mirror of
https://github.com/tinygrad/tinygrad.git
synced 2026-01-24 22:38:16 -05:00
Bring back WebGPU (#7063)
* Start from andredaprato:webgpu-clean * Fix infs * inf wgsl function is not needed * Emulated ulong for threefry, more tests passing * Randomness tests passing * Update model export to support new changes in webgpu, efficientnet export works again * Simplify shift emulation in wgsl * Delete test file * Fix bigger than u32 u32 literal * Why was skip copies added here? * Python3.12 for webgpu tests * Fix model export syntax error * Get test ops passing with some skips * Fix lint * Much simpler shift * Run more tests * Timestamp queries are not supported in CI, so skip search tests * All fancy indexing passing * r is ctx * Run more dtype tests by using is_dtype_supported * Cleanup ulong shift rendering * UPat -> Pat, UOps -> Ops * Pat -> UPat * Refactor render_ushift if-else * Pattern to avoid ulong mul * Remove vals_dtype * is_nan trick + rewrite, test_isnan passing * Rewrite a * select(1, nan, gate) -> select(a, nan, gate) * No arg, just op * Support char, uchar, short, ushort * Run test_index_mnis now that we have uint8 * Fix pyling * Save 3 lines by using base Compiler * No more long emulation * Remove fixup_binops * No more external_local_bufx wgsl specific cstyle modif, use base extra_pm * Simpler, faster copyin/out * Skip some new tests that use long * Fix typo * copyout touchup * Save lines by using render_cast * WebGL is not supported in core, delete it from is_dtype_supported * More narrow test skips for some unary tests * TernaryOps, UnaryOps -> Ops * TinyGrad supports WebGPU * StableDiffusion demo: f16tof32 gpu is a lib, update UI * Packed load/store, no more scale_size, no core tinygrad changes * Rename copyin, copyout * Device -> dev * Fix lint * Pattern matcher rule for packed load/store * Refactor * Shorter packed load/store * this should fix lint * Fix mypy * SD compile script working * New SD webgpu UI * New default prompt * New SD weights * Fix title when webgpu not available * Run symbolic tests, simplify is_nan, use round_up * Show step time on UI * Bump minimum wgpu version to v0.19 * Fix latent --------- Co-authored-by: George Hotz <72895+geohot@users.noreply.github.com>
This commit is contained in:
Ahmed Harmouche
@@ -1,6 +1,6 @@
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import unittest, contextlib
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import numpy as np
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from tinygrad import Tensor, GlobalCounters, dtypes, nn
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from tinygrad import Tensor, GlobalCounters, dtypes, nn, Device
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from tinygrad.helpers import CI, Context, getenv
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from tinygrad.engine.realize import run_schedule
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from tinygrad.codegen.kernel import Opt, OptOps, Kernel, KernelOptError
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@@ -139,7 +139,7 @@ class TestIndexing(unittest.TestCase):
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np.testing.assert_equal(X.numpy(), 0)
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@unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
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def test_index_mnist(self, noopt=1, op_limit=512*784*5):
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def test_index_mnist(self, noopt=1, op_limit=512*784*10):
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from tinygrad.nn.datasets import mnist
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X_train, Y_train, _, _ = mnist()
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with Context(NOOPT=noopt, FUSE_ARANGE=1, SPLIT_REDUCEOP=0):
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@@ -153,7 +153,7 @@ class TestIndexing(unittest.TestCase):
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@unittest.skip("not ready")
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def test_index_mnist_opt(self): self.test_index_mnist(0)
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@unittest.skipIf(getenv("PTX"), "broken on ptx for some reason")
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@unittest.skipIf(getenv("PTX") or Device.DEFAULT == "WEBGPU", "broken on ptx and WebGPU for some reason")
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def test_llama_embedding(self, noopt=1, op_limit=65536):
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# llama3 is 128256
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vocab_size, embed_size = (10, 3) if CI else (32000, 4096)
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@@ -35,11 +35,11 @@ def _test_to_np(a:Tensor, np_dtype, target):
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except AssertionError as e:
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raise AssertionError(f"\ntensor {a.numpy()} does not match target {target} with np_dtype {np_dtype}") from e
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def _assert_eq(tensor:Tensor, target_dtype:DType, target):
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def _assert_eq(tensor:Tensor, target_dtype:DType, target, tol_target_dtype:float=1e-7):
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if DEBUG >= 2: print(tensor.numpy())
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try:
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assert tensor.dtype == target_dtype
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np.testing.assert_allclose(tensor.numpy(), target, rtol={dtypes.float16:1e-3, dtypes.bfloat16:1e-2}.get(target_dtype, 1e-7))
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np.testing.assert_allclose(tensor.numpy(), target, rtol={dtypes.float16:1e-3, dtypes.bfloat16:1e-2}.get(target_dtype, tol_target_dtype))
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except AssertionError as e:
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raise AssertionError(f"\ntensor {tensor.numpy()} dtype {tensor.dtype} does not match target {target} with dtype {target_dtype}") from e
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@@ -541,7 +541,7 @@ class TestTypeSpec(unittest.TestCase):
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_assert_eq(Tensor.arange(5, dtype=dtypes.int64), dtypes.int64, np.arange(5))
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if is_dtype_supported(dtypes.float16):
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_assert_eq(Tensor.arange(5, dtype=dtypes.float16), dtypes.float16, np.arange(5))
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_assert_eq(Tensor.arange(3, 9, 0.7), dtypes.default_float, np.arange(3, 9, 0.7))
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_assert_eq(Tensor.arange(3, 9, 0.7), dtypes.default_float, np.arange(3, 9, 0.7), 1e-6 if Device.DEFAULT == "WEBGPU" else 1e-7)
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_assert_eq(Tensor.arange(3, 8.5, 3), dtypes.default_float, np.arange(3, 8.5, 3))
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# stop-start and step have different signs
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_assert_eq(Tensor.arange(3, 5, -2), dtypes.default_int, np.arange(3, 5, -2))
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@@ -11,7 +11,7 @@ from tinygrad.engine.realize import run_schedule
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from tinygrad.ops import GroupOp
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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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import pytest
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import pytest, math
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pytestmark = pytest.mark.filterwarnings("ignore")
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settings.register_profile("my_profile", max_examples=200, deadline=None, derandomize=getenv("DERANDOMIZE_CI", False))
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@@ -41,8 +41,8 @@ unary_operations = [(Tensor.exp, np.exp), (Tensor.log, np.log), (Tensor.sin, np.
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# TODO: (a+b)/2 in tensor.py's maximum can overflow. This requires a new implementation of maximum that can be backpropagated
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#binary_operations += [(Tensor.maximum, np.maximum)]
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# TODO: CI CUDA segfaults on sin
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if getenv("MOCKGPU") and Device.DEFAULT == "NV": unary_operations.remove((Tensor.sin, np.sin))
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# TODO: CI CUDA segfaults on sin, WEBGPU sin is not precise enough for large numbers
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if (getenv("MOCKGPU") and Device.DEFAULT == "NV") or Device.DEFAULT == "WEBGPU": unary_operations.remove((Tensor.sin, np.sin))
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class ht:
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float64 = strat.floats(width=64, allow_subnormal=False)
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@@ -88,6 +88,8 @@ def universal_test_cast(a, in_dtype, dtype):
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np.testing.assert_equal(tensor_value.numpy(), numpy_value)
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def universal_test_midcast(a, b, c, op1, op2, d1:DType, d2:DType):
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# the 'inf' and 'nan' cases are wrong on WEBGPU
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if (c in [math.inf, -math.inf] or math.isnan(c)) and Device.DEFAULT == "WEBGPU": return
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if not isinstance(op1, tuple): op1 = (op1, op1)
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if not isinstance(op2, tuple): op2 = (op2, op2)
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at, bt, ct = Tensor([a], dtype=d1), Tensor([b], dtype=d1), Tensor([c], dtype=d2)
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@@ -148,6 +150,7 @@ class TestDTypeALU(unittest.TestCase):
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@given(ht.int32, ht.int32, strat.sampled_from(integer_binary_operations))
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def test_int32(self, a, b, op): universal_test(a, b, dtypes.int32, op)
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@unittest.skipUnless(is_dtype_supported(dtypes.int64, Device.DEFAULT), f"no int64 on {Device.DEFAULT}")
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@given(ht.int64, ht.int64, strat.sampled_from(integer_binary_operations))
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def test_int64(self, a, b, op): universal_test(a, b, dtypes.int64, op)
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@@ -312,7 +312,8 @@ class TestOps(unittest.TestCase):
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def _test_cmp(self, fxn, reverse=True):
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# test different dtypes
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helper_test_op(None, fxn, fxn, forward_only=True, vals=[[0.,1,2], [2.,1,0]])
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helper_test_op(None, fxn, fxn, forward_only=True, vals=[[0,1,2], [2,1,0]])
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if is_dtype_supported(dtypes.long):
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helper_test_op(None, fxn, fxn, forward_only=True, vals=[[0,1,2], [2,1,0]])
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helper_test_op(None, fxn, fxn, forward_only=True, vals=[[True, True, False], [False,True,False]])
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# test broadcasting
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for shps in [[(3, 4, 5), (3, 4, 5)], [(3, 4, 5), (5,)], [(5,), (3, 4, 5)]]:
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@@ -382,6 +383,7 @@ class TestOps(unittest.TestCase):
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helper_test_op(None, torch.isinf, Tensor.isinf, vals=[val], forward_only=True)
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np.testing.assert_equal(Tensor(val).isinf(detect_positive=True, detect_negative=False).numpy(), [False, False, True, False, False])
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np.testing.assert_equal(Tensor(val).isinf(detect_positive=False, detect_negative=True).numpy(), [True, False, False, False, False])
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def test_isnan(self):
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helper_test_op(None, torch.isnan, Tensor.isnan, vals=[[float('-inf'), 0., float('inf'), float('nan'), 1.1]], forward_only=True)
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@@ -499,8 +501,9 @@ class TestOps(unittest.TestCase):
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helper_test_op(None, lambda x: x//2, forward_only=True, vals=np.array([[3, 4, 5]], dtype=np.int32))
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torch_idiv, tiny_idiv = functools.partial(torch.div, rounding_mode="trunc"), Tensor.idiv
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helper_test_op(None, torch_idiv, tiny_idiv, forward_only=True, vals=np.array([[5, -6, 7],[1, 2, 3]], dtype=np.int32))
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x = Tensor(2**64 - 1, dtype=dtypes.uint64).idiv(1)
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np.testing.assert_equal(x.numpy(), 2**64 - 1)
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if is_dtype_supported(dtypes.uint64):
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x = Tensor(2**64 - 1, dtype=dtypes.uint64).idiv(1)
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np.testing.assert_equal(x.numpy(), 2**64 - 1)
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def test_scalar_div(self):
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helper_test_op([(45,65)], lambda x: x/255)
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helper_test_op([(45,65)], lambda x: x/1)
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@@ -525,6 +528,7 @@ class TestOps(unittest.TestCase):
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def test_pow_full(self):
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helper_test_op([(45,65), (45,65)], lambda x,y: x**y)
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helper_test_op([(45,65), (45,65)], lambda x,y: x.pow(y))
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def test_pow(self):
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helper_test_op([(45,65)], lambda x: x**0)
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helper_test_op([(45,65)], lambda x: x**1)
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@@ -644,14 +648,14 @@ class TestOps(unittest.TestCase):
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helper_test_op([(45,65)], lambda x: x.sin())
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helper_test_op([()], lambda x: x.sin())
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# works on real CUDA but not CI
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if not (getenv("MOCKGPU") and Device.DEFAULT == "NV"):
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if not ((getenv("MOCKGPU") and Device.DEFAULT == "NV") or Device.DEFAULT == "WEBGPU"):
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helper_test_op(None, lambda x: x.sin(), vals=[[math.nan, math.inf, -math.inf, 0.0]])
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helper_test_op(None, lambda x: x.sin(), vals=[[1e1, 1e2, 1e3, 1e4, 1e5, 1e6, -1e1, -1e2, -1e3, -1e4, -1e5, -1e6]],
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atol=3e-3, rtol=3e-3, grad_atol=3e-3, grad_rtol=3e-3)
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def test_cos(self):
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helper_test_op([(45,65)], lambda x: x.cos())
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helper_test_op([()], lambda x: x.cos())
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if not (getenv("MOCKGPU") and Device.DEFAULT == "NV"):
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if not ((getenv("MOCKGPU") and Device.DEFAULT == "NV") or Device.DEFAULT == "WEBGPU"):
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helper_test_op(None, lambda x: x.sin(), vals=[[math.nan, math.inf, -math.inf, 0.0]])
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helper_test_op(None, lambda x: x.cos(), vals=[[1e1, 1e2, 1e3, 1e4, 1e5, 1e6, -1e1, -1e2, -1e3, -1e4, -1e5, -1e6]],
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atol=3e-3, rtol=3e-3, grad_atol=3e-3, grad_rtol=3e-3)
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@@ -660,7 +664,7 @@ class TestOps(unittest.TestCase):
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helper_test_op([(45,65)], lambda x: x.tan(), low=-1.5, high=1.5)
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helper_test_op([(45,65)], lambda x: x.tan(), low=-5, high=5, forward_only=True)
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helper_test_op([()], lambda x: x.tan())
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if not (getenv("MOCKGPU") and Device.DEFAULT == "NV"):
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if not ((getenv("MOCKGPU") and Device.DEFAULT == "NV") or Device.DEFAULT == "WEBGPU"):
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helper_test_op(None, lambda x: x.sin(), vals=[[math.nan, math.inf, -math.inf, 0.0]])
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helper_test_op(None, lambda x: x.cos(), vals=[[1e1, 1e2, 1e3, 1e4, 1e5, 1e6, -1e1, -1e2, -1e3, -1e4, -1e5, -1e6]],
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atol=3e-3, rtol=3e-3, grad_atol=3e-3, grad_rtol=3e-3)
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@@ -994,7 +998,8 @@ 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, "not supported on these in CI/IMAGE")
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@unittest.skipIf(CI and Device.DEFAULT in ["NV", "LLVM", "GPU", "CUDA"] or IMAGE \
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or Device.DEFAULT == "WEBGPU", "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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def test_gemm(self):
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@@ -1076,8 +1081,9 @@ class TestOps(unittest.TestCase):
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helper_test_op([(45,3)], lambda x: x.min().mul(0.5))
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helper_test_op([()], lambda x: x.min())
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helper_test_op(None, lambda x: x.type(torch.int32).min(), lambda x: x.cast(dtypes.int32).min(), forward_only=True, vals=[[0, -2**31]])
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helper_test_op(None, lambda x: x.type(torch.int32).min(), lambda x: x.cast(dtypes.int32).min(), forward_only=True, vals=[[-2**31, 0]])
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if is_dtype_supported(dtypes.long):
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helper_test_op(None, lambda x: x.type(torch.int32).min(), lambda x: x.cast(dtypes.int32).min(), forward_only=True, vals=[[0, -2**31]])
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helper_test_op(None, lambda x: x.type(torch.int32).min(), lambda x: x.cast(dtypes.int32).min(), forward_only=True, vals=[[-2**31, 0]])
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helper_test_op(None, lambda x: x.type(torch.bool).min(), lambda x: x.cast(dtypes.bool).min(), forward_only=True, vals=[[False, True]])
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helper_test_op(None, lambda x: x.type(torch.bool).min(), lambda x: x.cast(dtypes.bool).min(), forward_only=True, vals=[[True, False]])
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@@ -1088,8 +1094,9 @@ class TestOps(unittest.TestCase):
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helper_test_op([(3,4,5,6)], lambda x: x.max(axis=1)[0], lambda x: x.max(axis=1))
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helper_test_op([()], lambda x: x.max())
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helper_test_op(None, lambda x: x.type(torch.int32).max(), lambda x: x.cast(dtypes.int32).max(), forward_only=True, vals=[[0, -2**31]])
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helper_test_op(None, lambda x: x.type(torch.int32).max(), lambda x: x.cast(dtypes.int32).max(), forward_only=True, vals=[[-2**31, 0]])
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if is_dtype_supported(dtypes.long):
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helper_test_op(None, lambda x: x.type(torch.int32).max(), lambda x: x.cast(dtypes.int32).max(), forward_only=True, vals=[[0, -2**31]])
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helper_test_op(None, lambda x: x.type(torch.int32).max(), lambda x: x.cast(dtypes.int32).max(), forward_only=True, vals=[[-2**31, 0]])
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helper_test_op(None, lambda x: x.type(torch.bool).max(), lambda x: x.cast(dtypes.bool).max(), forward_only=True, vals=[[False, True]])
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helper_test_op(None, lambda x: x.type(torch.bool).max(), lambda x: x.cast(dtypes.bool).max(), forward_only=True, vals=[[True, False]])
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@@ -2343,16 +2350,19 @@ class TestOps(unittest.TestCase):
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helper_test_op([(32,10), (32,10)], lambda x,y: torch.nn.functional.cross_entropy(x, y, label_smoothing=ls),
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lambda x,y: x.cross_entropy(y, label_smoothing=ls))
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@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
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def test_nll_loss(self):
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helper_test_op([(32,10), (32)],
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lambda x,y: torch.nn.functional.nll_loss(torch.nn.functional.log_softmax(x, dim=1), torch.clip(y,0).type(torch.long)),
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lambda x,y: x.log_softmax(axis=1).nll_loss(y.clip(0).cast(dtypes.long)), forward_only=True)
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@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
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def test_nll_loss_3d(self):
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helper_test_op([(32,10,3,3,3), (32,3,3,3)],
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lambda x,y: torch.nn.functional.nll_loss(torch.nn.functional.log_softmax(x, dim=1), torch.clip(y,0).type(torch.long)),
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lambda x,y: x.log_softmax(axis=1).nll_loss(y.clip(0).cast(dtypes.long)), forward_only=True)
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@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
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def test_nll_loss_reductions(self):
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for r in ("mean", "sum", "none"):
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helper_test_op([(32,10), (32)],
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@@ -2362,6 +2372,7 @@ class TestOps(unittest.TestCase):
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lambda x,y: torch.nn.functional.nll_loss(x, torch.clip(y,0).type(torch.long), reduction="typo"),
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lambda x,y: x.nll_loss(y.clip(0).cast(dtypes.long), reduction="typo"), expected=ValueError)
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@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
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def test_nll_loss_weight(self):
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for r in ("mean", "sum", "none"):
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helper_test_op([(32,10), (32), (10)],
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@@ -2369,6 +2380,7 @@ class TestOps(unittest.TestCase):
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weight=z, reduction=r),
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lambda x,y,z: x.log_softmax(axis=1).nll_loss(y.clip(0).cast(dtypes.long), weight=z, reduction=r), forward_only=True)
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@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
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def test_nll_loss_3d_weight(self):
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for r in ("mean", "sum", "none"):
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helper_test_op([(32,10,3,3,3), (32,3,3,3), (10)],
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@@ -2376,6 +2388,7 @@ class TestOps(unittest.TestCase):
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weight=z, reduction=r),
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lambda x,y,z: x.log_softmax(axis=1).nll_loss(y.clip(0).cast(dtypes.long), weight=z, reduction=r), forward_only=True)
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||||
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@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
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||||
def test_nll_loss_ignore_index(self):
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logits = [[2.0, 0.5, -1.0],
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[1.5, 2.5, -0.5],
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||||
@@ -2405,7 +2418,8 @@ class TestOps(unittest.TestCase):
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||||
@unittest.skipIf(Device.DEFAULT == "QCOM", "OpenCL fails to compile this (both on GPU(qcom)/QCOM backends)")
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||||
def test_cast(self):
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helper_test_op([(3, 3)], lambda x: x.float())
|
||||
helper_test_op(None, lambda x: x.float(), vals=[[0, 1, 2, 3]], forward_only=True)
|
||||
if is_dtype_supported(dtypes.long):
|
||||
helper_test_op(None, lambda x: x.float(), vals=[[0, 1, 2, 3]], forward_only=True)
|
||||
helper_test_op(None, lambda x: x.float(), vals=[[True, False]], forward_only=True)
|
||||
helper_test_op([(3, 3)], lambda x: x.int(), forward_only=True)
|
||||
helper_test_op([(3, 3)], lambda x: x.bool(), forward_only=True)
|
||||
@@ -2440,6 +2454,7 @@ class TestOpsUint8(unittest.TestCase):
|
||||
lambda x: torch.nn.functional.interpolate((10*x).relu().type(torch.uint8), size=out_sz, mode="nearest-exact"),
|
||||
lambda x: Tensor.interpolate((10*x).relu().cast('uint8'), size=out_sz, mode="nearest-exact"), forward_only=True)
|
||||
|
||||
@unittest.skipUnless(is_dtype_supported(dtypes.long), f"no long on {Device.DEFAULT}")
|
||||
def test_min(self):
|
||||
helper_test_op(None,
|
||||
lambda x: x.type(torch.uint8).min(),
|
||||
|
||||
65
test/web/test_webgpu.js
Normal file
65
test/web/test_webgpu.js
Normal file
@@ -0,0 +1,65 @@
|
||||
const puppeteer = require("puppeteer");
|
||||
const { spawn } = require("child_process");
|
||||
const res = spawn("python", ["-m", "http.server", "8000"], { shell: true });
|
||||
|
||||
async function timeout(time) {
|
||||
return new Promise((resolve) => setTimeout(resolve, time));
|
||||
}
|
||||
|
||||
function cleanup(err) {
|
||||
console.log("cleaning up");
|
||||
res.kill();
|
||||
if (err != null) {
|
||||
console.error(err);
|
||||
process.exit(1);
|
||||
}
|
||||
process.exit(0);
|
||||
}
|
||||
|
||||
async function waitForText(selector, text) {
|
||||
let n = 0;
|
||||
let ready = false;
|
||||
while (n < 30) {
|
||||
const res = await (await selector.getProperty("textContent")).jsonValue();
|
||||
console.log(`waiting for text ${text} got ${res}`);
|
||||
if (res == text) {
|
||||
ready = true;
|
||||
break;
|
||||
}
|
||||
await timeout(1000);
|
||||
n += 1;
|
||||
}
|
||||
return ready;
|
||||
}
|
||||
|
||||
async function runTest() {
|
||||
const browser = await puppeteer.launch({
|
||||
headless: false,
|
||||
args: ["--enable-unsafe-webgpu"],
|
||||
});
|
||||
const page = await browser.newPage();
|
||||
|
||||
page
|
||||
.on("console", (message) =>
|
||||
console.log(`message from console ${message.text()}`),
|
||||
)
|
||||
.on("pageerror", ({ message }) =>
|
||||
console.log(`error from page ${message}`),
|
||||
);
|
||||
|
||||
const res = await page.goto("http://localhost:8000/examples/index.html");
|
||||
if (res.status() !== 200) throw new Error("Failed to load page");
|
||||
|
||||
const textSelector = await page.waitForSelector("#result");
|
||||
const buttonSelector = await page.waitForSelector("input[type=button]");
|
||||
const ready = await waitForText(textSelector, "ready");
|
||||
if (!ready) throw new Error("Failed to load page");
|
||||
|
||||
await buttonSelector.evaluate((e) => e.click());
|
||||
const done = await waitForText(textSelector, "hen");
|
||||
if (!done) throw new Error("failed to get hen");
|
||||
|
||||
cleanup(null);
|
||||
}
|
||||
|
||||
runTest().catch((err) => cleanup(err));
|
||||
Reference in New Issue
Block a user