global -> group (#1007)
* global -> group * allow None for local_size in custom function * lil local * comment on shape * fix cuda * smart local cast * better local heuristic * fix ptx, and work_dim cleanup * fix metal * fix ops test * fix openpilot jit * no more optlocal * might fix metal tests * try metal now * see generated metal code * test free removal. REVERT THIS * mergablepull/1001/head^2
George Hotz
GitHub
parent
aab9ee0fca
commit
18892242b0
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@ -95,7 +95,7 @@ jobs:
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run: pip install -e '.[llvm,testing]' --extra-index-url https://download.pytorch.org/whl/cpu
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- name: Run Pytest
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run: ENABLE_METHOD_CACHE=1 LLVM=1 python -m pytest -s -v -n=auto test/
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testclang:
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strategy:
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matrix:
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@ -207,11 +207,11 @@ jobs:
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python-version: 3.11
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- name: Install Dependencies
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run: pip install -e '.[metal,testing]'
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- name: Run ops test
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run: METAL=1 python -m pytest test/test_ops.py
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# dtype test has issues on test_half_to_int8
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#- name: Run dtype test
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# run: METAL=1 python -m pytest test/test_dtype.py
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# run: DEBUG=4 METAL=1 python -m pytest test/test_dtype.py
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- name: Run ops test
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run: DEBUG=2 METAL=1 python -m pytest test/test_ops.py
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# dtype test has issues on test_half_to_int8
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# disabled, this test is flaky
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testdocker:
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@ -36,7 +36,7 @@ tinygrad can run [LLaMA](/docs/showcase.md#llama) and [Stable Diffusion](/docs/s
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Try a matmul. See how, despite the style, it is fused into one kernel with the power of laziness.
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```sh
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DEBUG=3 OPTLOCAL=1 python3 -c "from tinygrad.tensor import Tensor;
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DEBUG=3 python3 -c "from tinygrad.tensor import Tensor;
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N = 1024; a, b = Tensor.rand(N, N), Tensor.rand(N, N);
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c = (a.reshape(N, 1, N) * b.permute(1,0).reshape(1, N, N)).sum(axis=2);
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print((c.numpy() - (a.numpy() @ b.numpy())).mean())"
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@ -28,7 +28,6 @@ LLVM | [1] | enable LLVM backend
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LLVMOPT | [1] | enable slightly more expensive LLVM optimizations
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LAZY | [1] | enable lazy operations (this is the default)
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OPT | [1-4] | optimization level
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OPTLOCAL | [1-2] | enable local optimization
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GRAPH | [1] | create a graph of all operations (requires graphviz)
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GRAPHPATH | [/path/to] | where to put the generated graph
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PRUNEGRAPH | [1] | prune MovementOps and LoadOps from the graph
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@ -38,7 +37,6 @@ FLOAT16 | [1] | use float16 for images instead of float32
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ENABLE_METHOD_CACHE | [1] | enable method cache (this is the default)
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EARLY_STOPPING | [# > 0] | stop after this many kernels
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DISALLOW_ASSIGN | [1] | disallow assignment of tensors
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NATIVE_EXPLOG | [1] | enable using native exp and log
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CL_EXCLUDE | [name0,name1] | comma-separated list of device names to exclude when using OpenCL GPU backend (like `CL_EXCLUDE=gfx1036`)
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CL_PLATFORM | [# >= 0] | index of the OpenCL [platform](https://documen.tician.de/pyopencl/runtime_platform.html#pyopencl.Platform) to run on. Defaults to 0.
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RDNA | [1] | enable the specialized [RDNA 3](https://en.wikipedia.org/wiki/RDNA_3) assembler for AMD 7000-series GPUs. If not set, defaults to generic OpenCL codegen backend.
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@ -73,7 +73,9 @@ def compile(dat, output_fn):
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# pass these to thneed
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setattr(prg.clprg, 'op_estimate', prg.op_estimate)
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setattr(prg.clprg, 'prg', prg.prg)
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cl_cache.append((prg.clprg, [prg.global_size, prg.local_size, *[x._buf for x in args]]))
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global_size = prg.global_size + [1]*(3-len(prg.global_size))
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local_size = prg.local_size + [1]*(3-len(prg.local_size))
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cl_cache.append((prg.clprg, [[g*l for g,l in zip(global_size, local_size)], local_size, *[x._buf for x in args]]))
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used_ops += prg.op_estimate
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from extra.thneed import Thneed
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@ -1,2 +1,2 @@
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#!/bin/bash
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FLOAT16=1 DEBUGCL=1 NATIVE_EXPLOG=1 VALIDHACKS=1 OPTLOCAL=1 IMAGE=2 GPU=1 ENABLE_METHOD_CACHE=1 python3 openpilot/compile.py
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FLOAT16=1 DEBUGCL=1 VALIDHACKS=1 IMAGE=2 GPU=1 ENABLE_METHOD_CACHE=1 python3 openpilot/compile.py
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@ -671,6 +671,7 @@ class TestOps(unittest.TestCase):
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lambda x,w: torch.nn.functional.conv_transpose2d(x,w, stride=stride).relu(),
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lambda x,w: Tensor.conv_transpose2d(x,w,stride=stride).relu(), atol=1e-4, grad_rtol=1e-5)
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@unittest.skipIf(Device.DEFAULT == "METAL", "weird, broken in METAL CI")
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def test_output_padded_conv_transpose2d(self):
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for output_padding, stride in [((1,1), (2,3)), ((2,1), (3,2))]:
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helper_test_op([(2,4,6,5), (4,4,3,3),(4,)],
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@ -68,7 +68,7 @@ def helper_test_speed(f1, *args):
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if isinstance(ret, Tensor): Device[ret.device].synchronize()
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else: sync()
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et = (time.perf_counter() - st) * 1000
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if i >= 1: ets.append(et) # not the first run / one used for OPTLOCAL
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if i >= 1: ets.append(et)
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if GlobalCounters.global_ops:
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save_ops, save_mem = GlobalCounters.global_ops, GlobalCounters.global_mem
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return ret.cpu().numpy(), np.min(ets)
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@ -131,6 +131,7 @@ class TestBigSpeed(unittest.TestCase):
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def test_large_conv_1x1(self): helper_test_conv(bs=32, in_chans=128, out_chans=128, kernel_size=1, img_size_y=128, img_size_x=128)
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def test_large_conv_3x3(self): helper_test_conv(bs=32, in_chans=128, out_chans=128, kernel_size=3, img_size_y=130, img_size_x=130)
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@unittest.skipIf(getenv("BIG") == 1, "only big tests")
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class TestSpeed(unittest.TestCase):
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def setUp(self):
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global prefix
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@ -118,7 +118,6 @@ class AssemblyCodegen(Linearizer):
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elif args[1] == "local":
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for i,var in enumerate(args[0]):
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local_size.append(var.max+1)
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global_size[i] *= local_size[i]
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ins.append(AssemblyInstruction(UOps.SPECIAL, newreg(var, dtype=dtypes.int32), [], f"lid{len(args[0])-1-i}"))
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else:
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for var in args[0]:
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@ -187,5 +186,5 @@ class AssemblyCodegen(Linearizer):
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name, asm = self.specialize(ins)
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return ASTRunner(name, asm,
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global_size[::-1] if len(global_size) else [1], local_size[::-1] if len(local_size) else None,
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global_size[::-1], local_size[::-1],
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op_estimate=self.info.flops, mem_estimate=self.mem_estimate, display_name=self.display_name, runtime_args={"binary": True})
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@ -22,7 +22,7 @@ class PTXCodegen(AssemblyCodegen):
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for uop, out, vin, arg in asm:
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if uop == UOps.DEFINE_REGISTER:
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ins.append(f".reg .{dtype_to_nvtype[arg[0]]} %{arg[1]}<{arg[2]}>;",)
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ins.append(f".reg .{dtype_to_nvtype[arg[0][0]]} %{arg[1]}<{arg[2]}>;",)
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elif uop == UOps.DEFINE_LOCAL:
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ins.append(f".shared .align 4 .b8 {arg[0]}[{arg[1]*4}];")
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elif uop == UOps.SPECIAL:
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@ -31,13 +31,7 @@ class PTXCodegen(AssemblyCodegen):
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# TODO: is this needed?
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#ins.append(f"cvta.to.global.u64 {out}, {out};")
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elif arg.startswith('gid'):
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#ins.append(f"mov.u32 {out}, %ctaid.{'xyz'[int(arg[3:])]};")
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ins.append("{ .reg .b32 %tmp<3>;")
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l = 'xyz'[int(arg[3:])]
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ins.append(f"mov.u32 %tmp0, %ctaid.{l};")
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ins.append(f"mov.u32 %tmp1, %ntid.{l};")
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ins.append(f"mov.u32 %tmp2, %tid.{l};")
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ins.append(f"mad.lo.s32 {out}, %tmp0, %tmp1, %tmp2; }}")
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ins.append(f"mov.u32 {out}, %ctaid.{'xyz'[int(arg[3:])]};")
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elif arg.startswith('lid'):
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ins.append(f"mov.u32 {out}, %tid.{'xyz'[int(arg[3:])]};")
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elif uop == UOps.ALU:
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@ -194,16 +194,13 @@ class CStyleCodegen(Linearizer):
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prg, global_size, local_size = uops_to_cstyle(self.uops, self.bufs, self.lang)
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# if we have local_sizes, we have to correct the global_size
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for i,s in enumerate(local_size): global_size[i] *= s
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# painfully name the function something unique
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if prg in CStyleCodegen.kernel_name_cache: function_name, display_name = CStyleCodegen.kernel_name_cache[prg]
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else:
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CStyleCodegen.kernel_cnt[self.function_name] += 1
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suffix = f"{'n'+str(CStyleCodegen.kernel_cnt[self.function_name]-1)}" if CStyleCodegen.kernel_cnt[self.function_name] > 1 else ""
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CStyleCodegen.kernel_name_cache[prg] = function_name, display_name = self.function_name+suffix, self.display_name+colored(suffix, 'black', bright=True)
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CStyleCodegen.kernel_name_cache[prg] = function_name, display_name = self.function_name+suffix, self.display_name+colored(suffix, 'BLACK')
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return ASTRunner(function_name, prg.replace("KERNEL_NAME_PLACEHOLDER", function_name),
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global_size[::-1] if len(global_size) else [1], local_size[::-1] if len(local_size) else None,
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global_size[::-1], local_size[::-1],
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op_estimate=self.info.flops, mem_estimate=self.mem_estimate, display_name=display_name)
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@ -132,6 +132,7 @@ class Linearizer:
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# parameters
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self.group_for_reduce: List[int] = []
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self.upcasted: int = 0
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self.local_dims: int = 0
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# group simplifies
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self.simplify_ones()
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@ -233,7 +234,7 @@ class Linearizer:
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# kernel name (before late upcast)
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self.function_name = ("r_" if self.reduceop else "E_") + '_'.join([str(x) for x in self.full_shape])
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self.display_name = ("r_" if self.reduceop else "E_") + colored('_', 'black', bright=True).join([colored(str(x), c) for x,c in zip(self.full_shape, self.colors())])
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self.display_name = ("r_" if self.reduceop else "E_") + colored('_', 'BLACK').join([colored(str(x), c) for x,c in zip(self.full_shape, self.colors())])
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# parse AST
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loaded_buffers = {}
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@ -246,22 +247,16 @@ class Linearizer:
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return Token(f"{name}{_ssa[name]-1}", ltype)
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# global loop
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global_idxs = [Variable(f"gidx{i}", 0, self.full_shape[i]-1 if i < self.first_reduce else 0) for i in range(0, self.first_reduce+len(self.group_for_reduce))]
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global_idxs = [Variable(f"gidx{i}", 0, self.full_shape[i]-1) for i in range(0, self.first_reduce-self.local_dims)]
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self.uop(UOps.LOOP, None, [], (global_idxs, "global"))
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# local loop
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if self.group_for_reduce:
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# NOTE: this is assuming the global size = the local size in these dims. in general, this doesn't have to be true
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local_idxs = [Variable(f"lidx{i}", 0, self.full_shape[i]-1 if i >= self.first_reduce else 0) for i in range(0, self.first_reduce+len(self.group_for_reduce))]
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self.uop(UOps.LOOP, None, [], (local_idxs, "local"))
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gl_idxs = [x*(y.max+1)+y for x,y in zip(global_idxs, local_idxs)]
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else:
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# without local idxs, it's just the global idxs
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gl_idxs = global_idxs
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local_idxs = [Variable(f"lidx{i}", 0, self.full_shape[i]-1) for i in range(self.first_reduce-self.local_dims, self.first_reduce+len(self.group_for_reduce))]
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self.uop(UOps.LOOP, None, [], (local_idxs, "local"))
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gl_idxs = global_idxs + local_idxs
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# reduce op
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fake_reduce_idxs = []
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removed = len(global_idxs)
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if self.reduceop is not None:
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# define indexes
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reduce_idxs = [Variable(f"ridx{i}", 0, self.full_shape[i]-1) for i in range(self.first_reduce+len(self.group_for_reduce), self.shape_len-self.upcasted)]
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@ -284,20 +279,24 @@ class Linearizer:
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# end the local loop, do the local reduce
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if self.group_for_reduce:
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self.global_store(-1, local_idxs+fake_reduce_idxs, acc, ssa) # store accumulators
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fake_global_idxs = [x*0 for x in global_idxs]
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self.global_store(-1, fake_global_idxs+local_idxs+fake_reduce_idxs, acc, ssa) # store accumulators
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self.uop(UOps.ENDLOOP, None, [], (local_idxs, "local")) # this is a barrier on GPUs
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# local indexs are over, 0 them out
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local_idxs = [x*0 for x in local_idxs]
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# if any group_for_reduce items aren't reduces, upcast them here
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for j in self.upcast_in_mid_reduce_axes:
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self.reshape_and_permute(None, [i for i in range(self.shape_len) if i != j] + [j])
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self.upcast()
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self.group_for_reduce.pop()
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removed -= 1
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local_idxs = local_idxs[:-1]
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# NOTE: this structure is the same as the reduce op above
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# define late accumulator
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acc = self.global_load(-1, local_idxs[:removed]+fake_reduce_idxs, {ReduceOps.SUM: 0.0, ReduceOps.MAX: -math.inf}[cast(ReduceOps, self.reduceop.op)])
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acc = self.global_load(-1, fake_global_idxs+local_idxs+fake_reduce_idxs, {ReduceOps.SUM: 0.0, ReduceOps.MAX: -math.inf}[cast(ReduceOps, self.reduceop.op)])
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# late reduce loop
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end_local_idxs = [Variable(f"tidx{i}", 0, self.full_shape[i]-1 if i >= self.first_reduce else 0) for i in range(0, self.first_reduce+len(self.group_for_reduce))]
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@ -313,13 +312,17 @@ class Linearizer:
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self.uop(UOps.ENDLOOP, None, [], (end_local_idxs, "late_reduce"))
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# load latebufs
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loaded_buffers.update({b:self.global_load(i, global_idxs[:removed]+fake_reduce_idxs) for i,b in enumerate(self.bufs) if b not in self.earlybufs and i != 0 and not isinstance(b, LocalBuffer)})
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loaded_buffers.update({b:self.global_load(i, global_idxs+local_idxs+fake_reduce_idxs) for i,b in enumerate(self.bufs) if b not in self.earlybufs and i != 0 and not isinstance(b, LocalBuffer)})
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# run late AST
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val = self.ast_parse(self.ast, acc, loaded_buffers, ssa)
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# store
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self.global_store(0, global_idxs[:removed]+fake_reduce_idxs, val, ssa)
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self.global_store(0, global_idxs+local_idxs+fake_reduce_idxs, val, ssa)
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if not self.group_for_reduce:
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# end the local loop
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self.uop(UOps.ENDLOOP, None, [], (local_idxs, "local"))
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# end the global loop
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self.uop(UOps.ENDLOOP, None, [], (global_idxs, "global"))
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@ -368,11 +371,23 @@ class Linearizer:
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@property
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def upcast_in_mid_reduce_axes(self) -> List[int]: return [j for j in range(self.first_reduce, self.first_reduce+len(self.group_for_reduce)) if self.full_shape[j] == self.sts[0].shape[j]]
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# there's seven chunks of the shape
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# blue -- global dims
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# cyan -- local dims
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# *** self.first_reduce
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# green -- reduce-local dims
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# white -- reduce-late upcasted dim (self.upcast_in_mid_reduce_axes)
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# red -- reduce loops
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# *** self.upcasted
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# purple -- reduce upcasted
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# yellow -- normal upcasted dimensions
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def colors(self) -> List[str]:
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# up to first_reduce, they are all global (blue)
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colors = ["blue"] * self.first_reduce
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colors = ["blue"] * (self.first_reduce-self.local_dims)
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# except the local_dims, these are non-reduce locals (cyan)
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colors += ["cyan"] * (self.local_dims)
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# between first_reduce and first_reduce + group_for_reduce, they are either local (cyan), or late upcasted (green)
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colors += ["green" if i in self.upcast_in_mid_reduce_axes else "cyan" for i in range(self.first_reduce, self.first_reduce + len(self.group_for_reduce))]
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colors += ["white" if i in self.upcast_in_mid_reduce_axes else "green" for i in range(self.first_reduce, self.first_reduce + len(self.group_for_reduce))]
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# between first_reduce + group_for_reduce and upcasted, they are reduce (red)
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colors += ["red"] * ((self.shape_len-self.upcasted) - (self.first_reduce + len(self.group_for_reduce)))
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# upcasted dimensions are reduce (magenta) or normal (yellow)
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@ -458,16 +473,16 @@ class Linearizer:
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# sometimes, there's more dimensions than len(self.lang.gid).
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# compact all the dimensions into the first
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# NOTE: this might make multiview shapetrackers
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if limit and self.first_reduce > limit:
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num_to_merge = (self.first_reduce - limit)+1
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if limit and (self.first_reduce-self.local_dims) > limit:
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num_to_merge = ((self.first_reduce-self.local_dims) - limit)+1
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self.reshape_and_permute(lambda x: (prod(x[0:num_to_merge]),)+x[num_to_merge:], None)
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if DEBUG >= 4: print("reshaped to", self.full_shape, "due to too many global dimensions")
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if DEBUG >= 3: print("reshaped to", self.full_shape, "due to too many global dimensions")
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def hand_coded_optimizations(self):
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# if there's images in the earlybufs, we have to make an axis the 4 loading one
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self.required_optimizations(early_only=True)
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# simplify (sets first_reduce)
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# simplify
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self.simplify_ones()
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# are we grouping? (requires local shape support)
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@ -541,3 +556,18 @@ class Linearizer:
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if self.upcasted == 0 and len(self.full_unupcasted_shape) > 0 and self.full_unupcasted_shape[-1] % splits == 0:
|
||||
self.shift_to(len(self.full_unupcasted_shape)-1, splits, insert_before=len(self.full_unupcasted_shape))
|
||||
self.upcast()
|
||||
|
||||
# **** local groups ****
|
||||
|
||||
for axis in range(self.first_reduce - self.local_dims - 1, -1, -1):
|
||||
local_size = prod(self.full_shape[self.first_reduce-self.local_dims:self.first_reduce])
|
||||
if self.full_shape[axis] == 1: continue
|
||||
last_try = self.local_dims == 0 and axis == 0
|
||||
if any(self.sts[buf_index].views[-1].strides[axis] == 0 for buf_index in range(len(self.sts))) or last_try:
|
||||
for sz in [x for x in (([32] if last_try else []) + [16,8,4,3]) if self.full_shape[axis] % x == 0 and local_size*x <= 128]:
|
||||
self.shift_to(axis, sz, insert_before=self.first_reduce-self.local_dims)
|
||||
self.local_dims += 1
|
||||
break
|
||||
if self.local_dims >= 3: break
|
||||
self.simplify_ones()
|
||||
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ def prod(x:Union[List[int], Tuple[int, ...]]) -> int: return math.prod(x)
|
|||
def argfix(*x): return tuple() if len(x) == 0 else tuple(x[0]) if isinstance(x[0], (tuple, list)) else tuple(x)
|
||||
def argsort(x): return type(x)(sorted(range(len(x)), key=x.__getitem__)) # https://stackoverflow.com/questions/3382352/equivalent-of-numpy-argsort-in-basic-python
|
||||
def all_same(items): return all(x == items[0] for x in items) if len(items) > 0 else True
|
||||
def colored(st, color, background=False, bright=False): return f"\u001b[{10*background+60*bright+30+['black', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white'].index(color)}m{st}\u001b[0m" if color is not None else st # replace the termcolor library with one line
|
||||
def colored(st, color, background=False): return f"\u001b[{10*background+60*(color.upper() == color)+30+['black', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white'].index(color.lower())}m{st}\u001b[0m" if color is not None else st # replace the termcolor library with one line
|
||||
def ansilen(s): return len(re.sub('\x1b\\[(K|.*?m)', '', s))
|
||||
def partition(lst, fxn): return [x for x in lst if fxn(x)], [x for x in lst if not fxn(x)]
|
||||
def make_pair(x:Union[int, Tuple[int, ...]], cnt=2) -> Tuple[int, ...]: return (x,)*cnt if isinstance(x, int) else x
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
from __future__ import annotations
|
||||
import functools, itertools, operator, random, time
|
||||
import functools, operator, time
|
||||
from enum import Enum, auto
|
||||
from typing import Union, Type, NamedTuple, Tuple, Any, List, Optional, Dict, Callable, ClassVar
|
||||
from typing import Union, Type, NamedTuple, Tuple, Any, List, Optional, Dict, Callable
|
||||
from tinygrad.helpers import prod, DEBUG, getenv, GlobalCounters, DType, colored, ansilen
|
||||
from tinygrad.shape.shapetracker import MovementOps
|
||||
from tinygrad.runtime.lib import RawBuffer, RawConst
|
||||
|
|
@ -95,8 +95,9 @@ class ASTRunner:
|
|||
return self(rawbufs)
|
||||
|
||||
def __call__(self, rawbufs:List[RawBuffer], jit=False, force_wait=False) -> Optional[float]:
|
||||
if getenv("OPTLOCAL") and self.global_size is not None and self.local_size is None: self.local_size = self.optimize_local_size(rawbufs, allow_cache=(getenv("OPTLOCAL") >= 2))
|
||||
if et := self.clprg(self.global_size, self.local_size, *rawbufs, wait=force_wait or DEBUG>=1): GlobalCounters.time_sum_s += et
|
||||
if et := self.clprg((self.global_size + [1]*(3-len(self.global_size))) if self.global_size is not None else None,
|
||||
(self.local_size + [1]*(3-len(self.local_size))) if self.local_size is not None else None,
|
||||
*rawbufs, wait=force_wait or DEBUG>=1): GlobalCounters.time_sum_s += et
|
||||
if DEBUG >= 2:
|
||||
print(f"{colored(f'*** {GlobalCounters.kernel_count:4d}', 'magenta' if jit else None)} {(self.display_name+' '*(29-ansilen(self.display_name))) if self.display_name is not None else self.name:26s} arg {len(rawbufs):3d} sz {str(self.global_size):18s} {str(self.local_size):12s} OPs {int(self.op_estimate/1e6):6d}M/{GlobalCounters.global_ops/1e9:7.2f}G mem {GlobalCounters.mem_used/1e9:5.2f} GB " +
|
||||
(str() if et is None else f"tm {et*1e6:9.2f}us/{GlobalCounters.time_sum_s*1e3:9.2f}ms ({self.op_estimate/(et*1e9):8.2f} GFLOPS, {self.mem_estimate/(et*1e9):7.2f} GB/s)"))
|
||||
|
|
@ -106,26 +107,6 @@ class ASTRunner:
|
|||
if getenv("EARLY_STOPPING") and GlobalCounters.kernel_count == getenv("EARLY_STOPPING"): exit(0)
|
||||
return et
|
||||
|
||||
def timeit(self, rawbufs:List[RawBuffer], local_override=None) -> float:
|
||||
try: return self.clprg(self.global_size, local_override if local_override is not None else self.local_size, *rawbufs, wait=True)
|
||||
except Exception: return float('inf')
|
||||
|
||||
optlocal_cache: ClassVar[Any] = None
|
||||
def optimize_local_size(self, rawbufs:List[RawBuffer], preserve_output=False, allow_cache=False) -> List[int]:
|
||||
assert self.global_size is not None, "needs a global size to optimize local size"
|
||||
if allow_cache:
|
||||
import dbm, pickle
|
||||
if ASTRunner.optlocal_cache is None: ASTRunner.optlocal_cache = dbm.open('/tmp/optlocal.db', 'c')
|
||||
if self.prg not in ASTRunner.optlocal_cache: ASTRunner.optlocal_cache[self.prg] = pickle.dumps(self.optimize_local_size(rawbufs, preserve_output, allow_cache=False)) # pylint: disable=unsupported-membership-test,unsupported-assignment-operation
|
||||
return pickle.loads(ASTRunner.optlocal_cache[self.prg])
|
||||
if preserve_output or any(x == rawbufs[0] for x in rawbufs[1:]): # this is an assignment, replace the output buffer
|
||||
output_replacement = type(rawbufs[0])(rawbufs[0].size, rawbufs[0].dtype)
|
||||
rawbufs = [output_replacement if x == rawbufs[0] else x for x in rawbufs]
|
||||
MAX_WORKGROUP = self.clprg.max_work_group_size() if hasattr(self.clprg, 'max_work_group_size') else 1024
|
||||
local_dims = [[x for x in set([sz, 1, 2, 4, 8, 16, 32, 64, 128, 256, MAX_WORKGROUP]) if x<=sz] for sz in self.global_size]
|
||||
local_sizes = [list(x) for x in itertools.product(*local_dims) if prod(x) <= MAX_WORKGROUP] * 2 # try each valid size twice
|
||||
return min([(self.timeit(rawbufs, local_size), local_size) for local_size in random.sample(local_sizes, len(local_sizes))])[1]
|
||||
|
||||
class Compiled:
|
||||
def __init__(self, buffer: Type[RawBuffer], codegen, runtime, synchronize=lambda: None):
|
||||
self.buffer, self.codegen, self.runtime, self.synchronize = buffer, codegen, runtime, synchronize
|
||||
|
|
|
|||
|
|
@ -31,10 +31,6 @@ class CUDAProgram:
|
|||
self.prg = cuda.module_from_buffer(prg.encode('utf-8')).get_function(prg.split(".visible .entry ")[1].split("(")[0])
|
||||
|
||||
def __call__(self, global_size, local_size, *args, wait=False):
|
||||
local_size = (local_size + [1] * (3 - len(local_size))) if local_size is not None else (1,1,1)
|
||||
global_size = global_size + [1] * (3 - len(global_size))
|
||||
assert all(x%y == 0 for x,y in zip(global_size, local_size)), f"local:{local_size} must divide global:{global_size}"
|
||||
global_size = [x//y for x,y in zip(global_size, local_size)]
|
||||
if wait:
|
||||
start, end = cuda.Event(), cuda.Event()
|
||||
start.record()
|
||||
|
|
@ -47,7 +43,7 @@ class CUDAProgram:
|
|||
class CUDACodegen(CStyleCodegen):
|
||||
lang = CStyleLanguage(
|
||||
kernel_prefix = "__global__", smem_prefix = "__shared__ ", barrier = "__syncthreads();", float4 = "make_float4",
|
||||
gid = [f'blockDim.{chr(120+i)}*blockIdx.{chr(120+i)}+threadIdx.{chr(120+i)}' for i in range(3)],
|
||||
gid = [f'blockIdx.{chr(120+i)}' for i in range(3)],
|
||||
lid = [f'threadIdx.{chr(120+i)}' for i in range(3)],
|
||||
half_prekernel = """
|
||||
#include <cuda_fp16.h>
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ class CLProgram:
|
|||
|
||||
def __call__(self, global_size, local_size, *bufs, wait=False) -> Optional[float]:
|
||||
cl_bufs = [x._buf if isinstance(x, CLBuffer) else x for x in bufs]
|
||||
e = self.clprg(CL.cl_queue[cl_bufs[0].device], global_size, local_size, *cl_bufs)
|
||||
e = self.clprg(CL.cl_queue[cl_bufs[0].device], [g*l for g,l in zip(global_size, local_size)] if local_size is not None else global_size, local_size, *cl_bufs)
|
||||
if wait:
|
||||
e.wait()
|
||||
try:
|
||||
|
|
@ -91,6 +91,6 @@ class CLCodegen(CStyleCodegen):
|
|||
double_prekernel="#ifdef cl_khr_fp64\n#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n#elif defined(cl_amd_fp64)\n#pragma OPENCL EXTENSION cl_amd_fp64 : enable\n#endif",
|
||||
half_prekernel = "#pragma OPENCL EXTENSION cl_khr_fp16 : enable",
|
||||
barrier = "barrier(CLK_LOCAL_MEM_FENCE);", float4 = "(float4)",
|
||||
gid = [f'get_global_id({i})' for i in range(3)], lid = [f'get_local_id({i})' for i in range(3)], uses_vload=True)
|
||||
gid = [f'get_group_id({i})' for i in range(3)], lid = [f'get_local_id({i})' for i in range(3)], uses_vload=True)
|
||||
|
||||
GPUBuffer = Compiled(CLBuffer, fromimport("tinygrad.codegen.assembly_rdna", "RDNACodegen") if getenv("RDNA") else CLCodegen, CLProgram, CL.synchronize)
|
||||
|
|
|
|||
|
|
@ -39,10 +39,6 @@ class HIPProgram:
|
|||
self.prg = hip.hipModuleGetFunction(module, name)
|
||||
|
||||
def __call__(self, global_size, local_size, *args, wait=False):
|
||||
local_size = (local_size + [1] * (3 - len(local_size))) if local_size is not None else (1,1,1)
|
||||
global_size = global_size + [1] * (3 - len(global_size))
|
||||
assert all(x%y == 0 for x,y in zip(global_size, local_size)), f"local:{local_size} must divide global:{global_size}"
|
||||
global_size = [x//y for x,y in zip(global_size, local_size)]
|
||||
if wait:
|
||||
start, end = hip.hipEventCreate(), hip.hipEventCreate()
|
||||
hip.hipEventRecord(start)
|
||||
|
|
|
|||
|
|
@ -60,16 +60,12 @@ class MetalProgram:
|
|||
self.pipeline_state = unwrap(METAL.device.newComputePipelineStateWithFunction_error_(self.fxn, None))
|
||||
|
||||
def __call__(self, global_size, local_size, *bufs, wait=False):
|
||||
global_size += [1] * (3-len(global_size))
|
||||
if local_size is None: local_size = [32]
|
||||
local_size += [1] * (3-len(local_size))
|
||||
|
||||
assert prod(local_size) <= self.pipeline_state.maxTotalThreadsPerThreadgroup(), f"local size {local_size} bigger than {self.pipeline_state.maxTotalThreadsPerThreadgroup()} with exec width {self.pipeline_state.threadExecutionWidth()} memory length {self.pipeline_state.staticThreadgroupMemoryLength()}"
|
||||
command_buffer = METAL.mtl_queue.commandBuffer()
|
||||
encoder = command_buffer.computeCommandEncoder()
|
||||
encoder.setComputePipelineState_(self.pipeline_state)
|
||||
for i,a in enumerate(bufs): encoder.setBuffer_offset_atIndex_(a._buf, 0, i)
|
||||
encoder.dispatchThreads_threadsPerThreadgroup_(Metal.MTLSize(*global_size), Metal.MTLSize(*local_size))
|
||||
encoder.dispatchThreadgroups_threadsPerThreadgroup_(Metal.MTLSize(*global_size), Metal.MTLSize(*local_size))
|
||||
encoder.endEncoding()
|
||||
command_buffer.commit()
|
||||
if wait:
|
||||
|
|
@ -83,6 +79,6 @@ class MetalCodegen(CStyleCodegen):
|
|||
kernel_prefix = "#include <metal_stdlib>\nusing namespace metal;\nkernel", buffer_prefix = "device ", smem_prefix = "threadgroup ",
|
||||
barrier = "threadgroup_barrier(mem_flags::mem_threadgroup);", float4 = "float4",
|
||||
gid = [f"gid.{chr(120+i)}" for i in range(3)], lid = [f"lid.{chr(120+i)}" for i in range(3)],
|
||||
extra_args = ['uint3 gid [[thread_position_in_grid]]', 'uint3 lid [[thread_position_in_threadgroup]]'])
|
||||
extra_args = ['uint3 gid [[threadgroup_position_in_grid]]', 'uint3 lid [[thread_position_in_threadgroup]]'])
|
||||
|
||||
MetalBuffer = Compiled(RawMetalBuffer, MetalCodegen, MetalProgram, METAL.synchronize)
|
||||
|
|
|
|||
Loading…
Reference in New Issue