fix onnx.py type annotations (#17811)

pads can be tuple

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

@@ -194,21 +194,29 @@ runs:
         echo "pkgs=$pkgs" >> "$GITHUB_OUTPUT"
         echo "hash=$(echo -n "$pkgs" | sha256sum | cut -d' ' -f1)" >> "$GITHUB_OUTPUT"
 
+        installed=true
+        for pkg in $pkgs; do
+          info=$(dpkg-query -W -f='${db:Status-Abbrev} ${Version}' "$pkg" 2> /dev/null || true)
+          echo "${pkg}: ${info:-not in dpkg database}"
+          [[ "$info" == ii* ]] || installed=false
+        done
+        echo "installed=$installed" >> "$GITHUB_OUTPUT"
+
     - name: Cache apt (PR)
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && github.event_name == 'pull_request'
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && github.event_name == 'pull_request' && steps.apt-pkgs.outputs.installed == 'false'
       uses: actions/cache/restore@v5
       with:
         path: /var/cache/apt/archives/
         key: ${{ runner.os }}-${{ runner.arch }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
     - name: Cache apt
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && github.event_name != 'pull_request'
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && github.event_name != 'pull_request' && steps.apt-pkgs.outputs.installed == 'false'
       uses: actions/cache@v5
       with:
         path: /var/cache/apt/archives/
         key: ${{ runner.os }}-${{ runner.arch }}-apt-${{ steps.apt-pkgs.outputs.hash }}-${{ env.CACHE_VERSION }}
 
     - name: Run apt Update + Install
-      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true')
+      if: runner.os == 'Linux' && (inputs.opencl == 'true' || inputs.amd == 'true' || inputs.webgpu == 'true' || inputs.llvm == 'true' || inputs.qemu == 'true' || inputs.ninja == 'true') && steps.apt-pkgs.outputs.installed == 'false'
       shell: bash
       run: |
         sudo apt -qq update || true

.github/workflows/benchmark.yml

@@ -94,7 +94,7 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
+      HCQ2: '0'
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -140,7 +140,7 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
+      HCQ2: '0'
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -188,7 +188,7 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
+      HCQ2: '0'
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -230,7 +230,7 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
+      HCQ2: '0'
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code
@@ -275,7 +275,7 @@ jobs:
         shell: bash -e -o pipefail {0}
     env:
       DEV: ${{ matrix.dev }}
-      HCQ2: ${{ matrix.dev == 'AMD' && '1' || '0' }}
+      HCQ2: '0'
     if: github.repository_owner == 'tinygrad'
     steps:
     - name: Checkout Code

examples/mlperf/model_train.py

@@ -1667,7 +1667,7 @@ def train_llama3():
 def train_gptoss():
   from examples.mlperf.models.gpt_oss import GPTOSS, GPT_OSS_20B, apply_grad, FP8_DTYPE
   from examples.mlperf.lr_schedulers import CosineAnnealingLRWithWarmup
-  from examples.mlperf.optim import GradAccClipAdamW, GradAccClipAdamWGroup, clip_grads
+  from examples.mlperf.optim import GradAccClipAdamW, GradAccClipAdamWGroup, fclip_grads
 
   BENCHMARK = getenv("BENCHMARK")
 
@@ -1785,12 +1785,10 @@ def train_gptoss():
 
     Tensor.realize(loss, *grads)
 
-    grad_norm = clip_grads(grads, 1, 1.0)
-    optim.fstep(grads, grad_norm)
+    clipped_grads, grad_norm = fclip_grads(grads, 1.0)
+    optim.fstep(clipped_grads, grad_norm)
     scheduler.step()
 
-    for g in grads: g.assign(0)
-
     loss_cpu = loss.flatten().float().to("CPU")
     lr_cpu = optim.lr.float().to("CPU")
     grad_norm_cpu = grad_norm.float().to("CPU")

examples/mlperf/models/gpt_oss.py

@@ -282,14 +282,14 @@ def apply_grad(grad_buf:Tensor, new_grad:UOp):
   pads = _get_pads(new_grad)
   if len(pads) <= 1:
     new_grad = new_grad.cast(grad_buf.dtype)
-    grad_buf.uop = grad_buf.uop.after(grad_buf.uop.store(grad_buf.uop + new_grad))
+    grad_buf.uop = grad_buf.uop.after(grad_buf.uop.store(new_grad))
     return
   cur = grad_buf.uop
   for pad in sorted(pads, key=lambda p: p.marg[0][0] if p.op == Ops.PAD else 0, reverse=True):
     if pad.op == Ops.PAD:
-      grad_shrink = tuple([(p[0], s+p[0]) for s,p in zip(pad.src[0].shape, pad.marg)])
+      grad_shrink = tuple((p[0], s+p[0]) for s,p in zip(pad.src[0].shape, pad.marg))
       buf_slice = cur.shrink(grad_shrink)
-      cur = cur.after(buf_slice.store(buf_slice + pad.src[0].cast(cur.dtype)))
+      cur = cur.after(buf_slice.store(pad.src[0].cast(cur.dtype)))
     else:
       cur = cur.after(cur.store(cur + pad.cast(cur.dtype)))
   grad_buf.uop = cur

examples/mlperf/optim.py

@@ -27,6 +27,11 @@ def clip_grads(grads:list[Tensor], grad_acc, clip_norm) -> Tensor:
   for g in grads: g.assign((g * (clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)).cast(g.dtype))
   return total_norm
 
+def fclip_grads(grads:list[Tensor], clip_norm) -> Tensor:
+  total_norm = Tensor.stack(*[g.float().square().sum() for g in grads]).sum().sqrt().contiguous()
+  scale = (clip_norm / (total_norm + 1e-6)).clamp(max_=1.0)
+  return [(g * scale).cast(g.dtype) for g in grads], total_norm
+
 class GradAccClipAdamW(Optimizer):
   def __init__(self, params:list[Tensor], lr=0.001, b1=0.9, b2=0.999, eps=1e-6, weight_decay=0.0, grad_acc=1, clip_norm=1.0, device=None, fused=FUSE_OPTIM):
     super().__init__(params, lr, device, fused)

extra/gemm/gemm_mxfp4.py

@@ -20,34 +20,39 @@ def v_mfma_fp4(dst, a, b, opsel, opsel_hi, scale_a, scale_b):
 def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
   k = Kernel()
   scale_k = K // 32
+  k.emit(s_and_b32(s[1], s[1], LIT, 65535))
   if (tile_m, tile_n) == (128, 512):
-    k.emit(s_and_b32(s[1], s[1], LIT, 65535))
     k.emit(s_mov_b32(s[47], s[2]))
     k.emit(s_mov_b32(s[48], s[3]))
-    k.emit(s_load_dwordx2(s[4:5], s[0:1], s[0], 0, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[8], 0))
-    k.emit(s_mov_b32(s[9], 0))
-    k.emit(s_load_dwordx2(s[12:13], s[0:1], s[0], 8, 0, 0, 0, 1))
-    k.emit(s_load_dwordx2(s[16:17], s[0:1], s[0], 16, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[36], N))
-    k.emit(s_mov_b32(s[37], K))
-    k.emit(s_mov_b32(s[38], K))
-    k.emit(s_mov_b32(s[43], M))
-    k.emit(s_mov_b32(s[44], N))
-    k.emit(s_mov_b32(s[45], K))
-    k.emit(s_load_dwordx2(s[20:21], s[0:1], s[0], 24, 0, 0, 0, 1))
-    k.emit(s_load_dwordx2(s[24:25], s[0:1], s[0], 32, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[39], scale_k))
-    k.emit(s_mov_b32(s[40], scale_k))
-    k.emit(v_lshrrev_b32_e32(v[1], 10))
-    k.emit(v_lshrrev_b32_e32(v[2], 10, v[1]))
-    k.emit(v_and_b32_e32(v[2], LIT, v[2], 1023))
-    k.emit(v_and_b32_e32(v[1], LIT, v[1], 1023))
-    k.emit(v_and_b32_e32(v[0], LIT, v[0], 1023))
-    k.emit(v_lshrrev_b32_e32(v[3], 6))
-    k.emit(v_and_b32_e32(v[0], 63))
-    k.emit(v_readfirstlane_b32_e32(v[46], v[3]))
-    k.emit(s_waitcnt(49279))
+  k.emit(s_load_dwordx2(s[4:5], s[0:1], s[0], 0, 0, 0, 0, 1))
+  k.emit(s_mov_b32(s[8], 0))
+  k.emit(s_mov_b32(s[9], 0))
+  k.emit(s_load_dwordx2(s[12:13], s[0:1], s[0], 8, 0, 0, 0, 1))
+  k.emit(s_load_dwordx2(s[16:17], s[0:1], s[0], 16, 0, 0, 0, 1))
+  k.emit(s_mov_b32(s[36], N))
+  k.emit(s_mov_b32(s[37], K))
+  k.emit(s_mov_b32(s[38], K))
+  k.emit(s_mov_b32(s[43], M))
+  k.emit(s_mov_b32(s[44], N))
+  k.emit(s_mov_b32(s[45], K))
+  k.emit(s_load_dwordx2(s[20:21], s[0:1], s[0], 24, 0, 0, 0, 1))
+  k.emit(s_load_dwordx2(s[24:25], s[0:1], s[0], 32, 0, 0, 0, 1))
+  k.emit(s_mov_b32(s[39], scale_k))
+  k.emit(s_mov_b32(s[40], scale_k))
+  k.emit(v_lshrrev_b32_e32(v[1], 10))
+  k.emit(v_lshrrev_b32_e32(v[2], 10, v[1]))
+  k.emit(v_and_b32_e32(v[2], LIT, v[2], 1023))
+  k.emit(v_and_b32_e32(v[1], LIT, v[1], 1023))
+  k.emit(v_and_b32_e32(v[0], LIT, v[0], 1023))
+  k.emit(v_lshrrev_b32_e32(v[3], 6))
+  k.emit(v_and_b32_e32(v[0], 63))
+  if (tile_m, tile_n) == (256, 256):
+    k.emit(s_mov_b32(s[49], s[2]))
+    k.emit(s_mov_b32(s[47], s[3]))
+  k.emit(v_readfirstlane_b32_e32(v[46], v[3]))
+  k.emit(s_waitcnt(49279))
+
+  if (tile_m, tile_n) == (128, 512):
     for i in range(2):
       k.emit(s_mov_b32(s[6 + i * 8], -16))
       k.emit(s_mov_b32(s[10 + i * 12], -16))
@@ -1213,31 +1218,6 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(s_waitcnt())
     k.emit(s_endpgm())
   elif (tile_m, tile_n) == (192, 256):
-    k.emit(s_and_b32(s[1], s[1], LIT, 65535))
-    k.emit(s_load_dwordx2(s[4:5], s[0:1], s[0], 0, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[8], 0))
-    k.emit(s_mov_b32(s[9], 0))
-    k.emit(s_load_dwordx2(s[12:13], s[0:1], s[0], 8, 0, 0, 0, 1))
-    k.emit(s_load_dwordx2(s[16:17], s[0:1], s[0], 16, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[36], N))
-    k.emit(s_mov_b32(s[37], K))
-    k.emit(s_mov_b32(s[38], K))
-    k.emit(s_mov_b32(s[43], M))
-    k.emit(s_mov_b32(s[44], N))
-    k.emit(s_mov_b32(s[45], K))
-    k.emit(s_load_dwordx2(s[20:21], s[0:1], s[0], 24, 0, 0, 0, 1))
-    k.emit(s_load_dwordx2(s[24:25], s[0:1], s[0], 32, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[39], scale_k))
-    k.emit(s_mov_b32(s[40], scale_k))
-    k.emit(v_lshrrev_b32_e32(v[1], 10))
-    k.emit(v_lshrrev_b32_e32(v[2], 10, v[1]))
-    k.emit(v_and_b32_e32(v[2], LIT, v[2], 1023))
-    k.emit(v_and_b32_e32(v[1], LIT, v[1], 1023))
-    k.emit(v_and_b32_e32(v[0], LIT, v[0], 1023))
-    k.emit(v_lshrrev_b32_e32(v[3], 6))
-    k.emit(v_and_b32_e32(v[0], 63))
-    k.emit(v_readfirstlane_b32_e32(v[46], v[3]))
-    k.emit(s_waitcnt(49279))
     k.emit(s_mul_i32(s[63], LIT, 8, 192))
     k.emit(v_cvt_f32_u32_e32(v[4], s[63]))
     k.emit(s_sub_i32(s[62], 0, s[63]))
@@ -2234,49 +2214,22 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(s_waitcnt())
     k.emit(s_endpgm())
   elif (tile_m, tile_n) == (256, 256):
-    k.emit(s_and_b32(s[1], s[1], LIT, 65535))
-    k.emit(s_load_dwordx2(s[4:5], s[0:1], s[0], 0, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[8], 0))
-    k.emit(s_mov_b32(s[9], 0))
-    k.emit(s_load_dwordx2(s[12:13], s[0:1], s[0], 8, 0, 0, 0, 1))
-    k.emit(s_load_dwordx2(s[16:17], s[0:1], s[0], 16, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[40], N))
-    k.emit(s_mov_b32(s[41], K))
-    k.emit(s_mov_b32(s[42], K))
-    k.emit(s_mov_b32(s[43], M))
-    k.emit(s_mov_b32(s[44], N))
-    k.emit(s_mov_b32(s[45], K))
-    k.emit(s_load_dwordx2(s[20:21], s[0:1], s[0], 24, 0, 0, 0, 1))
-    k.emit(s_load_dwordx2(s[24:25], s[0:1], s[0], 32, 0, 0, 0, 1))
-    k.emit(s_mov_b32(s[36], scale_k))
-    k.emit(s_mov_b32(s[37], scale_k))
-    k.emit(v_lshrrev_b32_e32(v[1], 10))
-    k.emit(v_lshrrev_b32_e32(v[2], 10, v[1]))
-    k.emit(v_and_b32_e32(v[2], LIT, v[2], 1023))
-    k.emit(v_and_b32_e32(v[1], LIT, v[1], 1023))
-    k.emit(v_and_b32_e32(v[0], LIT, v[0], 1023))
-    k.emit(v_lshrrev_b32_e32(v[3], 6))
-    k.emit(v_and_b32_e32(v[0], 63))
-    k.emit(s_mov_b32(s[46], s[2]))
-    k.emit(s_mov_b32(s[47], s[3]))
-    k.emit(v_readfirstlane_b32_e32(v[49], v[3]))
-    k.emit(s_waitcnt(49279))
     k.emit(s_add_u32(s[55], s[44], LIT, 255))
     k.emit(s_lshr_b32(s[54], s[55], 8))
     k.emit(s_mul_i32(s[48], s[54], s[47]))
-    k.emit(s_add_i32(s[48], s[48], s[46]))
+    k.emit(s_add_i32(s[48], s[48], s[49]))
     k.emit(s_add_u32(s[55], s[43], LIT, 255))
     k.emit(s_lshr_b32(s[52], s[55], 8))
     k.emit(s_lshl_b32(s[52], s[52], 5))
-    k.emit(s_mov_b32(s[46], 0))
+    k.emit(s_mov_b32(s[49], 0))
     k.label('L2_00E8')
     k.emit(s_cmp_lt_i32(s[48], s[52]))
     k.emit(s_cbranch_scc1(3), target='L2_00FC')
     k.emit(s_sub_i32(s[48], s[48], s[52]))
-    k.emit(s_add_i32(s[46], s[46], 32))
+    k.emit(s_add_i32(s[49], s[49], 32))
     k.emit(s_branch(65531), target='L2_00E8')
     k.label('L2_00FC')
-    k.emit(s_sub_i32(s[54], s[54], s[46]))
+    k.emit(s_sub_i32(s[54], s[54], s[49]))
     k.emit(s_cmp_lt_i32(s[54], 32))
     k.emit(s_cbranch_scc1(3), target='L2_0114')
     k.emit(s_lshr_b32(s[47], s[48], 5))
@@ -2311,7 +2264,7 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(s_mul_i32(s[52], s[54], s[47]))
     k.emit(s_sub_i32(s[52], s[48], s[52]))
     k.label('L2_0194')
-    k.emit(s_add_i32(s[46], s[52], s[46]))
+    k.emit(s_add_i32(s[49], s[52], s[49]))
     k.emit(s_mov_b32(s[6], -16))
     k.emit(s_mov_b32(s[10], -16))
     k.emit(s_mov_b32(s[18], -16))
@@ -2328,18 +2281,18 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(s_or_b32(s[9], s[9], LIT, 262144))
     k.emit(s_or_b32(s[17], s[17], LIT, 262144))
     k.emit(s_or_b32(s[13], s[13], LIT, 262144))
-    k.emit(s_lshr_b32(s[41], s[41], 1))
-    k.emit(s_mul_i32(s[52], s[41], s[43]))
+    k.emit(s_lshr_b32(s[37], s[37], 1))
+    k.emit(s_mul_i32(s[52], s[37], s[43]))
     k.emit(s_mov_b32(s[14], s[52]))
-    k.emit(s_lshr_b32(s[42], s[42], 1))
-    k.emit(s_mul_i32(s[52], s[42], s[44]))
+    k.emit(s_lshr_b32(s[38], s[38], 1))
+    k.emit(s_mul_i32(s[52], s[38], s[44]))
     k.emit(s_mov_b32(s[18], s[52]))
     k.emit(s_add_u32(s[52], s[43], 31))
     k.emit(s_lshr_b32(s[52], s[52], 5))
     k.emit(s_lshl_b32(s[52], s[52], 5))
-    k.emit(s_mul_i32(s[53], s[52], s[36]))
+    k.emit(s_mul_i32(s[53], s[52], s[39]))
     k.emit(s_mov_b32(s[22], s[53]))
-    k.emit(s_mul_i32(s[53], s[44], s[37]))
+    k.emit(s_mul_i32(s[53], s[44], s[40]))
     k.emit(s_mov_b32(s[26], s[53]))
     k.emit(s_mov_b32(s[23], LIT, 131072))
     k.emit(s_mov_b32(s[27], LIT, 131072))
@@ -2356,23 +2309,23 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(v_add_u32_e32(v[5], v[5], v[6]))
     k.emit(v_and_b32_e32(v[4], 1, v[4]))
     k.emit(v_add_u32_e32(v[5], v[5], v[4]))
-    k.emit(v_mul_lo_u32(v[212], s[41], v[5]))
+    k.emit(v_mul_lo_u32(v[212], s[37], v[5]))
     k.emit(v_and_b32_e32(v[4], 7))
     k.emit(v_lshlrev_b32_e32(v[4], 4, v[4]))
     k.emit(v_add_u32_e32(v[212], v[212], v[4]))
-    k.emit(s_lshr_b32(s[52], s[49], 1))
+    k.emit(s_lshr_b32(s[52], s[46], 1))
     k.emit(s_mul_i32(s[52], s[52], 8))
-    k.emit(s_and_b32(s[53], s[49], 1))
+    k.emit(s_and_b32(s[53], s[46], 1))
     k.emit(s_mul_i32(s[53], s[53], 2))
     k.emit(s_add_u32(s[52], s[52], s[53]))
     k.emit(s_mul_i32(s[53], s[47], LIT, 256))
     k.emit(s_add_u32(s[52], s[52], s[53]))
-    k.emit(s_mul_i32(s[52], s[41], s[52]))
+    k.emit(s_mul_i32(s[52], s[37], s[52]))
     k.emit(v_add_u32_e32(v[212], s[52], v[212]))
-    k.emit(s_mul_i32(s[52], s[41], 32))
+    k.emit(s_mul_i32(s[52], s[37], 32))
     for i in range(7):
       k.emit(v_add_u32_e32(v[213 + i * 1], s[52], v[212 + i * 1]))
-    k.emit(s_mul_i32(s[59], LIT, s[49], 1056))
+    k.emit(s_mul_i32(s[59], LIT, s[46], 1056))
     k.emit(s_add_u32(s[59], LIT, s[59], 4096))
     k.emit(v_and_b32_e32(v[4], 15))
     k.emit(v_lshrrev_b32_e32(v[5], 3, v[4]))
@@ -2396,35 +2349,35 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(v_add_u32_e32(v[221], LIT, v[220], 33792))
     k.emit(v_lshlrev_b32_e32(v[222], 2))
     k.emit(s_mul_i32(s[52], s[47], LIT, 256))
-    k.emit(s_mul_i32(s[53], s[49], 32))
+    k.emit(s_mul_i32(s[53], s[46], 32))
     k.emit(s_add_i32(s[52], s[53], s[52]))
-    k.emit(s_mul_i32(s[53], s[52], s[36]))
+    k.emit(s_mul_i32(s[53], s[52], s[39]))
     k.emit(v_add_u32_e32(v[222], s[53], v[222]))
-    k.emit(s_mul_i32(s[53], LIT, s[36], 128))
+    k.emit(s_mul_i32(s[53], LIT, s[39], 128))
     k.emit(v_add_u32_e32(v[223], s[53], v[222]))
-    k.emit(s_mul_i32(s[60], s[49], LIT, 256))
+    k.emit(s_mul_i32(s[60], s[46], LIT, 256))
     k.emit(s_add_i32(s[60], s[60], 0))
     k.emit(v_lshlrev_b32_e32(v[224], 2))
     k.emit(v_add_u32_e32(v[224], 0, v[224]))
     k.emit(v_lshlrev_b32_e32(v[225], 4))
-    k.emit(s_mul_i32(s[52], s[46], LIT, 256))
-    k.emit(s_mul_i32(s[53], s[49], 64))
+    k.emit(s_mul_i32(s[52], s[49], LIT, 256))
+    k.emit(s_mul_i32(s[53], s[46], 64))
     k.emit(s_add_u32(s[52], s[52], s[53]))
-    k.emit(s_mul_i32(s[52], s[52], s[42]))
+    k.emit(s_mul_i32(s[52], s[52], s[38]))
     k.emit(v_add_u32_e32(v[225], s[52], v[225]))
-    k.emit(s_mul_i32(s[52], 16, s[42]))
+    k.emit(s_mul_i32(s[52], 16, s[38]))
     k.emit(v_add_u32_e32(v[226], s[52], v[225]))
     k.emit(v_add_u32_e32(v[227], s[52], v[226]))
     k.emit(v_add_u32_e32(v[228], s[52], v[227]))
     for i in range(4):
       k.emit(v_add_u32_e32(v[229 + i * 1], LIT, v[225 + i * 1], 1024))
     k.emit(v_lshlrev_b32_e32(v[233], 2))
-    k.emit(s_mul_i32(s[52], s[46], LIT, 256))
-    k.emit(s_mul_i32(s[53], s[49], 64))
+    k.emit(s_mul_i32(s[52], s[49], LIT, 256))
+    k.emit(s_mul_i32(s[53], s[46], 64))
     k.emit(s_add_i32(s[52], s[53], s[52]))
-    k.emit(s_mul_i32(s[53], s[52], s[37]))
+    k.emit(s_mul_i32(s[53], s[52], s[40]))
     k.emit(v_add_u32_e32(v[233], s[53], v[233]))
-    k.emit(s_mul_i32(s[52], 32, s[37]))
+    k.emit(s_mul_i32(s[52], 32, s[40]))
     k.emit(v_add_u32_e32(v[234], s[52], v[233]))
     k.emit(s_mov_b32(s[61], LIT, 128))
     k.emit(s_mov_b32(s[62], LIT, 2048))
@@ -2510,18 +2463,18 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(ds_read_b32(v[201], v[224], v[0], v[0], 0, 0, 1))
     k.emit(ds_read_b32(v[202], v[224], v[0], v[0], 0, 0, 2))
     k.emit(ds_read_b32(v[203], v[224], v[0], v[0], 0, 0, 3))
-    k.emit(s_lshl_b32(s[40], s[40], 1))
+    k.emit(s_lshl_b32(s[36], s[36], 1))
     k.emit(s_mul_i32(s[52], s[47], LIT, 256))
-    k.emit(s_mul_hi_u32(s[53], s[52], s[40]))
+    k.emit(s_mul_hi_u32(s[53], s[52], s[36]))
     k.emit(s_add_u32(s[5], s[5], s[53]))
-    k.emit(s_mul_i32(s[53], s[52], s[40]))
+    k.emit(s_mul_i32(s[53], s[52], s[36]))
     k.emit(s_add_u32(s[4], s[4], s[53]))
     k.emit(s_addc_u32(s[5], 0, s[5]))
     k.emit(s_sub_i32(s[52], s[43], s[52]))
-    k.emit(s_mul_i32(s[52], s[52], s[40]))
+    k.emit(s_mul_i32(s[52], s[52], s[36]))
     k.emit(s_mov_b32(s[6], s[52]))
     k.emit(v_and_b32_e64(v[235], v[0], 15))
-    k.emit(v_mul_lo_u32(v[235], v[235], s[40]))
+    k.emit(v_mul_lo_u32(v[235], v[235], s[36]))
     k.emit(v_lshrrev_b32_e32(v[4], 5))
     k.emit(v_mul_i32_i24_e32(v[4], 16, v[4]))
     k.emit(v_add_u32_e32(v[235], v[4], v[235]))
@@ -2529,12 +2482,12 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
     k.emit(v_and_b32_e32(v[4], 1, v[4]))
     k.emit(v_mul_i32_i24_e32(v[4], 32, v[4]))
     k.emit(v_add_u32_e32(v[235], v[4], v[235]))
-    k.emit(s_mul_i32(s[52], s[46], LIT, 256))
-    k.emit(s_mul_i32(s[53], s[49], 64))
+    k.emit(s_mul_i32(s[52], s[49], LIT, 256))
+    k.emit(s_mul_i32(s[53], s[46], 64))
     k.emit(s_add_i32(s[52], s[52], s[53]))
     k.emit(s_lshl_b32(s[52], s[52], 1))
     k.emit(v_add_u32_e32(v[235], s[52], v[235]))
-    k.emit(s_mul_i32(s[53], s[40], 16))
+    k.emit(s_mul_i32(s[53], s[36], 16))
     for i in range(15):
       k.emit(v_add_u32_e64(v[236 + i * 1], v[235 + i * 1], s[53]))
     k.emit(s_mov_b32(s[50], 0))
@@ -2543,7 +2496,7 @@ def build_kernel(M: int, N: int, K: int, tile_m: int, tile_n: int):
       k.emit(s_cmp_lt_u32(LIT, s[51], 512 + i * -256))
       k.emit(s_cselect_b32(s[61 + i * 1], s[61 + i * 1], 0))
       k.emit(s_cselect_b32(s[63 + i * 1], s[63 + i * 1], 0))
-    k.emit(s_cmp_lt_i32(s[49], 2))
+    k.emit(s_cmp_lt_i32(s[46], 2))
     k.emit(s_cbranch_scc0(1367), target='L2_25B8')
     k.label('L2_105C')
     k.emit(s_waitcnt(122))

extra/llama_kernels/__init__.py

@@ -1,8 +1,6 @@
 from __future__ import annotations
 import functools, pathlib
-from dataclasses import replace
-from tinygrad import Tensor, dtypes
-from tinygrad.uop.ops import shape_to_shape_arg
+from tinygrad import Tensor
 from tinygrad.runtime.support.compiler_amd import HIPCCCompiler
 
 FP8_MAX = 448.0
@@ -12,7 +10,7 @@ NUM_WG, THREADS_PER_WG = 1024, 256
 @functools.cache
 def _local_abs_max_fxn(x_p, device):
   x = Tensor(x_p, device=device)
-  inner = Tensor(x.uop.replace(src=(shape_to_shape_arg(x.uop.shard_shape),), arg=replace(x.uop.arg, axis=None))) if x.uop.axis is not None else x
+  inner = Tensor(x.uop.src[0]) if x.uop.axis is not None else x  # the per-shard view of the flat param
   return (inner.abs().max(),)
 
 def local_abs_max(x:Tensor) -> Tensor:

spec/tinyspec.tex

@@ -50,10 +50,10 @@ All nodes in the tinygrad graph are \textbf{UOps}. A UOp is a tuple $(\mathrm{op
 \toprule
 \textbf{Op} & \textbf{src} & \textbf{arg} & \textbf{Semantics} \\
 \midrule
-\op{Param} & $(\mathbf{s})$ & slot, dtype, device?, addrspace? &
-  Placeholder with shape $\mathbf{s}$. Substituted in \op{Function}. \\[4pt]
-\op{Buffer} & $(\mathbf{s})$ & slot, dtype, device, addrspace &
-  Concrete buffer slot with shape $\mathbf{s}$. If device is a tuple, it creates the fully sized buffer across multiple devices. \\
+\op{Param} & () & slot, dtype, size?, device?, addrspace? &
+  Placeholder with flat storage of $\mathrm{size}$ elements. Substituted in \op{Call}. \\[4pt]
+\op{Buffer} & () & slot, dtype, size, device, addrspace &
+  Concrete buffer slot with flat storage of $\mathrm{size}$ elements. \\
 \op{Const} & () & value, dtype &
   A scalar constant with shape $(\ )$. \\
   & & & Form vector consts with \op{Stack} \\
@@ -256,9 +256,9 @@ Every UOp has a \textbf{dtype}, \textbf{shape}, \textbf{device}, \textbf{addrspa
 \toprule
 \textbf{Op} & \textbf{dtype} & \textbf{shape} & \textbf{device} & \textbf{min\_max} \\
 \midrule
-\op{Buffer} & from arg & from $\mathrm{src}[0]$ & from arg & dtype range \\
+\op{Buffer} & from arg & from arg ($\mathrm{size}$) & from arg & dtype range \\
 \op{Const} & from arg & $()$ & \textsc{null} & $[v, v]$ \\
-\op{Param} & from arg & from $\mathrm{src}[0]$ & from arg & from src or dtype range \\[3pt]
+\op{Param} & from arg & from arg ($\mathrm{size}$) & from arg & from src or dtype range \\[3pt]
 Movement ops & $\mathrm{src}[0].\mathrm{dtype}$ & (see op) & $\mathrm{src}[0].\mathrm{device}$ & $\mathrm{src}[0]$ \\
 \op{Unshard} & $\mathrm{src}[0].\mathrm{dtype}$ & $\mathrm{src}[0]$, each $a_k \times n_k$ & $\mathrm{src}[0].\mathrm{device}$ & $\mathrm{src}[0]$ \\
 \op{Reduce} & $\mathrm{src}[0].\mathrm{dtype}$ & remove first $n$ axes & $\mathrm{src}[0].\mathrm{device}$ & dtype range \\[3pt]

test/backend/test_dtype.py

@@ -258,6 +258,11 @@ class TestDoubleDType(TestDType):
       a = [2, 3, 4]
       np.testing.assert_allclose(func(Tensor(a, dtype=self.DTYPE)).numpy(), func(torch.tensor(a, dtype=torch.float64)), rtol=1e-12, atol=1e-12)
 
+  def test_float32_compare_selecting_float64(self):
+    a = Tensor([1.0, 2.0, 5.0, 9.0], dtype=dtypes.float32)
+    p, q = Tensor([10., 20., 30., 40.], dtype=self.DTYPE), Tensor([50., 60., 70., 80.], dtype=self.DTYPE)
+    _test_op(lambda: (a < 3.0).where(p, q), self.DTYPE, [10., 20., 70., 80.])
+
   def test_float64_to_float32_cast_inf(self):
     _test_op(lambda: Tensor([3.4e40, 3.4e38, 1, 0], dtype=dtypes.float64).cast(dtypes.float32),
              dtypes.float32, [float('inf'), 3.4e38, 1, 0])
@@ -423,6 +428,11 @@ class TestDtypeUsage(unittest.TestCase):
         t = Tensor([[1, 2], [3, 4]], dtype=d)
         (t*t).max().item()
 
+  def test_where_float16_compare_to_const(self):
+    # t > 0 is CMPLT(0, t): the float16 operand is on the right
+    t = Tensor([-1.0, 1.0], dtype=dtypes.float16)
+    np.testing.assert_equal((t > 0).where(Tensor.ones(2, dtype=dtypes.float16), Tensor.zeros(2, dtype=dtypes.float16)).numpy(), [0.0, 1.0])
+
 @unittest.skipUnless(dtypes.bfloat16 in supported_dtypes, f"no bfloat16 on {Device.DEFAULT}")
 class TestOpsBFloat16(unittest.TestCase):
   def test_cast(self):

test/backend/test_encodings.py

@@ -100,13 +100,6 @@ class TestEncodingsX86(unittest.TestCase):
     # vaddss xmm0, xmm0, xmm8
     self.assertEqual(bytes.fromhex(self.encode(add)), bytes.fromhex("C4 C1 7A 58 C0"))
 
-  # test ymm encoding
-  def test_ymm_encoding(self):
-    xmm0, xmm1 = def_reg(dtypes._uint256, XMM[0]), def_reg(dtypes._uint256, XMM[1])
-    add = ins(X86Ops.VADDPS, dtypes._uint256, (xmm0, xmm1), XMM[0])
-    # vaddps ymm0, ymm0, ymm1
-    self.assertEqual(bytes.fromhex(self.encode(add)), bytes.fromhex("C5 FC 58 C1"))
-
   # test encoding where register is in the immediate field
   def test_reg_in_imm_field(self):
     xmm0, xmm1, xmm2 = def_reg(dtypes.float32, XMM[0]), def_reg(dtypes.float32, XMM[1]), def_reg(dtypes.float32, XMM[2])

test/backend/test_jit.py

@@ -6,7 +6,7 @@ from test.helpers import assert_jit_cache_len, call_is_graph, not_support_multi_
 from test.unit.test_jit import _simple_test
 from tinygrad import Tensor, Variable, TinyJit, Device, dtypes
 from tinygrad.engine.jit import graph_class
-from tinygrad.helpers import JIT, DEV, GlobalCounters
+from tinygrad.helpers import JIT, DEV, GlobalCounters, HCQ2
 from tinygrad.uop.ops import Ops
 from tinygrad.renderer.isa.x86 import X86Renderer
 
@@ -235,6 +235,7 @@ class TestJitPrune(unittest.TestCase):
     assert_jit_cache_len(w2_prune, 1)
 
 class TestJitFree(unittest.TestCase):
+  @unittest.skipIf(HCQ2, "hcq2 keeps refs to intermediate buffers")
   def test_free_intermediates(self):
     ext_tensor = Tensor([1,24,23,45,1])
     @TinyJit

test/backend/test_multitensor.py

@@ -187,6 +187,13 @@ class TestMultiTensor(unittest.TestCase):
         a,b = jit_allreduce(Tensor.rand(256, 256))
         np.testing.assert_almost_equal(a.numpy(), b.numpy(), decimal=5)
 
+  def test_allreduce_all2all_jit(self):
+    with Context(ALL2ALL=2):
+      jit_allreduce = TinyJit(_test_allreduce)
+      for _ in range(5):
+        a,b = jit_allreduce(Tensor.rand(256, 256))
+        np.testing.assert_almost_equal(a.numpy(), b.numpy(), decimal=5)
+
   def test_multitensor_jit_input(self):
     @TinyJit
     def f(x): return (x+1).contiguous().sum()

test/backend/test_nn.py

@@ -135,7 +135,7 @@ class TestNN(unittest.TestCase):
   def test_conv2d_same_padding_large_kernel(self):
     self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=16, DIMS=[28, 33], C2=32, K=9, S=1, P='same')
   def test_conv2d_same_padding_with_dilation(self):
-    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=3, DIMS=[28, 28], C2=32, K=3, S=1, P='same', D=3)
+    self._test_conv(Conv2d, torch.nn.Conv2d, BS=16, C1=3, DIMS=[28, 31], C2=32, K=(3,5), S=1, P='same', D=(2,3))
 
   def test_conv2d_same_padding_invalid_stride(self):
     self.assertRaises(ValueError, Conv2d, in_channels=16, out_channels=32, kernel_size=2, stride=2, padding='same')

test/backend/test_ops.py

@@ -359,6 +359,13 @@ class TestOps(unittest.TestCase):
       lambda x: torch.where(x > 0.5, 4, 2).type(torch.int32).permute((1, 0)),
       lambda x: (x > 0.5).where(4, 2).clone().permute((1, 0)), forward_only=True)
 
+  @unittest.skipIf(Device.DEFAULT == "WEBGPU", "software vulkan evaluates a NaN != x as false")
+  def test_where_nan_cond(self):
+    # a NaN compares false against everything except !=.
+    for fxn in (lambda x: x<1, lambda x: x>1, lambda x: x!=1, lambda x: x==1):
+      helper_test_op(None, lambda x,a,b: torch.where(fxn(x), a, b), lambda x,a,b: fxn(x).where(a, b), forward_only=True,
+                     vals=[[math.nan, 1.0, 2.0, -1.0], [10, 20, 30, 40], [-1, -2, -3, -4]])
+
   def _test_cmp(self, fxn, reverse=True):
     # test different dtypes
     helper_test_op(None, fxn, fxn, forward_only=True, vals=[[0.,1,2], [2.,1,0]])
@@ -2810,7 +2817,7 @@ class TestOps(unittest.TestCase):
         lambda x: Tensor.interpolate(x, size=out_sz, mode="linear"))
 
   def test_interpolate_linear_corners_aligned(self):
-    for in_sz, out_sz in [((52,),(29,)), ((29,),(52,))]:
+    for in_sz, out_sz in [((52,),(29,)), ((29,),(52,)), ((29,),(1,))]:
       helper_test_op([(2,3)+in_sz],
         lambda x: torch.nn.functional.interpolate(x, size=out_sz, mode="linear", align_corners=True),
         lambda x: Tensor.interpolate(x, size=out_sz, mode="linear", align_corners=True))
@@ -2963,6 +2970,10 @@ class TestOps(unittest.TestCase):
     data = [math.inf, -math.inf, math.nan]
     helper_test_op((), lambda: torch.tensor(data)[torch.tensor([0, 1, 2])], lambda: Tensor(data)[Tensor([0, 1, 2])])
 
+  def test_fancy_indexing_index_dtypes(self):
+    helper_test_op((), lambda: torch.tensor([10., 20., 30., 40.])[torch.tensor([1, 2, 3, 0])],
+                       lambda: Tensor([10., 20., 30., 40.])[Tensor([1, 2, 3, 0], dtype=dtypes.uint8)])
+
   @slow_test
   def test_slice_fancy_indexing_no_dim_collapse(self):
     a,b,c,d,e,i,j,k,o,p = self._get_index_randoms()

test/backend/test_schedule.py

@@ -379,27 +379,30 @@ class TestCopyFolding(unittest.TestCase):
     check_schedule(a.clone(), 1, filter_sink=False)
 
   def test_shrink_copy(self):
-    a = Tensor.arange(4)
-    view = a.shrink(((0, 2),))
-    b = view.clone()
-    run_linear(*check_schedule(b, 1, filter_sink=False))
-    self.assertEqual(b.uop.base.buffer.size, 2)
-    self.assertEqual(b.uop.numel(), 2)
-    self.assertListEqual(b.tolist(), [0, 1])
+    a = Tensor.arange(4).clone("CPU:1").realize()
+    b = a.to("CPU:2").shrink(((1, 3),)).to("CPU:3")
+    GlobalCounters.reset()
+    run_linear(*check_schedule(b, 3, filter_sink=False))
+    # extra E kernel, copy exactly 4 bytes
+    self.assertEqual(GlobalCounters.global_mem, 4*4 + 2*4*2 + 2*4)
+    self.assertListEqual(b.tolist(), [1, 2])
 
   def test_expanded_copy(self):
-    a = Tensor.arange(2)
-    view = a.reshape(2, 1).expand(2, 2)
-    b = view.clone()
-    run_linear(*check_schedule(b, 1, filter_sink=False))
-    self.assertEqual(b.uop.base.buffer.size, 4)
-    self.assertEqual(b.uop.numel(), 4)
-    self.assertListEqual(b.tolist(), [[0, 0], [1, 1]])
+    a = Tensor.arange(4).clone("CPU:1").realize()
+    b = a.to("CPU:2").reshape(4, 1).expand(4, 2).to("CPU:3")
+    GlobalCounters.reset()
+    run_linear(*check_schedule(b, 3, filter_sink=False))
+    # TODO: expands before copy
+    self.assertEqual(GlobalCounters.global_mem, 4*4 + (4*4 + 8*4) + 8*4)
+    self.assertListEqual(b.tolist(), [[0, 0], [1, 1], [2, 2], [3, 3]])
 
   def test_permuted_copy(self):
-    a = Tensor.arange(4)
-    b = a.reshape(2, 2).permute(1, 0)
-    b.realize()
+    a = Tensor.arange(4).clone("CPU:1").realize()
+    b = a.to("CPU:2").reshape(2, 2).permute(1, 0).to("CPU:3")
+    GlobalCounters.reset()
+    run_linear(*check_schedule(b, 3, filter_sink=False))
+    # permutes before copy
+    self.assertEqual(GlobalCounters.global_mem, 4*4 + (4*4 + 4*4) + 4*4)
     self.assertListEqual(b.tolist(), [[0, 2], [1, 3]])
 
   def test_permute_on_disk(self):

test/device/test_hcq2.py

@@ -3,10 +3,10 @@ from unittest.mock import patch
 from tinygrad import Device, Tensor
 from tinygrad.device import Buffer
 from tinygrad.dtype import dtypes
-from tinygrad.helpers import getenv
+from tinygrad.helpers import HCQ2
 from tinygrad.runtime.support.hcq2 import HCQ_DEVS, all_devices_in
 
-@unittest.skipUnless(getenv("HCQ2") and all_devices_in(Device.DEFAULT, HCQ_DEVS), "hcq2 device required")
+@unittest.skipUnless(HCQ2 and all_devices_in(Device.DEFAULT, HCQ_DEVS), "hcq2 device required")
 class TestHCQ2(unittest.TestCase):
   def test_copy_without_copy_queue(self):
     with patch.object(Device[Device.DEFAULT], "has_copy_queue", False):

test/external/external_test_onnx_ops.py

@@ -235,6 +235,15 @@ class TestMainOnnxOps(TestOnnxOps):
     outputs = ["y"]
     self.helper_test_single_op("MaxUnpool", inputs, attributes, outputs)
 
+  def test_maxunpool_pads(self):
+    # per-axis pads shrink the output: spatial dim is (i-1)*stride + kernel - pad_begin - pad_end -> (2, 4), and indices index into that output
+    # NOTE: indices must be in bounds of that output; ORT aborts the process on out-of-bounds indices
+    xT = np.array([[[[5, 6], [7, 8]]]], dtype=np.float32)
+    xI = np.array([[[[0, 3], [4, 7]]]], dtype=np.int64)
+    inputs = {"x": xT, "indices": xI}
+    attributes = {"kernel_shape": [2, 2], "strides": [2, 2], "pads": [1, 0, 1, 0]}
+    self.helper_test_single_op("MaxUnpool", inputs, attributes, ["y"])
+
   def test_averagepool_3d_dilations_large_count_include_pad_is_1_ceil_mode_is_True(self):
     # https://github.com/onnx/onnx/blob/main/docs/Operators.md#examples-13
     inputs = {"x": np.random.randn(1, 1, 32, 32, 32).astype(np.float32)}

test/null/test_uop_graph.py

@@ -441,7 +441,8 @@ class TestMovementOps(unittest.TestCase):
     self.assertEqual(result.op, Ops.INDEX)
     self.assertIs(result.src[0], src)
     self.assertEqual(result.shape, (4,))
-    self.assertNotIn(Ops.RESHAPE, [u.op for u in result.toposort()])
+    # the only RESHAPE is src itself: the view of the flat param, the extra reshape was folded into the INDEX
+    self.assertEqual([u for u in result.toposort() if u.op is Ops.RESHAPE], [src])
 
   def test_pm_mops_partial_reshape_index_suffix_mismatch_does_nothing(self):
     from tinygrad.schedule.prepare import pm_mops

test/null/test_uops.py

@@ -84,11 +84,10 @@ class TestLowerIndexDtype(unittest.TestCase):
 
   def test_reg_buffer_size_lowers(self):
     reg = UOp.placeholder((4,), dtypes.float, 0, addrspace=AddrSpace.REG)
-    self.assertEqual(reg.src[0].dtype, dtypes.weakint)
+    self.assertEqual(reg.arg.size, 4)
     lowered = graph_rewrite(reg.sink(), pm_lower_weak)
     self.assertTrue(all(u.op is Ops.CONST for u in lowered.backward_slice_with_self if u.dtype in dtypes.weaks),
                     "lowering must resolve every weak width, except a typed literal's value half")
-    self.assertEqual(next(u for u in lowered.backward_slice_with_self if u.op is Ops.BUFFER).src[0].dtype, dtypes.int)
 
 class TestSafeCast(unittest.TestCase):
   def test_cast_folds(self):

test/null/test_viz.py

@@ -244,7 +244,7 @@ class TestViz(unittest.TestCase):
     self.assertEqual(len(lst), 1)
     graphs = [x["graph"] for x in viz.get_details(0, 0)]
     # const is always in the graph, client side hides exclude=True nodes by default
-    self.assertEqual(list(graphs[0]), [id(a.src[0]), id(a), id(z), id(alu), id(y), id(sink)])
+    self.assertEqual(list(graphs[0]), [id(a), id(z), id(alu), id(y), id(sink)])
     self.assertTrue(graphs[0][id(z)]["exclude"])
     self.assertTrue(graphs[0][id(y)]["exclude"])
     self.assertFalse(graphs[0][id(alu)]["exclude"])

test/unit/test_call.py

@@ -52,6 +52,11 @@ class TestCall(unittest.TestCase):
     np.testing.assert_allclose(a.grad.numpy(), gt_a_grad, rtol=1e-5)
     np.testing.assert_allclose(b.grad.numpy(), gt_b_grad, rtol=1e-5)
 
+  def test_call_scalar_param_shape_mismatch(self):
+    scalar_fxn = UOp.param(0, dtypes.float, ()) * 2
+    with self.assertRaisesRegex(TypeError, "shape mismatch: expected scalar"):
+      Tensor.call(Tensor.ones(2), fxn=scalar_fxn).realize()
+
   def test_call_gemm(self):
     M, K, N = 4, 8, 4
     a = Tensor.randn(M, K)

test/unit/test_gguf.py

@@ -5,17 +5,33 @@ from tinygrad.llm.gguf import _ggml_iq_grid, ggml_data_to_tensor, gguf_load
 from tinygrad.runtime.autogen import ggml_common as _ggml
 import numpy as np
 from gguf import GGUFReader, GGUFValueType, GGMLQuantizationType, GGML_QUANT_SIZES, dequantize, quantize
-from gguf.quants import IQ2_S, IQ3_S, IQ3_XXS
+from gguf.quants import IQ1_S, IQ2_S, IQ2_XS, IQ2_XXS, IQ3_S, IQ3_XXS
 
 ggml_test_block_count = 4
 supported_dtypes = Device[Device.DEFAULT].renderer.supported_dtypes()
 
 class TestGGUFTables(unittest.TestCase):
+  def test_iq2_xxs_grid_matches_gguf_py(self):
+    IQ2_XXS.init_grid()
+    grid = _ggml_iq_grid(Device.DEFAULT, _ggml.iq2xxs_grid, (256, 8)).numpy()
+    np.testing.assert_equal(grid, IQ2_XXS.grid.reshape(256, 8))
+
+  def test_iq2_xs_grid_matches_gguf_py(self):
+    IQ2_XS.init_grid()
+    grid = _ggml_iq_grid(Device.DEFAULT, _ggml.iq2xs_grid, (512, 8)).numpy()
+    np.testing.assert_equal(grid, IQ2_XS.grid.reshape(512, 8))
+
   def test_iq2_s_grid_matches_gguf_py(self):
     IQ2_S.init_grid()
     grid = _ggml_iq_grid(Device.DEFAULT, _ggml.iq2s_grid, (1024, 8)).numpy()
     np.testing.assert_equal(grid, IQ2_S.grid.reshape(1024, 8))
 
+  def test_iq1_s_grid_matches_gguf_py(self):
+    IQ1_S.init_grid()
+    grid = _ggml_iq_grid(Device.DEFAULT, _ggml.iq1s_grid, (2048, 8)).numpy()
+    grid = np.where(grid > 127, grid - 256, grid)
+    np.testing.assert_equal(grid, IQ1_S.grid.reshape(2048, 8))
+
   def test_iq3_xxs_grid_matches_gguf_py(self):
     IQ3_XXS.init_grid()
     grid = _ggml_iq_grid(Device.DEFAULT, _ggml.iq3xxs_grid, (256, 4)).numpy()
@@ -39,6 +55,56 @@ class TestGGUF(unittest.TestCase):
     expected = np.arange(1, 33, dtype=np.float32) * 2.0
     np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 32, GGMLQuantizationType.Q8_0.value).numpy().flatten(), expected)
 
+  def test_dequantization_q2_k_hardcoded(self):
+    # Q2_K: scales[16] + qs[64] + d(fp16) + dmin(fp16). 16 sub-blocks of 16, x = d*(scale&0xF)*q - dmin*(scale>>4)
+    scales, qs = bytes([0x11]*16), bytes([0x55]*64)  # scale=1, min=1; qs=0x55 -> 2-bit quants of 1
+    d, dmin = np.float16(1.0).tobytes(), np.float16(0.0).tobytes()
+    block = np.frombuffer(scales + qs + d + dmin, dtype=np.uint8).copy()
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 256, 10).numpy().flatten(), np.ones(256, dtype=np.float32))
+
+  def test_dequantization_q3_k_hardcoded(self):
+    # Q3_K: hmask[32] + qs[64] + scales[12] + d(fp16). 16 sub-blocks of 16, x = d * (scale-32) * (q - (hbit?0:4))
+    # 6-bit scales 32..47 so (scale-32) = 0..15; qs=0x55 -> 2-bit quants of 1; d=1.0
+    scales = bytes([0x80, 0x91, 0xA2, 0xB3, 0xC4, 0xD5, 0xE6, 0xF7, 0xAA, 0xAA, 0xAA, 0xAA])
+    d = np.float16(1.0).tobytes()
+    qs, ones = bytes([0x55]*64), np.ones(16, dtype=np.float32)
+    # hmask all-ones: high bit set, q=1; hmask zeros: subtract 4, q=-3
+    for hmask, q in ((bytes([0xFF]*32), 1.0), (bytes([0x00]*32), -3.0)):
+      block = np.frombuffer(hmask + qs + scales + d, dtype=np.uint8).copy()
+      expected = np.concatenate([q * s * ones for s in range(16)])
+      np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 256, 11).numpy().flatten(), expected)
+
+  def test_dequantization_iq2_xxs_hardcoded(self):
+    # IQ2_XXS: d + 8 groups of (4 grid bytes + uint32 signs/scale). grid[0]=all 0x08, scale=0, signs=0
+    # db = 1.0 * (0.5 + 0) * 0.25 = 0.125; 0.125 * 8 = 1.0
+    block = np.frombuffer(np.float16(1.0).tobytes() + bytes(64), dtype=np.uint8).copy()
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 256, 16).numpy().flatten(), np.ones(256, dtype=np.float32))
+
+  def test_dequantization_iq2_xs_hardcoded(self):
+    # IQ2_XS: d + 32 uint16 qs + 8 scale bytes. qs=0 -> grid[0]=all 0x08, signs=0; scales=0
+    block = np.frombuffer(np.float16(1.0).tobytes() + bytes(64) + bytes(8), dtype=np.uint8).copy()
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 256, 17).numpy().flatten(), np.ones(256, dtype=np.float32))
+
+  def test_dequantization_iq1_s_hardcoded(self):
+    # IQ1_S: d + qs[32] + qh[16]. qs=qh=0 -> grid[0]=all -1, scale=1, delta=+0.125 -> -0.875
+    block = np.frombuffer(np.float16(1.0).tobytes() + bytes(48), dtype=np.uint8).copy()
+    expected = np.full(256, -0.875, dtype=np.float32)
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 256, 19).numpy().flatten(), expected)
+
+  def test_dequantization_iq1_m_hardcoded(self):
+    # IQ1_M: qs[32] + qh[16] + scales[8]. f16 1.0=0x3C00 packed in high nibbles; qs=qh=0 -> -0.875
+    scales = bytes([0x00, 0x00, 0x00, 0x00, 0x00, 0xC0, 0x00, 0x30])
+    block = np.frombuffer(bytes(48) + scales, dtype=np.uint8).copy()
+    expected = np.full(256, -0.875, dtype=np.float32)
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 256, 29).numpy().flatten(), expected)
+
+  def test_dequantization_iq4_nl_hardcoded(self):
+    # IQ4_NL: 2-byte fp16 scale + 16 packed bytes. low nibbles first, then high
+    lut = list(_ggml.kvalues_iq4nl)
+    block = np.frombuffer(np.float16(1.0).tobytes() + bytes(range(16)), dtype=np.uint8).copy()
+    expected = np.array(lut + [lut[0]]*16, dtype=np.float32)
+    np.testing.assert_equal(ggml_data_to_tensor(Tensor(block), 32, 20).numpy().flatten(), expected)
+
   def test_dequantization_mxfp4_hardcoded(self):
     # MXFP4: 1 byte shared exponent E + 16 packed bytes (32 x 4-bit values)
     # nibble: bit3=sign, bit2:1=exp, bit0=mant; E=128 gives scale=1.0
@@ -52,13 +118,20 @@ class TestGGUF(unittest.TestCase):
   def test_dequantization_q5_0(self): self._test_dequantization(GGMLQuantizationType.Q5_0)
   def test_dequantization_q5_1(self): self._test_dequantization(GGMLQuantizationType.Q5_1)
   def test_dequantization_q8_0(self): self._test_dequantization(GGMLQuantizationType.Q8_0)
+  def test_dequantization_q2_k(self): self._test_dequantization(GGMLQuantizationType.Q2_K)
+  def test_dequantization_q3_k(self): self._test_dequantization(GGMLQuantizationType.Q3_K)
   def test_dequantization_q4_k(self): self._test_dequantization(GGMLQuantizationType.Q4_K)
   def test_dequantization_q5_k(self): self._test_dequantization(GGMLQuantizationType.Q5_K)
   def test_dequantization_q6_k(self): self._test_dequantization(GGMLQuantizationType.Q6_K)
+  def test_dequantization_iq2_xxs(self): self._test_dequantization(GGMLQuantizationType.IQ2_XXS)
+  def test_dequantization_iq2_xs(self): self._test_dequantization(GGMLQuantizationType.IQ2_XS)
   def test_dequantization_iq3_xxs(self): self._test_dequantization(GGMLQuantizationType.IQ3_XXS)
+  def test_dequantization_iq1_s(self): self._test_dequantization(GGMLQuantizationType.IQ1_S)
+  def test_dequantization_iq4_nl(self): self._test_dequantization(GGMLQuantizationType.IQ4_NL)
   def test_dequantization_iq3_s(self): self._test_dequantization(GGMLQuantizationType.IQ3_S)
   def test_dequantization_iq2_s(self): self._test_dequantization(GGMLQuantizationType.IQ2_S)
   def test_dequantization_iq4_xs(self): self._test_dequantization(GGMLQuantizationType.IQ4_XS)
+  def test_dequantization_iq1_m(self): self._test_dequantization(GGMLQuantizationType.IQ1_M)
   def test_dequantization_mxfp4(self): self._test_dequantization(GGMLQuantizationType.MXFP4)
   @unittest.skipUnless(dtypes.bfloat16 in supported_dtypes, "Backend must support bfloat16")
   def test_dequantization_bf16(self): self._test_dequantization(GGMLQuantizationType.BF16)
@@ -203,12 +276,23 @@ class TestGGUFGEMV(unittest.TestCase):
       q_data = rng.integers(0, 256, size=n_blocks * type_size, dtype=np.uint8).reshape(n_blocks, type_size)
       scales = np.float16(rng.standard_normal(n_blocks * 4)).view(np.uint8).reshape(n_blocks, -1)
       if qtype in (GGMLQuantizationType.Q5_0, GGMLQuantizationType.Q8_0,
-                   GGMLQuantizationType.IQ3_XXS,
-                   GGMLQuantizationType.IQ2_S,
+                   GGMLQuantizationType.IQ2_XXS, GGMLQuantizationType.IQ2_XS,
+                   GGMLQuantizationType.IQ3_XXS, GGMLQuantizationType.IQ4_NL,
+                   GGMLQuantizationType.IQ1_S, GGMLQuantizationType.IQ2_S,
                    GGMLQuantizationType.IQ3_S, GGMLQuantizationType.IQ4_XS): q_data[:, :2] = scales[:, :2]  # d at offset 0
       elif qtype in (GGMLQuantizationType.Q5_1, GGMLQuantizationType.Q4_K, GGMLQuantizationType.Q5_K):
         q_data[:, :4] = scales[:, :4]  # d, m/dmin at offset 0
-      elif qtype == GGMLQuantizationType.Q6_K: q_data[:, -2:] = scales[:, :2]               # d at end
+      elif qtype == GGMLQuantizationType.Q2_K: q_data[:, -4:] = scales[:, :4]  # d, dmin at end
+      elif qtype in (GGMLQuantizationType.Q6_K, GGMLQuantizationType.Q3_K): q_data[:, -2:] = scales[:, :2]  # d at end
+      elif qtype == GGMLQuantizationType.IQ1_M:
+        s = np.float16(rng.standard_normal(n_blocks)).view(np.uint16)
+        sc = q_data[:, -8:].copy().view(np.uint16).reshape(n_blocks, 4)
+        sc &= np.uint16(0x0FFF)
+        sc[:, 0] |= (s & np.uint16(0x000F)) << 12
+        sc[:, 1] |= (s & np.uint16(0x00F0)) << 8
+        sc[:, 2] |= (s & np.uint16(0x0F00)) << 4
+        sc[:, 3] |= (s & np.uint16(0xF000))
+        q_data[:, -8:] = sc.reshape(n_blocks, -1).view(np.uint8)
       elif qtype == GGMLQuantizationType.MXFP4: q_data[:, 0] = rng.integers(120, 136, size=n_blocks, dtype=np.uint8) # constrain byte0
       q_data = q_data.flatten()
     ref = dequantize(q_data, qtype).reshape(rows, cols)
@@ -235,13 +319,20 @@ class TestGGUFGEMV(unittest.TestCase):
   def test_gguf_gemv_q8_0(self): self._test_gguf_gemv(GGMLQuantizationType.Q8_0)
   def test_gguf_gemv_q5_0(self): self._test_gguf_gemv(GGMLQuantizationType.Q5_0)
   def test_gguf_gemv_q5_1(self): self._test_gguf_gemv(GGMLQuantizationType.Q5_1)
+  def test_gguf_gemv_q2_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q2_K)
+  def test_gguf_gemv_q3_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q3_K)
   def test_gguf_gemv_q4_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q4_K)
   def test_gguf_gemv_q5_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q5_K)
   def test_gguf_gemv_q6_k(self): self._test_gguf_gemv(GGMLQuantizationType.Q6_K)
+  def test_gguf_gemv_iq2_xxs(self): self._test_gguf_gemv(GGMLQuantizationType.IQ2_XXS)
+  def test_gguf_gemv_iq2_xs(self): self._test_gguf_gemv(GGMLQuantizationType.IQ2_XS)
   def test_gguf_gemv_iq3_xxs(self): self._test_gguf_gemv(GGMLQuantizationType.IQ3_XXS)
+  def test_gguf_gemv_iq1_s(self): self._test_gguf_gemv(GGMLQuantizationType.IQ1_S)
+  def test_gguf_gemv_iq4_nl(self): self._test_gguf_gemv(GGMLQuantizationType.IQ4_NL)
   def test_gguf_gemv_iq3_s(self): self._test_gguf_gemv(GGMLQuantizationType.IQ3_S)
   def test_gguf_gemv_iq2_s(self): self._test_gguf_gemv(GGMLQuantizationType.IQ2_S)
   def test_gguf_gemv_iq4_xs(self): self._test_gguf_gemv(GGMLQuantizationType.IQ4_XS)
+  def test_gguf_gemv_iq1_m(self): self._test_gguf_gemv(GGMLQuantizationType.IQ1_M)
   def test_gguf_gemv_mxfp4(self): self._test_gguf_gemv(GGMLQuantizationType.MXFP4)
   @unittest.skipUnless(dtypes.bfloat16 in supported_dtypes, "Backend must support bfloat16")
   def test_gguf_gemv_bf16(self): self._test_gguf_gemv(GGMLQuantizationType.BF16)

tinygrad/codegen/__init__.py

@@ -391,7 +391,15 @@ def full_rewrite_to_sink(ast:UOp, ren:Renderer, optimize:bool=True) -> UOp:
   sink = graph_rewrite(sink, pm_number_params, ctx=[num_params], name="number params with -1", walk=True)
 
   if VIZ: graph_rewrite(sink, PatternMatcher([]), name="View Output AST")
-  if SPEC: type_verify(sink, spec_program)
+  if SPEC:
+    import os
+    if os.environ.get("DBGTV"):
+      try: type_verify(sink, spec_program)
+      except RuntimeError:
+        from tinygrad.uop.render import print_uops
+        print_uops(list(sink.toposort()))
+        raise
+    else: type_verify(sink, spec_program)
 
   # return the rewritten sink
   return sink

tinygrad/codegen/decomp/dtype.py

@@ -140,8 +140,8 @@ def f2f_store(st, idx, val, fr:DType, to:DType):
 pm_long_decomp: PatternMatcher = PatternMatcher([
   # the decomp's own bottom-up rewrite can mint bare consts mid-flight: word splitting commits them at the long sibling's dtype
   (UPat(GroupOp.All, name='x'), lambda x: commit_weak_consts(x, next((s.dtype for s in x.src if s.dtype in l2i_dt), None))),
-  (UPat(GroupOp.Defines, tuple(l2i_dt.keys()), src=(UPat.var("sz"),), name="x"), lambda x,sz:
-   UOp(x.op, src=(sz*2,), arg=replace(x.arg, dtype=l2i_dt[x.dtype]), tag=x.tag)),
+  (UPat(GroupOp.Defines, tuple(l2i_dt.keys()), name="x"), lambda x:
+   UOp(x.op, arg=replace(x.arg, dtype=l2i_dt[x.dtype], size=None if x.arg.size is None else x.arg.size*2), tag=x.tag)),
   (UPat(Ops.INDEX, tuple(l2i_dt.keys()), name='x'), lambda x:
    reindex(x, x.tag[0]).replace(tag=None) if x.tag is not None else None),
   (UPat(Ops.STORE, src=(UPat.var('idx', tuple(l2i_dt.keys())), UPat.var('val')), name='st'), lambda st,idx,val:

tinygrad/codegen/gpudims.py

@@ -73,7 +73,7 @@ def add_gpudims(ctx:Renderer, s:UOp):
   subs = {}
   for r in s_topo:
     # look for local INDEXes that are not used in the GLOBAL store, then add them as an INVALID
-    if r.op is Ops.STORE and (idx := r.src[0]).src[0].addrspace == AddrSpace.GLOBAL:
+    if r.op is Ops.STORE and len((idx := r.src[0]).src) and idx.src[0].addrspace == AddrSpace.GLOBAL:
       missing_locals = [all_ranges[rng] for rng in local_dims if all_ranges[rng] not in idx.ranges]
       if len(missing_locals):
         assert len(idx.src) == 2, "index has 2 sources"

tinygrad/codegen/late/coalesce.py

@@ -1,7 +1,8 @@
 import itertools, functools
 from collections import defaultdict
+from dataclasses import replace
 from tinygrad.dtype import dtypes, AddrSpace, Invalid, DType
-from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, shape_to_shape_arg, graph_rewrite
+from tinygrad.uop.ops import UOp, Ops, PatternMatcher, UPat, GroupOp, graph_rewrite
 from tinygrad.uop.symbolic import uop_given_valid, parse_valid, invalid_gate, sym
 from tinygrad.helpers import getenv, IMAGE, OSX, ceildiv, is_image_shape
 from tinygrad.renderer import Renderer
@@ -85,7 +86,8 @@ def transform_to_image(ctx, buf:UOp, x:UOp) -> UOp|None:
   if len(cands) == 0: return None
   # and tiebreak with indexing complexity (ie. number of nodes)
   h, w, cidx = cands[0] if len(cands) == 1 else min(cands, key=lambda cand: len(cand[2].index(1).simplify().backward_slice))
-  buf = buf.replace(src=(shape_to_shape_arg((h, w, 4)),))
+  # the image dims are stored in the param's arg, the size stays the flat buffer len
+  buf = buf.replace(arg=replace(buf.arg, image=(h, w)))
   shapes[buf.arg.slot] = (h, w)
   if valid.op is not Ops.CONST or valid.val is not True:
     return buf.index(cidx.src[1].valid(valid), cidx.src[0].valid(valid))

tinygrad/dtype.py

@@ -120,8 +120,6 @@ class DTypes:
   uint32: Final[DType] = DType.new(6, 32, "unsigned int", 'I')
   int64: Final[DType] = DType.new(7, 64, "long", 'q')
   uint64: Final[DType] = DType.new(8, 64, "unsigned long", 'Q')
-  _uint128: Final[DType] = DType.new(8, 128, "uint128", None)
-  _uint256: Final[DType] = DType.new(8, 256, "uint256", None)
   weakfloat: Final[DType] = DType.new(9, 800, "weakfloat", None)
   fp8e4m3: Final[DType] = DType.new(10, 8, "float8_e4m3", None)
   fp8e5m2: Final[DType] = DType.new(11, 8, "float8_e5m2", None)

tinygrad/engine/jit.py

@@ -67,7 +67,7 @@ def jit_lower(linear:UOp, held_bufs:set[UOp], input_uops:list[UOp]) -> UOp:
   if VIZ: graph_rewrite(linear, PatternMatcher([]), name="View captured linear")
 
   # parametrize input buffers: map each input buffer UOp to a PARAM with the correct slot index
-  linear = linear.substitute({u: UOp.param(i, u.dtype, u.shape, u.device) for i,u in enumerate(input_uops)}, walk=True)
+  linear = linear.substitute({u: UOp.param(i, u.dtype, u.max_numel(), u.device) for i,u in enumerate(input_uops)}, walk=True)
   linear = memory_plan_rewrite(linear, held_bufs)
   linear = compile_linear(linear, beam=getenv("JITBEAM", BEAM.value))
   if JIT < 2: linear = graph_split_rewrite(linear, max_batch_size=JIT_BATCH_SIZE.value)

tinygrad/engine/realize.py

@@ -2,8 +2,8 @@ from __future__ import annotations
 from typing import cast, Iterator, Any, Sequence
 import random, itertools, math, weakref, array, decimal
 from dataclasses import dataclass, replace, field
-from tinygrad.helpers import colored, DEBUG, GlobalCounters, ansipad, all_int, prod, flatten, Context, getenv, to_tuple, tqdm, dedup
-from tinygrad.helpers import BEAM, size_to_str, time_to_str, VALIDATE_WITH_CPU, PROFILE, ProfilePointEvent, cpu_events, perf_counter_us
+from tinygrad.helpers import colored, DEBUG, GlobalCounters, ansipad, all_int, prod, flatten, Context, to_tuple, tqdm, dedup
+from tinygrad.helpers import BEAM, size_to_str, time_to_str, VALIDATE_WITH_CPU, HCQ2, PROFILE, ProfilePointEvent, cpu_events, perf_counter_us
 from tinygrad.uop.ops import Ops, PatternMatcher, UOp, UPat, AxisType, sym_infer, graph_rewrite, ProgramInfo
 from tinygrad.device import Device, Buffer, MultiBuffer, ProfileGraphEntry
 from tinygrad.dtype import dtypes
@@ -305,17 +305,17 @@ pm_exec = PatternMatcher([
   (UPat(Ops.CALL, src=(UPat(Ops.CUSTOM_FUNCTION, arg="validate", name="ast"),), name="call", allow_any_len=True), exec_validate),
 ])
 
-if getenv("HCQ2"): from tinygrad.runtime.support.hcq2 import hcq_compile, hcq_link, HCQ_RUNTIME_DEV # noqa: E402 # down here, hcq2 imports realize
+from tinygrad.runtime.support.hcq2 import hcq_compile, hcq_link, HCQ_RUNTIME_DEV # noqa: E402 # down here, hcq2 imports realize
 
 def compile_linear(linear:UOp, beam:int|None=None, validate=False, input_uops:list[UOp]|None=None, profile:bool|None=None) -> UOp:
   if validate: linear = graph_rewrite(linear, pm_validate, name="validate", walk=True)
   if (beam_val:=BEAM.value if beam is None else beam) >= 1: linear = graph_rewrite(linear, pm_beam, ctx=beam_val, walk=True)
   linear = lower_and_compile(linear)
   linear = graph_rewrite(linear, pm_optimize_local_size, name="optimize local size", walk=True)
-  if getenv("HCQ2"): linear = hcq_compile(linear, input_uops, bool(PROFILE or DEBUG >= 2) if profile is None else profile)
+  if HCQ2: linear = hcq_compile(linear, input_uops, bool(PROFILE or DEBUG >= 2) if profile is None else profile)
   return linear
 
-def link_linear(linear:UOp, cache=True) -> UOp: return hcq_link(linear, cache=cache) if getenv("HCQ2") else linear
+def link_linear(linear:UOp, cache=True) -> UOp: return hcq_link(linear, cache=cache) if HCQ2 else linear
 
 def run_linear(linear:UOp, var_vals:dict[str, int]|None=None, input_uops:Sequence[UOp]=(), update_stats=True, jit=False, wait=False):
   inputs = list(input_uops)

tinygrad/helpers.py

@@ -240,7 +240,7 @@ TRANSCENDENTAL, NOLOCALS = ContextVar("TRANSCENDENTAL", 1), ContextVar("NOLOCALS
 SPLIT_REDUCEOP, NO_MEMORY_PLANNER, LRU = ContextVar("SPLIT_REDUCEOP", 1), ContextVar("NO_MEMORY_PLANNER", 0), ContextVar("LRU", 1)
 RING, ALL2ALL, ALLREDUCE_CAST = ContextVar("RING", 1), ContextVar("ALL2ALL", 0), ContextVar("ALLREDUCE_CAST", 1)
 CACHELEVEL, IGNORE_BEAM_CACHE = ContextVar("CACHELEVEL", 2), ContextVar("IGNORE_BEAM_CACHE", 0)
-VALIDATE_WITH_CPU = ContextVar("VALIDATE_WITH_CPU", 0)
+VALIDATE_WITH_CPU, HCQ2 = ContextVar("VALIDATE_WITH_CPU", 0), ContextVar("HCQ2", 0)
 # TODO: this is broken for some indexing
 DISABLE_FAST_IDIV = ContextVar("DISABLE_FAST_IDIV", 1)
 FUSE_OPTIM = ContextVar("FUSE_OPTIM", 0)

tinygrad/llm/gguf.py

@@ -18,7 +18,9 @@ _GGML_NATIVE = {0: dtypes.float32, 1: dtypes.float16, 24: dtypes.int8, 25: dtype
 
 # quant types {ggml_type: (number of elements, number of bytes)}
 _GGML_QUANT = {2:(32,18), 3:(32,20), 6:(32,22), 7:(32,24), 8:(32,34),
-               12:(256,144), 13:(256,176), 14:(256,210), 18:(256,98), 21:(256,110), 22:(256,82), 23:(256,136), 39:(32,17), 41:(128,18)}
+               10:(256,84), 11:(256,110), 12:(256,144), 13:(256,176), 14:(256,210),
+               16:(256,66), 17:(256,74), 18:(256,98), 19:(256,50), 20:(32,18), 21:(256,110), 22:(256,82), 23:(256,136),
+               29:(256,56), 39:(32,17), 41:(128,18)}
 
 def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
   """
@@ -27,8 +29,9 @@ def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
   Supported native types: float32 (id: 0), float16 (id: 1), int8 (id: 24),
   int16 (id: 25), int32 (id: 26), int64 (id: 27), float64 (id: 28), bfloat16 (id: 30)
   Supported quantized types: Q4_0 (id: 2), Q4_1 (id: 3), Q5_0 (id: 6),
-  Q5_1 (id: 7), Q8_0 (id: 8), Q4_K (id: 12), Q5_K (id: 13),
-  Q6_K (id: 14), IQ3_XXS (id: 18), IQ3_S (id: 21), IQ2_S (id: 22), IQ4_XS (id: 23), MXFP4 (id: 39), Q1_0 (id: 41)
+  Q5_1 (id: 7), Q8_0 (id: 8), Q2_K (id: 10), Q3_K (id: 11), Q4_K (id: 12), Q5_K (id: 13),
+  Q6_K (id: 14), IQ2_XXS (id: 16), IQ2_XS (id: 17), IQ3_XXS (id: 18), IQ1_S (id: 19),
+  IQ4_NL (id: 20), IQ3_S (id: 21), IQ2_S (id: 22), IQ4_XS (id: 23), IQ1_M (id: 29), MXFP4 (id: 39), Q1_0 (id: 41)
   """
   # https://github.com/ggerganov/ggml/blob/323951f1bdcdfbd5b5ff3a9a7c3770e63b1a560e/include/ggml.h#L356
 
@@ -54,6 +57,19 @@ def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
       q = q_to_uint8(blocks[:,qh_off+4:], 4).bitcast(dtypes.int8) + qh * 16
       return q * d + (blocks[:,2:4].bitcast(dtypes.float16).cast(dtypes.float32) if ggml_type == 7 else -16 * d)
     if ggml_type == 8: return blocks[:,:2].bitcast(dtypes.float16).cast(dtypes.float32) * blocks[:,2:].bitcast(dtypes.int8)
+    # Q2_K: 256 elements per 84-byte block (scales:16, qs:64, d:2, dmin:2)
+    if ggml_type == 10:
+      d, dmin = (blocks[:,i:i+2].bitcast(dtypes.float16).cast(dtypes.float32).unsqueeze(-1) for i in [80, 82])
+      sc = blocks[:, :16]
+      q = q_to_uint8(blocks[:, 16:80].reshape((-1, 2, 32)), 2).reshape((-1, 16, 16))
+      return (d * sc.bitwise_and(0xF).unsqueeze(-1) * q - dmin * sc.rshift(4).unsqueeze(-1)).flatten(-2)
+    # Q3_K: 256 elements per 110-byte block (hmask:32, qs:64, scales:12, d:2)
+    if ggml_type == 11:
+      d = blocks[:,-2:].bitcast(dtypes.float16).cast(dtypes.float32).unsqueeze(-1)
+      sc = q_to_uint8(blocks[:,96:104], 4).bitwise_or(q_to_uint8(blocks[:,104:108], 2).lshift(4)).bitcast(dtypes.int8) - 32
+      q = q_to_uint8(blocks[:,32:96].reshape((-1, 2, 32)), 2).reshape((-1, 16, 16))
+      qh = q_to_uint8(blocks[:,:32], 1).reshape((-1, 16, 16))
+      return (d * sc.unsqueeze(-1) * (q.bitcast(dtypes.int8) - qh.bitwise_xor(1).lshift(2).bitcast(dtypes.int8))).flatten(-2)
      # Q4_K: 256 elements per 144-byte block (d:2, dmin:2, scales:12, qs:128)
      # Q5_K: 256 elements per 176-byte block (d:2, dmin:2, scales:12, qh:32, qs:128)
     if ggml_type in (12, 13):
@@ -79,6 +95,41 @@ def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
       signs = (q_to_uint8(even_signs[sign_idx].reshape((-1, 32, 1)), 1) == 0).where(1.0, -1.0).reshape((-1, 8, 4, 8))
       grid = _ggml_iq_grid(t.device, _ggml.iq3xxs_grid, (256, 4))[blocks[:, 2:66]].reshape((-1, 8, 4, 8))
       return (db * grid * signs).flatten(-3)
+    # IQ2_XXS: 256 elements per 66-byte block (d:2, qs:64). 8 groups of 32: 4 grid bytes + packed signs/scale.
+    if ggml_type == 16:
+      d = blocks[:, :2].bitcast(dtypes.float16).cast(dtypes.float32).reshape((-1, 1, 1, 1))
+      qs_u32 = blocks[:, 2:].bitcast(dtypes.uint32).reshape((-1, 8, 2))
+      db = d * (qs_u32[:, :, 1].rshift(28).cast(dtypes.float32) + 0.5).reshape((-1, 8, 1, 1)) * 0.25
+      sign_idx = qs_u32[:, :, 1].unsqueeze(-1).rshift(Tensor.const((0, 7, 14, 21), dtypes.uint32))
+      sign_idx = sign_idx.bitwise_and(0x7F).reshape((-1, 32)).cast(dtypes.int32)
+      even_signs = Tensor([i | (0x80 if i.bit_count() % 2 else 0) for i in range(128)], dtype=dtypes.uint8, device=t.device)
+      signs = (q_to_uint8(even_signs[sign_idx].reshape((-1, 32, 1)), 1) == 0).where(1.0, -1.0).reshape((-1, 8, 4, 8))
+      grid = _ggml_iq_grid(t.device, _ggml.iq2xxs_grid, (256, 8))[blocks[:, 2:].reshape((-1, 8, 8))[:, :, :4]].reshape((-1, 8, 4, 8))
+      return (db * grid * signs).flatten(-3)
+    # IQ2_XS: 256 elements per 74-byte block (d:2, qs:64 as uint16, scales:8)
+    if ggml_type == 17:
+      d = blocks[:, :2].bitcast(dtypes.float16).cast(dtypes.float32).reshape((-1, 1, 1, 1))
+      db = d * (q_to_uint8(blocks[:, 66:74].reshape((-1, 8, 1)), 4).reshape((-1, 16)).cast(dtypes.float32) + 0.5).reshape((-1, 16, 1, 1)) * 0.25
+      qs = blocks[:, 2:66].bitcast(dtypes.uint16)
+      sign_idx = qs.rshift(9).cast(dtypes.int32)
+      even_signs = Tensor([i | (0x80 if i.bit_count() % 2 else 0) for i in range(128)], dtype=dtypes.uint8, device=t.device)
+      signs = (q_to_uint8(even_signs[sign_idx].reshape((-1, 32, 1)), 1) == 0).where(1.0, -1.0).reshape((-1, 16, 2, 8))
+      grid = _ggml_iq_grid(t.device, _ggml.iq2xs_grid, (512, 8))[qs.bitwise_and(511)].reshape((-1, 16, 2, 8))
+      return (db * grid * signs).flatten(-3)
+    # IQ1_S: 256 elements per 50-byte block (d:2, qs:32, qh:16). grid bytes are int8 {-1,0,1}.
+    if ggml_type == 19:
+      d = blocks[:, :2].bitcast(dtypes.float16).cast(dtypes.float32).reshape((-1, 1, 1, 1))
+      qh = blocks[:, 34:50].bitcast(dtypes.uint16)
+      dl = d * (qh.rshift(12).bitwise_and(7).cast(dtypes.float32) * 2 + 1).reshape((-1, 8, 1, 1))
+      delta = (qh.bitwise_and(0x8000) == 0).where(0.125, -0.125).reshape((-1, 8, 1, 1))
+      qh_hi = qh.unsqueeze(-1).rshift(Tensor.const((0, 3, 6, 9), dtypes.uint16)).bitwise_and(7).lshift(8)
+      q = blocks[:, 2:34].cast(dtypes.uint16) + qh_hi.reshape((-1, 32))
+      grid = _ggml_iq_grid(t.device, _ggml.iq1s_grid, (2048, 8))[q].reshape((-1, 8, 4, 8))
+      grid = (grid > 127).where(grid - 256, grid)
+      return (dl * (grid + delta)).flatten(-3)
+    if ggml_type == 20:
+      d = blocks[:, :2].bitcast(dtypes.float16).cast(dtypes.float32)
+      return d * Tensor(list(_ggml.kvalues_iq4nl), dtype=dtypes.float32, device=t.device)[q_to_uint8(blocks[:, 2:], 4)]
     if ggml_type == 21:
       d = blocks[:, :2].bitcast(dtypes.float16).cast(dtypes.float32).reshape((-1, 1, 1, 1))
       scales = (1 + 2 * q_to_uint8(blocks[:, 106:110].reshape((-1, 4, 1)), 4).reshape((-1, 8))).cast(dtypes.float32).reshape((-1, 8, 1, 1))
@@ -102,6 +153,20 @@ def ggml_data_to_tensor(t: Tensor, n: int, ggml_type: int) -> Tensor:
       scales = (scales_l.bitwise_or(scales_h.lshift(4)).bitcast(dtypes.int8) - 32).cast(dtypes.float32).reshape((-1, 8, 1))
       q = (qs:=blocks[:, 8:].reshape((-1, 8, 16))).bitwise_and(0xF).cat(qs.rshift(4), dim=2)
       return (d * scales * iq4_xs_lut[q]).flatten(-2)
+    # IQ1_M: 256 elements per 56-byte block (qs:32, qh:16, scales:8). f16 scale packed in high nibbles.
+    if ggml_type == 29:
+      sc16 = blocks[:, 48:56].bitcast(dtypes.uint16)
+      d = sc16.bitwise_and(0xF000).rshift(Tensor.const((12, 8, 4, 0), dtypes.uint16))
+      d = d[:, 0:1].bitwise_or(d[:, 1:2]).bitwise_or(d[:, 2:3]).bitwise_or(d[:, 3:4])
+      d = d.bitcast(dtypes.float16).cast(dtypes.float32).reshape((-1, 1, 1, 1, 1))
+      scales = sc16.unsqueeze(-1).rshift(Tensor.const((0, 3, 6, 9), dtypes.uint16)).bitwise_and(7)
+      dl = d * (scales.cast(dtypes.float32) * 2 + 1).reshape((-1, 8, 2, 1, 1))
+      qh_n = Tensor.stack(blocks[:, 32:48].bitwise_and(0x0F), blocks[:, 32:48].rshift(4), dim=-1).reshape((-1, 32))
+      q = blocks[:, :32].cast(dtypes.uint16) + qh_n.bitwise_and(7).cast(dtypes.uint16).lshift(8)
+      delta = (qh_n.bitwise_and(0x08) == 0).where(0.125, -0.125).reshape((-1, 8, 2, 2, 1))
+      grid = _ggml_iq_grid(t.device, _ggml.iq1s_grid, (2048, 8))[q].reshape((-1, 8, 2, 2, 8))
+      grid = (grid > 127).where(grid - 256, grid)
+      return (dl * (grid + delta)).flatten(-4)
     if ggml_type == 39:
       e = blocks[:, 0].cast(dtypes.uint32)
       small_bits = Tensor([0x00200000, 0x00400000], dtype=dtypes.uint32, device=t.device)[e.clip(0, 1).cast(dtypes.int32)] # e = 0 or e = 1 case

tinygrad/mixin/gradient.py

@@ -31,6 +31,10 @@ def call_gradient(ctx:UOp, k:UOp, needed:set[int]) -> tuple[UOp|None, ...]:
     return (None,) + k.arg.grad_fxn(on_dev(ctx, 0), k)
   assert fxn.op is Ops.TUPLE, f"expected TUPLE body for gradient, got {fxn.op}"
   params = {x.arg.slot:x for x in fxn.toposort(enter_calls=False) if x.op == Ops.PARAM}
+  # grads are collected at the flat param storage: reshape to each arg's view (max view shrunk to symbolic)
+  def shaped_grad(grad:UOp, i:int) -> UOp:
+    a = args[i]
+    return grad.view_as(a.shard_shape, a.axis) if a.axis is not None and isinstance(a.device, tuple) else grad.view_as(a._shape)
   grad_args = ctx.src
   root_grad = UOp(Ops.TUPLE, src=tuple(UOp(Ops.NOOP) if g.op is Ops.NOOP else
     g if g.device is None else g.param_like(len(args)+i) for i,g in enumerate(grad_args)))
@@ -39,7 +43,7 @@ def call_gradient(ctx:UOp, k:UOp, needed:set[int]) -> tuple[UOp|None, ...]:
   fwd_subs = {src: src.param_like(len(args)+len(grad_args)+i) for i, src in enumerate(fxn.src)} if k.arg.precompile else {}
   fwd_outs = tuple(k.gettuple(i) for i in range(len(fxn.src))) if k.arg.precompile else ()
   # collect needed gradient bodies, compact unused params, create a single backward CALL
-  grad_bodies = [(i, grads[p]) for i in needed if (p:=params.get(i)) is not None and p in grads]
+  grad_bodies = [(i, shaped_grad(grads[p], i)) for i in needed if (p:=params.get(i)) is not None and p in grads]
   bwd_body = UOp.maketuple(*(gb for _, gb in grad_bodies)).substitute(fwd_subs, walk=True)
   bwd_body = renumber_invalid_outputs(bwd_body)
   bwd_body, compact_args = _compact_params(bwd_body, (*args, *grad_args, *fwd_outs))

tinygrad/mixin/op.py

@@ -1067,7 +1067,7 @@ class OpMixin(ElementwiseMixin, ReduceMixin):
       reshape[i] = expand[i] = size[i]
       if mode == "linear":
         arr = type(self).arange(size[i])
-        num, den = (arr*(in_sz-1), size[i]-1) if align_corners else ((arr*2+1)*in_sz - size[i], size[i]*2)
+        num, den = (arr*(in_sz-1), max(size[i]-1, 1)) if align_corners else ((arr*2+1)*in_sz - size[i], size[i]*2)
         num = num.clip(0, (in_sz-1)*den)
         low, high, perc = [y.reshape(reshape).expand(expand) for y in (num//den, (num+den-1)//den, (num % den).cast(dtypes.float32)/den)]
         x = x.gather(i, low).lerp(x.gather(i, high), perc)

tinygrad/nn/__init__.py

@@ -99,7 +99,7 @@ class Conv2d:
     if isinstance(padding, str):
       if padding.lower() != 'same': raise ValueError(f"Invalid padding string {padding!r}, only 'same' is supported")
       if stride != 1: raise ValueError("padding='same' is not supported for strided convolutions")
-      pad = [(d*(k-1)//2, d*(k-1) - d*(k-1)//2) for d,k in zip(make_tuple(dilation, len(self.kernel_size)), self.kernel_size[::-1])]
+      pad = [(d*(k-1)//2, d*(k-1) - d*(k-1)//2) for d,k in zip(make_tuple(dilation, len(self.kernel_size))[::-1], self.kernel_size[::-1])]
       padding = tuple(flatten(pad))
     self.stride, self.dilation, self.groups, self.padding = stride, dilation, groups, padding
     scale = 1 / math.sqrt(in_channels * prod(self.kernel_size))

tinygrad/nn/onnx.py

@@ -556,8 +556,8 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     return tuple(t if cond else e for t,e in zip(then_out.values(), else_out.values()))
 
   def Identity(x:Tensor): return x
-  def Constant(sparse_value:Tensor|None=None, value:Tensor|None=None, value_float:float|None=None, value_floats:list[float]|None=None,
-              value_int:int|None=None, value_ints:list[int]|None=None, value_string:str|None=None, value_strings:list[str]|None=None):
+  def Constant(sparse_value:Tensor|None=None, value:Tensor|None=None, value_float:float|None=None, value_floats:tuple[float, ...]|None=None,
+              value_int:int|None=None, value_ints:tuple[int, ...]|None=None, value_string:str|None=None, value_strings:tuple[str, ...]|None=None):
     if value is not None: return value
     if value_float is not None: return Tensor(value_float, dtype=dtypes.float32)
     if value_floats is not None: return Tensor(list(value_floats), dtype=dtypes.float32)
@@ -594,7 +594,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
 
   # ***** Unary Ops (math) *****
   def Not(x:Tensor): return x.logical_not()
-  def Clip(x: Tensor, min:Tensor|None=None, max:Tensor|None=None): return x if min is None and max is None else x.clip(min, max)  # noqa: A002 # pylint: disable=redefined-builtin
+  def Clip(x: Tensor, min:Tensor|float|None=None, max:Tensor|float|None=None): return x if min is None and max is None else x.clip(min, max)  # noqa: A002 # pylint: disable=redefined-builtin
   def IsInf(x:Tensor, detect_negative:int=1, detect_positive:int=1): return x.isinf(bool(detect_positive), bool(detect_negative))
 
   # ***** Unary Ops (activation) *****
@@ -643,26 +643,26 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
   def Min(*data_0:Tensor): return functools.reduce(Tensor.minimum, data_0)
   def Sum(*data_0:Tensor): return functools.reduce(Tensor.add, data_0)
   def Mean(*data_0:Tensor): return Sum(*data_0) / len(data_0)
-  def ReduceMax(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceMax(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return data.max(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
-  def ReduceMin(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceMin(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return data.min(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
-  def ReduceSum(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceSum(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return data.sum(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
-  def ReduceMean(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceMean(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return data.mean(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
-  def ReduceSumSquare(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceSumSquare(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return ReduceSum(data.square(), axes, keepdims, noop_with_empty_axes)
-  def ReduceProd(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceProd(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return data.prod(_axes(axes, noop_with_empty_axes), keepdim=keepdims)
-  def ReduceL1(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceL1(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return ReduceSum(data.abs(), axes, keepdims, noop_with_empty_axes)
-  def ReduceL2(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceL2(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     dtype = dtypes.float if data.dtype in (dtypes.float16, dtypes.bfloat16) else data.dtype
     return ReduceSum(data.cast(dtype).square(), axes, keepdims, noop_with_empty_axes).sqrt().cast(data.dtype)
-  def ReduceLogSum(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceLogSum(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return ReduceSum(data, axes, keepdims, noop_with_empty_axes).log()
-  def ReduceLogSumExp(data:Tensor, axes:list[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
+  def ReduceLogSumExp(data:Tensor, axes:Sequence[int]|None=None, keepdims:int=1, noop_with_empty_axes:int=0):
     return ReduceSum(data.exp(), axes, keepdims, noop_with_empty_axes).log()
   def ArgMax(x:Tensor, axis:int=0, keepdims:int=1, select_last_index:int=0):
     if select_last_index: return ((int(x.shape[axis])-1) - x.flip(axis).argmax(axis, keepdim=keepdims)).cast(dtypes.int64)
@@ -671,32 +671,32 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     return ArgMax(-x, axis=axis, keepdims=keepdims, select_last_index=select_last_index)
 
   # ***** Movement Ops *****
-  def Reshape(data:Tensor, shape:list[int], allowzero:int=0):
+  def Reshape(data:Tensor, shape:Sequence[int], allowzero:int=0):
     return data.reshape([x if x != 0 else (0 if allowzero else data.shape[i]) for i,x in enumerate(shape)])
   def Flatten(x:Tensor, axis:int=1): return x.reshape(prod(x.shape[0:axis]), -1)
   def Expand(x:Tensor, shape:list[int]): return x.expand(_broadcast_shape(x.shape, tuple(shape)))
   def Shrink(x:Tensor, bias:float=0.0, lambd:float=0.5): return (x < -lambd)*(x+bias) + (x > lambd)*(x-bias)
-  def Transpose(x:Tensor, perm:list[int]|None=None): return x.permute(order=perm or list(range(x.ndim)[::-1]))
+  def Transpose(x:Tensor, perm:tuple[int, ...]|None=None): return x.permute(order=perm or list(range(x.ndim)[::-1]))
 
-  def Squeeze(data:Tensor, axes:list[int]|None=None):
+  def Squeeze(data:Tensor, axes:Sequence[int]|None=None):
     return data.squeeze() if axes is None else functools.reduce(lambda d, dim: d.squeeze(dim), sorted(axes, reverse=True), data)
-  def Unsqueeze(data:Tensor, axes:list[int]): return functools.reduce(lambda d, dim: d.unsqueeze(dim), sorted(axes), data)
+  def Unsqueeze(data:Tensor, axes:Sequence[int]): return functools.reduce(lambda d, dim: d.unsqueeze(dim), sorted(axes), data)
 
   def Tile(x:Tensor, repeats:list[int]): return x.repeat(repeats)
   def Concat(*xs:Tensor, axis:int): return Tensor.cat(*xs, dim=axis)
-  def Slice(data:Tensor, starts:list[int], ends:list[int], axes:list[int]|None=None, steps:list[int]|None=None):
+  def Slice(data:Tensor, starts:Sequence[int], ends:Sequence[int], axes:Sequence[int]|None=None, steps:list[int]|None=None):
     axes = axes or list(range(data.ndim))
     steps = steps or [1] * data.ndim
     slices = [slice(None)] * data.ndim
     for i, axis in enumerate(axes): slices[axis] = slice(starts[i], ends[i], steps[i])
     return data[tuple(slices)]
 
-  def Split(data:Tensor, split:list[int]|None=None, num_outputs:int=0, axis:int=0):
+  def Split(data:Tensor, split:Sequence[int]|None=None, num_outputs:int=0, axis:int=0):
     sz = int(data.shape[axis])
     if split is None: split = [sz // num_outputs + (1 if i < sz % num_outputs else 0) for i in range(num_outputs)]
     return data.split(split, axis)
 
-  def Pad(x:Tensor, pads:list[int], constant_value:ConstType|None=None, axes:list[int]|None=None,
+  def Pad(x:Tensor, pads:Sequence[int], constant_value:ConstType|None=None, axes:list[int]|None=None,
           mode:Literal["constant", "reflect", "edge", "wrap"]="constant", value=0):
     value = _resolve_const(value if constant_value is None else constant_value)
     axes = axes or list(range(x.ndim))
@@ -704,7 +704,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     for i,axis in enumerate(axes): real_pads[axis%x.ndim], real_pads[axis%x.ndim+x.ndim] = pads[i], pads[i+len(axes)]
     return x.pad(padding=_onnx_pads_to_tiny_pads(real_pads), mode={"edge":"replicate", "wrap":"circular"}.get(mode, mode), value=value)
 
-  def CenterCropPad(t:Tensor, shape:list[int], axes:list[int]|None=None):
+  def CenterCropPad(t:Tensor, shape:list[int], axes:tuple[int, ...]|None=None):
     shrink_arg:list[None|tuple[sint,sint]] = [None] * t.ndim
     pad_arg:list[None|tuple[sint,sint]] = [None] * t.ndim
     for s, x in zip(shape, axes or range(t.ndim)):
@@ -714,26 +714,26 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     return t.shrink(tuple(shrink_arg)).pad(tuple(pad_arg))
 
   # ***** Processing Ops *****
-  def AveragePool(X: Tensor, kernel_shape:list[int], auto_pad:AUTO_PAD_OPTIONS="NOTSET", ceil_mode:int=0, count_include_pad:int=0,
-                  dilations:list[int]|int=1, pads:list[int]|int=0, strides:list[int]|int=1):
+  def AveragePool(X: Tensor, kernel_shape:tuple[int, ...], auto_pad:AUTO_PAD_OPTIONS="NOTSET", ceil_mode:int=0, count_include_pad:int=0,
+                  dilations:tuple[int, ...]|int=1, pads:tuple[int, ...]|int=0, strides:tuple[int, ...]|int=1):
     pool_pads = _resolve_pool_pads(X, pads, kernel_shape, dilations, strides, auto_pad)
     return X.avg_pool2d(tuple(kernel_shape), strides, dilations, pool_pads, ceil_mode=ceil_mode, count_include_pad=count_include_pad)
 
-  def MaxPool(X: Tensor, kernel_shape:list[int], auto_pad:AUTO_PAD_OPTIONS="NOTSET", ceil_mode:int=0, dilations:list[int]|int=1, pads:list[int]|int=0,
-              storage_order:int=0, strides:list[int]|int=1):
+  def MaxPool(X: Tensor, kernel_shape:tuple[int, ...], auto_pad:AUTO_PAD_OPTIONS="NOTSET", ceil_mode:int=0, dilations:tuple[int, ...]|int=1,
+              pads:tuple[int, ...]|int=0, storage_order:int=0, strides:tuple[int, ...]|int=1):
     pool_pads = _resolve_pool_pads(X, pads, kernel_shape, dilations, strides, auto_pad)
     out = X.max_pool2d(tuple(kernel_shape), strides, dilations, pool_pads, ceil_mode=ceil_mode, return_indices=True)
     ret, idx = cast(tuple[Tensor, Tensor], out)
     return ret, idx.transpose(-2, -1).cast(dtypes.int64) if storage_order else idx.cast(dtypes.int64)
 
-  def Conv(X: Tensor, W: Tensor, B:Tensor|None=None, auto_pad:AUTO_PAD_OPTIONS="NOTSET", dilations:list[int]|int=1, group:int=1,
-          kernel_shape:list[int]|None=None, pads:list[int]|int=0, strides:list[int]|int=1):
+  def Conv(X: Tensor, W: Tensor, B:Tensor|None=None, auto_pad:AUTO_PAD_OPTIONS="NOTSET", dilations:tuple[int, ...]|int=1, group:int=1,
+          kernel_shape:tuple[int, ...]|None=None, pads:tuple[int, ...]|int=0, strides:tuple[int, ...]|int=1):
     return X.conv2d(W, B, stride=strides, groups=group, dilation=dilations,
                     padding=_resolve_pool_pads(X, pads, kernel_shape or W.shape[2:], dilations, strides, auto_pad))
 
-  def ConvTranspose(X: Tensor, W: Tensor, B:Tensor|None=None, auto_pad:AUTO_PAD_OPTIONS="NOTSET", dilations:list[int]|int=1, group:int=1,
-                    kernel_shape:list[int]|None=None, pads:list[int]|None=None, output_shape:list[int]|None=None, output_padding:list[int]|int=0,
-                    strides:list[int]|int=1):
+  def ConvTranspose(X: Tensor, W: Tensor, B:Tensor|None=None, auto_pad:AUTO_PAD_OPTIONS="NOTSET", dilations:tuple[int, ...]|int=1, group:int=1,
+                    kernel_shape:tuple[int, ...]|None=None, pads:Sequence[int]|None=None, output_shape:Sequence[int]|None=None,
+                    output_padding:tuple[int, ...]|int=0, strides:tuple[int, ...]|int=1):
     input_shape_, kernel_shape_ = X.shape[2:], (kernel_shape or W.shape[2:])
     strides_, dilations_, output_padding_ = (make_tuple(x, len(input_shape_)) for x in (strides, dilations, output_padding))
     if output_shape is not None: # we pad according to output_shape
@@ -747,10 +747,10 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     pads = _onnx_pads_to_tiny_pads(pads)
     return X.conv_transpose2d(W, B, group, strides_, dilations_, pads, output_padding_)
 
-  def MaxUnpool(xT: Tensor, xI: Tensor, outshape: list[int]|None=None, kernel_shape:list[int]|None=None, pads:list[int]|int=0,
-                strides:list[int]|int=1):
+  def MaxUnpool(xT: Tensor, xI: Tensor, outshape: list[int]|None=None, kernel_shape:Sequence[int]|None=None, pads:tuple[int, ...]|int=0,
+                strides:tuple[int, ...]|int=1):
     if kernel_shape is None: kernel_shape = []
-    pads_: int | tuple[int, ...] = tuple(pads) if isinstance(pads, list) else pads
+    pads_: int | tuple[int, ...] = pads if isinstance(pads, int) else _onnx_pads_to_tiny_pads(pads)
     return Tensor.max_unpool2d(xT, xI, tuple(kernel_shape), strides, 1, pads_, outshape if outshape is None else tuple(outshape))
 
   def GlobalAveragePool(X:Tensor): return X.mean(axis=tuple(range(2, X.ndim)), keepdim=True)
@@ -761,7 +761,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     if C is not None: ret = ret + beta * (C if broadcast == 0 else C.reshape([-1 if i < len(C.shape) else 1 for i in range(ret.ndim)][::-1]))
     return ret
 
-  def Einsum(*Inputs:list[Tensor], equation:str): return Tensor.einsum(equation, *Inputs)
+  def Einsum(*Inputs:Tensor, equation:str): return Tensor.einsum(equation, *Inputs)
 
   def CumSum(X:Tensor, axis:int|list[int], exclusive:int=0, reverse:int=0):
     axis = X._resolve_dim(_resolve_const(axis))
@@ -774,8 +774,8 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     k_ = _resolve_const(k)
     return x.triu(k_) if upper else x.tril(k_)
 
-  def Resize(X:Tensor, roi:list[float]|None=None, scales:list[float]|None=None, sizes:list[int]|None=None, antialias:int=0,
-        axes:list[int]|None=None, coordinate_transformation_mode:str='half_pixel', cubic_coeff_a:float=-0.75, exclude_outside:int=0,
+  def Resize(X:Tensor, roi:list[float]|None=None, scales:Sequence[float]|None=None, sizes:list[int]|None=None, antialias:int=0,
+        axes:Sequence[int]|None=None, coordinate_transformation_mode:str='half_pixel', cubic_coeff_a:float=-0.75, exclude_outside:int=0,
         extrapolation_value:float=0.0, keep_aspect_ratio_policy:str='stretch', mode:str='nearest', nearest_mode:str='round_prefer_floor'):
     def _apply_transformation(input_sz, output_sz, scale_dim, mode):
       index = Tensor.arange(output_sz)
@@ -876,7 +876,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
         gathered_values = [X.gather(i, idx) for idx in expanded_indices]
         X = sum(v * c for v, c in zip(gathered_values, expanded_coeffs))
     return X.permute(*argsort(perm)) if perm else X
-  def Upsample(X, scales, mode): return Resize(X=X, scales=scales, mode=mode)  # deprecated
+  def Upsample(X:Tensor, scales:Sequence[float], mode:str): return Resize(X=X, scales=scales, mode=mode)  # deprecated
 
   def TopK(X:Tensor, K:int|list[int], axis:int=-1, largest:int=1, sorted:int=1):  # noqa: A002 # pylint: disable=redefined-builtin
     val, idx = X.topk(_resolve_const(K), axis, bool(largest), bool(sorted))
@@ -937,7 +937,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
     if segment_embedding is not None: embedding_sum = embedding_sum + embedding(segment_ids, segment_embedding.shape[0], segment_embedding)
     out = embedding_sum.layernorm(eps=epsilon) * gamma + beta
     return out, None, embedding_sum
-  def MeanVarianceNormalization(x:Tensor, axis:list[int]|None=None):
+  def MeanVarianceNormalization(x:Tensor, axis:Sequence[int]|None=None):
     if axis is None: axis = [0,2,3]
     return (x - x.mean(axis, keepdim=True)) / (x.std(axis, keepdim=True, correction=0) + 1e-9)
 
@@ -1001,7 +1001,7 @@ def get_onnx_ops() -> dict[str, types.FunctionType|dict[OpSetId, types.FunctionT
 
   def attention_contrib(x:Tensor, weights:Tensor, bias:Tensor|None=None, mask_index:Tensor|None=None, past:Tensor|None=None,
                         attention_bias:Tensor|None=None, past_sequence_length:Tensor|None=None, do_rotary:int=0, mask_filter_value:float=-10000.0,
-                        num_heads:int|None=None, past_present_share_buffer:int|None=None, qkv_hidden_sizes:list[int]|None=None,
+                        num_heads:int|None=None, past_present_share_buffer:int|None=None, qkv_hidden_sizes:Sequence[int]|None=None,
                         rotary_embedding_dim:int|None=None, scale:float|None=None, unidirectional:int=0):
     assert not do_rotary and not attention_bias, "TODO"
     if qkv_hidden_sizes is None: qkv_hidden_sizes = [int(weights.shape[1] // 3)] * 3

tinygrad/renderer/isa/x86.py

@@ -4,7 +4,7 @@ import sys, struct, functools
 from typing import cast
 from tinygrad.dtype import dtypes, DType, truncate, AddrSpace
 from tinygrad.uop import FastEnum, auto, Ops, GroupOp
-from tinygrad.uop.ops import UOp, UPat, PatternMatcher
+from tinygrad.uop.ops import UOp, UPat, PatternMatcher, promo_dtype
 from tinygrad.renderer.isa import ISARenderer, IselContext, Register, PreRegAllocContext, greg
 from tinygrad.helpers import getenv, NUM_CPU_THREADS, unwrap, Target
 
@@ -33,7 +33,6 @@ class X86Ops(FastEnum):
   # bitcasts
   VMOVD = auto(); VMOVQ = auto(); VMOVDm = auto(); VMOVQm = auto()
   # comparisons
-  VUCOMISS = auto(); VUCOMISD = auto()
   VCMPSS = auto(); VCMPSD = auto(); VCMPPS = auto(); VCMPPD = auto()
   VPCMPGTB = auto(); VPCMPGTW = auto(); VPCMPGTD = auto(); VPCMPGTQ = auto()
   VPCMPEQB = auto(); VPCMPEQW = auto(); VPCMPEQD = auto(); VPCMPEQQ = auto()
@@ -95,7 +94,7 @@ class X86GroupOp:
                 X86Ops.VPMULLW, X86Ops.VPMULLD, X86Ops.VROUNDSS, X86Ops.VROUNDSD, X86Ops.VSQRTSS, X86Ops.VSQRTSD, X86Ops.VINSERTPS,
                 X86Ops.VPINSRB, X86Ops.VPINSRW, X86Ops.VPINSRD, X86Ops.VPINSRQ, X86Ops.VPAND, X86Ops.VPOR, X86Ops.VPXOR, X86Ops.VPSLLVD,
                 X86Ops.VPSLLVQ, X86Ops.VPSRLVD, X86Ops.VPSRLVQ, X86Ops.VPSRAVD, X86Ops.CMOVNE, X86Ops.CMOVE, X86Ops.CMOVL, X86Ops.CMOVB,
-                X86Ops.VCVTSI2SS, X86Ops.VCVTSI2SD, X86Ops.VCVTSS2SD, X86Ops.VCVTSD2SS, X86Ops.VUCOMISS, X86Ops.VUCOMISD, X86Ops.IDIV, X86Ops.DIV}
+                X86Ops.VCVTSI2SS, X86Ops.VCVTSI2SD, X86Ops.VCVTSS2SD, X86Ops.VCVTSD2SS, X86Ops.IDIV, X86Ops.DIV}
 
   # X86Ops that can write to memory
   WriteMem = {X86Ops.MOVm, X86Ops.MOVi, X86Ops.VMOVSSm, X86Ops.VMOVSDm, X86Ops.VMOVUPSm, X86Ops.VMOVDm, X86Ops.VMOVQm,
@@ -110,7 +109,7 @@ class X86GroupOp:
   # X86Ops that write flags or can modify flags to undefined values
   WriteFlags = {X86Ops.CMP, X86Ops.CMPi, X86Ops.ADD, X86Ops.ADDi, X86Ops.SUB, X86Ops.SUBi, X86Ops.IMUL, X86Ops.IMULi, X86Ops.IDIV, X86Ops.DIV,
                 X86Ops.SHL, X86Ops.SHLi, X86Ops.SHR, X86Ops.SHRi, X86Ops.SAR, X86Ops.SARi, X86Ops.AND, X86Ops.ANDi, X86Ops.XOR, X86Ops.XORi,
-                X86Ops.OR, X86Ops.ORi, X86Ops.VUCOMISS, X86Ops.VUCOMISD}
+                X86Ops.OR, X86Ops.ORi}
 
   # X86Ops whose first src is the rm field
   Rm1st = ReadMem1st | (ReadMem2nd & TwoAddress) | {X86Ops.VPSRLDQ}
@@ -118,8 +117,6 @@ class X86GroupOp:
   # X86Ops whose second src is the rm field
   Rm2nd = ReadMem2nd
 
-  All = set(X86Ops)
-
 # ***** X86 legalization *****
 
 extra_matcher = PatternMatcher([
@@ -145,14 +142,14 @@ extra_matcher = PatternMatcher([
   # float16 alus are done in float32
   (UPat(GroupOp.ALU, dtypes.float16, name="x"), lambda x: UOp(x.op,
    src=tuple(s.cast(dtypes.float) if s.dtype != dtypes.bool else s for s in x.src)).cast(x.dtype)),
-  (UPat(GroupOp.Comparison, src=(UPat.var("a", dtypes.float16), UPat.var("b")), name="x"),
-   lambda x,a,b: UOp(x.op, src=(a.cast(dtypes.float32), b.cast(dtypes.float32))).cast(x.dtype)),
+  (UPat(GroupOp.Comparison, src=[UPat(dtype=dtypes.float16), UPat()], name="x"),
+   lambda x: UOp(x.op, src=tuple(s.cast(dtypes.float32) for s in x.src)).cast(x.dtype)),
   # no cmpne for packed ints, y != x => !(y==x)
   (UPat(Ops.CMPNE, src=(UPat.var("y", dtypes.ints), UPat.var("x")), name="cmp"),
    lambda y,x,cmp: UOp(Ops.CMPEQ, src=(y,x))^True if y.max_numel() > 1 else None),
-  # float WHERE needs a mask unless its comparison already has a float operand
+  # a float WHERE blends at the width of its value, so it needs a comparison at that width to make the mask
   (UPat.var("m", dtypes.bool).where(UPat.var("a", dtypes.floats+(dtypes.weakfloat,)), UPat.var("b")).named("w"),
-   lambda m,a,b,w: m.cast(w.dtype).ne(0).where(a, b) if w.dtype in dtypes.floats and not dtypes.is_float(m.src[0].dtype) else None),
+   lambda m,a,b,w: m.cast(w.dtype).ne(0).where(a, b) if w.dtype in dtypes.floats and promo_dtype(m.src) is not w.dtype else None),
   # rewrite -x -> 0 - x
   (UPat(Ops.NEG, name="x"), lambda x: UOp(Ops.SUB, src=(x.const_like(0),) + x.src)),
   # TODO: add support for mod, requires support for accessing the 2nd+ reg of a multi output instruction
@@ -177,6 +174,12 @@ def gated_store(addr:UOp, gate:UOp, val:UOp):
   sel = gate.where(addr.replace(dtype=dtypes.uint64), local.index(UOp.cconst(0, dtypes.int32), dtype=dtypes.uint64))
   return UOp(Ops.AFTER, addr.dtype, (sel,)).store(val)
 
+# a gate the flags can be picked with, or the bool compared to zero that replaces one they can't: only an integer
+# comparison sets the flags, see cmp. NOTE: the 0 is int so the bool zero-extends and compares as int (a byte compare renders
+# different kernels)
+def flag_gate(m:UOp) -> UOp|None:
+  return None if m.op in GroupOp.Comparison and m.src[0].dtype not in dtypes.floats else m.ne(UOp.cconst(0, dtypes.int))
+
 # legalize the new style graph for isel. NOTE: this runs after the spec is verified, some of these rewrites violate it
 pre_isel_matcher = PatternMatcher([
   # noop casts: zero extending scalar 32bit int, same-width signed/unsigned, narrowing scalar int
@@ -191,11 +194,9 @@ pre_isel_matcher = PatternMatcher([
   # gated load/store become a conditional move on the address, the load/store are unconditional
   (UPat((Ops.INDEX, Ops.SHRINK), name="addr").load(UPat.var("alt"), UPat.var("gate"), name="x"), gated_load),
   (UPat((Ops.INDEX, Ops.SHRINK), name="addr").store(UPat.var("val"), UPat.var("gate")), gated_store),
-  # TODO: remove this once we allow all flag producing ops in cmove
-  # if gate in scalar int cmove is not a comparison need to add one to set the flag
-  # NOTE: the 0 is int so the bool gate zero-extends and compares as int (a byte compare renders different kernels)
-  (UPat.var("m", dtypes.bool).where(UPat.var("a"), UPat.var("b")),
-   lambda m,a,b: m.ne(UOp.cconst(0, dtypes.int)).where(a,b) if m.op not in GroupOp.Comparison else None),
+  # a conditional backedge picks with the flags, and so does the cmove, which is legalized in isel
+  (UPat(Ops.END, src=(UPat(), UPat(), UPat.var("m", dtypes.bool)), name="x"),
+   lambda m,x: x.replace(src=x.src[:2]+(g,)) if (g:=flag_gate(m)) is not None else None),
 ])
 
 # ***** X86 registers *****
@@ -217,7 +218,7 @@ CALLEE_SAVED = (RBX, RBP, GPR[12], GPR[13], GPR[14], GPR[15]) + ((RSI, RDI) + XM
 
 reg_strs = {"rax": {4:"eax", 2:"ax", 1:"al"}, "rcx": {4:"ecx", 2:"cx", 1:"cl"}, "rdx": {4:"edx", 2:"dx", 1:"dl"}, "rbx": {4:"ebx", 2:"bx", 1:"bl"},
         "rsp": {4:"esp", 2:"sp", 1:"spl"}, "rbp": {4:"ebp", 2:"bp", 1:"bpl"}, "rsi": {4:"esi", 2:"si", 1:"sil"}, "rdi": {4:"edi", 2:"di", 1:"dil"},
-        **{f"r{i}": {4:f"r{i}d", 2:f"r{i}w", 1:f"r{i}b"} for i in range(8, 16)}, **{f"xmm{i}": {64:f"zmm{i}", 32:f"ymm{i}"} for i in range(16)}}
+        **{f"r{i}": {4:f"r{i}d", 2:f"r{i}w", 1:f"r{i}b"} for i in range(8, 16)}}
 
 # ***** X86 instruction selection *****
 def base(x:UOp, i:int) -> UOp: return s.src[0] if (s:=x.src[i]).op is Ops.INDEX else s
@@ -230,9 +231,10 @@ def to_imm(c:UOp) -> UOp|None:
   if c.dtype in dtypes.int64s: return imm(dtypes.int32, v.val) if not v.overflows(dtypes.int32) else None
   if c.dtype in dtypes.ints+(dtypes.bool,): return imm(c.dtype, v.val)
   return None
+# the flag path, which only an integer comparison can take: an x86 float compare sets carry, zero and parity together when an
+# operand is NaN, so a NaN reads as "below" and as "equal", and it clears sign and overflow, so nothing reads as "less"
 def cmp(x:UOp) -> UOp:
-  if x.src[0].dtype is dtypes.float32: return x.ins(X86Ops.VUCOMISS, dtype=dtypes.void)
-  if x.src[0].dtype is dtypes.float64: return x.ins(X86Ops.VUCOMISD, dtype=dtypes.void)
+  if x.src[0].dtype in dtypes.floats: raise RuntimeError(f"no flag compare for {x.src[0].dtype}, a float gate must be a mask")
   return x.ins(X86Ops.CMP, dtype=dtypes.void) if (i:=to_imm(x.src[1])) is None else x.ins(X86Ops.CMPi, dtype=dtypes.void, src=(x.src[0], i))
 def vcmp(x:UOp) -> UOp:
   v = imm(dtypes.uint8, {Ops.CMPLT: 1, Ops.CMPNE: 4, Ops.CMPEQ: 0}[x.op])
@@ -282,7 +284,7 @@ def shift(x:UOp, op:X86Ops) -> UOp:
 # it is materialized as an immediate so the address stays correct if the base register is ever spilled and refilled
 def fold_address(x:UOp) -> tuple[UOp, UOp, UOp, UOp]:
   def _disp(v:int) -> UOp: return imm(dtypes.int32 if abs(v) > dtypes.int8.max else dtypes.int8, v)
-  def _cast(v:UOp) -> UOp: return v.cast(dtypes.int64) if v.vmin < 0 else v
+  def _cast(v:UOp) -> UOp: return v.cast(dtypes.int64) if v.vmin < 0 else v.cast(dtypes.uint32) if v.dtype.itemsize < 4 else v
   if x.op not in {Ops.INDEX, Ops.SHRINK}: return (x, UOp(Ops.NOOP), _disp(0), imm(dtypes.uint8, x.dtype.itemsize))
   base, idx = x.src[0], x.src[1]
   # buffers are indexed by element, everything else (the stack pointer) by byte
@@ -350,8 +352,6 @@ def alloc_vregs(ctx:IselContext, x:UOp) -> UOp|None:
 
 isel_matcher = PatternMatcher([
   # **** Op -> Op ****
-  # cast of void is a noop
-  (UPat.var("y").cast(name="x"), lambda y,x: y if y.dtype == dtypes.void else None),
   # range is lowered to acc, cmp, jmp after regalloc
   (UPat(Ops.RANGE, src=(UPat.cvar("c").cast(),), allow_any_len=True, name="x"), lambda c,x: x.replace(src=(imm(x.dtype, c.val),) + x.src[1:])),
   (UPat(Ops.RANGE, name="x"), lambda ctx,x: x.replace(tag=(ctx.vreg(WGPR),)) if not isinstance(x.tag, tuple) else None),
@@ -382,6 +382,9 @@ isel_matcher = PatternMatcher([
   (UPat(GroupOp.Comparison, dtypes.bool, (UPat.var("y", (dtypes.float32, dtypes.float64)), UPat()), name="x"), lambda y,x:
    UOp(Ops.AND, src=(x.replace(dtype=y.dtype).bitcast(dt:=to_int(y.dtype)), UOp.cconst(1, dt))).f(Ops.NOOP, dtype=dtypes.bool)),
   # conditional moves that use flags
+  # TODO: remove this once we allow all flag producing ops in cmove
+  # the blends took every float gate a mask can serve, so a gate that is still not an integer comparison becomes one here
+  (UPat.var("m", dtypes.bool).where(UPat.var("a"), UPat.var("b")), lambda m,a,b: g.where(a, b) if (g:=flag_gate(m)) is not None else None),
   (UPat(Ops.CMPLT, src=(UPat(dtype=dtypes.sints), UPat()), name="m").where(UPat.var("a"), UPat.var("b")), lambda m,a,b:
    a.ins(X86Ops.CMOVL, src=(b, a, cmp(m)))),
   (UPat(Ops.CMPLT, name="m").where(UPat.var("a"), UPat.var("b")), lambda m,a,b: a.ins(X86Ops.CMOVB, src=(b, a, cmp(m)))),
@@ -629,9 +632,8 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
     r, _x, b = reg >> 3, idx >> 3, rm >> 3
     if sel: # VEX bytes
       vvvv = cast(Register, greg(vvvv_uop)).index if vvvv_uop is not None else 0
-      l = (max(reg_sz, rm_sz) > 16) & 0b1
-      if sel == 1 and _x == b == we == 0: inst += bytes([0xC5, (~r & 0b1) << 7 | (~vvvv & 0b1111) << 3 | l << 2 | pp])
-      else: inst += bytes([0xC4, (~r & 0b1) << 7 | (~_x & 0b1) << 6 | (~b & 0b1) << 5 | sel, we << 7 | (~vvvv & 0b1111) << 3 | l << 2 | pp])
+      if sel == 1 and _x == b == we == 0: inst += bytes([0xC5, (~r & 0b1) << 7 | (~vvvv & 0b1111) << 3 | pp])
+      else: inst += bytes([0xC4, (~r & 0b1) << 7 | (~_x & 0b1) << 6 | (~b & 0b1) << 5 | sel, we << 7 | (~vvvv & 0b1111) << 3 | pp])
     else: # optional PREFIX and REX bytes
       # PREFIX byte signaling 16 bit variant of instruction
       if sz == 2: inst += bytes([0x66])
@@ -695,7 +697,7 @@ def encode(x:UOp, opc:int, reg:int|None=None, pp:int=0, sel:int=0, we:int=0) ->
   if x.arg in X86GroupOp.Rm2nd:
     if len(x.src) > 4: address, rest = x.src[1:5], x.src[:1] + x.src[5:]
     else: address, rest = (x.src[1], None, None, None), x.src[:1] + x.src[2:]
-    # cmp/vucomiss reg, rm don't define a new register
+    # cmp reg, rm doesn't define a new register
     return _encode(x, *address, *rest) if x.dtype is not dtypes.void else _encode(rest[0], *address)
 
   return None
@@ -770,8 +772,6 @@ encodings = {
   X86Ops.VPSUBB: lambda x: encode(x, 0xF8, pp=1, sel=1), X86Ops.VPSUBW: lambda x: encode(x, 0xF9, pp=1, sel=1),
   X86Ops.VPSUBD: lambda x: encode(x, 0xFA, pp=1, sel=1), X86Ops.VPSUBQ: lambda x: encode(x, 0xFB, pp=1, sel=1),
   X86Ops.VPSRAVD: lambda x: encode(x, 0x46, pp=1, sel=2),
-  # float cmp
-  X86Ops.VUCOMISS: lambda x: encode(x, 0x2E, pp=0, sel=1), X86Ops.VUCOMISD: lambda x: encode(x, 0x2E, pp=1, sel=1),
   # scalar / packed float binary
   X86Ops.VADDSS: lambda x: encode(x, 0x58, pp=2, sel=1), X86Ops.VADDPS: lambda x: encode(x, 0x58, pp=0, sel=1),
   X86Ops.VADDSD: lambda x: encode(x, 0x58, pp=3, sel=1), X86Ops.VADDPD: lambda x: encode(x, 0x58, pp=1, sel=1),

tinygrad/runtime/ops_amd.py

@@ -8,7 +8,7 @@ from tinygrad.runtime.support.hcq import MMIOInterface, BumpAllocator, hcq_filte
 from tinygrad.uop.ops import sint
 from tinygrad.device import Compiled, BufferSpec, TinyELF
 from tinygrad.helpers import getenv, round_up, data64_le, DEBUG, PROFILE, ProfileEvent, lo32, hi32, colored, prod, ContextVar, TracingKey
-from tinygrad.helpers import VIZ, ceildiv, unwrap, pluralize
+from tinygrad.helpers import VIZ, HCQ2, ceildiv, unwrap, pluralize
 from tinygrad.renderer.cstyle import HIPRenderer, HIPCCRenderer
 from tinygrad.renderer.llvmir import AMDLLVMRenderer
 from tinygrad.runtime.autogen import kfd, hsa, sqtt, amdgpu_kd, amdgpu_drm
@@ -1153,4 +1153,4 @@ class AMDDevice(HCQCompiled):
 
   def hw_copy_queues(self): return [(f"SDMA:{i}", functools.partial(unwrap(self.hw_copy_queue_t), queue_idx=i)) for i in self.sdma_queues]
 
-if getenv("HCQ2"): from extra.hcq2.ops_amd2 import * # noqa: F401, F403 # pylint: disable=unused-import
+if HCQ2: from extra.hcq2.ops_amd2 import * # noqa: F401, F403 # pylint: disable=unused-import

tinygrad/runtime/support/hcq.py

@@ -1,24 +1,16 @@
 from __future__ import annotations
 from typing import cast, Callable, Type, TypeVar, Generic, Any
-import contextlib, decimal, statistics, time, ctypes, array, os, struct, collections, itertools
+import contextlib, decimal, statistics, time, ctypes, array, os, collections, itertools
 try: import fcntl # windows misses that
 except ImportError: fcntl = None #type:ignore[assignment]
-from tinygrad.helpers import DEV, PROFILE, getenv, to_mv, from_mv, cpu_profile, ProfileRangeEvent, unwrap
+from tinygrad.helpers import DEV, PROFILE, getenv, from_mv, cpu_profile, ProfileRangeEvent, unwrap
 from tinygrad.helpers import suppress_finalizing, pluralize, TracingKey
 from tinygrad.device import Device, BufferSpec, Compiled, LRUAllocator, ProfileDeviceEvent, ProfileProgramEvent, Program, TinyELF
 from tinygrad.uop.ops import sym_infer, sint, UOp
 from tinygrad.runtime.autogen import libc
-from tinygrad.runtime.support.memory import BumpAllocator
+from tinygrad.runtime.support.memory import BumpAllocator, MMIOInterface
 from tinygrad.renderer import Renderer
 
-class MMIOInterface:
-  def __init__(self, addr:int, nbytes:int, fmt='B'): self.mv, self.addr, self.nbytes, self.fmt = to_mv(addr, nbytes).cast(fmt), addr, nbytes, fmt
-  def __len__(self): return self.nbytes // struct.calcsize(self.fmt)
-  def __getitem__(self, k): return (self.mv[k] if self.fmt == 'B' else self.mv[k].tolist()) if isinstance(k, slice) else self.mv[k]
-  def __setitem__(self, k, v): self.mv[k] = v
-  def view(self, offset:int=0, size:int|None=None, fmt=None) -> MMIOInterface:
-    return MMIOInterface(self.addr+offset, (self.nbytes - offset) if size is None else size, fmt=fmt or self.fmt)
-
 class FileIOInterface:
   """
   Hardware Abstraction Layer for HCQ devices. The class provides a unified interface for interacting with hardware devices.

tinygrad/runtime/support/hcq2.py

@@ -1,5 +1,5 @@
 from __future__ import annotations
-from typing import cast, TypeVar, Generic, Any, Sequence, Iterable
+from typing import cast, TypeVar, Generic, Any, Sequence, Iterable, TYPE_CHECKING
 import struct, functools, time, collections, itertools, decimal, statistics
 from dataclasses import replace, dataclass, field
 from tinygrad.helpers import suppress_finalizing, dedup, pluralize, JIT_BATCH_SIZE, unwrap, PROFILE
@@ -9,11 +9,11 @@ from tinygrad.device import ProfileDeviceEvent, ProfileGraphEntry, ProfileGraphE
 from tinygrad.uop.ops import Ops, sint, UOp, UPat, PatternMatcher, KernelInfo, graph_rewrite, rewrite_group, GroupOp
 from tinygrad.uop.symbolic import symbolic
 from tinygrad.dtype import dtypes, truncate, DType
-from tinygrad.runtime.support.hcq import MMIOInterface, HCQBuffer
-from tinygrad.runtime.support.memory import BumpAllocator
+from tinygrad.runtime.support.memory import BumpAllocator, MMIOInterface
 from tinygrad.renderer import Renderer, Estimates
-from tinygrad.engine.realize import to_program, get_call_arg_uops, get_call_name, get_call_outs_ins, estimate_uop
-from tinygrad.engine.realize import pm_flatten_linear, lower_and_compile
+from tinygrad.engine.realize import to_program, get_call_arg_uops, get_call_name, get_call_outs_ins, estimate_uop, pm_flatten_linear,lower_and_compile
+
+if TYPE_CHECKING: from tinygrad.runtime.support.hcq import HCQBuffer # TODO: remove that
 
 # *****************
 # 0. helpers
@@ -168,8 +168,8 @@ class BatchCtx:
   slots:dict[str, int] = field(default_factory=lambda: collections.defaultdict(lambda: next(UOp.unique_num)))
 
 def _get_call_bufs_by_lane(call:UOp, devices:tuple[str, ...]) -> list[list[Any]]:
-  return [[b if (b:=_lane(a, lane)).op is Ops.PARAM or (b.op is Ops.MSELECT and b.src[0].op is Ops.PARAM) else b.buffer
-           for a in get_call_arg_uops(call)] for lane in range(len(devices))]
+  def dep_buf(b:UOp) -> Any: return base if (base:=(b.src[0] if b.op is Ops.MSELECT else b).base).op is Ops.PARAM else b.buffer
+  return [[dep_buf(_lane(a, lane)) for a in get_call_arg_uops(call)] for lane in range(len(devices))]
 
 def _wait_ins(ctx:BatchCtx, bufs_by_lane:list[list[Any]], write, devices:tuple[str, ...], queue:str, tag:int) -> list[UOp]:
   deps:list[Dep] = []
@@ -462,7 +462,7 @@ def hcq_lower(linear:UOp, pm_encode:PatternMatcher) -> UOp:
   linear = graph_rewrite(linear, pm_split_patches, walk=True, name="split patches")
 
   # and compile it
-  return lower_and_compile(graph_rewrite(linear, pm_replace_params, walk=True, name="replace params"))
+  with Context(EMULATED_DTYPES=""): return lower_and_compile(graph_rewrite(linear, pm_replace_params, walk=True, name="replace params"))
 
 @rewrite_group(lambda linear,input_uops,profile,ret: f"HCQ Compile {pluralize('Kernel', len(ret.src))}")
 def hcq_compile(linear:UOp, input_uops:list[UOp]|None, profile:bool) -> UOp:

tinygrad/runtime/support/memory.py

@@ -1,6 +1,15 @@
-import collections, functools, dataclasses, enum
+from __future__ import annotations
+import collections, functools, dataclasses, enum, struct
 from typing import Any, ClassVar
-from tinygrad.helpers import round_up, getenv
+from tinygrad.helpers import round_up, getenv, to_mv
+
+class MMIOInterface:
+  def __init__(self, addr:int, nbytes:int, fmt='B'): self.mv, self.addr, self.nbytes, self.fmt = to_mv(addr, nbytes).cast(fmt), addr, nbytes, fmt
+  def __len__(self): return self.nbytes // struct.calcsize(self.fmt)
+  def __getitem__(self, k): return (self.mv[k] if self.fmt == 'B' else self.mv[k].tolist()) if isinstance(k, slice) else self.mv[k]
+  def __setitem__(self, k, v): self.mv[k] = v
+  def view(self, offset:int=0, size:int|None=None, fmt=None) -> MMIOInterface:
+    return MMIOInterface(self.addr+offset, (self.nbytes - offset) if size is None else size, fmt=fmt or self.fmt)
 
 class BumpAllocator:
   def __init__(self, size:int, base:int=0, wrap:bool=True): self.size, self.ptr, self.base, self.wrap = size, 0, base, wrap

tinygrad/schedule/__init__.py

@@ -94,7 +94,7 @@ pm_post_sched_cache = PatternMatcher([
   # only resolve buffer PARAMs (slot>=0); ALU/shape vars use slot=-1 and must not be swapped for call args
   (UPat(Ops.PARAM, name="x"), lambda ctx,x: ctx[1][x.arg.slot] if x.arg.slot >= 0 else None),
   # create new BUFFERs
-  (UPat(Ops.BUFFER, src=(UPat(),), name="b"), lambda ctx,b:
+  (UPat(Ops.BUFFER, src=(), name="b"), lambda ctx,b:
    create_new_buffer(ctx, b) if isinstance(b.arg, ParamArg) and b.addrspace is AddrSpace.GLOBAL else None),
 ])
 

tinygrad/schedule/multi.py

@@ -280,13 +280,8 @@ def rewrite_into_function(call:UOp):
                            for i, s in enumerate(new_body.src)])
   return call.replace(src=(new_body,)+new_args)
 
-def param_to_multi(p:UOp):
-  if p.axis is None: return None
-  return UOp.param(p.arg.slot, p.dtype, p.shard_shape, p.device, p.arg.vmin_vmax, p.arg.multiple_of, p.arg.name, p.arg.addrspace).unshard(p.axis)
-
 # NOTE: this is the same pattern as unrolled ranges
 multi_pm = PatternMatcher([
-  (UPat(Ops.PARAM, name="p"), param_to_multi),
   (UPat(GroupOp.ALU, name="root", custom_early_reject=set([Ops.UNSHARD])), alu_multi),
   (UPat(Ops.REDUCE, src=(UPat(Ops.UNSHARD, name="multi"), ), name="root"), reduce_multi),
   (UPat(Ops.RESHAPE, src=(UPat(Ops.UNSHARD, name="multi"), UPat()), name="root"), reshape_multi),

tinygrad/schedule/prepare.py

@@ -1,7 +1,7 @@
 import itertools
 from tinygrad.dtype import dtypes, to_dtype
 from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp
-from tinygrad.uop.ops import graph_rewrite, rewrite_group, shape_to_shape_arg, ParamArg, identity_element
+from tinygrad.uop.ops import graph_rewrite, rewrite_group, ParamArg, identity_element
 from tinygrad.uop.movement import mop_cleanup
 from tinygrad.helpers import prod, getenv, all_int, DEBUG, SPLIT_REDUCEOP, OPENPILOT_HACKS, FLOAT16, argsort
 from tinygrad.schedule.indexing import apply_movement_op
@@ -100,10 +100,19 @@ def resolve_function(c:UOp, allow_param_mismatch=True) -> UOp|None:
     if [x.arg.slot for x in params] != list(range(len(params))): raise RuntimeError(f"params not in order: {[x.arg.slot for x in params]}")
     if len(params) != len(args): raise TypeError(f"expected {len(params)} args, got {len(args)}")
 
+  # params have a flat storage size in the arg, the logical shape is a view (RESHAPE/SHRINK/UNSHARD) on top of it.
+  # substitute args by their flat max-shaped storage view so the movement views on the params stay valid
+  def flat_storage(a:UOp) -> tuple[int, UOp]:  # returns (size, view of a as flat max-shaped storage)
+    shp = a.max_shard_shape if a.axis is not None and isinstance(a.device, tuple) else a.max_shape
+    return (n:=prod(shp)), a if a.shape == (n,) else a.pad_to(shp).reshape((n,))
   dict_map = {x:args[x.arg.slot] for x in params}
   for i, (p, a) in enumerate(dict_map.items()):
-    if p.axis != a.axis: raise TypeError(f"arg {i} axis mismatch: expected {p.axis}, got {a.axis}")
-    if p.max_shape != a.max_shape: raise TypeError(f"arg {i} shape mismatch: expected {p.shape}, got {a.shape}")
+    if p.arg.size is not None:
+      n, flat = flat_storage(a)
+      if p.arg.size != n: raise TypeError(f"arg {i} shape mismatch: expected size {p.arg.size}, got {a.shape}")
+      dict_map[p] = flat
+    elif a.shape != ():
+      raise TypeError(f"arg {i} shape mismatch: expected scalar, got {a.shape}")
     if p.dtype != a.dtype: raise TypeError(f"arg {i} dtype mismatch: expected {p.dtype}, got {a.dtype}")
   return c.src[0].substitute(dict_map, walk=True)
 
@@ -192,9 +201,9 @@ def convert_copy_to_store(ctx, copy:UOp, existing_buf:UOp|None=None):
     # if there's already a buffer, we just use it
     return existing_buf.flatten().store(input_src)
   # create the output buffer
-  buf = UOp(Ops.BUFFER, src=(shape_to_shape_arg(input_src.max_shape),), arg=ParamArg(next(ctx), copy.dtype, device=copy.device))
+  buf = UOp(Ops.BUFFER, arg=ParamArg(next(ctx), copy.dtype, size=prod(input_src.max_shape), device=copy.device))
   # reshape back to input
-  return buf.after(buf.store(input_src)).reshape(copy.shape)
+  return buf.reshape(input_src.max_shape).after(buf.store(input_src)).reshape(copy.shape)
 
 pm_copy_to_store = PatternMatcher([
   (UPat(name="existing_buf").store(UPat(Ops.COPY, name="copy")), convert_copy_to_store),

tinygrad/schedule/rangeify.py

@@ -2,7 +2,7 @@ from dataclasses import dataclass, field, replace
 from typing import cast
 import itertools
 from tinygrad.dtype import dtypes, AddrSpace, Invalid
-from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, KernelInfo, ParamArg, shape_to_shape_arg
+from tinygrad.uop.ops import PatternMatcher, UPat, Ops, UOp, resolve, GroupOp, KernelInfo, ParamArg
 from tinygrad.uop.ops import graph_rewrite, sint, AxisType, BottomUpGate, rewrite_group
 from tinygrad.uop.symbolic import symbolic
 from tinygrad.helpers import prod, dedup, DEBUG_RANGEIFY, VIZ, MAX_KERNEL_BUFFERS, SPEC
@@ -162,6 +162,12 @@ pm_no_indexing_calls = PatternMatcher([
   (UPat(Ops.CALL, name="u"), no_indexing_calls),
 ])
 
+# the kernel graph is what gets executed: no shape views left in it, the storage of a value is just the storage
+pm_no_views = PatternMatcher([
+  (UPat((Ops.RESHAPE, Ops.SHRINK), name="v", src=(UPat((Ops.AFTER, Ops.PARAM, Ops.UNSHARD, Ops.MSTACK, Ops.BUFFER)),), allow_any_len=True), lambda v:
+   v.src[0]),
+])
+
 DEVICE_MAX_BUFS = {"METAL": 31, "WEBGPU": 8, "CPU": 31} # TODO: get from device?
 @dataclass
 class LimitBufsContext:
@@ -223,7 +229,7 @@ def bufferize_to_store(ctx:itertools.count, x:UOp, idx:UOp, allow_locals=True):
 
   # NOTE: the local BUFFER needs to be disambiguated here
   if x.arg.addrspace == AddrSpace.GLOBAL:
-    buf = UOp(Ops.BUFFER, src=(shape_to_shape_arg((size,)),), arg=ParamArg(next(ctx), x.dtype, device=x.arg.device, addrspace=AddrSpace.GLOBAL))
+    buf = UOp(Ops.BUFFER, arg=ParamArg(next(ctx), x.dtype, size=size, device=x.arg.device, addrspace=AddrSpace.GLOBAL))
     do_store = buf.index(idx).store(x.src[0]).end(*rngs)
     return buf.after(do_store)
 
@@ -282,8 +288,7 @@ class LocalAddBufferContext:
 def debuf(ctx:LocalAddBufferContext, buf:UOp):
   # Variables (ALU buffers with a value range) are scalar symbolic values, not real buffers: they become ALU params with no slot
   if buf.is_variable: return buf.replace(op=Ops.PARAM)
-  param = UOp(Ops.PARAM, src=(UOp.const(prod(buf.max_shape)),),
-              arg=ParamArg(ctx.dg, buf.dtype, addrspace=buf.addrspace, device=buf.device))
+  param = UOp(Ops.PARAM, arg=ParamArg(ctx.dg, buf.dtype, prod(buf.max_shape), addrspace=buf.addrspace, device=buf.device))
   ret = param.reshape(buf.max_shape)
   # if the buffer has symbolic shape, shrink the max-sized view to the actual shape
   if buf.max_shape != buf.shape: ret = ret.shrink(tuple((0, s) for s in buf.shape))
@@ -383,6 +388,7 @@ def get_kernel_graph(tsink:UOp) -> UOp:
   tsink = graph_rewrite(tsink, pm_add_buffers+pm_add_param_range_tags, ctx=itertools.count(paramarg_start), bottom_up=True, name="stage to store")
   tsink = graph_rewrite(tsink, split_kernels, bottom_up=True, name="split kernels")
   tsink = graph_rewrite(tsink, pm_no_indexing_calls, name="remove indexing from call args")
+  tsink = graph_rewrite(tsink, pm_no_views, name="remove views from the kernel graph")
 
   if VIZ: graph_rewrite(tsink, PatternMatcher([]), name="View Kernel Graph")
   if SPEC:

tinygrad/tensor.py

@@ -198,9 +198,7 @@ def finalize_after(ctx:AllocCtx, x:UOp):
 
 def replace_input_buffer(ctx:AllocCtx, b:UOp):
   ctx.replacements.append(b)
-  if b.is_bound_var or b.is_variable: return b.param_like(len(ctx.replacements)-1)
-  return UOp.param(len(ctx.replacements)-1, b.dtype, b.shape, b.device,
-                   addrspace=b.addrspace if b.addrspace is not None else AddrSpace.GLOBAL)
+  return b.param_like(len(ctx.replacements)-1)
 
 def replace_input_view(ctx:AllocCtx, b:UOp): return replace_input_buffer(ctx, b) if b in ctx.views else None
 
@@ -211,7 +209,7 @@ pm_finalize_call = PatternMatcher([
 
 pm_replace_buf = PatternMatcher([
   # replace BUFFER with PARAM for cache key normalization
-  (UPat(Ops.BUFFER, src=(UPat(),), name="b"), lambda ctx,b:
+  (UPat(Ops.BUFFER, src=(), name="b"), lambda ctx,b:
    replace_input_buffer(ctx, b) if isinstance(b.arg, ParamArg) and b.addrspace is AddrSpace.GLOBAL else None),
   # replace SHRINK with PARAM
   (UPat(Ops.SHRINK, src=(UPat(Ops.BUFFER),), name="b", allow_any_len=True), replace_input_view),
@@ -381,7 +379,7 @@ class Tensor(RandMixin):
   # ***** data handlers ****
 
   def as_param(self, slot:int):
-    return Tensor(UOp.param(slot, self.dtype, self.uop.shard_shape, self.device, axis=self.uop.axis))
+    return Tensor(self.uop.param_like(slot))
 
   def call(self, *lst:Tensor, fxn:Tensor|UOp, grad_fxn:Callable|None=None) -> Tensor:
     fret = fxn._uop.call(*[t.uop for t in (self,)+lst], grad_fxn=grad_fxn)

tinygrad/uop/ops.py

@@ -23,17 +23,21 @@ class AxisType(Enum):
 class ParamArg:
   slot: int
   dtype: DType
+  # number of elements in the buffer. always a concrete int (never symbolic), None for scalars (shape ())
+  size: int|None = None
   vmin_vmax: tuple[PyConst, PyConst]|None = None
   multiple_of: int|None = None
   name: str|None = None
   addrspace: AddrSpace|None = AddrSpace.GLOBAL
-  axis: int|None = None
   device: str|tuple[str, ...]|None = None
   volatile: bool = False
+  # (h, w) if this is an image2d buffer, then size == h*w*4
+  image: tuple[int, int]|None = None
   def __repr__(self):
-    fields = (("vmin_vmax", None), ("multiple_of", None), ("name", None), ("addrspace", AddrSpace.GLOBAL), ("axis", None), ("device", None),
-              ("volatile", False))
-    args = [repr(self.slot), repr(self.dtype)] + [f"{k}={v!r}" for k,default in fields if (v:=getattr(self, k)) != default]
+    fields = (("vmin_vmax", None), ("multiple_of", None), ("name", None), ("addrspace", AddrSpace.GLOBAL), ("device", None),
+              ("volatile", False), ("image", None))
+    args = [repr(self.slot), repr(self.dtype)] + ([repr(self.size)] if self.size is not None else []) + \
+      [f"{k}={v!r}" for k,default in fields if (v:=getattr(self, k)) != default]
     return f"ParamArg({', '.join(args)})"
 axis_letters = {AxisType.DEVICE: "d", AxisType.GLOBAL: "g", AxisType.THREAD: "t", AxisType.LOCAL: "l", AxisType.WARP: "w", AxisType.WEAK: "L",
                 AxisType.LOOP: "L", AxisType.UPCAST: "u", AxisType.GROUP_REDUCE: "G", AxisType.REDUCE: "R", AxisType.UNROLL: "r"}
@@ -381,9 +385,10 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
       case Ops.GETADDR: return ()
       case Ops.RANGE | Ops.SPECIAL: return ()
       case Ops.BINARY: return (len(self.arg),)
-      case Ops.BUFFER:
-        if len(self.src): return self.src[0].as_shape
-        return ()
+      case Ops.BUFFER | Ops.PARAM:
+        # PARAM/BUFFER don't have a shape input, they have a size in the arg: int gives shape (size,), None gives ()
+        if (img:=self.arg.image) is not None: return (img[0], img[1], 4)
+        return () if self.arg.size is None else (self.arg.size,)
       case Ops.CUSTOM | Ops.CUSTOMI:
         if self.dtype is dtypes.void: return None
         input_shapes = [x._shape for x in self.src if x._shape is not None]
@@ -394,10 +399,6 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
         # STAGE adds the existing shape to the front, opposite of INDEX
         return tuple([int(r.vmax+1) for r in self.src[1:]])+self.src[0].shape
 
-      # param has shape as the only arg
-      case Ops.PARAM:
-        return self.src[0].as_shape
-
       # wmma output shape = accumulator shape (src[2])
       case Ops.WMMA:
         wmma_b = _broadcast_shape(self.src[0].shape[:-1], self.src[1].shape[:-1], self.src[2].shape[:-1])
@@ -708,7 +709,7 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
     if self.op is Ops.GETTUPLE:
       in_tuple = self.src[0].src[0] if self.src[0].op is Ops.FUNCTION else self.src[0]
       return in_tuple.src[self.arg].axis if in_tuple.op is Ops.TUPLE else None
-    if self.op is Ops.PARAM: return self.arg.axis
+    if self.op is Ops.PARAM: return None
     # NOTE: they all have to share an axis, we always choose [-1]. src axes are right-aligned into the output shape
     if self.op in GroupOp.ALU.union({Ops.STACK}):
       return axes[-1] if (axes := dedup([x.axis+len(self.shape)-len(x.shape) for x in self.src if x.axis is not None])) else None
@@ -821,8 +822,9 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
   @staticmethod
   def new_buffer(device:str|tuple[str, ...], size:int, dtype:DType, num=None):
     if dtype in dtypes.weaks: raise RuntimeError(f"cannot create storage for weak dtype {dtype}")
+    assert isinstance(size, int), f"new_buffer size must be a concrete int, got {size}"
     slot = next(UOp.unique_num) if num is None else num
-    return UOp(Ops.BUFFER, src=(shape_to_shape_arg((size,)),), arg=ParamArg(slot, dtype, device=device))
+    return UOp(Ops.BUFFER, arg=ParamArg(slot, dtype, size=size, device=device))
   @staticmethod
   def from_buffer(opaque:Buffer, device:str|tuple[str, ...]|None=None):
     if (uop:=UOp.new_buffer(device or opaque.device, opaque.size, opaque.dtype, num=-id(opaque))) not in buffers: buffers[uop] = opaque.ref(1)
@@ -978,7 +980,7 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
     # a Variable is a 0-d BUFFER in the ALU addrspace; binding it is storing a CONST into it
     # param=True creates the kernel-side form directly: an ALU PARAM (what the BUFFER becomes inside kernels)
     arg = ParamArg(-1, dtype, name=name, vmin_vmax=(min_val, max_val), multiple_of=multiple_of, addrspace=AddrSpace.ALU)
-    return UOp(Ops.PARAM if param else Ops.BUFFER, src=(shape_to_shape_arg(()),), arg=arg)
+    return UOp(Ops.PARAM if param else Ops.BUFFER, arg=arg)
   @property
   def is_variable(self) -> bool:
     # a Variable is a 0-d BUFFER in the ALU addrspace that carries a value range (it becomes a PARAM inside kernels)
@@ -1148,11 +1150,11 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
     dtype = strong_dtype(dtype)  # storage is never weak: a placeholder commits the width of what's put in it
     if slot is None: slot = next(UOp.unique_num)
     if addrspace is AddrSpace.GLOBAL:
-      ret = UOp(Ops.PARAM, src=(shape_to_shape_arg((prod(shape),)),), arg=ParamArg(slot, dtype, addrspace=addrspace, device=device,volatile=volatile))
+      ret = UOp(Ops.PARAM, arg=ParamArg(slot, dtype, size=prod(shape), addrspace=addrspace, device=device, volatile=volatile))
     else:
       assert addrspace in (AddrSpace.LOCAL, AddrSpace.REG)
       assert device is None, "LOCAL and REG placeholders cannot have a device"
-      ret = UOp(Ops.BUFFER, src=(shape_to_shape_arg((prod(shape),)),), arg=ParamArg(slot, dtype, addrspace=addrspace))
+      ret = UOp(Ops.BUFFER, arg=ParamArg(slot, dtype, size=prod(shape), addrspace=addrspace))
     if tag is not None: ret = ret.rtag(tag)
     if len(shape) > 1: ret = ret.reshape(shape)
     return ret
@@ -1167,20 +1169,34 @@ class UOp(RandMixin, metaclass=UOpMetaClass):
   # TODO: this should replace placeholder
   @staticmethod
   def param(slot:int, dtype:DType, shape:tuple[sint, ...]|sint|None=None, device=None, vmin_vmax:tuple[PyConst, PyConst]|None=None,
-            multiple_of:int|None=None, name=None, addrspace=AddrSpace.GLOBAL, axis:int|None=None, volatile:bool=False):
+            multiple_of:int|None=None, name=None, addrspace=AddrSpace.GLOBAL, volatile:bool=False):
+    """create a PARAM: a single sint or 1-d shape gives a flat param of that size, a None shape gives a scalar param.
+    the arg only stores the concrete max size (never symbolic): a multi-dim shape is a RESHAPE on top of the flat param,
+    a symbolic shape is a max-size param shrunk to the real shape"""
     if dtype in dtypes.weaks: raise RuntimeError(f"cannot create param for weak dtype {dtype}")
     if isinstance(shape, (int, UOp)): shape = (shape,)
-    if shape is not None and axis is not None and isinstance(device, tuple):
-      shape = tuple(s*len(device) if i == axis else s for i,s in enumerate(shape))
-    src: tuple[UOp, ...] = (UOp(Ops.NOOP) if shape is None else shape_to_shape_arg(shape),)
-    return UOp(Ops.PARAM, src=src, arg=ParamArg(slot, dtype, vmin_vmax, multiple_of, name, addrspace, axis, device, volatile))
+    if shape is None or len(shape) == 0:
+      return UOp(Ops.PARAM, arg=ParamArg(slot, dtype, None, vmin_vmax, multiple_of, name, addrspace, device, volatile))
+    max_shape = to_max_shape(shape)
+    ret = UOp(Ops.PARAM, arg=ParamArg(slot, dtype, prod(max_shape), vmin_vmax, multiple_of, name, addrspace, device, volatile))
+    return ret.view_as(shape)
   def param_like(self, slot:int):
     # Variables become ALU params in the call body; the stored value (if bound) stays in the call args
     if self.is_bound_var or self.is_variable:
       b = self.src[0] if self.op is Ops.AFTER else self
-      return UOp(Ops.PARAM, src=b.src, arg=replace(b.arg, slot=slot, name=f"p{slot}"))
+      return UOp(Ops.PARAM, arg=replace(b.arg, slot=slot, name=f"p{slot}"))
     addrspace = self.addrspace if self.addrspace is not None else AddrSpace.GLOBAL
-    return UOp.param(slot, self.dtype, self.shard_shape if self.axis is not None else self._shape, self.device, addrspace=addrspace, axis=self.axis)
+    # multi-device values become a per-shard sized param wrapped in UNSHARD: the sharding lives in the graph, not the arg
+    if self.axis is not None and isinstance(self.device, tuple):
+      return UOp(Ops.PARAM, arg=ParamArg(slot, self.dtype, prod(to_max_shape(self.shard_shape)),
+                                         addrspace=addrspace, device=self.device)).view_as(self.shard_shape, self.axis)
+    return UOp.param(slot, self.dtype, self._shape, self.device, addrspace=addrspace)
+  def view_as(self:UOp, shape:tuple[sint, ...], axis:int|None=None) -> UOp:
+    """view flat storage as the given (possibly symbolic) shape, optionally sharded on axis, the UNSHARD gives back the multiplied shape"""
+    max_shape = to_max_shape(shape)
+    ret = self.reshape(max_shape) if len(shape) > 1 else self
+    if tuple(max_shape) != tuple(shape): ret = ret.shrink_to(shape)
+    return ret if axis is None else ret.unshard(axis)
 
   @staticmethod
   def custom_function(name:str, *src:UOp) -> UOp: return UOp(Ops.CUSTOM_FUNCTION, src=src, arg=name)

tinygrad/uop/render.py

@@ -84,7 +84,7 @@ pm_pyrender_extra = PatternMatcher([
   (UPat(Ops.CONST, src=(), name="x"), lambda x: f"UOp.const({x.val})"),
   (UPat((Ops.CAST, Ops.BITCAST), name="x"), lambda ctx,x: f"{ctx[x.src[0]]}.{x.op.name.lower()}({x.dtype})" if x.dtype != x.src[0].dtype else None),
   (UPat(Ops.SPECIAL, src=(UPat(Ops.CONST),), name="x"), lambda x: f"UOp.special({x.src[0].val}, {repr(x.arg)})"),
-  (UPat(Ops.BUFFER, src=(UPat(),), name="x"), lambda x:
+  (UPat(Ops.BUFFER, src=(), name="x"), lambda x:
     f"UOp.new_buffer({repr(x.arg.device)}, {x.max_numel()}, {x.dtype}, {x.arg.slot})"
     if isinstance(x.arg, ParamArg) and x.addrspace is AddrSpace.GLOBAL else None),
   (UPat(Ops.COPY, src=(UPat(name="x"),), name="copy"), lambda ctx,x,copy: f"{ctx[x]}.copy_to_device({repr(copy.arg)})"),

tinygrad/uop/spec.py

@@ -87,10 +87,9 @@ spec_shared = PatternMatcher([
   # a loop-ended END requires a trailing bool condition for the backedge (loop again while true)
   (UPat(Ops.END, src=(UPat(), UPat(Ops.RANGE, dtypes.void), UPat(dtype=dtypes.bool))), lambda: True),
 
-  # PARAM
-  (UPat(Ops.PARAM, name="x"), lambda x: isinstance(x.arg, ParamArg)),
-  (UPat(Ops.BUFFER, src=(UPat(),), name="x"), lambda x:
-   isinstance(x.arg, ParamArg) and x.addrspace in (AddrSpace.REG, AddrSpace.LOCAL)),
+  # PARAM/BUFFER have a size in the arg, no shape input
+  (UPat(Ops.PARAM, src=(), name="x"), lambda x: isinstance(x.arg, ParamArg)),
+  (UPat(Ops.BUFFER, src=(), name="x"), lambda x: isinstance(x.arg, ParamArg) and x.addrspace in (AddrSpace.REG, AddrSpace.LOCAL)),
 
   # GROUP of stores (or groups, or NOOPs)
   (UPat(Ops.GROUP, dtypes.void, src=UPat((Ops.GROUP, Ops.STORE, Ops.NOOP, Ops.INS, Ops.END))), lambda: True),
@@ -141,12 +140,12 @@ spec_tensor = PatternMatcher([
    lambda u: dtypes.is_float(u.dtype) or u.src[0].base.is_invalid),
 
   # BUFFER
-  (UPat(Ops.BUFFER, src=(UPat(),), name="buf"), lambda buf:
-   (isinstance(buf.dtype, DType) and matches_dtype(buf.src[0], dtypes.weakint) and is_device(buf.arg.device))
+  (UPat(Ops.BUFFER, src=(), name="buf"), lambda buf:
+   (isinstance(buf.dtype, DType) and isinstance(buf.arg.size, int) and is_device(buf.arg.device))
    if isinstance(buf.arg, ParamArg) and buf.addrspace is AddrSpace.GLOBAL else None),
 
   # a Variable is a 0-d ALU BUFFER with a value range and no device
-  (UPat(Ops.BUFFER, src=(UPat(),), name="buf"), lambda buf: buf.arg.device is None if buf.is_variable else None),
+  (UPat(Ops.BUFFER, src=(), name="buf"), lambda buf: buf.arg.device is None if buf.is_variable else None),
 
   # custom function
   (UPat(Ops.CUSTOM_FUNCTION, name="x"), lambda x: isinstance(x.arg, str)),
@@ -229,7 +228,8 @@ spec_program = PatternMatcher([
 ])+spec_shared
 
 spec_hcq = PatternMatcher([
-  (UPat(Ops.GETADDR, dtypes.uint64, src=(UPat((Ops.BUFFER, Ops.PARAM)).or_after(),), name="x"), lambda x: is_device(x.arg)),
+  (UPat(Ops.GETADDR, dtypes.uint64, src=(UPat((Ops.BUFFER, Ops.PARAM, Ops.SHRINK, Ops.BITCAST, Ops.MSTACK)).or_after(),), name="x"),
+   lambda x: is_device(x.arg)),
   (UPat(Ops.PROGRAM, dtypes.void, src=(UPat((Ops.BUFFER, Ops.PARAM)).or_after(),)), lambda: True),
 ])+spec_shared
 
@@ -260,11 +260,10 @@ spec_kernel_graph = PatternMatcher([
   (UPat(Ops.STACK, name="s"), lambda s: all(x.op in (Ops.CONST, Ops.PARAM) or x.is_variable or x.is_bound_var for x in s.src) or None),
   # linear for more kernels (TODO: we should enter non sink calls)
   #(UPat(Ops.LINEAR), lambda: True),
-  # param is outside buffer, buffer is local buffer
-  (UPat(Ops.PARAM, name="x"), lambda x: isinstance(x.arg, ParamArg)),
+  # param is outside buffer, buffer is local buffer. params have a size in the arg, no shape input
+  (UPat(Ops.PARAM, src=(), name="x"), lambda x: isinstance(x.arg, ParamArg)),
   (UPat(Ops.BUFFER, name="x"), lambda x: isinstance(x.arg, ParamArg) and x.addrspace in (AddrSpace.GLOBAL, AddrSpace.ALU)),
-  # RESHAPE/BITCAST are NOOPs in the kernel graph (do we need them?)
-  (UPat((Ops.RESHAPE, Ops.BITCAST)), lambda: True),
+  (UPat(Ops.BITCAST), lambda: True),
   # mstack/mselect
   (UPat(Ops.MSTACK, name="x"), lambda x: all(isinstance(s.device, str) for s in x.src) or (all_same(x.src) and x.src[0].device is None)),
   (UPat(Ops.MSELECT, name="x"), lambda x: isinstance(x.src[0].device, tuple) and x.arg < len(x.src[0].device)),