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v4.2 release. (#2587)

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* Fix default cluster callback values to 1 to avoid profiler failure when these values are not set in command line.

* v4.2 release.
This commit is contained in:
Junkai-Wu
2025-08-23 06:11:24 +08:00
committed by GitHub
parent 11cad1f67b
commit a49a78ffef
351 changed files with 28182 additions and 2032 deletions
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34
test/python/cutlass/emit/pytorch.py
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@@ -40,9 +40,9 @@ import unittest
from cutlass_library import ConvMode
import cutlass
import cutlass_cppgen
if cutlass.utils.datatypes.is_torch_available():
if cutlass_cppgen.utils.datatypes.is_torch_available():
import torch
@@ -95,7 +95,7 @@ def _generate_conv2d_problem(conv_kind, dtype, ps):
:type conv_kind: str
:param dtype: data type of tensors
:param problem_size: the conv2d problem size
:type problem_size: cutlass.shape.Conv2DProblemSize
:type problem_size: cutlass_cppgen.shape.Conv2DProblemSize
:return: initialized tensors A, B, C, and D
:rtype: list
@@ -116,18 +116,18 @@ def _generate_conv2d_problem(conv_kind, dtype, ps):
return [torch.ceil(torch.empty(size, dtype=dtype, device='cuda').uniform_(-4.5, 3.5)).to(memory_format=torch.channels_last) for size in sizes]
@unittest.skipIf(not cutlass.utils.datatypes.is_torch_available(), 'PyTorch must be available to run PyTorch extension tests')
@unittest.skipIf(not cutlass_cppgen.utils.datatypes.is_torch_available(), 'PyTorch must be available to run PyTorch extension tests')
class PyTorchExtensionTest(unittest.TestCase):
def test_gemm(self):
random.seed(2023)
dtype = torch.float16
plan = cutlass.op.Gemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
plan = cutlass_cppgen.op.Gemm(element=dtype, layout=cutlass_cppgen.LayoutType.RowMajor)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name='gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
mod = cutlass_cppgen.emit.pytorch(op, name='gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
A, B, C, _ = _initialize(dtype, 1024, 256, 512)
@@ -154,11 +154,11 @@ class PyTorchExtensionTest(unittest.TestCase):
random.seed(2023)
dtype = torch.float16
plan = cutlass.op.GroupedGemm(element=dtype, layout=cutlass.LayoutType.RowMajor)
plan = cutlass_cppgen.op.GroupedGemm(element=dtype, layout=cutlass_cppgen.LayoutType.RowMajor)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name='grouped_gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
mod = cutlass_cppgen.emit.pytorch(op, name='grouped_gemm_mod', cc=plan.cc, sourcedir=tmpdir, jit=True)
As, Bs, Cs, _ = _generate_problems(dtype, 50)
@@ -189,14 +189,14 @@ class PyTorchExtensionTest(unittest.TestCase):
torch.manual_seed(2023)
dtype = torch.float16
plan = cutlass.op.Conv2d(kind="fprop", element=dtype, element_accumulator=torch.float32)
plan = cutlass_cppgen.op.Conv2d(kind="fprop", element=dtype, element_accumulator=torch.float32)
plan.activation = "relu"
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name="conv2d_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
mod = cutlass_cppgen.emit.pytorch(op, name="conv2d_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
problem_size = cutlass.shape.Conv2DProblemSize(
problem_size = cutlass_cppgen.shape.Conv2DProblemSize(
1, 4, 4, 16,
8, 3, 3, 16,
0, 0,
@@ -231,13 +231,13 @@ class PyTorchExtensionTest(unittest.TestCase):
def test_conv2d_dgrad(self):
torch.manual_seed(2023)
dtype = torch.float16
plan = cutlass.op.Conv2d(kind="dgrad", element=dtype, element_accumulator=torch.float32)
plan = cutlass_cppgen.op.Conv2d(kind="dgrad", element=dtype, element_accumulator=torch.float32)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name="conv2d_dgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
mod = cutlass_cppgen.emit.pytorch(op, name="conv2d_dgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
problem_size = cutlass.shape.Conv2DProblemSize(
problem_size = cutlass_cppgen.shape.Conv2DProblemSize(
1, 4, 4, 16,
8, 3, 3, 16,
0, 0,
@@ -265,13 +265,13 @@ class PyTorchExtensionTest(unittest.TestCase):
def test_conv2d_wgrad(self):
torch.manual_seed(2023)
dtype = torch.float16
plan = cutlass.op.Conv2d(kind="wgrad", element=dtype, element_accumulator=torch.float32)
plan = cutlass_cppgen.op.Conv2d(kind="wgrad", element=dtype, element_accumulator=torch.float32)
op = plan.construct()
with tempfile.TemporaryDirectory() as tmpdir:
mod = cutlass.emit.pytorch(op, name="conv2d_wgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
mod = cutlass_cppgen.emit.pytorch(op, name="conv2d_wgrad_mod", cc=plan.cc, sourcedir=tmpdir, jit=True)
problem_size = cutlass.shape.Conv2DProblemSize(
problem_size = cutlass_cppgen.shape.Conv2DProblemSize(
1, 4, 4, 16,
8, 3, 3, 16,
0, 0,