[example] fused_linear_jsd and tests

Author: v0i0

benchmarks/run.py

@@ -180,6 +180,11 @@ class RunResult:
         "examples.jagged_softmax",
         "jagged_softmax_tritonbench",
     ),
+    "fused_linear_jsd": (
+        "tritonbench.operators.fused_linear_jsd.operator",
+        "examples.fused_linear_jsd",
+        "fused_linear_jsd_fwd_tritonbench",
+    ),
     # Multiple kernel variants:
     "gemm": (
         "tritonbench.operators.gemm.operator",

examples/fused_linear_jsd.py

@@ -0,0 +1,139 @@
+"""
+Fused Linear JSD Example
+===========================
+
+This example demonstrates how to implement a JSD kernel using Helion and
+fuse it with a linear layer.
+"""
+
+# %%
+# Imports
+# -------
+from __future__ import annotations
+
+from typing import Callable
+
+import torch
+
+import helion
+from helion._testing import run_example
+import helion.language as hl
+
+
+# %%
+# Helion Kernel
+# -------------------
+@helion.kernel()
+def fused_linear_jsd_fwd(
+    beta: float,
+    ignore_index: int,
+    temperature: float,
+    student_weight: torch.Tensor,
+    teacher_weight: torch.Tensor,
+    student_input: torch.Tensor,
+    teacher_input: torch.Tensor,
+) -> torch.Tensor:
+    student_logits = student_input @ student_weight.T
+    teacher_logits = teacher_input @ teacher_weight.T
+    loss = student_logits.new_empty(student_input.shape[0], dtype=torch.float)
+    for batch in hl.tile(student_logits.shape[0]):
+        student_prob = torch.log_softmax(student_logits[batch, :] / temperature, dim=-1)
+        teacher_prob = torch.log_softmax(teacher_logits[batch, :] / temperature, dim=-1)
+        student_prob = student_prob.to(torch.float).view(-1, student_prob.size(-1))
+        teacher_prob = teacher_prob.to(torch.float).view(-1, teacher_prob.size(-1))
+        m = torch.exp(student_prob) + beta * (
+            torch.exp(teacher_prob) - torch.exp(student_prob)
+        )
+        teacher_div = torch.nn.functional.kl_div(
+            torch.log(m), teacher_prob, reduction="none", log_target=True
+        ).sum(dim=-1)
+        student_div = torch.nn.functional.kl_div(
+            torch.log(m), student_prob, reduction="none", log_target=True
+        ).sum(dim=-1)
+        batch_loss = student_div + beta * (teacher_div - student_div)
+        loss[batch] = batch_loss
+    return (loss / student_logits.shape[0]).sum()
+
+
+# %%
+# Benchmark Entry Point Function
+# -------------------
+def fused_linear_jsd_fwd_tritonbench(
+    tb_op: object,
+    student_input: torch.Tensor,
+    teacher_input: torch.Tensor,
+    label: torch.Tensor | None = None,
+) -> Callable[[], torch.Tensor]:
+    assert label is None
+    baseline_op = tb_op.baseline_op  # pyright: ignore[reportAttributeAccessIssue]
+    beta = baseline_op.jsd.beta
+    ignore_index = baseline_op.jsd.ignore_index
+    temperature = baseline_op.temperature
+    student_weight = baseline_op.student_lin.weight
+    teacher_weight = baseline_op.teacher_lin.weight
+    return lambda: fused_linear_jsd_fwd(
+        beta,
+        ignore_index,
+        temperature,
+        student_weight,
+        teacher_weight,
+        student_input,
+        teacher_input,
+    )
+
+
+# %%
+# Reference Implementation
+# --------------------
+def fused_linear_jsd_pytorch(
+    beta: float,
+    ignore_index: int,
+    temperature: float,
+    student_weight: torch.Tensor,
+    teacher_weight: torch.Tensor,
+    student_input: torch.Tensor,
+    teacher_input: torch.Tensor,
+) -> torch.Tensor:
+    student_logits = student_input @ student_weight.T
+    teacher_logits = teacher_input @ teacher_weight.T
+    student_prob = torch.log_softmax(student_logits / temperature, dim=-1)
+    teacher_prob = torch.log_softmax(teacher_logits / temperature, dim=-1)
+    student_prob = student_prob.to(torch.float).view(-1, student_prob.size(-1))
+    teacher_prob = teacher_prob.to(torch.float).view(-1, teacher_prob.size(-1))
+    m = torch.exp(student_prob) + beta * (
+        torch.exp(teacher_prob) - torch.exp(student_prob)
+    )
+    teacher_div = torch.nn.functional.kl_div(
+        torch.log(m), teacher_prob, reduction="none", log_target=True
+    ).sum(dim=-1)
+    student_div = torch.nn.functional.kl_div(
+        torch.log(m), student_prob, reduction="none", log_target=True
+    ).sum(dim=-1)
+    loss = student_div + beta * (teacher_div - student_div)
+    return (loss / student_logits.shape[0]).sum()
+
+
+# %%
+# Verification Function
+# -------------------
+def check(m: int, n: int, k: int) -> None:
+    student_input = torch.rand([m, n], device="cuda", dtype=torch.float)
+    teacher_input = torch.rand([m, n], device="cuda", dtype=torch.float)
+    student_weight = torch.rand([k, n], device="cuda", dtype=torch.float)
+    teacher_weight = torch.rand([k, n], device="cuda", dtype=torch.float)
+    run_example(
+        fused_linear_jsd_fwd,
+        fused_linear_jsd_pytorch,
+        (0.5, 1, 1.0, student_weight, teacher_weight, student_input, teacher_input),
+    )
+
+
+# %%
+# Main Function
+# -----------
+def main() -> None:
+    check(1024, 4096, 128256)
+
+
+if __name__ == "__main__":
+    main()

test/test_examples.expected

@@ -841,6 +841,78 @@ def fp8_gemm(x: torch.Tensor, y: torch.Tensor, *, _launcher=_default_launcher):
     _launcher(_helion_fp8_gemm, (triton.cdiv(256, _BLOCK_SIZE_0) * triton.cdiv(256, _BLOCK_SIZE_1),), x, y, out, _BLOCK_SIZE_0, _BLOCK_SIZE_1, _BLOCK_SIZE_2, num_warps=4, num_stages=3)
     return out
 
+--- assertExpectedJournal(TestExamples.test_fused_linear_jsd)
+from __future__ import annotations
+
+import torch
+import triton
+import triton.language as tl
+from torch._inductor.runtime.triton_helpers import math as tl_math
+from torch._inductor.runtime.triton_compat import libdevice
+from helion.runtime import default_launcher as _default_launcher
+
+@triton.jit
+def _helion_fused_linear_jsd_fwd(student_logits, teacher_logits, loss, student_input_size_0, student_weight_size_0, loss_stride_0, student_logits_stride_0, student_logits_stride_1, teacher_logits_stride_0, teacher_logits_stride_1, temperature, beta, _BLOCK_SIZE_0: tl.constexpr, _RDIM_SIZE_1: tl.constexpr):
+    pid_0 = tl.program_id(0)
+    offset_0 = pid_0 * _BLOCK_SIZE_0
+    indices_0 = (offset_0 + tl.arange(0, _BLOCK_SIZE_0)).to(tl.int32)
+    mask_0 = indices_0 < student_input_size_0
+    indices_1 = tl.arange(0, _RDIM_SIZE_1).to(tl.int32)
+    mask_1 = indices_1 < student_weight_size_0
+    load = tl.load(student_logits + (indices_0[:, None] * student_logits_stride_0 + indices_1[None, :] * student_logits_stride_1), mask_0[:, None] & mask_1[None, :], other=0)
+    v_0 = load / temperature
+    _mask_to = tl.where(mask_0[:, None] & mask_1[None, :], v_0, tl.full([], float('-inf'), tl.float32))
+    amax = tl.cast(tl.reshape(tl.max(_mask_to, 1), [_BLOCK_SIZE_0, 1]), tl.float32)
+    v_1 = v_0 - amax
+    v_2 = libdevice.exp(v_1)
+    _mask_to_1 = tl.where(mask_0[:, None] & mask_1[None, :], v_2, tl.full([], 0, tl.float32))
+    sum_1 = tl.cast(tl.reshape(tl.sum(_mask_to_1, 1), [_BLOCK_SIZE_0, 1]), tl.float32)
+    v_3 = tl_math.log(sum_1)
+    v_4 = v_1 - v_3
+    load_1 = tl.load(teacher_logits + (indices_0[:, None] * teacher_logits_stride_0 + indices_1[None, :] * teacher_logits_stride_1), mask_0[:, None] & mask_1[None, :], other=0)
+    v_5 = load_1 / temperature
+    _mask_to_2 = tl.where(mask_0[:, None] & mask_1[None, :], v_5, tl.full([], float('-inf'), tl.float32))
+    amax_1 = tl.cast(tl.reshape(tl.max(_mask_to_2, 1), [_BLOCK_SIZE_0, 1]), tl.float32)
+    v_6 = v_5 - amax_1
+    v_7 = libdevice.exp(v_6)
+    _mask_to_3 = tl.where(mask_0[:, None] & mask_1[None, :], v_7, tl.full([], 0, tl.float32))
+    sum_2 = tl.cast(tl.reshape(tl.sum(_mask_to_3, 1), [_BLOCK_SIZE_0, 1]), tl.float32)
+    v_8 = tl_math.log(sum_2)
+    v_9 = v_6 - v_8
+    student_prob_1 = tl.reshape(v_4, [_BLOCK_SIZE_0, _RDIM_SIZE_1])
+    teacher_prob_1 = tl.reshape(v_9, [_BLOCK_SIZE_0, _RDIM_SIZE_1])
+    v_10 = libdevice.exp(student_prob_1)
+    v_11 = libdevice.exp(teacher_prob_1)
+    v_12 = libdevice.exp(student_prob_1)
+    v_13 = v_11 - v_12
+    v_14 = v_13 * beta
+    v_15 = v_10 + v_14
+    v_16 = tl_math.log(v_15)
+    v_17 = teacher_prob_1 - v_16
+    v_18 = libdevice.exp(teacher_prob_1)
+    v_19 = v_18 * v_17
+    _mask_to_4 = tl.where(mask_0[:, None] & mask_1[None, :], v_19, tl.full([], 0, tl.float32))
+    teacher_div = tl.cast(tl.sum(_mask_to_4, 1), tl.float32)
+    v_20 = tl_math.log(v_15)
+    v_21 = student_prob_1 - v_20
+    v_22 = libdevice.exp(student_prob_1)
+    v_23 = v_22 * v_21
+    _mask_to_5 = tl.where(mask_0[:, None] & mask_1[None, :], v_23, tl.full([], 0, tl.float32))
+    student_div = tl.cast(tl.sum(_mask_to_5, 1), tl.float32)
+    v_24 = teacher_div - student_div
+    v_25 = v_24 * beta
+    v_26 = student_div + v_25
+    tl.store(loss + indices_0 * loss_stride_0, v_26, mask_0)
+
+def fused_linear_jsd_fwd(beta: float, ignore_index: int, temperature: float, student_weight: torch.Tensor, teacher_weight: torch.Tensor, student_input: torch.Tensor, teacher_input: torch.Tensor, *, _launcher=_default_launcher):
+    student_logits = student_input @ student_weight.T
+    teacher_logits = teacher_input @ teacher_weight.T
+    loss = student_logits.new_empty(student_input.shape[0], dtype=torch.float)
+    _BLOCK_SIZE_0 = 32
+    _RDIM_SIZE_1 = triton.next_power_of_2(student_weight.size(0))
+    _launcher(_helion_fused_linear_jsd_fwd, (triton.cdiv(student_input.size(0), _BLOCK_SIZE_0),), student_logits, teacher_logits, loss, student_input.size(0), student_weight.size(0), loss.stride(0), student_logits.stride(0), student_logits.stride(1), teacher_logits.stride(0), teacher_logits.stride(1), temperature, beta, _BLOCK_SIZE_0, _RDIM_SIZE_1, num_warps=4, num_stages=3)
+    return (loss / student_logits.shape[0]).sum()
+
 --- assertExpectedJournal(TestExamples.test_geglu)
 from __future__ import annotations
 

test/test_examples.py

@@ -1147,6 +1147,41 @@ def test_int4_gemm(self):
             )
         )
 
+    def test_fused_linear_jsd(self):
+        beta = 0.5
+        ignore_index = 1
+        temperature = 1.0
+        m, n, k = 64, 128, 256
+
+        student_input = torch.randn([m, n], device=DEVICE, dtype=torch.float32)
+        teacher_input = torch.randn([m, n], device=DEVICE, dtype=torch.float32)
+        student_weight = torch.randn([k, n], device=DEVICE, dtype=torch.float32)
+        teacher_weight = torch.randn([k, n], device=DEVICE, dtype=torch.float32)
+
+        args = (
+            beta,
+            ignore_index,
+            temperature,
+            student_weight,
+            teacher_weight,
+            student_input,
+            teacher_input,
+        )
+
+        # Import and use the reference implementation
+        mod = import_path(EXAMPLES_DIR / "fused_linear_jsd.py")
+        expected = mod.fused_linear_jsd_pytorch(*args)
+
+        self.assertExpectedJournal(
+            check_example(
+                "fused_linear_jsd",
+                args,
+                expected,
+                fn_name="fused_linear_jsd_fwd",
+                block_sizes=[32],
+            )
+        )
+
 
 if __name__ == "__main__":
     unittest.main()