[BACKEND][FRONTEND] Fix problems with test_matmul (#973)

1. Handle induction variable when step is negative
2. Restore async_wait that accidentally deleted
3. Add missing induction variable in prefetch
4. Add device property functions

Co-authored-by: Philippe Tillet <Phil.Tillet@gmail.com>

lib/Conversion/TritonGPUToLLVM/TritonGPUToLLVM.cpp

@@ -4783,10 +4783,15 @@ private:
       decomposed = true;
     });
 
-    // async wait is supported in Ampere and later
     mod.walk([&](triton::gpu::AsyncWaitOp asyncWaitOp) -> void {
-      if (!triton::gpu::AsyncWaitOp::isSupported(computeCapability) ||
-          decomposed) {
+      if (!triton::gpu::AsyncWaitOp::isSupported(computeCapability)) {
+        // async wait is supported in Ampere and later
+        asyncWaitOp.erase();
+      } else if (decomposed) {
+        // Wait for all previous async ops
+        OpBuilder builder(asyncWaitOp);
+        auto newAsyncWaitOp =
+            builder.create<triton::gpu::AsyncWaitOp>(asyncWaitOp.getLoc(), 0);
         asyncWaitOp.erase();
       }
     });

lib/Conversion/TritonToTritonGPU/TritonToTritonGPU.cpp

@@ -262,10 +262,10 @@ struct TritonCatPattern : public OpConversionPattern<triton::CatOp> {
     // For now, this behaves like generic, but this will evolve when
     // we add support for `can_reorder=False`
     Type retType = this->getTypeConverter()->convertType(op.getType());
-    rewriter.replaceOpWithNewOp<triton::CatOp>(op, retType, adaptor.getOperands());
+    rewriter.replaceOpWithNewOp<triton::CatOp>(op, retType,
+                                               adaptor.getOperands());
     return success();
   }
-
 };
 
 struct TritonTransPattern : public OpConversionPattern<triton::TransOp> {
@@ -450,13 +450,11 @@ void populateTritonPatterns(TritonGPUTypeConverter &typeConverter,
       TritonGenericPattern<triton::IntToPtrOp>,
       TritonGenericPattern<triton::PtrToIntOp>,
       TritonGenericPattern<triton::SplatOp>, TritonBroadcastPattern,
-      TritonGenericPattern<triton::AddPtrOp>, 
-      TritonCatPattern,
-      TritonReducePattern,
-      TritonTransPattern, TritonExpandDimsPattern, TritonMakeRangePattern,
-      TritonDotPattern, TritonLoadPattern, TritonStorePattern,
-      TritonExtElemwisePattern, TritonPrintfPattern, TritonAtomicRMWPattern>(
-      typeConverter, context);
+      TritonGenericPattern<triton::AddPtrOp>, TritonCatPattern,
+      TritonReducePattern, TritonTransPattern, TritonExpandDimsPattern,
+      TritonMakeRangePattern, TritonDotPattern, TritonLoadPattern,
+      TritonStorePattern, TritonExtElemwisePattern, TritonPrintfPattern,
+      TritonAtomicRMWPattern>(typeConverter, context);
 }
 
 //

lib/Dialect/TritonGPU/Transforms/Combine.cpp

@@ -782,8 +782,8 @@ public:
                                                  newRetType.getEncoding()));
     a = rewriter.create<triton::gpu::ConvertLayoutOp>(a.getLoc(), newAType, a);
     b = rewriter.create<triton::gpu::ConvertLayoutOp>(b.getLoc(), newBType, b);
-    auto newDot = rewriter.create<triton::DotOp>(
-        dotOp.getLoc(), newRetType, a, b, newAcc, dotOp.allowTF32());
+    auto newDot = rewriter.create<triton::DotOp>(dotOp.getLoc(), newRetType, a,
+                                                 b, newAcc, dotOp.allowTF32());
 
     rewriter.replaceOpWithNewOp<triton::gpu::ConvertLayoutOp>(
         op, oldRetType, newDot.getResult());

lib/Dialect/TritonGPU/Transforms/Prefetch.cpp

@@ -225,6 +225,7 @@ scf::ForOp Prefetcher::createNewForOp() {
   BlockAndValueMapping mapping;
   for (const auto &arg : llvm::enumerate(forOp.getRegionIterArgs()))
     mapping.map(arg.value(), newForOp.getRegionIterArgs()[arg.index()]);
+  mapping.map(forOp.getInductionVar(), newForOp.getInductionVar());
 
   for (Operation &op : forOp.getBody()->without_terminator()) {
     Operation *newOp = builder.clone(op, mapping);

python/tests/test_matmul.py

@@ -0,0 +1,101 @@
+import itertools
+
+import pytest
+import torch
+
+import triton
+import triton._C.libtriton.triton as _triton
+
+
+@pytest.mark.parametrize(
+    "BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE",
+    itertools.chain(
+        *[
+            [
+                # 1 warp
+                (16, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (32, 16, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (16, 32, 16, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (16, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (32, 16, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (16, 32, 32, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (16, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (64, 16, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                (16, 64, 64, 1, 1, 2, None, None, None, AT, BT, DTYPE),
+                # 2 warp
+                (64, 32, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE),
+                (32, 64, 64, 1, 2, 2, None, None, None, AT, BT, DTYPE),
+                (64, 32, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE),
+                (32, 64, 16, 1, 2, 2, None, None, None, AT, BT, DTYPE),
+                (128, 32, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE),
+                (32, 128, 32, 1, 2, 2, None, None, None, AT, BT, DTYPE),
+                # 4 warp
+                (128, 64, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE),
+                (64, 128, 16, 1, 4, 2, None, None, None, AT, BT, DTYPE),
+                (128, 32, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE),
+                (32, 128, 32, 1, 4, 2, None, None, None, AT, BT, DTYPE),
+                (128, 32, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE),
+                (32, 128, 64, 1, 4, 2, None, None, None, AT, BT, DTYPE),
+                # 8 warp
+                (128, 256, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE),
+                (256, 128, 16, 1, 8, 2, None, None, None, AT, BT, DTYPE),
+                (256, 128, 32, 1, 8, 2, None, None, None, AT, BT, DTYPE),
+                # split-k
+                (64, 64, 16, 2, 4, 2, None, None, None, AT, BT, DTYPE),
+                (64, 64, 16, 4, 4, 2, None, None, None, AT, BT, DTYPE),
+                (64, 64, 16, 8, 4, 2, None, None, None, AT, BT, DTYPE),
+                # variable input
+                (128, 128, 32, 1, 4, 2, 1024, 1024, 1024, AT, BT, DTYPE),
+                (128, 128, 32, 1, 4, 2, 384, 128, 640, AT, BT, DTYPE),
+                (128, 128, 32, 1, 4, 2, 107, 233, 256, AT, BT, DTYPE),
+                (128, 128, 32, 1, 4, 2, 107, 233, 311, AT, BT, DTYPE),
+            ] for DTYPE in ["float16", "bfloat16", "float32"] for AT in [False, True] for BT in [False, True]
+        ],
+        # n-stage
+        *[
+            [
+                (16, 16, 16, 1, 1, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
+                (64, 32, 64, 1, 2, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
+                (128, 64, 16, 1, 4, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
+                (256, 128, 32, 1, 8, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
+                (128, 128, 32, 1, 4, STAGES, 384, 128, 640, AT, BT, DTYPE),
+                # split-k
+                (64, 64, 16, 8, 4, STAGES, 1024, 1024, 1024, AT, BT, DTYPE),
+                (64, 64, 16, 8, 4, STAGES, 1024, 1024, 32, AT, BT, DTYPE),
+            ] for DTYPE in ["float16", "bfloat16", "float32"] for AT in [False, True] for BT in [False, True] for STAGES in [2, 3, 4]
+        ]
+    ),
+)
+def test_op(BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, NWARP, NSTAGE, M, N, K, AT, BT, DTYPE):
+    capability = torch.cuda.get_device_capability()
+    if capability[0] < 7:
+        pytest.skip("Only test tl.dot() on devices with sm >= 70")
+    if capability[0] < 8 and DTYPE == "bfloat16":
+        pytest.skip("Only test bfloat16 on devices with sm >= 80")
+    #if DTYPE == "bfloat16" and SPLIT_K != 1:
+    #    pytest.skip("bfloat16 matmuls don't allow split_k for now")
+    if DTYPE == "bfloat16":
+        pytest.skip("bfloat16 matmuls doesn't support for now")
+    torch.manual_seed(0)
+    # nuke kernel decorators -- will set meta-parameters manually
+    kwargs = {'BLOCK_M': BLOCK_M, 'BLOCK_N': BLOCK_N, 'BLOCK_K': BLOCK_K, 'SPLIT_K': SPLIT_K}
+    pre_hook = None if SPLIT_K == 1 else lambda nargs: nargs['C'].zero_()
+    configs = [triton.Config(kwargs=kwargs, num_warps=NWARP, num_stages=NSTAGE, pre_hook=pre_hook)]
+    kernel = triton.ops._matmul.kernel
+    kernel.configs = configs
+    # kernel.run = kernel.run.run.run
+
+    # get matrix shape
+    M = BLOCK_M if M is None else M
+    N = BLOCK_N if N is None else N
+    K = BLOCK_K * SPLIT_K if K is None else K
+    # allocate/transpose inputs
+    DTYPE = {"float16": torch.float16, "bfloat16": torch.bfloat16, "float32": torch.float32}[DTYPE]
+    a = .1 * torch.randn((K, M) if AT else (M, K), device="cuda", dtype=DTYPE)
+    b = .1 * torch.randn((N, K) if BT else (K, N), device="cuda", dtype=DTYPE)
+    a = a.t() if AT else a
+    b = b.t() if BT else b
+    # run test
+    th_c = torch.matmul(a, b)
+    tt_c = triton.testing.catch_oor(lambda: triton.ops.matmul(a, b), pytest)
+    triton.testing.assert_almost_equal(th_c, tt_c)

python/tests/test_vecadd.py

@@ -68,7 +68,7 @@ def test_vecadd_scf_mask(shape, num_warps, block_size, iter_size):
         @num_elements: number of elements
         '''
         pid = tl.program_id(axis=0)
-        for i in range(math.ceil(block_size / iter_size)):
+        for i in range(tl.cdiv(block_size, iter_size)):
             # TODO: a bug here, if put the offset outside the forloop, there will be a GPU mis-aligned error.
             offset = pid * block_size + tl.arange(0, iter_size)
             x_ptrs = x_ptr + offset

python/triton/compiler.py

@@ -329,10 +329,6 @@ class CodeGenerator(ast.NodeVisitor):
     def visit_BinOp(self, node):
         lhs = self.visit(node.left)
         rhs = self.visit(node.right)
-        if isinstance(lhs, triton.language.constexpr):
-            lhs = lhs.value
-        if isinstance(rhs, triton.language.constexpr):
-            rhs = rhs.value
         fn = {
             ast.Add: '__add__',
             ast.Sub: '__sub__',
@@ -591,8 +587,10 @@ class CodeGenerator(ast.NodeVisitor):
                         ast.NodeVisitor.generic_visit(self, stmt)
                 return
         # handle negative constant step (not supported by scf.for in MLIR)
+        negative_step = False
         if isinstance(step, triton.language.constexpr) and step.value < 0:
             step = triton.language.constexpr(-step.value)
+            negative_step = True
             lb, ub = ub, lb
         # lb/ub/step might be constexpr, we need to cast them to tensor
         lb = triton.language.core._to_tensor(lb, self.builder).handle
@@ -640,6 +638,9 @@ class CodeGenerator(ast.NodeVisitor):
             # update induction variable with actual value, and replace all uses
             self.builder.set_insertion_point_to_start(for_op.get_body(0))
             iv = self.builder.create_index_to_si(for_op.get_induction_var())
+            if negative_step:
+                ub_si = self.builder.create_index_to_si(ub)
+                iv = self.builder.create_sub(ub_si, iv)
             self.lscope[node.target.id].handle.replace_all_uses_with(iv)
             self.set_value(name, triton.language.core.tensor(iv, triton.language.core.int32))
 
@@ -890,9 +891,9 @@ def ttir_to_ttgir(mod, num_warps, num_stages, compute_capability):
     pm = _triton.ir.pass_manager(mod.context)
     pm.add_convert_triton_to_tritongpu_pass(num_warps)
     pm.enable_debug()
-    # Convert blocked layout to mma layout for dot ops so that pipeline
-    # can get shared memory swizzled correctly.
     pm.add_coalesce_pass()
+    # The combine pass converts blocked layout to mma layout
+    # for dot ops so that pipeline can get shared memory swizzled correctly.
     pm.add_triton_gpu_combine_pass(compute_capability)
     pm.add_tritongpu_pipeline_pass(num_stages)
     # Prefetch must be done after pipeline pass because pipeline pass
@@ -1395,20 +1396,20 @@ def compile(fn, **kwargs):
     extern_libs = kwargs.get("extern_libs", dict())
     device = kwargs.get("device", torch.cuda.current_device())
     capability = torch.cuda.get_device_capability()
-    capability = capability[0]*10 + capability[1]
+    capability = capability[0] * 10 + capability[1]
     # build compilation stages
     stages = {
-      "ast" : (lambda path: fn, None),
-      "ttir": (lambda path: _triton.ir.parse_mlir_module(path, context), 
-               lambda src: ast_to_ttir(src, signature, configs[0], constants)),
-      "ttgir": (lambda path: _triton.ir.parse_mlir_module(path, context), 
-                lambda src: ttir_to_ttgir(src, num_warps, num_stages, capability)),
-      "llir": (lambda path: Path(path).read_bytes(), 
-              lambda src: ttgir_to_llir(src, extern_libs, capability)),
-      "ptx":  (lambda path: Path(path).read_text(), 
-              lambda src: llir_to_ptx(src, capability)),
-      "cubin": (lambda path: Path(path).read_bytes(), 
-               lambda src: ptx_to_cubin(src, capability))
+        "ast": (lambda path: fn, None),
+        "ttir": (lambda path: _triton.ir.parse_mlir_module(path, context),
+                 lambda src: ast_to_ttir(src, signature, configs[0], constants)),
+        "ttgir": (lambda path: _triton.ir.parse_mlir_module(path, context),
+                  lambda src: ttir_to_ttgir(src, num_warps, num_stages, capability)),
+        "llir": (lambda path: Path(path).read_bytes(),
+                 lambda src: ttgir_to_llir(src, extern_libs, capability)),
+        "ptx": (lambda path: Path(path).read_text(),
+                lambda src: llir_to_ptx(src, capability)),
+        "cubin": (lambda path: Path(path).read_bytes(),
+                  lambda src: ptx_to_cubin(src, capability))
     }
     # find out the signature of the function
     if isinstance(fn, triton.runtime.JITFunction):
@@ -1467,8 +1468,8 @@ def compile(fn, **kwargs):
       if ir == ext:
         next_module = parse(fn)
       elif os.path.exists(path) and\
-           ir in metadata["ctime"] and\
-           os.path.getctime(path) == metadata["ctime"][ir]:
+              ir in metadata["ctime"] and\
+              os.path.getctime(path) == metadata["ctime"][ir]:
         next_module = parse(path)
       else:
         next_module = compile(module)
@@ -1504,8 +1505,7 @@ class CompiledKernel:
         self.asm = asm
         device = torch.cuda.current_device()
         global cuda_utils
-        if cuda_utils is None:
-            cuda_utils = CudaUtils()
+        init_cuda_utils()
         mod, func, n_regs, n_spills = cuda_utils.load_binary(metadata["name"], self.asm["cubin"], self.shared, device)
         self.cu_module = mod
         self.cu_function = func
@@ -1562,6 +1562,34 @@ class CudaUtils(object):
 
         #define CUDA_CHECK(ans) { gpuAssert((ans), __FILE__, __LINE__); if(PyErr_Occurred()) return NULL; }
 
+        static PyObject* getDeviceProperties(PyObject* self, PyObject* args){
+            int device_id;
+            if(!PyArg_ParseTuple(args, "i", &device_id))
+                return NULL;
+            // Get device handle
+            CUdevice device;
+            cuDeviceGet(&device, device_id);
+
+            // create a struct to hold device properties
+            int max_shared_mem;
+            int multiprocessor_count;
+            int sm_clock_rate;
+            int mem_clock_rate;
+            int mem_bus_width;
+            CUDA_CHECK(cuDeviceGetAttribute(&max_shared_mem, CU_DEVICE_ATTRIBUTE_MAX_SHARED_MEMORY_PER_BLOCK, device));
+            CUDA_CHECK(cuDeviceGetAttribute(&multiprocessor_count, CU_DEVICE_ATTRIBUTE_MULTIPROCESSOR_COUNT, device));
+            CUDA_CHECK(cuDeviceGetAttribute(&sm_clock_rate, CU_DEVICE_ATTRIBUTE_CLOCK_RATE, device));
+            CUDA_CHECK(cuDeviceGetAttribute(&mem_clock_rate, CU_DEVICE_ATTRIBUTE_MEMORY_CLOCK_RATE, device));
+            CUDA_CHECK(cuDeviceGetAttribute(&mem_bus_width, CU_DEVICE_ATTRIBUTE_GLOBAL_MEMORY_BUS_WIDTH, device));
+
+
+            return Py_BuildValue("{s:i, s:i, s:i, s:i, s:i}", "max_shared_mem", max_shared_mem,
+                                       "multiprocessor_count", multiprocessor_count,
+                                       "sm_clock_rate", sm_clock_rate,
+                                       "mem_clock_rate", mem_clock_rate,
+                                       "mem_bus_width", mem_bus_width);
+        }
+
         static PyObject* loadBinary(PyObject* self, PyObject* args) {
             const char* name;
             const char* data;
@@ -1601,6 +1629,7 @@ class CudaUtils(object):
 
         static PyMethodDef ModuleMethods[] = {
           {"load_binary", loadBinary, METH_VARARGS, "Load provided cubin into CUDA driver"},
+          {"get_device_properties", getDeviceProperties, METH_VARARGS, "Get the properties for a given device"},
           {NULL, NULL, 0, NULL} // sentinel
         };
 
@@ -1640,6 +1669,13 @@ class CudaUtils(object):
         mod = importlib.util.module_from_spec(spec)
         spec.loader.exec_module(mod)
         self.load_binary = mod.load_binary
+        self.get_device_properties = mod.get_device_properties
+
+
+def init_cuda_utils():
+    global cuda_utils
+    if cuda_utils is None:
+        cuda_utils = CudaUtils()
 
 
 cuda_utils = None

python/triton/language/core.py

@@ -9,6 +9,7 @@ from triton._C.libtriton.triton import ir
 
 T = TypeVar('T')
 
+
 def _to_tensor(x, builder):
     if isinstance(x, bool):
         return tensor(builder.get_int1(x), int1)
@@ -348,6 +349,9 @@ class constexpr:
     def __mul__(self, other):
         return constexpr(self.value * other.value)
 
+    def __mod__(self, other):
+        return constexpr(self.value % other.value)
+
     def __rmul__(self, other):
         return constexpr(other.value * self.value)
 
@@ -726,10 +730,12 @@ def broadcast_to(input, shape, _builder=None):
     """
     return semantic.broadcast_impl_shape(input, shape, _builder)
 
+
 @builtin
 def trans(input, _builder=None):
     return semantic.trans(input, _builder)
 
+
 @builtin
 def cat(input, other, can_reorder=False, _builder=None):
     """
@@ -762,6 +768,7 @@ def view(input, shape, _builder=None):
     shape = [x.value for x in shape]
     return semantic.view(input, shape, _builder)
 
+
 @builtin
 def reshape(input, shape, _builder=None):
     # TODO: should be more than just a view

python/triton/language/semantic.py

@@ -481,7 +481,8 @@ def view(input: tl.tensor,
          dst_shape: List[int],
          builder: ir.builder) -> tl.tensor:
     # TODO: disable when TritonToTritonGPU handles views properly
-    assert len(input.shape) == len(dst_shape)
+    
+    # assert len(input.shape) == len(dst_shape)
     numel = 1
     for s in dst_shape:
         numel *= s

python/triton/ops/matmul.py

@@ -26,9 +26,6 @@ def get_configs_io_bound():
     return configs
 
 
-@triton.heuristics({
-    'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
-})
 @triton.autotune(
     configs=[
         # basic configs for compute-bound matmuls
@@ -59,6 +56,9 @@ def get_configs_io_bound():
         'top_k': 10
     },
 )
+@triton.heuristics({
+    'EVEN_K': lambda args: args['K'] % (args['BLOCK_K'] * args['SPLIT_K']) == 0,
+})
 @triton.jit
 def _kernel(A, B, C, M, N, K,
             stride_am, stride_ak,

python/triton/ops/matmul_perf_model.py

@@ -10,7 +10,9 @@ from triton.testing import get_dram_gbps, get_max_simd_tflops, get_max_tensorcor
 def get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype):
     ''' return compute throughput in TOPS '''
     total_warps = num_ctas * min(num_warps, 4)
-    num_subcores = _triton.runtime.num_sm(backend, device) * 4  # on recent GPUs
+    triton.compiler.init_cuda_utils()
+
+    num_subcores = triton.compiler.cuda_utils.get_device_properties(device)["multiprocessor_count"] * 4  # on recent GPUs
     tflops = min(num_subcores, total_warps) / num_subcores * get_max_tensorcore_tflops(dtype, backend, device)
     return tflops
 
@@ -18,14 +20,14 @@ def get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype):
 def get_simd_tflops(backend, device, num_ctas, num_warps, dtype):
     ''' return compute throughput in TOPS '''
     total_warps = num_ctas * min(num_warps, 4)
-    num_subcores = _triton.runtime.num_sm(backend, device) * 4  # on recent GPUs
+    num_subcores = triton.compiler.cuda_utils.get_device_properties(device)["multiprocessor_count"] * 4  # on recent GPUs
     tflops = min(num_subcores, total_warps) / num_subcores * get_max_simd_tflops(dtype, backend, device)
     return tflops
 
 
 def get_tflops(backend, device, num_ctas, num_warps, dtype):
-    cc = _triton.runtime.cc(backend, device)
-    if cc < 80 and dtype == torch.float32:
+    capability = torch.cuda.get_device_capability(device)
+    if capability[0] < 8 and dtype == torch.float32:
         return get_simd_tflops(backend, device, num_ctas, num_warps, dtype)
     return get_tensorcore_tflops(backend, device, num_ctas, num_warps, dtype)
 
@@ -59,7 +61,7 @@ def estimate_matmul_time(
     compute_ms = total_ops / tput
 
     # time to load data
-    num_sm = _triton.runtime.num_sm(backend, device)
+    num_sm = triton.compiler.cuda_utils.get_device_properties(device)["multiprocessor_count"]
     active_cta_ratio = min(1, num_ctas / num_sm)
     active_cta_ratio_bw1 = min(1, num_ctas / 32)  # 32 active ctas are enough to saturate
     active_cta_ratio_bw2 = max(min(1, (num_ctas - 32) / (108 - 32)), 0)  # 32-108, remaining 5%
@@ -99,7 +101,7 @@ def estimate_matmul_time(
 def early_config_prune(configs, named_args):
     backend = _triton.runtime.backend.CUDA
     device = torch.cuda.current_device()
-    cc = _triton.runtime.cc(backend, device)
+    capability = torch.cuda.get_device_capability()
     # BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps, num_stages
     dtsize = named_args['A'].element_size()
     dtype = named_args['A'].dtype
@@ -110,7 +112,10 @@ def early_config_prune(configs, named_args):
         kw = config.kwargs
         BLOCK_M, BLOCK_N, BLOCK_K, num_stages = \
             kw['BLOCK_M'], kw['BLOCK_N'], kw['BLOCK_K'], config.num_stages
-        max_shared_memory = _triton.runtime.max_shared_memory(backend, device)
+
+        # TODO: move to `cuda_utils` submodule
+        triton.compiler.init_cuda_utils()
+        max_shared_memory = triton.compiler.cuda_utils.get_device_properties(device)["max_shared_mem"]
         required_shared_memory = (BLOCK_M + BLOCK_N) * BLOCK_K * num_stages * dtsize
         if required_shared_memory <= max_shared_memory:
             pruned_configs.append(config)
@@ -136,7 +141,7 @@ def early_config_prune(configs, named_args):
     pruned_configs = []
     for k, v in configs_map.items():
         BLOCK_M, BLOCK_N, BLOCK_K, SPLIT_K, num_warps = k
-        if cc >= 80:
+        if capability[0] >= 8:
             # compute cycles (only works for ampere GPUs)
             mmas = BLOCK_M * BLOCK_N * BLOCK_K / (16 * 8 * 16)
             mma_cycles = mmas / min(4, num_warps) * 8

python/triton/testing.py

@@ -16,6 +16,9 @@ except ImportError:
     _cutlass = None
     has_cutlass = False
 
+# TODO: move to separate module
+import triton
+
 
 def catch_oor(kernel, pytest_handle=None):
     try:
@@ -330,8 +333,8 @@ def get_dram_gbps(backend=None, device=None):
         backend = _triton.runtime.backend.CUDA
     if not device:
         device = torch.cuda.current_device()
-    mem_clock_khz = _triton.runtime.memory_clock_rate(backend, device)
-    bus_width = _triton.runtime.global_memory_bus_width(backend, device)
+    mem_clock_khz = triton.compiler.cuda_utils.get_device_properties(device)["mem_clock_rate"] # in kHz
+    bus_width = triton.compiler.cuda_utils.get_device_properties(device)["mem_bus_width"] 
     bw_gbps = mem_clock_khz * bus_width * 2 / 1e6 / 8  # In GB/s
     return bw_gbps
 
@@ -341,11 +344,13 @@ def get_max_tensorcore_tflops(dtype: torch.dtype, backend=None, device=None, clo
         backend = _triton.runtime.backend.CUDA
     if not device:
         device = torch.cuda.current_device()
-    num_subcores = _triton.runtime.num_sm(backend, device) * 4  # on recent GPUs
+
+    triton.compiler.init_cuda_utils()
+    num_subcores = triton.compiler.cuda_utils.get_device_properties(device)["multiprocessor_count"] * 4
     if not clock_rate:
-        clock_rate = _triton.runtime.clock_rate(backend, device)  # in kHz
-    cc = _triton.runtime.cc(backend, device)
-    if cc < 80:
+        clock_rate = triton.compiler.cuda_utils.get_device_properties(device)["sm_clock_rate"] # in kHz
+    capability = torch.cuda.get_device_capability(device)
+    if capability[0] < 8:
         assert dtype == torch.float16
         ops_per_sub_core = 256  # 2 4x4x4 Tensor Cores
     else: