: BENCHMARK_LOG=resnet_10steps_6gpu DEV=AMD CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=1536 GPUS=6 MODEL=resnet python3 examples/mlperf/model_train.py
-    - name: Run 10 MLPerf Bert training steps (6 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps_6gpu DEV=AMD CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=72 GPUS=6 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
  testcommalatest:
    name: comma Benchmark (0.11.0)
    runs-on: [self-hosted, Linux, comma]
@@ -648,31 +412,28 @@ jobs:
    - name: openpilot run_pickle 0.10.1 driving_vision
      run: BENCHMARK_LOG=usbgpu_openpilot_0_10_1_vision_run_pickle RUN_PICKLE=1 PYTHONPATH="." GMMU=0 DEV=USB+AMD ASSERT_MIN_STEP_TIME=50 python3 examples/openpilot/compile3.py

-  testreddriverbenchmark:
-    name: AM Benchmark
+  driverbenchmarks:
+    name: PCI Driver Benchmark (DEV=${{ matrix.dev }})
    runs-on: [self-hosted, Linux, tinyboxrandom]
+    strategy:
+      fail-fast: false
+      matrix:
+        dev: ['AMD', 'NV']
    timeout-minutes: 20
    defaults:
      run:
        shell: bash -e -o pipefail {0}
+    env:
+      DEV: ${{ matrix.dev }}
    if: github.repository_owner == 'tinygrad'
    steps:
    - name: Checkout Code
      uses: actions/checkout@v6
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove amd modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py amd kill_pids
-    - name: Symlink models and datasets
+    - name: Setup
      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
+        ./extra/amdpci/setup_python_cap.sh
+        ./extra/hcq/hcq_smi.py ${{ matrix.dev == 'AMD' && 'amd' || 'nv' }} rmmod
+        ./extra/hcq/hcq_smi.py ${{ matrix.dev == 'AMD' && 'amd' || 'nv' }} kill_pids
        mkdir -p extra/datasets
        ln -s /raid/datasets/imagenet extra/datasets/imagenet
    - name: setup staging db
@@ -683,104 +444,47 @@ jobs:
    - name: reset process replay
      run: test/external/process_replay/reset.py
    - name: Test driver cold start time
-      run: time DEBUG=3 DEV=AMD AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
+      run: time DEBUG=3 AM_RESET=1 python3 test/test_tiny.py TestTiny.test_plus
    - name: Test driver warm start time
-      run: time DEBUG=3 DEV=AMD python3 test/test_tiny.py TestTiny.test_plus
+      if: ${{ matrix.dev == 'AMD' }}
+      run: time DEBUG=3 python3 test/test_tiny.py TestTiny.test_plus
    - name: Test GPU crash recovery
-      run: DEV=AMD python3 -m pytest -rA test/external/external_test_gpu_crash.py
-    # Fails on 9070
-    # - name: Test tensor cores
-    #   run: |
-    #     DEV=AMD python3 test/test_linearizer.py test/opt/test_tensor_cores.py
-    #     DEV=AMD:LLVM python3 test/test_linearizer.py test/opt/test_tensor_cores.py
-    #     DEV=AMD SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
-    - name: Run Tensor Core GEMM (AMD)
-      run: DEV=AMD SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
-    - name: Test DEV=AMD
-      run: DEBUG=2 DEV=AMD python -m pytest -rA test/test_tiny.py
+      if: ${{ matrix.dev == 'AMD' }}
+      run: python3 -m pytest -rA test/external/external_test_gpu_crash.py
+    - name: Test tensor cores
+      run: |
+        if [[ "${{ matrix.dev }}" == "AMD" ]]; then
+          # Fails on 9070
+          # python3 test/test_linearizer.py test/opt/test_tensor_cores.py
+          # DEV=AMD:LLVM python3 test/test_linearizer.py test/opt/test_tensor_cores.py
+          # SHOULD_USE_TC=1 BFLOAT16=1 DEBUG=2 python3 extra/gemm/simple_matmul.py
+          SHOULD_USE_TC=1 HALF=1 DEBUG=2 ATOL=2e-2 python3 extra/gemm/simple_matmul.py
+        else
+          ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
+        fi
    - name: Test DISK copy time
-      run: DEV=AMD TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
+      run: TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
    - name: Test CPU copy time
      run: |
-        DEV=AMD GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
-        DEV=AMD GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
+        GRAPH_ONE_KERNEL=1 NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
+        GRAPH_ONE_KERNEL=1 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 DEV=AMD DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    # - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-    #   run: BENCHMARK_LOG=resnet_10steps DEV=AMD MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py
+      run: time BENCHMARK_LOG=cifar DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
+    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
+      if: ${{ matrix.dev == 'NV' }}
+      run: BENCHMARK_LOG=resnet_10steps MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py
    - name: Run 10 MLPerf Bert training steps (1 gpu)
      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps DEV=AMD CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
+      run: BENCHMARK_LOG=bert_10steps CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
    - name: Remote
      run: |
        pkill -f 'extra/remote/serve.py' || true
        PYTHONPATH=. python3 extra/remote/serve.py 6482 &
        sleep 1
-        DEBUG=2 PYTHONPATH=. REMOTE=127.0.0.1:6482 AM_RESET=1 DEV=PCI+AMD python3 test/test_tiny.py
-        DEBUG=2 PYTHONPATH=. REMOTE=127.0.0.1:6482 AM_RESET=1 DEV=PCI+AMD AMD_AQL=1 python3 test/test_tiny.py
-        pkill -f 'extra/remote/serve.py' || true
-    - name: Run process replay tests
-      uses: ./.github/actions/process-replay
-
-  testgreendriverbenchmark:
-    name: NV Benchmark
-    runs-on: [self-hosted, Linux, tinyboxrandom]
-    timeout-minutes: 20
-    defaults:
-      run:
-        shell: bash -e -o pipefail {0}
-    if: github.repository_owner == 'tinygrad'
-    steps:
-    - name: Checkout Code
-      uses: actions/checkout@v6
-    - name: Setcap to python
-      run: ./extra/amdpci/setup_python_cap.sh
-    - name: Remove nv modules
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv rmmod
-    - name: Kill stale pids
-      run: PYTHONPATH=. ./extra/hcq/hcq_smi.py nv kill_pids
-    - name: Symlink models and datasets
-      run: |
-        mkdir -p weights
-        ln -s ~/tinygrad/weights/bpe_simple_vocab_16e6.txt.gz weights/bpe_simple_vocab_16e6.txt.gz
-        ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
-        ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
-        ln -s /raid/weights/mixtral-8x7b-32kseqlen weights/mixtral-8x7b-32kseqlen
-        ln -s /raid/weights/LLaMA-2 weights/LLaMA-2
-        mkdir -p extra/datasets
-        ln -s /raid/datasets/imagenet extra/datasets/imagenet
-    - name: setup staging db
-      if: github.ref == 'refs/heads/update_benchmark_staging'
-      run: |
-        echo "CACHEDB=/tmp/staging.db" >> $GITHUB_ENV
-        rm -f /tmp/staging.db /tmp/staging.db-shm /tmp/staging.db-wal
-    - name: reset process replay
-      run: test/external/process_replay/reset.py
-    - name: Test driver start time
-      run: time DEBUG=3 DEV=NV python3 test/test_tiny.py TestTiny.test_plus
-    - name: Test tensor cores
-      run: DEV=NV ALLOW_TF32=1 python3 test/opt/test_tensor_cores.py
-    - name: Test DISK copy time
-      run: DEV=NV TESTFILE=/raid/downloads/llama3-8b-sfr/model-00001-of-00004.safetensors python3 test/external/external_benchmark_disk_raw.py
-    - name: Test CPU copy time
-      run: |
-        DEV=NV GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyDefaulttoCPUJit
-        DEV=NV GRAPH_ONE_KERNEL=1 PYTHONPATH=. NSZ=8192 python3 test/speed/external_test_copy_speed.py TestCopySpeed.testCopyCPUtoDefaultJit
-    - name: Test LLAMA-3
-      run: BENCHMARK_LOG=llama3_beam DEV=NV JITBEAM=2 IGNORE_BEAM_CACHE=1 python3 examples/llama3.py --size 8B --benchmark --temperature 0
-    - name: Run full CIFAR training w 1 GPU
-      run: time BENCHMARK_LOG=cifar DEV=NV DEFAULT_FLOAT=HALF STEPS=1000 TARGET_EVAL_ACC_PCT=93.0 python3 examples/hlb_cifar10.py
-    - name: Run 10 MLPerf ResNet50 training steps (1 gpu)
-      run: BENCHMARK_LOG=resnet_10steps DEV=NV MNISTMOCK=1 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=256 GPUS=1 MODEL=resnet python3 examples/mlperf/model_train.py
-    - name: Run 10 MLPerf Bert training steps (1 gpu)
-      # TODO: remove BERT_LAYERS once scheduler is fast
-      run: BENCHMARK_LOG=bert_10steps DEV=NV CAPTURE_PROCESS_REPLAY=0 DEFAULT_FLOAT=HALF BENCHMARK=10 BS=66 GPUS=1 BERT_LAYERS=2 MODEL=bert python3 examples/mlperf/model_train.py
-    - name: Remote
-      run: |
-        pkill -f 'extra/remote/serve.py' || true
-        PYTHONPATH=. python3 extra/remote/serve.py 6483 &
-        sleep 1
-        DEBUG=2 PYTHONPATH=. REMOTE=127.0.0.1:6483 DEV=NV python3 test/test_tiny.py
+        DEBUG=2 PYTHONPATH=. REMOTE=127.0.0.1:6482 AM_RESET=1 python3 test/test_tiny.py
+        if [[ "${{ matrix.dev }}" == "AMD" ]]; then
+          DEBUG=2 PYTHONPATH=. REMOTE=127.0.0.1:6482 AM_RESET=1 AMD_AQL=1 python3 test/test_tiny.py
+        fi
        pkill -f 'extra/remote/serve.py' || true
    - name: Run process replay tests
      uses: ./.github/actions/process-replay
@@ -327,7 +327,7 @@ jobs:
          llvm: 'true'
      - name: Test openpilot model kernel count and gate usage
        run: |
-          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1468 ALLOWED_GATED_READ_IMAGE=10 FLOAT16=1 DEV=CL IMAGE=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
+          ALLOWED_KERNEL_COUNT=123 ALLOWED_READ_IMAGE=1361 ALLOWED_GATED_READ_IMAGE=55 FLOAT16=1 DEV=CL IMAGE=1 python examples/openpilot/compile3.py https://gitlab.com/commaai/openpilot-lfs.git/gitlab-lfs/objects/cf6376aa9a090f0da26c280ef69eabf9bbdd51d1faac9ed392919c3db69be916
      - name: Test openpilot CL compile fp32 (test correctness)
        run: |
          DEV=CL IMAGE=1 SELFTEST=1 python examples/openpilot/compile3.py https://github.com/haraschax/filedump/raw/refs/heads/master/driving_vision_fp32.onnx
@@ -370,7 +370,6 @@ jobs:
        with:
          key: optim
          deps: testing
-          pydeps: "tensorflow==2.19"
          opencl: 'true'
      #- name: Test Optimization Helpers
      #  run: DEBUG=1 python3 extra/optimization/test_helpers.py
@@ -379,7 +378,7 @@ jobs:
      - name: Test Beam Search
        run: DEV=CL IGNORE_BEAM_CACHE=1 python3 -m pytest extra/optimization/test_beam_search.py
      - name: Test MLPerf stuff
-        run: DEV=CL 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
+        run: DEV=CL python -m pytest -n=auto test/external/external_test_lr_schedule.py test/external/external_test_losses.py test/external/external_test_metrics.py test/external/external_test_datasets.py --durations=20
      - name: DEV=NULL beautiful_mnist_multigpu
        run: DEV=NULL NULL_ALLOW_COPYOUT=1 python examples/beautiful_mnist_multigpu.py
      - name: Test Bert training
@@ -38,7 +38,7 @@ def quantize_fp8(x:Tensor, amax_state:Tensor|None=None):

 def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_scale:Tensor|None=None,
           x_fp8:Tensor|None=None, x_new_amax:Tensor|None=None,
-           grad_amax_state:Tensor|None=None) -> tuple[Tensor,...]:
+           grad_amax_state:Tensor|None=None, x_prequant_mx:tuple|None=None) -> tuple[Tensor,...]:
  if not fp8:
    if ASM_GEMM:
      from extra.gemm.cdna_asm_gemm import can_use_asm_gemm, asm_gemm
@@ -47,12 +47,14 @@ def matmul(x:Tensor, w:Tensor, fp8:bool=True, amax_x:Tensor|None=None, w_inv_sca
  assert w_inv_scale is not None, "fp8 matmul requires w_inv_scale (weights must be stored in fp8 with per-tensor scale)"
  if MXFP8:
    from extra.gemm.cdna_asm_gemm import asm_gemm, quantize_mxfp8, mx_pack, can_use_asm_gemm, _mx_block_scale
-    x_q, x_e8, x_si = quantize_mxfp8(x.reshape(-1, x.shape[-1]))
+    if x_prequant_mx is not None: x_q, x_e8, x_si = x_prequant_mx       # fused producer already quantized (2d)
+    else: x_q, x_e8, x_si = quantize_mxfp8(x.reshape(-1, x.shape[-1]))
+    l_shape = x.shape[:-1] if x is not None else x_q.shape[:-1]
    if can_use_asm_gemm(x_q, w.T):
      out = asm_gemm(x_q, w.T, mx=True, mx_scales=(x_si, x_e8, mx_pack(w_inv_scale), w_inv_scale),
-                     mx_w_stored=True).reshape(*x.shape[:-1], w.shape[0])
+                     mx_w_stored=True).reshape(*l_shape, w.shape[0])
    else:
-      x_phys = (x_q.cast(dtypes.bfloat16) * _mx_block_scale(x_e8)).reshape(*x.shape[:-1], x.shape[-1])
+      x_phys = (x_q.cast(dtypes.bfloat16) * _mx_block_scale(x_e8)).reshape(*l_shape, x_q.shape[-1])
      out = x_phys @ (w.cast(dtypes.bfloat16) * _mx_block_scale(w_inv_scale)).T
    return out, (amax_x.detach() if amax_x is not None else None), x_q
  if x_fp8 is None:
@@ -214,8 +216,15 @@ class FlatTransformer:
      x_w3, new_amax, *s = matmul(inp, kwargs["w3"], amax_x=kwargs["amax_x3"], w_inv_scale=kwargs["s_3"], grad_amax_state=kwargs["grad_amax_xw3"])
      amaxs.append(new_amax)
      saves.extend([*s, x_w3])
-      out, new_amax, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], amax_x=kwargs["amax_x2"], w_inv_scale=kwargs["s_2"],
-                                 grad_amax_state=kwargs["grad_amax_xout"])
+      if FUSED_SILU_W13 and MXFP8:
+        from extra.llama_kernels.fused_silu_mul_quantize_mxfp8 import fused_silu_mul_quantize_mxfp8
+        aq, ae8, asi = fused_silu_mul_quantize_mxfp8(x_w1.reshape(-1, x_w1.shape[-1]), x_w3.reshape(-1, x_w3.shape[-1]))
+        out, new_amax, *s = matmul(None, kwargs["w2"], x_prequant_mx=(aq, ae8, asi), amax_x=kwargs["amax_x2"],
+                                   w_inv_scale=kwargs["s_2"], grad_amax_state=kwargs["grad_amax_xout"])
+        out = out.reshape(*x_w1.shape[:-1], kwargs["w2"].shape[0])
+      else:
+        out, new_amax, *s = matmul(x_w1.silu() * x_w3, kwargs["w2"], amax_x=kwargs["amax_x2"], w_inv_scale=kwargs["s_2"],
+                                   grad_amax_state=kwargs["grad_amax_xout"])
      amaxs.append(new_amax)
      saves.extend([*s, out])
    else:
@@ -1,6 +1,6 @@
 #!/bin/bash
-export BENCHMARK=5
+export BENCHMARK=${BENCHMARK:-5}
 export EVAL_BS=0
 VIZ=${VIZ:--1} FULL_LAYERS=1 DEBUG=${DEBUG:--0} examples/mlperf/training_submission_v6.0/tinycorp/benchmarks/llama31_8b/implementations/tinybox_8xMI350X/dev_beam.sh
 SRC="AMD"; [[ $DEV == NULL* ]] && SRC="NULL"
-python -m tinygrad.viz.cli -s "$SRC" -t --interval "train @ 2" "train @ 3"
+[ "$BENCHMARK" -le 3 ] || python -m tinygrad.viz.cli -s "$SRC" -t --interval "train @ 2" "train @ 3"
@@ -2899,7 +2899,7 @@ def custom_mx_gemm_bw(gradient:UOp, kernel:UOp, has_w_post:bool, w_stored:bool=F

  g = Tensor(gradient, device=aq.device)[:aq.shape[0]].reshape(aq.shape[0]*aq.shape[1], bq.shape[0]).cast(dtypes.bfloat16)
  grad_a = asm_gemm(g, b_phys, mx=True)
-  grad_b = asm_gemm(g.T, a_phys, mx=True)
+  grad_b = asm_gemm(g.T, a_phys, mx=True, a_pretranspose=g)

  grad_a = (grad_a * _mx_block_scale(ae8)).reshape(aq.shape)
  if not w_stored: grad_b = grad_b * _mx_block_scale(be8)
@@ -2909,7 +2909,8 @@ def custom_mx_gemm_bw(gradient:UOp, kernel:UOp, has_w_post:bool, w_stored:bool=F
 # ** main gemm function

 def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=None, grad_amax_state:Tensor|None=None,
-             w_post_scale:Tensor|None=None, mx:bool=False, mx_scales:tuple|None=None, mx_w_stored:bool=False, g_scale:Tensor|None=None) -> Tensor:
+             w_post_scale:Tensor|None=None, mx:bool=False, mx_scales:tuple|None=None, mx_w_stored:bool=False, g_scale:Tensor|None=None,
+             a_pretranspose:Tensor|None=None) -> Tensor:
  assert can_use_asm_gemm(a, b), f"{counters['todos'][-1]}"
  counters["used"] += 1
  unfold_batch = a.ndim == 3 and isinstance(a.device, tuple) and a.uop.axis == 2 and b.uop.axis == 0
@@ -2946,6 +2947,11 @@ def asm_gemm(a:Tensor, b:Tensor, x_scale:Tensor|None=None, w_scale:Tensor|None=N
      if mx_scales is not None:
        a_si, a_e8, b_si, b_e8 = mx_scales
        a_q, b_q = a.reshape(-1, a.shape[-1]), b.T
+      elif (a_pretranspose is not None and getenv("FUSED_GRAD_QUANTIZE", 0) and a_pretranspose.dtype == dtypes.bfloat16
+            and a_pretranspose.shape[0] % 32 == 0 and a_pretranspose.shape[1] % 256 == 0):
+        from extra.llama_kernels.transpose_quantize_mxfp8 import transpose_quantize_mxfp8
+        a_q, a_e8, a_si = transpose_quantize_mxfp8(a_pretranspose)
+        b_q, b_e8, b_si = quantize_mxfp8(b.T)
      else:
        a_q, a_e8, a_si = quantize_mxfp8(a.reshape(-1, a.shape[-1]))
        b_q, b_e8, b_si = quantize_mxfp8(b.T)
@@ -143,14 +143,17 @@ def make_getaddr(u, device=None):
 def make_ins(op, *srcs):
  return UOp(Ops.INS, dtypes.void, tuple(UOp.const(dtypes.uint32, s) if isinstance(s, int) else s.cast(dtypes.uint32) for s in srcs), op)

+def make_patch(buf:UOp, off:sint, val:UOp, dtype=None) -> UOp:
+  dt = dtype or val.dtype
+  return UOp(Ops.SHRINK, buf.dtype.base, (buf, UOp.const(dtypes.int, off), UOp.const(dtypes.int, dt.itemsize))).bitcast(dt).store(val.cast(dt))
+
 def make_cmdbuf(lin, devs, tag):
  blob, patches = b'', []
  for s in (s for ins in lin.src for s in ins.src):
    if s.op is not Ops.CONST: patches.append((len(blob), s))
    blob += struct.pack(f'<{s.dtype.fmt}', s.arg if s.op is Ops.CONST else 0x0)
  buf = UOp.new_buffer(devs, len(blob), dtypes.uint8).rtag(tag)
-  stores = [buf.index(UOp.const(dtypes.int, off), dtype=buf.dtype.ptr()).cast(s.dtype.ptr()).store(s) for off, s in patches]
-  return buf.after(buf.store(UOp(Ops.BINARY, dtypes.void, src=(), arg=blob)), *stores)
+  return buf.after(buf.store(UOp(Ops.BINARY, dtypes.void, src=(), arg=blob)), *[make_patch(buf, off, s) for off, s in patches])

 def make_mstack(uops): return uops[0] if len(uops) == 1 else UOp(Ops.MSTACK, uops[0].dtype, tuple(uops))

@@ -211,15 +214,11 @@ def prep_program(call:UOp, prg:UOp) -> UOp|None:
  return prg.replace(src=(buf.after(buf.store(blob)),), arg=(data, prg.arg)).call(*call.src[1:], aux=HCQInfo.from_call(call))

 def prep_kernargs(call:UOp, prg:UOp) -> UOp:
-  data, info = prg.arg
-  patches = [(i*dtypes.uint64.itemsize, UOp(Ops.GETADDR, dtypes.uint64, src=(call.src[1+gi], UOp(Ops.DEVICE, arg=call.src[1+gi].device))),
-              dtypes.uint64) for i,gi in enumerate(info.globals)] \
-          + [(len(info.globals)*dtypes.uint64.itemsize + i*dtypes.uint32.itemsize, v, dtypes.uint32) for i,v in enumerate(info.vars)]
-
-  buf = UOp.new_buffer(call.src[1].device, data.kernargs_alloc_size, dtypes.uint8).rtag("kernargs")
-  kernargs = buf.after(*tuple(buf.index(UOp.const(dtypes.int, o), dtype=buf.dtype.ptr()).cast(dt.ptr()).store(val.cast(dt)) for o, val, dt in patches))
-
-  return call.replace(src=(prg.replace(src=prg.src + (kernargs,), arg=(data, info)),) + call.src[1:])
+  (data, info), dev_uop = prg.arg, UOp(Ops.DEVICE, arg=call.src[1].device)
+  buf = UOp.new_buffer(dev_uop.arg, data.kernargs_alloc_size, dtypes.uint8).rtag("kernargs")
+  patches = [make_patch(buf, i*8, UOp(Ops.GETADDR, dtypes.uint64, src=(call.src[1+gi], dev_uop))) for i,gi in enumerate(info.globals)] \
+          + [make_patch(buf, len(info.globals)*8 + i*4, v, dtypes.uint32) for i,v in enumerate(info.vars)]
+  return call.replace(src=(prg.replace(src=prg.src + (buf.after(*patches),), arg=(data, info)),) + call.src[1:])

 pm_prep_runtime = PatternMatcher([
  # bind generic PROGRAM device to the call's actual dev(s), then run device-specific lowering
@@ -532,9 +531,9 @@ pm_resolve_patches = PatternMatcher([
  (UPat(GroupOp.ALU, src=[UPat(Ops.STACK, name="s"), UPat(Ops.CONST)], name="op"), push_stack),
  (UPat(Ops.CAST, src=(UPat(Ops.STACK, name="s"),), name="op"), push_stack),

-  # index on slice is index
-  (UPat(Ops.INDEX, src=(UPat(Ops.SLICE, name="bv"), UPat()), name="idx", allow_any_len=True),
-    lambda idx, bv: idx.replace(src=(bv.src[0], idx.src[1] + bv.src[1].cast(idx.src[1].dtype), *idx.src[2:]))),
+  # shrink on slice is shrink on base at offset
+  (UPat(Ops.SHRINK, src=(UPat(Ops.SLICE, name="bv"), UPat(), UPat()), name="shr"),
+    lambda shr, bv: shr.replace(src=(bv.src[0], shr.src[1] + bv.src[1].cast(shr.src[1].dtype), shr.src[2]))),

  # getaddr
  (UPat(Ops.GETADDR, src=(UPat(Ops.SLICE, name="bv"), UPat(Ops.DEVICE, name="dev"))), resolve_getaddr_slice), # getaddr(slice(x)) -> offset+getaddr(x)
@@ -542,8 +541,8 @@ pm_resolve_patches = PatternMatcher([

  # folders
  (UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf").store(UPat(Ops.BINARY, name="blob")), fold_blob_store),
-  (UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf").index(UPat.cvar("off")).or_casted().store(UPat.any(UPat.cvar("val"), UPat(Ops.STACK, name="val"))),
-    fold_const_store),
+  (UPat(Ops.SHRINK, src=(UPat({Ops.BUFFER, Ops.SLICE, Ops.MSTACK}, name="buf"), UPat.cvar("off"), UPat(Ops.CONST))).bitcast()
+    .store(UPat.any(UPat.cvar("val"), UPat(Ops.STACK, name="val"))), fold_const_store),
 ]) + symbolic_simple

 # *****************
@@ -0,0 +1,104 @@
+import functools
+from tinygrad import Tensor, dtypes
+from tinygrad.uop.ops import UOp, Ops, KernelInfo, AxisType
+from extra.llama_kernels import FP8_MAX, THREADS_PER_WG, alloc_like
+
+BLK = 32
+PACK = 4
+LOG2E = 1.4426950408889634
+
+@functools.cache
+def _custom_silu_mul_quantize_mxfp8(fp8_out:UOp, e8_out:UOp, si_out:UOp, x_w1:UOp, x_w3:UOp) -> UOp:
+  rows, K = x_w1.shape
+  scale_K = K // BLK
+  n_elems = rows * K
+  n_super = n_elems // (BLK * PACK)
+  sk4 = scale_K // PACK
+  assert n_super % THREADS_PER_WG == 0, f"{n_super=} must divide over {THREADS_PER_WG=}"
+  nwg = n_super // THREADS_PER_WG
+
+  x_w1, x_w3 = x_w1.reshape(n_elems), x_w3.reshape(n_elems)
+  fp8_out = fp8_out.reshape(n_elems)
+  e8_out = e8_out.reshape(rows * scale_K)
+  si_out = si_out.reshape(sk4 * rows)
+
+  wg = UOp.range(nwg, 0, AxisType.GLOBAL)
+  tid = UOp.range(THREADS_PER_WG, 1, AxisType.LOCAL)
+  sb = UOp.range(PACK, 2, AxisType.UNROLL)
+  lane = UOp.range(BLK, 3, AxisType.UNROLL)
+
+  super_idx = wg * THREADS_PER_WG + tid
+  idx = super_idx * (BLK * PACK) + sb * BLK + lane
+
+  w1 = x_w1[idx].cast(dtypes.float)
+  w3 = x_w3[idx].cast(dtypes.float)
+  sig = (1.0 + (w1 * -LOG2E).exp2()).reciprocal()
+  act = w1 * sig * w3
+  abs_a = (act < 0.0).where(-act, act)
+  blk_max = abs_a.reduce(lane, arg=Ops.MAX)
+  e8f = (blk_max.maximum(1e-38).log2().floor() + 127.0).maximum(0.0).minimum(254.0)
+  qscale = (127.0 - e8f).exp2()
+  scaled = (act * qscale).maximum(-FP8_MAX).minimum(FP8_MAX)
+  e8u8 = e8f.cast(dtypes.uint8)
+
+  fp8_store = fp8_out[idx].store(scaled.cast(fp8_out.dtype.base)).end(lane)
+  e8_store = e8_out.after(fp8_store)[super_idx * PACK + sb].store(e8u8)
+  packed = (e8u8.cast(dtypes.uint32) << (sb.cast(dtypes.uint32) * 8)).reduce(sb, arg=Ops.ADD)
+  row, col4 = super_idx // sk4, super_idx % sk4
+  si_store = si_out.after(e8_store.end(sb))[col4 * rows + row].store(packed)
+  return si_store.end(tid, wg).sink(arg=KernelInfo(f"silu_mul_quantize_mxfp8_{n_elems}", opts_to_apply=()))
+
+@functools.cache
+def _custom_silu_mul_bwd_mxfp8(gx1_out:UOp, gx3_out:UOp, x_w1:UOp, x_w3:UOp, grad_aq:UOp, e8:UOp) -> UOp:
+  rows, K = x_w1.shape
+  scale_K = K // BLK
+  n_elems = rows * K
+  VEC = 8
+  assert n_elems % (THREADS_PER_WG * VEC) == 0, f"{n_elems=} must divide {THREADS_PER_WG*VEC=}"
+  nwg = n_elems // (THREADS_PER_WG * VEC)
+  x_w1, x_w3, grad_aq = x_w1.reshape(n_elems), x_w3.reshape(n_elems), grad_aq.reshape(n_elems)
+  gx1_out, gx3_out, e8 = gx1_out.reshape(n_elems), gx3_out.reshape(n_elems), e8.reshape(rows * scale_K)
+
+  wg = UOp.range(nwg, 0, AxisType.GLOBAL)
+  tid = UOp.range(THREADS_PER_WG, 1, AxisType.LOCAL)
+  lane = UOp.range(VEC, 2, AxisType.UNROLL)
+  idx = (wg * THREADS_PER_WG + tid) * VEC + lane
+
+  e8v = e8[idx // BLK].cast(dtypes.float)
+  qscale = (127.0 - e8v).exp2()
+  ga = grad_aq[idx].cast(dtypes.float) * qscale
+  w1 = x_w1[idx].cast(dtypes.float)
+  w3 = x_w3[idx].cast(dtypes.float)
+  sig = (1.0 + (w1 * -LOG2E).exp2()).reciprocal()
+  s = w1 * sig
+  sprime = sig * (1.0 + w1 * (1.0 - sig))
+  gx1 = gx1_out[idx].store((ga * sprime * w3).cast(gx1_out.dtype.base))
+  gx3 = gx3_out.after(gx1)[idx].store((ga * s).cast(gx3_out.dtype.base))
+  return gx3.end(lane, tid, wg).sink(arg=KernelInfo(f"silu_mul_bwd_mxfp8_{n_elems}", opts_to_apply=()))
+
+def _silu_mul_quantize_mxfp8_bwd(gradient:UOp, kernel:UOp):
+  _, e8_out, _, x_w1, x_w3 = kernel.src[1:]
+  device = x_w1.device
+  rows, K = x_w1.shape
+  axis = x_w1.axis if isinstance(device, tuple) else None
+  gx1 = alloc_like((rows, K), dtypes.bfloat16, device, axis)
+  gx3 = alloc_like((rows, K), dtypes.bfloat16, device, axis)
+  gx1, gx3, *_ = Tensor.custom_kernel(gx1, gx3, Tensor(x_w1, device=device), Tensor(x_w3, device=device),
+                                      Tensor(gradient, device=device).cast(dtypes.bfloat16), Tensor(e8_out.after(kernel), device=device),
+                                      fxn=_custom_silu_mul_bwd_mxfp8)
+  return (None, None, None, gx1.uop, gx3.uop)
+
+def fused_silu_mul_quantize_mxfp8(x_w1:Tensor, x_w3:Tensor) -> tuple[Tensor, Tensor, Tensor]:
+  assert x_w1.shape == x_w3.shape, f"{x_w1.shape} != {x_w3.shape}"
+  assert x_w1.dtype == dtypes.bfloat16 and x_w3.dtype == dtypes.bfloat16
+  assert x_w1.ndim == 2, f"expected 2d, got {x_w1.shape}"
+  from extra.gemm.cdna_asm_gemm import FP8_DTYPE
+  rows, K = x_w1.shape
+  scale_K = K // BLK
+  axis = x_w1.uop.axis if isinstance(x_w1.device, tuple) else None
+  fp8_out = alloc_like((rows, K), FP8_DTYPE, x_w1.device, axis)
+  e8_out = alloc_like((rows, scale_K), dtypes.uint8, x_w1.device, axis)
+  si_out = alloc_like((scale_K // PACK, rows), dtypes.uint32, x_w1.device, None if axis is None else (1 if axis == 0 else 0))
+  fp8_out, e8_out, si_out, *_ = Tensor.custom_kernel(fp8_out, e8_out, si_out, x_w1, x_w3,
+                                                     fxn=_custom_silu_mul_quantize_mxfp8, grad_fxn=_silu_mul_quantize_mxfp8_bwd)
+  return fp8_out, e8_out, si_out
@@ -42,8 +42,8 @@ def _custom_quantize_fp8_with_amax(fp8_out:UOp, amax_partial:UOp, x:UOp, amax_st
  step = THREADS_PER_WG // 2
  while step:
    active = tid < step
-    other = lds[tid + step].load(UOp.const(dtypes.float, 0.0), active)
-    lds = lds.after(lds[tid].store(lds[tid].maximum(other), gate=active).barrier())
+    other = lds[(tid + step).valid(active)].load()
+    lds = lds.after(lds[tid.valid(active)].store(lds[tid].maximum(other)).barrier())
    step //= 2

  amax_store = amax_partial[tid.eq(0).where(wg, UOp.invalid())].store(lds[0])
@@ -0,0 +1,37 @@
+from __future__ import annotations
+import functools, pathlib
+from tinygrad import Tensor, dtypes
+from tinygrad.uop.ops import UOp, Ops, KernelInfo
+from tinygrad.renderer import Estimates
+from extra.llama_kernels import THREADS_PER_WG, alloc_like, dname_of, compile_hip
+
+TILE_N = THREADS_PER_WG   # 256
+BLK = 32
+
+@functools.cache
+def _custom_transpose_quantize_mxfp8(q:UOp, e8:UOp, g:UOp, dname:str) -> UOp:
+  M, N = g.shape
+  num_wg = (M // BLK) * (N // TILE_N)
+  threads, workgroups = UOp.special(THREADS_PER_WG, "lidx0"), UOp.special(num_wg, "gidx0")
+  mem = M * N * 2 + M * N + (M // BLK) * N   # read bf16, write fp8 + e8
+  sink = UOp.sink(q.base, e8.base, g.base, threads, workgroups,
+                  arg=KernelInfo(f"transpose_quantize_mxfp8_{M}_{N}", estimates=Estimates(ops=M*N, mem=mem)))
+  src = (pathlib.Path(__file__).parent/"transpose_quantize_mxfp8.cpp").read_text()
+  defines = [f"-DM_DIM={M}", f"-DN_DIM={N}", f"-DTHREADS_PER_WG={THREADS_PER_WG}"]
+  return UOp(Ops.PROGRAM, src=(sink, UOp(Ops.DEVICE, arg=dname), UOp(Ops.LINEAR, src=(*sink.src, sink)),
+                               UOp(Ops.SOURCE, arg=src), UOp(Ops.BINARY, arg=compile_hip(src, defines))))
+
+def transpose_quantize_mxfp8(g:Tensor) -> tuple[Tensor, Tensor, Tensor]:
+  # fused g.T quantize: returns (q, e8, si) == quantize_mxfp8(g.T) — q (N,M) fp8, e8 (N, M/32), si packed (M/128, N)
+  assert g.ndim == 2 and g.dtype == dtypes.bfloat16, f"{g.shape} {g.dtype}"
+  from extra.gemm.cdna_asm_gemm import FP8_DTYPE, mx_pack
+  M, N = g.shape
+  assert M % BLK == 0 and N % TILE_N == 0, f"M={M} must%{BLK}, N={N} must%{TILE_N}"
+  device = g.device
+  axis = g.uop.axis if isinstance(device, tuple) else None
+  out_axis = None if axis is None else (1 if axis == 0 else 0)
+  q = alloc_like((N, M), FP8_DTYPE, device, out_axis)
+  e8 = alloc_like((N, M // BLK), dtypes.uint8, device, out_axis)
+  fxn = functools.partial(_custom_transpose_quantize_mxfp8, dname=dname_of(device))
+  q, e8, *_ = Tensor.custom_kernel(q, e8, g, fxn=fxn)
+  return q, e8, mx_pack(e8)
@@ -0,0 +1,62 @@
+#include <hip/hip_runtime.h>
+#include <hip/hip_fp8.h>
+#include <hip/hip_bf16.h>
+
+#ifndef M_DIM
+#define M_DIM 8192
+#endif
+#ifndef N_DIM
+#define N_DIM 14336
+#endif
+#ifndef THREADS_PER_WG
+#define THREADS_PER_WG 256
+#endif
+
+constexpr int BLK = 32;
+constexpr int TILE_M = BLK;                 // one mxfp8 block along M per tile
+constexpr int TILE_N = THREADS_PER_WG;      // 256, one output column per thread
+constexpr int LDS_STRIDE = TILE_N + 1;      // +1 pad: stride 257 ≡ 1 (mod 32) -> conflict-free column reads
+constexpr int N_TILES_N = N_DIM / TILE_N;
+constexpr float FP8_MAX = 448.0f;
+
+static_assert(M_DIM % TILE_M == 0, "M_DIM must be a multiple of 32");
+static_assert(N_DIM % TILE_N == 0, "N_DIM must be a multiple of 256");
+
+extern "C" __global__ __launch_bounds__(THREADS_PER_WG) void
+transpose_quantize_mxfp8(__hip_fp8_storage_t* __restrict__ q,        // (N_DIM, M_DIM)
+                         uint8_t* __restrict__ e8_out,               // (N_DIM, M_DIM/32)
+                         const __hip_bfloat16* __restrict__ g)       // (M_DIM, N_DIM)
+{
+  __shared__ __hip_bfloat16 lds[TILE_M * LDS_STRIDE];
+  const int tid    = threadIdx.x;
+  const int tile_m = blockIdx.x / N_TILES_N;     // which 32-block along M
+  const int tile_n = blockIdx.x % N_TILES_N;
+
+  #pragma unroll
+  for (int mm = 0; mm < TILE_M; mm++)
+    lds[mm * LDS_STRIDE + tid] = g[(long long)(tile_m * TILE_M + mm) * N_DIM + (tile_n * TILE_N + tid)];
+  __syncthreads();
+
+  float vals[TILE_M];
+  float amax = 0.0f;
+  #pragma unroll
+  for (int mm = 0; mm < TILE_M; mm++) {
+    float v = (float)lds[mm * LDS_STRIDE + tid];
+    vals[mm] = v;
+    amax = fmaxf(amax, fabsf(v));
+  }
+  int e8 = (int)floorf(log2f(fmaxf(amax, 1e-38f))) + 127;
+  e8 = max(0, min(254, e8));
+  float qscale = exp2f((float)(127 - e8));
+
+  const long long n = tile_n * TILE_N + tid;
+  __hip_fp8_storage_t out[TILE_M];
+  #pragma unroll
+  for (int mm = 0; mm < TILE_M; mm++)
+    out[mm] = __hip_cvt_float_to_fp8(fmaxf(-FP8_MAX, fminf(FP8_MAX, vals[mm] * qscale)), __HIP_SATFINITE, __HIP_E4M3);
+  // 32 contiguous fp8 along M -> two 16-byte vector stores
+  long long obase = n * M_DIM + (long long)(tile_m * TILE_M);
+  *reinterpret_cast<uint4*>(&q[obase])      = *reinterpret_cast<uint4*>(&out[0]);
+  *reinterpret_cast<uint4*>(&q[obase + 16]) = *reinterpret_cast<uint4*>(&out[16]);
+  e8_out[n * (M_DIM / BLK) + tile_m] = (uint8_t)e8;
+}
@@ -140,7 +140,7 @@ class TestLinearizer(unittest.TestCase):
                       renderer=Device[Device.DEFAULT].renderer).src[2].src)
    num_loads = len([uop for uop in uops if uop.op is Ops.LOAD])
    assert num_loads <= 4, "more load uops than needed"
-    assert num_loads >= 4, "unexpected number of uops, maybe this test needs updating?"
+    assert num_loads >= 1, "expected at least one load uop"

  @unittest.skip("this is handled at higher level now")
  def test_upcast_cse(self):
@@ -0,0 +1,67 @@
+import unittest, math
+import numpy as np
+from tinygrad.tensor import Tensor
+from tinygrad.nn.optim import AdamW
+from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup, LambdaLR, LambdaLinearScheduler
+
+np.random.seed(1337)
+x_init = np.random.randn(1,4).astype(np.float32)
+W_init = np.random.randn(4,4).astype(np.float32)
+m_init = np.random.randn(1,4).astype(np.float32)
+
+class TinyNet:
+  def __init__(self):
+    self.x = Tensor(x_init.copy())
+    self.W = Tensor(W_init.copy())
+    self.m = Tensor(m_init.copy())
+
+  def forward(self):
+    out = self.x.matmul(self.W).relu()
+    out = out.log_softmax(1)
+    out = out.mul(self.m).add(self.m).sum()
+    return out
+
+class TestCosineAnnealingLRWithWarmup(unittest.TestCase):
+  # only tests the lr
+  def _test_lr(self, base_lr, end_lr, warmup_steps, decay_steps):
+    net = TinyNet()
+    optim = AdamW([net.W], lr=0.0)
+    tiny_lr = CosineAnnealingLRWithWarmup(optim, base_lr, end_lr, warmup_steps, decay_steps)
+    lr = []
+    for _ in range(warmup_steps+decay_steps):
+      lr.append(optim.lr.item())
+      tiny_lr.step()
+    # reimplemented in python
+    expected = []
+    for i in range(warmup_steps): expected.append((i+1)/warmup_steps*base_lr)
+    for i in range(decay_steps): expected.append(end_lr+(base_lr-end_lr)*(1+math.cos((i+1)/decay_steps*math.pi))/2)
+    np.testing.assert_allclose(lr, expected, rtol=1e-5)
+
+  def test_lr_0(self): self._test_lr(3e-4, 8e-5, 3, 5)
+  def test_lr_1(self): self._test_lr(3e-4, 8e-5, 10, 20)
+  def test_lr_llama3(self): self._test_lr(8e-5, 8e-7, 20, 100)
+
+class TestLambdaLRLinearWarmup(unittest.TestCase):
+  def test_linear_lr_warmup(self):
+    BS, BASE_LR = 304, 2.5e-7
+    lr = BS * BASE_LR
+    # Use a dummy Tensor parameter for optimizer because the lr_scheduler only needs the optimizer's device and lr, the params aren't touched.
+    optimizer = AdamW([Tensor([1.])])
+    lambda_lr_callback = LambdaLinearScheduler(1000, 1.0, 1.0, 1e-06, 10000000000000).schedule
+    lr_scheduler = LambdaLR(optimizer, Tensor(lr, device=optimizer.device), lambda_lr_callback)
+    lrs = {}
+
+    # with above settings, optimizer.lr should warm up to lr over 1000 steps linearly
+    for i in range(1200):
+      lr_scheduler.step()
+      if i in {0, 499, 998, 999, 1000, 1199}:
+        lrs[i] = optimizer.lr.item()
+
+    np.testing.assert_allclose(lr, lrs[999], rtol=0, atol=1e-11)
+    np.testing.assert_equal(lrs[999], lrs[1000])
+    np.testing.assert_equal(lrs[999], lrs[1199])
+    np.testing.assert_allclose(lrs[999] / lrs[0], 1000, rtol=0, atol=1)
+    np.testing.assert_allclose(lrs[999] / lrs[499], 2, rtol=0, atol=1e-5)
+
+if __name__ == '__main__':
+  unittest.main()
@@ -1,5 +1,5 @@
 #!/usr/bin/env python
-import unittest, math
+import unittest
 import numpy as np
 import tensorflow as tf
 from tensorflow.keras.optimizers import Lamb
@@ -7,11 +7,11 @@ from tensorflow.python.ops import math_ops
 from extra.lr_scheduler import LRSchedulerGroup

 from tinygrad.tensor import Tensor
-from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup, AdamW
+from tinygrad.nn.optim import LAMB, LARS, SGD, OptimizerGroup

 from test.external.mlperf_resnet.lars_optimizer import LARSOptimizer

-from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup, CosineAnnealingLRWithWarmup, LambdaLR, LambdaLinearScheduler
+from examples.mlperf.lr_schedulers import PolynomialDecayWithWarmup
 from test.external.mlperf_resnet.lars_util import PolynomialDecayWithWarmup as PolynomialDecayWithWarmup_tf

 np.random.seed(1337)
@@ -173,48 +173,5 @@ class ExternalTestOptim(unittest.TestCase):
      'warmup': steps_per_epoch * warmup_epochs,
    }, 1e-5, 1e-5, do_optim=False)

-
-class TestCosineAnnealingLRWithWarmup(unittest.TestCase):
-  # only tests the lr
-  def _test_lr(self, base_lr, end_lr, warmup_steps, decay_steps):
-    net = TinyNet()
-    optim = AdamW([net.W], lr=0.0)
-    tiny_lr = CosineAnnealingLRWithWarmup(optim, base_lr, end_lr, warmup_steps, decay_steps)
-    lr = []
-    for _ in range(warmup_steps+decay_steps):
-      lr.append(optim.lr.item())
-      tiny_lr.step()
-    # reimplemented in python
-    expected = []
-    for i in range(warmup_steps): expected.append((i+1)/warmup_steps*base_lr)
-    for i in range(decay_steps): expected.append(end_lr+(base_lr-end_lr)*(1+math.cos((i+1)/decay_steps*math.pi))/2)
-    np.testing.assert_allclose(lr, expected, rtol=1e-5)
-
-  def test_lr_0(self): self._test_lr(3e-4, 8e-5, 3, 5)
-  def test_lr_1(self): self._test_lr(3e-4, 8e-5, 10, 20)
-  def test_lr_llama3(self): self._test_lr(8e-5, 8e-7, 20, 100)
-
-class TestLambdaLRLinearWarmup(unittest.TestCase):
-  def test_linear_lr_warmup(self):
-    BS, BASE_LR = 304, 2.5e-7
-    lr = BS * BASE_LR
-    # Use a dummy Tensor parameter for optimizer because the lr_scheduler only needs the optimizer's device and lr, the params aren't touched.
-    optimizer = AdamW([Tensor([1.])])
-    lambda_lr_callback = LambdaLinearScheduler(1000, 1.0, 1.0, 1e-06, 10000000000000).schedule
-    lr_scheduler = LambdaLR(optimizer, Tensor(lr, device=optimizer.device), lambda_lr_callback)
-    lrs = {}
-
-    # with above settings, optimizer.lr should warm up to lr over 1000 steps linearly
-    for i in range(1200):
-      lr_scheduler.step()
-      if i in {0, 499, 998, 999, 1000, 1199}:
-        lrs[i] = optimizer.lr.item()
-
-    np.testing.assert_allclose(lr, lrs[999], rtol=0, atol=1e-11)
-    np.testing.assert_equal(lrs[999], lrs[1000])
-    np.testing.assert_equal(lrs[999], lrs[1199])
-    np.testing.assert_allclose(lrs[999] / lrs[0], 1000, rtol=0, atol=1)
-    np.testing.assert_allclose(lrs[999] / lrs[499], 2, rtol=0, atol=1e-5)
-
 if __name__ == '__main__':
  unittest.main()
@@ -16,41 +16,17 @@ def simplify_image_idx(sink: UOp) -> UOp: return graph_rewrite(sink, sym+pm_move
 def get_gated_load_uop(valid:UOp, idx:UOp):
  return UOp(Ops.LOAD, dtypes.float, (
    UOp.param(0, dtypes.float.ptr()).index(idx.valid(valid), ptr=True),
-    UOp.const(dtypes.float, 0.0)
  ))

 def get_load_image_uop(image_shape:tuple[int, ...], valid:UOp, idx:tuple[UOp, UOp]):
  return UOp(Ops.LOAD, dtypes.float.vec(4), (
    UOp.param(0, dtypes.imagef(image_shape)).index(idx[1].valid(valid), idx[0].valid(valid), ptr=True),
-    UOp(Ops.STACK, dtypes.float.vec(4), src=(UOp.const(dtypes.float, 0.0),) * 4)
  ))

 def Special(expr, nmax): return UOp(Ops.SPECIAL, dtypes.weakint, (UOp.const(dtypes.weakint, nmax),), expr)
 def Variable(expr, nmin, nmax): return UOp.variable(expr, nmin, nmax)
 def Range(n, nmax): return UOp.range(nmax, n)

-class TestHelpers(unittest.TestCase):
-  def test_is_increasing(self):
-    idx1 = Special("idx1", 32)
-    idx2 = Special("idx2", 64)
-    ridx0 = Variable("ridx0", 0, 5)
-    ridx1 = Variable("ridx1", 0, 2)
-    ridx2 = Variable("ridx2", 0, 2)
-    # (ridx0+(idx1*48)+(ridx2*6)+(-6)),((idx2*2)+ridx1+(-1)))
-    f0 = ((idx1*24)+(ridx2*3)+ridx0+765)%768
-    f1 = ridx0+(idx1*48)+(ridx2*6)+(-6)
-    f2 = (idx2*2)+ridx1+((idx1+((ridx2+7)//8)+31)//32)+(-2)
-    f3 = (idx2*2)+ridx1+(-1)
-
-    self.assertFalse(f0.is_increasing())
-    self.assertTrue(f1.is_increasing())
-    self.assertTrue(f2.is_increasing())
-    self.assertTrue(f3.is_increasing())
-
-    rng = UOp.range(5, 2)
-    self.assertTrue(rng.is_increasing())
-    self.assertTrue((rng+2).is_increasing())
-
 class TestValidIdxSimplification(unittest.TestCase):
  def check(self, load, sidx, svalid, extra=()):
    load = simplify_valid_idx(UOp.sink(load, *extra)).src[0]
@@ -506,6 +482,16 @@ class TestImageSimplification(unittest.TestCase):
      self.check(load, "(((lidx1<1)!=True)&(((lidx0+r0)<3)!=True)&((lidx0+r0)<11))",
                       "(lidx2+gidx0*4+lidx1*256+(lidx0*1024+r0*1024)+-3264)", "0")

+  def test_drop_non_monotonic_window(self):
+    # two-sided window valid (645 <= gidx0 < 653) on a non-monotonic index (lane split via %4 and //4):
+    # gidx0 outside the window pushes idx_x out of the (1, 48) image, so the gate is dropped
+    gidx0 = Special("gidx0", 1064)
+    r12 = Range(12, 3)
+    valid = ((gidx0 < 645).ne(True)) & (gidx0 < 653)
+    idx = (r12*4 + (gidx0+3)%4 + (gidx0+3)//4*24 - 3888, UOp.const(dtypes.weakint, 0))
+    load = get_load_image_uop((1, 48, 4), valid, idx)
+    self.check(load, None, "(r12*4+(gidx0+3)%4+(gidx0+3)//4*24+-3888)", "0")
+
 class TestDropTrueGate(unittest.TestCase):
  def test_drop_true_gate_on_index(self):
    # test that INDEX with a constant True valid gets simplified to drop the valid
@@ -12,17 +12,18 @@ from tinygrad.dtype import dtypes, PtrDType, ImageDType

 # import all pattern matchers here
 from tinygrad.codegen.gpudims import pm_add_gpudims
-from tinygrad.uop.symbolic import sym, symbolic_simple, gep_pushing, symbolic, pm_move_where_on_load, pm_clean_up_group_sink
-from tinygrad.uop.decompositions import get_late_rewrite_patterns, get_transcendental_patterns, pm_dtype_decomps
-from tinygrad.codegen.late.devectorizer import load_store_indexing, ReduceContext, pm_render, pm_make_images
+from tinygrad.uop.symbolic import sym, symbolic_simple, gep_pushing, symbolic, pm_move_where_on_load, pm_clean_up_group_sink, pm_remove_invalid
+from tinygrad.uop.decompositions import get_late_rewrite_patterns, get_transcendental_patterns, pm_dtype_decomps, get_simplifying_rewrite_patterns
+from tinygrad.codegen.late.expander import expander, pm_pre_expander, pm_group_for_reduce
+from tinygrad.codegen.late.devectorizer import load_store_folding, load_store_indexing, devectorize_buf_and_index, devectorize_alu, pm_reduce, \
+  ReduceContext, correct_load_store, pm_render, pm_add_loads, pm_make_images
 from tinygrad.codegen.opt.postrange import apply_opts
 from tinygrad.codegen.late.gater import pm_move_gates_from_index
 from tinygrad.codegen.simplify import pm_simplify_ranges, pm_flatten_range, pm_split_ranges, pm_load_collapse
 from tinygrad.schedule.rangeify import pm_add_buffers_local, rangeify_codegen, pm_mops, pm_syntactic_sugar, pm_store_ranges
 from tinygrad.codegen.late.linearizer import CFGContext, pm_split_ends, pm_add_control_flow, linearize
 from tinygrad.codegen.late.regalloc import LinearScanRegallocContext, pm_regalloc_rewrite
-
-from tinygrad.codegen.codegen2 import expander2, pm_move_regs, devectorizer2, unbroadcast, pm_reduce_local, pm_horizontal_reduce, memory_coalesing
+from tinygrad.codegen.late.coalese import memory_coalesing

 pm_index_is_shrink = PatternMatcher([
  # rewrite non-image INDEX to SHRINK
@@ -52,6 +53,10 @@ pm_number_params = PatternMatcher([
  (UPat(Ops.PARAM, name="x"), do_number_param),
 ])

+pm_no_weakints = PatternMatcher([
+  (UPat(GroupOp.All, dtype=dtypes.weakint, name="x"), lambda x: x.replace(dtype=dtypes.int))
+])
+
 def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
  if VIZ: graph_rewrite(ast, PatternMatcher([]), name="View Base AST")
  if DEBUG >= 5: print(pyrender(ast))
@@ -81,16 +86,14 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
  sink = graph_rewrite(sink, sym+pm_move_where_on_load+pm_flatten_range, name="postopt symbolic")

  # expand
-  #sink = graph_rewrite(sink, sym+pm_pre_expander+pm_group_for_reduce+expander, name="expander")
-  sink = graph_rewrite(sink, expander2, ctx={}, name="expander", bottom_up=True)
+  sink = graph_rewrite(sink, sym+pm_pre_expander+pm_group_for_reduce+expander, name="expander")

  # add locals
  sink = graph_rewrite(sink, pm_add_buffers_local+rangeify_codegen, ctx=itertools.count(0), name="add local buffers")

  # ** devectorizer (full_graph_rewrite) **
  # remove reduce
-  #sink = graph_rewrite(sink, pm_reduce+gep_pushing, ctx=ReduceContext(), name="remove_reduce")
-  sink = graph_rewrite(sink, pm_reduce_local+pm_horizontal_reduce, ctx=ReduceContext(), name="remove_reduce")
+  sink = graph_rewrite(sink, pm_reduce+gep_pushing, ctx=ReduceContext(), name="remove_reduce")

  # add gpu dims (late). this works after devectorize, but it's faster here
  sink = graph_rewrite(sink, pm_add_gpudims, ctx=ren, name="add gpudims")
@@ -98,21 +101,15 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
  # **** optimizations are done, now we lower to actual code ****

  # add loads and remove invalids
-  #sink = graph_rewrite(sink, pm_add_loads+pm_remove_invalid, name="** add loads (code)")
-  sink = graph_rewrite(sink, pm_move_regs,  name="** add loads")
+  sink = graph_rewrite(sink, pm_add_loads+pm_remove_invalid, name="** add loads (code)")

  # create image buffers
  if IMAGE and ren.target.device in {"QCOM", "CL", "PYTHON", "NULL"}:
    sink = graph_rewrite(sink, pm_make_images, name="create image buffers", bottom_up=True, ctx=ren.target.arch)

-  # hreduce
-  #sink = graph_rewrite(sink, pm_mops+pm_horizontal_reduce, name="hreduce")
-
  # devectorize
-  #sink = graph_rewrite(sink, sym+devectorize_alu+devectorize_buf_and_index+load_store_folding+correct_load_store+load_store_indexing,
-  #                     ctx=ren, name="devectorize")
-  sink = graph_rewrite(sink, unbroadcast, name="*** unbroadcast")
-  sink = graph_rewrite(sink, symbolic_simple+devectorizer2, ctx=ren, name="devectorize2")
+  sink = graph_rewrite(sink, sym+devectorize_alu+devectorize_buf_and_index+load_store_folding+correct_load_store+load_store_indexing,
+                       ctx=ren, name="devectorize")

  # lower the index dtype to a concrete int
  sink = graph_rewrite(sink, pm_lower_index_dtype+load_store_indexing+gep_pushing, name="lower all index dtypes")
@@ -121,36 +118,30 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
  # optional pre matcher
  if ren.pre_matcher is not None: sink = graph_rewrite(sink, ren.pre_matcher, name="pre_matcher")

-  # dtypes
-  sink = graph_rewrite(sink, pm_dtype_decomps, ctx=(set(), ren), name="decomp dtypes")
-
-  # memory coalesing
-  sink = memory_coalesing(sink)
-
-  # again
-  sink = graph_rewrite(sink, pm_lower_index_dtype+load_store_indexing+gep_pushing, name="lower all index dtypes")
-  sink = graph_rewrite(sink, symbolic, name="post index symbolic")
-
-  # decompositions
+  # floordiv+mod / dtype decomp (early)
  supported_ops = tuple(ren.code_for_op.keys())
-  pm_decomp = symbolic_simple+get_late_rewrite_patterns(supported_ops, bool(DISABLE_FAST_IDIV))
-  pm_transcendental = symbolic_simple+get_transcendental_patterns(supported_ops, TRANSCENDENTAL>=2)
-  sink = graph_rewrite(sink, pm_decomp, ctx=ren, name="decompositions")
-  sink = graph_rewrite(sink, pm_transcendental, name="transcendental")
+  pm_decomp = symbolic_simple+get_simplifying_rewrite_patterns(supported_ops)
+  sink = graph_rewrite(sink, pm_decomp, name="early decompositions")

-  # GEP/STACK stuff
+  # do memory coalesing (late)
+  sink = memory_coalesing(sink, ren)
+
+  # this is new style (TODO: this should all be removed)
  sink = graph_rewrite(sink, pm_render, name="pm_render gep/stack")
-
-  # this is new style
  sink = graph_rewrite(sink, pm_index_is_shrink, name="index is shrink")
  sink = graph_rewrite(sink, pm_remove_vec_dtypes, name="transform to new style")

-  # move gates from unrenderable INVALID where
+  # late decomps + move gates from unrenderable INVALID where
+  sink = graph_rewrite(sink, pm_dtype_decomps, ctx=(set(), ren), name="decomp dtypes")
+  pm_decomp = pm_decomp+\
+    get_late_rewrite_patterns(supported_ops, bool(DISABLE_FAST_IDIV))+\
+    get_transcendental_patterns(supported_ops, TRANSCENDENTAL>=2)
+  sink = graph_rewrite(sink, pm_decomp, ctx=ren, name="late decompositions")
  sink = graph_rewrite(sink, pm_move_gates_from_index, name="move gates from index")

  # final rules for the renderer (without sym)
  extra_matcher = ren.extra_matcher if ren.extra_matcher is not None else PatternMatcher([])
-  pm_final_rewrite = pm_decomp+extra_matcher+pm_split_ends
+  pm_final_rewrite = pm_decomp+extra_matcher+pm_split_ends+pm_no_weakints
  sink = graph_rewrite(sink, pm_final_rewrite, ctx=ren, name="final rewrite")

  # this was the linearizer
@@ -1,161 +0,0 @@
-from typing import Any
-import itertools, functools
-from tinygrad.schedule.rangeify import pm_mops
-from tinygrad.codegen.simplify import pm_flatten_range
-from tinygrad.uop.ops import PatternMatcher, UPat, GroupOp, Ops, UOp, AxisType, resolve, graph_rewrite
-from tinygrad.dtype import dtypes, AddrSpace, ImageDType, Invalid
-from tinygrad.helpers import all_same, flatten, getenv
-from tinygrad.uop.ops import _align_left, _broadcast_shape, identity_element
-from tinygrad.codegen.late.devectorizer import ReduceContext
-from tinygrad.uop.symbolic import pm_clean_up_group_sink
-from collections import defaultdict
-
-def maybe_load(u:UOp): return u.load() if u.addrspace in (AddrSpace.GLOBAL, AddrSpace.LOCAL, AddrSpace.REG) else u
-pm_move_regs = PatternMatcher([
-  # BITCAST?
-  (UPat(GroupOp.Elementwise|{Ops.REDUCE}, name="x"), lambda x: x.replace(src=tuple([maybe_load(u) for u in x.src]))),
-  (UPat(Ops.STORE, name="x"), lambda x: x.replace(src=(x.src[0], maybe_load(x.src[1]))+x.src[2:])),
-])
-
-pm_lower_weakints = PatternMatcher([
-  (UPat(GroupOp.All, dtype=dtypes.weakint, name="x"), lambda x: x.replace(dtype=dtypes.int)),
-])
-
-def build_range_map(ctx, sink:UOp):
-  for x in sink.toposort():
-    if x.op is Ops.RANGE and x.arg[1] in {AxisType.UNROLL, AxisType.UPCAST}:
-      ctx[x.arg[0]] = len(ctx)
-
-def fix_reduce(ctx, r:UOp):
-  range_to_axis = {u:ctx[u.arg[0]] for u in r.ended_ranges if u.arg[0] in ctx if u.arg[1] == AxisType.UNROLL}
-  return r.replace(src=tuple([u for u in r.src if u not in range_to_axis]), arg=(r.arg[0], r.arg[1]+tuple(range_to_axis.values())))
-
-expander2 = PatternMatcher([
-  (UPat(Ops.SINK, name="sink"), build_range_map),
-  (UPat(Ops.REDUCE, name="r"), fix_reduce),
-  (UPat(Ops.RANGE, name="r"),
-   lambda ctx, r: UOp.const(r.dtype, tuple(range(r.vmax+1))) \
-    .reshape(tuple([r.vmax+1 if i == ctx[r.arg[0]] else 1 for i in range(len(ctx))])) if r.arg[0] in ctx else None),
-])+pm_flatten_range
-
-def broadcast_binary(x:UOp):
-  shapes = [u.shape for u in x.src]
-  if all_same(shapes): return None
-  shaped_aligned = _align_left(*shapes)
-  broadcasted = _broadcast_shape(*shapes)
-  src_reshaped = [u.reshape(shp).expand(broadcasted) for u,shp in zip(x.src, shaped_aligned)]
-  return x.replace(src=tuple(src_reshaped))
-
-unbroadcast = PatternMatcher([
-  (UPat(GroupOp.Binary|GroupOp.Ternary|{Ops.STORE}, name="x"), broadcast_binary),
-])
-
-def do_devectorize(b:UOp):
-  if b.shape == (): return None
-  # broadcasting needs to be already unpacked
-  if not all_same([x.shape for x in b.src]): return None
-  src = []
-  for idx in itertools.product(*[range(x) for x in b.shape]):
-    idx_c = [UOp.const(dtypes.weakint, i) for i in idx]
-    src.append(b.replace(src=tuple([x.index(*idx_c) for x in b.src])))
-  return UOp.vectorize(*src).reshape(b.shape) if b.op is not Ops.STORE else UOp.group(*src)
-
-devectorizer2 = pm_mops+PatternMatcher([
-  # unpack broadcasting
-  (UPat(GroupOp.Elementwise|{Ops.LOAD,Ops.STORE}, name="b"), do_devectorize),
-  # const INDEX into STACK is src
-  (UPat(Ops.INDEX, src=(UPat(Ops.STACK, name="a"), UPat.cvar("i"))), lambda a,i: a.src[i.arg]),
-  # stacked INDEX is many INDEX
-  (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.STACK, name="s"))),
-   lambda b,s: UOp.vectorize(*[b.index(u) for u in s.src])),
-  # INDEX into RESHAPE moves the RESHAPE
-  (UPat(Ops.INDEX, src=(UPat((Ops.PARAM, Ops.BUFFER), name="b"), UPat(Ops.RESHAPE, name="s"))),
-   lambda b,s: b.index(s.src[0]).reshape(s.shape)),
-  # RESHAPE a void is removed (hack for AFTER)
-  (UPat(Ops.RESHAPE, dtype=dtypes.void, name="x"), lambda x: x.src[0]),
-  # reshape of a single element shaped value to scalar is an index
-  (UPat(Ops.RESHAPE, name="x"), lambda x: x.src[0].index(UOp.const(dtypes.weakint, 0)) if x.marg == () and x.src[0].shape == (1,) else None),
-  # INDEX without src is nothing
-  (UPat(Ops.INDEX, src=(UPat.var('x'),)), lambda x: x),
-  # RESHAPE+EXPAND -> STACK
-  (UPat(Ops.EXPAND, src=(UPat(Ops.RESHAPE, src=(UPat.var("x"), UPat())), UPat()), name="out"),
-   lambda x,out: UOp.vectorize(*([x]*out.max_numel())) if out.shape == (out.max_numel(),) else None),
-])
-
-def reduce_ranges_to_acc(ctx:ReduceContext, r:UOp):
-  acc = UOp.placeholder_like(r, ctx.acc_num, AddrSpace.REG)
-  ctx.acc_num += 1
-  topo = r.src[0].toposort()
-  ended_ranges = flatten([x.ended_ranges for x in topo if x.op is Ops.END])
-  input_ranges = tuple(x for x in topo if x.op is Ops.RANGE and x not in r.src[1:] and x not in ended_ranges)
-  acc_init = acc.after(*input_ranges).store(identity_element(r.arg[0], r.dtype.scalar()))
-  acc_initted = acc.after(acc_init, *r.src[1:])
-  inp = r.src[0].reduce(arg=r.arg) if r.arg[1] else r.src[0]
-  acc_out = acc_initted.store(acc_initted.alu(r.arg[0], inp)).end(*r.src[1:])
-  return acc.after(acc_out)
-
-def expand_horizontal_reduce(r:UOp):
-  axes = r.arg[1]
-  vals = [r.src[0].shrink(tuple((idx[axes.index(i)], idx[axes.index(i)]+1) if i in axes else None for i in range(r.src[0].ndim)))
-          for idx in itertools.product(*[range(r.src[0].max_shape[a]) for a in axes])]
-  return functools.reduce(lambda x,y: x.alu(r.arg[0], y), vals)
-
-pm_reduce_local = PatternMatcher([
-  (UPat(Ops.REDUCE, src=(UPat(), UPat()), allow_any_len=True, name="r"), reduce_ranges_to_acc),
-])+pm_clean_up_group_sink
-
-pm_horizontal_reduce = PatternMatcher([
-  (UPat(Ops.REDUCE, src=(UPat(),), name="r"), expand_horizontal_reduce),
-])
-
-# *** memory coalesing ***
-
-def memory_coalesing(sink:UOp):
-  if getenv("DMC"): return sink
-
-  # collect
-  memory: defaultdict[tuple[UOp, UOp, UOp], dict[int, list[UOp]]]  = defaultdict(dict)
-  for u in sink.toposort():
-    if u.op in {Ops.LOAD, Ops.STORE} and u.src[0].addrspace != AddrSpace.REG:
-      assert u.src[0].op is Ops.INDEX
-      buf,idx_u = u.src[0].src
-      idx: Any = idx_u.src[1] if idx_u.op is Ops.WHERE and idx_u.src[2].arg is Invalid else idx_u
-      valid: Any = idx_u.src[0] if idx_u.op is Ops.WHERE and idx_u.src[2].arg is Invalid else None
-      if idx.op is Ops.ADD and idx.src[1].op is Ops.CONST: root_src, arg = idx.src[0], idx.src[1].arg
-      elif idx.op is Ops.ADD and idx.src[0].op is Ops.CONST: root_src, arg = idx.src[1], idx.src[0].arg
-      elif idx.op is Ops.CONST and idx.arg is Invalid: root_src, arg = "INVALID", 0
-      elif idx.op is Ops.CONST: root_src, arg = "CONST", idx.arg
-      else: root_src, arg = idx, 0
-      memory[(u.op, buf, root_src, valid)].setdefault(arg, []).append(u)
-
-  # allowed lengths
-  lengths = [8,4,2,1]
-
-  # build replacements
-  replacements = {}
-  for (op,buf,base,valid),offsets in memory.items():
-    grouped_offsets = [[x for _,x in group] for _,group in itertools.groupby(enumerate(sorted(offsets.keys())), lambda x: x[1]-x[0])]
-    for full_grp in grouped_offsets:
-      while len(full_grp):
-        offset = (base+full_grp[0]) if isinstance(base, UOp) else UOp.const(dtypes.weakint, full_grp[0])
-        length = [l for l in lengths if l <= len(full_grp) and offset.divides(l) is not None][0]
-        grp = full_grp[:length]
-        idx = buf._mop(Ops.SHRINK, arg=[(offset, len(grp))]) if len(grp) > 1 else buf.index(offset)
-        if op is Ops.STORE:
-          datas = []
-          for i,g in enumerate(grp):
-            assert len(offsets[g]) == 1
-            datas.append(offsets[g][0].src[1])
-          data = UOp.vectorize(*datas) if len(datas) > 1 else datas[0]
-          store = idx.store(data, valid) if valid is not None else idx.store(data)
-          for i,g in enumerate(grp): replacements[offsets[g][0]] = store
-        else:
-          ld = idx.load(idx.vconst_like(0), valid) if valid is not None else idx.load()
-          for i,g in enumerate(grp):
-            for oo in offsets[g]:
-              replacements[oo] = ld.index(UOp.const(dtypes.int, i)) if len(grp) > 1 else ld
-        full_grp = full_grp[length:]
-
-  # apply
-  return sink.substitute(replacements, name="memory coalesing")
-
@@ -0,0 +1,73 @@
+from typing import Any
+import itertools
+from collections import defaultdict
+from tinygrad.dtype import dtypes, AddrSpace, Invalid, ImageDType
+from tinygrad.uop.ops import UOp, Ops
+from tinygrad.helpers import getenv
+from tinygrad.renderer import Renderer
+
+def memory_coalesing(sink:UOp, ctx:Renderer) -> UOp:
+  if getenv("DMC"): return sink
+
+  # collect
+  memory: defaultdict[tuple[Ops, UOp, Any, Any], dict[int, list[UOp]]]  = defaultdict(dict)
+  for u in sink.toposort():
+    # TODO: this should handle images too, it's just memory coalesing
+    if u.op in {Ops.LOAD, Ops.STORE} and not isinstance(u.src[0].src[0].dtype, ImageDType):
+      assert len(u.src) == (2 if u.op is Ops.STORE else 1), "memory coalesing does not support gated loads/stores"
+      if u.src[0].op is not Ops.INDEX: continue
+      buf, idx_u = u.src[0].src
+      if buf.addrspace == AddrSpace.REG: continue
+      idx: Any = idx_u.src[1] if idx_u.op is Ops.WHERE and idx_u.src[2].arg is Invalid else idx_u
+      valid: Any = idx_u.src[0] if idx_u.op is Ops.WHERE and idx_u.src[2].arg is Invalid else None
+      if idx.op is Ops.ADD and idx.src[1].op is Ops.CONST: root_src, arg = idx.src[0], idx.src[1].arg
+      elif idx.op is Ops.ADD and idx.src[0].op is Ops.CONST: root_src, arg = idx.src[1], idx.src[0].arg
+      elif idx.op is Ops.CONST and idx.arg is Invalid: root_src, arg = "INVALID", 0
+      elif idx.op is Ops.CONST: root_src, arg = "CONST", idx.arg
+      else: root_src, arg = idx, 0
+      memory[(u.op, buf, root_src, valid)].setdefault(arg, []).append(u)
+
+  # build replacements
+  replacements = {}
+  for (op,buf,base,valid),offsets in memory.items():
+    # allowed lengths (copied in)
+    lengths = []
+    must_divide = True
+    if ctx is not None and ctx.target.device == "DSP":
+      lengths = [128,64,32,16,8,4]
+      must_divide = False
+    elif buf.dtype.base not in (dtypes.float, dtypes.half, *dtypes.fp8s) and not isinstance(buf.dtype, ImageDType):
+      pass
+    elif buf.addrspace == AddrSpace.REG:
+      pass
+    elif isinstance(buf.dtype, ImageDType):
+      lengths = [4]
+    elif ctx is not None and ctx.supports_float4:
+      # TODO: a better way to get this than ctx
+      lengths = [8,4,2] if buf.dtype.base == dtypes.half and getenv("ALLOW_HALF8") else [4,2]
+    lengths.append(1)  # worst case, it's not folded
+    # do the grouping
+    grouped_offsets = [[x for _,x in group] for _,group in itertools.groupby(enumerate(sorted(offsets.keys())), lambda x: x[1]-x[0])]
+    for full_grp in grouped_offsets:
+      while len(full_grp):
+        offset = (base+full_grp[0]) if isinstance(base, UOp) else UOp.const(dtypes.int, full_grp[0])
+        length = [l for l in lengths if l <= len(full_grp) and (not must_divide or offset.divides(l) is not None)][0]
+        grp = full_grp[:length]
+        idx = UOp(Ops.SHRINK, dtype=buf.dtype, src=(buf, offset, UOp.const(dtypes.int, len(grp)))) if len(grp) > 1 else buf.index(offset)
+        if op == Ops.STORE:
+          datas = []
+          for i,g in enumerate(grp):
+            assert len(offsets[g]) == 1, f"attempting multiple stores: {len(offsets[g])}"
+            datas.append(offsets[g][0].src[1])
+          data = UOp.vectorize(*datas) if len(datas) > 1 else datas[0]
+          store = idx.store(data, valid) if valid is not None else idx.store(data)
+          for i,g in enumerate(grp): replacements[offsets[g][0]] = store
+        else:
+          ld = idx.load(idx.vconst_like(0), valid) if valid is not None else idx.load()
+          for i,g in enumerate(grp):
+            for oo in offsets[g]:
+              replacements[oo] = ld.index(UOp.const(dtypes.int, i)) if len(grp) > 1 else ld
+        full_grp = full_grp[length:]
+
+  # apply
+  return sink.substitute(replacements, name="memory coalesing")
@@ -5,7 +5,7 @@ from dataclasses import dataclass
 from tinygrad.dtype import dtypes, ImageDType, DType, AddrSpace, Invalid, PtrDType
 from tinygrad.uop.ops import UOp, Ops, UPat, PatternMatcher, GroupOp, identity_element
 from tinygrad.uop.symbolic import uop_given_valid, parse_valid, invalid_gate
-from tinygrad.helpers import getenv, flatten, prod
+from tinygrad.helpers import getenv, flatten, prod, OSX, ceildiv
 from tinygrad.renderer import Renderer

 # ***** image load valid simplification *****
@@ -14,7 +14,7 @@ from tinygrad.renderer import Renderer
 def _drop_valid_stmts(valid:UOp, idx:UOp, height:int, width:int) -> list[UOp]:
  # can drop valid if idx is out of bound when valid is False
  drop_stmt = []
-  for stmt in valid.split_uop(Ops.AND):
+  for i,stmt in enumerate(valid.split_uop(Ops.AND)):
    if (res:=parse_valid(stmt)) is None: continue
    X, is_upper_bound, c = res

@@ -25,12 +25,12 @@ def _drop_valid_stmts(valid:UOp, idx:UOp, height:int, width:int) -> list[UOp]:
        drop_stmt.append(stmt)
        continue

-    # if X <= c, check if it's out of bound when X = c+1
-    # if X >= c, check if it's out of bound when X = c-1
-    test_value = c + 1 if is_upper_bound else c - 1
-    for i,b in zip(idx.src, (width, height)):
-      if i.is_increasing():
-        rw = i.substitute({X:X.const_like(test_value)})
+    # check if idx is out of bound when X is on the wrong side of the bound: X in [c+1, vmax] or [vmin, c-1]
+    lo, hi = (c + 1, X.vmax) if is_upper_bound else (X.vmin, c - 1)
+    if lo <= hi:
+      fake = UOp.variable(f"fake{i}", lo, hi, X.dtype)
+      for coord,b in zip(idx.src, (width, height)):
+        rw = coord.substitute({X:fake}).simplify()
        if rw.vmin >= b or rw.vmax < 0:
          drop_stmt.append(stmt)
          break
@@ -60,6 +60,15 @@ load_store_indexing = PatternMatcher([

 # ***** load/store grouping *****

+# get list of (height, width) that do not require pitch padding
+def image_valid_dims(base:DType, size:int, arch:str) -> list[tuple[int,int]]:
+  if (ALIGN:=next((int(p.split('=')[1]) for p in arch.split(',') if p.startswith("IMAGE_PITCH_ALIGNMENT=")), 0)) == 0: return []
+  MAXW, pxls = 16384, size // 4
+  if base not in (dtypes.half, dtypes.float) or size > 4*MAXW*MAXW: return []
+  # height=1 images just need to abide by alignment requirements in bytes, not pixels!
+  if size % (ALIGN * 4) != 0: return [] if (base.itemsize * size) % (64 if OSX else ALIGN) != 0 or pxls > MAXW else [(1, pxls)]
+  return [(pxls//ALIGN//k, ALIGN*k) for k in range(ceildiv(pxls//ALIGN, MAXW), min(pxls//ALIGN, MAXW//ALIGN)+1) if (pxls//ALIGN)%k == 0]
+
 def expand_index(ctx, buf:UOp, vec:UOp):
  # determine optimal image shapes
  if isinstance(dt:=buf.dtype, ImageDType):
@@ -68,7 +77,7 @@ def expand_index(ctx, buf:UOp, vec:UOp):
    x, valid = idxs.gep(lane), valids.gep(lane)
    # search for dims that drop the most valid statements
    best_drop, cands = -1, []
-    for ch, cw in ImageDType.valid_dims(dt, ctx.target.arch):
+    for ch, cw in image_valid_dims(dt.base, dt.size, ctx.target.arch):
      if (dropped:=len(_drop_valid_stmts(valid, cidx:=uop_given_valid(valid, UOp.vectorize((x//4)%cw, x//(4*cw))), ch, cw))) > best_drop:
        best_drop, cands = dropped, [(ch, cw, cidx)]
      elif dropped == best_drop: cands.append((ch, cw, cidx))
@@ -162,18 +171,8 @@ def split_load_store(ctx:Renderer|None, ls:UOp, idx:UOp):
  # determine fold lengths
  lengths = []
  must_divide = True
-  if ctx is not None and ctx.target.device == "DSP":
-    lengths = [128,64,32,16,8,4]
-    must_divide = False
-  elif buf.dtype.base not in (dtypes.float, dtypes.half, *dtypes.fp8s) and not isinstance(buf.dtype, ImageDType):
-    pass
-  elif buf.addrspace == AddrSpace.REG:
-    pass
-  elif isinstance(buf.dtype, ImageDType):
-    lengths = [4]
-  elif ctx is not None and ctx.supports_float4:
-    # TODO: a better way to get this than ctx
-    lengths = [8,4,2] if buf.dtype.base == dtypes.half and getenv("ALLOW_HALF8") else [4,2]
+  # TODO: this belongs in coalese
+  if isinstance(buf.dtype, ImageDType): lengths = [4]
  lengths.append(1)  # worst case, it's not folded

  # filter fold lengths that don't divide
@@ -384,7 +383,7 @@ pm_imageh_store = PatternMatcher([

 def make_image(ctx, ls, buf, off):
  if (vcount:=buf.dtype.vcount) != 1: buf = buf.src[0]
-  if buf.op == Ops.PARAM and not isinstance(dt:=buf.dtype, ImageDType) and (dims:=ImageDType.valid_dims(dt, ctx)):
+  if buf.op == Ops.PARAM and not isinstance(dt:=buf.dtype, ImageDType) and (dims:=image_valid_dims(dt.base, dt.size, ctx)):
    buf = buf.replace(dtype=(dtypes.imageh if dt.base == dtypes.half else dtypes.imagef)((*dims[0], 4))).flatten()
    if vcount != 1: buf = UOp.vectorize(*([buf] * vcount))
    if ls.op is Ops.LOAD: return ls.replace(src=(buf.index(off, ptr=True),), dtype=dtypes.float.vec(ls.dtype.vcount)).cast(dt.base)
@@ -1,8 +1,9 @@
 import itertools
 from tinygrad.codegen.opt import Opt, OptOps, KernelOptError
 from tinygrad.helpers import getenv, DEBUG, prod, NOLOCALS, TC_OPT, TC_SELECT, USE_TC, IMAGE
-from tinygrad.dtype import PtrDType, ImageDType
+from tinygrad.dtype import PtrDType
 from tinygrad.uop.ops import Ops, resolve, AxisType
+from tinygrad.codegen.late.devectorizer import image_valid_dims
 from tinygrad.codegen.opt.postrange import Scheduler

 def hand_coded_optimizations(k:Scheduler) -> Scheduler:
@@ -50,7 +51,7 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
  # upcast float4 images, this must be early so we don't accidentally add locals before the upcast
  if IMAGE:
    for buf_index,buf in enumerate(k.bufs):
-      if isinstance(buf.src[0].dtype, PtrDType) and ImageDType.valid_dims(buf.src[0].dtype, k.ren.target.arch):
+      if isinstance(buf.src[0].dtype, PtrDType) and image_valid_dims(buf.src[0].dtype.base, buf.src[0].dtype.size, k.ren.target.arch):
        # part of is_expanded
        unit_stride_axes_mul_4 = [k.rngs.index(c) for c in k.bufs[buf_index].src[1].get_idx().split_uop(Ops.ADD) if
          c.op is Ops.RANGE and (c.vmax+1)%4 == 0]
@@ -101,6 +102,12 @@ def hand_coded_optimizations(k:Scheduler) -> Scheduler:
    # for Schedule, we check if the range is used in INDEX gates or WHERE gates
    is_masked = k.rngs[axis] in where_gate_rngs
    if k.full_shape[axis] <= 7 and is_masked and prod(k.full_shape[j] for j in to_upcast) * k.full_shape[axis] <= 7 * 7:
+      # upcasting a masked global axis moves that range out of the launch grid into each work-item
+      # under IMAGE, skip the upcast unless enough global work-items remain after it to hide memory latency
+      if IMAGE and k.axis_types[axis] is AxisType.GLOBAL:
+        global_upcast = prod(k.full_shape[i] for i in to_upcast if k.axis_types[i] is AxisType.GLOBAL) * k.full_shape[axis]
+        global_items_after = prod(k.full_shape[i] for i in k.axes_of(AxisType.GLOBAL)) // global_upcast
+        if resolve(global_items_after < getenv("OCCUPANCY_FLOOR", 4096), False): continue
      if DEBUG >= 4: print(f"upcasting masked axis : {axis}")
      to_upcast.append(axis)
  for axis in to_upcast[::-1]: k.apply_opt(Opt(OptOps.UPCAST, axis, 0))
@@ -2,7 +2,7 @@ from __future__ import annotations
 from typing import Final, ClassVar, Callable, Literal
 import math, struct, ctypes, functools
 from dataclasses import dataclass, fields
-from tinygrad.helpers import ceildiv, getenv, prod, round_up, OSX
+from tinygrad.helpers import getenv, prod, round_up, OSX
 from enum import IntEnum, auto

 class ConstFloat(float):
@@ -136,15 +136,6 @@ class ImageDType(PtrDType):
  @property
  def pitch(self): return (round_up(self.shape[1], 256) if OSX else self.shape[1]) * 4 * self.itemsize

-  # get list of (height, width) that do not require pitch padding
-  @staticmethod
-  def valid_dims(ptr:PtrDType, arch:str) -> list[tuple[int,int]]:
-    if (ALIGN:=next((int(p.split('=')[1]) for p in arch.split(',') if p.startswith("IMAGE_PITCH_ALIGNMENT=")), 0)) == 0: return []
-    MAXW, pxls = 16384, ptr.size // 4
-    if ptr.base not in (dtypes.half, dtypes.float) or ptr.size > 4*MAXW*MAXW: return []
-    # height=1 images just need to abide by alignment requirements in bytes, not pixels!
-    if ptr.size % (ALIGN * 4) != 0: return [] if ptr.nbytes() % (64 if OSX else ALIGN) != 0 or pxls > MAXW else [(1, pxls)]
-    return [(pxls//ALIGN//k, ALIGN*k) for k in range(ceildiv(pxls//ALIGN, MAXW), min(pxls//ALIGN, MAXW//ALIGN)+1) if (pxls//ALIGN)%k == 0]

 class dtypes:
  @staticmethod
@@ -275,7 +275,7 @@ SCACHE = ContextVar("SCACHE", 1)
 # allow use of atomics for embedding backward
 USE_ATOMICS = ContextVar("USE_ATOMICS", 0)
 # don't allow broadcast
-DISALLOW_BROADCAST = ContextVar("DISALLOW_BROADCAST", 0)
+DISALLOW_BROADCAST = ContextVar("DISALLOW_BROADCAST", 1)

@dataclass(frozen=True)
 class Metadata:
@@ -6,7 +6,7 @@ from tinygrad.mixin.movement import MovementMixin
 from tinygrad.mixin.reduce import ReduceMixin
 from tinygrad.uop import Ops
 from tinygrad.uop.ops import _broadcast_shape, resolve, smax, smin, identity_element
-from tinygrad.dtype import ConstType, DTypeLike, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
+from tinygrad.dtype import ConstType, DTypeLike, Invalid, PtrDType, PyConst, dtypes, least_upper_dtype, sum_acc_dtype, to_dtype
 from tinygrad.helpers import all_int, argfix, argsort, ceildiv, flatten, flat_to_grouped, fully_flatten, get_shape, make_tuple, merge_dicts, prod
 from tinygrad.helpers import resolve_pool_pads, round_up

@@ -285,7 +285,7 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
    pads = tuple((smax(pB,0), smax(pA,0)) for pB,pA in pX) if has_neg else pX
    base = MovementMixin.pad(X, pads)
    if value == 0: return base
-    base = base.cast(least_upper_dtype(base.dtype, dtypes.from_py(value)))
+    if value is not Invalid: base = base.cast(least_upper_dtype(base.dtype, dtypes.from_py(value)))
    return MovementMixin.pad(X.const_like(1).cast(dtypes.bool), pads).where(base, base.const_like(value))

  def _pad_circular(self, pX:tuple[tuple[sint, sint], ...]) -> Self:
@@ -22,6 +22,7 @@ base_rewrite = PatternMatcher([
  (UPat(Ops.CAST, name="x"), lambda ctx,x: f"__builtin_convertvector({ctx[x.src[0]]}, {ctx.render_type(x)})" \
    if x.max_numel() > 1 and x.addrspace is AddrSpace.REG else None),
  (UPat(Ops.CAST, name="x"), lambda ctx,x: f"({ctx.render_cast(x, ctx[x.src[0]])})"),
+  (UPat(Ops.BITCAST, name="x"), lambda ctx,x: ctx[x.src[0]] if x.addrspace in (AddrSpace.GLOBAL, AddrSpace.LOCAL) else None),
  (UPat(Ops.BITCAST, name="x"), lambda ctx,x: f"__builtin_bit_cast({ctx.render_type(x)}, ({ctx.render_type(x.src[0])})({ctx[x.src[0]]}))"),

  # GPU stuff
@@ -21,7 +21,7 @@ class CUDAGraph(MultiGraphRunner):

        c_deps, new_node = self.new_node([b.base for b in bufs], ast.arg.outs)
        c_args, vargs = encode_args([b._buf for b in bufs], [device_vars.get(x.expr, 0) for x in ast.arg.vars])
-        kern_params = cuda.CUDA_KERNEL_NODE_PARAMS_v1(runtime.prg, *global_size, *local_size, 0,
+        kern_params = cuda.CUDA_KERNEL_NODE_PARAMS_v1(runtime.prg, *global_size, *local_size, runtime.smem,
                                                      ctypes.cast(0, ctypes.POINTER(ctypes.c_void_p)), vargs)
        check(cuda.cuGraphAddKernelNode(ctypes.byref(new_node), self.graph, c_deps, len(c_deps or []), ctypes.byref(kern_params)))

@@ -105,7 +105,7 @@ class PythonProgram:
          if u.src[0].addrspace == AddrSpace.ALU:
            ret = [src_values[0][i][t] for t,i in enumerate(src_values[1])]
          elif isinstance(src_dtypes[0], ImageDType):
-            assert len(src_values) == 3, "image index must be 3 srcs"
+            assert len(src_values) == 3, f"image index must be 3 srcs, not {len(src_values)}"
            for m,oy,ox in zip(*src_values):
              if ox < 0 or ox >= src_dtypes[0].shape[1] or oy < 0 or oy >= src_dtypes[0].shape[0]: ret.append((m, None))
              else: ret.append((m, ox*4 + oy*src_dtypes[0].shape[1]*4))
@@ -141,7 +141,7 @@ def apply_movement_op(op:Ops, in_shape:tuple[sint,...], arg:tuple, rngs:tuple[UO
        symbolic+pm_simplify_valid, name="pad").where(r-off, UOp.invalid()) for r,sh,(off,sz) in zip(rngs, in_shape, arg))
    case Ops.RESHAPE:
      sink = UOp.sink(*rngs).simplify() # NOTE: this applies any commutative flips to the rngs early
-      sub_array = {r:UOp.range(r.src[0], i, AxisType.PLACEHOLDER) for i,r in enumerate(sink.ranges)}
+      sub_array = {r:UOp.range(r.src[0], i, AxisType.PLACEHOLDER, dtype=r.dtype) for i,r in enumerate(sink.ranges)}
      rngs = _apply_reshape(in_shape, arg, sink.substitute(sub_array)).substitute({v:k for k,v in sub_array.items()}).src
    case _: raise RuntimeError(f"{op} is not a MovementOp")
  return rngs
@@ -18,7 +18,7 @@ import sys
 sys.setrecursionlimit(10000)

 def add_ranges_to_store(ctx, x):
-  if x.src[0]._shape is None or x.src[1]._shape is None or x.src[0].shape == (): return None
+  if x.src[0]._shape is None or x.src[1]._shape is None or x.src[0].shape == () or x.src[0].max_numel() == x.src[1].max_numel() == 1: return None
  assert x.src[0].shape == x.src[1].shape, "bad store shape"
  idxs = [UOp.range(r, next(ctx), AxisType.LOOP) for r in x.src[0].shape]
  return UOp.store(x.src[0].index(*idxs), x.src[1].index(*idxs)).end(*idxs)
@@ -859,8 +859,7 @@ class Tensor(RandMixin, metaclass=TensorMeta):
    def is_pow2(v): return v > 0 and v & (v - 1) == 0
    # pad dimension i to amt with invalids
    def ipad(t, i, amt):
-      shape = (None,)*i + (amt,) + (None,)*(t.ndim-i-1)
-      return Tensor(True, device=t.device).expand(t.shape).pad_to(shape).where(t.pad_to(shape), Invalid) if amt != t.shape[i] else t
+      return t.pad(tuple(None if d != i else (0, amt-s) for d,s in enumerate(t.shape)), value=Invalid) if amt != t.shape[i] else t
    # align a dimension, use at to specify the dimension to pad in, defaults to first
    def pad_align(t, dim, at=None, force=False):
      # align to 64 pixels when height is real, otherwise 64 bytes is sufficient
@@ -895,13 +894,10 @@ class Tensor(RandMixin, metaclass=TensorMeta):
    # prepare weights
    w = w.permute(0,4,2,5,1,3).reshape((1, 1, 1, *group_shape, *rcout_expand, rcin_hi, rcin_lo, H, W))

-    added_ox = round_up(ox, math.lcm(cout, 64 // dtsz) // cout) - ox
-    if added_ox: x = x.pad_to(None, None, ox + added_ox, None, None, None, None, None, None, None, None)
-
    # the conv!
    ret = (x*w).cast(dtypes.float32).sum((-4, -3, -2, -1), dtype=dtype)

-    ret = ret.reshape(bs, oy, ox + added_ox, groups, rcout)[:, :, :ox, :, :]
+    ret = ret.reshape(bs, oy, ox, groups, rcout)
    # undo hack for non multiples of 4 on C.rcout
    if added_output_channels: ret = ret[:, :, :, :, :-added_output_channels]
    # NCHW output
@@ -454,7 +454,8 @@ def floormod_to_mod(a:UOp, b:UOp) -> UOp:

 powers_of_two: dict[int, int] = {2**i:i for i in range(64)}
@functools.cache
-def get_late_rewrite_patterns(ops:tuple[Ops, ...], disable_fast_idiv:bool) -> PatternMatcher:
+def get_simplifying_rewrite_patterns(ops:tuple[Ops, ...]) -> PatternMatcher:
+  # these are rewrites that make things simpler
  pat: list[tuple[UPat, Callable]] = [(UPat.var("a")//UPat.var("b"), floordiv_to_idiv)]
  # FLOORMOD by 2**y -> x & (2**y-1) (correct floor mod for any sign in two's complement); fires before floormod_to_mod
  if Ops.AND in ops: pat.append((UPat.var("x", dtypes.ints)%UPat.cvar("c"), lambda x,c: x & (c.arg-1) if c.arg in powers_of_two else None))
@@ -463,6 +464,11 @@ def get_late_rewrite_patterns(ops:tuple[Ops, ...], disable_fast_idiv:bool) -> Pa
  if Ops.THREEFRY not in ops: pat.append((UPat(Ops.THREEFRY, dtype=dtypes.uint64, src=(UPat.var("x"), UPat.var("key"))), threefry2x32))
  # MAX can be rewritten as CMPLT + WHERE (max function is annoying on many cstyle backends)
  if Ops.MAX not in ops and Ops.CMPLT in ops: pat.append((UPat(Ops.MAX, name="m"), lambda m: (m.src[0] < m.src[1]).where(m.src[1], m.src[0])))
+  return PatternMatcher(pat)
+
+@functools.cache
+def get_late_rewrite_patterns(ops:tuple[Ops, ...], disable_fast_idiv:bool) -> PatternMatcher:
+  pat: list[tuple[UPat, Callable]] = []
  if Ops.OR in ops: pat += [(UPat.var("x", dtypes.bool).logical_not()&UPat.var("y", dtypes.bool).logical_not(),
    lambda x,y: (x | y).logical_not())]
  # rewrite MUL/CDIV to SHL+SHR: x*(2**y) -> shl(x,y) and x//(2**y) -> shr(x,y)
@@ -84,8 +84,8 @@ def multirange_str(rngs:Iterable[UOp], color=False, pad=None) -> str:

 def shape_to_shape_arg(arg:tuple[sint, ...]) -> UOp:
  if len(arg) == 0: return UOp(Ops.STACK)
-  elif len(arg) == 1: return UOp.const(dtypes.weakint, arg[0])
-  else: return UOp(Ops.STACK, dtypes.weakint, tuple(UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in arg))
+  elif all_int(arg): return UOp.const(dtypes.weakint.vec(len(arg)), arg)
+  else: return UOp(Ops.STACK, dtypes.weakint.vec(len(arg)), tuple(UOp.const(dtypes.weakint, x) if isinstance(x, int) else x for x in arg))

 def consumer_map_from_toposort(lst:Iterable[UOp]):
  ret: dict[UOp, dict[UOp, None]] = {}
@@ -305,6 +305,10 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
      case Ops.MSTACK | Ops.MSELECT | Ops.DETACH | Ops.CONTIGUOUS | Ops.CONTIGUOUS_BACKWARD | Ops.AFTER | Ops.LOAD | \
           Ops.COPY | Ops.ALLREDUCE | Ops.STORE | Ops.END:
        return self.src[0]._shape
+      # REDUCE with empty axis is passthrough (lowered form)
+      case Ops.REDUCE if len(self.arg[1]) == 0:
+        # these can mismatch if there's a horizonal reduce
+        return (self.dtype.count,) if self.dtype.count > 1 else ()

      # TODO: disallow shape changing bitcast
      case Ops.BITCAST:
@@ -469,7 +473,7 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
    if len(srcs) == 1 and isinstance(srcs[0], UOp): return srcs[0]
    return UOp(Ops.GROUP, dtypes.void, tuple([x for x in srcs if x is not None]))
  def vectorize(self, *srcs):
-    return UOp(Ops.STACK, self.dtype, (self,)+srcs)
+    return UOp(Ops.STACK, self.dtype.vec(len(srcs)+1), (self,)+srcs)
  def index(self, *srcs:UOp|None, ptr=False, **kwargs):
    return UOp(Ops.INDEX, kwargs.pop("dtype", self.dtype if ptr else self.dtype.base), (self,)+tuple([x for x in srcs if x is not None]), **kwargs)
  def __getitem__(self, idx):
@@ -915,12 +919,6 @@ class UOp(RandMixin, metaclass=UOpMetaClass):

  # *** uop symbolic stuff ***

-  def is_increasing(self:UOp) -> bool:
-    # is f a monotonically increasing function regards its input
-    if self.op in GroupOp.Irreducible: return True
-    if self.op is Ops.ADD: return self.src[0].is_increasing() and self.src[1].is_increasing()
-    if self.op in (Ops.MUL, Ops.CDIV, Ops.FLOORDIV) and self.src[1].op is Ops.CONST and self.src[1].arg >= 0: return self.src[0].is_increasing()
-    return False  # False if not sure
  def const_factor(self) -> int:
    """largest known int that divides self"""
    # TODO: for negatives it's not the largest
@@ -192,8 +192,8 @@ spec_program = PatternMatcher([
  # no more of these in programs
  (UPat(Ops.GEP), lambda: False),

-  # weakint is not allowed in programs, except on CONST and STACK
-  (UPat(GroupOp.All-{Ops.CONST,Ops.STACK}, dtypes.weakint), lambda: False),
+  # weakint is not allowed in programs
+  (UPat(GroupOp.All, dtypes.weakint), lambda: False),

  # allow special SHRINK
  (UPat(Ops.SHRINK, src=(UPat((Ops.PARAM, Ops.BUFFER, Ops.AFTER)), UPat(), UPat(Ops.CONST))), lambda: True),
@@ -121,6 +121,8 @@ symbolic_simple = propagate_invalid + PatternMatcher([
  # TODO: combine this with "# rules for threefry" below
  ((UPat.var("x") & UPat.cvar("mask")) >> UPat.cvar("k"),
   lambda x,mask,k: x >> k.arg if mask.arg | ((1 << k.arg) - 1) == -1 else None),
+  ((UPat.var("x") & UPat.cvar("mask")) // UPat.cvar("c"),
+   lambda x,mask,c: x // c.arg if c.arg > 0 and c.arg & (c.arg-1) == 0 and mask.arg | (c.arg-1) == -1 else None),
  (UPat.var("x", dtype=dtypes.ints+(dtypes.bool, dtypes.weakint)) != UPat.var("x"),
   lambda x: x.const_like(False).cast(dtypes.bool.vec(x.dtype.count))), # x != x -> False (only ints)
  # ** constant folding **
@@ -160,6 +162,7 @@ symbolic_simple = propagate_invalid + PatternMatcher([
  (((UPat.var(None, dtypes.uint64)*(1<<32)) | UPat.var('y',  dtypes.uint32).cast(dtypes.uint64)).cast(dtypes.uint32), lambda y: y),
  (((UPat.var('x',  dtypes.uint64)*(1<<32)) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))//(1<<32), lambda x: x),
  (((UPat.var(None, dtypes.uint64)<<32) | UPat.var('y',  dtypes.uint32).cast(dtypes.uint64)).cast(dtypes.uint32), lambda y: y),
+  (((UPat.var('x',  dtypes.uint64)<<32) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))//(1<<32), lambda x: x),
  (((UPat.var('x',  dtypes.uint64)<<32) | UPat.var(None, dtypes.uint32).cast(dtypes.uint64))>>32, lambda x: x),
  # ** simple where folding **
  # a conditional with the same results either way is a noop, also fold const conditionals
@@ -167,6 +170,9 @@ symbolic_simple = propagate_invalid + PatternMatcher([
  (UPat.cvar("gate").where(UPat.var("c0"), UPat.var("c1")), lambda gate, c0, c1: c0 if gate.arg else c1),
  # a.where(b.where(c, d), d) -> (a & b).where(c, d)
  (UPat.var("a").where(UPat.var("b").where(UPat.var("c"), UPat.var("d")), UPat.var("d")), lambda a,b,c,d: (a&b).where(c,d)),
+  # STACK on INDEX CONST (TODO: remove all the GEP crap)
+  (UPat(Ops.STACK, src=UPat(Ops.INDEX, src=(UPat.var("src"), UPat(Ops.CONST))), name="stk"),
+   lambda src,stk: src if stk.shape == src.shape and list(range(len(stk.src))) == [x.src[1].arg for x in stk.src] else None),
 ])

 # ******** phase 2 builds on phase 1, it includes the old "symbolic", rules that match deeper ********
Author	SHA1	Message	Date
geohot	ca25160d59	move down dtype decomps	2026-06-24 22:12:53 -07:00
George HotzandGitHub	2f91a401a1	move late decomp after new style (#16734 )	2026-06-24 22:04:59 -07:00
qazalandGitHub	cb9df8bd33	fix profile.sh BENCHMARK=3 exit status (#16736 )	2026-06-25 13:41:03 +09:00
sirhcmandGitHub	63f0e08593	start benchmarks cleanup (#16729 )	2026-06-24 23:33:46 -04:00
wozeparrotandGitHub	e0c69d7a12	llama: fused grad quantize (#16731 )	2026-06-24 23:25:39 -04:00
chenyuandGitHub	65dd099b63	support Invalid in _pad_constant [PR] (#16733 ) and simplified image_conv2d	2026-06-24 21:03:36 -04:00
George HotzandGitHub	9d5d2253b3	refactor to image_valid_dims (#16730 )	2026-06-24 16:53:09 -07:00
nimlgenandGitHub	b99b9e1875	do not assert index in mem coalesing (#16723 )	2026-06-24 14:28:49 +03:00
Nikolay Murzin GitHub Claude Opus 4.8	f38836d14d	cuda: pass kernel dynamic shared memory in the graph runner (#16699 ) The CUDA graph runner hardcoded sharedMemBytes=0 in the kernel node params, so a custom_kernel using dynamic shared memory (extern __shared__) failed with CUDA error 700 on graph replay while the eager path worked. Pass runtime.smem (0 for normal kernels, which use static shared). Co-authored-by: Claude Opus 4.8 (1M context) <[email protected]>	2026-06-24 14:20:10 +03:00
qazalandGitHub	535c806c9f	llama: profile.sh BENCHMARK=3 (#16722 )	2026-06-24 19:20:51 +09:00
chenyuandGitHub	857a435af2	remove dead added_ox (#16721 )	2026-06-23 23:59:17 -04:00
chenyuandGitHub	687ade119e	IMAGE hand_coded_optimizations update (#16720 )	2026-06-23 21:55:28 -04:00
George HotzandGitHub	0a8e61d0c5	switch to the new memory coaleser [pr] (#16716 ) * switch to the new memory coalese * move that stuff * copy in allowed length logic * mulitple buffers * new coalese is better * fine * earlier * fixes * work * work * valid * stack on index const	2026-06-23 18:03:48 -07:00
wozeparrotandGitHub	dfea9e7994	llama: fused silu mul quantize mxfp8 (#16704 )	2026-06-23 16:59:50 -07:00
chenyuandGitHub	ce87d80911	better _drop_valid_stmts [pr] (#16719 ) also dropped the unused is_increasing	2026-06-23 19:35:01 -04:00
George HotzandGitHub	5a2b3b7b06	early dtype decomp (#16718 ) * early dtype decomp * simplify * cleanup * that goes there * doing too much * stupid symbolic rules	2026-06-23 16:07:20 -07:00
sirhcmandGitHub	116045cc8e	ci: remove tensorflow from testoptim (#16717 )	2026-06-23 18:11:48 -04:00
nimlgenandGitHub	7c1d0b6d9a	hcq2: use shrink(bitcast) (#16713 ) * hcq2: use shrink(bitcast) * x	2026-06-23 18:11:39 +03:00