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################################################################################
#
# Copyright (c) 20123 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: BSD-3-Clause
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
################################################################################
"""
Testbed classes of EVT
"""
import torch
import unittest
import cutlass
from cutlass import Tensor
import cutlass.backend.evt
from cutlass.shape import GemmCoord
from cutlass.utils.datatypes import torch_type
from cutlass.utils.profiler import CUDAEventProfiler
class EVTReferenceModule:
def __init__(self, layout_A, layout_B, layout_C, epilogue_visitor):
self.layout_A = layout_A
self.layout_B = layout_B
self.layout_C = layout_C
self.epilogue_visitor = epilogue_visitor
def run(self, A, B, C, problem_size, alpha, beta, batch=1):
if self.layout_A == cutlass.LayoutType.RowMajor:
A_row = A.view((batch, problem_size.m, problem_size.k))
else:
A_col = A.view((batch, problem_size.k, problem_size.m))
A_row = torch.permute(A_col, (0, 2, 1))
if self.layout_B == cutlass.LayoutType.RowMajor:
B_row = B.view((batch, problem_size.k, problem_size.n))
else:
B_col = B.view((batch, problem_size.n, problem_size.k))
B_row = torch.permute(B_col, (0, 2, 1))
if self.layout_C == cutlass.LayoutType.RowMajor:
C_row = C.view((batch, problem_size.m, problem_size.n))
else:
C_col = C.view((batch, problem_size.n, problem_size.m))
C_row = torch.permute(C_col, (0, 2, 1))
out_row = torch.matmul(A_row, B_row) * alpha + C_row * beta
if self.layout_C == cutlass.LayoutType.ColumnMajor:
out = torch.permute(out_row, (0, 2, 1))
else:
out = out_row
return torch.flatten(out)
def __call__(self, A, B, C, problem_size, batch=1, epilogue_args=None):
# Running the mainloop
accum = self.run(
A, B, C, problem_size, 1.0, 0.0, batch=batch
).reshape(batch, problem_size.m, problem_size.n)
# Running the epilogue
epilogue_args["accum"] = accum
references = self.epilogue_visitor(**epilogue_args)
# Return the results
if not isinstance(references, tuple):
references = (references,)
return references
class EVTTestBed:
"""
Epilogue Visitor Testbed
"""
def __init__(self, element, evt_fn, example_inputs, profile=False, **kwargs) -> None:
self.element = element
layout = cutlass.LayoutType.RowMajor
self.example_inputs = example_inputs
# Create the Gemm plan
self.plan = cutlass.op.Gemm(element=element, layout=layout, element_accumulator=torch.float32)
if "tile_description" in kwargs:
self.plan.tile_description = kwargs["tile_description"]
if "swizzling_functor" in kwargs:
self.plan.swizzling_functor = kwargs["swizzling_functor"]
# Compile the epilogue visitor
epilogue_visitor = cutlass.epilogue.trace(evt_fn, example_inputs)
if "epilogue_stages" in kwargs:
epilogue_visitor.epilogue_stages = kwargs["epilogue_stages"]
self.plan.epilogue_visitor = epilogue_visitor
# Reference model
self.reference_fn = EVTReferenceModule(layout, layout, layout, epilogue_visitor)
self.profile = profile
def get_torch_tensor(self, shape, dtype=None, fill=None):
if dtype is None:
dtype = self.element
dtype = torch_type(dtype)
if fill is None:
return torch.ceil(
torch.empty(size=shape, dtype=dtype, device="cuda").uniform_(-4.5, 3.5)
)
else:
return torch.full(shape, fill, dtype=dtype, device="cuda")
def verify(self, problem_size, input_keys, result_keys, batch_count=1):
"""
Verify the results
"""
problem_size = GemmCoord(*problem_size)
# Initiate the GEMM arguments
tensor_A = self.get_torch_tensor((batch_count, problem_size.m, problem_size.k))
tensor_B = self.get_torch_tensor((batch_count, problem_size.k, problem_size.n))
# Initialize the epilogue args
epilogue_args = {}
for key in self.example_inputs.keys():
if key in input_keys:
tensor = self.example_inputs[key]
if isinstance(tensor, Tensor):
epilogue_args[key] = self.get_torch_tensor(tensor.shape, tensor.element)
else:
epilogue_args[key] = tensor
elif key in result_keys:
tensor = self.example_inputs[key]
if isinstance(tensor, Tensor):
if "max" in key:
fill = -1000
else:
fill = 0
epilogue_args[key] = self.get_torch_tensor(tensor.shape, tensor.element, fill=fill)
else:
epilogue_args[key] = tensor
tensor_D = epilogue_args["D"]
if "C" in epilogue_args:
tensor_C = epilogue_args["C"]
else:
tensor_C = tensor_D
# Run the device kernel
self.plan.run(tensor_A, tensor_B, tensor_C, tensor_D, visitor_args=epilogue_args)
# Run the host reference
evt_args_inputs = {}
for key in input_keys:
evt_args_inputs[key] = epilogue_args[key]
reference_results = self.reference_fn(
tensor_A, tensor_B, tensor_C, problem_size, batch_count, evt_args_inputs)
# Compare the results
for result, ref in zip(result_keys, reference_results):
assert torch.equal(epilogue_args[result].flatten(), ref.flatten())
# Run profile
if self.profile:
profiler = CUDAEventProfiler(
self.plan, 100, 100, tensor_A, tensor_B, tensor_C, tensor_D,
visitor_args = epilogue_args
)
print(f"Cutlass Python Duration: {profiler()}")
class EVTTestCaseBase(unittest.TestCase):
"""
Base class for EVT Unittest
"""
def __init__(self, methodName: str = "runTest", lmnk=(6, 512, 256, 128)) -> None:
super().__init__(methodName)
self.element = cutlass.DataType.f16
self.l, self.m, self.n, self.k = lmnk
self.problem_size = (self.m, self.n, self.k)
torch.random.manual_seed(42)
def fake_tensor(self, element, shape):
return Tensor(element=element, shape=shape, layout_tag=cutlass.LayoutType.RowMajor)
def get_problem_sizes(self, alignment, k=None, batch_count=[3,]):
k = k if k else self.k
problem_size_m = [alignment, 512 - 3 * alignment]
problem_size_n = [alignment, 512 - alignment]
if alignment % 8 == 0:
problem_size_m.append(768)
problem_size_n.append(768)
problem_size_l = batch_count
problem_sizes = []
for m in problem_size_m:
for n in problem_size_n:
for l in problem_size_l:
problem_sizes.append((m, n, k, l))
return problem_sizes
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