model: support arch DbrxForCausalLM

README.md

@@ -94,6 +94,7 @@ Typically finetunes of the base models below are supported as well.
 - [x] LLaMA 2 🦙🦙
 - [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
 - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
+- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
 - [X] Falcon
 - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
 - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)

convert-hf-to-gguf.py

@@ -1427,6 +1427,101 @@ def write_tensors(self):
             self.gguf_writer.add_tensor(new_name, data)
 
 
+@Model.register("DbrxForCausalLM")
+class DbrxModel(Model):
+    model_arch = gguf.MODEL_ARCH.DBRX
+
+    def set_gguf_parameters(self):
+        ffn_config = self.hparams["ffn_config"]
+        attn_config = self.hparams["attn_config"]
+        self.gguf_writer.add_name(self.hparams["model_type"])
+        self.gguf_writer.add_block_count(self.hparams["n_layers"])
+
+        self.gguf_writer.add_context_length(self.hparams["max_seq_len"])
+        self.gguf_writer.add_embedding_length(self.hparams["d_model"])
+        self.gguf_writer.add_feed_forward_length(ffn_config["ffn_hidden_size"])
+
+        self.gguf_writer.add_head_count(self.hparams["n_heads"])
+        self.gguf_writer.add_head_count_kv(attn_config["kv_n_heads"])
+
+        self.gguf_writer.add_rope_freq_base(attn_config["rope_theta"])
+
+        self.gguf_writer.add_clamp_kqv(attn_config["clip_qkv"])
+        self.gguf_writer.add_file_type(self.ftype)
+
+        self.gguf_writer.add_expert_count(ffn_config["moe_num_experts"])
+        self.gguf_writer.add_expert_used_count(ffn_config["moe_top_k"])
+
+        self.gguf_writer.add_layer_norm_eps(1e-5)
+
+        self.gguf_writer.add_file_type(self.ftype)
+        print(f"gguf: file type = {self.ftype}")
+
+    def write_tensors(self):
+        block_count = self.hparams.get("n_layers")
+        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
+        for name, data_torch in self.get_tensors():
+            n_expert = self.hparams["ffn_config"]["moe_num_experts"]
+            n_ff = self.hparams["ffn_config"]["ffn_hidden_size"]
+            n_embd = self.hparams["d_model"]
+
+            # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose
+            # original implementation expects (n_expert, n_ff, n_embd) for all experts weights
+            # But llama.cpp moe graph works differently
+            # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions
+            # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor
+            exp_tensor_names = {"ffn.experts.mlp.w1": None,       # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff,   n_expert}
+                                "ffn.experts.mlp.w2": (0, 2, 1),  # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff,   n_embd, n_expert}
+                                "ffn.experts.mlp.v1": None}       # LLM_TENSOR_FFN_UP_EXPS   ggml_tensor->ne{n_embd, n_ff,   n_expert}
+            experts = False
+            for exp_tensor_name in exp_tensor_names.keys():
+                if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1:
+                    experts = True
+                    data_torch = data_torch.view(n_expert, n_ff, n_embd)
+                    if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None:
+                        data_torch = data_torch.permute(*permute_tensor)
+                    break
+
+            old_dtype = data_torch.dtype
+
+            # convert any unsupported data types to float32
+            if data_torch.dtype not in (torch.float16, torch.float32):
+                data_torch = data_torch.to(torch.float32)
+
+            data = data_torch.squeeze().numpy()
+
+            # map tensor names
+            # In MoE models the ffn tensors are typically most of the model weights,
+            # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight.
+            # Every other model has the weight names ending in .weight,
+            # let's assume that is the convention which is not the case for dbrx:
+            # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15
+            new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",))
+            if new_name is None:
+                print(f"Can not map tensor {name!r}")
+                sys.exit()
+
+            n_dims = len(data.shape)
+            data_dtype = data.dtype
+
+            # Most of the codebase that takes in 1D tensors only handles F32 tensors
+            # and most of the outputs tensors are F32.
+            if data_dtype != np.float32 and n_dims == 1:
+                print(f"Can not map tensor {name!r}: all 1D tensors must be F32")
+                sys.exit()
+
+            # if f32 desired, convert any float16 to float32
+            if self.ftype == 0 and data_dtype == np.float16:
+                data = data.astype(np.float32)
+
+            # if f16 desired, convert any float32 2-dim weight tensors to float16
+            if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1:
+                data = data.astype(np.float16)
+
+            print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}")
+
+            self.gguf_writer.add_tensor(new_name, data)
+
 @Model.register("MiniCPMForCausalLM")
 class MiniCPMModel(Model):
     model_arch = gguf.MODEL_ARCH.MINICPM

examples/eval-callback/eval-callback.cpp

@@ -28,14 +28,27 @@ static std::string ggml_ne_string(const ggml_tensor * t) {
 }
 
 static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
+    GGML_ASSERT(n > 0);
     float sum = 0;
     for (int64_t i3 = 0; i3 < ne[3]; i3++) {
         printf("                                     [\n");
-        for (int64_t i2 = 0; i2 < ne[2] && i2 < n; i2++) {
+        for (int64_t i2 = 0; i2 < ne[2]; i2++) {
+            if (i2 == n && ne[2] > 2*n) {
+                printf("                                      ..., \n");
+                i2 = ne[2] - n;
+            }
             printf("                                      [\n");
-            for (int64_t i1 = 0; i1 < ne[1] && i1 < n; i1++) {
+            for (int64_t i1 = 0; i1 < ne[1]; i1++) {
+                if (i1 == n && ne[1] > 2*n) {
+                    printf("                                       ..., \n");
+                    i1 = ne[1] - n;
+                }
                 printf("                                       [");
-                for (int64_t i0 = 0; i0 < ne[0] && i0 < n; i0++) {
+                for (int64_t i0 = 0; i0 < ne[0]; i0++) {
+                    if (i0 == n && ne[0] > 2*n) {
+                        printf("..., ");
+                        i0 = ne[0] - n;
+                    }
                     size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
                     float v;
                     if (type == GGML_TYPE_F16) {
@@ -51,17 +64,14 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne
                     } else {
                         GGML_ASSERT(false);
                     }
-                    printf("%8.4f", v);
+                    printf("%12.4f", v);
                     sum += v;
-                    if (i0 < ne[0] - 1 && i0 < n - 1) printf(", ");
+                    if (i0 < ne[0] - 1) printf(", ");
                 }
-                if (ne[0] > n) printf(", ...");
                 printf("],\n");
             }
-            if (ne[1] > n) printf("                                       ...\n");
             printf("                                      ],\n");
         }
-        if (ne[2] > n) printf("                                     ...\n");
         printf("                                     ]\n");
         printf("                                     sum = %f\n", sum);
     }

gguf-py/gguf/constants.py

@@ -126,6 +126,7 @@ class MODEL_ARCH(IntEnum):
     MAMBA      = auto()
     XVERSE     = auto()
     COMMAND_R  = auto()
+    DBRX       = auto()
 
 
 class MODEL_TENSOR(IntEnum):
@@ -195,6 +196,7 @@ class MODEL_TENSOR(IntEnum):
     MODEL_ARCH.MAMBA:          "mamba",
     MODEL_ARCH.XVERSE:         "xverse",
     MODEL_ARCH.COMMAND_R:      "command-r",
+    MODEL_ARCH.DBRX:           "dbrx",
 }
 
 TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@@ -642,6 +644,19 @@ class MODEL_TENSOR(IntEnum):
         MODEL_TENSOR.ATTN_K_NORM,
         MODEL_TENSOR.ATTN_Q_NORM,
     ],
+    MODEL_ARCH.DBRX: [
+        MODEL_TENSOR.TOKEN_EMBD,
+        MODEL_TENSOR.OUTPUT_NORM,
+        MODEL_TENSOR.OUTPUT,
+        MODEL_TENSOR.ATTN_NORM,
+        MODEL_TENSOR.ATTN_QKV,
+        MODEL_TENSOR.ATTN_OUT,
+        MODEL_TENSOR.ATTN_OUT_NORM,
+        MODEL_TENSOR.FFN_GATE_INP,
+        MODEL_TENSOR.FFN_GATE_EXP,
+        MODEL_TENSOR.FFN_DOWN_EXP,
+        MODEL_TENSOR.FFN_UP_EXP,
+    ],
     # TODO
 }
 

gguf-py/gguf/tensor_mapping.py

@@ -10,7 +10,7 @@ class TensorNameMap:
         # Token embeddings
         MODEL_TENSOR.TOKEN_EMBD: (
             "gpt_neox.embed_in",                         # gptneox
-            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen
+            "transformer.wte",                           # gpt2 gpt-j mpt refact qwen dbrx
             "transformer.word_embeddings",               # falcon
             "word_embeddings",                           # bloom
             "model.embed_tokens",                        # llama-hf
@@ -48,7 +48,7 @@ class TensorNameMap:
         # Output
         MODEL_TENSOR.OUTPUT: (
             "embed_out",                 # gptneox
-            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba
+            "lm_head",                   # gpt2 mpt falcon llama-hf baichuan qwen mamba dbrx
             "output",                    # llama-pth bloom internlm2
             "word_embeddings_for_head",  # persimmon
             "lm_head.linear",            # phi2
@@ -60,7 +60,7 @@ class TensorNameMap:
             "transformer.ln_f",                        # gpt2 gpt-j falcon
             "model.norm",                              # llama-hf baichuan internlm2
             "norm",                                    # llama-pth
-            "transformer.norm_f",                      # mpt
+            "transformer.norm_f",                      # mpt dbrx
             "ln_f",                                    # refact bloom qwen gpt2
             "language_model.encoder.final_layernorm",  # persimmon
             "model.final_layernorm",                   # persimmon
@@ -96,6 +96,7 @@ class TensorNameMap:
             "model.layers.{bid}.norm",                              # mamba-qbert
             "backbone.layers.{bid}.norm",                           # mamba
             "transformer.decoder_layer.{bid}.rms_norm",             # Grok
+            "transformer.blocks.{bid}.norm_attn_norm.norm_1",       # dbrx
         ),
 
         # Attention norm 2
@@ -108,6 +109,7 @@ class TensorNameMap:
             "gpt_neox.layers.{bid}.attention.query_key_value",                     # gptneox
             "transformer.h.{bid}.attn.c_attn",                                     # gpt2 qwen
             "transformer.blocks.{bid}.attn.Wqkv",                                  # mpt
+            "transformer.blocks.{bid}.norm_attn_norm.attn.Wqkv",                   # dbrx
             "transformer.h.{bid}.self_attention.query_key_value",                  # falcon
             "h.{bid}.self_attention.query_key_value",                              # bloom
             "language_model.encoder.layers.{bid}.self_attention.query_key_value",  # persimmon
@@ -152,30 +154,32 @@ class TensorNameMap:
 
         # Attention output
         MODEL_TENSOR.ATTN_OUT: (
-            "gpt_neox.layers.{bid}.attention.dense",                     # gptneox
-            "transformer.h.{bid}.attn.c_proj",                           # gpt2 refact qwen
-            "transformer.blocks.{bid}.attn.out_proj",                    # mpt
-            "transformer.h.{bid}.self_attention.dense",                  # falcon
-            "h.{bid}.self_attention.dense",                              # bloom
-            "model.layers.{bid}.self_attn.o_proj",                       # llama-hf
-            "layers.{bid}.attention.wo",                                 # llama-pth
-            "encoder.layer.{bid}.attention.output.dense",                # bert
-            "transformer.h.{bid}.attn.out_proj",                         # gpt-j
-            "language_model.encoder.layers.{bid}.self_attention.dense",  # persimmon
-            "model.layers.{bid}.self_attn.dense",                        # persimmon
-            "h.{bid}.attn.c_proj",                                       # gpt2
-            "transformer.h.{bid}.mixer.out_proj",                        # phi2
-            "model.layers.layers.{bid}.self_attn.o_proj",                # plamo
-            "model.layers.{bid}.attention.wo",                           # internlm2
-            "encoder.layers.{bid}.attn.out_proj",                        # nomic-bert
-            "transformer.decoder_layer.{bid}.multi_head_attention.linear"# Grok
+            "gpt_neox.layers.{bid}.attention.dense",                        # gptneox
+            "transformer.h.{bid}.attn.c_proj",                              # gpt2 refact qwen
+            "transformer.blocks.{bid}.attn.out_proj",                       # mpt
+            "transformer.h.{bid}.self_attention.dense",                     # falcon
+            "h.{bid}.self_attention.dense",                                 # bloom
+            "model.layers.{bid}.self_attn.o_proj",                          # llama-hf
+            "layers.{bid}.attention.wo",                                    # llama-pth
+            "encoder.layer.{bid}.attention.output.dense",                   # bert
+            "transformer.h.{bid}.attn.out_proj",                            # gpt-j
+            "language_model.encoder.layers.{bid}.self_attention.dense",     # persimmon
+            "model.layers.{bid}.self_attn.dense",                           # persimmon
+            "h.{bid}.attn.c_proj",                                          # gpt2
+            "transformer.h.{bid}.mixer.out_proj",                           # phi2
+            "model.layers.layers.{bid}.self_attn.o_proj",                   # plamo
+            "model.layers.{bid}.attention.wo",                              # internlm2
+            "encoder.layers.{bid}.attn.out_proj",                           # nomic-bert
+            "transformer.decoder_layer.{bid}.multi_head_attention.linear",  # Grok
+            "transformer.blocks.{bid}.norm_attn_norm.attn.out_proj",        # dbrx
         ),
 
         # Attention output norm
         MODEL_TENSOR.ATTN_OUT_NORM: (
             "encoder.layer.{bid}.attention.output.LayerNorm",  # bert
             "encoder.layers.{bid}.norm1",                      # nomic-bert
             "transformer.decoder_layer.{bid}.rms_norm_1",      # Grok
+            "transformer.blocks.{bid}.norm_attn_norm.norm_2",  # dbrx
         ),
 
         # Rotary embeddings
@@ -202,9 +206,10 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_GATE_INP: (
-            "layers.{bid}.feed_forward.gate",           # mixtral
-            "model.layers.{bid}.block_sparse_moe.gate", # mixtral
-            "transformer.decoder_layer.{bid}.router"    # Grok
+            "layers.{bid}.feed_forward.gate",             # mixtral
+            "model.layers.{bid}.block_sparse_moe.gate",   # mixtral
+            "transformer.decoder_layer.{bid}.router",     # Grok
+            "transformer.blocks.{bid}.ffn.router.layer",  # dbrx
         ),
 
         # Feed-forward up
@@ -233,6 +238,7 @@ class TensorNameMap:
         MODEL_TENSOR.FFN_UP_EXP: (
             "layers.{bid}.feed_forward.experts.w3",                 # mixtral (merged)
             "transformer.decoder_layer.{bid}.moe.linear_v",         # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.v1",          # dbrx
         ),
 
         # AWQ-activation gate
@@ -251,8 +257,9 @@ class TensorNameMap:
         ),
 
         MODEL_TENSOR.FFN_GATE_EXP: (
-            "layers.{bid}.feed_forward.experts.w1",                 # mixtral (merged)
-            "transformer.decoder_layer.{bid}.moe.linear"            # Grok (merged)
+            "layers.{bid}.feed_forward.experts.w1",         # mixtral (merged)
+            "transformer.decoder_layer.{bid}.moe.linear",   # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w1",  # dbrx
         ),
 
         # Feed-forward down
@@ -280,6 +287,7 @@ class TensorNameMap:
         MODEL_TENSOR.FFN_DOWN_EXP: (
             "layers.{bid}.feed_forward.experts.w2",                 # mixtral (merged)
             "transformer.decoder_layer.{bid}.moe.linear_1",         # Grok (merged)
+            "transformer.blocks.{bid}.ffn.experts.mlp.w2",          # dbrx
         ),
 
         MODEL_TENSOR.ATTN_Q_NORM: (