mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-06-12 07:55:14 +08:00
239 lines
12 KiB
Python
239 lines
12 KiB
Python
# ruff: noqa: E501
|
|
import numpy as np
|
|
import tempfile, unittest
|
|
from tinygrad import Tensor, Context, Device, dtypes, UOp
|
|
from tinygrad.uop.ops import Ops
|
|
from tinygrad.codegen.opt import Opt, OptOps
|
|
from tinygrad.engine.realize import run_linear
|
|
from tinygrad.codegen import to_program
|
|
from test.helpers import replace_opts
|
|
|
|
N = 512
|
|
|
|
def create_gemm_model(model_path:str, batch_size=N, in_size=N, out_size=N, bias=False):
|
|
import onnx
|
|
from onnx import helper, numpy_helper, TensorProto
|
|
# Define input and output
|
|
input_tensor = helper.make_tensor_value_info("input", TensorProto.FLOAT, [batch_size, in_size])
|
|
output_tensor = helper.make_tensor_value_info("output", TensorProto.FLOAT, [batch_size, out_size])
|
|
|
|
# Create random weights and bias
|
|
W_data = np.random.randn(in_size, out_size).astype(np.float32)
|
|
W_init = numpy_helper.from_array(W_data, name="W")
|
|
|
|
if bias:
|
|
B_data = np.random.randn(out_size).astype(np.float32)
|
|
B_init = numpy_helper.from_array(B_data, name="B")
|
|
gemm_node = helper.make_node("Gemm", inputs=["input", "W", "B"], outputs=["output"], alpha=1.0, beta=1.0, transB=0)
|
|
graph_def = helper.make_graph([gemm_node], "SingleGemmGraph", [input_tensor], [output_tensor], initializer=[W_init, B_init])
|
|
else:
|
|
gemm_node = helper.make_node("Gemm", inputs=["input", "W"], outputs=["output"], alpha=1.0, beta=1.0, transB=0)
|
|
graph_def = helper.make_graph([gemm_node], "SingleGemmGraph", [input_tensor], [output_tensor], initializer=[W_init])
|
|
|
|
# Create and save the model
|
|
#model_def = helper.make_model(graph_def, producer_name="single_gemm_example")
|
|
# TODO remove this once ORT supports 1.18.0
|
|
model_def = helper.make_model(graph_def, producer_name="single_gemm_example", ir_version=10, opset_imports=[helper.make_opsetid("", 22)])
|
|
onnx.save_model(model_def, model_path)
|
|
return model_path
|
|
|
|
def sexec(out:Tensor, opts:list[Opt], replace_src=None, run_count=3):
|
|
linear = out.schedule_linear()
|
|
call = linear.src[-1]
|
|
prg = to_program(replace_opts(call.src[0], opts), renderer=Device[Device.DEFAULT].renderer)
|
|
if replace_src is not None:
|
|
old_name = prg.src[3].arg.split("__attribute__((noinline)) void ")[1].split("(")[0]
|
|
new_src = replace_src + "/* DSP boilerplate */" + prg.src[3].arg.split("/* DSP boilerplate */")[1].replace(old_name, "fxn")
|
|
# drop BINARY and replace SOURCE so run_linear recompiles
|
|
prg = prg.replace(src=prg.src[:3] + (UOp(Ops.SOURCE, arg=new_src),))
|
|
linear = linear.replace(src=linear.src[:-1] + (call.replace(src=(prg, *call.src[1:])),))
|
|
for _ in range(run_count): run_linear(linear)
|
|
|
|
def get_quantized_model(sz):
|
|
from onnxruntime.quantization import quantize_static, QuantFormat, QuantType, CalibrationDataReader
|
|
class FakeDataReader(CalibrationDataReader):
|
|
def __init__(self): self.cnt = 0
|
|
def get_next(self) -> dict:
|
|
self.cnt += 1
|
|
if self.cnt == 100: return None
|
|
return {"input": np.random.uniform(size=(sz, sz)).astype(np.float32)}
|
|
tmpdir = tempfile.mkdtemp()
|
|
out_file = f"{tmpdir}/test_out.onnx"
|
|
quantize_static(create_gemm_model(f"{tmpdir}/test_in.onnx", sz, sz, sz), out_file,
|
|
FakeDataReader(), quant_format=QuantFormat.QDQ, per_channel=False, reduce_range=False,
|
|
activation_type=QuantType.QUInt8, weight_type=QuantType.QInt8,
|
|
extra_options={"ActivationSymmetric": False})
|
|
return out_file
|
|
|
|
@unittest.skip("this is broken")
|
|
@unittest.skipIf(Device.DEFAULT != "CPU", "only tests for CPU")
|
|
class TestQuantizeOnnxCPU(unittest.TestCase):
|
|
def test_quant_128(self, sz=128):
|
|
try:
|
|
import onnx # noqa: F401 # pylint: disable=unused-import
|
|
except ImportError:
|
|
raise unittest.SkipTest()
|
|
from tinygrad.nn.onnx import OnnxRunner
|
|
out_file = get_quantized_model(sz)
|
|
run_onnx = OnnxRunner(out_file)
|
|
inp = Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32))
|
|
with Context(QUANTIZE=1):
|
|
linear = run_onnx({"input":inp})["output"].schedule_linear()
|
|
prg = to_program(linear.src[-2].src[0], renderer=Device[Device.DEFAULT].renderer)
|
|
daccs = [u for u in tuple(prg.src[2].src) if u.op is Ops.DEFINE_REG]
|
|
assert all(u.dtype.scalar() is dtypes.int for u in daccs)
|
|
|
|
@unittest.skipIf(Device.DEFAULT != "DSP", "only tests for DSP")
|
|
class TestQuantizeOnnx(unittest.TestCase):
|
|
def test_quant_128(self): self.test_quant(128)
|
|
def test_quant(self, sz=512):
|
|
from examples.benchmark_onnx import load_onnx_model
|
|
# divide is ~1500-2000 without reduce_range, 750-900 with it
|
|
out_file = get_quantized_model(sz)
|
|
run_onnx_jit, _ = load_onnx_model(out_file)
|
|
run_onnx_jit(input=Tensor(np.random.uniform(size=(sz, sz)).astype(np.float32)))
|
|
|
|
def test_prequant_conv2d_1x1(self):
|
|
X = Tensor(np.random.uniform(0, 255, size=(1, 32, 128, 128)).astype(np.uint8))
|
|
W = Tensor(np.random.uniform(0, 255, size=(64, 32, 1, 1)).astype(np.uint8))
|
|
out = X.conv2d(W, dtype=X.dtype)
|
|
opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
|
|
sexec(out, opts)
|
|
|
|
def test_prequant_gemm(self):
|
|
N = 512
|
|
X = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8))
|
|
W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8))
|
|
out = X.matmul(W, dtype=X.dtype)
|
|
opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
|
|
sexec(out, opts)
|
|
|
|
# TODO: this has to work
|
|
def test_prequant_gemm_intacc_early(self, xi=np.int8, wi=np.int8):
|
|
N = 512
|
|
X = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(xi))
|
|
W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(wi))
|
|
# this divide is interesting and forces the accumulator to actually be an int
|
|
out = (X.cast("int").matmul(W.cast("int"))//1000).cast("int8")
|
|
opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
|
|
sexec(out, opts)
|
|
|
|
def test_prequant_gemm_handcode(self):
|
|
src = """typedef int int128 __attribute__((aligned(512),vector_size(512)));
|
|
typedef int int32 __attribute__((aligned(128),vector_size(128)));
|
|
typedef int int64 __attribute__((aligned(256),vector_size(256)));
|
|
typedef unsigned char unsigned_char4 __attribute__((aligned(4),vector_size(4)));
|
|
typedef signed char signed_char128 __attribute__((aligned(128),vector_size(128)));
|
|
typedef unsigned char unsigned_char128 __attribute__((aligned(128),vector_size(128)));
|
|
typedef unsigned char unsigned_char256 __attribute__((aligned(256),vector_size(256)));
|
|
union V256 {
|
|
unsigned_char256 vec256;
|
|
struct {
|
|
unsigned_char128 lo128;
|
|
unsigned_char128 hi128;
|
|
};
|
|
};
|
|
__attribute__((noinline)) void fxn(unsigned char* restrict __attribute__((align_value(128))) data0,
|
|
unsigned char* restrict __attribute__((align_value(128))) data1,
|
|
signed char* restrict __attribute__((align_value(128))) data2) {
|
|
for (int ridx0 = 0; ridx0 < 512; ridx0++) {
|
|
int alu0 = (ridx0<<9);
|
|
for (int ridx1 = 0; ridx1 < 4; ridx1++) {
|
|
int alu1 = (ridx1<<7);
|
|
int32 acc0 = __builtin_HEXAGON_V6_vd0_128B();
|
|
int32 acc1 = __builtin_HEXAGON_V6_vd0_128B();
|
|
int32 acc2 = __builtin_HEXAGON_V6_vd0_128B();
|
|
int32 acc3 = __builtin_HEXAGON_V6_vd0_128B();
|
|
|
|
for (int ridx2 = 0; ridx2 < 128; ridx2++) {
|
|
unsigned_char4 val0 = *((unsigned_char4*)((data1+(alu0+(ridx2<<2)))));
|
|
int alu2 = (alu1+(ridx2<<11));
|
|
signed_char128 x0 = *((signed_char128*)((data2+alu2)));
|
|
signed_char128 x1 = *((signed_char128*)((data2+(alu2+512))));
|
|
signed_char128 x2 = *((signed_char128*)((data2+(alu2+1024))));
|
|
signed_char128 x3 = *((signed_char128*)((data2+(alu2+1536))));
|
|
|
|
union V256 ss01;
|
|
// ss01.lo128 = (x0[0], x1[0], x0[2], x1[2], x0[4], x1[4], ...)
|
|
// ss01.hi128 = (x0[1], x1[1], x0[3], x1[3], x0[5], x1[5], ...)
|
|
ss01.vec256 = __builtin_HEXAGON_V6_vshufoeb_128B(x1, x0);
|
|
|
|
union V256 ss23;
|
|
// ss23.lo128 = (x2[0], x3[0], x2[2], x3[2], x2[4], x3[4], ...)
|
|
// ss23.hi128 = (x2[1], x3[1], x2[3], x3[3], x2[5], x3[5], ...)
|
|
ss23.vec256 = __builtin_HEXAGON_V6_vshufoeb_128B(x3, x2);
|
|
|
|
union V256 sslo;
|
|
// sslo.lo128 = (x0[0], x1[0], x2[0], x3[0], x0[4], x1[4], ...)
|
|
// sslo.hi128 = (x0[2], x1[2], x2[2], x3[2], x0[6], x1[6], ...)
|
|
sslo.vec256 = __builtin_HEXAGON_V6_vdealvdd_128B(ss23.lo128, ss01.lo128, 2);
|
|
|
|
union V256 sshi;
|
|
// sshi.lo128 = (x0[1], x1[1], x2[1], x3[1], x0[5], x1[5], ...)
|
|
// sshi.hi128 = (x0[3], x1[3], x2[3], x3[3], x0[7], x1[7], ...)
|
|
sshi.vec256 = __builtin_HEXAGON_V6_vdealvdd_128B(ss23.hi128, ss01.hi128, 2);
|
|
|
|
//unsigned_char128 w0 = (unsigned_char128){val0[0],val0[1],val0[2],val0[3],val0[0],val0[1],val0[2],val0[3],...
|
|
unsigned_char128 w0 = __builtin_HEXAGON_V6_lvsplatw_128B(*((unsigned int*)&val0));
|
|
|
|
acc0 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc0, w0, sslo.lo128);
|
|
acc1 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc1, w0, sshi.lo128);
|
|
acc2 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc2, w0, sslo.hi128);
|
|
acc3 = __builtin_HEXAGON_V6_vrmpybusv_acc_128B(acc3, w0, sshi.hi128);
|
|
}
|
|
acc0 /= 1000;
|
|
acc1 /= 1000;
|
|
acc2 /= 1000;
|
|
acc3 /= 1000;
|
|
// ','.join([f"acc{j}[{i}]" for i in range(32) for j in range(4)])
|
|
// acc0[0], acc0[1], acc0[2], ..... acc3[30], acc3[31]
|
|
unsigned_char128 packed = __builtin_HEXAGON_V6_vpackhub_sat_128B(__builtin_HEXAGON_V6_vpackwh_sat_128B(acc3, acc2),
|
|
__builtin_HEXAGON_V6_vpackwh_sat_128B(acc1, acc0));
|
|
packed = __builtin_HEXAGON_V6_vshuffb_128B(packed);
|
|
packed = __builtin_HEXAGON_V6_vshuffb_128B(packed);
|
|
// acc0[0], acc1[0], acc2[0], ..... acc2[31], acc3[31]
|
|
*((unsigned_char128*)((data0+(alu0+alu1)))) = packed;
|
|
}
|
|
}
|
|
}"""
|
|
self.test_prequant_gemm_intacc(np.uint8, np.int8, src)
|
|
|
|
def test_prequant_gemm_intacc_32(self):
|
|
opts = [Opt(op=OptOps.UPCAST, axis=1, arg=0), Opt(op=OptOps.UPCAST, axis=0, arg=4), Opt(op=OptOps.UNROLL, axis=0, arg=0)]
|
|
self.test_prequant_gemm_intacc(np.uint8, np.int8, N=32, opts=opts)
|
|
def test_prequant_gemm_intacc_128(self): self.test_prequant_gemm_intacc(np.uint8, np.int8, N=128)
|
|
def test_prequant_gemm_intacc_256(self): self.test_prequant_gemm_intacc(np.uint8, np.int8, N=256)
|
|
def test_prequant_gemm_intacc(self, xi=np.uint8, wi=np.uint8, replace_src=None, N=512, clip=True, opts=None):
|
|
X = Tensor(m1:=(np.random.uniform(0, 255, size=(N,N)).astype(xi))).realize()
|
|
W = Tensor(m2:=(np.random.uniform(0, 255, size=(N,N)).astype(wi))).realize()
|
|
tg_dtype = dtypes.int8 if xi == np.int8 else dtypes.uint8
|
|
out = (X.int().matmul(W.int())//1000)
|
|
if clip: out = out.clip(tg_dtype.min, tg_dtype.max)
|
|
out = out.cast(tg_dtype)
|
|
opts = [Opt(op=OptOps.UPCAST, axis=1, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)] if opts is None else opts
|
|
sexec(out, opts, replace_src, run_count=1)
|
|
tout = out.numpy()
|
|
mout = ((m1.astype(np.int32) @ m2.astype(np.int32)) // 1000)
|
|
if clip: mout = mout.clip(tg_dtype.min, tg_dtype.max)
|
|
mout = mout.astype(xi)
|
|
print(tout)
|
|
print(mout)
|
|
np.testing.assert_equal(tout, mout)
|
|
|
|
def test_prequant_gemm_intacc_wi(self): self.test_prequant_gemm_intacc(wi=np.int8)
|
|
def test_prequant_gemm_intacc_xiwi(self): self.test_prequant_gemm_intacc(xi=np.int8, wi=np.int8)
|
|
def test_prequant_gemm_intacc_xiwi_noclip(self): self.test_prequant_gemm_intacc(xi=np.int8, wi=np.int8, clip=False)
|
|
|
|
def test_prequant_gemv(self):
|
|
N = 2048
|
|
X = Tensor(np.random.uniform(0, 255, size=(1,N)).astype(np.uint8)).realize()
|
|
W = Tensor(np.random.uniform(0, 255, size=(N,N)).astype(np.uint8)).realize()
|
|
#out = X.cast(dtypes.int) @ W.cast(dtypes.int)
|
|
#out = X @ W
|
|
out = X.matmul(W, dtype=X.dtype)
|
|
opts = [Opt(op=OptOps.UPCAST, axis=0, arg=128), Opt(op=OptOps.UNROLL, axis=0, arg=4)]
|
|
sexec(out, opts)
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|