Merge branch 'master' into loadams/update-mii-transformers

csrc/transformer/inference/csrc/apply_rotary_pos_emb.cu

@@ -101,7 +101,15 @@ __global__ void apply_rotary_pos_half(T* mixed_query,
 
 #if defined(__HIP_PLATFORM_AMD__) and ROCM_WAVEFRONT_SIZE == 64
 #define LAUNCH_FOR_ALIGNMENT(ALIGNMENT)         \
-    if (threads_per_head == 64) {               \
+    if (threads_per_head == 4) {                \
+        LAUNCH_ROT_POS_EMB_HALF(4, ALIGNMENT);  \
+    } else if (threads_per_head == 8) {         \
+        LAUNCH_ROT_POS_EMB_HALF(8, ALIGNMENT);  \
+    } else if (threads_per_head == 16) {        \
+        LAUNCH_ROT_POS_EMB_HALF(16, ALIGNMENT); \
+    } else if (threads_per_head == 32) {        \
+        LAUNCH_ROT_POS_EMB_HALF(32, ALIGNMENT); \
+    } else if (threads_per_head == 64) {        \
         LAUNCH_ROT_POS_EMB_HALF(64, ALIGNMENT); \
     } else {                                    \
         assert(false);                          \

tests/unit/ops/transformer/inference/test_rope.py

@@ -0,0 +1,38 @@
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+import pytest
+import torch
+import deepspeed
+from deepspeed.ops.op_builder import InferenceBuilder
+from deepspeed.accelerator import get_accelerator
+
+if not deepspeed.ops.__compatible_ops__[InferenceBuilder.NAME]:
+    pytest.skip("Inference ops are not available on this system", allow_module_level=True)
+
+
+@pytest.mark.inference_ops
+@pytest.mark.parametrize("num_heads", [64, 32, 16, 8])
+def test_rope_warp_size_alignment(num_heads):
+    if get_accelerator().device_name() != "cuda":
+        pytest.skip("This test runs only on GPU")
+
+    batch = 1
+    head = 8
+    seq_len = 1024
+    head_dim = 32
+    rotary_dim = 32
+    offset = 8
+    rotate_half = False
+    rope_theta = 2
+
+    cuda0 = torch.device('cuda:0')
+    query = torch.randn(batch, head, seq_len, head_dim, device=cuda0)
+    key = torch.randn(batch, head, seq_len, head_dim, device=cuda0)
+
+    inference = InferenceBuilder().load()
+    # For num_heads values of 64, 32, 16, 8
+    # corresponding threads_per_head (defined in apply_rotary_pos_emb.cu) values are 4, 8, 16, 32
+    inference.apply_rotary_pos_emb(query, key, rotary_dim, offset, num_heads, rotate_half, rope_theta)