gaspersic/triton
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

[FRONTEND] Added compilation flag to force use of .nc cache modifier (#134)

Browse Source 
in DRAM loads. /!\ USE CAREFULLY - THIS CAN BREAK CORRECTNESS IF MISUSED
/!\
This commit is contained in:
Philippe Tillet
2021-07-20 17:58:06 -04:00
committed by Philippe Tillet
parent 2824345065
commit 01276b5153
7 changed files with 27 additions and 18 deletions
Download Patch File Download Diff File Expand all files Collapse all files

15
python/triton/code_gen.py
View File

@@ -408,7 +408,7 @@ class CodeGenerator(ast.NodeVisitor):
class Binary:
def __init__(self, module, kernel, num_warps, num_stages, shared_mem, ir_asm):
def __init__(self, module, kernel, num_warps, num_stages, force_nc_cache, shared_mem, ir_asm):
# cache ir asm
self.ir_asm = ir_asm
self.module = module
@@ -416,6 +416,7 @@ class Binary:
self.shared_mem = shared_mem
self.num_warps = num_warps
self.num_stages = num_stages
self.force_nc_cache = force_nc_cache
self.sass = None
def asm(self, mode):
@@ -524,7 +525,7 @@ class Kernel:
def __init__(self, fn):
self.fn = fn
def _compile(self, *wargs, device, attributes, constants, num_warps, num_stages, **meta):
def _compile(self, *wargs, device, attributes, constants, num_warps, num_stages, force_nc_cache, **meta):
# explicitly set device
torch.cuda.set_device(device.index)
# create IR module
@@ -546,12 +547,12 @@ class Kernel:
raise CompilationError(self.fn.src, node, e)
tt_device = _triton.driver.cu_device(device.index, False)
# Compile to machine code
mod, ker, shared_mem, ir_asm = _triton.code_gen.add_passes_to_emit_bin(generator.module, tt_device, num_warps, num_stages)
mod, ker, shared_mem, ir_asm = _triton.code_gen.add_passes_to_emit_bin(generator.module, tt_device, num_warps, num_stages, force_nc_cache)
if shared_mem > tt_device.max_shared_memory():
raise OutOfResources(shared_mem, tt_device.max_shared_memory(), "shared memory")
return Binary(mod, ker, num_warps, num_stages, shared_mem, ir_asm)
return Binary(mod, ker, num_warps, num_stages, force_nc_cache, shared_mem, ir_asm)
def __call__(self, *wargs, grid, num_warps=4, num_stages=2, **meta):
def __call__(self, *wargs, grid, num_warps=4, num_stages=2, force_nc_cache=False, **meta):
# device inference
tensor_idxs = [i for i, arg in enumerate(wargs) if hasattr(arg, 'data_ptr')]
if len(tensor_idxs) == 0:
@@ -573,7 +574,9 @@ class Kernel:
if key not in cache:
# compile and cache configuration if necessary
cache[key] = self._compile(
*wargs, device=device, attributes=attributes, num_warps=num_warps, num_stages=num_stages, constants=constants, **meta
*wargs, device=device, attributes=attributes,
num_warps=num_warps, num_stages=num_stages, force_nc_cache=force_nc_cache,
constants=constants, **meta
)
# pack arguments
fmt = ''.join(['P' if i in tensor_idxs else Kernel._type_name(arg.__class__) for i, arg in enumerate(wargs)])