[Inference] Kernel: no pad rotary embedding (#5252)

* fix bugs

* comment

* use more accurate atol

* fix

Author: CjhHa1

colossalai/kernel/triton/__init__.py

@@ -11,11 +11,13 @@
     from .context_attn_unpad import context_attention_unpadded
     from .fused_layernorm import layer_norm
     from .gptq_triton import gptq_fused_linear_triton
+    from .no_pad_rotary_embedding import rotary_embedding
     from .softmax import softmax
 
     __all__ = [
         "context_attention_unpadded",
         "softmax",
         "layer_norm",
         "gptq_fused_linear_triton",
+        "rotary_embedding",
     ]

colossalai/kernel/triton/no_pad_rotary_embedding.py

@@ -0,0 +1,149 @@
+import torch
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def rotary_embedding_kernel(
+    q,
+    k,
+    cos,
+    sin,
+    q_token_stride,
+    q_head_stride,
+    k_token_stride,
+    k_head_stride,
+    head_dim_stride,
+    cos_token_stride,
+    cos_stride,
+    q_total_tokens,
+    Q_HEAD_NUM: tl.constexpr,
+    K_HEAD_NUM: tl.constexpr,
+    HEAD_DIM: tl.constexpr,
+    BLOCK_HEAD: tl.constexpr,
+    BLOCK_TOKENS: tl.constexpr,
+):
+    block_head_index = tl.program_id(0)
+    block_token_index = tl.program_id(1)
+
+    rotary_data = q
+    HEAD_NUM = Q_HEAD_NUM
+    head_stride = q_head_stride
+    token_stride = q_token_stride
+
+    if block_token_index * BLOCK_TOKENS >= q_total_tokens:
+        block_token_index = block_token_index - tl.cdiv(q_total_tokens, BLOCK_TOKENS)
+        rotary_data = k
+        HEAD_NUM = K_HEAD_NUM
+        head_stride = k_head_stride
+        token_stride = k_token_stride
+
+    tokens_range = block_token_index * BLOCK_TOKENS + tl.arange(0, BLOCK_TOKENS)
+    head_range = block_head_index * BLOCK_HEAD + tl.arange(0, BLOCK_HEAD)
+
+    dim_range0 = tl.arange(0, HEAD_DIM // 2)
+    dim_range1 = tl.arange(HEAD_DIM // 2, HEAD_DIM)
+
+    off_data0 = (
+        tokens_range[:, None, None] * token_stride
+        + head_range[None, :, None] * head_stride
+        + dim_range0[None, None, :] * head_dim_stride
+    )
+    off_data1 = (
+        tokens_range[:, None, None] * token_stride
+        + head_range[None, :, None] * head_stride
+        + dim_range1[None, None, :] * head_dim_stride
+    )
+
+    loaded_data0 = tl.load(
+        rotary_data + off_data0,
+        mask=((head_range[None, :, None] < HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
+        other=0.0,
+    )
+    loaded_data1 = tl.load(
+        rotary_data + off_data1,
+        mask=((head_range[None, :, None] < HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
+        other=0.0,
+    )
+
+    off_cos_sin = tokens_range[:, None] * cos_token_stride + dim_range0[None, :] * cos_stride
+
+    loaded_cos = tl.load(cos + off_cos_sin, mask=(tokens_range[:, None] < q_total_tokens), other=0.0)
+    loaded_sin = tl.load(sin + off_cos_sin, mask=(tokens_range[:, None] < q_total_tokens), other=0.0)
+
+    out0 = loaded_data0 * loaded_cos[:, None, :] - loaded_data1 * loaded_sin[:, None, :]
+    out1 = loaded_data0 * loaded_sin[:, None, :] + loaded_data1 * loaded_cos[:, None, :]
+
+    # concat
+    tl.store(
+        rotary_data + off_data0,
+        out0,
+        mask=((head_range[None, :, None] < HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
+    )
+    tl.store(
+        rotary_data + off_data1,
+        out1,
+        mask=((head_range[None, :, None] < HEAD_NUM) & (tokens_range[:, None, None] < q_total_tokens)),
+    )
+
+
+@torch.no_grad()
+def rotary_embedding(
+    q: torch.Tensor,
+    k: torch.Tensor,
+    cos: torch.Tensor,
+    sin: torch.Tensor,
+):
+    """
+    Args:
+        q: query tensor, [total_tokens, head_num, head_dim]
+        k: key tensor, [total_tokens, head_num, head_dim]
+        cos: cosine for rotary embedding, [total_tokens, head_dim]
+        sin: sine for rotary embedding, [total_tokens, head_dim]
+    """
+    q_total_tokens, q_head_num, head_dim = q.shape
+    assert q.shape[0] == cos.shape[0] and q.shape[0] == sin.shape[0], f"q shape {q.shape} cos shape {cos.shape}"
+    BLOCK_HEAD = 4
+    BLOCK_TOKENS = 8
+    grid = (triton.cdiv(q_head_num, BLOCK_HEAD), 2 * triton.cdiv(q_total_tokens, BLOCK_TOKENS))
+
+    if head_dim >= 128:
+        num_warps = 8
+    else:
+        num_warps = 4
+
+    q_token_stride = q.stride(0)
+    q_head_stride = q.stride(1)
+    head_dim_stride = q.stride(2)
+
+    k_token_stride = k.stride(0)
+    k_head_stride = k.stride(1)
+
+    k_head_num = q.shape[1]
+
+    cos_token_stride = cos.stride(0)
+    cos_stride = cos.stride(1)
+
+    rotary_embedding_kernel[grid](
+        q,
+        k,
+        cos,
+        sin,
+        q_token_stride,
+        q_head_stride,
+        k_token_stride,
+        k_head_stride,
+        head_dim_stride,
+        cos_token_stride,
+        cos_stride,
+        q_total_tokens,
+        Q_HEAD_NUM=q_head_num,
+        K_HEAD_NUM=k_head_num,
+        HEAD_DIM=head_dim,
+        BLOCK_HEAD=BLOCK_HEAD,
+        BLOCK_TOKENS=BLOCK_TOKENS,
+        num_warps=num_warps,
+        num_stages=1,
+    )
+
+    return

tests/test_infer_ops/triton/test_rotary_embdding_unpad.py

@@ -0,0 +1,56 @@
+import pytest
+import torch
+from transformers.models.llama.modeling_llama import LlamaRotaryEmbedding, apply_rotary_pos_emb
+
+from colossalai.kernel.triton import rotary_embedding
+
+
+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)
+
+
+@pytest.mark.parametrize("BATCH_SIZE", [4])
+@pytest.mark.parametrize("SEQ_LEN", [64])
+@pytest.mark.parametrize("H", [32])
+@pytest.mark.parametrize("D", [64])
+@pytest.mark.parametrize("dtype", [torch.float32])
+def test_rotary_emb(BATCH_SIZE, SEQ_LEN, H, D, dtype):
+    TOTAL_TOKENS = BATCH_SIZE * SEQ_LEN
+    # our crafted op equals to Transformers
+    x0 = torch.randn(TOTAL_TOKENS, SEQ_LEN, D)
+    x1 = torch.randn(TOTAL_TOKENS, SEQ_LEN, D)
+    emb = LlamaRotaryEmbedding(D)
+    cos, sin = emb(x0, TOTAL_TOKENS)
+    cos_2 = cos[:, :32]
+    sin_2 = sin[:, :32]
+    position_ids = torch.arange(TOTAL_TOKENS)
+    embd_x0, _ = apply_rotary_pos_emb(x0, x1, cos, sin, position_ids)
+    embd_stimulated_x = torch_rotary_emb(x0, cos_2, sin_2)
+    assert torch.allclose(embd_x0, embd_stimulated_x)
+
+    # create data
+    q_shape = (TOTAL_TOKENS, H, D)
+    q = -2.3 + 0.5 * torch.randn(q_shape, dtype=dtype, device="cuda")
+    k_shape = (TOTAL_TOKENS, H, D)
+    k = -2.3 + 0.5 * torch.randn(k_shape, dtype=dtype, device="cuda")
+    cos_shape = (TOTAL_TOKENS, D // 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")
+
+    q_ref = torch_rotary_emb(q, cos, sin)
+    k_ref = torch_rotary_emb(k, cos, sin)
+    rotary_embedding(q, k, cos, sin)
+
+    assert torch.allclose(q, q_ref, atol=1e-4, rtol=1e-4)
+    assert torch.allclose(k, k_ref, atol=1e-4, rtol=1e-4)
+
+
+if __name__ == "__main__":
+    test_rotary_emb(4, 64, 32, 64, torch.float32)