Add rotary embedding sweep (#15743)

.github/workflows/ttnn-run-sweeps.yaml

@@ -348,6 +348,7 @@ on:
           - transformer.split_query_key_value_and_split_heads.split_query_key_value_and_split_heads_kv_input
           - transformer.attention_softmax.attention_softmax
           - transformer.attention_softmax.attention_softmax_
+          - transformer.rotary_embedding.rotary_embedding
           - data_movement.stack.stack_pytorch2
           - data_movement.repeat.repeat_pytorch2
           - data_movement.split.split_pytorch2

tests/sweep_framework/sweep_utils/utils.py

@@ -220,6 +220,28 @@ def gen_split_qkv_heads_spec(
         }
 
 
+def gen_rotary_embedding_spec(
+    input_shape_list,
+    cache_size_list,
+    use_token_idx_list=[True, False],
+):
+    for input_shape, cache_size, use_token_idx in itertools.product(
+        input_shape_list, cache_size_list, use_token_idx_list
+    ):
+        input_shape_ = input_shape.copy()
+        if use_token_idx is True:
+            token_idx = random.randint(1, cache_size - 1)
+            input_shape_[0] = 1
+        else:
+            token_idx = None
+
+        yield {
+            "input_shape": input_shape_,
+            "cache_size": cache_size,
+            "token_idx": token_idx,
+        }
+
+
 def gen_complex_tensor(input_shape, low, high, dtype=ttnn.bfloat16):
     torch_real = gen_func_with_cast_tt(partial(torch_random, low=-100, high=100, dtype=torch.float32), dtype)(
         input_shape

tests/sweep_framework/sweeps/transformer/rotary_embedding/rotary_embedding.py

@@ -0,0 +1,137 @@
+# SPDX-FileCopyrightText: © 2024 Tenstorrent Inc.
+
+# SPDX-License-Identifier: Apache-2.0
+
+from typing import Optional, Tuple
+from functools import partial
+
+import torch
+import random
+import ttnn
+from tests.sweep_framework.sweep_utils.utils import gen_shapes, gen_rotary_embedding_spec
+from tests.tt_eager.python_api_testing.sweep_tests.generation_funcs import gen_func_with_cast_tt
+
+from tests.ttnn.utils_for_testing import check_with_pcc, start_measuring_time, stop_measuring_time
+from models.utility_functions import torch_random
+
+# Override the default timeout in seconds for hang detection.
+TIMEOUT = 30
+
+random.seed(0)
+
+
+# Parameters provided to the test vector generator are defined here.
+# They are defined as dict-type suites that contain the arguments to the run function as keys, and lists of possible inputs as values.
+# Each suite has a key name (in this case "suite_1" and "suite_2") which will associate the test vectors to this specific suite of inputs.
+# Developers can create their own generator functions and pass them to the parameters as inputs.
+parameters = {
+    "nightly": {
+        "input_spec": gen_rotary_embedding_spec(
+            input_shape_list=gen_shapes([1, 1, 32, 64], [6, 12, 256, 512], [1, 1, 32, 64], 16),
+            cache_size_list=[random.randint(1, 2048) for i in range(8)],
+        ),
+        "input_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_layout": [ttnn.ROW_MAJOR_LAYOUT, ttnn.TILE_LAYOUT],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "output_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+    },
+}
+
+
+def invalidate_vector(test_vector) -> Tuple[bool, Optional[str]]:
+    if test_vector["input_layout"] == ttnn.ROW_MAJOR_LAYOUT and test_vector["input_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b/bfloat4_b requires TILE_LAYOUT!"
+    if test_vector["input_spec"]["input_shape"][-1] % 64 != 0:
+        return True, "Input X dimension (133) must be divisible by 64 for tiling"
+    if test_vector["input_spec"]["token_idx"] and test_vector["input_spec"]["input_shape"][0] != 1:
+        return True, "When passing token_idx, sequence length must be 1"
+    return False, None
+
+
+# This is the run instructions for the test, defined by the developer.
+# The run function must take the above-defined parameters as inputs.
+# The runner will call this run function with each test vector, and the returned results from this function will be stored.
+# If you defined a mesh_device_fixture above, the object you yielded will be passed into this function as 'device'. Otherwise, it will be the default ttnn device opened by the infra.
+def run(
+    input_spec,
+    input_dtype,
+    input_layout,
+    input_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    input_shape, cache_size, token_idx = input_spec.values()
+    seq_length, batch_size, num_heads, head_dim = input_shape
+
+    sin_cos_cache_shape = [1, 1, cache_size, head_dim]
+
+    torch_input_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_dtype
+    )(input_shape)
+    torch_cos_cache_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_dtype
+    )(sin_cos_cache_shape)
+    torch_sin_cache_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), input_dtype
+    )(sin_cos_cache_shape)
+
+    if token_idx:
+        golden_function = partial(ttnn.get_golden_function(ttnn.experimental.rotary_embedding), token_idx=token_idx)
+    else:
+
+        def rotate_half(x):
+            x1 = x[..., : x.shape[-1] // 2]
+            x2 = x[..., x.shape[-1] // 2 :]
+            return torch.cat((-x2, x1), dim=-1)
+
+        def apply_rotary_pos_emb(x, cos_cached, sin_cached, token_idx=None):
+            seq_len = x.shape[-2]
+            if token_idx is None:
+                cos = cos_cached[:, :, :seq_len, ...]
+                sin = sin_cached[:, :, :seq_len, ...]
+            else:
+                cos = cos_cached[:, :, token_idx : token_idx + 1, ...]
+                sin = sin_cached[:, :, token_idx : token_idx + 1, ...]
+
+            x_embed = (x * cos) + (rotate_half(x) * sin)
+            return x_embed
+
+        golden_function = apply_rotary_pos_emb
+
+    torch_output_tensor = golden_function(torch_input_tensor, torch_cos_cache_tensor, torch_sin_cache_tensor)
+
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_memory_config,
+    )
+    cos_cache_tensor = ttnn.from_torch(
+        torch_cos_cache_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_memory_config,
+    )
+    sin_cache_tensor = ttnn.from_torch(
+        torch_sin_cache_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    output_tensor = ttnn.experimental.rotary_embedding(
+        input_tensor, cos_cache_tensor, sin_cache_tensor, token_idx, memory_config=output_memory_config
+    )
+    e2e_perf = stop_measuring_time(start_time)
+
+    output_tensor = ttnn.to_torch(output_tensor)
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]