[Inference]Move benchmark-related code to the example directory.

examples/inference/benchmark_ops/benchmark_context_attn_unpad.py

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+import torch
+from transformers.modeling_attn_mask_utils import AttentionMaskConverter
+
+from colossalai.inference.modeling.layers.attention import PagedAttention
+from colossalai.kernel.triton import context_attention_unpadded
+from colossalai.utils import get_current_device
+from tests.test_infer.test_ops.triton.kernel_utils import generate_caches_and_block_tables_v2, torch_attn_ref
+
+try:
+    import triton  # noqa
+
+except ImportError:
+    print("please install triton from https://github.com/openai/triton")
+
+HEAD_DIM = 32
+BATCH = 16
+BLOCK_SIZE = 32
+SAME_LEN = True
+WARM_UPS = 10
+REPS = 100
+configs = [
+    triton.testing.Benchmark(
+        x_names=["KV_LEN"],
+        x_vals=[2**i for i in range(8, 13)],
+        # x_vals=[x for x in range(256, 8192, 256)],
+        line_arg="provider",
+        line_vals=["torch", "triton"],
+        line_names=["Torch", "Triton"],
+        styles=[("red", "-"), ("blue", "-")],
+        ylabel="ms",
+        plot_name=f"context_attn-block_size-{BLOCK_SIZE}-batch{BATCH}",
+        args={"bsz": BATCH, "block_size": BLOCK_SIZE, "same_context_len": SAME_LEN, "kv_group_num": 1},
+    )
+]
+
+
+@triton.testing.perf_report(configs)
+def bench_kernel(
+    bsz,
+    KV_LEN,
+    provider,
+    block_size: int,
+    kv_group_num: int,
+    same_context_len: bool,
+):
+    num_attn_heads = 16
+    max_num_blocks_per_seq = triton.cdiv(KV_LEN, block_size)
+    max_seq_len = block_size * max_num_blocks_per_seq
+
+    num_kv_heads = num_attn_heads // kv_group_num
+    assert isinstance(num_kv_heads, int) and num_kv_heads > 0, "Invalid number of kv heads."
+    dtype = torch.float16
+    device = get_current_device()
+
+    if same_context_len:
+        context_lengths = torch.tensor([max_seq_len for _ in range(bsz)], dtype=torch.int32, device=device)
+    else:
+        context_lengths = torch.randint(low=1, high=max_seq_len, size=(bsz,), dtype=torch.int32, device=device)
+    num_tokens = torch.sum(context_lengths).item()
+
+    qkv_size = (num_tokens, num_attn_heads + 2 * num_kv_heads, HEAD_DIM)
+    qkv_unpad = torch.empty(size=qkv_size, dtype=dtype, device=device).normal_(mean=0.0, std=0.5)
+    q_unpad, k_unpad, v_unpad = torch.split(qkv_unpad, [num_attn_heads, num_kv_heads, num_kv_heads], dim=-2)
+    q_unpad = q_unpad.contiguous()
+    k_cache_ref, v_cache_ref, block_tables = generate_caches_and_block_tables_v2(
+        k_unpad, v_unpad, context_lengths, bsz, max_num_blocks_per_seq, block_size, dtype, device
+    )
+    block_tables = block_tables.to(device=device)
+
+    quantiles = [0.5, 0.2, 0.8]
+    if provider == "torch":
+        q_padded = PagedAttention.pad_and_reshape(q_unpad, context_lengths, max_seq_len, num_attn_heads, HEAD_DIM)
+        k_padded = PagedAttention.pad_and_reshape(k_unpad, context_lengths, max_seq_len, num_kv_heads, HEAD_DIM)
+        v_padded = PagedAttention.pad_and_reshape(v_unpad, context_lengths, max_seq_len, num_kv_heads, HEAD_DIM)
+        q_padded, k_padded, v_padded = (
+            q_padded.to(device=device),
+            k_padded.to(device=device),
+            v_padded.to(device=device),
+        )
+        q_padded = q_padded.transpose(1, 2)
+        k_padded = PagedAttention.repeat_kv(k_padded.transpose(1, 2), kv_group_num)
+        v_padded = PagedAttention.repeat_kv(v_padded.transpose(1, 2), kv_group_num)
+        # This benchmark ignores the padding mask. *Only* use the-same-length inputs for benchmarkings
+        attn_mask = AttentionMaskConverter._make_causal_mask(
+            (bsz, max_seq_len), q_padded.dtype, q_padded.device, past_key_values_length=0
+        )
+        attn_mask = attn_mask.to(device=q_padded.device)
+        fn = lambda: torch_attn_ref(
+            q_padded,
+            k_padded,
+            v_padded,
+            attn_mask,
+            bsz,
+            max_seq_len,
+            max_seq_len,
+            num_attn_heads,
+            num_kv_heads,
+            HEAD_DIM,
+        )
+        ms, min_ms, max_ms = triton.testing.do_bench(fn, warmup=WARM_UPS, rep=REPS, quantiles=quantiles)
+    if provider == "triton":
+        k_cache_triton = torch.zeros_like(k_cache_ref)
+        v_cache_triton = torch.zeros_like(v_cache_ref)
+        fn = lambda: context_attention_unpadded(
+            q_unpad, k_unpad, v_unpad, k_cache_triton, v_cache_triton, context_lengths, block_tables, block_size
+        )
+        ms, min_ms, max_ms = triton.testing.do_bench(fn, warmup=WARM_UPS, rep=REPS, quantiles=quantiles)
+
+    return ms, min_ms, max_ms
+
+
+if __name__ == "__main__":
+    bench_kernel.run(save_path=".", print_data=True)

examples/inference/benchmark_ops/benchmark_decoding_attn.py

@@ -0,0 +1,110 @@
+import torch
+
+from colossalai.kernel.triton import flash_decoding_attention
+from colossalai.utils import get_current_device
+from tests.test_infer.test_ops.triton.kernel_utils import (
+    convert_kv_unpad_to_padded,
+    generate_caches_and_block_tables_v2,
+    prepare_padding_mask,
+    torch_attn_ref,
+)
+from tests.test_infer.test_ops.triton.test_decoding_attn import prepare_data
+
+try:
+    import triton  # noqa
+
+except ImportError:
+    print("please install triton from https://github.com/openai/triton")
+
+Q_LEN = 1
+HEAD_DIM = 128
+BATCH = 16
+BLOCK_SIZE = 32
+SAME_LEN = True
+WARM_UPS = 10
+REPS = 100
+configs = [
+    triton.testing.Benchmark(
+        x_names=["KV_LEN"],
+        x_vals=[2**i for i in range(8, 14)],
+        # x_vals=[x for x in range(256, 8192, 256)],
+        line_arg="provider",
+        line_vals=["torch", "triton"],
+        line_names=["Torch", "Triton"],
+        styles=[("red", "-"), ("blue", "-")],
+        ylabel="ms",
+        plot_name=f"decoding-block_size-{BLOCK_SIZE}-batch{BATCH}",
+        args={"bsz": BATCH, "block_size": BLOCK_SIZE, "same_context_len": SAME_LEN, "kv_group_num": 1},
+    )
+]
+
+
+@triton.testing.perf_report(configs)
+def bench_kernel(
+    bsz,
+    KV_LEN,
+    provider,
+    block_size: int,
+    kv_group_num: int,
+    same_context_len: bool,
+):
+    num_attn_heads = 16
+    max_num_blocks_per_seq = triton.cdiv(KV_LEN, block_size)
+    max_seq_len = block_size * max_num_blocks_per_seq
+
+    num_kv_heads = num_attn_heads // kv_group_num
+    assert isinstance(num_kv_heads, int) and num_kv_heads > 0, "Invalid number of kv heads."
+    block_size * max_num_blocks_per_seq
+    dtype = torch.float16
+    device = get_current_device()
+
+    q, k_unpad, v_unpad, kv_lengths = prepare_data(
+        bsz, num_attn_heads, num_kv_heads, HEAD_DIM, same_context_len, Q_LEN, max_seq_len, dtype, device
+    )
+    max_seq_len_in_b = kv_lengths.max().item()  # for random lengths
+
+    quantiles = [0.5, 0.2, 0.8]
+    if provider == "torch":
+        k_torch = convert_kv_unpad_to_padded(k_unpad, kv_lengths, bsz, max_seq_len_in_b)
+        v_torch = convert_kv_unpad_to_padded(v_unpad, kv_lengths, bsz, max_seq_len_in_b)
+        torch_padding_mask = prepare_padding_mask(kv_lengths, bsz, max_seq_len_in_b, q.device)
+        fn = lambda: torch_attn_ref(
+            q, k_torch, v_torch, torch_padding_mask, bsz, 1, max_seq_len_in_b, num_attn_heads, num_kv_heads, HEAD_DIM
+        )
+        ms, min_ms, max_ms = triton.testing.do_bench(fn, warmup=WARM_UPS, rep=REPS, quantiles=quantiles)
+    if provider == "triton":
+        k_cache, v_cache, block_tables = generate_caches_and_block_tables_v2(
+            k_unpad, v_unpad, kv_lengths, bsz, max_num_blocks_per_seq, block_size, dtype, device
+        )
+        block_tables = block_tables.to(device=device)
+        # the maximum block length splitted on kv should be the kv cache block size
+        kv_max_split_num = (max_seq_len_in_b + block_size - 1) // block_size
+        output = torch.empty((bsz, num_attn_heads, HEAD_DIM), dtype=dtype, device=device)
+        mid_output = torch.empty(
+            size=(bsz, num_attn_heads, kv_max_split_num, HEAD_DIM), dtype=torch.float32, device=q.device
+        )
+        mid_output_lse = torch.empty(size=(bsz, num_attn_heads, kv_max_split_num), dtype=torch.float32, device=q.device)
+        sm_scale = 1.0 / (HEAD_DIM**0.5)
+        fn = lambda: flash_decoding_attention(
+            # Here we use q.squeeze(2) because we hide the q_len dimension (which is equivalent to 1),
+            # refer to attention forward in modeling.
+            q.squeeze(2),
+            k_cache,
+            v_cache,
+            kv_lengths,
+            block_tables,
+            block_size,
+            max_seq_len_in_b,
+            output,
+            mid_output,
+            mid_output_lse,
+            sm_scale=sm_scale,
+            kv_group_num=kv_group_num,
+        )  # [bsz, 1, num_heads, head_dim]
+        ms, min_ms, max_ms = triton.testing.do_bench(fn, warmup=WARM_UPS, rep=REPS, quantiles=quantiles)
+
+    return ms, min_ms, max_ms
+
+
+if __name__ == "__main__":
+    bench_kernel.run(save_path=".", print_data=True)

examples/inference/benchmark_ops/benchmark_fused_rotary_embedding.py

@@ -0,0 +1,65 @@
+import torch
+import triton
+
+from colossalai.kernel.triton.fused_rotary_embedding import fused_rotary_embedding
+
+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", "-")],
+        ylabel="ms",
+        plot_name=f"rotary_emb-batch-{BATCH}",
+        args={"num_kv_heads": 16},
+    )
+]
+
+
+def torch_rotary_emb(x, cos, sin):
+    seq_len, h, dim = x.shape
+    x0 = x[:, :, 0 : dim // 2]
+    x1 = x[:, :, dim // 2 : dim]
+    cos = cos.view((seq_len, 1, dim // 2))
+    sin = sin.view((seq_len, 1, dim // 2))
+    o0 = x0 * cos - x1 * sin
+    o1 = x0 * sin + x1 * cos
+    return torch.cat((o0, o1), dim=-1)
+
+
+@triton.testing.perf_report(configs)
+def benchmark_rotary_emb(
+    provider: str,
+    num_tokens: int,
+    num_kv_heads: int,
+):
+    warmup = 10
+    rep = 100
+
+    head_dim = 128
+    dtype = torch.float16
+    q_shape = (num_tokens, num_kv_heads, head_dim)
+    q = -2.3 + 0.5 * torch.randn(q_shape, dtype=dtype, device="cuda")
+    k_shape = (num_tokens, num_kv_heads, head_dim)
+    k = -2.3 + 0.5 * torch.randn(k_shape, dtype=dtype, device="cuda")
+    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)
+    else:
+        raise ValueError("Undefined provider")
+
+    ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+    return ms
+
+
+if __name__ == "__main__":
+    benchmark_rotary_emb.run(save_path=".", print_data=True)

examples/inference/benchmark_ops/benchmark_rmsnorm_triton.py

@@ -0,0 +1,78 @@
+import torch
+import triton
+
+from colossalai.kernel.triton import rms_layernorm
+
+try:
+    import triton  # noqa
+
+except ImportError:
+    print("please install triton from https://github.com/openai/triton")
+
+
+# Triton benchmark plot attributions
+configs = [
+    triton.testing.Benchmark(
+        x_names=["SEQUENCE_TOTAL"],
+        x_vals=[i for i in range(128, 1025, 128)],
+        line_arg="provider",
+        line_vals=[
+            "vllm_rms_layernorm",
+            "triton_rms_layernorm",
+            "triton_rms_layernorm_with_residual",
+            "vllm_rms_layernorm_with_residual",
+        ],
+        line_names=[
+            "vllm_rms_layernorm",
+            "triton_rms_layernorm",
+            "triton_rms_layernorm_with_residual",
+            "vllm_rms_layernorm_with_residual",
+        ],
+        styles=[("red", "-"), ("blue", "-"), ("yellow", "-"), ("green", "-")],
+        ylabel="ms",
+        plot_name=f"RMSNorm benchmarking results",
+        args={"HIDDEN_SIZE": 1024},
+    )
+]
+
+
+@triton.testing.perf_report(configs)
+def benchmark_rms_layernorm(
+    provider: str,
+    SEQUENCE_TOTAL: int,
+    HIDDEN_SIZE: int,
+):
+    try:
+        from vllm.model_executor.layers.layernorm import RMSNorm
+    except ImportError:
+        raise ImportError("Please install vllm from https://github.com/vllm-project/vllm")
+
+    warmup = 10
+    rep = 1000
+
+    dtype = torch.float16
+    eps = 1e-5
+    x_shape = (SEQUENCE_TOTAL, HIDDEN_SIZE)
+    w_shape = (x_shape[-1],)
+    residual = torch.rand(x_shape, dtype=dtype, device="cuda")
+    weight = torch.ones(w_shape, dtype=dtype, device="cuda")
+    vllm_norm = RMSNorm(hidden_size=HIDDEN_SIZE, eps=eps).to(dtype=dtype, device="cuda")
+    x = -2.3 + 0.5 * torch.randn(x_shape, dtype=dtype, device="cuda")
+    if provider == "vllm_rms_layernorm":
+        fn = lambda: vllm_norm(x)
+    elif provider == "triton_rms_layernorm":
+        fn = lambda: rms_layernorm(x, weight, eps=eps)
+    elif provider == "vllm_rms_layernorm_with_residual":
+        fn = lambda: vllm_norm(x, residual=residual)
+    elif provider == "triton_rms_layernorm_with_residual":
+        fn = lambda: rms_layernorm(x, weight, eps=eps, residual=residual)
+    else:
+        raise ValueError("Undefined provider.")
+
+    ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep)
+
+    return ms
+
+
+if __name__ == "__main__":
+    benchmark_rms_layernorm.run(save_path=".", print_data=True)