apeskov commented on code in PR #11508:
URL: https://github.com/apache/tvm/pull/11508#discussion_r886041844
##########
src/runtime/contrib/dnnl/dnnl_json_runtime.cc:
##########
@@ -1084,6 +1086,47 @@ class DNNLJSONRuntime : public JSONRuntimeBase {
{DNNL_ARG_VARIANCE, variance_memory}});
}
+ void LayerNorm(const size_t& nid) {
+ auto node = nodes_[nid];
+
+ auto data_entry = node.GetInputs()[0];
+ auto gamma_entry = node.GetInputs()[1];
+ auto beta_entry = node.GetInputs()[2];
+
+ dnnl::memory::dims data_shape =
nodes_[data_entry.id_].GetOpShape()[data_entry.index_];
+
+ float epsilon =
std::stof(node.GetAttr<std::vector<std::string>>("epsilon")[0]);
+
+ // Memory description.
+ dnnl::memory::desc data_md = GenDNNLMemDescByShape(data_shape, dt::f32);
+
+ // LN description.
+ auto lnorm_desc = dnnl::layer_normalization_forward::desc(
+ dnnl::prop_kind::forward_inference, data_md, epsilon,
+ dnnl::normalization_flags::use_scale |
dnnl::normalization_flags::use_shift);
+
+ auto lnorm_prim_desc =
dnnl::layer_normalization_forward::primitive_desc(lnorm_desc, engine_);
+ auto lnorm_prim = dnnl::layer_normalization_forward(lnorm_prim_desc);
+
+ net_.push_back(lnorm_prim);
+
+ // Memories.
+ auto data_memory = BindDNNLMemory(data_entry, data_md);
+ JSONGraphNodeEntry out_entry(nid, 0);
+ auto dst_memory = BindDNNLMemory(out_entry, data_md);
+ auto scale_memory = BindDNNLMemory(gamma_entry, data_md);
+ auto shift_memory = BindDNNLMemory(beta_entry, data_md);
+ auto mean_memory = dnnl::memory(lnorm_prim_desc.mean_desc(), engine_);
+ auto variance_memory = dnnl::memory(lnorm_prim_desc.variance_desc(),
engine_);
+
+ net_args_.push_back({{DNNL_ARG_SRC, data_memory},
+ {DNNL_ARG_MEAN, mean_memory},
+ {DNNL_ARG_VARIANCE, variance_memory},
+ {DNNL_ARG_SCALE, scale_memory},
+ {DNNL_ARG_SHIFT, shift_memory},
Review Comment:
DNNL_ARG_SCALE and DNNL_ARG_SHIFT was introduced only in v2.3. Unconditional
using of that will break compilation with pervious version of oneDNN. Are you
sure that it's your goal?
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