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0f804c9a83* include clip pr diff * updated unet and sd init * dehardcode default device * revert beam hang workaround --------- Co-authored-by: chenyu <chenyu@fastmail.com>
114 lines
No EOL
6.3 KiB
Python
114 lines
No EOL
6.3 KiB
Python
import unittest
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import numpy as np
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from pathlib import Path
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from tinygrad import Tensor, dtypes, Device
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from tinygrad.helpers import getenv
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from tinygrad.nn.state import get_parameters
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from extra.models import clip
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from examples.mlperf.initializers import gelu_erf, init_stable_diffusion, attn_f32_softmax
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from typing import Literal
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clip_params = {"dims": 1024, "n_heads": 16, "layers": 24, "return_pooled": False, "ln_penultimate": True, "clip_tokenizer_version": "sd_mlperf_v5_0"}
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def get_cond_stage_model(GPUS:list[str]|None=None) -> clip.FrozenOpenClipEmbedder:
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clip.gelu = gelu_erf
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model = clip.FrozenOpenClipEmbedder(**clip_params)
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if GPUS and len(GPUS) > 1:
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for p in get_parameters(model): p.to_(GPUS)
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return model
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def get_tokens(BS:int) -> Tensor: return Tensor([0] * 77 * BS, dtype=dtypes.int32).reshape(-1, 77)
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class TestOpenClip(unittest.TestCase):
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def test_tokenizer(self):
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prompt = "Beautiful is better than ugly.\nExplicit is better than implicit.\nSimple is better than complex.\nComplex is better than complicated."
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model = get_cond_stage_model()
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tokens = model.tokenizer.encode(prompt, pad_with_zeros=True)
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expected = [49406, 1215, 533, 1539, 1126, 8159, 269, 33228, 533, 1539, 1126, 15269, 585, 269, 4129, 533, 1539, 1126, 6324, 269, 6324, 533,
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1539, 1126, 16621, 269, 49407] + [0]*50
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self.assertEqual(tokens, expected)
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def test_clip_gelu_init(self):
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for resblock in get_cond_stage_model().model.transformer.resblocks:
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self.assertEqual(resblock.mlp.gelu, gelu_erf)
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def test_multigpu_clip_embed(self):
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BS = 304
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GPUS = [f"{Device.DEFAULT}:{i}" for i in range(8)]
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model = get_cond_stage_model(GPUS)
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tokens = get_tokens(BS)
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embeds = model.embed_tokens(tokens.shard(GPUS, axis=0)).realize()
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self.assertEqual(embeds.shape, (BS, 77, 1024))
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self.assertEqual(embeds.dtype, dtypes.float32)
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def test_multigpu_clip_score(self):
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BS = 240
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GPUS = [f"{Device.DEFAULT}:{i}" for i in range(8)]
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vision_cfg = {'width': 1280, 'layers': 32, 'd_head': 80, 'image_size': 224, 'patch_size': 14}
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text_cfg = {'width': 1024, 'n_heads': 16, 'layers': 24, 'vocab_size': 49408, 'ctx_length': 77}
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clip.gelu = gelu_erf
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clip_encoder = clip.OpenClipEncoder(1024, text_cfg, vision_cfg)
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for p in get_parameters(clip_encoder): p.to_(GPUS)
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tokens = get_tokens(BS)
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imgs = Tensor.zeros(BS,3,224,224).contiguous()
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scores = clip_encoder.get_clip_score(tokens.shard(GPUS, axis=0), imgs.shard(GPUS, axis=0)).realize()
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self.assertEqual(scores.shape, (BS,))
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self.assertEqual(scores.dtype, dtypes.float32)
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class TestInitStableDiffusion(unittest.TestCase):
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def setUp(self):
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# NOTE: set env variable based on where checkpoints are on the system
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self.CKPTDIR = Path(getenv("CKPTDIR", "/raid/weights/stable_diffusion"))
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def helper_test_init(self, version:Literal["v2-mlperf-train", "v2-mlperf-eval"]):
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model, unet, sqrt_acp, sqrt_omacp = init_stable_diffusion(version, self.CKPTDIR / "sd" / "512-base-ema.ckpt", ["CPU"])
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with self.subTest("test that StableDiffusion has correct models"):
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self.assertEqual(model.model.diffusion_model, unet)
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has_encoder = True if version=="v2-mlperf-eval" else False
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self.assertEqual(hasattr(model, "first_stage_model"), has_encoder, "only the eval model uses the encoder")
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self.assertTrue(isinstance(model.cond_stage_model, clip.FrozenOpenClipEmbedder))
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with self.subTest("test for mlperf unique attributes"):
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self.assertEqual(model.cond_stage_model.tokenizer.version, 'sd_mlperf_v5_0')
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self.assertEqual(unet.out[0].num_groups, 16)
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self.assertEqual(unet.input_blocks[1][1].norm.eps, 1e-6)
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self.assertEqual(unet.input_blocks[1][1].transformer_blocks[0].attn1.attn, attn_f32_softmax)
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with self.subTest("test loaded clip parameters"):
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sample = model.cond_stage_model.model.transformer.resblocks[8].mlp.c_fc.bias.flatten()[42:46].numpy()
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expected = np.array([-0.49812260270118713, -0.3039605915546417, -0.40284937620162964, -0.45069342851638794], dtype=np.float32)
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np.testing.assert_allclose(sample, expected, rtol=1e-7, atol=0, err_msg="loaded clip parameters are incorrect")
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if version=="v2-mlperf-train":
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with self.subTest("test that zero_module worked"):
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self.assertTrue((unet.out[2].weight == 0).all().item(), "expected all zeroes")
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self.assertTrue((unet.out[2].bias == 0).all().item(), "expected all zeroes")
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elif version=="v2-mlperf-eval":
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with self.subTest("test loaded vae parameters"):
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sample = model.first_stage_model.decoder.up[0]['block'][1].conv2.weight.flatten()[42:46].numpy()
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expected = np.array([0.08192943036556244, 0.040095631033182144, 0.07541035860776901, 0.1475081741809845], dtype=np.float32)
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np.testing.assert_allclose(sample, expected, rtol=1e-7, atol=0, err_msg="loaded vae parameters are incorrect")
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with self.subTest("check schedules"):
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expected = np.array([0.9995748996734619, 0.06826484948396683], dtype=np.float32)
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np.testing.assert_allclose(sqrt_acp[[0,-1]].numpy(), expected, rtol=1e-7, atol=0, err_msg="sqrt_acp is incorrect")
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expected = np.array([0.029155133292078972, 0.9976672530174255], dtype=np.float32)
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np.testing.assert_allclose(sqrt_omacp[[0,-1]].numpy(), expected, rtol=1e-7, atol=0, err_msg="sqrt_omacp is incorrect")
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with self.subTest("check mixed precision"):
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out = unet.input_blocks[2][1].proj_in(Tensor.randn(320, dtype=dtypes.float32))
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self.assertEqual(out.dtype, dtypes.bfloat16, "expected float32 to be downcast to bfloat16 by Linear")
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out = unet.out[2](Tensor.randn(304,320,64,64, dtype=dtypes.float32))
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self.assertEqual(out.dtype, dtypes.bfloat16, "expected float32 to be downcast to bfloat16 by Conv2d")
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out = unet.input_blocks[1][1].transformer_blocks[0].norm1(Tensor.randn(320, dtype=dtypes.bfloat16))
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self.assertEqual(out.dtype, dtypes.float32, "expected bfloat16 to be upcast to float32 by LayerNorm")
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out = unet.input_blocks[5][0].in_layers[0](Tensor.randn(304, 640, dtype=dtypes.bfloat16))
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self.assertEqual(out.dtype, dtypes.float32, "expected bfloat16 to be upcast to float32 by GroupNorm")
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def test_train_model(self):
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self.helper_test_init("v2-mlperf-train")
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def test_eval_model(self):
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self.helper_test_init("v2-mlperf-eval")
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if __name__=="__main__":
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unittest.main() |