v4.5 tag update (#3202)
* Python DSL examples reorganization. * v4.5 tag update.
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Junkai-Wu
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# Copyright (c) 2025 - 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: BSD-3-Clause
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are met:
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# 1. Redistributions of source code must retain the above copyright notice, this
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# list of conditions and the following disclaimer.
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# 2. Redistributions in binary form must reproduce the above copyright notice,
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# this list of conditions and the following disclaimer in the documentation
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# and/or other materials provided with the distribution.
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# 3. Neither the name of the copyright holder nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
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# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
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# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
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# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
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# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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from typing import Optional
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import torch
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import cutlass
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import cutlass.cute as cute
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import cutlass.torch as cutlass_torch
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import cutlass.utils.mixed_input_helpers as mixed_input_utils
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from cutlass.cute.runtime import from_dlpack
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"""
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This file contains common host-side utilities for mixed-input GEMM.
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"""
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def create_cumsum_tensor(
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num_groups: int,
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fused_n: int,
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alignment: int,
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uniform_distribution: bool = False,
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) -> tuple[cute.Tensor, torch.Tensor]:
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"""
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Create a tensor of shape (num_groups + 1) recording the cumulative sum of the elements in each group.
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"""
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assert fused_n % alignment == 0, "fused_n must be divisible by alignment"
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if uniform_distribution:
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# keep a uniform distribution for debug and performance collection
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group_counts = torch.tensor([fused_n // num_groups] * num_groups)
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else:
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# sample group sizes with equal probability for each group
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probs = torch.ones(num_groups) / num_groups
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group_sizes = torch.multinomial(probs, fused_n // alignment, replacement=True)
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group_counts = torch.bincount(group_sizes, minlength=num_groups) * alignment
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print(group_counts.tolist())
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# Create cumulative sum
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cumsum_torch = torch.cat([torch.tensor([0]), group_counts.cumsum(0)])
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print(cumsum_torch.tolist())
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cumsum_tensor, _ = cutlass_torch.cute_tensor_like(
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cumsum_torch, cutlass.Int32, is_dynamic_layout=False
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)
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return cumsum_tensor, cumsum_torch.to("cpu")
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def create_i4_tensor_and_scale(
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l: int,
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m: int,
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k: int,
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is_m_major: bool,
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dtype: type[cutlass.Numeric],
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shuffle_a: bool,
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scale_granularity_m: int,
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scale_granularity_k: int,
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is_dynamic_layout: bool = True,
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init_config: tuple = (
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cutlass_torch.TensorInitType.RANDOM,
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cutlass_torch.RandomInitConfig(min_val=-7, max_val=6),
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),
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divisibility: int = 16,
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transformed_dtype: Optional[type[cutlass.Numeric]] = None,
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) -> tuple[
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cute.Tensor,
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torch.Tensor,
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torch.Tensor,
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cute.Tensor,
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torch.Tensor,
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torch.Tensor,
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]:
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"""
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Create quantized 4-bit tensor and corresponding scale tensor.
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"""
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lb_4b = -8 if dtype == cutlass.Int4 else 0
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up_4b = 7 if dtype == cutlass.Int4 else 15
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if not (
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init_config[0] == cutlass_torch.TensorInitType.RANDOM
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or init_config[0] == cutlass_torch.TensorInitType.SCALAR
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):
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raise ValueError(
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"Only random and scalar initialization is supported for 4bit data type"
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)
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# Construct reference tensor in f32
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ref_fp32 = cutlass_torch.matrix(l, m, k, is_m_major, cutlass.Float32, *init_config)
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# Generate scale data and perform quantization
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num_scales = k // scale_granularity_k
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ref = ref_fp32.to(dtype=cutlass_torch.dtype(transformed_dtype)).reshape(
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m, num_scales, scale_granularity_k, l
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)
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# Get elements with maximum absolute value to compute scaling factors
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a_max = (
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torch.maximum(ref / up_4b, ref / lb_4b)
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if dtype == cutlass.Int4
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else ref / up_4b
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)
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a_scales, _ = torch.max(a_max, dim=2, keepdim=True)
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a_scale_inv = torch.where(a_scales == 0, 0, 1 / a_scales)
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a_quant = ref * a_scale_inv
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# Convert values to integer to avoid computation errors
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a_quant = a_quant.to(dtype=torch.int32).reshape((m, k, l)).to(dtype=torch.float32)
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# Construct cute scale tensor
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a_scales = a_scales.random_(-3, 3).reshape((m, num_scales, l))
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# Scale tensor is always m-major
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a_scales = a_scales.permute(2, 1, 0).contiguous().permute(2, 1, 0).to(device="cuda")
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if shuffle_a:
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# shuffle within each group of 8 elements
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perm = torch.tensor([0, 2, 1, 3, 4, 6, 5, 7], device=a_quant.device)
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a_shuffled = (
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a_quant.view(m, k // 8, 8, l)[:, :, perm, :]
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.reshape(a_quant.shape)
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.permute(2, 0, 1)
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.contiguous()
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.permute(1, 2, 0)
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)
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# Construct A quantized tensor
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cute_a_quant_tensor, torch_a_quant_tensor = cutlass_torch.cute_tensor_like(
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a_shuffled,
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dtype,
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is_dynamic_layout=is_dynamic_layout,
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assumed_align=divisibility,
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)
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else:
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# Construct A quantized tensor
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cute_a_quant_tensor, torch_a_quant_tensor = cutlass_torch.cute_tensor_like(
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a_quant,
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dtype,
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is_dynamic_layout=is_dynamic_layout,
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assumed_align=divisibility,
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)
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cute_scale_tensor = from_dlpack(a_scales, assumed_align=divisibility)
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for i, stride in enumerate(a_scales.stride()):
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if stride == 1:
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leading_dim = i
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break
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if is_dynamic_layout:
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cute_scale_tensor = cute_scale_tensor.mark_layout_dynamic(
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leading_dim=leading_dim
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)
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return (
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cute_a_quant_tensor,
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torch_a_quant_tensor,
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a_quant.to("cpu"),
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cute_scale_tensor,
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a_scales,
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a_scales.to("cpu"),
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)
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def create_tensor_a(
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l: int,
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m: int,
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k: int,
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a_major: str,
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a_dtype: type[cutlass.Numeric],
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shuffle_a: bool,
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scale_granularity_m: int = 0,
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scale_granularity_k: int = 0,
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transformed_dtype: Optional[type[cutlass.Numeric]] = None,
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) -> tuple[cute.Tensor, Optional[cute.Tensor], torch.Tensor, Optional[torch.Tensor]]:
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"""
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Create tensor A and scale tensor.
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"""
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a_scale_tensor = None
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a_scale_torch_cpu = None
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if a_dtype in (cutlass.Int4,):
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(
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a_tensor,
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a_torch_gpu,
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a_torch_cpu,
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a_scale_tensor,
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a_scale_torch_gpu,
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a_scale_torch_cpu,
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) = create_i4_tensor_and_scale(
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l,
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m,
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k,
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a_major == "m",
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a_dtype,
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shuffle_a,
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scale_granularity_m,
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scale_granularity_k,
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divisibility=mixed_input_utils.get_divisibility(m if a_major == "m" else k),
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transformed_dtype=transformed_dtype,
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)
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else:
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a_torch_cpu = cutlass_torch.matrix(
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l,
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m,
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k,
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a_major == "m",
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a_dtype,
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)
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a_tensor, _ = cutlass_torch.cute_tensor_like(
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a_torch_cpu,
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a_dtype,
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is_dynamic_layout=True,
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assumed_align=mixed_input_utils.get_divisibility(
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m if a_major == "m" else k
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),
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)
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return a_tensor, a_scale_tensor, a_torch_cpu, a_scale_torch_cpu
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def create_tensors_for_contiguous_grouped_mixed_input_gemm(
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l: int,
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m: int,
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n: int,
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k: int,
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a_major: str,
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b_major: str,
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c_major: str,
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a_dtype: type[cutlass.Numeric],
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b_dtype: type[cutlass.Numeric],
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c_dtype: type[cutlass.Numeric],
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shuffle_a: bool = False,
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scale_granularity_m: int = 0,
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scale_granularity_k: int = 0,
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uniform_group_sizes: bool = False,
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) -> tuple:
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"""
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Create all input and output tensors for the contiguous grouped mixed-input GEMM.
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"""
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a_tensor, a_scale_tensor, a_torch_cpu, a_scale_torch_cpu = create_tensor_a(
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l,
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m,
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k,
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a_major,
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a_dtype,
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shuffle_a,
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scale_granularity_m,
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scale_granularity_k,
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b_dtype,
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)
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# In GROUP mode, l specifies the number of groups. We'll fuse group into the n mode for tensor B and C.
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# Batch mode will be set to 1.
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num_groups = l
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fused_n = n * num_groups
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b_torch_cpu = cutlass_torch.matrix(
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1, # batch=1
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fused_n,
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k,
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b_major == "n",
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b_dtype,
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cutlass_torch.TensorInitType.RANDOM,
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cutlass_torch.RandomInitConfig(min_val=-10, max_val=10),
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)
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b_tensor, _ = cutlass_torch.cute_tensor_like(
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b_torch_cpu,
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b_dtype,
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is_dynamic_layout=True,
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assumed_align=mixed_input_utils.get_divisibility(n if b_major == "n" else k),
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)
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c_torch_cpu = cutlass_torch.matrix(
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1, # batch=1
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m,
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fused_n,
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c_major == "m",
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c_dtype,
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)
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c_tensor, c_torch_gpu = cutlass_torch.cute_tensor_like(
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c_torch_cpu,
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c_dtype,
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is_dynamic_layout=True,
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assumed_align=mixed_input_utils.get_divisibility(m if c_major == "m" else n),
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)
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c_tensor = c_tensor.mark_compact_shape_dynamic(
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mode=(0 if c_major == "m" else 1),
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stride_order=(2, 1, 0) if c_major == "m" else (2, 0, 1),
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divisibility=mixed_input_utils.get_divisibility(m if c_major == "m" else n),
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)
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# We need to ensure mode N satisfies 16B alignment for each group
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alignment_n = 16 * 8 // b_dtype.width
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cumsum_tensor, cumsum_torch = create_cumsum_tensor(
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num_groups, fused_n, alignment_n, uniform_distribution=uniform_group_sizes
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)
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return (
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a_tensor,
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a_scale_tensor,
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b_tensor,
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cumsum_tensor,
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c_tensor,
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a_torch_cpu,
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a_scale_torch_cpu,
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b_torch_cpu,
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cumsum_torch,
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c_torch_gpu,
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)
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def create_tensors_for_batched_mixed_input_gemm(
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l: int,
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m: int,
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n: int,
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k: int,
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a_major: str,
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b_major: str,
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c_major: str,
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a_dtype: type[cutlass.Numeric],
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b_dtype: type[cutlass.Numeric],
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c_dtype: type[cutlass.Numeric],
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shuffle_a: bool = False,
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scale_granularity_m: int = 0,
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scale_granularity_k: int = 0,
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) -> tuple:
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"""
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Create all input and output tensors for the batched mixed-input GEMM.
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"""
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torch.manual_seed(2025)
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a_tensor, a_scale_tensor, a_torch_cpu, a_scale_torch_cpu = create_tensor_a(
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l,
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m,
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k,
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a_major,
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a_dtype,
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shuffle_a,
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scale_granularity_m,
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scale_granularity_k,
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b_dtype,
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)
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b_torch_cpu = cutlass_torch.matrix(
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l,
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n,
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k,
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b_major == "n",
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b_dtype,
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cutlass_torch.TensorInitType.RANDOM,
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cutlass_torch.RandomInitConfig(min_val=-10, max_val=10),
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)
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c_torch_cpu = cutlass_torch.matrix(
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l,
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m,
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n,
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c_major == "m",
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c_dtype,
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)
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b_tensor, _ = cutlass_torch.cute_tensor_like(
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b_torch_cpu,
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b_dtype,
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is_dynamic_layout=True,
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assumed_align=mixed_input_utils.get_divisibility(n if b_major == "n" else k),
|
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)
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c_tensor, c_torch_gpu = cutlass_torch.cute_tensor_like(
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c_torch_cpu,
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c_dtype,
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is_dynamic_layout=True,
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assumed_align=mixed_input_utils.get_divisibility(m if c_major == "m" else n),
|
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)
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c_tensor = c_tensor.mark_compact_shape_dynamic(
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mode=(0 if c_major == "m" else 1),
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stride_order=(2, 1, 0) if c_major == "m" else (2, 0, 1),
|
||||
divisibility=mixed_input_utils.get_divisibility(m if c_major == "m" else n),
|
||||
)
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||||
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return (
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a_tensor,
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a_scale_tensor,
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b_tensor,
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||||
c_tensor,
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a_torch_cpu,
|
||||
a_scale_torch_cpu,
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b_torch_cpu,
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||||
c_torch_gpu,
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||||
)
|
||||
|
||||
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||||
def run_contiguous_grouped_ref_and_compare(
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a_torch_cpu: torch.Tensor,
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b_torch_cpu: torch.Tensor,
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a_scale_torch_cpu: Optional[torch.Tensor],
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||||
cumsum_torch_cpu: torch.Tensor,
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||||
c_torch_gpu: torch.Tensor,
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||||
c_dtype: type[cutlass.Numeric],
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||||
tolerance: float,
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||||
) -> None:
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"""
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Compare kernel result with reference computation.
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||||
"""
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||||
kernel_result = c_torch_gpu.cpu()
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assert kernel_result.shape[2] == 1, "batch mode must be 1"
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||||
kernel_result = kernel_result.reshape(
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||||
kernel_result.shape[0], kernel_result.shape[1]
|
||||
)
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||||
# Compute reference result
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||||
a_for_gemm = a_torch_cpu
|
||||
if a_scale_torch_cpu is not None:
|
||||
scale_shape = a_scale_torch_cpu.shape
|
||||
a_shape = a_torch_cpu.shape
|
||||
a_scale_torch_cpu = a_scale_torch_cpu.to(dtype=torch.float32).reshape(
|
||||
scale_shape[0], scale_shape[1], 1, scale_shape[2]
|
||||
)
|
||||
a_torch_cpu = a_torch_cpu.to(dtype=torch.float32).reshape(
|
||||
a_torch_cpu.shape[0], scale_shape[1], -1, a_torch_cpu.shape[2]
|
||||
)
|
||||
a_for_gemm = (a_torch_cpu * a_scale_torch_cpu).reshape(a_shape)
|
||||
# A in (m, k, l), b in (n, k), c in (m, n)
|
||||
assert cumsum_torch_cpu.shape[0] == a_for_gemm.shape[-1] + 1, (
|
||||
"cumsum tensor must have one more element than a_for_gemm"
|
||||
)
|
||||
assert b_torch_cpu.shape[2] == 1, (
|
||||
"b_torch_cpu must have a singleton dimension in the last position"
|
||||
)
|
||||
prev_idx = 0
|
||||
ref = torch.zeros((a_for_gemm.shape[0], b_torch_cpu.shape[0]), dtype=torch.float32)
|
||||
for group_idx in range(1, cumsum_torch_cpu.shape[0]):
|
||||
# No computation for current group
|
||||
if cumsum_torch_cpu[group_idx] == prev_idx:
|
||||
continue
|
||||
# Get A slice for current group
|
||||
sliced_a = a_for_gemm[:, :, group_idx - 1]
|
||||
# Get B slice for current group
|
||||
sliced_b = b_torch_cpu[prev_idx : cumsum_torch_cpu[group_idx], :, 0]
|
||||
sliced_ref = torch.einsum(
|
||||
"mk,nk->mn",
|
||||
sliced_a.to(dtype=torch.float32),
|
||||
sliced_b.to(dtype=torch.float32),
|
||||
)
|
||||
ref[:, prev_idx : cumsum_torch_cpu[group_idx]] = sliced_ref
|
||||
prev_idx = cumsum_torch_cpu[group_idx]
|
||||
# Convert ref to c_dtype
|
||||
_, ref_torch_gpu = cutlass_torch.cute_tensor_like(
|
||||
ref, c_dtype, is_dynamic_layout=True, assumed_align=16
|
||||
)
|
||||
ref_result = ref_torch_gpu.cpu()
|
||||
|
||||
# Assert close results
|
||||
torch.testing.assert_close(kernel_result, ref_result, atol=tolerance, rtol=1e-05)
|
||||
|
||||
|
||||
def run_batched_mixed_input_ref_and_compare(
|
||||
a_torch_cpu: torch.Tensor,
|
||||
b_torch_cpu: torch.Tensor,
|
||||
a_scale_torch_cpu: Optional[torch.Tensor],
|
||||
c_torch_gpu: torch.Tensor,
|
||||
c_dtype: type[cutlass.Numeric],
|
||||
tolerance: float,
|
||||
) -> None:
|
||||
"""
|
||||
Compare kernel result with reference computation.
|
||||
"""
|
||||
kernel_result = c_torch_gpu.cpu()
|
||||
# Compute reference result
|
||||
if a_scale_torch_cpu is not None:
|
||||
scale_shape = a_scale_torch_cpu.shape
|
||||
a_shape = a_torch_cpu.shape
|
||||
a_scale_torch_cpu = a_scale_torch_cpu.to(dtype=torch.float32).reshape(
|
||||
scale_shape[0], scale_shape[1], 1, scale_shape[2]
|
||||
)
|
||||
a_torch_cpu = a_torch_cpu.to(dtype=torch.float32).reshape(
|
||||
a_torch_cpu.shape[0], scale_shape[1], -1, a_torch_cpu.shape[2]
|
||||
)
|
||||
a_dequant = a_torch_cpu * a_scale_torch_cpu
|
||||
ref = torch.einsum(
|
||||
"mkl,nkl->mnl",
|
||||
a_dequant.reshape(a_shape),
|
||||
b_torch_cpu.to(dtype=torch.float32),
|
||||
)
|
||||
else:
|
||||
ref = torch.einsum(
|
||||
"mkl,nkl->mnl",
|
||||
a_torch_cpu.to(dtype=torch.float32),
|
||||
b_torch_cpu.to(dtype=torch.float32),
|
||||
)
|
||||
# Convert ref to c_dtype
|
||||
_, ref_torch_gpu = cutlass_torch.cute_tensor_like(
|
||||
ref, c_dtype, is_dynamic_layout=True, assumed_align=16
|
||||
)
|
||||
ref_result = ref_torch_gpu.cpu()
|
||||
|
||||
# Assert close results
|
||||
torch.testing.assert_close(kernel_result, ref_result, atol=tolerance, rtol=1e-05)
|
||||
Reference in New Issue
Block a user