Merge OpenAI Triton commit 78c8054

python/test/unit/language/test_core.py

@@ -1490,18 +1490,30 @@ def kernel(X):
                           for shape in [(2, 2), (2, 8), (8, 2), (8, 8), (32, 32), (64, 64)]
                           for axis in [0, 1]
                           for num_ctas in num_ctas_list
-                          for dtype_x_str in ['float32', 'uint64', 'int64', 'float64']])
+                          for dtype_x_str in ['float16', 'float32', 'uint64', 'int64', 'float64']])
 def test_tensor_atomic_rmw(shape, axis, num_ctas, dtype_x_str, device):
     check_type_supported(dtype_x_str, device)
+    if is_interpreter() and dtype_x_str == 'float16':
+        pytest.skip('float16 atomic_add does not work in the interpreter mode')
     shape0, shape1 = shape
     # triton kernel
 
     @triton.jit
-    def kernel(Z, X, OLD, AXIS: tl.constexpr, SHAPE0: tl.constexpr, SHAPE1: tl.constexpr):
+    def kernel(Z, X, OLD, AXIS: tl.constexpr, SHAPE0: tl.constexpr, SHAPE1: tl.constexpr, DTYPE: tl.constexpr):
         off0 = tl.arange(0, SHAPE0)
         off1 = tl.arange(0, SHAPE1)
         x = tl.load(X + off0[:, None] * SHAPE1 + off1[None, :])
+
+        if DTYPE == tl.float16:
+            # sum can have bad numerics when accumulating in float16.
+            # if we're dealing with float16, do the sum in float32.
+            x = x.to(tl.float32)
+
         z = tl.sum(x, axis=AXIS)
+
+        if DTYPE == tl.float16:
+            z = z.to(DTYPE)
+
         if AXIS == 1:
             old = tl.atomic_add(Z + off0, z)
             tl.store(OLD + off0, old)
@@ -1515,13 +1527,23 @@ def kernel(Z, X, OLD, AXIS: tl.constexpr, SHAPE0: tl.constexpr, SHAPE1: tl.const
     z = numpy_random(z_shape, dtype_str=dtype_x_str, rs=rs)
     old = np.zeros(z_shape, dtype=getattr(np, dtype_x_str))
     # reference results
-    z_ref = z + np.sum(x, axis=axis, keepdims=False)
+    if x.dtype == np.float16:
+        # do the sum in float32 to reduce numerical variation
+        z_ref = z + np.sum(x.astype(np.float32), axis=axis, keepdims=False).astype(x.dtype)
+    else:
+        z_ref = z + np.sum(x, axis=axis, keepdims=False)
     old_ref = np.copy(z)
     # triton result
     x_tri = to_triton(x, device=device)
     z_tri = to_triton(z, device=device)
     old_tri = to_triton(old, device=device)
-    kernel[(1, )](z_tri, x_tri, old_tri, axis, shape0, shape1, num_ctas=num_ctas)
+
+    def torch_to_triton_dtype(t):
+        if t == torch.float16:
+            return tl.float16
+        return None
+
+    kernel[(1, )](z_tri, x_tri, old_tri, axis, shape0, shape1, torch_to_triton_dtype(x_tri.dtype), num_ctas=num_ctas)
     np.testing.assert_allclose(z_ref, to_numpy(z_tri), rtol=1e-4)
     np.testing.assert_equal(old_ref, to_numpy(old_tri))
 

python/triton/compiler/compiler.py

@@ -15,6 +15,7 @@
 import re
 import functools
 import os
+import sysconfig
 
 # - ^\s*tt\.func\s+ : match the start of the string, any leading whitespace, the keyword func,
 #    and any following whitespace
@@ -151,7 +152,8 @@ def triton_key():
 
     # backend
     libtriton_hash = hashlib.sha256()
-    with open(os.path.join(TRITON_PATH, "_C/libtriton.so"), "rb") as f:
+    ext = sysconfig.get_config_var("EXT_SUFFIX").split(".")[-1]
+    with open(os.path.join(TRITON_PATH, f"_C/libtriton.{ext}"), "rb") as f:
         while True:
             chunk = f.read(1024**2)
             if not chunk:

test/Conversion/amd/tritongpu_to_llvm.mlir

@@ -62,3 +62,35 @@ module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 :
     tt.return
   }
 }
+
+// -----
+
+#blocked1 = #triton_gpu.blocked<{sizePerThread = [2], threadsPerWarp = [32], warpsPerCTA = [4], order = [0]}>
+module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32} {
+  // CHECK-LABEL: atomic_add_f16
+  tt.func @atomic_add_f16(%arg0: !tt.ptr<f16> {tt.divisibility = 16 : i32}, %arg1 : tensor<256xi1, #blocked1>, %arg2 : tensor<256xf16, #blocked1>) {
+    %range = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32, #blocked1>
+    %base_ptr = tt.splat %arg0 : !tt.ptr<f16> -> tensor<256x!tt.ptr<f16>, #blocked1>
+    %ptr = tt.addptr %base_ptr, %range : tensor<256x!tt.ptr<f16>, #blocked1>, tensor<256xi32, #blocked1>
+    // CHECK: llvm.cond_br
+    // CHECK: llvm.atomicrmw fadd {{.*}} vector<2xf16>
+    %0 =  tt.atomic_rmw fadd, relaxed, gpu, %ptr, %arg2, %arg1 : (tensor<256x!tt.ptr<f16>, #blocked1>, tensor<256xf16, #blocked1>, tensor<256xi1, #blocked1>) -> tensor<256xf16, #blocked1>
+    tt.return
+  }
+}
+
+// -----
+
+#blocked2 = #triton_gpu.blocked<{sizePerThread = [2], threadsPerWarp = [32], warpsPerCTA = [4], order = [0]}>
+module attributes {"triton_gpu.num-ctas" = 1 : i32, "triton_gpu.num-warps" = 4 : i32} {
+  // CHECK-LABEL: atomic_add_bf16
+  tt.func @atomic_add_bf16(%arg0: !tt.ptr<bf16> {tt.divisibility = 16 : i32}, %arg1 : tensor<256xi1, #blocked2>, %arg2 : tensor<256xbf16, #blocked2>) {
+    %range = tt.make_range {end = 256 : i32, start = 0 : i32} : tensor<256xi32, #blocked2>
+    %base_ptr = tt.splat %arg0 : !tt.ptr<bf16> -> tensor<256x!tt.ptr<bf16>, #blocked2>
+    %ptr = tt.addptr %base_ptr, %range : tensor<256x!tt.ptr<bf16>, #blocked2>, tensor<256xi32, #blocked2>
+    // CHECK: llvm.cond_br
+    // CHECK: llvm.atomicrmw fadd {{.*}} vector<2xbf16>
+    %0 =  tt.atomic_rmw fadd, relaxed, gpu, %ptr, %arg2, %arg1 : (tensor<256x!tt.ptr<bf16>, #blocked2>, tensor<256xbf16, #blocked2>, tensor<256xi1, #blocked2>) -> tensor<256xbf16, #blocked2>
+    tt.return
+  }
+}

third_party/amd/lib/TritonAMDGPUToLLVM/LoadStoreOpToLLVM.cpp

@@ -768,7 +768,11 @@ struct AtomicRMWOpConversion
     // tensor
     if (tensorTy) {
       auto valTy = cast<RankedTensorType>(val.getType());
-      vec = std::min<unsigned>(vec, valTy.getElementType().isF16() ? 2 : 1);
+      Type elTy = valTy.getElementType();
+      vec = std::min<unsigned>(vec, llvm::isa<FloatType>(elTy) &&
+                                            elTy.getIntOrFloatBitWidth() == 16
+                                        ? 2
+                                        : 1);
       // mask
       numElems = tensorTy.getNumElements();
     }
@@ -783,13 +787,22 @@ struct AtomicRMWOpConversion
     auto vecTy = vec_ty(valueElemTy, vec);
     auto retType = vec == 1 ? valueElemTy : vecTy;
     SmallVector<Value> resultVals(elemsPerThread);
-    const bool f16v2 = vec == 2 && valueElemTy.isF16();
     for (size_t i = 0; i < elemsPerThread; i += vec) {
       Value rmwPtr = ptrElements[i];
       // TODO: in case llMask is zero we can create only one branch for all
       // elemsPerThread.
       Value rmwMask = llMask ? and_(mask, maskElements[i]) : mask;
 
+      Value operand;
+      if (vec == 1) {
+        operand = valElements[i];
+      } else {
+        operand = undef(vecTy);
+        for (size_t ii = 0; ii < vec; ++ii)
+          operand =
+              insert_element(vecTy, operand, valElements[i + ii], i32_val(ii));
+      }
+
       Value undefVal = undef(retType);
       // Build blocks to bypass the atomic instruction for ~rmwMask.
       auto *curBlock = rewriter.getInsertionBlock();
@@ -806,25 +819,11 @@ struct AtomicRMWOpConversion
       auto maybeKind = matchAtomicOp(atomicRmwAttr);
       // TODO: use rocdl.raw.buffer.atomic from ROCDL dialect to use efficient
       // atomics for MI-* series of AMD GPU.
-      Value atom = rewriter
-                       .create<LLVM::AtomicRMWOp>(
-                           loc, *maybeKind, rmwPtr, valElements[i],
-                           atomicMemOrdering, StringRef("agent"))
-                       .getResult();
-
-      // NV for the f16v2 case generates one packed instruction. We have to
-      // create two separate instructions since LLVM::AtomicRMWOp doesn't
-      // support this. Can be optimized out with rocdl.raw.buffer.atomic.
-      if (f16v2) {
-        Value atom2 =
-            rewriter
-                .create<LLVM::AtomicRMWOp>(
-                    loc, *maybeKind, ptrElements[i + 1], valElements[i + 1],
-                    atomicMemOrdering, StringRef("agent"))
-                .getResult();
-        auto tmp = insert_element(vecTy, undef(vecTy), atom, i32_val(0));
-        atom = insert_element(vecTy, tmp, atom2, i32_val(1)).getResult();
-      }
+      Value atom =
+          rewriter
+              .create<LLVM::AtomicRMWOp>(loc, *maybeKind, rmwPtr, operand,
+                                         atomicMemOrdering, StringRef("agent"))
+              .getResult();
       if (!tensorTy) {
         if (atomicNeedsSharedMemory(op.getResult())) {
           Value atomPtr =

third_party/nvidia/backend/driver.py

@@ -1,5 +1,6 @@
 import functools
 import os
+import sysconfig
 import hashlib
 import subprocess
 import tempfile
@@ -48,15 +49,16 @@ def library_dirs():
 def compile_module_from_src(src, name):
     key = hashlib.sha256(src.encode("utf-8")).hexdigest()
     cache = get_cache_manager(key)
-    cache_path = cache.get_file(f"{name}.so")
+    ext = sysconfig.get_config_var("EXT_SUFFIX").split(".")[-1]
+    cache_path = cache.get_file(f"{name}.{ext}")
     if cache_path is None:
         with tempfile.TemporaryDirectory() as tmpdir:
             src_path = os.path.join(tmpdir, "main.c")
             with open(src_path, "w") as f:
                 f.write(src)
             so = _build(name, src_path, tmpdir, library_dirs(), include_dir, libraries)
             with open(so, "rb") as f:
-                cache_path = cache.put(f.read(), f"{name}.so", binary=True)
+                cache_path = cache.put(f.read(), f"{name}.{ext}", binary=True)
     import importlib.util
     spec = importlib.util.spec_from_file_location(name, cache_path)
     mod = importlib.util.module_from_spec(spec)