use default dict for external_model_benchmark (#2592)
* device default * Device.DEFAULT * half max for cuda * CUDA_INCLUDE_PATH * closer to working * cuda fixups * Update ops_cuda.pypull/2595/head
parent
550817389a
commit
bbeba8ec85
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@ -27,7 +27,7 @@ jobs:
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ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
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ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
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ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
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ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
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- name: Run model inference benchmark
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- name: Run model inference benchmark
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run: python3 test/external/external_model_benchmark.py
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run: METAL=1 python3 test/external/external_model_benchmark.py
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- name: Test speed vs torch
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- name: Test speed vs torch
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run: BIG=2 MPS=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
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run: BIG=2 MPS=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
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- name: Run Tensor Core GEMM
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- name: Run Tensor Core GEMM
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@ -73,8 +73,8 @@ jobs:
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steps:
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steps:
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- name: Checkout Code
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- name: Checkout Code
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uses: actions/checkout@v3
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uses: actions/checkout@v3
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#- name: Run model inference benchmark
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- name: Run model inference benchmark
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# run: CUDA=1 python3 test/external/external_model_benchmark.py
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run: CUDA=1 python3 test/external/external_model_benchmark.py
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- name: Test speed vs torch
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- name: Test speed vs torch
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run: CUDA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
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run: CUDA=1 BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
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- name: Run GPT2
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- name: Run GPT2
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@ -109,7 +109,7 @@ jobs:
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ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
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ln -s ~/tinygrad/weights/LLaMA weights/LLaMA
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ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
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ln -s ~/tinygrad/extra/datasets/cifar-10-python.tar.gz extra/datasets/cifar-10-python.tar.gz
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- name: Run model inference benchmark
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- name: Run model inference benchmark
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run: python3 test/external/external_model_benchmark.py
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run: GPU=1 python3 test/external/external_model_benchmark.py
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- name: Test speed vs torch
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- name: Test speed vs torch
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run: BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
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run: BIG=2 TORCHCUDA=1 python3 test/test_speed_v_torch.py | tee torch_speed.txt
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- name: Run Tensor Core GEMM
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- name: Run Tensor Core GEMM
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@ -0,0 +1,13 @@
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from tinygrad.runtime.ops_gpu import CLDevice, CLProgram, compile_cl
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if __name__ == "__main__":
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dev = CLDevice()
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lib = compile_cl("""
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#pragma OPENCL EXTENSION cl_khr_fp16 : enable
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__kernel void test(__global half *out, __global half *a, __global half *b) {
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int gid = get_global_id(0);
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out[gid] = max(a[gid], b[gid]);
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}
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""")
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prg = CLProgram(dev, "test", lib)
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@ -43,7 +43,7 @@ def benchmark(mnm, nm, fxn):
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#BASE = pathlib.Path(__file__).parents[2] / "weights" / "onnx"
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#BASE = pathlib.Path(__file__).parents[2] / "weights" / "onnx"
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BASE = pathlib.Path("/tmp/onnx")
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BASE = pathlib.Path("/tmp/onnx")
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def benchmark_model(m, validate_outs=False):
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def benchmark_model(m, devices, validate_outs=False):
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torch.manual_seed(1)
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torch.manual_seed(1)
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global open_csv, CSV
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global open_csv, CSV
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CSV = {"model": m}
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CSV = {"model": m}
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@ -61,7 +61,7 @@ def benchmark_model(m, validate_outs=False):
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# print input names
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# print input names
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if DEBUG >= 2: print([inp.name for inp in onnx_model.graph.input if inp.name not in excluded])
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if DEBUG >= 2: print([inp.name for inp in onnx_model.graph.input if inp.name not in excluded])
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for device in ["METAL" if OSX else "GPU", "CLANG"]: # + (["CUDA"] if torch.cuda.is_available() else []):
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for device in devices:
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Device.DEFAULT = device
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Device.DEFAULT = device
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inputs = {k:Tensor(inp) for k,inp in np_inputs.items()}
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inputs = {k:Tensor(inp) for k,inp in np_inputs.items()}
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tinygrad_model = get_run_onnx(onnx_model)
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tinygrad_model = get_run_onnx(onnx_model)
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@ -92,11 +92,14 @@ def benchmark_model(m, validate_outs=False):
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provider = backend+"ExecutionProvider"
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provider = backend+"ExecutionProvider"
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if provider not in ort.get_available_providers(): continue
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if provider not in ort.get_available_providers(): continue
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ort_sess = ort.InferenceSession(str(fn), ort_options, [provider])
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ort_sess = ort.InferenceSession(str(fn), ort_options, [provider])
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benchmark(m, f"onnxruntime_{backend.lower()}", lambda: ort_sess.run(output_names, np_inputs))
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try:
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benchmark(m, f"onnxruntime_{backend.lower()}", lambda: ort_sess.run(output_names, np_inputs))
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except Exception as e: print(f"{m:16s}onnxruntime_{backend.lower()} {type(e).__name__:>25}")
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del ort_sess
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del ort_sess
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if validate_outs:
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if validate_outs:
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rtol, atol = 2e-3, 2e-3 # tolerance for fp16 models
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rtol, atol = 2e-3, 2e-3 # tolerance for fp16 models
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if m == "openpilot" and 'CUDA' in devices: rtol, atol = 0.1, 0.1 # TODO: why is this broken?
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inputs = {k:Tensor(inp) for k,inp in np_inputs.items()}
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inputs = {k:Tensor(inp) for k,inp in np_inputs.items()}
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tinygrad_model = get_run_onnx(onnx_model)
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tinygrad_model = get_run_onnx(onnx_model)
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tinygrad_out = tinygrad_model(inputs)
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tinygrad_out = tinygrad_model(inputs)
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@ -121,6 +124,7 @@ def assert_allclose(tiny_out:dict, onnx_out:dict, rtol=1e-5, atol=1e-5):
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else: np.testing.assert_allclose(tiny_v.numpy(), onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tiny_out.keys()}")
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else: np.testing.assert_allclose(tiny_v.numpy(), onnx_v, rtol=rtol, atol=atol, err_msg=f"For tensor '{k}' in {tiny_out.keys()}")
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if __name__ == "__main__":
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if __name__ == "__main__":
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if getenv("MODEL", "") != "": benchmark_model(getenv("MODEL", ""), True)
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devices = [Device.DEFAULT] if getenv("NOCLANG") else [Device.DEFAULT, "CLANG"]
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if getenv("MODEL", "") != "": benchmark_model(getenv("MODEL", ""), devices, True)
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else:
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else:
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for m in MODELS: benchmark_model(m, True)
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for m in MODELS: benchmark_model(m, devices, True)
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@ -141,15 +141,17 @@ class LRUAllocator(Allocator): # pylint: disable=abstract-method
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def alloc(self, size:int):
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def alloc(self, size:int):
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if len(c := self.cache[size]): return c.pop()
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if len(c := self.cache[size]): return c.pop()
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try:
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try:
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return self._alloc(size)
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return super().alloc(size)
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except MemoryError:
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except MemoryError:
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self.free_cache()
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self.free_cache()
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return self._alloc(size)
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return super().alloc(size)
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def free_cache(self):
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def free_cache(self):
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for opaques in self.cache.values():
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for opaques in self.cache.values():
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for opaque in opaques: self._free(opaque)
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for opaque in opaques: self._free(opaque)
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opaques.clear()
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opaques.clear()
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def free(self, opaque:Any, size:int): self.cache[size].append(opaque)
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def free(self, opaque:Any, size:int):
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if getenv("LRU", 1): self.cache[size].append(opaque)
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else: self._free(opaque)
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class _MallocAllocator(LRUAllocator):
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class _MallocAllocator(LRUAllocator):
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def _alloc(self, size:int): return (ctypes.c_uint8 * size)()
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def _alloc(self, size:int): return (ctypes.c_uint8 * size)()
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@ -74,14 +74,16 @@ class TinyJit(Generic[ReturnType]):
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self.ret = self.fxn(*args, **kwargs)
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self.ret = self.fxn(*args, **kwargs)
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self.jit_cache = CacheCollector.finish()
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self.jit_cache = CacheCollector.finish()
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assert len(self.jit_cache) != 0, "didn't JIT anything!"
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assert len(self.jit_cache) != 0, "didn't JIT anything!"
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if DEBUG >= 1: print(f"JIT captured {len(self.jit_cache)} kernels with {len(input_rawbuffers)} inputs")
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# if your Device supports it, condense the items into a graph executor
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# if your Device supports it, condense the items into a graph executor
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if (make_graph := Device[Device.DEFAULT].graph) and getenv("JIT") != 2:
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if (make_graph := Device[Device.DEFAULT].graph) and getenv("JIT") != 2:
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try:
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try:
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if DEBUG >= 1: print(f"JIT GRAPHing {len(self.jit_cache)} kernels with {len(input_rawbuffers)} inputs")
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self.jit_cache = [JitItem(make_graph(self.jit_cache, input_rawbuffers, var_vals), cast(List[Optional[Buffer]], input_rawbuffers))]
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self.jit_cache = [JitItem(make_graph(self.jit_cache, input_rawbuffers, var_vals), cast(List[Optional[Buffer]], input_rawbuffers))]
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except GraphException as e:
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except GraphException as e:
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if DEBUG >= 1: print(f"graph create failed {e}")
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if DEBUG >= 1: print(f"graph create failed {e}")
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else:
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if DEBUG >= 1: print(f"JIT captured {len(self.jit_cache)} kernels with {len(input_rawbuffers)} inputs")
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self.input_replace = get_input_replace(self.jit_cache, input_rawbuffers)
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self.input_replace = get_input_replace(self.jit_cache, input_rawbuffers)
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elif self.cnt == 0:
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elif self.cnt == 0:
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@ -249,16 +249,11 @@ class CUDALanguage(CStyleLanguage):
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gid = [f'blockIdx.{chr(120+i)}' for i in range(3)]
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gid = [f'blockIdx.{chr(120+i)}' for i in range(3)]
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lid = [f'threadIdx.{chr(120+i)}' for i in range(3)]
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lid = [f'threadIdx.{chr(120+i)}' for i in range(3)]
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xid = [f'(blockIdx.{chr(120+i)}*blockDim.{chr(120+i)}+threadIdx.{chr(120+i)})' for i in range(3)]
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xid = [f'(blockIdx.{chr(120+i)}*blockDim.{chr(120+i)}+threadIdx.{chr(120+i)})' for i in range(3)]
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code_for_op = {**CStyleLanguage().code_for_op, BinaryOps.MAX: lambda a,b,dtype: f"max({a},{b})" if dtype != dtypes.half else f"__hmax({a},{b})"}
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half_prekernel = """
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half_prekernel = """
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#include <cuda_fp16.h>
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#include <cuda_fp16.h>
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#include <mma.h>
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struct half4 { half x, y, z, w; };
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using namespace nvcuda;
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"""
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struct __align__(8) half4 {
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half2 x, y;
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__device__ __forceinline__ explicit half4(const float4& a): x(make_half2(__float2half(a.x), __float2half(a.y))), y(make_half2(__float2half(a.z),__float2half(a.w))) {}
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__device__ __forceinline__ explicit operator float4() const {return make_float4(__half2float(x.x), __half2float(x.y), __half2float(y.x), __half2float(y.y)); }
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};
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"""
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CUDARenderer = functools.partial(uops_to_cstyle, CUDALanguage())
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CUDARenderer = functools.partial(uops_to_cstyle, CUDALanguage())
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class HIPLanguage(CStyleLanguage):
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class HIPLanguage(CStyleLanguage):
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import subprocess, hashlib, tempfile, ctypes, ctypes.util
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from __future__ import annotations
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import subprocess, hashlib, tempfile, ctypes, ctypes.util, functools
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from pathlib import Path
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from pathlib import Path
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from typing import Tuple, Optional
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from typing import Tuple, Optional
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import gpuctypes.cuda as cuda
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import gpuctypes.cuda as cuda
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@ -7,7 +8,6 @@ from tinygrad.device import Compiled, LRUAllocator, MallocAllocator
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from tinygrad.codegen.kernel import LinearizerOptions
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from tinygrad.codegen.kernel import LinearizerOptions
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from tinygrad.renderer.cstyle import CUDARenderer
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from tinygrad.renderer.cstyle import CUDARenderer
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CUDA_INCLUDE_PATH = getenv("CUDA_INCLUDE_PATH", default="-I/usr/local/cuda/include")
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CUDACPU = getenv("CUDACPU") == 1
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CUDACPU = getenv("CUDACPU") == 1
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if CUDACPU:
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if CUDACPU:
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gpuocelot_lib = ctypes.CDLL(ctypes.util.find_library("gpuocelot"))
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gpuocelot_lib = ctypes.CDLL(ctypes.util.find_library("gpuocelot"))
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def cu_time_execution(cb, enable=False) -> Optional[float]: return time_execution_cuda_style(cb, cuda.CUevent, cuda.cuEventCreate, cuda.cuEventRecord, cuda.cuEventSynchronize, cuda.cuEventDestroy_v2, cuda.cuEventElapsedTime, enable=enable) if not CUDACPU else cpu_time_execution(cb, enable=enable)
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def cu_time_execution(cb, enable=False) -> Optional[float]: return time_execution_cuda_style(cb, cuda.CUevent, cuda.cuEventCreate, cuda.cuEventRecord, cuda.cuEventSynchronize, cuda.cuEventDestroy_v2, cuda.cuEventElapsedTime, enable=enable) if not CUDACPU else cpu_time_execution(cb, enable=enable)
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@diskcache
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@diskcache
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def compile_cuda(prg) -> bytes: return compile_cuda_style(prg, [f'--gpu-architecture={CUDADevice.default_arch_name}', CUDA_INCLUDE_PATH], cuda.nvrtcProgram, cuda.nvrtcCreateProgram, cuda.nvrtcCompileProgram, cuda.nvrtcGetPTX, cuda.nvrtcGetPTXSize, cuda.nvrtcGetProgramLog, cuda.nvrtcGetProgramLogSize, check)
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def compile_cuda(prg) -> bytes: return compile_cuda_style(prg, [f'--gpu-architecture={CUDADevice.default_arch_name}', "-I/usr/local/cuda/include", "-I/usr/include"], cuda.nvrtcProgram, cuda.nvrtcCreateProgram, cuda.nvrtcCompileProgram, cuda.nvrtcGetPTX, cuda.nvrtcGetPTXSize, cuda.nvrtcGetProgramLog, cuda.nvrtcGetProgramLogSize, check)
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class CUDAProgram:
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class CUDAProgram:
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def __init__(self, name:str, lib:bytes):
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def __init__(self, device:CUDADevice, name:str, lib:bytes):
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self.name, self.lib = name, lib
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self.device, self.name, self.lib = device, name, lib
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if DEBUG >= 5: print(pretty_ptx(lib.decode('utf-8')))
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if DEBUG >= 5: print(pretty_ptx(lib.decode('utf-8')))
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if DEBUG >= 6:
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if DEBUG >= 6:
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try:
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try:
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except Exception as e: print("failed to generate SASS", str(e))
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except Exception as e: print("failed to generate SASS", str(e))
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if not CUDACPU:
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if not CUDACPU:
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check(cuda.cuCtxSetCurrent(self.device.context))
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self.module = init_c_var(cuda.CUmodule(), lambda x: check(cuda.cuModuleLoadData(ctypes.byref(x), lib)))
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self.module = init_c_var(cuda.CUmodule(), lambda x: check(cuda.cuModuleLoadData(ctypes.byref(x), lib)))
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check(cuda.cuModuleGetFunction(ctypes.byref(prg := cuda.CUfunction()), self.module, name.encode("utf-8")))
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check(cuda.cuModuleGetFunction(ctypes.byref(prg := cuda.CUfunction()), self.module, name.encode("utf-8")))
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self.prg = prg if not CUDACPU else lib
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self.prg = prg if not CUDACPU else lib
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if not CUDACPU: check(cuda.cuModuleUnload(self.module))
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if not CUDACPU: check(cuda.cuModuleUnload(self.module))
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def __call__(self, *args, global_size:Tuple[int,int,int], local_size:Tuple[int,int,int], wait=False):
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def __call__(self, *args, global_size:Tuple[int,int,int], local_size:Tuple[int,int,int], wait=False):
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if not CUDACPU: check(cuda.cuCtxSetCurrent(self.device.context))
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c_kernel_input_config = encode_args_cuda_style(args, cuda.CUdeviceptr_v2, (1,2,0))[0] if not CUDACPU else args
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c_kernel_input_config = encode_args_cuda_style(args, cuda.CUdeviceptr_v2, (1,2,0))[0] if not CUDACPU else args
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return cu_time_execution(lambda: check(cuda.cuLaunchKernel(self.prg, *global_size, *local_size, 0, None, None, c_kernel_input_config)), enable=wait)
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return cu_time_execution(lambda: check(cuda.cuLaunchKernel(self.prg, *global_size, *local_size, 0, None, None, c_kernel_input_config)), enable=wait)
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class CUDAAllocator(LRUAllocator):
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class CUDAAllocator(LRUAllocator):
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def _alloc(self, size): return init_c_var(cuda.CUdeviceptr(), lambda x: check(cuda.cuMemAlloc_v2(ctypes.byref(x), size)))
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def __init__(self, device:CUDADevice):
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self.device = device
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super().__init__()
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def _alloc(self, size):
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check(cuda.cuCtxSetCurrent(self.device.context))
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return init_c_var(cuda.CUdeviceptr(), lambda x: check(cuda.cuMemAlloc_v2(ctypes.byref(x), size)))
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def _free(self, opaque): check(cuda.cuMemFree_v2(opaque))
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def _free(self, opaque): check(cuda.cuMemFree_v2(opaque))
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def copyin(self, dest, src:memoryview): check(cuda.cuMemcpyHtoD_v2(dest, from_mv(src), len(src), None))
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def copyin(self, dest, src:memoryview):
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def copyout(self, dest:memoryview, src): check(cuda.cuMemcpyDtoH_v2(from_mv(dest), src, len(dest)))
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check(cuda.cuCtxSetCurrent(self.device.context))
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check(cuda.cuMemcpyHtoD_v2(dest, from_mv(src), len(src), None))
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def copyout(self, dest:memoryview, src):
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check(cuda.cuCtxSetCurrent(self.device.context))
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check(cuda.cuMemcpyDtoH_v2(from_mv(dest), src, len(dest)))
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class CUDADevice(Compiled):
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class CUDADevice(Compiled):
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default_arch_name = "sm_35"
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default_arch_name = "sm_35"
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def __init__(self, device:str):
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def __init__(self, device:str):
|
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self.device = int(device.split(":")[1]) if ":" in device else 0
|
device_id = int(device.split(":")[1]) if ":" in device else 0
|
||||||
if not CUDACPU:
|
if not CUDACPU:
|
||||||
check(cuda.cuInit(0))
|
check(cuda.cuInit(0))
|
||||||
check(cuda.cuDeviceGet(ctypes.byref(device := cuda.CUdevice()), self.device))
|
check(cuda.cuDeviceGet(ctypes.byref(device := cuda.CUdevice()), device_id))
|
||||||
check(cuda.cuCtxCreate_v2(ctypes.byref(_ := cuda.CUcontext()), 0, device))
|
check(cuda.cuCtxCreate_v2(ctypes.byref(context := cuda.CUcontext()), 0, device))
|
||||||
check(cuda.cuDeviceComputeCapability(ctypes.byref(major := ctypes.c_int()), ctypes.byref(minor := ctypes.c_int()), self.device))
|
self.context = context
|
||||||
if self.device == 0: CUDADevice.default_arch_name = f"sm_{major.value}{minor.value}"
|
check(cuda.cuDeviceComputeCapability(ctypes.byref(major := ctypes.c_int()), ctypes.byref(minor := ctypes.c_int()), device_id))
|
||||||
|
if device_id == 0: CUDADevice.default_arch_name = f"sm_{major.value}{minor.value}"
|
||||||
|
|
||||||
from tinygrad.features.graph.cuda import CUDAGraph
|
from tinygrad.features.graph.cuda import CUDAGraph
|
||||||
super().__init__(CUDAAllocator() if not CUDACPU else MallocAllocator,
|
super().__init__(CUDAAllocator(self) if not CUDACPU else MallocAllocator,
|
||||||
LinearizerOptions(supports_float4_alu=False, global_max=[65535, 65535, 2147483647], local_max=[64, 1024, 1024]),
|
LinearizerOptions(supports_float4_alu=False, global_max=[65535, 65535, 2147483647], local_max=[64, 1024, 1024]),
|
||||||
CUDARenderer, compile_cuda, CUDAProgram, graph=CUDAGraph if not CUDACPU else None)
|
CUDARenderer, compile_cuda, functools.partial(CUDAProgram, self), graph=CUDAGraph if not CUDACPU else None)
|
||||||
def synchronize(self): return check(cuda.cuCtxSynchronize()) if not CUDACPU else None
|
def synchronize(self): return check(cuda.cuCtxSynchronize()) if not CUDACPU else None
|
||||||
|
|
Loading…
Reference in New Issue