mlir/test/Integration/GPU/CUDA/sm90/python/matmul.py - third_party/llvm-project - Git at Google

 # RUN: env SUPPORT_LIB=%mlir_cuda_runtime \
 # RUN:   %PYTHON %s | FileCheck %s


 # ===--- GEMM Hopper Tensor Core Integration Test ---===
 #
 # This test aims to validate the correctness of the supported GEMM kernels in
 # NVGPU dialects, with current support for Multistage and Warp Specialization
 # kernels.
 # The test constructs and metaprograms IR using Python bindings, allowing
 # generic IR building. This flexibility enables changes to the shape,
 # tile size, or data type of the GEMM for testing purposes.
 # The entry function is `matmul`, where one can specify GEMM shape, tile size,
 # data type, GEMM algorithm (Multistage or Warp Specialization), and the maximum
 # number of stages.
 # Verification is done via numpy's matmul operation.
 #
 # Example:
 # matmul(input_type=np.float16,                # input types
 #        output_type=np.float32,               # output type
 #        M=4096, N=4096, K=4096,               # Shape
 #        BLOCK_M=128, BLOCK_N=128, BLOCK_K=64, # Tile Size
 #        use_warp_specialization=True,         # Enable Warp Specialization
 #        max_num_stages=3)                     # Number of stages in shared memory
 #
 # ===--- Parallelism Across CTAs  ---===
 #
 # GEMM includes three loops defining the shape of the GEMM, specified in the
 # `matmul` function.
 # The program builds IR using the following loop structure, tiling the loops
 # with the given tile size and parallelizing the two outermost loops into the
 # first and second dimensions of CTAs.
 #
 # for(bi = 0; i < M; i += BLOCK_M)          # parallelize across blockIdx.x
 #     for(bj = 0; j < N; j += BLOCK_N)      # parallelize across blockIdx.y
 #         for(bk = 0; k < K; K += BLOCK_K)
 #             for(i = bi; i < (bi + BLOCK_M); ++i)
 #                 for(j = bj; j < (bj + BLOCK_N); ++j)
 #                     for(k = bk; k < (bk + BLOCK_K); ++k)
 #
 # ===--- Multistage Kernel ---===
 #
 # This kernel launches a single warp group (128 threads). The primary thread
 # (pthread) requests load from TMA. Threads collectively wait for the data and
 # perform mma operations. After completing the shape, threads together store
 # first fragmented registers to shared memory, then from shared memory to global
 # memory; this part is called the epilogue.
 #
 # Execution Timeline of Multistage Kernel with 3 stages:
 # +-------+----------------+--------------------+--------------------+--------------------+-----+-----------------------+
 # |       |Prologue ---->   |MainLoop ---->                                                                  |Epilogue  |
 # +-------+----------------+--------------------+--------------------+--------------------+-----+-----------------------+
 # |pthread|[tma-0,1,2]     |[wait-0][mma][tma-2]|[wait-1][mma][tma-0]|[wait-2][mma][tma-1]| ... | [mma-wait] |[epilogue]|
 # |wgroup | ........       |[wait-0][mma]       |[wait-1][mma]       |[wait-2][mma]       | ... | [mma-wait] |[epilogue]|
 # +-------+----------------+--------------------+--------------------+--------------------+-----+-----------------------+
 #
 # ===--- Warp Specialization Kernel  ---===
 #
 # This kernel launches 2 warp groups (2x128 threads) per CTA, specializing one
 # as `producer warp group` and another as `consumer warp group`. The
 # `producer warp group` is responsible for requesting TMA load, while the
 # `consumer warp group` performs the mma operation. The epilogue section is
 # handled by the `consumer warp group` as its threads own the fragmented registers.
 #
 # Execution Timeline of Warp Specialization Kernel with 2 stages:
 # +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+
 # |        |MainLoop ---->                                                    | 1st Epilogue | 2nd Epilogue    |
 # +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+
 # |pthread1|[tma-0] | [tma-1] | [tma-0] | [tma-1] | ..........................| ...........  | [shmem->global] |
 # |wgroup1 | .......|         |         |         |                           |              | [shmem->global] |
 # +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+
 # |wgroup2 |[wait-0][mma], [wait-1][mma], [wait-0][mma], [wait-1][mma], ......| [reg->shmem] | [shmem->global]|
 # +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+

 import errno
 import numpy as np
 import subprocess
 import ctypes
 from tools import nvgpucompiler
 from tools import matmulBuilder
 import contextlib
 import os
 import sys
 import pathlib
 import ctypes
 from mlir import runtime as rt


 def generate_matmul(
     input_type=np.float16,
     output_type=np.float32,
     M=4096,
     N=4096,
     K=4096,
     BLOCK_M=128,
     BLOCK_N=128,
     BLOCK_K=64,
     use_warp_specialization=True,
     saveIR=False,
     max_num_stages=3,
     options=f"cubin-chip=sm_90a cubin-features=+ptx80 opt-level=3",
 ):
     with matmulBuilder.ir.Context() as ctx, matmulBuilder.ir.Location.unknown():
         if use_warp_specialization:
             mlir_nvgpu_module = matmulBuilder.generate_matmul_ws(
                 input_type,
                 output_type,
                 M,
                 N,
                 K,
                 BLOCK_M,
                 BLOCK_N,
                 BLOCK_K,
                 max_num_stages,
             )
         else:
             mlir_nvgpu_module = matmulBuilder.generate_matmul_multistage(
                 input_type,
                 output_type,
                 M,
                 N,
                 K,
                 BLOCK_M,
                 BLOCK_N,
                 BLOCK_K,
                 max_num_stages,
             )

         mlir_nvgpu_module.operation.verify()

         # Save generated IR
         if saveIR:
             # print(mlir_nvgpu_module)
             original_stdout = sys.stdout
             with open("gemm.mlir", "w") as f:
                 sys.stdout = f
                 print(mlir_nvgpu_module)
                 sys.stdout = original_stdout

         # Get compiler
         support_lib = os.getenv("SUPPORT_LIB")
         if not os.path.exists(support_lib):
             raise FileNotFoundError(
                 errno.ENOENT, os.strerror(errno.ENOENT), support_lib
             )
         compiler = nvgpucompiler.NvgpuCompiler(
             options, opt_level=3, shared_libs=[support_lib]
         )

         # Compile
         engine = compiler.compile_and_jit(mlir_nvgpu_module)
         return engine


 def matmul(
     input_type=np.float16,
     output_type=np.float32,
     M=128,
     N=128,
     K=128,
     BLOCK_M=128,
     BLOCK_N=128,
     BLOCK_K=64,
     use_warp_specialization=True,
     saveIR=False,
     max_num_stages=3,
     print_results=False,
     no_verify=False,
 ):
     # Print the configuration
     required_stages = (M * K + K * N) // (BLOCK_M * BLOCK_K + BLOCK_K * BLOCK_N)
     num_stages = min(required_stages, max_num_stages)
     ity = "f16" if input_type == np.float16 else "f32"
     oty = "f16" if output_type == np.float16 else "f32"
     gemmty = "Warp specialization" if use_warp_specialization else "Multistage"
     print(
         "===-- Running GEMM "
         + gemmty
         + " "
         + oty
         + " += "
         + ity
         + " * "
         + ity
         + ", Size "
         + str(M)
         + "x"
         + str(N)
         + "x"
         + str(K)
         + ", Tile "
         + str(BLOCK_M)
         + "x"
         + str(BLOCK_N)
         + "x"
         + str(BLOCK_K)
         + ", stages "
         + str(num_stages)
         + " --==="
     )

     # Build IR and compile
     engine = generate_matmul(
         input_type,
         output_type,
         M,
         N,
         K,
         BLOCK_M,
         BLOCK_N,
         BLOCK_K,
         use_warp_specialization,
         saveIR,
         num_stages,
     )

     # Allocate matrices and invoke the matmul
     c = np.zeros((M, N), output_type)
     a = np.random.randn(M, K).astype(input_type)
     b = np.random.randn(K, N).astype(input_type)
     mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
     mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
     mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
     kernelName = matmulBuilder.make_kernel_name(
         input_type,
         output_type,
         M,
         N,
         K,
         BLOCK_M,
         BLOCK_N,
         BLOCK_K,
         num_stages,
         use_warp_specialization,
     )

     # Launch the MLIR generated kernel
     engine.invoke(kernelName, mem_a, mem_b, mem_c)

     float_formatter = "{:.2f}".format
     np.set_printoptions(formatter={"float_kind": float_formatter})

     if print_results:
         print(c)

     # Verify the results
     if not no_verify:
         ref = a.astype(input_type) @ b.astype(input_type)
         if print_results:
             print(ref)
         np.testing.assert_allclose(c, ref, rtol=5e-03, atol=1e-01)

     print("PASS ")


 # Takes longer time to run
 def test_long():
     for stages in range(1, 7):
         for M in [128, 512, 1024, 4096, 8192]:
             for N in [128, 512, 1024, 4096, 8192]:
                 for K in [64, 128, 512, 1024, 4096, 8192]:
                     matmul(
                         np.float16,
                         np.float32,
                         M,
                         N,
                         K,
                         max_num_stages=stages,
                         use_warp_specialization=False,
                         no_verify=True,
                     )
                     matmul(
                         np.float16,
                         np.float32,
                         M,
                         N,
                         K,
                         max_num_stages=stages,
                         use_warp_specialization=True,
                     )


 def test_short():
     for stages in [1, 3]:
         for M in [128, 512]:
             for N in [128]:
                 for K in [64, 256]:
                     matmul(
                         np.float16,
                         np.float32,
                         M,
                         N,
                         K,
                         max_num_stages=stages,
                         use_warp_specialization=False,
                     )
                     matmul(
                         np.float16,
                         np.float32,
                         M,
                         N,
                         K,
                         max_num_stages=stages,
                         use_warp_specialization=True,
                     )


 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 3 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 3 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 2 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 2 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 3 --===
 # CHECK: PASS
 # CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 3 --===
 # CHECK: PASS

 test_short()
	# RUN: env SUPPORT_LIB=%mlir_cuda_runtime \
	# RUN: %PYTHON %s \| FileCheck %s


	# ===--- GEMM Hopper Tensor Core Integration Test ---===
	#
	# This test aims to validate the correctness of the supported GEMM kernels in
	# NVGPU dialects, with current support for Multistage and Warp Specialization
	# kernels.
	# The test constructs and metaprograms IR using Python bindings, allowing
	# generic IR building. This flexibility enables changes to the shape,
	# tile size, or data type of the GEMM for testing purposes.
	# The entry function is `matmul`, where one can specify GEMM shape, tile size,
	# data type, GEMM algorithm (Multistage or Warp Specialization), and the maximum
	# number of stages.
	# Verification is done via numpy's matmul operation.
	#
	# Example:
	# matmul(input_type=np.float16, # input types
	# output_type=np.float32, # output type
	# M=4096, N=4096, K=4096, # Shape
	# BLOCK_M=128, BLOCK_N=128, BLOCK_K=64, # Tile Size
	# use_warp_specialization=True, # Enable Warp Specialization
	# max_num_stages=3) # Number of stages in shared memory
	#
	# ===--- Parallelism Across CTAs ---===
	#
	# GEMM includes three loops defining the shape of the GEMM, specified in the
	# `matmul` function.
	# The program builds IR using the following loop structure, tiling the loops
	# with the given tile size and parallelizing the two outermost loops into the
	# first and second dimensions of CTAs.
	#
	# for(bi = 0; i < M; i += BLOCK_M) # parallelize across blockIdx.x
	# for(bj = 0; j < N; j += BLOCK_N) # parallelize across blockIdx.y
	# for(bk = 0; k < K; K += BLOCK_K)
	# for(i = bi; i < (bi + BLOCK_M); ++i)
	# for(j = bj; j < (bj + BLOCK_N); ++j)
	# for(k = bk; k < (bk + BLOCK_K); ++k)
	#
	# ===--- Multistage Kernel ---===
	#
	# This kernel launches a single warp group (128 threads). The primary thread
	# (pthread) requests load from TMA. Threads collectively wait for the data and
	# perform mma operations. After completing the shape, threads together store
	# first fragmented registers to shared memory, then from shared memory to global
	# memory; this part is called the epilogue.
	#
	# Execution Timeline of Multistage Kernel with 3 stages:
	# +-------+----------------+--------------------+--------------------+--------------------+-----+-----------------------+
	# \| \|Prologue ----> \|MainLoop ----> \|Epilogue \|
	# +-------+----------------+--------------------+--------------------+--------------------+-----+-----------------------+
	# \|pthread\|[tma-0,1,2] \|[wait-0][mma][tma-2]\|[wait-1][mma][tma-0]\|[wait-2][mma][tma-1]\| ... \| [mma-wait] \|[epilogue]\|
	# \|wgroup \| ........ \|[wait-0][mma] \|[wait-1][mma] \|[wait-2][mma] \| ... \| [mma-wait] \|[epilogue]\|
	# +-------+----------------+--------------------+--------------------+--------------------+-----+-----------------------+
	#
	# ===--- Warp Specialization Kernel ---===
	#
	# This kernel launches 2 warp groups (2x128 threads) per CTA, specializing one
	# as `producer warp group` and another as `consumer warp group`. The
	# `producer warp group` is responsible for requesting TMA load, while the
	# `consumer warp group` performs the mma operation. The epilogue section is
	# handled by the `consumer warp group` as its threads own the fragmented registers.
	#
	# Execution Timeline of Warp Specialization Kernel with 2 stages:
	# +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+
	# \| \|MainLoop ----> \| 1st Epilogue \| 2nd Epilogue \|
	# +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+
	# \|pthread1\|[tma-0] \| [tma-1] \| [tma-0] \| [tma-1] \| ..........................\| ........... \| [shmem->global] \|
	# \|wgroup1 \| .......\| \| \| \| \| \| [shmem->global] \|
	# +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+
	# \|wgroup2 \|[wait-0][mma], [wait-1][mma], [wait-0][mma], [wait-1][mma], ......\| [reg->shmem] \| [shmem->global]\|
	# +--------+--------+---------+---------+---------+-----------------------+---+--------------+-----------------+

	import errno
	import numpy as np
	import subprocess
	import ctypes
	from tools import nvgpucompiler
	from tools import matmulBuilder
	import contextlib
	import os
	import sys
	import pathlib
	import ctypes
	from mlir import runtime as rt


	def generate_matmul(
	input_type=np.float16,
	output_type=np.float32,
	M=4096,
	N=4096,
	K=4096,
	BLOCK_M=128,
	BLOCK_N=128,
	BLOCK_K=64,
	use_warp_specialization=True,
	saveIR=False,
	max_num_stages=3,
	options=f"cubin-chip=sm_90a cubin-features=+ptx80 opt-level=3",
	):
	with matmulBuilder.ir.Context() as ctx, matmulBuilder.ir.Location.unknown():
	if use_warp_specialization:
	mlir_nvgpu_module = matmulBuilder.generate_matmul_ws(
	input_type,
	output_type,
	M,
	N,
	K,
	BLOCK_M,
	BLOCK_N,
	BLOCK_K,
	max_num_stages,
	)
	else:
	mlir_nvgpu_module = matmulBuilder.generate_matmul_multistage(
	input_type,
	output_type,
	M,
	N,
	K,
	BLOCK_M,
	BLOCK_N,
	BLOCK_K,
	max_num_stages,
	)

	mlir_nvgpu_module.operation.verify()

	# Save generated IR
	if saveIR:
	# print(mlir_nvgpu_module)
	original_stdout = sys.stdout
	with open("gemm.mlir", "w") as f:
	sys.stdout = f
	print(mlir_nvgpu_module)
	sys.stdout = original_stdout

	# Get compiler
	support_lib = os.getenv("SUPPORT_LIB")
	if not os.path.exists(support_lib):
	raise FileNotFoundError(
	errno.ENOENT, os.strerror(errno.ENOENT), support_lib
	)
	compiler = nvgpucompiler.NvgpuCompiler(
	options, opt_level=3, shared_libs=[support_lib]
	)

	# Compile
	engine = compiler.compile_and_jit(mlir_nvgpu_module)
	return engine


	def matmul(
	input_type=np.float16,
	output_type=np.float32,
	M=128,
	N=128,
	K=128,
	BLOCK_M=128,
	BLOCK_N=128,
	BLOCK_K=64,
	use_warp_specialization=True,
	saveIR=False,
	max_num_stages=3,
	print_results=False,
	no_verify=False,
	):
	# Print the configuration
	required_stages = (M * K + K * N) // (BLOCK_M * BLOCK_K + BLOCK_K * BLOCK_N)
	num_stages = min(required_stages, max_num_stages)
	ity = "f16" if input_type == np.float16 else "f32"
	oty = "f16" if output_type == np.float16 else "f32"
	gemmty = "Warp specialization" if use_warp_specialization else "Multistage"
	print(
	"===-- Running GEMM "
	+ gemmty
	+ " "
	+ oty
	+ " += "
	+ ity
	+ " * "
	+ ity
	+ ", Size "
	+ str(M)
	+ "x"
	+ str(N)
	+ "x"
	+ str(K)
	+ ", Tile "
	+ str(BLOCK_M)
	+ "x"
	+ str(BLOCK_N)
	+ "x"
	+ str(BLOCK_K)
	+ ", stages "
	+ str(num_stages)
	+ " --==="
	)

	# Build IR and compile
	engine = generate_matmul(
	input_type,
	output_type,
	M,
	N,
	K,
	BLOCK_M,
	BLOCK_N,
	BLOCK_K,
	use_warp_specialization,
	saveIR,
	num_stages,
	)

	# Allocate matrices and invoke the matmul
	c = np.zeros((M, N), output_type)
	a = np.random.randn(M, K).astype(input_type)
	b = np.random.randn(K, N).astype(input_type)
	mem_a = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(a)))
	mem_b = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(b)))
	mem_c = ctypes.pointer(ctypes.pointer(rt.get_ranked_memref_descriptor(c)))
	kernelName = matmulBuilder.make_kernel_name(
	input_type,
	output_type,
	M,
	N,
	K,
	BLOCK_M,
	BLOCK_N,
	BLOCK_K,
	num_stages,
	use_warp_specialization,
	)

	# Launch the MLIR generated kernel
	engine.invoke(kernelName, mem_a, mem_b, mem_c)

	float_formatter = "{:.2f}".format
	np.set_printoptions(formatter={"float_kind": float_formatter})

	if print_results:
	print(c)

	# Verify the results
	if not no_verify:
	ref = a.astype(input_type) @ b.astype(input_type)
	if print_results:
	print(ref)
	np.testing.assert_allclose(c, ref, rtol=5e-03, atol=1e-01)

	print("PASS ")


	# Takes longer time to run
	def test_long():
	for stages in range(1, 7):
	for M in [128, 512, 1024, 4096, 8192]:
	for N in [128, 512, 1024, 4096, 8192]:
	for K in [64, 128, 512, 1024, 4096, 8192]:
	matmul(
	np.float16,
	np.float32,
	M,
	N,
	K,
	max_num_stages=stages,
	use_warp_specialization=False,
	no_verify=True,
	)
	matmul(
	np.float16,
	np.float32,
	M,
	N,
	K,
	max_num_stages=stages,
	use_warp_specialization=True,
	)


	def test_short():
	for stages in [1, 3]:
	for M in [128, 512]:
	for N in [128]:
	for K in [64, 256]:
	matmul(
	np.float16,
	np.float32,
	M,
	N,
	K,
	max_num_stages=stages,
	use_warp_specialization=False,
	)
	matmul(
	np.float16,
	np.float32,
	M,
	N,
	K,
	max_num_stages=stages,
	use_warp_specialization=True,
	)


	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x64, Tile 128x128x64, stages 1 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 3 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 128x128x256, Tile 128x128x64, stages 3 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 2 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x64, Tile 128x128x64, stages 2 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Multistage f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 3 --===
	# CHECK: PASS
	# CHECK: ===-- Running GEMM Warp specialization f32 += f16 * f16, Size 512x128x256, Tile 128x128x64, stages 3 --===
	# CHECK: PASS

	test_short()