[Inference/kernel]Add Fused Rotary Embedding and KVCache Memcopy CUDA Kernel

colossalai/inference/modeling/models/nopadding_llama.py

@@ -81,8 +81,8 @@ def llama_model_forward(
     # NOTE: After testing, the performance of this configuration is relatively good. With updates
     # and optimizations to the CUDA kernel implementation, a more detailed analysis of this configuration's
     # selection should be conducted.
-    if batch_size >= 32 and kv_seq_len > 512:
-        use_cuda_kernel = False
+    # if batch_size >= 32 and kv_seq_len > 512:
+    #     use_cuda_kernel = False
 
     hidden_states = self.embed_tokens(input_ids)
 
@@ -298,8 +298,12 @@ def forward(
             )
 
         block_size = k_cache.size(-2)
+
         if is_prompts:
-            rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
+            if use_cuda_kernel:
+                inference_ops.rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
+            else:
+                rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
             attn_output = context_attention_unpadded(
                 q=query_states,
                 k=key_states,
@@ -315,9 +319,21 @@ def forward(
             )
         else:
             if use_cuda_kernel:
-                rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
-                inference_ops.decode_kv_cache_memcpy(
-                    key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables
+                # using non-fused operation
+                # inference_ops.rotary_embedding(query_states, key_states, cos_sin[0], cos_sin[1])
+                # inference_ops.decode_kv_cache_memcpy(
+                #     key_states, value_states, k_cache, v_cache, sequence_lengths, block_tables
+                # )
+                inference_ops.rotary_embedding_and_cache_copy(
+                    query_states,
+                    key_states,
+                    value_states,
+                    cos_sin[0],
+                    cos_sin[1],
+                    k_cache,
+                    v_cache,
+                    sequence_lengths,
+                    block_tables,
                 )
             else:
                 decoding_fused_rotary_embedding(

colossalai/inference/utils.py

@@ -47,5 +47,5 @@ def init_to_get_rotary(self, base=10000, use_elem=False):
     t = torch.arange(max_seq_len + 1024 * 64, device="cpu", dtype=torch.float32) / rope_scaling_factor
     freqs = torch.outer(t, inv_freq)
 
-    self._cos_cached = torch.cos(freqs).to(torch.float16).cuda()
-    self._sin_cached = torch.sin(freqs).to(torch.float16).cuda()
+    self._cos_cached = torch.cos(freqs).to(self.dtype).cuda()
+    self._sin_cached = torch.sin(freqs).to(self.dtype).cuda()

examples/inference/benchmark_ops/benchmark_rotary_embdding_unpad.py → examples/inference/benchmark_ops/benchmark_fused_rotary_embdding_unpad.py

@@ -1,8 +1,11 @@
 import torch
 
+from colossalai.kernel.kernel_loader import InferenceOpsLoader
 from colossalai.kernel.triton import copy_kv_to_blocked_cache, decoding_fused_rotary_embedding, rotary_embedding
 from tests.test_infer.test_ops.triton.kernel_utils import mock_alloc_block_table_and_kvcache_v2, mock_alloc_single_token
 
+inference_ops = InferenceOpsLoader().load()
+
 try:
     import triton  # noqa
 
@@ -16,9 +19,19 @@
         x_names=["num_tokens"],
         x_vals=[2**i for i in range(4, 11)],
         line_arg="provider",
-        line_vals=["no_fused_rotary_emb_func", "fused_triton_rotary_emb_func"],
-        line_names=["no_fused_rotary_emb_func", "fused_triton_rotary_emb_func"],
-        styles=[("red", "-"), ("blue", "-")],
+        line_vals=[
+            "no_fused_triton_rotary_emb_func",
+            "fused_triton_rotary_emb_func",
+            "no_fused_cuda_rotary_emb_func",
+            "fused_cuda_rotary_emb_func",
+        ],
+        line_names=[
+            "no_fused_triton_rotary_emb_func",
+            "fused_triton_rotary_emb_func",
+            "no_fused_cuda_rotary_emb_func",
+            "fused_cuda_rotary_emb_func",
+        ],
+        styles=[("red", "-"), ("blue", "-"), ("green", "-"), ("yellow", "-")],
         ylabel="ms",
         plot_name=f"rotary_emb-batch-{BATCH}",
         args={"num_kv_heads": 16},
@@ -32,7 +45,7 @@ def benchmark_rotary_emb(
     num_tokens: int,
     num_kv_heads: int,
 ):
-    BATCH_SIZE = 4
+    BATCH_SIZE = 16
     SEQ_LEN = num_tokens // BATCH_SIZE
     max_num_blocks_per_seq = 8
     block_size = 64
@@ -68,7 +81,7 @@ def benchmark_rotary_emb(
     kv_seq_lengths = past_kv_seq_lengths + 1
     block_tables = block_tables.to(device="cuda")
 
-    if provider == "no_fused_rotary_emb_func":
+    if provider == "no_fused_triton_rotary_emb_func":
         fn = lambda: [
             rotary_embedding(new_q, new_k, cos, sin),
             copy_kv_to_blocked_cache(
@@ -77,7 +90,16 @@ def benchmark_rotary_emb(
         ]
     elif provider == "fused_triton_rotary_emb_func":
         fn = lambda: decoding_fused_rotary_embedding(
-            new_q, new_k, new_k, cos, sin, k_cache, k_cache, block_tables, kv_seq_lengths
+            new_q, new_k, new_v, cos, sin, k_cache, v_cache, block_tables, kv_seq_lengths
+        )
+    elif provider == "no_fused_cuda_rotary_emb_func":
+        fn = lambda: [
+            inference_ops.rotary_embedding(new_q, new_k, cos, sin),
+            inference_ops.decode_kv_cache_memcpy(new_k, new_v, k_cache, v_cache, kv_seq_lengths, block_tables),
+        ]
+    elif provider == "fused_cuda_rotary_emb_func":
+        fn = lambda: inference_ops.rotary_embedding_and_cache_copy(
+            new_q, new_k, new_v, cos, sin, k_cache, v_cache, kv_seq_lengths, block_tables
         )
     else:
         raise ValueError("Undefined provider")

examples/inference/benchmark_ops/benchmark_fused_rotary_embedding.py → examples/inference/benchmark_ops/benchmark_rotary_embedding.py

@@ -1,17 +1,21 @@
 import torch
 import triton
+from vllm._C import ops
 
-from colossalai.kernel.triton.fused_rotary_embedding import fused_rotary_embedding
+from colossalai.kernel.kernel_loader import InferenceOpsLoader
+from colossalai.kernel.triton import rotary_embedding
+
+inference_ops = InferenceOpsLoader().load()
 
 BATCH = 16
 configs = [
     triton.testing.Benchmark(
         x_names=["num_tokens"],
         x_vals=[2**i for i in range(4, 12)],
         line_arg="provider",
-        line_vals=["torch_rotary_emb_func", "triton_rotary_emb_func"],
-        line_names=["torch_rotary_emb_func", "triton_rotary_emb_func"],
-        styles=[("red", "-"), ("blue", "-")],
+        line_vals=["triton_func", "colossal_cuda_func", "vllm_cuda_func"],
+        line_names=["triton_func", "colossal_cuda_func", "vllm_cuda_func"],
+        styles=[("red", "-"), ("blue", "-"), ("yellow", "-")],
         ylabel="ms",
         plot_name=f"rotary_emb-batch-{BATCH}",
         args={"num_kv_heads": 16},
@@ -48,12 +52,19 @@ def benchmark_rotary_emb(
     cos_shape = (4096, head_dim // 2)
     cos = -1.2 + 0.5 * torch.randn(cos_shape, dtype=dtype, device="cuda")
     sin = -2.0 + 0.5 * torch.randn(cos_shape, dtype=dtype, device="cuda")
-    lengths = torch.tensor([3, 4, 6, 7], device="cuda")
 
-    if provider == "torch_rotary_emb_func":
-        fn = lambda: torch_rotary_emb(q, cos[:num_tokens], sin[:num_tokens])
-    elif provider == "triton_rotary_emb_func":
-        fn = lambda: fused_rotary_embedding(q, k, cos, sin, lengths)
+    cos_sin = torch.stack((cos, sin), dim=1).contiguous()
+
+    positions = torch.arange(num_tokens).cuda()
+
+    if provider == "triton_func":
+        fn = lambda: rotary_embedding(q, k, cos, sin)
+    elif provider == "colossal_cuda_func":
+        fn = lambda: inference_ops.rotary_embedding(q, k, cos, sin)
+    elif provider == "vllm_cuda_func":
+        q = q.view(num_tokens, -1)
+        k = k.view(num_tokens, -1)
+        fn = lambda: ops.rotary_embedding(positions, q, k, head_dim, cos_sin, True)
     else:
         raise ValueError("Undefined provider")
 

examples/inference/benchmark_ops/benchmark_xine_copy.py

@@ -0,0 +1,54 @@
+import torch
+
+from colossalai.kernel.triton import get_xine_cache
+from tests.test_infer.test_ops.triton.test_xine_copy import get_cos_sin
+
+try:
+    import triton  # noqa
+
+except ImportError:
+    print("please install triton from https://github.com/openai/triton")
+
+
+configs = [
+    triton.testing.Benchmark(
+        x_names=["max_num_tokens"],
+        x_vals=[2**i for i in range(6, 12)],
+        line_arg="provider",
+        line_vals=["torch_get_cos_sin", "triton_get_cos_sin"],
+        line_names=["torch_get_cos_sin", "triton_get_cos_sin"],
+        styles=[("red", "-"), ("blue", "-")],
+        ylabel="ms",
+        plot_name="Get_cos-sin_func",
+        args={"batch_size": 16, "head_dim": 256},
+    )
+]
+
+
+@triton.testing.perf_report(configs)
+def benchmark_get_xine_cache(
+    provider: str,
+    max_num_tokens: int,
+    batch_size: int,
+    head_dim: int,
+):
+    warmup = 10
+    rep = 1000
+    dtype = torch.float16
+    cos_cache = torch.randn((8912, head_dim), dtype=dtype, device="cuda")
+    sin_cache = torch.randn((8912, head_dim), dtype=dtype, device="cuda")
+    lengths = torch.randint(2, max_num_tokens, (batch_size,), device="cuda")
+
+    if provider == "torch_get_cos_sin":
+        fn = lambda: get_cos_sin(lengths, cos_cache, sin_cache, is_prompts=True, dtype=dtype)
+    elif provider == "triton_get_cos_sin":
+        fn = lambda: get_xine_cache(lengths, cos_cache, sin_cache, is_prompts=True)
+    else:
+        raise ValueError("Undefined provider")
+
+    ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    return ms
+
+
+if __name__ == "__main__":
+    benchmark_get_xine_cache.run(save_path=".", print_data=True)

extensions/csrc/cuda/colossal_inference_C_frontend.cpp

@@ -9,7 +9,30 @@ void decode_kv_cache_memcpy(
     torch::Tensor& sequence_lengths,  // [batch_size]
     torch::Tensor& block_tables);     // [batch_size, max_seq_len]
 
+void rotary_embedding(
+    torch::Tensor& query,  // [total_tokens, head_num, head_dim]
+    torch::Tensor& key,    // [total_tokens, kv_head_num, head_dim]
+    torch::Tensor& cos,    // [total_tokens, head_dim]
+    torch::Tensor& sin);   // [total_tokens, head_dim]
+
+void rotary_embedding_and_cache_copy(
+    torch::Tensor& query,      // [num_tokens, head_num, head_dim]
+    torch::Tensor& key,        // [num_tokens, kv_head_num, head_dim]
+    torch::Tensor& value,      // [num_tokens, num_heads, head_dim]
+    torch::Tensor& cos,        // [num_tokens, head_dim]
+    torch::Tensor& sin,        // [num_tokens, head_dim]
+    torch::Tensor& key_cache,  // [num_blocks, num_heads, block_size, head_dim]
+    torch::Tensor&
+        value_cache,  // [num_blocks, num_heads, block_size, head_dim]
+    torch::Tensor& sequence_lengths,  // [batch_size]
+    torch::Tensor& block_tables);     // [batch_size, max_seq_len]
+
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("decode_kv_cache_memcpy", &decode_kv_cache_memcpy,
         "Copy the GPU memory of kvcache during the decode stage.");
+  m.def(
+      "rotary_embedding_and_cache_copy", &rotary_embedding_and_cache_copy,
+      "performing Rotary Embedding-related calculations and KVCache Memcopy.");
+  m.def("rotary_embedding", &rotary_embedding,
+        "performing Rotary Embedding-related calculations.");
 }