mirror of https://github.com/commaai/tinygrad.git
126 lines
5.2 KiB
Python
126 lines
5.2 KiB
Python
import os
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#os.environ["METAL"] = "1"
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import numpy as np
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import time, torch, torch.mps
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from tinygrad.helpers import GlobalCounters
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from tinygrad.tensor import Tensor
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from tinygrad.jit import TinyJit
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from tinygrad import Device
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from tinygrad.helpers import colored, getenv, CI
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import os
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os.environ["METAL"] = "1"
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import time
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import numpy as np
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from tinygrad.helpers import dtypes, getenv
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from tinygrad.runtime.ops_metal import RawMetalBuffer, MetalProgram, compile_metal
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N = 16384
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M = 4096
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FLOPS = N*M*2
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nb = np.random.default_rng().standard_normal(size=(N), dtype=np.float32) #.astype(np.int32).astype(np.float32)
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nc = np.random.default_rng().standard_normal(size=(N,M), dtype=np.float32) #.astype(np.int32).astype(np.float32)
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import torch, torch.mps
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b = torch.from_numpy(nb).to('mps')
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c = torch.from_numpy(nc).to('mps')
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def torch_prog(b, c):
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st = time.perf_counter()
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a = b@c
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torch.mps.synchronize()
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return time.perf_counter() - st
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tm = min([torch_prog(b, c) for _ in range(200)])
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print(f"{N:d}x{M:d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS matvec in torch")
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torch_a = (b@c).cpu()
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WORKSIZE_ROW = 16
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WORKSIZE_COL = 1
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LOCAL_SIZE = [32, WORKSIZE_COL, WORKSIZE_ROW]
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GLOBAL_SIZE = [M//(LOCAL_SIZE[0]*LOCAL_SIZE[1]*4), 1, 1]
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prog = compile_metal(f"""
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#include <metal_stdlib>
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using namespace metal;
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kernel void test(device float* data0, const device float* data1, const device float* data2, uint3 gid [[threadgroup_position_in_grid]], uint3 lid [[thread_position_in_threadgroup]]) {{
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int gidx0 = gid.x; /* {GLOBAL_SIZE[0]} */
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int lidx1 = lid.x; /* {LOCAL_SIZE[0]} */
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int lidx2 = lid.y; /* {LOCAL_SIZE[1]} */
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int lidx3 = lid.z; /* {LOCAL_SIZE[2]} */
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// 4 rows per thread
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threadgroup float4 acc0[{LOCAL_SIZE[0]*LOCAL_SIZE[1]*LOCAL_SIZE[2]}];
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int acc0_index = ((lidx1*{LOCAL_SIZE[1]})+lidx2)+({LOCAL_SIZE[0]*LOCAL_SIZE[1]}*lidx3);
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acc0[acc0_index] = float4(0.0f,0.0f,0.0f,0.0f);
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threadgroup float4 val1[{LOCAL_SIZE[0]*LOCAL_SIZE[1]*LOCAL_SIZE[2]}];
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// iterate over the columns
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for (int ridx2 = 0; ridx2 < {N//(4*LOCAL_SIZE[0]*LOCAL_SIZE[1]*(LOCAL_SIZE[2]))}; ++ridx2) {{
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// load 4*threadgroup_size columns into shared memory
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int col_1 = (((lidx3*{N//(4*LOCAL_SIZE[0]*LOCAL_SIZE[1]*(LOCAL_SIZE[2]))})+ridx2)*{LOCAL_SIZE[0]*LOCAL_SIZE[1]})+(lidx1*{LOCAL_SIZE[1]})+lidx2;
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val1[(lidx3*{LOCAL_SIZE[1]*LOCAL_SIZE[0]})+((lidx1*{LOCAL_SIZE[1]})+lidx2)] = *((device float4*)(data1+(col_1*4)));
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threadgroup_barrier(mem_flags::mem_threadgroup);
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for (int ridx3 = 0; ridx3 < {LOCAL_SIZE[0]*LOCAL_SIZE[1]}; ++ridx3) {{
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int col = ((((lidx3*{N//(4*LOCAL_SIZE[0]*LOCAL_SIZE[1]*(LOCAL_SIZE[2]))})+ridx2)*{LOCAL_SIZE[0]*LOCAL_SIZE[1]})+ridx3);
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float4 val1_0 = val1[(lidx3*{LOCAL_SIZE[1]*LOCAL_SIZE[0]})+ridx3];
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float4 val2_0 = (float4)(*((device float4*)(data2+(gidx0*{M//GLOBAL_SIZE[0]})+(((lidx1*{LOCAL_SIZE[1]})+lidx2)*4)+(col*{M*4})+{M*0})));
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float4 val2_1 = (float4)(*((device float4*)(data2+(gidx0*{M//GLOBAL_SIZE[0]})+(((lidx1*{LOCAL_SIZE[1]})+lidx2)*4)+(col*{M*4})+{M*1})));
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float4 val2_2 = (float4)(*((device float4*)(data2+(gidx0*{M//GLOBAL_SIZE[0]})+(((lidx1*{LOCAL_SIZE[1]})+lidx2)*4)+(col*{M*4})+{M*2})));
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float4 val2_3 = (float4)(*((device float4*)(data2+(gidx0*{M//GLOBAL_SIZE[0]})+(((lidx1*{LOCAL_SIZE[1]})+lidx2)*4)+(col*{M*4})+{M*3})));
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acc0[acc0_index] = ((val1_0.x*val2_0)+acc0[acc0_index]);
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acc0[acc0_index] = ((val1_0.y*val2_1)+acc0[acc0_index]);
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acc0[acc0_index] = ((val1_0.z*val2_2)+acc0[acc0_index]);
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acc0[acc0_index] = ((val1_0.w*val2_3)+acc0[acc0_index]);
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}}
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threadgroup_barrier(mem_flags::mem_threadgroup);
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}} /* reduce */
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if (lidx3 == 0) {{
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float4 out = float4(0.0f,0.0f,0.0f,0.0f);
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for (int n = 0; n < {LOCAL_SIZE[2]}; n++) {{
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out += acc0[((lidx1*{LOCAL_SIZE[1]})+lidx2)+({LOCAL_SIZE[0]*LOCAL_SIZE[1]}*n)];
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}}
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*( (device float4 *) (data0 + (gidx0*{M//GLOBAL_SIZE[0]}) + ( ( (lidx1*{LOCAL_SIZE[1]})+lidx2 ) * 4 ) ) ) = out;
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}}
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}}
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""")
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prog = MetalProgram("test", prog)
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# print(prog_string)
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na = np.zeros(M, dtype=np.float32)
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b = RawMetalBuffer.fromCPU(nb)
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c = RawMetalBuffer.fromCPU(nc)
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def metalrun():
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a = RawMetalBuffer.fromCPU(na)
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prog(a, b, c, global_size=GLOBAL_SIZE, local_size=LOCAL_SIZE, wait=True)
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return a
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def timeit(fxn):
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st = time.perf_counter()
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et = fxn()
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# NOTE: et doesn't contain the launch overhead
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return time.perf_counter() - st
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tm = min([timeit(metalrun) for _ in range(200)])
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print(f"{N:d}x{M:d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS matvec in metal")
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metal_a = metalrun().toCPU().reshape(M)
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np.testing.assert_allclose(metal_a, torch_a, atol=5e-3)
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from tinygrad.tensor import Tensor
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from tinygrad.jit import TinyJit
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from tinygrad.runtime.ops_metal import METAL
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b = Tensor(nb)
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c = Tensor(nc)
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# TODO: slowness without the JIT I suspect comes from a lack of a caching allocator
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@TinyJit
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def tiny_jit(b, c):
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return (b@c).realize()
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def tiny_prog(b, c):
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st = time.perf_counter()
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a = tiny_jit(b, c)
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METAL.synchronize()
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return time.perf_counter() - st
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tm = min([tiny_prog(b, c) for _ in range(200)])
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print(f"{N:d}x{M:d} {tm*1e6:9.2f} us, would be {FLOPS*1e-9/tm:9.2f} GFLOPS matvec in tinygrad")
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tiny_a = tiny_jit(b, c).numpy()
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np.testing.assert_allclose(tiny_a, torch_a, atol=5e-3) |