[FFN Fusion] Support FFN_fusion for the ChatGLM2

neural_speed/convert/convert_chatglm.py

@@ -50,6 +50,7 @@ def bytes_to_unicode():
 
 
 class SentencePieceVocab:
+
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
         self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
         added_tokens: Dict[str, int]
@@ -149,11 +150,11 @@ def chatglm2_convert_gguf(model, tokenizer, dir_model, fname_out, ftype, hparams
     print("ChatGLM-2.gguf converting: ")
     list_vars = model.state_dict()
     for name in list_vars.keys():
-        print(name, list_vars[name].shape, list_vars[name].dtype)
+        print("%-80s" % name, list_vars[name].shape, list_vars[name].dtype)
 
     print(hparams)
 
-    gguf_file = fname_out + '.gguf'
+    gguf_file = fname_out
     gguf_writer = gguf.GGUFWriter(gguf_file, "chatglm2")
 
     arch = "chatglm2."
@@ -285,35 +286,68 @@ def write_vocab_gguf(dir_model):
     print("gguf: get tensor metadata")
     for name in list_vars.keys():
         data = list_vars[name].squeeze().numpy()
-
-        print("Processing variable: " + name + " with shape: ", data.shape)
         if 'inv_freq' in name:
+            print("Converting: %-75s" % name, " shape: %-15s" % str(data.shape))
             continue
 
+        print("Converting: %-75s" % name, " shape: %-15s" % str(data.shape), end="      ")
         n_dims = len(data.shape)
 
         # ftype == 0 -> float32, ftype == 1 -> float16
         ftype_cur = 0
         if ftype != 0:
             if name[-7:] == ".weight" and n_dims == 2:
-                print("  Converting to float16")
+                print("  to float16".rjust(15))
                 data = data.astype(np.float16)
                 ftype_cur = 1
             else:
-                print("  Converting to float32")
+                print("  to float32".rjust(15))
                 data = data.astype(np.float32)
                 ftype_cur = 0
         else:
             if data.dtype != np.float32:
-                print("  Converting to float32")
+                print("  to float32".rjust(15))
                 data = data.astype(np.float32)
                 ftype_cur = 0
 
-        # print(f"[{i+1:{padi}d}/{len(model)}]
-        # Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4}")
-
         gguf_writer.add_tensor(name, data)
 
+        if "mlp.dense_h_to_4h" in name:
+            name_0 = name.replace("dense_h_to_4h", "dense_h_to_4h_0")
+            name_1 = name.replace("dense_h_to_4h", "dense_h_to_4h_1")
+            shape_0 = data.shape[0]
+            half_shape_0 = int(shape_0 / 2)
+            data_0 = data[0:half_shape_0, :]
+            data_1 = data[half_shape_0:shape_0, :]
+
+            print("Converting: %-75s" % name_0, " shape: %-15s" % str(data_0.shape))
+            print("Converting: %-75s" % name_1, " shape: %-15s" % str(data_1.shape))
+
+            n_dims = len(data_0.shape)
+            assert (len(data_0.shape) == len(data_1.shape))
+            # ftype == 0 -> float32, ftype == 1 -> float16
+            ftype_cur = 0
+            if ftype != 0:
+                if name_0[-7:] == ".weight" and n_dims == 2:
+                    print("  to float16".rjust(15))
+                    data_0 = data_0.astype(np.float16)
+                    data_1 = data_1.astype(np.float32)
+                    ftype_cur = 1
+                else:
+                    print("  to float32".rjust(15))
+                    data_0 = data_0.astype(np.float32)
+                    data_1 = data_1.astype(np.float32)
+                    ftype_cur = 0
+            else:
+                if data_0.dtype != np.float32:
+                    print("  to float32".rjust(15))
+                    data_0 = data_0.astype(np.float32)
+                    data_1 = data_1.astype(np.float32)
+                    ftype_cur = 0
+
+            gguf_writer.add_tensor(name_0, data_0)
+            gguf_writer.add_tensor(name_1, data_1)
+
     print("gguf: write header")
     gguf_writer.write_header_to_file()
     print("gguf: write metadata")
@@ -363,9 +397,9 @@ def chatglm2_convert(model, tokenizer, dir_model, fname_out, ftype, hparams):
     fout.write(struct.pack("f", 10000.0))  # freq_base
     fout.write(struct.pack("f", 1.0))  # rope_factor
 
-    fout.write(struct.pack("f", 0.0)) # config.json "rope_scaling.factor", not enabled
-    fout.write(struct.pack("i", 0))   # rope_scaling.original_max_position_embeddings
-    fout.write(struct.pack("i", 0))   # params["rope_scaling"]["type"] =="yarn" else 0))
+    fout.write(struct.pack("f", 0.0))  # config.json "rope_scaling.factor", not enabled
+    fout.write(struct.pack("i", 0))  # rope_scaling.original_max_position_embeddings
+    fout.write(struct.pack("i", 0))  # params["rope_scaling"]["type"] =="yarn" else 0))
 
     fout.write(struct.pack("i", tokenizer.bos_token_id if tokenizer.bos_token_id is not None else 1))
     fout.write(struct.pack("i", tokenizer.eos_token_id if tokenizer.eos_token_id is not None else 2))
@@ -419,10 +453,56 @@ def chatglm2_convert(model, tokenizer, dir_model, fname_out, ftype, hparams):
         for i in range(n_dims):
             fout.write(struct.pack("i", data.shape[n_dims - 1 - i]))
         fout.write(str)
-
         # data
         data.tofile(fout)
 
+        if "mlp.dense_h_to_4h" in name:
+            name_0 = name.replace("dense_h_to_4h", "dense_h_to_4h_0")
+            name_1 = name.replace("dense_h_to_4h", "dense_h_to_4h_1")
+            shape_0 = data.shape[0]
+            half_shape_0 = int(shape_0 / 2)
+            data_0 = data[0:half_shape_0, :]
+            data_1 = data[half_shape_0:shape_0, :]
+
+            print("Converting: %-75s" % name_0, " shape: ", data_0.shape)
+            print("Converting: %-75s" % name_1, " shape: ", data_1.shape)
+
+            n_dims = len(data_0.shape)
+            assert (len(data_0.shape) == len(data_1.shape))
+            # ftype == 0 -> float32, ftype == 1 -> float16
+            ftype_cur = 0
+            if ftype != 0:
+                if name_0[-7:] == ".weight" and n_dims == 2:
+                    print("  to float16".rjust(15))
+                    data_0 = data_0.astype(np.float16)
+                    data_1 = data_1.astype(np.float32)
+                    ftype_cur = 1
+                else:
+                    print("  to float32".rjust(15))
+                    data_0 = data_0.astype(np.float32)
+                    data_1 = data_1.astype(np.float32)
+                    ftype_cur = 0
+            else:
+                if data_0.dtype != np.float32:
+                    print("  to float32".rjust(15))
+                    data_0 = data_0.astype(np.float32)
+                    data_1 = data_1.astype(np.float32)
+                    ftype_cur = 0
+
+            str_0 = name_0.encode("utf-8")
+            fout.write(struct.pack("iii", n_dims, len(str_0), ftype_cur))
+            for i in range(n_dims):
+                fout.write(struct.pack("i", data_0.shape[n_dims - 1 - i]))
+            fout.write(str_0)
+            data_0.tofile(fout)
+
+            str_1 = name_1.encode("utf-8")
+            fout.write(struct.pack("iii", n_dims, len(str_1), ftype_cur))
+            for i in range(n_dims):
+                fout.write(struct.pack("i", data_1.shape[n_dims - 1 - i]))
+            fout.write(str_1)
+            data_1.tofile(fout)
+
     fout.close()
 
     print("Done. Output file: " + fname_out)

neural_speed/models/chatglm/chatglm2.cpp

@@ -298,19 +298,25 @@ static bool chatglm_model_eval_internal(model_context* ctx, const model_input* i
 
     // mlp.forward
     struct ne_tensor* mlp_output = ne_rms_norm(ctx0, hidden_states, hparams.rms_norm_eps);
-    ne_set_name(mlp_output, "mlp_output");
-    // mlp_output = ne_mul(ctx0, mlp_output, model.layers[il].norm[1]);
     mlp_output = ne_mul(ctx0, ne_repeat(ctx0, model.layers[il].norm[1], mlp_output), mlp_output);
 
-    mlp_output = ne_mul_mat(ctx0, model.layers[il].ffn[0], mlp_output);
-    struct ne_tensor* x0 = ne_view_2d(ctx0, mlp_output, mlp_output->ne[0] / 2, mlp_output->ne[1], mlp_output->nb[1], 0);
-    x0 = ne_silu(ctx0, x0);
-    struct ne_tensor* x1 = ne_view_2d(ctx0, mlp_output, mlp_output->ne[0] / 2, mlp_output->ne[1], mlp_output->nb[1],
-                                      mlp_output->ne[0] / 2 * ne_element_size(mlp_output));
-    ne_set_name(x0, "x0");
-    ne_set_name(x1, "x1");
-    mlp_output = ne_mul(ctx0, x0, x1);
-    mlp_output = ne_mul_mat(ctx0, model.layers[il].ffn[1], mlp_output);
+    if (model.layers[il].ffn_fusion &&
+        bestla_fusion_FFN_SiLu_f32f32_support(model.layers[il].ffn[2]->data, model.layers[il].ffn[1]->data,
+                                              model.layers[il].ffn[3]->data, N, int(cur->ne[0] / 2),
+                                              model.layers[il].ffn[2]->ne[1], model.layers[il].ffn[1]->ne[1])) {
+      mlp_output =
+          ne_ffn_silu(ctx0, model.layers[il].ffn[2], model.layers[il].ffn[1], model.layers[il].ffn[3], mlp_output);
+    } else {
+      // mlp.forward
+      mlp_output = ne_mul_mat(ctx0, model.layers[il].ffn[0], mlp_output);
+      struct ne_tensor* x0 =
+          ne_view_2d(ctx0, mlp_output, mlp_output->ne[0] / 2, mlp_output->ne[1], mlp_output->nb[1], 0);
+      x0 = ne_silu(ctx0, x0);
+      struct ne_tensor* x1 = ne_view_2d(ctx0, mlp_output, mlp_output->ne[0] / 2, mlp_output->ne[1], mlp_output->nb[1],
+                                        mlp_output->ne[0] / 2 * ne_element_size(mlp_output));
+      mlp_output = ne_mul(ctx0, x0, x1);
+      mlp_output = ne_mul_mat(ctx0, model.layers[il].ffn[1], mlp_output);
+    }
 
 #ifdef NS_TP_MODEL
     if (enable_tp) {

neural_speed/models/chatglm/chatglm2_utils.cpp

@@ -67,14 +67,13 @@ void CHATGLM2::init(const char* path_model, model_context* ctx, int n_gpu_layer_
   model.hparams = ml->file_loaders.at(0)->hparams;
   model_file_version file_version = ml->file_loaders.at(0)->file_version;
   auto& hparams = model.hparams;
-  n_ff = 4 * hparams.n_embd;
-  fprintf(stderr, "%s: n_vocab    = %u\n", __func__, hparams.n_vocab);
-  fprintf(stderr, "%s: n_embd     = %u\n", __func__, hparams.n_embd);
-  fprintf(stderr, "%s: n_mult     = %u\n", __func__, hparams.n_mult);
-  fprintf(stderr, "%s: n_head     = %u\n", __func__, hparams.n_head);
-  fprintf(stderr, "%s: n_layer    = %u\n", __func__, hparams.n_layer);
-  fprintf(stderr, "%s: n_rot      = %u\n", __func__, hparams.n_rot);
-  fprintf(stderr, "%s: n_ff       = %u\n", __func__, n_ff);
+  fprintf(stderr, "%s: hparams.n_vocab         = %u\n", __func__, hparams.n_vocab);
+  fprintf(stderr, "%s: hparams.n_embd          = %u\n", __func__, hparams.n_embd);
+  fprintf(stderr, "%s: hparams.n_mult          = %u\n", __func__, hparams.n_mult);
+  fprintf(stderr, "%s: hparams.n_head          = %u\n", __func__, hparams.n_head);
+  fprintf(stderr, "%s: hparams.n_layer         = %u\n", __func__, hparams.n_layer);
+  fprintf(stderr, "%s: hparams.n_rot           = %u\n", __func__, hparams.n_rot);
+  fprintf(stderr, "%s: hparams.ffn_hidden_size = %u\n", __func__, hparams.ffn_hidden_size);
   fprintf(stderr, "%s: n_parts    = %zu\n", __func__, ml->file_loaders.size());
   n_embd = hparams.n_embd;
   n_vocab = hparams.n_vocab;
@@ -149,6 +148,15 @@ void CHATGLM2::load(model_context* ctx, model_progress_callback progress_callbac
     layer.ffn[1] =
         ml->get_tensor(layers_i + ".mlp.dense_4h_to_h.weight", {uint32_t(hparams.ffn_hidden_size), n_embd}, backend);
 
+    if (ml->verify_tensor(layers_i + ".mlp.dense_h_to_4h_0.weight") &&
+        ml->verify_tensor(layers_i + ".mlp.dense_h_to_4h_1.weight")) {
+      layer.ffn[2] = ml->get_tensor(layers_i + ".mlp.dense_h_to_4h_0.weight",
+                                    {n_embd, uint32_t(hparams.ffn_hidden_size)}, backend);
+      layer.ffn[3] = ml->get_tensor(layers_i + ".mlp.dense_h_to_4h_1.weight",
+                                    {n_embd, uint32_t(hparams.ffn_hidden_size)}, backend);
+      layer.ffn_fusion = true;
+    }
+
     // kv-cache
     layer.k_cache = nullptr;  // kv-cache will be init later in model_utils
     layer.v_cache = nullptr;  // kv-cache will be init later in model_utils
@@ -161,6 +169,7 @@ void CHATGLM2::load(model_context* ctx, model_progress_callback progress_callbac
     }
   }
 
+  fprintf(stderr, "%s: layers[0].ffn_fusion    = %u\n", __func__, model.layers[0].ffn_fusion);
   // print memory requirements
   // this is the total memory required to run the inference
   const size_t mem_required = ctx_size + mmapped_size - vram_total +  // weights in VRAM not in memory

neural_speed/models/model_utils/model_types.h

@@ -160,6 +160,8 @@ struct model_layer {
 
   struct ne_tensor* k_cache;
   struct ne_tensor* v_cache;
+
+  bool ffn_fusion = false;
 };
 
 typedef int32_t model_pos;

tests/model-test/cpp_graph_inference.sh

@@ -221,7 +221,7 @@ function main() {
         infer_cmd="./build/bin/run_dolly"
     elif [[ "${model}" == "chatglm2" ]]; then
         quant_script="./build/bin/quant_chatglm2"
-        convert_script="${convert_script}/convert_chatglm.py"
+        convert_script="${convert_script}/convert_chatglm.py --format=GGUF"
         infer_cmd="./build/bin/run_chatglm2"
     elif [[ "${model}" == "chatglm-6b" ]]; then
         quant_script="./build/bin/quant_chatglm"