modify rms norm

csrc/extensions.cpp

@@ -197,21 +197,6 @@ void extApplyPenalty(at::Tensor& logits, const at::Tensor& presence_penalty,
             p_token_ids, p_token_counts, p_cumsum_seq_len, p_max_len_in_batch);
 }
 
-// For lightllm, rms_norm reuses the diopi implementation of internlm
-auto extRmsNormLightllm(const at::Tensor& x, const at::Tensor& weight,
-                        float eps) {
-  at::ScalarType acc_type = x.scalar_type();
-  if (x.scalar_type() == at::kBFloat16 || x.scalar_type() == at::kHalf) {
-    acc_type = at::kFloat;
-  }
-  auto inv_rms = at::empty_like(x, acc_type);
-  auto out = at::empty_like(x);
-  auto bias = at::empty_like(weight);
-  at::OptionalIntArrayRef normalized_shape = weight.sizes();
-  callDiopi(diopiRMSNorm, out, inv_rms, x, normalized_shape, weight, bias, eps);
-  return out;
-}
-
 // For lightllm, rotary_embedding reuses the diopi implementation of internlm
 void extRotaryEmb(at::Tensor& q, const at::Tensor& cos, const at::Tensor& sin) {
   auto seq_len = q.size(0);
@@ -229,9 +214,6 @@ void extRotaryEmb(at::Tensor& q, const at::Tensor& cos, const at::Tensor& sin) {
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   if (&diopiRMSNorm != nullptr) {  // Check if weak symbol defined
     m.def("rms_norm", &extRmsNorm, "deeplink ext_rms_norm");
-    m.def("rms_norm_lightllm", &extRmsNormLightllm,
-          "deeplink ext_rms_norm for lightllm", py::arg("x"), py::arg("weight"),
-          py::arg("eps"));
   }
   if (&diopiRMSNormBackward != nullptr) {
     m.def("rms_norm_backward", &extRmsNormBackward,

csrc/pybind_type_cast.h

@@ -21,28 +21,4 @@ using OptionalIntArray = c10::optional<IntArray>;
 
 }  // namespace dipu::dipu_ext
 
-namespace pybind11::detail {
-
-namespace py = pybind11;
-
-template <>
-struct type_caster<at::OptionalIntArrayRef> {
- public:
-  PYBIND11_TYPE_CASTER(dipu::dipu_ext::OptionalIntArray, _("OptionalIntArray"));
-
-  bool load(py::handle src, bool /*unused*/) {
-    if (PyList_Check(src.ptr())) {
-      value = py::cast<dipu::dipu_ext::IntArray>(src);
-      return true;
-    }
-    if (src.is_none()) {
-      value = c10::nullopt;
-      return true;
-    }
-    return false;
-  }
-};
-
-}  // namespace pybind11::detail
-
 #endif /* end of include guard: PYBIND_TYPE_CAST_H_PXMGELYW */

deeplink_ext/internlm_ops/rms_norm/deeplink.py

@@ -1,64 +1,110 @@
 # Copyright (c) 2024, DeepLink.
 
 import torch
-import deeplink_ext.cpp_extensions as ext
+import deeplink_ext.cpp_extensions as cpp_ext
 
-assert hasattr(ext, "rms_norm")
+assert hasattr(cpp_ext, "rms_norm")
 
 
-# 定义自定义的 autograd 函数
-class _DeepLinkRMSNormFunction(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, hidden_states, weight, bias, eps):
-        output = torch.empty_like(hidden_states)
-        inv_rms_shape = list(hidden_states.shape[:-1]) + [1]
-        inv_rms = torch.empty(
-            inv_rms_shape, dtype=hidden_states.dtype, device=hidden_states.device
-        )
-        ext.rms_norm(output, inv_rms, hidden_states, weight.shape, weight, bias, eps)
+def rms_norm_out(output, inv_rms, input, normalized_shape, weight, bias, eps):
+    if None == normalized_shape:
+        cpp_ext.rms_norm(output, inv_rms, input, weight.shape, weight, bias, eps)
+    else:
+        cpp_ext.rms_norm(output, inv_rms, input, normalized_shape, weight, bias, eps)
 
-        ctx.save_for_backward(hidden_states, inv_rms, weight, bias, torch.tensor(eps))
-        return output
 
-    @staticmethod
-    def backward(ctx, grad_output):
-        hidden_states, inv_rms, weight, bias, eps_tensor = ctx.saved_tensors
-        eps = eps_tensor.item()
+def rms_norm(input, normalized_shape, weight, bias, eps):
+    output = torch.empty_like(input)
+    inv_rms_shape = list(input.shape[:-1]) + [1]
+    inv_rms = torch.empty(inv_rms_shape, dtype=input.dtype, device=input.device)
+    rms_norm_out(output, inv_rms, input, normalized_shape, weight, bias, eps)
+
+    return [output, inv_rms]
 
-        grad_input = torch.empty_like(hidden_states)
-        grad_weight = torch.empty_like(weight)
-        grad_bias = torch.empty_like(bias)
-        ext.rms_norm_backward(
+
+def rms_norm_backward_out(
+    grad_input,
+    grad_weight,
+    grad_bias,
+    grad_output,
+    input,
+    weight,
+    bias,
+    inv_rms,
+    normalized_shape,
+    eps,
+):
+    if None == normalized_shape:
+        cpp_ext.rms_norm_backward(
             grad_input,
             grad_weight,
             grad_bias,
             grad_output,
-            hidden_states,
+            input,
             weight,
             bias,
             inv_rms,
             weight.shape,
             eps,
         )
+    else:
+        cpp_ext.rms_norm_backward(
+            grad_input,
+            grad_weight,
+            grad_bias,
+            grad_output,
+            input,
+            weight,
+            bias,
+            inv_rms,
+            normalized_shape,
+            eps,
+        )
+
+
+def rms_norm_backward(input, grad_output, inv_rms, normalized_shape, weight, bias, eps):
+    grad_input = torch.empty_like(input)
+    grad_weight = torch.empty_like(weight)
+    grad_bias = torch.empty_like(bias)
+    rms_norm_backward_out(
+        grad_input,
+        grad_weight,
+        grad_bias,
+        grad_output,
+        input,
+        weight,
+        bias,
+        inv_rms,
+        normalized_shape,
+        eps,
+    )
+
+    return [grad_input, grad_weight, grad_bias]
+
+
+# 定义自定义的 autograd 函数
+class _DeepLinkRMSNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, hidden_states, weight, bias, eps):
+        output, inv_rms = rms_norm(hidden_states, None, weight, bias, eps)
+        ctx.save_for_backward(hidden_states, inv_rms, weight, bias, torch.tensor(eps))
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        hidden_states, inv_rms, weight, bias, eps_tensor = ctx.saved_tensors
+        eps = eps_tensor.item()
+        grad_input, grad_weight, grad_bias = rms_norm_backward(
+            hidden_states, grad_output, inv_rms, None, weight, bias, eps
+        )
         return grad_input, grad_weight, grad_bias, None
 
 
 class _DeepLinkRMSNormFunctionWithNormalizedShape(torch.autograd.Function):
     @staticmethod
     def forward(ctx, hidden_states, weight, bias, eps, normalized_shape):
-        output = torch.empty_like(hidden_states, dtype=torch.float32)
-        inv_rms_shape = list(hidden_states.shape[:-1]) + [1]
-        inv_rms = torch.empty(
-            inv_rms_shape, dtype=torch.float32, device=hidden_states.device
-        )
-        ext.rms_norm(
-            output,
-            inv_rms,
-            hidden_states.float(),
-            normalized_shape,
-            weight.float(),
-            bias.float(),
-            eps,
+        output, inv_rms = rms_norm(
+            hidden_states.float(), normalized_shape, weight.float(), bias.float(), eps
         )
         output = output.half()
         inv_rms = inv_rms.half()
@@ -72,24 +118,15 @@ def backward(ctx, grad_output):
         eps = eps_tensor.item()
         normalized_shape = ctx.intermediate_results
 
-        grad_input = torch.empty_like(hidden_states, dtype=torch.float32)
-        grad_weight = torch.empty_like(weight, dtype=torch.float32)
-        grad_bias = torch.empty_like(bias, dtype=torch.float32)
-        ext.rms_norm_backward(
-            grad_input,
-            grad_weight,
-            grad_bias,
-            grad_output.float(),
+        grad_input, grad_weight, grad_bias = rms_norm_backward(
             hidden_states.float(),
-            weight.float(),
-            bias.float(),
+            grad_output.float(),
             inv_rms.float(),
             normalized_shape,
+            weight.float(),
+            bias.float(),
             eps,
         )
-        grad_output = grad_output.half()
-        hidden_states = hidden_states.half()
-        inv_rms = inv_rms.half()
         return grad_input, grad_weight, grad_bias, None, None
 
 

deeplink_ext/patch_lightllm.py

@@ -51,7 +51,18 @@ def patch_token_softmax_reducev_inference():
             )
 
         def patch_rms_norm_lightllm():
-            rms_norm_pack.rmsnorm_forward = ext.rms_norm_lightllm
+            import torch
+
+            def rms_norm_lightllm(x, weight, eps):
+                output = torch.empty_like(x)
+                inv_rms_dtype = torch.float16 if x.dtype == torch.bfloat16 else x.dtype
+                inv_rms = torch.empty_like(x, dtype=inv_rms_dtype)
+                bias = torch.empty_like(weight)
+                ext.rms_norm(output, inv_rms, x, weight.shape, weight, bias, eps)
+
+                return output
+
+            rms_norm_pack.rmsnorm_forward = rms_norm_lightllm
 
         def patch_rotary_emb():
             rotary_emb_pack.rotary_emb_fwd = ext.rotary_emb

tests/test_rms_internlm.py

@@ -5,7 +5,7 @@
 import deeplink_ext.internlm_ops.rms_norm as ext
 
 
-def test_rms_norm(BaseRmsNorm, DeeplinkRmsNorm, rtol=1e-4, atol=1e-3):
+def rms_norm_test(BaseRmsNorm, DeeplinkRmsNorm, rtol=1e-4, atol=1e-3):
     x_base = torch.randn(5, 5, requires_grad=True).cuda()
     x_base.retain_grad()
 
@@ -29,9 +29,9 @@ def test_rms_norm(BaseRmsNorm, DeeplinkRmsNorm, rtol=1e-4, atol=1e-3):
 
 print(
     "Test case: normalized_shape == None: grad_inputs closed ? ",
-    test_rms_norm(ext.fallback.RMSNorm, ext.DeepLinkRMSNorm),
+    rms_norm_test(ext.fallback.RMSNorm, ext.DeepLinkRMSNorm),
 )
 print(
     "Test case: normalized_shape == weight.size(): grad_inputs closed ? ",
-    test_rms_norm(ext.fallback.RMSNorm, ext.DeepLinkRMSNormWithNormalizedShape),
+    rms_norm_test(ext.fallback.RMSNorm, ext.DeepLinkRMSNormWithNormalizedShape),
 )

tests/test_rms_lightlm.py

@@ -1,7 +1,7 @@
 # Copyright (c) 2023, DeepLink.
 
 import torch
-import deeplink_ext.cpp_extensions as ext
+from deeplink_ext.internlm_ops.rms_norm.deeplink import rms_norm, rms_norm_backward
 
 # 定义输入张量
 input = torch.randn(5, 5, requires_grad=True).cuda()
@@ -17,26 +17,10 @@
 normalized_shape = torch.tensor([5, 5], dtype=torch.long).cuda()
 
 print(input.is_dipu)
-output = torch.empty_like(input)
-inv_rms_shape = list(input.shape[:-1]) + [1]
-inv_rms = torch.empty(inv_rms_shape, dtype=torch.float32, device=input.device)
-ext.rms_norm(output, inv_rms, input, weight.shape, weight, bias, 1e-6)
-
-# 使用 RMS normalization 反向传播
-grad_input = torch.empty_like(grad_output)
-grad_weight = torch.empty_like(weight)
-grad_bias = torch.empty_like(bias)
-ext.rms_norm_backward(
-    grad_input,
-    grad_weight,
-    grad_bias,
-    grad_output,
-    input,
-    weight,
-    bias,
-    inv_rms,
-    weight.shape,
-    1e-6,
+output, inv_rms = rms_norm(input, None, weight, bias, 1e-6)
+
+grad_input, grad_weight, grad_bias = rms_norm_backward(
+    input, grad_output, inv_rms, None, weight, bias, 1e-6
 )
 
 print("Output:", output)