mirror of https://github.com/commaai/tinygrad.git
120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
# tinygrad is a tensor library, and as a tensor library it has multiple parts
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# 1. a "runtime". this allows buffer management, compilation, and running programs
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# 2. a "Device" that uses the runtime but specifies compute in an abstract way for all
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# 3. a "LazyBuffer" that fuses the compute into kernels, using memory only when needed
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# 4. a "Tensor" that provides an easy to use frontend with autograd ".backward()"
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print("******** first, the runtime ***********")
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from tinygrad.runtime.ops_clang import ClangProgram, ClangCompiler, MallocAllocator
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# allocate some buffers
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out = MallocAllocator.alloc(4)
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a = MallocAllocator.alloc(4)
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b = MallocAllocator.alloc(4)
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# load in some values (little endian)
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MallocAllocator.copyin(a, bytearray([2,0,0,0]))
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MallocAllocator.copyin(b, bytearray([3,0,0,0]))
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# compile a program to a binary
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lib = ClangCompiler().compile("void add(int *out, int *a, int *b) { out[0] = a[0] + b[0]; }")
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# create a runtime for the program (ctypes.CDLL)
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fxn = ClangProgram("add", lib)
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# run the program
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fxn(out, a, b)
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# check the data out
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print(val := MallocAllocator.as_buffer(out).cast("I").tolist()[0])
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assert val == 5
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print("******** second, the Device ***********")
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DEVICE = "CLANG" # NOTE: you can change this!
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import struct
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from tinygrad.dtype import PtrDType, dtypes
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from tinygrad.device import Buffer, Device
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from tinygrad.ops import BinaryOps, MetaOps, UOp, UOps
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from tinygrad.shape.shapetracker import ShapeTracker
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# allocate some buffers + load in values
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out = Buffer(DEVICE, 1, dtypes.int32).allocate()
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a = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
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b = Buffer(DEVICE, 1, dtypes.int32).allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
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# NOTE: a._buf is the same as the return from MallocAllocator.alloc
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# describe the computation
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buf_1 = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int32), (), 1)
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buf_2 = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int32), (), 2)
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ld_1 = UOp(UOps.LOAD, dtypes.int32, (buf_1, ShapeTracker.from_shape((1,)).to_uop()))
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ld_2 = UOp(UOps.LOAD, dtypes.int32, (buf_2, ShapeTracker.from_shape((1,)).to_uop()))
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alu = ld_1 + ld_2
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output_buf = UOp(UOps.DEFINE_GLOBAL, PtrDType(dtypes.int32), (), 0)
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st_0 = UOp(UOps.STORE, dtypes.void, (output_buf, ShapeTracker.from_shape((1,)).to_uop(), alu))
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s = UOp(UOps.SINK, dtypes.void, (st_0,))
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# convert the computation to a "linearized" format (print the format)
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from tinygrad.engine.realize import get_kernel, CompiledRunner
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kernel = get_kernel(Device[DEVICE].renderer, s).linearize()
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# compile a program (and print the source)
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fxn = CompiledRunner(kernel.to_program())
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print(fxn.p.src)
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# NOTE: fxn.clprg is the ClangProgram
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# run the program
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fxn.exec([out, a, b])
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# check the data out
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assert out.as_buffer().cast('I')[0] == 5
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print("******** third, the LazyBuffer ***********")
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from tinygrad.lazy import LazyBuffer
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from tinygrad.engine.realize import run_schedule
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from tinygrad.engine.schedule import create_schedule
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# allocate some values + load in values
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a = LazyBuffer.metaop(MetaOps.EMPTY, (1,), dtypes.int32, DEVICE)
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b = LazyBuffer.metaop(MetaOps.EMPTY, (1,), dtypes.int32, DEVICE)
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a.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 2))))
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b.buffer.allocate().copyin(memoryview(bytearray(struct.pack("I", 3))))
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del a.srcs
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del b.srcs
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# describe the computation
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out = a.alu(BinaryOps.ADD, b)
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# schedule the computation as a list of kernels
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sched = create_schedule([out])
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for si in sched: print(si.ast.op) # NOTE: the first two convert it to CLANG
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# DEBUGGING: print the compute ast
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print(sched[-1].ast)
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# NOTE: sched[-1].ast is the same as st_0 above
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# run that schedule
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run_schedule(sched)
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# check the data out
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assert out.realized.as_buffer().cast('I')[0] == 5
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print("******** fourth, the Tensor ***********")
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from tinygrad import Tensor
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a = Tensor([2], dtype=dtypes.int32, device=DEVICE)
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b = Tensor([3], dtype=dtypes.int32, device=DEVICE)
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out = a + b
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# check the data out
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print(val:=out.item())
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assert val == 5
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