Cleanup the test

sharktank/tests/layers/mmdit_test.py

@@ -15,13 +15,9 @@
 from iree.turbine import aot
 from sharktank.layers import (
     MMDITDoubleBlock,
-    PagedLlamaAttentionBlock,
-    PagedKVCache,
-    RotaryEmbeddingLayer,
 )
 import sharktank.ops as ops
 from sharktank.layers.testing import (
-    make_llama_attention_block_theta,
     make_mmdit_block_theta,
 )
 from sharktank.types.tensors import DefaultPrimitiveTensor
@@ -32,24 +28,10 @@ def setUp(self):
         torch.manual_seed(12345)
         self.hidden_size = 3072
         self.num_heads = 24
-
-        self.transformer_block_count = 13
-        self.block_index = 1
-        self.shard_count = 3
-        self.head_count_kv = 2 * self.shard_count
-        self.attention_head_count = 5 * self.head_count_kv
-        self.attention_head_dim = 24
-        self.rms_epsilon = 0.01
-        self.cache_partition_count = 2
-        self.page_count = 23
-        self.embedding_length = self.attention_head_count * self.attention_head_dim
-        self.rope_dimension_count = self.attention_head_dim
         self.block_seqlen = 7
         self.block_seq_stride = 17
         self.max_seqlen = self.block_seq_stride * self.block_seqlen
-        self.rope_freq_base = None
         self.batch_size = 3
-        self.start_index = 0
 
     def testExport(self):
         dtype = torch.float32
@@ -66,36 +48,16 @@ def testExport(self):
             num_heads=self.num_heads,
         )
 
-        seq_block_ids = torch.arange(self.batch_size * self.block_seqlen).view(
-            self.batch_size, -1
-        )
-
-        embedding_module = RotaryEmbeddingLayer(
-            rope_dimension_count=self.rope_dimension_count,
-            max_seqlen=self.max_seqlen,
-            rope_freq_base=self.rope_freq_base,
-        )
-
-        class MyModule(torch.nn.Module):
-            def forward(self, img, txt, vec, pe):
-                return mmdit.forward(
-                    img,
-                    txt,
-                    vec,
-                    pe,
-                )
-
-        mod = MyModule()
         img = torch.rand([self.batch_size, 1024, self.hidden_size])
         txt = torch.rand([self.batch_size, 512, self.hidden_size])
         vec = torch.rand([self.batch_size, self.hidden_size])
         rot = torch.rand([self.batch_size, 1, 1536, 64, 2, 2])
-        mod.forward(img, txt, vec, rot)
-        fxb = aot.FxProgramsBuilder(mod)
+        mmdit.forward(img, txt, vec, rot)
+        fxb = aot.FxProgramsBuilder(mmdit)
 
         @fxb.export_program(name="mmdit", args=(img, txt, vec, rot), strict=False)
         def _(model, img, txt, vec, rot) -> torch.Tensor:
-            return model(img, txt, vec, rot)
+            return model.forward(img, txt, vec, rot)
 
         output = aot.export(fxb)
         output.verify()