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unify cpu and llvm (#11982)
* try unify cpu and llvm * fixes * fix * ops * no llvm * fix * rm * lvmm is ot * oops * override * no llvm * ignore * skip llvm * ooops
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@@ -2,7 +2,7 @@ import time, math, unittest, functools, platform, warnings
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import numpy as np
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from typing import List, Callable
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import torch
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from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, AMD_LLVM
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from tinygrad.helpers import getenv, IMAGE, DEBUG, CI, Context, TRANSCENDENTAL, CPU_LLVM, AMD_LLVM
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from tinygrad import Tensor, Device, dtypes
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from tinygrad.tensor import _to_np_dtype
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from tinygrad.device import is_dtype_supported
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@@ -1304,7 +1304,7 @@ class TestOps(unittest.TestCase):
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np.arange(64,128,dtype=np.float32).reshape(8,8)])
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def test_small_gemm_eye(self):
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helper_test_op(None, lambda x,y: x.matmul(y), lambda x,y: x@y, vals=[np.eye(8).astype(np.float32), np.eye(8).astype(np.float32)])
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@unittest.skipIf(CI and Device.DEFAULT in ["NV", "LLVM", "GPU", "CUDA"] or IMAGE
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@unittest.skipIf(CI and Device.DEFAULT in ["NV", "GPU", "CUDA"] or (Device.DEFAULT == "CPU" and CPU_LLVM) or IMAGE
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or (Device.DEFAULT == "WEBGPU" and platform.system() == "Windows"), "not supported on these in CI/IMAGE")
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def test_gemm_fp16(self):
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helper_test_op([(64,64), (64,64)], lambda x,y: x.half().matmul(y.half()), atol=5e-3, rtol=5e-3)
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@@ -2262,7 +2262,7 @@ class TestOps(unittest.TestCase):
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lambda x,w: torch.nn.functional.conv2d(x,w,stride=2),
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lambda x,w: Tensor.conv2d(x,w,stride=2))
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@unittest.skipIf(Device.DEFAULT != "LLVM", "DEVECTORIZE=0 only for LLVM")
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@unittest.skipUnless(Device.DEFAULT == "CPU" and CPU_LLVM, "DEVECTORIZE=0 only for LLVM")
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def test_strided_conv2d_simple_vec(self):
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with Context(DEVECTORIZE=0): self.test_strided_conv2d_simple()
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