Enhancements in test_llama_attention.py / floating-point comparison, variable naming, and code cleanup

ai_scaling.py

@@ -0,0 +1,135 @@
+import pytest
+import torch
+from packaging import version
+
+try:
+    from colossalai.kernel.triton import int8_rotary_embedding_fwd
+
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+    print("please install triton from https://github.com/openai/triton")
+
+try:
+    from colossalai.inference.quant.smoothquant.models import LLamaSmoothquantAttention
+
+    HAS_TORCH_INT = True
+except ImportError:
+    HAS_TORCH_INT = False
+    print("Please install torch_int from https://github.com/Guangxuan-Xiao/torch-int")
+
+TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse("11.4")
+
+import math
+import torch
+from torch.nn import functional as F
+
+def ai_predict_scaling(input_tensor):
+    # Placeholder AI model for predicting scaling factors
+    # Implement AI model loading and prediction here
+    predicted_scale = 1.0  # Example predicted scale value
+    return predicted_scale
+
+def torch_context_attention(xq, xk, xv, bs, seqlen, num_head, head_dim):
+    xq = xq.view(bs, seqlen, num_head, head_dim)
+    xk = xk.view(bs, seqlen, num_head, head_dim)
+    xv = xv.view(bs, seqlen, num_head, head_dim)
+    mask = torch.tril(torch.ones(seqlen, seqlen), diagonal=0).unsqueeze(0).unsqueeze(0).cuda()
+    mask[mask == 0.0] = -100000000.0
+    mask = mask.repeat(bs, num_head, 1, 1)
+    keys = xk
+    values = xv
+    xq = xq.transpose(1, 2)
+    keys = keys.transpose(1, 2)
+    values = values.transpose(1, 2)
+    scores = torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(head_dim)
+    scores = F.softmax(scores.float() + mask, dim=-1).type_as(xq)
+    output = torch.matmul(scores, values).transpose(1, 2).contiguous().reshape(-1, num_head, head_dim)
+
+    return output
+
+@pytest.mark.skipif(
+    not TRITON_CUDA_SUPPORT or not HAS_TRITON or not HAS_TORCH_INT,
+    reason="triton requires cuda version to be higher than 11.4 or not install torch_int",
+)
+def test_llama_context_attention():
+    head_num = 2
+    seq_len = 32
+    head_dim = 64
+    dtype = torch.float
+    hidden_size = head_num * head_dim
+
+    smooth_attn = LLamaSmoothquantAttention(head_num * head_dim, head_num)
+
+    smooth_attn.q_proj.weight = torch.ones(hidden_size, hidden_size, device="cuda").to(torch.int8)
+    smooth_attn.k_proj.weight = torch.ones(hidden_size, hidden_size, device="cuda").to(torch.int8)
+    smooth_attn.v_proj.weight = torch.ones(hidden_size, hidden_size, device="cuda").to(torch.int8)
+    smooth_attn.out_proj.weight = torch.ones(hidden_size, hidden_size, device="cuda").to(torch.int8)
+    smooth_attn.out_proj.weight[:, 1:hidden_size] = torch.zeros(hidden_size - 1, device="cuda").to(torch.int8)
+
+    qkv_weight_scale = 1.0
+
+    ones_matrix = torch.ones(hidden_size, hidden_size, dtype=torch.float, device="cuda")
+
+    smooth_attn = smooth_attn.to("cuda")
+
+    input_tensor = torch.randint(-20, 20, (1, seq_len, head_num * head_dim), dtype=torch.int8, device="cuda")
+    input_scale = ai_predict_scaling(input_tensor)  # Dynamic scaling based on AI predictions
+
+    output_tensor = torch.matmul(input_tensor.to(torch.float) * input_scale, ones_matrix)
+    qkv_max_out = torch.max(torch.abs(output_tensor)) / 127
+    smooth_attn.q_proj.a = torch.tensor(input_scale * qkv_weight_scale / qkv_max_out)
+    smooth_attn.k_proj.a = torch.tensor(input_scale * qkv_weight_scale / qkv_max_out)
+    smooth_attn.v_proj.a = torch.tensor(input_scale * qkv_weight_scale / qkv_max_out)
+
+    q = smooth_attn.q_proj(input_tensor)
+    k = smooth_attn.k_proj(input_tensor)
+    v = smooth_attn.v_proj(input_tensor)
+
+    cos_shape = (seq_len, head_dim // 2)
+    cos = torch.ones(cos_shape, dtype=dtype, device="cuda")
+    sin = torch.zeros(cos_shape, dtype=dtype, device="cuda")
+    in_scale = torch.tensor([qkv_max_out], device="cuda")
+    out_scale = torch.tensor([qkv_max_out], device="cuda")
+    int8_rotary_embedding_fwd(q.view(-1, head_num, head_dim), cos, sin, in_scale.item(), out_scale.item())
+    int8_rotary_embedding_fwd(k.view(-1, head_num, head_dim), cos, sin, in_scale.item(), out_scale.item())
+
+    q = q.to(torch.float) * out_scale
+    k = k.to(torch.float) * out_scale
+    v = v.to(torch.float) * out_scale
+    torch_out = torch_context_attention(q.clone(), k.clone(), v.clone(), 1, seq_len, head_num, head_dim)
+    attn_out_max = torch.max(torch.abs(torch_out)) / 127
+
+    output_tensor = torch.matmul(torch_out.view(-1, seq_len, head_num * head_dim), ones_matrix)
+    smooth_attn.q_output_scale = torch.tensor(qkv_max_out)
+    smooth_attn.k_output_scale = torch.tensor(qkv_max_out)
+
+    smooth_attn.v_output_scale = torch.tensor(qkv_max_out)
+    smooth_attn.q_rotary_output_scale = torch.tensor(qkv_max_out)
+    smooth_attn.k_rotary_output_scale = torch.tensor(qkv_max_out)
+
+    smooth_attn.attn_output_scale = torch.tensor(attn_out_max)
+    smooth_attn.out_proj.a = torch.tensor([attn_out_max])
+
+    torch_out = (
+        (torch_out / smooth_attn.attn_output_scale)
+        .round()
+        .clamp(-128, 127)
+        .to(torch.int8)
+        .view(-1, seq_len, head_num * head_dim)
+    )
+
+    torch_out = smooth_attn.out_proj(torch_out)
+    torch_out = torch_out.to(torch.float)
+
+    smooth_attn = smooth_attn.to("cuda")
+    smooth_out, _, _ = smooth_attn(input_tensor, (cos, sin))
+    smooth_out = smooth_out.to(torch.float)
+
+    # Use torch.allclose with a tolerance for floating-point comparisons
+    assert torch.allclose(
+        torch_out.cpu(), smooth_out.cpu(), rtol=1e-1, atol=1e-1
+    ), "outputs from triton and torch are not matched"
+
+if __name__ == "__main__":
+    test_llama_context_attention()