Add embedding sweep (#14278)

* #11512: Add embedding sweep

* #11512: Add embedding to ttnn-run-sweeps.yaml

* #11512: Remove device_mesh_fixture from embedding sweep

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

@@ -228,6 +228,7 @@ on:
           - eltwise.ternary.where.where_pytorch2
           - reduction.topk.topk
           - reduction.argmax.argmax
+          - embedding.embedding
           - matmul.full.matmul_default_block_sharded
           - matmul.full.matmul_default_height_sharded
           - matmul.full.matmul_default_interleaved

tests/sweep_framework/sweeps/embedding/embedding.py

@@ -0,0 +1,109 @@
+# 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
+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": {
+        "embedding_args": gen_shapes([1, 32, 32, 128], [4, 2080, 4128, 550], [1, 32, 32, 32], 32),
+        "input_dtype": [ttnn.uint32],
+        "weight_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "output_dtype": [ttnn.bfloat16, ttnn.bfloat8_b],
+        "input_layout": [ttnn.ROW_MAJOR_LAYOUT, ttnn.TILE_LAYOUT],
+        "weight_layout": [ttnn.ROW_MAJOR_LAYOUT, ttnn.TILE_LAYOUT],
+        "input_memory_config": [ttnn.DRAM_MEMORY_CONFIG, ttnn.L1_MEMORY_CONFIG],
+        "weight_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.TILE_LAYOUT:
+        return True, "Input must be in row major layout"
+    if test_vector["weight_layout"] == ttnn.TILE_LAYOUT:
+        return True, "Weights must in row major layout"
+    if test_vector["output_dtype"] == ttnn.bfloat8_b:
+        return True, "bloat8_b is not supported for output tensor"
+    if test_vector["weight_layout"] == ttnn.ROW_MAJOR_LAYOUT and test_vector["weight_dtype"] == ttnn.bfloat8_b:
+        return True, "bfloat8_b is only supported on tiled layout"
+    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(
+    embedding_args,
+    input_dtype,
+    weight_dtype,
+    output_dtype,
+    input_layout,
+    weight_layout,
+    input_memory_config,
+    weight_memory_config,
+    output_memory_config,
+    *,
+    device,
+) -> list:
+    data_seed = random.randint(0, 20000000)
+    torch.manual_seed(data_seed)
+
+    batch_size, seq_length, embeddings_dim, num_embeddings = embedding_args
+
+    input_shape = (batch_size, seq_length)
+    weight_shape = (num_embeddings, embeddings_dim)
+
+    torch_input_tensor = torch_random(input_shape, 0, num_embeddings, torch.int64)
+    torch_weight_tensor = gen_func_with_cast_tt(
+        partial(torch_random, low=-100, high=100, dtype=torch.float32), weight_dtype
+    )(weight_shape)
+
+    golden_function = ttnn.get_golden_function(ttnn.embedding)
+    torch_output_tensor = golden_function(torch_input_tensor, torch_weight_tensor).squeeze(dim=0)
+    # torch_output_tensor = torch.nn.functional.embedding(torch_input_tensor, torch_weight_tensor)
+
+    input_tensor = ttnn.from_torch(
+        torch_input_tensor,
+        dtype=input_dtype,
+        layout=input_layout,
+        device=device,
+        memory_config=input_memory_config,
+    )
+    weight_tensor = ttnn.from_torch(
+        torch_weight_tensor,
+        dtype=weight_dtype,
+        layout=weight_layout,
+        device=device,
+        memory_config=weight_memory_config,
+    )
+
+    start_time = start_measuring_time()
+    output_tensor = ttnn.embedding(input_tensor, weight_tensor, dtype=output_dtype, memory_config=output_memory_config)
+    e2e_perf = stop_measuring_time(start_time)
+
+    output_tensor = ttnn.to_torch(output_tensor).squeeze()
+
+    return [check_with_pcc(torch_output_tensor, output_tensor, 0.999), e2e_perf]