tinygrad/extra/thneed.py

288 lines
11 KiB
Python

# this can be constructed from a cl_cache or loaded from a thneed file
import time
import struct
import json
import traceback
import numpy as np
from tinygrad.runtime.ops_gpu import CLProgram, compile_gpu
from tinygrad.device import Device
from tinygrad.helpers import DEBUG, getenv
from collections import defaultdict
import pyopencl as cl
from tinygrad.runtime.ops_gpu import OSX_TIMING_RATIO
CL = Device["GPU"]
DEBUGCL = getenv("DEBUGCL", 0)
FLOAT16 = getenv("FLOAT16", 0)
class Thneed:
def __init__(self, cl_cache=[], inputs={}):
self.cl_cache, self.inputs = cl_cache[:], inputs
self.gobj = 0
# build graph
# NOTE: if CLCACHE=1, this is wrong!
nodes = defaultdict(lambda: {'in_edges': [], 'out_edges': []})
for _, args in self.cl_cache:
# output is always the first parameter
for a in args[3:]:
nodes[a]['out_edges'].append(args[2])
nodes[args[2]]['in_edges'].append(a)
# get buffers to save
self.buffers_to_save = set()
self.outputs = []
for n in nodes.keys():
if len(nodes[n]['in_edges']) == 0:
self.buffers_to_save.add(n)
if len(nodes[n]['out_edges']) == 0:
self.outputs.append(n)
fake_inputs = []
for k,n in self.inputs.items():
if n in self.buffers_to_save:
self.buffers_to_save.remove(n)
else:
print(f"WARNING: {k} was not a used input, removing it")
fake_inputs.append(k)
for k in fake_inputs:
del self.inputs[k]
def load(self, input_fn):
float32 = not FLOAT16
mf = cl.mem_flags
image_fmt = cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.FLOAT if float32 else cl.channel_type.HALF_FLOAT)
image_fmt_32 = cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.FLOAT)
with open(input_fn, "rb") as f:
json_len = struct.unpack("I", f.read(4))[0]
jdat = json.loads(f.read(json_len).decode('latin_1'))
weights = f.read()
# load in the buffers
bufs = {'\x00\x00\x00\x00\x00\x00\x00\x00': None}
bufs_loaded = {}
ptr = 0
for o in jdat['objects']:
#print(o)
if o['needs_load']:
nptr = ptr + o['size']
o['data'] = weights[ptr:nptr]
ptr = nptr
if o['arg_type'] == "image2d_t" or o['arg_type'] == "image1d_t":
tfmt = image_fmt_32 if 'float32' in o and o['float32'] else image_fmt
if o['arg_type'] == "image2d_t":
if 'buffer_id' in o and o['height'] == 1 and not bufs_loaded[o['buffer_id']]:
# hack: use a image1d since we can back that with a buffer
buf = cl.Image(CL.ctx, mf.READ_WRITE, tfmt, shape=(o['width'],), buffer=bufs[o['buffer_id']])
else:
# buffer isn't supported in image2d, copy buffer into image
if 'buffer_id' in o and bufs_loaded[o['buffer_id']]:
arr = np.zeros(bufs[o['buffer_id']].size // 2, dtype=np.float16)
cl.enqueue_copy(CL.queue, arr, bufs[o['buffer_id']])
buf = cl.Image(CL.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, tfmt,
shape=(o['width'], o['height']), pitches=(o['row_pitch'],), hostbuf=arr)
elif o['needs_load']:
buf = cl.Image(CL.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, tfmt,
shape=(o['width'], o['height']), pitches=(o['row_pitch'],), hostbuf=o['data'])
else:
buf = cl.Image(CL.ctx, mf.READ_WRITE, tfmt, shape=(o['width'], o['height']))
if o['arg_type'] == "image1d_t":
assert not o['needs_load']
assert not bufs_loaded[o['buffer_id']]
buf = cl.Image(CL.ctx, mf.READ_WRITE, tfmt, shape=(o['width'],), buffer=bufs[o['buffer_id']])
else:
if 'data' in o:
buf = cl.Buffer(CL.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=o['data'])
else:
# zero out buffers
buf = cl.Buffer(CL.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=b'\x00'*o['size'])
bufs[o['id']] = buf
bufs_loaded[o['id']] = 'data' in o
# if it's loaded, it's saved
if 'data' in o:
self.buffers_to_save.add(buf)
# load binaries
prgs = {}
for o in jdat['binaries']:
nptr = ptr + o['length']
prgs[o['name']] = CLProgram(Device["GPU"], o['name'], weights[ptr:nptr])
ptr = nptr
# populate the cl_cache
for i,k in enumerate(jdat['kernels']):
kernel = prgs[k['name']]
aaa = []
for j,(a,sz) in enumerate(zip(k['args'], k['args_size'])):
if len(a) == 0:
aa = cl.LocalMemory(sz)
elif len(a) == 4:
a = a.encode('latin_1')
aa = np.uint32(struct.unpack("I", a)[0])
elif len(a) == 2:
a = a.encode('latin_1')
aa = np.uint16(struct.unpack("H", a)[0])
elif len(a) == 8:
#print(i,j,struct.unpack("Q", a.encode('latin_1'))[0])
aa = bufs[a]
aaa.append(aa)
self.cl_cache.append((kernel, [k['global_work_size'], k['local_work_size'], *aaa]))
if DEBUG >= 1: print(f"thneed: total bufs loaded: {len(bufs.keys())}")
# load inputs
for k in jdat['inputs']:
self.inputs[k['name']] = bufs[k['buffer_id']]
# load outputs
for k in jdat['outputs']:
self.outputs.append(bufs[k['buffer_id']])
def save(self, output_fn):
# this is the struct that will be saved
jdat = {"binaries": [], "programs": {}, "kernels": [], "objects": []}
# build the pieces of this struct
weights = []
binaries = []
saved_objs = set()
saved_binaries = set()
for prg, args in self.cl_cache:
# get binaries for saving
if prg.name not in saved_binaries:
binary = prg.clprogram.get_info(cl.program_info.BINARIES)
assert len(binary) == 1
jdat['binaries'].append({"name":prg.name, "length":len(binary[0])})
binaries.append(binary[0])
saved_binaries.add(prg.name)
# get the args from the kernel, some need the data saved
targs, args_size = [], []
argdtypes = [None]*(len(args)-2)
for a,d in zip(args[2:], argdtypes):
if d == np.int16:
targs.append(struct.pack("H", a).decode("latin_1"))
args_size.append(2)
elif d == np.int32:
targs.append(struct.pack("I", a).decode("latin_1"))
args_size.append(4)
elif isinstance(a, cl.LocalMemory):
targs.append("")
args_size.append(a.size)
elif d is None:
if getattr(a, "global_id", None) is None:
setattr(a, "global_id", self.gobj)
self.gobj += 1
ptr = struct.pack("Q", a.global_id).decode("latin_1")
if ptr not in saved_objs:
if isinstance(a, cl.Buffer):
needs_load = a in self.buffers_to_save
jdat['objects'].append({
"id": ptr, "arg_type": "float*", "needs_load": needs_load, "size": a.size,
})
if needs_load:
data = np.empty(a.size//4, dtype=np.float32)
cl.enqueue_copy(CL.queue, data, a, is_blocking=True)
weights.append(data.tobytes())
elif isinstance(a, cl.Image):
assert a.format == cl.ImageFormat(cl.channel_order.RGBA, cl.channel_type.HALF_FLOAT if FLOAT16 else cl.channel_type.FLOAT), "wrong type"
needs_load = a in self.buffers_to_save
row_pitch = (a.shape[0]*4*(2 if FLOAT16 else 4) + 63)//64 * 64
size = row_pitch * a.shape[1]
# this is *2 if float16 and *4 if float32
buf = cl.Buffer(CL.ctx, cl.mem_flags.READ_WRITE, size=size * (2 if FLOAT16 else 1))
# zero out the buffer
cl.enqueue_copy(CL.queue, buf, b'\x00'*buf.size, is_blocking=True)
CLProgram(CL, "from_image_strided", compile_gpu("""
__kernel void from_image_strided(read_only image2d_t in, __global float4 *out, int row_pitch) {
const sampler_t smp = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
int2 l;
l.y = get_global_id(1);
l.x = get_global_id(0);
out[l.y*row_pitch + l.x] = read_imagef(in, smp, l);
}
"""), bufs=2, vars=1)(a, buf, row_pitch//(4*(2 if FLOAT16 else 4)), global_size=a.shape)
# multiple of 32 isn't enough
jdat['objects'].append({
"id": ptr, "needs_load": needs_load, "size": size, "arg_type": "image2d_t",
"width": a.shape[0], "height": a.shape[1], "row_pitch": row_pitch, "float32": not FLOAT16,
})
if needs_load:
data = np.empty(size//(2 if FLOAT16 else 4), dtype=np.float32)
cl.enqueue_copy(CL.queue, data, buf, is_blocking=True)
if FLOAT16: data = data.astype(np.float16)
weights.append(data.tobytes())
else:
raise Exception("unknown object", a)
#print(jdat['objects'][-1])
saved_objs.add(ptr)
targs.append(ptr)
args_size.append(8)
else:
raise Exception("idk this type")
# save the kernel itself
jdat['kernels'].append({
"name": prg.name,
"work_dim": len(args[0]),
"global_work_size": args[0],
# TODO: C++ thneed requires a local_work_size, so we fill it with ones
"local_work_size": [1 for _ in args[0]] if args[1] is None else args[1],
"num_args": len(args)-2,
"args": targs,
"args_size": args_size
})
jdat['outputs'] = [{
"buffer_id": struct.pack("Q", x.global_id).decode("latin_1"),
"size": x.size,
} for x in self.outputs]
jdat['inputs'] = [{
"buffer_id": struct.pack("Q", v.global_id).decode("latin_1"),
"size": v.size,
"name": k
} for k,v in self.inputs.items()][::-1]
print(f"saving thneed to {output_fn}")
with open(output_fn, "wb") as f:
j = json.dumps(jdat, ensure_ascii=False).encode('latin_1')
f.write(struct.pack("I", len(j)))
f.write(j)
f.write(b''.join(weights))
f.write(b''.join(binaries))
def run(self):
events = []
st = time.monotonic()
for prg, args in self.cl_cache:
events.append(prg.clprg(CL.queue, *args))
mt = time.monotonic()
Device["GPU"].synchronize()
et = time.monotonic() - st
print(f"submit in {(mt-st)*1000.0:.2f} ms, total runtime is {et*1000.0:.2f} ms")
if DEBUGCL >= 2:
for i, ((prg, args), e) in enumerate(zip(self.cl_cache, events)):
print(f"{i:3d} {prg.name:25s} " + "queued @ %5.2f ms, submit @ %5.2fms, start @ %5.2f ms, end @ %5.2f ms" % tuple((x*OSX_TIMING_RATIO - st*1e9)/1e6 for x in [e.profile.queued, e.profile.submit, e.profile.start, e.profile.end]))
if DEBUGCL >= 1:
total_runtime = 0
for i, ((prg, args), e) in enumerate(zip(self.cl_cache, events)):
runtime = (e.profile.end - e.profile.start) * OSX_TIMING_RATIO
print(f"{i:3d} time {total_runtime/1e6:5.2f} ms running {prg.name:25s} with {str(args[0]):15s} {str(args[1]):15s} count {len(args)-2:2d} runtime {runtime/1e3:7.2f} us {(getattr(prg, 'op_estimate', float('nan')))/runtime:9.2f} GFLOPS -> {args[2].shape if hasattr(args[2], 'shape') else args[2].size}")
if hasattr(prg, 'prg') and ((DEBUGCL >= 2 and getenv("PRINT_KERNEL", -1) == i) or DEBUGCL >= 3):
print(prg.prg)
total_runtime += runtime
print(f"total runtime: {total_runtime/1e6:.2f} ms wall time: {et*1000.0:.2f} ms")
return total_runtime/1e9
return et