Fix a performance issue with Helion-emitted Flash Attention

examples/attention.py

@@ -12,9 +12,9 @@
 @helion.kernel(
     config=helion.Config(
         # This config was autotuned on a 3090, it won't be fast for other architectures
-        block_sizes=[32, 16],
-        num_warps=1,
-        num_stages=2,
+        block_sizes=[128, 64],
+        num_warps=4,
+        num_stages=3,
         indexing="block_ptr",
     ),
     # Static shapes provides a speedup for attention

helion/_compiler/inductor_lowering.py

@@ -777,52 +777,106 @@ def apply_dot_requirements(
     return LambdaLowering(handler, masked_value_fn=masked_value_fn)
 
 
+def reduce_3d_dot(
+    ctx: GraphInterpreter, node: torch.fx.Node, with_acc: bool
+) -> ast.AST:
+    datatype = CompileEnvironment.current().settings.dot_precision
+    acc = None
+    if with_acc:
+        acc, lhs, rhs = map_arg(node.args, lambda arg: ctx.env[arg])
+        assert isinstance(acc, ast.AST)
+        lhs_node = node.args[1]
+        rhs_node = node.args[2]
+    else:
+        lhs, rhs = map_arg(node.args, lambda arg: ctx.env[arg])
+        lhs_node = node.args[0]
+        rhs_node = node.args[1]
+    assert isinstance(lhs, ast.AST)
+    assert isinstance(rhs, ast.AST)
+
+    lhs_size = lhs_node.meta["val"].size()
+    rhs_size = rhs_node.meta["val"].size()
+    # check to see if it is 3D and the highest dim is 1
+    reduce_dim = False
+    if len(lhs_size) == 3:
+        env = CompileEnvironment.current()
+        lhs_dim_idx = env.get_block_id(lhs_size[0])
+        rhs_dim_idx = env.get_block_id(rhs_size[0])
+        if lhs_dim_idx is not None and rhs_dim_idx is not None:
+            lhs_dim_val = env.block_sizes[lhs_dim_idx]
+            rhs_dim_val = env.block_sizes[rhs_dim_idx]
+            if (
+                lhs_dim_val.from_config(ctx.cg.device_function.config) == 1
+                and rhs_dim_val.from_config(ctx.cg.device_function.config) == 1
+            ):
+                reduce_dim = True
+
+    if not reduce_dim:
+        if with_acc:
+            return expr_from_string(
+                f"tl.dot(lhs, rhs, acc=acc, input_precision={datatype!r})",
+                lhs=lhs,
+                rhs=rhs,
+                acc=acc,
+            )
+        # without accumulator
+        return expr_from_string(
+            f"tl.dot(lhs, rhs, input_precision={datatype!r})", lhs=lhs, rhs=rhs
+        )
+
+    # create reshape, dot, then reshape
+    lhs_shape_str = ctx.cg.device_function.tile_strategy.shape_str(
+        [*lhs_node.meta["val"].size()[1:]]
+    )
+    rhs_shape_str = ctx.cg.device_function.tile_strategy.shape_str(
+        [*rhs_node.meta["val"].size()[1:]]
+    )
+    out_shape_str = ctx.cg.device_function.tile_strategy.shape_str(
+        [*node.meta["val"].size()]
+    )
+    lhs_reshape = expr_from_string(f"tl.reshape(lhs, {lhs_shape_str})", lhs=lhs)
+    rhs_reshape = expr_from_string(f"tl.reshape(rhs, {rhs_shape_str})", rhs=rhs)
+    if with_acc:
+        acc_shape_str = ctx.cg.device_function.tile_strategy.shape_str(
+            [*node.args[0].meta["val"].size()[1:]]
+        )
+        acc_reshape = expr_from_string(f"tl.reshape(rhs, {acc_shape_str})", rhs=acc)
+        comp = expr_from_string(
+            f"tl.dot(lhs, rhs, acc=acc, input_precision={datatype!r})",
+            lhs=lhs_reshape,
+            rhs=rhs_reshape,
+            acc=acc_reshape,
+        )
+    else:
+        comp = expr_from_string(
+            f"tl.dot(lhs, rhs, input_precision={datatype!r})",
+            lhs=lhs_reshape,
+            rhs=rhs_reshape,
+        )
+    return expr_from_string(f"tl.reshape(lhs, {out_shape_str})", lhs=comp)
+
+
 @register_lowering(torch.ops.aten.bmm.default, apply_dot_requirements)
 # pyre-fixme[56]
 @register_lowering(torch.ops.aten.mm.default, apply_dot_requirements)
 def codegen_mm(ctx: GraphInterpreter, node: torch.fx.Node) -> ast.AST:
     assert not node.kwargs, "matmul kwargs not supported"
-    lhs, rhs = map_arg(node.args, lambda arg: ctx.env[arg])
-    assert isinstance(lhs, ast.AST)
-    assert isinstance(rhs, ast.AST)
-    tf32 = CompileEnvironment.current().settings.dot_precision
-    return expr_from_string(
-        f"tl.dot(lhs, rhs, input_precision={tf32!r})", lhs=lhs, rhs=rhs
-    )
+
+    return reduce_3d_dot(ctx, node, False)
 
 
 # pyre-fixme[56]
 @register_lowering(torch.ops.aten.addmm.default, apply_dot_requirements)
 def codegen_addmm(ctx: GraphInterpreter, node: torch.fx.Node) -> ast.AST:
     assert not node.kwargs, "addmm kwargs not supported"
-    acc, lhs, rhs = map_arg(node.args, lambda arg: ctx.env[arg])
-    assert isinstance(acc, ast.AST)
-    assert isinstance(lhs, ast.AST)
-    assert isinstance(rhs, ast.AST)
-    tf32 = CompileEnvironment.current().settings.dot_precision
-    return expr_from_string(
-        f"tl.dot(lhs, rhs, acc=acc, input_precision={tf32!r})",
-        lhs=lhs,
-        rhs=rhs,
-        acc=acc,
-    )
+    return reduce_3d_dot(ctx, node, True)
 
 
 # pyre-fixme[56]
 @register_lowering(torch.ops.aten.baddbmm.default, apply_dot_requirements)
 def codegen_baddbmm(ctx: GraphInterpreter, node: torch.fx.Node) -> ast.AST:
     assert not node.kwargs, "baddbmm kwargs not supported"
-    acc, lhs, rhs = map_arg(node.args, lambda arg: ctx.env[arg])
-    assert isinstance(acc, ast.AST)
-    assert isinstance(lhs, ast.AST)
-    assert isinstance(rhs, ast.AST)
-    tf32 = CompileEnvironment.current().settings.dot_precision
-    return expr_from_string(
-        f"tl.dot(lhs, rhs, acc=acc, input_precision={tf32!r})",
-        lhs=lhs,
-        rhs=rhs,
-        acc=acc,
-    )
+    return reduce_3d_dot(ctx, node, True)
 
 
 class GenerateASTFromInductor(DefaultHandler):

test/test_examples.py

@@ -1144,7 +1144,7 @@ def _attention_kernel(q_view, k_view, v_view, out, _BLOCK_SIZE_1: tl.constexpr,
         l_i_copy_0 = l_i_copy
         acc_copy_0 = acc_copy
         k = tl.load(k_view + (indices_0[:, None, None] * 32768 + indices_4[None, :, None] * 1 + indices_2[None, None, :] * 64), None)
-        qk = tl.dot(q_copy_0, k, input_precision='tf32')
+        qk = tl.reshape(tl.dot(tl.reshape(q_copy_0, [_BLOCK_SIZE_1, 64]), tl.reshape(k, [64, _BLOCK_SIZE_3]), input_precision='tf32'), [1, _BLOCK_SIZE_1, _BLOCK_SIZE_3])
         amax = tl.max(qk, 2)
         v_0 = 0.18033688
         v_1 = amax * v_0
@@ -1162,7 +1162,7 @@ def _attention_kernel(q_view, k_view, v_view, out, _BLOCK_SIZE_1: tl.constexpr,
         subscript_1 = v_8[:, :, None]
         v_11 = acc_copy_0 * subscript_1
         v = tl.load(v_view + (indices_0[:, None, None] * 32768 + indices_2[None, :, None] * 64 + indices_4[None, None, :] * 1), None)
-        acc = tl.dot(v_6, v, acc=v_11, input_precision='tf32')
+        acc = tl.reshape(tl.dot(tl.reshape(v_6, [_BLOCK_SIZE_1, _BLOCK_SIZE_3]), tl.reshape(v, [_BLOCK_SIZE_3, 64]), acc=tl.reshape(v_11, [_BLOCK_SIZE_1, 64]), input_precision='tf32'), [1, _BLOCK_SIZE_1, 64])
     subscript_2 = l_i[:, :, None]
     v_12 = acc / subscript_2
     tl.store(out + (indices_0[:, None, None] * 32768 + indices_1[None, :, None] * 64 + indices_4[None, None, :] * 1), v_12, None)
@@ -1249,7 +1249,7 @@ def _attention_kernel(q_view, k_view, v_view, out, _BLOCK_SIZE_1: tl.constexpr,
         l_i_copy_0 = l_i_copy
         acc_copy_0 = acc_copy
         k = tl.load(tl.make_block_ptr(k_view, [64, 64, 512], [32768, 1, 64], [offset_0, 0, offset_2], [1, 64, _BLOCK_SIZE_3], [2, 0, 1]), boundary_check=[0, 1, 2], padding_option='zero')
-        qk = tl.dot(q_copy_0, k, input_precision='tf32')
+        qk = tl.reshape(tl.dot(tl.reshape(q_copy_0, [_BLOCK_SIZE_1, 64]), tl.reshape(k, [64, _BLOCK_SIZE_3]), input_precision='tf32'), [1, _BLOCK_SIZE_1, _BLOCK_SIZE_3])
         amax = tl.max(qk, 2)
         v_0 = tl.full([], 0.18033688, tl.float16)
         v_1 = amax * v_0
@@ -1270,7 +1270,7 @@ def _attention_kernel(q_view, k_view, v_view, out, _BLOCK_SIZE_1: tl.constexpr,
         v_13 = acc_copy_0 * subscript_1
         v = tl.load(tl.make_block_ptr(v_view, [64, 512, 64], [32768, 64, 1], [offset_0, offset_2, 0], [1, _BLOCK_SIZE_3, 64], [2, 1, 0]), boundary_check=[0, 1, 2], padding_option='zero')
         v_14 = v_8.to(tl.float16)
-        acc = tl.dot(v_14, v, acc=v_13, input_precision='tf32')
+        acc = tl.reshape(tl.dot(tl.reshape(v_14, [_BLOCK_SIZE_1, _BLOCK_SIZE_3]), tl.reshape(v, [_BLOCK_SIZE_3, 64]), acc=tl.reshape(v_13, [_BLOCK_SIZE_1, 64]), input_precision='tf32'), [1, _BLOCK_SIZE_1, 64])
     subscript_2 = l_i[:, :, None]
     v_15 = acc / subscript_2
     v_16 = v_15.to(tl.float16)
@@ -1361,7 +1361,7 @@ def _attention_kernel(q_view, k_view, v_view, out, k_view_size_0, k_view_size_2,
         l_i_copy_0 = l_i_copy
         acc_copy_0 = acc_copy
         k = tl.load(tl.make_block_ptr(k_view, [k_view_size_0, 64, k_view_size_2], [k_view_stride_0, k_view_stride_1, k_view_stride_2], [offset_0, 0, offset_2], [1, 64, _BLOCK_SIZE_3], [2, 0, 1]), boundary_check=[0, 1, 2], padding_option='zero')
-        qk = tl.dot(q_copy_0, k, input_precision='tf32')
+        qk = tl.reshape(tl.dot(tl.reshape(q_copy_0, [_BLOCK_SIZE_1, 64]), tl.reshape(k, [64, _BLOCK_SIZE_3]), input_precision='tf32'), [1, _BLOCK_SIZE_1, _BLOCK_SIZE_3])
         _mask_to_2 = tl.where(tl.broadcast_to(mask_1[None, :, None] & mask_3[None, None, :], [1, _BLOCK_SIZE_1, _BLOCK_SIZE_3]), qk, float('-inf'))
         amax = tl.max(_mask_to_2, 2)
         v_0 = 0.18033688
@@ -1381,7 +1381,7 @@ def _attention_kernel(q_view, k_view, v_view, out, k_view_size_0, k_view_size_2,
         subscript_1 = v_8[:, :, None]
         v_11 = acc_copy_0 * subscript_1
         v = tl.load(tl.make_block_ptr(v_view, [v_view_size_0, v_view_size_1, 64], [v_view_stride_0, v_view_stride_1, v_view_stride_2], [offset_0, offset_2, 0], [1, _BLOCK_SIZE_3, 64], [2, 1, 0]), boundary_check=[0, 1, 2], padding_option='zero')
-        acc = tl.dot(_mask_to_3, v, acc=v_11, input_precision='tf32')
+        acc = tl.reshape(tl.dot(tl.reshape(_mask_to_3, [_BLOCK_SIZE_1, _BLOCK_SIZE_3]), tl.reshape(v, [_BLOCK_SIZE_3, 64]), acc=tl.reshape(v_11, [_BLOCK_SIZE_1, 64]), input_precision='tf32'), [1, _BLOCK_SIZE_1, 64])
     subscript_2 = l_i[:, :, None]
     v_12 = acc / subscript_2
     tl.store(tl.make_block_ptr(out, [out_size_0, out_size_1, 64], [out_stride_0, out_stride_1, out_stride_2], [offset_0, offset_1, 0], [1, _BLOCK_SIZE_1, 64], [2, 1, 0]), v_12, boundary_check=[0, 1, 2])
@@ -1398,10 +1398,10 @@ def attention(q_in: torch.Tensor, k_in: torch.Tensor, v_in: torch.Tensor):
     out = torch.empty_like(q_view)
     sm_scale = 1.0 / math.sqrt(head_dim)
     qk_scale = sm_scale * 1.44269504
-    _BLOCK_SIZE_1 = 32
+    _BLOCK_SIZE_1 = 128
     _RDIM_SIZE_2 = 64
-    _BLOCK_SIZE_3 = 16
-    _attention_kernel[q_in.size(1) * triton.cdiv(m_dim, _BLOCK_SIZE_1),](q_view, k_view, v_view, out, k_view.size(0), k_view.size(2), out.size(0), out.size(1), q_in.size(1), q_view.size(0), q_view.size(1), v_view.size(0), v_view.size(1), k_view.stride(0), k_view.stride(1), k_view.stride(2), out.stride(0), out.stride(1), out.stride(2), q_view.stride(0), q_view.stride(1), q_view.stride(2), v_view.stride(0), v_view.stride(1), v_view.stride(2), m_dim, n_dim, _BLOCK_SIZE_1, _BLOCK_SIZE_3, num_warps=1, num_stages=2)
+    _BLOCK_SIZE_3 = 64
+    _attention_kernel[q_in.size(1) * triton.cdiv(m_dim, _BLOCK_SIZE_1),](q_view, k_view, v_view, out, k_view.size(0), k_view.size(2), out.size(0), out.size(1), q_in.size(1), q_view.size(0), q_view.size(1), v_view.size(0), v_view.size(1), k_view.stride(0), k_view.stride(1), k_view.stride(2), out.stride(0), out.stride(1), out.stride(2), q_view.stride(0), q_view.stride(1), q_view.stride(2), v_view.stride(0), v_view.stride(1), v_view.stride(2), m_dim, n_dim, _BLOCK_SIZE_1, _BLOCK_SIZE_3, num_warps=4, num_stages=3)
     return out.view(q_in.size())
 
 def _attention_make_precompiler(q_in: torch.Tensor, k_in: torch.Tensor, v_in: torch.Tensor):
@@ -1416,11 +1416,11 @@ def _attention_make_precompiler(q_in: torch.Tensor, k_in: torch.Tensor, v_in: to
     out = torch.empty_like(q_view)
     sm_scale = 1.0 / math.sqrt(head_dim)
     qk_scale = sm_scale * 1.44269504
-    _BLOCK_SIZE_1 = 32
+    _BLOCK_SIZE_1 = 128
     _RDIM_SIZE_2 = 64
-    _BLOCK_SIZE_3 = 16
+    _BLOCK_SIZE_3 = 64
     from helion.runtime.precompile_shim import make_precompiler
-    return make_precompiler(_attention_kernel)(q_view, k_view, v_view, out, k_view.size(0), k_view.size(2), out.size(0), out.size(1), q_in.size(1), q_view.size(0), q_view.size(1), v_view.size(0), v_view.size(1), k_view.stride(0), k_view.stride(1), k_view.stride(2), out.stride(0), out.stride(1), out.stride(2), q_view.stride(0), q_view.stride(1), q_view.stride(2), v_view.stride(0), v_view.stride(1), v_view.stride(2), m_dim, n_dim, _BLOCK_SIZE_1, _BLOCK_SIZE_3, num_warps=1, num_stages=2)""",
+    return make_precompiler(_attention_kernel)(q_view, k_view, v_view, out, k_view.size(0), k_view.size(2), out.size(0), out.size(1), q_in.size(1), q_view.size(0), q_view.size(1), v_view.size(0), v_view.size(1), k_view.stride(0), k_view.stride(1), k_view.stride(2), out.stride(0), out.stride(1), out.stride(2), q_view.stride(0), q_view.stride(1), q_view.stride(2), v_view.stride(0), v_view.stride(1), v_view.stride(2), m_dim, n_dim, _BLOCK_SIZE_1, _BLOCK_SIZE_3, num_warps=4, num_stages=3)""",
         )
 
     def test_concat(self):

test/test_matmul.py

@@ -8,7 +8,6 @@
 
 import helion
 from helion import Config
-from helion._compat import get_triton_tensor_descriptor_class_import_path
 from helion._compat import supports_tensor_descriptor
 from helion._testing import DEVICE
 from helion._testing import code_and_output
@@ -353,13 +352,13 @@ def test_matmul_tensor_descriptor(self):
         code = examples_matmul.bind(args).to_triton_code(config)
         self.assertExpectedInline(
             code,
-            f"""\
+            """\
 from __future__ import annotations
 
 import torch
 import triton
 import triton.language as tl
-{get_triton_tensor_descriptor_class_import_path()}
+from triton.tools.tensor_descriptor import TensorDescriptor
 
 @triton.jit
 def _matmul_kernel(x_desc, y_desc, out_desc, _BLOCK_SIZE_0: tl.constexpr, _BLOCK_SIZE_1: tl.constexpr, _BLOCK_SIZE_2: tl.constexpr):
@@ -376,15 +375,16 @@ def _matmul_kernel(x_desc, y_desc, out_desc, _BLOCK_SIZE_0: tl.constexpr, _BLOCK
     acc = tl.full([_BLOCK_SIZE_0, _BLOCK_SIZE_1], 0.0, tl.float32)
     for offset_2 in range(0, 128, _BLOCK_SIZE_2):
         acc_copy = acc
+        acc_copy_0 = acc_copy
         load = x_desc.load([offset_0, offset_2])
         load_1 = y_desc.load([offset_2, offset_1])
-        acc = tl.dot(load, load_1, acc=acc_copy, input_precision='tf32')
+        acc = tl.dot(load, load_1, acc=acc_copy_0, input_precision='tf32')
     out_desc.store([offset_0, offset_1], acc)
 
 def matmul(x: torch.Tensor, y: torch.Tensor):
     m, k = x.size()
     k2, n = y.size()
-    assert k == k2, f'size mismatch {{k}} != {{k2}}'
+    assert k == k2, f'size mismatch {k} != {k2}'
     out = torch.empty([m, n], dtype=torch.promote_types(x.dtype, y.dtype), device=x.device)
     _BLOCK_SIZE_0 = 16
     _BLOCK_SIZE_1 = 16
@@ -395,7 +395,7 @@ def matmul(x: torch.Tensor, y: torch.Tensor):
 def _matmul_make_precompiler(x: torch.Tensor, y: torch.Tensor):
     m, k = x.size()
     k2, n = y.size()
-    assert k == k2, f'size mismatch {{k}} != {{k2}}'
+    assert k == k2, f'size mismatch {k} != {k2}'
     out = torch.empty([m, n], dtype=torch.promote_types(x.dtype, y.dtype), device=x.device)
     _BLOCK_SIZE_0 = 16
     _BLOCK_SIZE_1 = 16