Uploaded using `kernel-builder`.

by kernels-bot - opened 10 days ago

base: refs/heads/main

←

from: refs/pr/7

Discussion Files changed

+35

-44

Files changed (8) hide show

README.md +3 -0
build/torch-cuda/_ops.py +8 -2
build/torch-cuda/esmfold2_trimul/__init__.py +0 -26
build/torch-cuda/fused_ln_residual.py +6 -2
build/torch-cuda/layers.py +2 -0
build/torch-cuda/metadata.json +9 -8
build/torch-cuda/metadata.json.sigstore +1 -1
build/torch-cuda/trimul_with_residual.py +6 -5

README.md CHANGED Viewed

@@ -21,6 +21,9 @@ ESMFold2TriangleMultiplication(...)
 - `ESMFold2TriangleMultiplication`
 - `triangle_multiplicative_update_with_residual`
 ## Benchmarks
 No benchmark available yet.

 - `ESMFold2TriangleMultiplication`
 - `triangle_multiplicative_update_with_residual`
+## Available layers
+- `ESMFold2TriangleMultiplication`
 ## Benchmarks
 No benchmark available yet.

build/torch-cuda/_ops.py CHANGED Viewed

@@ -4,7 +4,13 @@ def get_backend() -> str:
     """Detect the backend by inspecting torch."""
     import torch
-    if hasattr(torch, "neuron"):
         # Needs to be sorted before specific Torch builds, since Neuron
         # extension can be loaded into e.g. CUDA Torch builds.
         return "neuron"
@@ -22,7 +28,7 @@ def get_backend() -> str:
 def _find_ops_name() -> str:
     kernel_name = "esmfold2_trimul"
-    unique_id = "f3cf427"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

     """Detect the backend by inspecting torch."""
     import torch
+    if hasattr(torch.backends, "tpu"):
+        # torch_tpu sets torch.backends.tpu when it is imported (via
+        # torch's device-backend autoload), regardless of whether TPU
+        # hardware is present — analogous to torch.version.cuda being
+        # set on CUDA builds without a GPU.
+        return "tpu"
+    elif hasattr(torch, "neuron"):
         # Needs to be sorted before specific Torch builds, since Neuron
         # extension can be loaded into e.g. CUDA Torch builds.
         return "neuron"
 def _find_ops_name() -> str:
     kernel_name = "esmfold2_trimul"
+    unique_id = "cafe8d5"
     backend = get_backend()
     return f"_{kernel_name}_{backend}_{unique_id}"

build/torch-cuda/esmfold2_trimul/__init__.py DELETED Viewed

@@ -1,26 +0,0 @@
-import ctypes
-import importlib.util
-import sys
-from pathlib import Path
-from types import ModuleType
-def _import_from_path(file_path: Path) -> ModuleType:
-    # We cannot use the module name as-is, after adding it to `sys.modules`,
-    # it would also be used for other imports. So, we make a module name that
-    # depends on the path for it to be unique using the hex-encoded hash of
-    # the path.
-    path_hash = "{:x}".format(ctypes.c_size_t(hash(file_path.absolute())).value)
-    module_name = path_hash
-    spec = importlib.util.spec_from_file_location(module_name, file_path)
-    if spec is None:
-        raise ImportError(f"Cannot load spec for {module_name} from {file_path}")
-    module = importlib.util.module_from_spec(spec)
-    if module is None:
-        raise ImportError(f"Cannot load module {module_name} from spec")
-    sys.modules[module_name] = module
-    spec.loader.exec_module(module)  # type: ignore
-    return module
-globals().update(vars(_import_from_path(Path(__file__).parent.parent / "__init__.py")))

build/torch-cuda/fused_ln_residual.py CHANGED Viewed

@@ -60,7 +60,9 @@ def _ln_fwd_kernel(
     TILE_M: tl.constexpr,
 ):
     pid = tl.program_id(axis=0).to(tl.int64)
-    M64 = M.to(tl.int64)
     offs_m = pid * TILE_M + tl.arange(0, TILE_M).to(tl.int64)
     offs_d = tl.arange(0, D).to(tl.int64)
@@ -108,7 +110,9 @@ def _ln_bwd_kernel(
     TILE_M: tl.constexpr,
 ):
     pid = tl.program_id(axis=0).to(tl.int64)
-    M64 = M.to(tl.int64)
     offs_m = pid * TILE_M + tl.arange(0, TILE_M).to(tl.int64)
     offs_d = tl.arange(0, D).to(tl.int64)

     TILE_M: tl.constexpr,
 ):
     pid = tl.program_id(axis=0).to(tl.int64)
+    # tl.cast, not M.to: M is a plain int when Triton specializes it (M == 1),
+    # and when torch.compile analyzes the kernel.
+    M64 = tl.cast(M, tl.int64)
     offs_m = pid * TILE_M + tl.arange(0, TILE_M).to(tl.int64)
     offs_d = tl.arange(0, D).to(tl.int64)
     TILE_M: tl.constexpr,
 ):
     pid = tl.program_id(axis=0).to(tl.int64)
+    # tl.cast, not M.to: M is a plain int when Triton specializes it (M == 1),
+    # and when torch.compile analyzes the kernel.
+    M64 = tl.cast(M, tl.int64)
     offs_m = pid * TILE_M + tl.arange(0, TILE_M).to(tl.int64)
     offs_d = tl.arange(0, D).to(tl.int64)

build/torch-cuda/layers.py CHANGED Viewed

@@ -33,6 +33,8 @@ def _bf16(t):
 class ESMFold2TriangleMultiplication(nn.Module):
     def forward(self, pair_grid: torch.Tensor, visibility: torch.Tensor | None = None) -> torch.Tensor:
         lat = self.dim
         pb = self.proj_bundle.weight

 class ESMFold2TriangleMultiplication(nn.Module):
+    can_torch_compile = True
     def forward(self, pair_grid: torch.Tensor, visibility: torch.Tensor | None = None) -> torch.Tensor:
         lat = self.dim
         pb = self.proj_bundle.weight

build/torch-cuda/metadata.json CHANGED Viewed

@@ -1,9 +1,11 @@
 {
   "name": "esmfold2-trimul",
-  "id": "_esmfold2_trimul_cuda_f3cf427",
   "version": 1,
   "license": "Apache-2.0",
   "python-depends": [],
   "backend": {
     "type": "cuda"
   },
@@ -11,23 +13,22 @@
     "algorithm": "sha256",
     "files": {
       "__init__.py": "QEOcPj5Hqkgyj1VMevVV+y5HuW6RfuNgx1ssRFeMqb8=",
-      "_ops.py": "/4Wxb/Y81Pq33vEqjfD22UtiVsXwYag7k/twtMh83js=",
-      "esmfold2_trimul/__init__.py": "DFYPlrhXwYjEqCl/8n0SmWGZV8NFml5DPhMjKfv98GY=",
       "fused_dual_gemm.py": "5wIgwoeeDYcMG4yeKfkxDS1nhxVpDc8Nxx1wo32u+Kk=",
-      "fused_ln_residual.py": "6GsiK/wIdtf7Lf8gGdLDrudzyIQjIM8O4B3+bD3Xk6s=",
-      "layers.py": "iDMKA5XPxtPYl4KXDAPVF2O3SkVTvTPipHjxPmmdTyc=",
       "trimul_einsum_triton.py": "77kcCDRV/eHG/LoycgXMvoc0UsAOcgG/+j8E4DiwcTw=",
-      "trimul_with_residual.py": "gxqMyKd488c9HJqu9z9WIJ0oxeMz7cEwbKp4cdmGzMI="
     }
   },
   "provenance": {
     "kernel-builder": {
       "version": "0.17.0-dev0",
-      "sha": "81580bb92577f2f7228661ca2a221fc052375709",
       "dirty": false
     },
     "kernel": {
-      "sha": "f3cf4277a3fb75e04f9a9594b4abf8edf94ddd3d",
       "dirty": false
     }
   }

 {
   "name": "esmfold2-trimul",
+  "id": "_esmfold2_trimul_cuda_cafe8d5",
   "version": 1,
+  "kernels-minver": "0.14.0",
   "license": "Apache-2.0",
   "python-depends": [],
+  "kernel-depends": [],
   "backend": {
     "type": "cuda"
   },
     "algorithm": "sha256",
     "files": {
       "__init__.py": "QEOcPj5Hqkgyj1VMevVV+y5HuW6RfuNgx1ssRFeMqb8=",
+      "_ops.py": "2/hlli9ZZts2pwZsCA8kTb1G8ArN/vNtDrOaqaDlnbQ=",
       "fused_dual_gemm.py": "5wIgwoeeDYcMG4yeKfkxDS1nhxVpDc8Nxx1wo32u+Kk=",
+      "fused_ln_residual.py": "inG8a2sHTGrIJSXH8bB10zzNOyaMtLmjAMlc/e6vSlc=",
+      "layers.py": "UuuCrpDruJguKKaruy3Btgn5Q7jH/JwkjwB81zWNT6A=",
       "trimul_einsum_triton.py": "77kcCDRV/eHG/LoycgXMvoc0UsAOcgG/+j8E4DiwcTw=",
+      "trimul_with_residual.py": "KutJ8jQf2f4ngrXNVfl52bRedkqYWyIvySpIoD5RKWk="
     }
   },
   "provenance": {
     "kernel-builder": {
       "version": "0.17.0-dev0",
+      "commit": "a7f0afdb29a6a3372b1d47180cc0c182454c5e3b",
       "dirty": false
     },
     "kernel": {
+      "commit": "cafe8d54340b6a2e0466a13ef60986d05193f9cb",
       "dirty": false
     }
   }

build/torch-cuda/metadata.json.sigstore CHANGED Viewed

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build/torch-cuda/trimul_with_residual.py CHANGED Viewed

@@ -76,8 +76,10 @@ def _gated_gemm_with_residual_kernel(
     M,
     N,
     K,
-    STRIDE_B: tl.constexpr,  # = N_ROW * N_COL  (for decoding b from m)
-    N_COL: tl.constexpr,  # = L_col  (for decoding j from m)
     TILE_M: tl.constexpr,
     TILE_N: tl.constexpr,
     TILE_K: tl.constexpr,
@@ -215,8 +217,8 @@ def _gated_gemm_with_residual_backward_kernel(
     M,
     N,
     K,
-    STRIDE_B: tl.constexpr,
-    N_COL: tl.constexpr,
     TILE_M: tl.constexpr,
     TILE_N: tl.constexpr,
     TILE_K: tl.constexpr,
@@ -531,7 +533,6 @@ def _gated_gemm_with_residual(
     )
-@torch._dynamo.disable
 def triangle_multiplicative_update_with_residual(
     pair: torch.Tensor,
     direction: str,

     M,
     N,
     K,
+    # Runtime args, not constexpr: both scale with sequence length, so constexpr
+    # would recompile the kernel (and torch.compile the graph) per distinct L.
+    STRIDE_B,  # = N_ROW * N_COL  (for decoding b from m)
+    N_COL,  # = L_col  (for decoding j from m)
     TILE_M: tl.constexpr,
     TILE_N: tl.constexpr,
     TILE_K: tl.constexpr,
     M,
     N,
     K,
+    STRIDE_B,  # runtime, not constexpr: see the forward kernel
+    N_COL,
     TILE_M: tl.constexpr,
     TILE_N: tl.constexpr,
     TILE_K: tl.constexpr,
     )
 def triangle_multiplicative_update_with_residual(
     pair: torch.Tensor,
     direction: str,