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@ -1395,14 +1395,6 @@ static void common_chat_parse_seed_oss(common_chat_msg_parser & builder) {
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builder.consume_reasoning_with_xml_tool_calls(form, "<seed:think>", "</seed:think>");
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}
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static void common_chat_parse_solar_open(common_chat_msg_parser & builder) {
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builder.try_parse_reasoning("<|think|>", "<|end|><|begin|>assistant<|content|>");
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// TODO: Tool calling
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builder.add_content(builder.consume_rest());
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}
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static void common_chat_parse_content_only(common_chat_msg_parser & builder) {
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builder.try_parse_reasoning("<think>", "</think>");
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builder.add_content(builder.consume_rest());
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@ -1487,9 +1479,6 @@ static void common_chat_parse(common_chat_msg_parser & builder) {
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case COMMON_CHAT_FORMAT_XIAOMI_MIMO:
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common_chat_parse_xiaomi_mimo(builder);
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break;
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case COMMON_CHAT_FORMAT_SOLAR_OPEN:
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common_chat_parse_solar_open(builder);
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break;
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default:
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throw std::runtime_error(std::string("Unsupported format: ") + common_chat_format_name(builder.syntax().format));
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}
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@ -380,8 +380,8 @@ std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const json & too
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const auto & function = tool.at("function");
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result.push_back({
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/* .name = */ function.at("name"),
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/* .description = */ function.value("description", ""),
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/* .parameters = */ function.value("parameters", json::object()).dump(),
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/* .description = */ function.at("description"),
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/* .parameters = */ function.at("parameters").dump(),
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});
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}
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}
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@ -669,7 +669,6 @@ const char * common_chat_format_name(common_chat_format format) {
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case COMMON_CHAT_FORMAT_QWEN3_CODER_XML: return "Qwen3 Coder";
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case COMMON_CHAT_FORMAT_APRIEL_1_5: return "Apriel 1.5";
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case COMMON_CHAT_FORMAT_XIAOMI_MIMO: return "Xiaomi MiMo";
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case COMMON_CHAT_FORMAT_SOLAR_OPEN: return "Solar Open";
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case COMMON_CHAT_FORMAT_PEG_SIMPLE: return "peg-simple";
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case COMMON_CHAT_FORMAT_PEG_NATIVE: return "peg-native";
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case COMMON_CHAT_FORMAT_PEG_CONSTRUCTED: return "peg-constructed";
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@ -2518,27 +2517,6 @@ static common_chat_params common_chat_params_init_granite(const common_chat_temp
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return data;
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}
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static common_chat_params common_chat_params_init_solar_open(const common_chat_template & tmpl, const struct templates_params & inputs) {
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common_chat_params data;
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// TODO: Reasoning effort
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json additional_context = {};
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data.prompt = apply(tmpl, inputs, std::nullopt, std::nullopt, additional_context);
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data.format = COMMON_CHAT_FORMAT_SOLAR_OPEN;
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data.preserved_tokens = {
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"<|think|>",
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"<|content|>",
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"<|begin|>",
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"<|end|>",
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};
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// TODO: Tool calling
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return data;
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}
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static common_chat_params common_chat_params_init_without_tools(const common_chat_template & tmpl, const struct templates_params & inputs) {
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common_chat_params data;
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data.prompt = apply(tmpl, inputs);
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@ -2802,13 +2780,6 @@ static common_chat_params common_chat_templates_apply_jinja(
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return common_chat_params_init_magistral(tmpl, params);
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}
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// Solar Open
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if (src.find("<|tool_response:begin|>") != std::string::npos &&
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src.find("<|tool_response:name|>") != std::string::npos &&
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src.find("<|tool_response:result|>") != std::string::npos) {
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return common_chat_params_init_solar_open(tmpl, params);
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}
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// Plain handler (no tools)
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if (params.tools.is_null() || inputs.tool_choice == COMMON_CHAT_TOOL_CHOICE_NONE) {
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return common_chat_params_init_without_tools(tmpl, params);
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@ -124,7 +124,6 @@ enum common_chat_format {
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COMMON_CHAT_FORMAT_QWEN3_CODER_XML,
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COMMON_CHAT_FORMAT_APRIEL_1_5,
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COMMON_CHAT_FORMAT_XIAOMI_MIMO,
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COMMON_CHAT_FORMAT_SOLAR_OPEN,
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// These are intended to be parsed by the PEG parser
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COMMON_CHAT_FORMAT_PEG_SIMPLE,
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@ -1062,9 +1062,6 @@ class TextModel(ModelBase):
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if chkhsh == "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273":
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# ref: https://huggingface.co/alvarobartt/grok-2-tokenizer
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res = "grok-2"
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if chkhsh == "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df":
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# ref: https://huggingface.co/aari1995/German_Semantic_V3
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res = "jina-v2-de"
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if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5":
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# ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B
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res = "llama-bpe"
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@ -1233,12 +1230,6 @@ class TextModel(ModelBase):
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if chkhsh == "4a2e2abae11ca2b86d570fc5b44be4d5eb5e72cc8f22dd136a94b37da83ab665":
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# ref: https://huggingface.co/KORMo-Team/KORMo-tokenizer
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res = "kormo"
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if chkhsh == "9d70134b369a70e5735009b6de918f7581b5211f7c074d1f89f753aea8248af1":
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# ref: https://huggingface.co/tencent/Youtu-LLM-2B
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res = "youtu"
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if chkhsh == "16389f0a1f51ee53e562ffd51c371dc508639ab0e4261502071836e50e223e91":
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# ref: https://huggingface.co/upstage/Solar-Open-100B
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res = "solar-open"
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if res is None:
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logger.warning("\n")
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@ -2495,7 +2486,6 @@ class StableLMModel(TextModel):
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"VLlama3ForCausalLM",
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"LlavaForConditionalGeneration",
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"VoxtralForConditionalGeneration",
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"IQuestCoderForCausalLM",
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"LlamaModel")
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class LlamaModel(TextModel):
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model_arch = gguf.MODEL_ARCH.LLAMA
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@ -3513,7 +3503,7 @@ class QwenModel(TextModel):
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self._set_vocab_qwen()
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@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration", "KORMoForCausalLM", "AudioFlamingo3ForConditionalGeneration")
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@ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration", "KORMoForCausalLM")
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class Qwen2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.QWEN2
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@ -5294,14 +5284,13 @@ class BertModel(TextModel):
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self.gguf_writer.add_token_type_count(self.hparams.get("type_vocab_size", 1))
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# convert to phantom space vocab
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def phantom(tok, toktype):
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if toktype == gguf.TokenType.CONTROL:
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def phantom(tok):
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if tok.startswith("[") and tok.endswith("]"):
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return tok
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if tok.startswith("##"):
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return tok[2:]
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return "\u2581" + tok
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assert len(tokens) == len(toktypes)
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tokens = list(map(phantom, tokens, toktypes))
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tokens = list(map(phantom, tokens))
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# add vocab to gguf
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self.gguf_writer.add_tokenizer_model("bert")
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@ -6415,17 +6404,6 @@ class ARwkv7Model(Rwkv7Model):
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self.gguf_writer.add_head_count(0)
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@ModelBase.register("MaincoderForCausalLM")
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class MaincoderModel(TextModel):
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model_arch = gguf.MODEL_ARCH.MAINCODER
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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if (head_dim := self.hparams.get("head_dim")) is not None:
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self.gguf_writer.add_rope_dimension_count(head_dim)
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@ModelBase.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
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class MambaModel(TextModel):
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model_arch = gguf.MODEL_ARCH.MAMBA
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@ -7203,7 +7181,6 @@ class DeepseekModel(TextModel):
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"DeepseekV2ForCausalLM",
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"DeepseekV3ForCausalLM",
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"KimiVLForConditionalGeneration",
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"YoutuForCausalLM",
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)
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class DeepseekV2Model(TextModel):
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model_arch = gguf.MODEL_ARCH.DEEPSEEK2
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@ -7270,15 +7247,7 @@ class DeepseekV2Model(TextModel):
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super().set_gguf_parameters()
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hparams = self.hparams
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# first_k_dense_replace: number of leading layers using dense FFN instead of MoE
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# For non-MoE models (like Youtu), set to n_layer to use dense FFN for all layers
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# For MoE models (like DeepSeek-V2), this is the number of leading non-MoE layers
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has_moe = hparams.get("n_routed_experts") is not None
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first_k_dense_replace = hparams.get("first_k_dense_replace")
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if first_k_dense_replace is None:
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# Default: if no MoE, all layers are dense; if MoE, none are dense
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first_k_dense_replace = hparams["num_hidden_layers"] if not has_moe else 0
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self.gguf_writer.add_leading_dense_block_count(first_k_dense_replace)
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self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"])
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self.gguf_writer.add_vocab_size(hparams["vocab_size"])
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if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None:
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self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"])
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@ -7290,24 +7259,11 @@ class DeepseekV2Model(TextModel):
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self.gguf_writer.add_key_length_mla(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"])
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self.gguf_writer.add_value_length_mla(hparams["v_head_dim"])
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# MoE parameters (required by C++ code for DEEPSEEK2 arch)
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# For non-MoE models like Youtu, use intermediate_size as expert_feed_forward_length
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moe_intermediate_size = self.find_hparam(["moe_intermediate_size", "intermediate_size"], optional=False)
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self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
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if (n_routed_experts := hparams.get("n_routed_experts")) is not None:
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self.gguf_writer.add_expert_count(n_routed_experts)
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# expert_shared_count is required by C++ code, default to 0 for non-MoE models
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n_shared_experts = hparams.get("n_shared_experts", 0)
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self.gguf_writer.add_expert_shared_count(n_shared_experts)
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# When not set, C++ code will use scale_w = false to skip the no-op scaling
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if (routed_scaling_factor := hparams.get("routed_scaling_factor")) is not None:
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self.gguf_writer.add_expert_weights_scale(routed_scaling_factor)
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if (norm_topk_prob := hparams.get("norm_topk_prob")) is not None and norm_topk_prob:
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self.gguf_writer.add_expert_weights_norm(norm_topk_prob)
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self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"])
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self.gguf_writer.add_expert_count(hparams["n_routed_experts"])
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self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"])
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self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"])
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self.gguf_writer.add_expert_weights_norm(hparams["norm_topk_prob"])
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self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"])
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@ -7323,17 +7279,10 @@ class DeepseekV2Model(TextModel):
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# skip vision tensors and remove "language_model." for Kimi-VL
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if "vision_tower" in name or "multi_modal_projector" in name:
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return []
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if name.startswith("siglip2.") or name.startswith("merger."):
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return []
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if name.startswith("language_model."):
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name = name.replace("language_model.", "")
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# skip lm_head.weight if tie_word_embeddings is True
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if self.hparams.get("tie_word_embeddings", False):
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if name == "lm_head.weight" or name == "model.lm_head.weight":
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logger.info("Skipping tied output layer 'lm_head.weight' (will use token_embd.weight)")
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return []
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# rename e_score_correction_bias tensors
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if name.endswith("e_score_correction_bias"):
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name = name.replace("e_score_correction_bias", "e_score_correction.bias")
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@ -9343,19 +9292,6 @@ class VoxtralWhisperEncoderModel(WhisperEncoderModel):
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self.gguf_writer.add_audio_stack_factor(4) # == intermediate_size // hidden_size
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@ModelBase.register("AudioFlamingo3ForConditionalGeneration")
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class AudioFlamingo3WhisperEncoderModel(WhisperEncoderModel):
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.MUSIC_FLAMINGO)
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def tensor_force_quant(self, name, new_name, bid, n_dims):
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if ".conv" in name and ".weight" in name:
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# Was trained in BF16, being safe, avoiding quantizing to FP16
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return gguf.GGMLQuantizationType.F32
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return super().tensor_force_quant(name, new_name, bid, n_dims)
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@ModelBase.register("FalconH1ForCausalLM")
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class FalconH1Model(Mamba2Model):
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model_arch = gguf.MODEL_ARCH.FALCON_H1
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@ -10668,79 +10604,6 @@ class JanusProVisionModel(MmprojModel):
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return []
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@ModelBase.register("YOUTUVLForConditionalGeneration", "YOUTUVLForCausalLM")
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class YOUTUVLVisionModel(MmprojModel):
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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assert self.hparams_vision is not None
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self.hparams_vision["image_size"] = self.hparams_vision.get("image_size", 560)
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def set_gguf_parameters(self):
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super().set_gguf_parameters()
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self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.YOUTUVL)
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self.gguf_writer.add_vision_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-6))
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# Handle activation function
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hidden_act = str(self.hparams.get("hidden_act", "gelu_pytorch_tanh")).lower()
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if hidden_act in ("gelu", "gelu_pytorch_tanh", "gelu_fast", "gelu_new", "gelu_accurate"):
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self.gguf_writer.add_vision_use_gelu(True)
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elif hidden_act == "silu":
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self.gguf_writer.add_vision_use_silu(True)
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else:
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raise ValueError(f"Unsupported activation function for YOUTUVL: {hidden_act}")
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self.gguf_writer.add_vision_spatial_merge_size(self.hparams.get("spatial_merge_size", 2))
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window_size = self.hparams.get("window_size")
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if window_size is not None:
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self.gguf_writer.add_vision_window_size(window_size)
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# fullatt_block_indexes contains explicit layer indices that use full attention
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# e.g., [2, 5, 8, 11] means layers 2, 5, 8, 11 use full attention
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# All other layers use window attention
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fullatt_block_indexes = self.hparams.get("fullatt_block_indexes")
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assert fullatt_block_indexes is not None, "fullatt_block_indexes is required for youtuvl"
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# Store the explicit layer indices for YoutuVL (irregular pattern approach)
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self.gguf_writer.add_vision_wa_layer_indexes(layers=fullatt_block_indexes)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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# Skip language model tensors
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skip_prefixes = ('lm_head.', 'model.layers.', 'model.embed_tokens.', 'model.norm.')
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if name.startswith(skip_prefixes):
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return []
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# Try to map the tensor using TensorNameMap (handles vision encoder and projector)
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try:
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new_name = self.map_tensor_name(name)
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return [(new_name, data_torch)]
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except ValueError:
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# If mapping fails, log warning and skip
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logger.warning(f"Cannot map tensor: {name}")
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return []
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@ModelBase.register("SolarOpenForCausalLM")
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class SolarOpenModel(Glm4MoeModel):
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model_arch = gguf.MODEL_ARCH.GLM4_MOE
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def set_vocab(self):
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from transformers import AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
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special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True)
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tokens, toktypes, tokpre = self.get_vocab_base()
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self.gguf_writer.add_tokenizer_model("gpt2")
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self.gguf_writer.add_tokenizer_pre(tokpre)
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"])
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special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|endoftext|>"])
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special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<unk>"])
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special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|startoftext|>"])
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special_vocab.add_to_gguf(self.gguf_writer)
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###### CONVERSION LOGIC ######
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|
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@ -145,8 +145,6 @@ models = [
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{"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
|
||||
{"name": "minimax-m2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },
|
||||
{"name": "kormo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/KORMo-Team/KORMo-tokenizer", },
|
||||
{"name": "youtu", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
|
||||
{"name": "solar-open", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
|
||||
]
|
||||
|
||||
# some models are known to be broken upstream, so we will skip them as exceptions
|
||||
|
|
@ -167,8 +165,6 @@ pre_computed_hashes = [
|
|||
{"name": "kimi-k2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base", "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
|
||||
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
|
||||
{"name": "grok-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
|
||||
# jina-v2-de variants
|
||||
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
|
||||
]
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -32,7 +32,7 @@ Legend:
|
|||
| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
|
||||
| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ |
|
||||
| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ |
|
||||
| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
|
||||
| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ |
|
||||
| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ |
|
||||
| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
|
||||
| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ |
|
||||
|
|
|
|||
|
|
@ -965,7 +965,6 @@
|
|||
"Metal","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,1,2560],ne_kernel=[3,3,1,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
|
||||
"Metal","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[12,12,2,2560],ne_kernel=[3,3,2,2560],s0=1,s1=1,p0=1,p1=1,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
|
||||
"Metal","IM2COL","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[5,5,1,32],ne_kernel=[3,4,1,32],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
|
||||
"Metal","IM2COL","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[2,2,1536,729],ne_kernel=[2,2,1536,4096],s0=1,s1=1,p0=0,p1=0,d0=1,d1=1,is_2D=1","support","1","yes","Metal"
|
||||
"Metal","IM2COL_3D","type_input=f32,type_kernel=f32,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","Metal"
|
||||
"Metal","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f32,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","Metal"
|
||||
"Metal","IM2COL_3D","type_input=f32,type_kernel=f16,dst_type=f16,ne_input=[10,10,10,9],ne_kernel=[3,3,3,1],IC=3,s0=1,s1=1,s2=1,p0=1,p1=1,p2=1,d0=1,d1=1,d2=1,v=0","support","0","no","Metal"
|
||||
|
|
@ -4965,9 +4964,8 @@
|
|||
"Metal","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","Metal"
|
||||
"Metal","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","1","yes","Metal"
|
||||
"Metal","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","1","yes","Metal"
|
||||
"Metal","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","1","yes","Metal"
|
||||
"Metal","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","Metal"
|
||||
"Metal","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","Metal"
|
||||
"Metal","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","0","no","Metal"
|
||||
"Metal","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","0","no","Metal"
|
||||
"Metal","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","Metal"
|
||||
"Metal","ARGMAX","type=f32,ne=[32,513,1,1]","support","1","yes","Metal"
|
||||
"Metal","ARGMAX","type=f32,ne=[100,10,1,1]","support","1","yes","Metal"
|
||||
|
|
@ -5717,15 +5715,15 @@
|
|||
"Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal"
|
||||
"Metal","RMS_NORM","type=f32,ne=[64,5,4,3],v=0,eps=0.000001,inplace=1","support","1","yes","Metal"
|
||||
"Metal","L2_NORM","type=f32,ne=[64,5,4,3]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[3,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[6,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[3,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[3,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[6,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[3,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[3,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[6,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[3,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[3,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[8,1536,1,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,1536,4,1],ne_b=[3,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[3,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[8,1024,1,1],ne_b=[4,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,1024,4,1],ne_b=[4,1024,1,1]","support","1","yes","Metal"
|
||||
|
|
@ -5735,15 +5733,6 @@
|
|||
"Metal","SSM_CONV","type=f32,ne_a=[4,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[8,2048,1,1],ne_b=[4,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[4,2048,4,1],ne_b=[4,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[9,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[18,1024,1,1],ne_b=[9,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[9,1024,4,1],ne_b=[9,1024,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[9,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[18,1536,1,1],ne_b=[9,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[9,1536,4,1],ne_b=[9,1536,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[9,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[18,2048,1,1],ne_b=[9,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_CONV","type=f32,ne_a=[9,2048,4,1],ne_b=[9,2048,1,1]","support","1","yes","Metal"
|
||||
"Metal","SSM_SCAN","type=f32,d_state=16,head_dim=1,n_head=1024,n_group=1,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal"
|
||||
"Metal","SSM_SCAN","type=f32,d_state=128,head_dim=64,n_head=16,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal"
|
||||
"Metal","SSM_SCAN","type=f32,d_state=256,head_dim=64,n_head=8,n_group=2,n_seq_tokens=32,n_seqs=4","support","1","yes","Metal"
|
||||
|
|
@ -8927,8 +8916,6 @@
|
|||
"Metal","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=0,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=0.000000,inplace=0","support","1","yes","Metal"
|
||||
"Metal","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
|
||||
"Metal","SOFT_MAX","type=f32,ne=[32,2,32,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
|
||||
"Metal","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f32,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
|
||||
"Metal","SOFT_MAX","type=f32,ne=[200001,2,3,1],mask=1,sinks=1,m_prec=f16,nr23=[1,1],scale=0.100000,max_bias=8.000000,inplace=0","support","1","yes","Metal"
|
||||
"Metal","SOFT_MAX_BACK","type=f32,ne=[16,16,1,1],scale=1.000000,max_bias=0.000000","support","0","no","Metal"
|
||||
"Metal","SOFT_MAX_BACK","type=f32,ne=[15,15,1,1],scale=1.000000,max_bias=0.000000","support","0","no","Metal"
|
||||
"Metal","SOFT_MAX_BACK","type=f32,ne=[16,16,2,3],scale=1.000000,max_bias=0.000000","support","0","no","Metal"
|
||||
|
|
@ -9555,311 +9542,311 @@
|
|||
"Metal","ARGSORT","type=f32,ne=[2048,2,1,3],order=1","support","1","yes","Metal"
|
||||
"Metal","ARGSORT","type=f32,ne=[2049,2,1,3],order=1","support","1","yes","Metal"
|
||||
"Metal","ARGSORT","type=f32,ne=[2,8,8192,1],order=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[12,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=100,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=500,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=1023,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=9999,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=1,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=2,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=3,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[12,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[13,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[15,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[19,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[27,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[43,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[64,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[75,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[128,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[139,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[256,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[267,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[512,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[523,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1035,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2059,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4096,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[4107,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8192,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[8203,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16395,1,2,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32768,1,1,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[32779,1,2,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65536,1,1,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[65547,1,2,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131072,1,1,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[131083,1,2,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262144,1,1,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[262155,1,2,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=100","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=500","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=1023","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524288,1,1,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[524299,1,2,1],k=9999","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=1","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=2","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=3","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=7","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16,10,10,10],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[60,10,10,10],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1023,2,1,3],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1024,2,1,3],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[1025,2,1,3],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[16384,1,1,1],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2047,2,1,3],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2048,2,1,3],k=15","support","1","yes","Metal"
|
||||
"Metal","TOP_K","type=f32,ne=[2049,2,1,3],k=15","support","1","yes","Metal"
|
||||
"Metal","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","Metal"
|
||||
"Metal","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","Metal"
|
||||
"Metal","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","Metal"
|
||||
|
|
@ -9904,9 +9891,8 @@
|
|||
"Metal","GROUP_NORM","type=f32,ne=[64,64,320,1],num_groups=32,eps=0.000001","support","1","yes","Metal"
|
||||
"Metal","GROUP_NORM","type=f32,ne=[9,9,1280,1],num_groups=32,eps=0.000001","support","1","yes","Metal"
|
||||
"Metal","ACC","type=f32,ne_a=[256,17,1,1],ne_b=[256,16,1,1]","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1,circular=0","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[33,17,2,1],pad_0=4,pad_1=3,circular=1","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0,circular=0","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],pad_0=1,pad_1=1","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,3,1],lp0=1,rp0=1,lp1=1,rp1=1,lp2=1,rp2=1,lp3=1,rp3=1,v=0","support","0","no","Metal"
|
||||
"Metal","PAD_REFLECT_1D","type=f32,ne_a=[512,34,2,1],pad_0=10,pad_1=9","support","1","yes","Metal"
|
||||
"Metal","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","Metal"
|
||||
"Metal","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","0","no","Metal"
|
||||
|
|
@ -9937,41 +9923,17 @@
|
|||
"Metal","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","1","yes","Metal"
|
||||
"Metal","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","1","yes","Metal"
|
||||
"Metal","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","1","yes","Metal"
|
||||
"Metal","DIAG","type=f32,ne=[10,1,4,3]","support","0","no","Metal"
|
||||
"Metal","DIAG","type=f32,ne=[79,1,19,13]","support","0","no","Metal"
|
||||
"Metal","DIAG","type=f32,ne=[256,1,8,16]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[30,30,7,1],ne_rhs=[8,30,7,1]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[64,64,2,2]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[79,79,5,3],ne_rhs=[417,79,5,3]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,2],ne_rhs=[32,128,4,2]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[80,80,2,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[79,80,2,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,2,8],ne_rhs=[81,80,2,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[80,80,8,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[79,80,8,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[80,80,8,8],ne_rhs=[81,80,8,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[84,84,4,4],ne_rhs=[32,84,4,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[95,95,8,8],ne_rhs=[40,95,8,8]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[32,128,4,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,3,4],ne_rhs=[32,128,3,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,1],ne_rhs=[32,128,4,1]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[200,64,4,4]","support","0","no","Metal"
|
||||
"Metal","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[384,64,4,4]","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=0","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=0","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0,circular=1","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0,circular=1","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=0","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=0","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1,circular=1","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1,circular=1","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","0","no","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","1","yes","Metal"
|
||||
"Metal","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=1","support","0","no","Metal"
|
||||
"Metal","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","1","yes","Metal"
|
||||
"Metal","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","1","yes","Metal"
|
||||
"Metal","FLASH_ATTN_EXT","hsk=40,hsv=40,nh=4,nr23=[1,1],kv=113,nb=1,mask=1,sinks=1,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","1","yes","Metal"
|
||||
|
|
|
|||
|
Can't render this file because it is too large.
|
|
|
@ -4,7 +4,7 @@ project("ggml" C CXX ASM)
|
|||
### GGML Version
|
||||
set(GGML_VERSION_MAJOR 0)
|
||||
set(GGML_VERSION_MINOR 9)
|
||||
set(GGML_VERSION_PATCH 5)
|
||||
set(GGML_VERSION_PATCH 4)
|
||||
set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")
|
||||
|
||||
find_program(GIT_EXE NAMES git git.exe NO_CMAKE_FIND_ROOT_PATH)
|
||||
|
|
|
|||
|
|
@ -358,7 +358,7 @@ extern "C" {
|
|||
typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
|
||||
|
||||
// Compare the output of two backends
|
||||
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data, struct ggml_tensor const * const * test_nodes, size_t num_test_nodes);
|
||||
GGML_API bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data, struct ggml_tensor * test_node);
|
||||
|
||||
// Tensor initialization
|
||||
GGML_API enum ggml_status ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
|
||||
|
|
|
|||
|
|
@ -2053,7 +2053,7 @@ void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy) {
|
|||
ggml_free(copy.ctx_unallocated);
|
||||
}
|
||||
|
||||
bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data, struct ggml_tensor const * const * test_nodes, size_t num_test_nodes) {
|
||||
bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data, struct ggml_tensor * test_node) {
|
||||
struct ggml_backend_graph_copy copy = ggml_backend_graph_copy(backend2, graph);
|
||||
if (copy.buffer == NULL) {
|
||||
return false;
|
||||
|
|
@ -2064,22 +2064,22 @@ bool ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t
|
|||
|
||||
assert(g1->n_nodes == g2->n_nodes);
|
||||
|
||||
if (num_test_nodes != 0) {
|
||||
GGML_ASSERT(test_nodes);
|
||||
// Compute the whole graph and only test the output for specific tensors
|
||||
if (test_node != nullptr) {
|
||||
// Compute the whole graph and only test the output for a specific tensor
|
||||
ggml_backend_graph_compute(backend1, g1);
|
||||
ggml_backend_graph_compute(backend2, g2);
|
||||
|
||||
bool verified = false;
|
||||
int test_node_idx = -1;
|
||||
for (int i = 0; i < g1->n_nodes; i++) {
|
||||
for (size_t j = 0; j < num_test_nodes; ++j) {
|
||||
if (g1->nodes[i] == test_nodes[j]) {
|
||||
callback(i, g1->nodes[i], g2->nodes[i], user_data);
|
||||
verified = true;
|
||||
}
|
||||
struct ggml_tensor * t1 = g1->nodes[i];
|
||||
if (t1 == test_node) {
|
||||
test_node_idx = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
GGML_ASSERT(verified);
|
||||
GGML_ASSERT(test_node_idx != -1);
|
||||
|
||||
callback(test_node_idx, g1->nodes[test_node_idx], g2->nodes[test_node_idx], user_data);
|
||||
} else {
|
||||
for (int i = 0; i < g1->n_nodes; i++) {
|
||||
struct ggml_tensor * t1 = g1->nodes[i];
|
||||
|
|
|
|||
|
|
@ -29,8 +29,8 @@ static void argsort_f32_i32_cuda_cub(ggml_cuda_pool & pool,
|
|||
const int nrows,
|
||||
ggml_sort_order order,
|
||||
cudaStream_t stream) {
|
||||
ggml_cuda_pool_alloc<int> temp_indices_alloc(pool, ((size_t) ncols) * nrows);
|
||||
ggml_cuda_pool_alloc<float> temp_keys_alloc(pool, ((size_t) ncols) * nrows);
|
||||
ggml_cuda_pool_alloc<int> temp_indices_alloc(pool, ncols * nrows);
|
||||
ggml_cuda_pool_alloc<float> temp_keys_alloc(pool, ncols * nrows);
|
||||
ggml_cuda_pool_alloc<int> offsets_alloc(pool, nrows + 1);
|
||||
|
||||
int * temp_indices = temp_indices_alloc.get();
|
||||
|
|
|
|||
|
|
@ -12,11 +12,11 @@ const int CUDA_CPY_BLOCK_NM = 8; // block size of 3rd dimension if available
|
|||
const int CUDA_CPY_BLOCK_ROWS = 8; // block dimension for marching through rows
|
||||
|
||||
template <cpy_kernel_t cpy_1>
|
||||
static __global__ void cpy_scalar(const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11,
|
||||
const int64_t nb12, const int64_t nb13) {
|
||||
const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
|
||||
static __global__ void cpy_scalar(const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
|
||||
const int nb12, const int nb13) {
|
||||
const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
if (i >= ne) {
|
||||
return;
|
||||
|
|
@ -40,10 +40,10 @@ static __global__ void cpy_scalar(const char * cx, char * cdst, const int64_t ne
|
|||
}
|
||||
|
||||
template <typename T>
|
||||
static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11,
|
||||
const int64_t nb12, const int64_t nb13) {
|
||||
static __global__ void cpy_scalar_transpose(const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
|
||||
const int nb12, const int nb13) {
|
||||
|
||||
const T* src = reinterpret_cast<const T*>(cx);
|
||||
T* dst = reinterpret_cast<T*>(cdst);
|
||||
|
|
@ -117,60 +117,60 @@ static __device__ void cpy_blck_q_f32(const char * cxi, char * cdsti) {
|
|||
}
|
||||
|
||||
template <cpy_kernel_t cpy_blck, int qk>
|
||||
static __global__ void cpy_f32_q(const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11,
|
||||
const int64_t nb12, const int64_t nb13) {
|
||||
const int64_t i = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*qk;
|
||||
static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
|
||||
const int nb12, const int nb13) {
|
||||
const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
|
||||
|
||||
if (i >= ne) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t i03 = i/(ne00 * ne01 * ne02);
|
||||
const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
|
||||
const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
|
||||
const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
|
||||
const int64_t x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
|
||||
const int i03 = i/(ne00 * ne01 * ne02);
|
||||
const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
|
||||
const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
|
||||
const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
|
||||
const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
|
||||
|
||||
const int64_t i13 = i/(ne10 * ne11 * ne12);
|
||||
const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
|
||||
const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
|
||||
const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
|
||||
const int64_t dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
|
||||
const int i13 = i/(ne10 * ne11 * ne12);
|
||||
const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
|
||||
const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
|
||||
const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
|
||||
const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
|
||||
|
||||
cpy_blck(cx + x_offset, cdst + dst_offset);
|
||||
}
|
||||
|
||||
template <cpy_kernel_t cpy_blck, int qk>
|
||||
static __global__ void cpy_q_f32(const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11,
|
||||
const int64_t nb12, const int64_t nb13) {
|
||||
const int64_t i = ((int64_t)blockDim.x*blockIdx.x + threadIdx.x)*qk;
|
||||
static __global__ void cpy_q_f32(const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
|
||||
const int nb12, const int nb13) {
|
||||
const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk;
|
||||
|
||||
if (i >= ne) {
|
||||
return;
|
||||
}
|
||||
|
||||
const int64_t i03 = i/(ne00 * ne01 * ne02);
|
||||
const int64_t i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
|
||||
const int64_t i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
|
||||
const int64_t i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
|
||||
const int64_t x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
|
||||
const int i03 = i/(ne00 * ne01 * ne02);
|
||||
const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01);
|
||||
const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00;
|
||||
const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00;
|
||||
const int x_offset = (i00/qk)*nb00 + i01*nb01 + i02*nb02 + i03 * nb03;
|
||||
|
||||
const int64_t i13 = i/(ne10 * ne11 * ne12);
|
||||
const int64_t i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
|
||||
const int64_t i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
|
||||
const int64_t i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
|
||||
const int64_t dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
|
||||
const int i13 = i/(ne10 * ne11 * ne12);
|
||||
const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11);
|
||||
const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10;
|
||||
const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10;
|
||||
const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13*nb13;
|
||||
|
||||
cpy_blck(cx + x_offset, cdst + dst_offset);
|
||||
}
|
||||
|
||||
template<typename src_t, typename dst_t>
|
||||
static __global__ void cpy_scalar_contiguous(const char * cx, char * cdst, const int64_t ne) {
|
||||
const int64_t i = (int64_t)blockDim.x*blockIdx.x + threadIdx.x;
|
||||
const int64_t i = blockDim.x*blockIdx.x + threadIdx.x;
|
||||
|
||||
if (i >= ne) {
|
||||
return;
|
||||
|
|
@ -188,20 +188,19 @@ static void ggml_cpy_scalar_contiguous_cuda(
|
|||
cudaStream_t stream) {
|
||||
|
||||
const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
cpy_scalar_contiguous<src_t, dst_t><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
||||
(cx, cdst, ne);
|
||||
}
|
||||
|
||||
template<typename src_t, typename dst_t, bool transposed = false>
|
||||
static void ggml_cpy_scalar_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
if (transposed) {
|
||||
GGML_ASSERT(ne == ne00*ne01*ne02); // ne[3] is 1 assumed
|
||||
int64_t ne00n, ne01n, ne02n;
|
||||
int ne00n, ne01n, ne02n;
|
||||
if (nb00 <= nb02) { // most likely safe to handle nb00 = nb02 case here
|
||||
ne00n = ne00;
|
||||
ne01n = ne01;
|
||||
|
|
@ -212,159 +211,143 @@ static void ggml_cpy_scalar_cuda(
|
|||
ne02n = 1;
|
||||
}
|
||||
|
||||
int64_t grid_x = (ne01n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D;
|
||||
int64_t grid_y = (ne00n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D;
|
||||
int64_t grid_z = (ne/(ne01n*ne00n) + CUDA_CPY_BLOCK_NM - 1) / CUDA_CPY_BLOCK_NM;
|
||||
GGML_ASSERT(grid_x < UINT_MAX);
|
||||
GGML_ASSERT(grid_y < USHRT_MAX);
|
||||
GGML_ASSERT(grid_z < USHRT_MAX);
|
||||
dim3 dimGrid(grid_x, grid_y, grid_z);
|
||||
dim3 dimGrid( (ne01n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D,
|
||||
(ne00n + CUDA_CPY_TILE_DIM_2D - 1) / CUDA_CPY_TILE_DIM_2D,
|
||||
(ne/(ne01n*ne00n) + CUDA_CPY_BLOCK_NM - 1) / CUDA_CPY_BLOCK_NM);
|
||||
dim3 dimBlock(CUDA_CPY_TILE_DIM_2D, CUDA_CPY_BLOCK_ROWS, 1);
|
||||
cpy_scalar_transpose<dst_t><<<dimGrid, dimBlock, 0, stream>>>
|
||||
(cx, cdst, ne, ne00n, ne01n, ne02n, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
} else {
|
||||
const int64_t num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE;
|
||||
cpy_scalar<cpy_1_scalar<src_t, dst_t>><<<num_blocks, CUDA_CPY_BLOCK_SIZE, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
}
|
||||
|
||||
static void ggml_cpy_f32_q8_0_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(ne % QK8_0 == 0);
|
||||
const int64_t num_blocks = ne / QK8_0;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne / QK8_0;
|
||||
cpy_f32_q<cpy_blck_f32_q8_0, QK8_0><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_q8_0_f32_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
const int64_t num_blocks = ne;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne;
|
||||
cpy_q_f32<cpy_blck_q8_0_f32, QK8_0><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_f32_q4_0_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(ne % QK4_0 == 0);
|
||||
const int64_t num_blocks = ne / QK4_0;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne / QK4_0;
|
||||
cpy_f32_q<cpy_blck_f32_q4_0, QK4_0><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_q4_0_f32_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02,
|
||||
const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
|
||||
const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02,
|
||||
const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12,
|
||||
const int nb10, const int nb11, const int nb12, const int nb13,
|
||||
cudaStream_t stream) {
|
||||
const int64_t num_blocks = ne;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne;
|
||||
cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0><<<num_blocks, 1, 0, stream>>>(
|
||||
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
|
||||
ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_f32_q4_1_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(ne % QK4_1 == 0);
|
||||
const int64_t num_blocks = ne / QK4_1;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne / QK4_1;
|
||||
cpy_f32_q<cpy_blck_f32_q4_1, QK4_1><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_q4_1_f32_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02,
|
||||
const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
|
||||
const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02,
|
||||
const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12,
|
||||
const int nb10, const int nb11, const int nb12, const int nb13,
|
||||
cudaStream_t stream) {
|
||||
const int64_t num_blocks = ne;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne;
|
||||
cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1><<<num_blocks, 1, 0, stream>>>(
|
||||
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
|
||||
ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_f32_q5_0_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(ne % QK5_0 == 0);
|
||||
const int64_t num_blocks = ne / QK5_0;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne / QK5_0;
|
||||
cpy_f32_q<cpy_blck_f32_q5_0, QK5_0><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_q5_0_f32_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02,
|
||||
const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
|
||||
const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02,
|
||||
const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12,
|
||||
const int nb10, const int nb11, const int nb12, const int nb13,
|
||||
cudaStream_t stream) {
|
||||
const int64_t num_blocks = ne;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne;
|
||||
cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0><<<num_blocks, 1, 0, stream>>>(
|
||||
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
|
||||
ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_f32_q5_1_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(ne % QK5_1 == 0);
|
||||
const int64_t num_blocks = ne / QK5_1;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne / QK5_1;
|
||||
cpy_f32_q<cpy_blck_f32_q5_1, QK5_1><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_q5_1_f32_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02,
|
||||
const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12,
|
||||
const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13,
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02,
|
||||
const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12,
|
||||
const int nb10, const int nb11, const int nb12, const int nb13,
|
||||
cudaStream_t stream) {
|
||||
const int64_t num_blocks = ne;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne;
|
||||
cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1><<<num_blocks, 1, 0, stream>>>(
|
||||
cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
|
||||
ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
||||
static void ggml_cpy_f32_iq4_nl_cuda(
|
||||
const char * cx, char * cdst, const int64_t ne,
|
||||
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t nb00, const int64_t nb01, const int64_t nb02,
|
||||
const int64_t nb03, const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t nb10, const int64_t nb11, const int64_t nb12, const int64_t nb13, cudaStream_t stream) {
|
||||
const char * cx, char * cdst, const int ne,
|
||||
const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02,
|
||||
const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) {
|
||||
|
||||
GGML_ASSERT(ne % QK4_NL == 0);
|
||||
const int64_t num_blocks = ne / QK4_NL;
|
||||
GGML_ASSERT(num_blocks < UINT_MAX);
|
||||
const int num_blocks = ne / QK4_NL;
|
||||
cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL><<<num_blocks, 1, 0, stream>>>
|
||||
(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13);
|
||||
}
|
||||
|
|
@ -373,6 +356,9 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, gg
|
|||
const int64_t ne = ggml_nelements(src0);
|
||||
GGML_ASSERT(ne == ggml_nelements(src1));
|
||||
|
||||
GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
|
||||
GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
|
||||
|
||||
const int64_t ne00 = src0->ne[0];
|
||||
const int64_t ne01 = src0->ne[1];
|
||||
const int64_t ne02 = src0->ne[2];
|
||||
|
|
|
|||
|
|
@ -918,9 +918,7 @@ void launch_fattn(
|
|||
blocks_num.y = 1;
|
||||
blocks_num.z = 1;
|
||||
|
||||
if (ntiles_total % blocks_num.x != 0) { // Fixup is only needed if the SMs work on fractional tiles.
|
||||
dst_tmp_meta.alloc((size_t(blocks_num.x) * ncols * (2 + DV/2)));
|
||||
}
|
||||
dst_tmp_meta.alloc(blocks_num.x*ncols * (2*2 + DV) * sizeof(float));
|
||||
} else {
|
||||
const int ntiles_KQ = (K->ne[1] + nbatch_fa - 1) / nbatch_fa; // Max. number of parallel blocks limited by tensor size.
|
||||
|
||||
|
|
|
|||
|
|
@ -201,6 +201,16 @@ static ggml_cuda_device_info ggml_cuda_init() {
|
|||
GGML_ASSERT(info.device_count <= GGML_CUDA_MAX_DEVICES);
|
||||
|
||||
int64_t total_vram = 0;
|
||||
#ifdef GGML_CUDA_FORCE_MMQ
|
||||
GGML_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__);
|
||||
#else
|
||||
GGML_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__);
|
||||
#endif // GGML_CUDA_FORCE_MMQ
|
||||
#ifdef GGML_CUDA_FORCE_CUBLAS
|
||||
GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: yes\n", __func__);
|
||||
#else
|
||||
GGML_LOG_INFO("%s: GGML_CUDA_FORCE_CUBLAS: no\n", __func__);
|
||||
#endif // GGML_CUDA_FORCE_CUBLAS
|
||||
GGML_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count);
|
||||
|
||||
std::vector<std::pair<int, std::string>> turing_devices_without_mma;
|
||||
|
|
|
|||
|
|
@ -85,16 +85,13 @@ static void glu_swiglu_fp32_per_thread(const struct htp_tensor * src0,
|
|||
struct htp_spad * dst_spad,
|
||||
uint32_t nth,
|
||||
uint32_t ith,
|
||||
uint32_t src0_nrows_per_thread,
|
||||
dma_queue * dma_queue) {
|
||||
uint32_t src0_nrows_per_thread) {
|
||||
htp_act_preamble3;
|
||||
|
||||
size_t src0_row_size = nb01;
|
||||
size_t src1_row_size = nb11;
|
||||
size_t dst_row_size = nb1;
|
||||
|
||||
|
||||
|
||||
const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows
|
||||
|
||||
const uint32_t src0_start_row = src0_nrows_per_thread * ith;
|
||||
|
|
@ -108,6 +105,12 @@ static void glu_swiglu_fp32_per_thread(const struct htp_tensor * src0,
|
|||
uint64_t t1, t2;
|
||||
t1 = HAP_perf_get_qtimer_count();
|
||||
|
||||
int is_aligned = 1;
|
||||
if (!htp_is_aligned((void *) src0->data, VLEN) || !htp_is_aligned((void *) dst->data, VLEN)) {
|
||||
is_aligned = 0;
|
||||
FARF(HIGH, "swiglu-f32: unaligned addresses in elementwise op, possibly slower execution\n");
|
||||
}
|
||||
|
||||
const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
|
||||
const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
|
||||
uint8_t * restrict data_dst = (uint8_t *) dst->data;
|
||||
|
|
@ -124,81 +127,37 @@ static void glu_swiglu_fp32_per_thread(const struct htp_tensor * src0,
|
|||
data_src1 += swapped ? 0 : nc_in_bytes;
|
||||
}
|
||||
|
||||
const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
|
||||
const size_t src1_row_size_aligned = htp_round_up(src1_row_size, VLEN);
|
||||
const size_t dst_row_size_aligned = htp_round_up(dst_row_size, VLEN);
|
||||
uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_row_size);
|
||||
uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_row_size);
|
||||
uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_row_size);
|
||||
|
||||
uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
|
||||
uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread);
|
||||
uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread);
|
||||
const bool opt_path = ((1 == is_aligned) && !(nb01 & (VLEN - 1)));
|
||||
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
|
||||
const float * restrict src0 = (float *) (data_src0 + (ir * src0_row_size));
|
||||
const float * restrict src1 = (float *) (data_src1 + (ir * src1_row_size));
|
||||
float * restrict dst = (float *) (data_dst + (ir * dst_row_size));
|
||||
|
||||
// While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0
|
||||
size_t src0_spad_half_size = src0_spad->size_per_thread / 2;
|
||||
size_t src1_spad_half_size = src1_spad->size_per_thread / 2;
|
||||
size_t dst_spad_half_size = dst_spad->size_per_thread / 2;
|
||||
|
||||
const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block
|
||||
if (BLOCK == 0) {
|
||||
FARF(ERROR,
|
||||
"swiglu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n",
|
||||
src0_spad->size_per_thread, src0_row_size_aligned);
|
||||
return;
|
||||
}
|
||||
|
||||
// See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379
|
||||
for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) {
|
||||
const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
|
||||
|
||||
// Dummy DMA transation for sequencing (interleaving dst,src,dst,...)
|
||||
dma_queue_push_vtcm_to_ddr(dma_queue,
|
||||
dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)),
|
||||
dst_row_size, dst_row_size_aligned, 0);
|
||||
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue,
|
||||
dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)),
|
||||
src0_row_size_aligned, src0_row_size, block_size);
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue,
|
||||
dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)),
|
||||
src1_row_size_aligned, src1_row_size, block_size);
|
||||
}
|
||||
|
||||
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
|
||||
const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
|
||||
|
||||
float * dst_spad = (float *) dma_queue_pop(dma_queue).src;
|
||||
float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
|
||||
float * src1_spad = (float *) dma_queue_pop(dma_queue).dst;
|
||||
|
||||
for (uint32_t ib = 0; ib < block_size; ib++) {
|
||||
const float * src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float));
|
||||
const float * src1_spad_ptr = src1_spad + ib * (src1_row_size_aligned / sizeof(float));
|
||||
float * dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float));
|
||||
|
||||
//swiglu(x) = x1 * sigmoid(x0)
|
||||
hvx_fast_sigmoid_f32((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
|
||||
hvx_mul_mul_f32_opt((const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr,
|
||||
(const uint8_t *) src1_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
|
||||
if (ir + 1 < src0_end_row) {
|
||||
htp_l2fetch(src0 + src0_row_size, 1, src0_row_size, src0_row_size);
|
||||
}
|
||||
|
||||
dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size,
|
||||
dst_row_size_aligned, block_size);
|
||||
if (opt_path) {
|
||||
hvx_fast_sigmoid_f32((const uint8_t *) src0, (uint8_t *) src0_spad_data, nc);
|
||||
hvx_mul_mul_f32_opt((const uint8_t *) src0, (const uint8_t *) src0_spad_data, (const uint8_t *) src1,
|
||||
(uint8_t *) dst, nc);
|
||||
} else {
|
||||
hvx_exp_f32((const uint8_t *) src0, src0_spad_data, nc, true);
|
||||
hvx_add_scalar_f32(src0_spad_data, 1.0, src1_spad_data, nc);
|
||||
hvx_inverse_f32(src1_spad_data, src0_spad_data, nc);
|
||||
|
||||
// prefetch N+2 loop iteration if any
|
||||
const uint32_t pref_block = (ir + BLOCK * 2);
|
||||
if (pref_block < src0_end_row) {
|
||||
const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block);
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)),
|
||||
src0_row_size_aligned, src0_row_size, pref_block_size);
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)),
|
||||
src1_row_size_aligned, src1_row_size, pref_block_size);
|
||||
hvx_mul_f32((const uint8_t *) src0, src0_spad_data, dst_spad_data, nc);
|
||||
hvx_mul_f32(dst_spad_data, (const uint8_t *) src1, (uint8_t *) dst, nc);
|
||||
}
|
||||
}
|
||||
|
||||
dma_queue_flush(dma_queue);
|
||||
|
||||
t2 = HAP_perf_get_qtimer_count();
|
||||
|
||||
FARF(HIGH, "swiglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth,
|
||||
FARF(HIGH, "swiglu-f32 %d/%d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, opt_path,
|
||||
ne00, ne01, ne02, ne03, src0_start_row, src0_end_row, ne10, ne11, ne12, ne13, ne0, ne1, ne2, ne3,
|
||||
(unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
|
||||
}
|
||||
|
|
@ -212,16 +171,15 @@ static void glu_swiglu_oai_fp32_per_thread(const struct htp_tensor * src0,
|
|||
struct htp_spad * dst_spad,
|
||||
uint32_t nth,
|
||||
uint32_t ith,
|
||||
uint32_t src0_nrows_per_thread,
|
||||
dma_queue * dma_queue) {
|
||||
uint32_t src0_nrows_per_thread) {
|
||||
htp_act_preamble3;
|
||||
|
||||
uint64_t t1, t2;
|
||||
t1 = HAP_perf_get_qtimer_count();
|
||||
|
||||
size_t src0_row_size = nb01;
|
||||
size_t src1_row_size = nb11;
|
||||
size_t dst_row_size = nb1;
|
||||
const size_t src0_row_size = nb01;
|
||||
const size_t src1_row_size = nb11;
|
||||
const size_t dst_row_size = nb1;
|
||||
|
||||
const uint32_t src0_nrows = ne01 * ne02 * ne03; // src0 rows
|
||||
|
||||
|
|
@ -233,110 +191,66 @@ static void glu_swiglu_oai_fp32_per_thread(const struct htp_tensor * src0,
|
|||
return;
|
||||
}
|
||||
|
||||
if (!htp_is_aligned((void *) src0->data, VLEN) || !htp_is_aligned((void *) dst->data, VLEN)) {
|
||||
FARF(HIGH, "act-f32: unaligned addresses in activations op, possibly slower execution\n");
|
||||
}
|
||||
|
||||
const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
|
||||
const uint8_t * restrict data_src1 = (const uint8_t *) src1->data;
|
||||
uint8_t * restrict data_dst = (uint8_t *) dst->data;
|
||||
|
||||
const bool src1_valid = src1->ne[0];
|
||||
const int nc = (src1_valid) ? ne00 : ne00 / 2;
|
||||
bool src1_valid = src1->ne[0];
|
||||
if (!src1_valid) {
|
||||
const int32_t swapped = op_params[1];
|
||||
data_src1 = data_src0;
|
||||
src1_row_size = src0_row_size;
|
||||
|
||||
const size_t nc_in_bytes = nc * SIZEOF_FP32;
|
||||
data_src0 += swapped ? nc_in_bytes : 0;
|
||||
data_src1 += swapped ? 0 : nc_in_bytes;
|
||||
data_src1 = data_src0;
|
||||
}
|
||||
|
||||
const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
|
||||
const size_t src1_row_size_aligned = htp_round_up(src1_row_size, VLEN);
|
||||
const size_t dst_row_size_aligned = htp_round_up(dst_row_size, VLEN);
|
||||
uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_row_size);
|
||||
uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_row_size);
|
||||
uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_row_size);
|
||||
|
||||
uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
|
||||
uint8_t * restrict src1_spad_data = src1_spad->data + (ith * src1_spad->size_per_thread);
|
||||
uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread);
|
||||
const int32_t swapped = op_params[1];
|
||||
const float alpha = ((const float *) (op_params))[2];
|
||||
const float limit = ((const float *) (op_params))[3];
|
||||
|
||||
// While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0
|
||||
size_t src0_spad_half_size = src0_spad->size_per_thread / 2;
|
||||
size_t src1_spad_half_size = src1_spad->size_per_thread / 2;
|
||||
size_t dst_spad_half_size = dst_spad->size_per_thread / 2;
|
||||
const int nc = (src1_valid) ? ne00 : ne00 / 2;
|
||||
|
||||
const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block
|
||||
if (BLOCK == 0) {
|
||||
FARF(ERROR,
|
||||
"swiglu-oai-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least "
|
||||
"%zu\n",
|
||||
src0_spad->size_per_thread, src0_row_size_aligned);
|
||||
return;
|
||||
}
|
||||
const float alpha = ((const float *) (op_params))[2];
|
||||
const float limit = ((const float *) (op_params))[3];
|
||||
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
|
||||
const float * restrict src0 = (float *) (data_src0 + (ir * src0_row_size));
|
||||
const float * restrict src1 = (float *) (data_src1 + (ir * src1_row_size));
|
||||
float * restrict dst = (float *) (data_dst + (ir * dst_row_size));
|
||||
|
||||
// See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379
|
||||
for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) {
|
||||
const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
|
||||
|
||||
// Dummy DMA transation for sequencing (interleaving dst,src,dst,...)
|
||||
dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)),
|
||||
dst_row_size, dst_row_size_aligned, 0);
|
||||
|
||||
dma_queue_push_ddr_to_vtcm(
|
||||
dma_queue,
|
||||
dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)),
|
||||
src0_row_size_aligned, src0_row_size, block_size);
|
||||
dma_queue_push_ddr_to_vtcm(
|
||||
dma_queue,
|
||||
dma_make_ptr(src1_spad_data + (spad_idx * src1_spad_half_size), data_src1 + (ir * src1_row_size)),
|
||||
src1_row_size_aligned, src1_row_size, block_size);
|
||||
}
|
||||
|
||||
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
|
||||
const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
|
||||
|
||||
float * dst_spad = (float *) dma_queue_pop(dma_queue).src;
|
||||
float * src0_spad = (float *) dma_queue_pop(dma_queue).dst;
|
||||
float * src1_spad = (float *) dma_queue_pop(dma_queue).dst;
|
||||
|
||||
for (uint32_t ib = 0; ib < block_size; ib++) {
|
||||
const float * src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float));
|
||||
const float * src1_spad_ptr = src1_spad + ib * (src1_row_size_aligned / sizeof(float));
|
||||
float * dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float));
|
||||
|
||||
// x (src0_spad_data) = std::min(src0_p[k], limit);
|
||||
hvx_min_scalar_f32((const uint8_t *) src0_spad_ptr, limit, (uint8_t *) src0_spad_ptr, nc);
|
||||
// y1 (src1_spad_data) = std::clamp(src1_p[k], -limit, limit);
|
||||
hvx_clamp_scalar_f32((const uint8_t *) src1_spad_ptr, -limit, limit, (uint8_t *) src1_spad_ptr, nc);
|
||||
// y (src1_spad_data) = y1 + 1.f
|
||||
hvx_add_scalar_f32((const uint8_t *) src1_spad_ptr, 1.0, (uint8_t *) src1_spad_ptr, nc);
|
||||
// x1 (dst_spad_data) = alpha * (x)
|
||||
hvx_mul_scalar_f32((const uint8_t *) src0_spad_ptr, alpha, (uint8_t *) dst_spad_ptr, nc);
|
||||
// x2 (dst_spad_data) = sigmoid(x1) = 1/(1+exp(-x1))
|
||||
hvx_fast_sigmoid_f32((const uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
|
||||
// out = x * sigmoid(alpha * x) * (y + 1.f)
|
||||
hvx_mul_mul_f32_opt((const uint8_t *) src0_spad_ptr, (const uint8_t *) dst_spad_ptr,
|
||||
(const uint8_t *) src1_spad_ptr, (uint8_t *) dst_spad_ptr, nc);
|
||||
if (ir + 1 < src0_end_row) {
|
||||
htp_l2fetch(src0 + src0_row_size, 1, src0_row_size, src0_row_size);
|
||||
}
|
||||
|
||||
dma_queue_push_vtcm_to_ddr(dma_queue, dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad), dst_row_size,
|
||||
dst_row_size_aligned, block_size);
|
||||
|
||||
// prefetch N+2 loop iteration if any
|
||||
const uint32_t pref_block = (ir + BLOCK * 2);
|
||||
if (pref_block < src0_end_row) {
|
||||
const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block);
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)),
|
||||
src0_row_size_aligned, src0_row_size, pref_block_size);
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue, dma_make_ptr(src1_spad, data_src1 + (pref_block * src1_row_size)),
|
||||
src1_row_size_aligned, src1_row_size, pref_block_size);
|
||||
if (!src1) {
|
||||
src0 += swapped ? nc : 0;
|
||||
src1 += swapped ? 0 : nc;
|
||||
}
|
||||
}
|
||||
|
||||
dma_queue_flush(dma_queue);
|
||||
// x (src0_spad_data) = std::min(src0_p[k], limit);
|
||||
hvx_min_scalar_f32((const uint8_t *) src0, limit, src0_spad_data, nc);
|
||||
// y1 (src1_spad_data) = std::clamp(src1_p[k], -limit, limit);
|
||||
hvx_clamp_scalar_f32((const uint8_t *) src1, -limit, limit, src1_spad_data, nc);
|
||||
// y (src1_spad_data) = y1 + 1.f
|
||||
hvx_add_scalar_f32(src1_spad_data, 1.0, src1_spad_data, nc);
|
||||
// x1 (dst_spad_data) = alpha * (x)
|
||||
hvx_mul_scalar_f32(src0_spad_data, alpha, dst_spad_data, nc);
|
||||
// x2 (dst_spad_data) = expf(-x1)
|
||||
hvx_exp_f32(dst_spad_data, dst_spad_data, nc, true);
|
||||
// x3 (dst_spad_data) = x2 + 1.f
|
||||
hvx_add_scalar_f32(dst_spad_data, 1.0, dst_spad_data, nc);
|
||||
// x4 (dst_spad_data) = 1 / x3
|
||||
hvx_inverse_f32(dst_spad_data, dst_spad_data, nc);
|
||||
// out_glu(dst_spad_data) = x * x4
|
||||
hvx_mul_f32(src0_spad_data, dst_spad_data, dst_spad_data, nc);
|
||||
// out = out_glu * (y + 1.f);
|
||||
hvx_mul_f32(dst_spad_data, src1_spad_data, (uint8_t *) dst, nc);
|
||||
}
|
||||
|
||||
t2 = HAP_perf_get_qtimer_count();
|
||||
|
||||
FARF(HIGH, "swiglu-oai-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, src0->ne[0],
|
||||
FARF(HIGH, "swiglu-f32 %d/%d: %ux%ux%ux%u (%u:%u) x %ux%ux%ux%u -> %ux%ux%ux%u usec %u\n", ith, nth, src0->ne[0],
|
||||
src0->ne[1], src0->ne[2], src0->ne[3], src0_start_row, src0_end_row, src1->ne[0], src1->ne[1], src1->ne[2],
|
||||
src1->ne[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
|
||||
}
|
||||
|
|
@ -457,8 +371,7 @@ static void unary_silu_fp32_per_thread(const struct htp_tensor * src0,
|
|||
struct htp_spad * dst_spad,
|
||||
uint32_t nth,
|
||||
uint32_t ith,
|
||||
uint32_t src0_nrows_per_thread,
|
||||
dma_queue * dma_queue) {
|
||||
uint32_t src0_nrows_per_thread) {
|
||||
htp_act_preamble2;
|
||||
|
||||
uint64_t t1, t2;
|
||||
|
|
@ -466,8 +379,6 @@ static void unary_silu_fp32_per_thread(const struct htp_tensor * src0,
|
|||
|
||||
const size_t src0_row_size = nb01;
|
||||
const size_t dst_row_size = nb1;
|
||||
const size_t src0_row_size_aligned = htp_round_up(src0_row_size, VLEN);
|
||||
const size_t dst_row_size_aligned = htp_round_up(dst_row_size, VLEN);
|
||||
|
||||
const uint32_t src0_nrows = ne01 * ne02 * ne03;
|
||||
|
||||
|
|
@ -479,91 +390,64 @@ static void unary_silu_fp32_per_thread(const struct htp_tensor * src0,
|
|||
return;
|
||||
}
|
||||
|
||||
const uint8_t * data_src0 = (const uint8_t *) src0->data;
|
||||
uint8_t * data_dst = (uint8_t *) dst->data;
|
||||
|
||||
uint8_t * src0_spad_data = src0_spad->data + (ith * src0_spad->size_per_thread);
|
||||
uint8_t * dst_spad_data = dst_spad->data + (ith * dst_spad->size_per_thread);
|
||||
|
||||
// While given src0_spad->size_per_thread, divide it to two ping-pong buffer for src0
|
||||
size_t src0_spad_half_size = src0_spad->size_per_thread / 2;
|
||||
size_t dst_spad_half_size = dst_spad->size_per_thread / 2;
|
||||
|
||||
const int BLOCK = src0_spad_half_size / src0_row_size_aligned; // How many rows can we process in one block
|
||||
|
||||
if (BLOCK == 0) {
|
||||
FARF(ERROR, "silu-f32 : current VTCM reservation %zu is too small for even 1 row per thread, needed at least %zu\n",
|
||||
src0_spad->size_per_thread, src0_row_size_aligned);
|
||||
return;
|
||||
int is_aligned = 1;
|
||||
int opt_path = 0;
|
||||
if (!htp_is_aligned((void *) src0->data, VLEN) || !htp_is_aligned((void *) dst->data, VLEN)) {
|
||||
is_aligned = 0;
|
||||
FARF(HIGH, "silu-f32: unaligned addresses in elementwise op, possibly slower execution\n");
|
||||
}
|
||||
if ((1 == is_aligned) && !(nb01 & (VLEN - 1))) {
|
||||
opt_path = 1;
|
||||
}
|
||||
|
||||
// See discussion: https://github.com/ggml-org/llama.cpp/pull/18151#issuecomment-3678235379
|
||||
for (uint32_t ir = src0_start_row, spad_idx = 0; ir < src0_end_row && spad_idx < 2; ir += BLOCK, spad_idx++) {
|
||||
const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
|
||||
const uint8_t * restrict data_src0 = (const uint8_t *) src0->data;
|
||||
uint8_t * restrict data_dst = (uint8_t *) dst->data;
|
||||
|
||||
// Dummy DMA transation for sequencing (interleaving dst,src,dst,...)
|
||||
dma_queue_push_vtcm_to_ddr(dma_queue,
|
||||
dma_make_ptr(data_dst, dst_spad_data + (spad_idx * dst_spad_half_size)),
|
||||
dst_row_size, dst_row_size_aligned, 0);
|
||||
uint8_t * restrict src0_spad_data = src0_spad->data + (ith * src0_row_size);
|
||||
uint8_t * restrict dst_spad_data = dst_spad->data + (ith * dst_row_size);
|
||||
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue,
|
||||
dma_make_ptr(src0_spad_data + (spad_idx * src0_spad_half_size), data_src0 + (ir * src0_row_size)),
|
||||
src0_row_size_aligned, src0_row_size, block_size);
|
||||
}
|
||||
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir++) {
|
||||
const float * restrict src0 = (float *) (data_src0 + (ir * src0_row_size));
|
||||
float * restrict dst = (float *) (data_dst + (ir * dst_row_size));
|
||||
|
||||
for (uint32_t ir = src0_start_row; ir < src0_end_row; ir += BLOCK) {
|
||||
const uint32_t block_size = MIN(BLOCK, src0_end_row - ir);
|
||||
|
||||
float* dst_spad = (float *) dma_queue_pop(dma_queue).src;
|
||||
float* src0_spad = (float *) dma_queue_pop(dma_queue).dst;
|
||||
|
||||
for (uint32_t ib = 0; ib < block_size; ib++) {
|
||||
const float* src0_spad_ptr = src0_spad + ib * (src0_row_size_aligned / sizeof(float));
|
||||
float* dst_spad_ptr = dst_spad + ib * (dst_row_size_aligned / sizeof(float));
|
||||
|
||||
// silu = x * sigmoid(x)
|
||||
hvx_fast_sigmoid_f32((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, ne0);
|
||||
hvx_mul_f32_opt((const uint8_t *) src0_spad_ptr, (uint8_t *) dst_spad_ptr, (uint8_t *) dst_spad_ptr, ne0);
|
||||
if (ir + 1 < src0_end_row) {
|
||||
htp_l2fetch(src0 + src0_row_size, 1, src0_row_size, src0_row_size);
|
||||
}
|
||||
|
||||
dma_queue_push_vtcm_to_ddr(dma_queue,
|
||||
dma_make_ptr(data_dst + (ir * dst_row_size), dst_spad),
|
||||
dst_row_size, dst_row_size_aligned, block_size);
|
||||
if (1 == opt_path) {
|
||||
hvx_fast_sigmoid_f32((const uint8_t *) src0, (uint8_t *) src0_spad_data, ne0);
|
||||
hvx_mul_f32_opt((const uint8_t *) src0, src0_spad_data, (uint8_t *) dst, ne0);
|
||||
} else {
|
||||
hvx_exp_f32((const uint8_t *) src0, src0_spad_data, ne0, true);
|
||||
hvx_add_scalar_f32(src0_spad_data, 1.0, dst_spad_data, ne0);
|
||||
hvx_inverse_f32(dst_spad_data, src0_spad_data, ne0);
|
||||
|
||||
// prefetch N+2 loop iteration if any
|
||||
const uint32_t pref_block = (ir + BLOCK * 2);
|
||||
if (pref_block < src0_end_row) {
|
||||
const uint32_t pref_block_size = MIN(BLOCK, src0_end_row - pref_block);
|
||||
dma_queue_push_ddr_to_vtcm(dma_queue,
|
||||
dma_make_ptr(src0_spad, data_src0 + (pref_block * src0_row_size)),
|
||||
src0_row_size_aligned, src0_row_size, pref_block_size);
|
||||
hvx_mul_f32((const uint8_t *) src0, src0_spad_data, (uint8_t *) dst, ne0);
|
||||
}
|
||||
}
|
||||
|
||||
dma_queue_flush(dma_queue);
|
||||
|
||||
t2 = HAP_perf_get_qtimer_count();
|
||||
|
||||
FARF(HIGH, "silu-f32 %d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, ne00, ne01, ne02,
|
||||
FARF(HIGH, "silu-f32 %d/%d/%d: %ux%ux%ux%u (%u:%u) -> %ux%ux%ux%u usec %u\n", ith, nth, opt_path, ne00, ne01, ne02,
|
||||
ne03, src0_start_row, src0_end_row, ne0, ne1, ne2, ne3, (unsigned) HAP_perf_qtimer_count_to_us(t2 - t1));
|
||||
}
|
||||
|
||||
static void unary_silu_fp32(unsigned int n, unsigned int i, void * data) {
|
||||
struct htp_ops_context * octx = (struct htp_ops_context *) data;
|
||||
unary_silu_fp32_per_thread(&octx->src0, &octx->dst, octx->op_params, &octx->src0_spad, &octx->dst_spad, n, i,
|
||||
octx->src0_nrows_per_thread, octx->ctx->dma[i]);
|
||||
octx->src0_nrows_per_thread);
|
||||
}
|
||||
|
||||
static void glu_swiglu_fp32(unsigned int n, unsigned int i, void * data) {
|
||||
struct htp_ops_context * octx = (struct htp_ops_context *) data;
|
||||
glu_swiglu_fp32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
|
||||
&octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
|
||||
&octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread);
|
||||
}
|
||||
|
||||
static void glu_swiglu_oai_fp32(unsigned int n, unsigned int i, void * data) {
|
||||
struct htp_ops_context * octx = (struct htp_ops_context *) data;
|
||||
glu_swiglu_oai_fp32_per_thread(&octx->src0, &octx->src1, &octx->dst, octx->op_params, &octx->src0_spad,
|
||||
&octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread, octx->ctx->dma[i]);
|
||||
&octx->src1_spad, &octx->dst_spad, n, i, octx->src0_nrows_per_thread);
|
||||
}
|
||||
|
||||
static int execute_op_activations_fp32(struct htp_ops_context * octx) {
|
||||
|
|
|
|||
|
|
@ -1684,60 +1684,3 @@ ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_opt_step_sgd(ggm
|
|||
|
||||
return res;
|
||||
}
|
||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_memset(ggml_metal_library_t lib, const ggml_tensor * op) {
|
||||
GGML_ASSERT(op->type == GGML_TYPE_I64);
|
||||
|
||||
char base[256];
|
||||
char name[256];
|
||||
|
||||
snprintf(base, 256, "kernel_memset_%s", ggml_type_name(op->type));
|
||||
snprintf(name, 256, "%s", base);
|
||||
|
||||
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
|
||||
if (!res.pipeline) {
|
||||
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_count_equal(ggml_metal_library_t lib, const ggml_tensor * op) {
|
||||
assert(op->op == GGML_OP_COUNT_EQUAL);
|
||||
|
||||
GGML_TENSOR_LOCALS(int64_t, ne0, op->src[0], ne);
|
||||
|
||||
GGML_ASSERT(op->src[0]->type == op->src[1]->type);
|
||||
GGML_ASSERT(op->src[0]->type == GGML_TYPE_I32);
|
||||
GGML_ASSERT(op->type == GGML_TYPE_I64);
|
||||
|
||||
// note: the kernel only supports i32 output due to metal atomic add only supporting atomic_int
|
||||
GGML_ASSERT(ggml_nelements(op->src[0]) < (1LL << 31));
|
||||
|
||||
char base[256];
|
||||
char name[256];
|
||||
|
||||
int nsg = 1;
|
||||
while (32*nsg < ne00 && nsg < 32) {
|
||||
nsg *= 2;
|
||||
}
|
||||
|
||||
snprintf(base, 256, "kernel_count_equal_%s", ggml_type_name(op->src[0]->type));
|
||||
snprintf(name, 256, "%s_nsg=%d", base, nsg);
|
||||
|
||||
ggml_metal_pipeline_with_params res = ggml_metal_library_get_pipeline(lib, name);
|
||||
if (!res.pipeline) {
|
||||
ggml_metal_cv_t cv = ggml_metal_cv_init();
|
||||
|
||||
ggml_metal_cv_set_int16(cv, nsg, FC_COUNT_EQUAL + 0);
|
||||
|
||||
res = ggml_metal_library_compile_pipeline(lib, base, name, cv);
|
||||
|
||||
ggml_metal_cv_free(cv);
|
||||
}
|
||||
|
||||
res.smem = 32 * sizeof(int32_t);
|
||||
res.nsg = nsg;
|
||||
|
||||
return res;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -147,8 +147,6 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_arange
|
|||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_timestep_embedding(ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_opt_step_adamw (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_opt_step_sgd (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_memset (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_count_equal (ggml_metal_library_t lib, const struct ggml_tensor * op);
|
||||
|
||||
struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_flash_attn_ext_pad(
|
||||
ggml_metal_library_t lib,
|
||||
|
|
|
|||
|
|
@ -1023,11 +1023,6 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
|
|||
return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
|
||||
case GGML_OP_L2_NORM:
|
||||
return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
|
||||
case GGML_OP_COUNT_EQUAL:
|
||||
return has_simdgroup_reduction &&
|
||||
op->src[0]->type == GGML_TYPE_I32 &&
|
||||
op->src[1]->type == GGML_TYPE_I32 &&
|
||||
op->type == GGML_TYPE_I64;
|
||||
case GGML_OP_ARGMAX:
|
||||
return has_simdgroup_reduction;
|
||||
case GGML_OP_NORM:
|
||||
|
|
|
|||
|
|
@ -78,7 +78,6 @@
|
|||
#define FC_MUL_MM 700
|
||||
#define FC_ROPE 800
|
||||
#define FC_SSM_CONV 900
|
||||
#define FC_COUNT_EQUAL 1000
|
||||
|
||||
// op-specific constants
|
||||
#define OP_FLASH_ATTN_EXT_NQPTG 8
|
||||
|
|
@ -895,25 +894,6 @@ typedef struct {
|
|||
float step;
|
||||
} ggml_metal_kargs_arange;
|
||||
|
||||
typedef struct {
|
||||
int64_t val;
|
||||
} ggml_metal_kargs_memset;
|
||||
|
||||
typedef struct {
|
||||
int32_t ne00;
|
||||
int32_t ne01;
|
||||
int32_t ne02;
|
||||
int32_t ne03;
|
||||
uint64_t nb00;
|
||||
uint64_t nb01;
|
||||
uint64_t nb02;
|
||||
uint64_t nb03;
|
||||
uint64_t nb10;
|
||||
uint64_t nb11;
|
||||
uint64_t nb12;
|
||||
uint64_t nb13;
|
||||
} ggml_metal_kargs_count_equal;
|
||||
|
||||
typedef struct {
|
||||
int32_t k0;
|
||||
int32_t k1;
|
||||
|
|
|
|||
|
|
@ -448,11 +448,7 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
|
|||
{
|
||||
n_fuse = ggml_metal_op_opt_step_sgd(ctx, idx);
|
||||
} break;
|
||||
case GGML_OP_COUNT_EQUAL:
|
||||
{
|
||||
n_fuse = ggml_metal_op_count_equal(ctx, idx);
|
||||
} break;
|
||||
default:
|
||||
default:
|
||||
{
|
||||
GGML_LOG_ERROR("%s: error: node %3d, op = %8s not implemented\n", __func__, idx, ggml_op_name(node->op));
|
||||
GGML_ABORT("fatal error");
|
||||
|
|
@ -2181,11 +2177,7 @@ size_t ggml_metal_op_flash_attn_ext_extra_pad(const ggml_tensor * op) {
|
|||
|
||||
const bool has_mask = op->src[3] != nullptr;
|
||||
|
||||
// note: the non-vec kernel requires more extra memory, so always reserve for it
|
||||
GGML_ASSERT(OP_FLASH_ATTN_EXT_NCPSG >= OP_FLASH_ATTN_EXT_VEC_NCPSG);
|
||||
|
||||
//if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
|
||||
if (false) {
|
||||
if (ggml_metal_op_flash_attn_ext_use_vec(op)) {
|
||||
// note: always reserve the padding space to avoid graph reallocations
|
||||
//const bool has_kvpad = ne11 % OP_FLASH_ATTN_EXT_VEC_NCPSG != 0;
|
||||
const bool has_kvpad = true;
|
||||
|
|
@ -4098,64 +4090,3 @@ int ggml_metal_op_opt_step_sgd(ggml_metal_op_t ctx, int idx) {
|
|||
|
||||
return 1;
|
||||
}
|
||||
|
||||
int ggml_metal_op_count_equal(ggml_metal_op_t ctx, int idx) {
|
||||
ggml_tensor * op = ctx->node(idx);
|
||||
|
||||
ggml_metal_library_t lib = ctx->lib;
|
||||
ggml_metal_encoder_t enc = ctx->enc;
|
||||
|
||||
GGML_TENSOR_LOCALS(int32_t, ne0, op->src[0], ne);
|
||||
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
|
||||
GGML_TENSOR_LOCALS(uint64_t, nb1, op->src[1], nb);
|
||||
|
||||
{
|
||||
ggml_metal_kargs_memset args = { /*.val =*/ 0 };
|
||||
|
||||
auto pipeline = ggml_metal_library_get_pipeline_memset(lib, op);
|
||||
|
||||
ggml_metal_encoder_set_pipeline(enc, pipeline);
|
||||
ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 1);
|
||||
|
||||
ggml_metal_encoder_dispatch_threadgroups(enc, 1, 1, 1, 1, 1, 1);
|
||||
}
|
||||
|
||||
ggml_metal_op_concurrency_reset(ctx);
|
||||
|
||||
{
|
||||
ggml_metal_kargs_count_equal args = {
|
||||
/*.ne00 =*/ ne00,
|
||||
/*.ne01 =*/ ne01,
|
||||
/*.ne02 =*/ ne02,
|
||||
/*.ne03 =*/ ne03,
|
||||
/*.nb00 =*/ nb00,
|
||||
/*.nb01 =*/ nb01,
|
||||
/*.nb02 =*/ nb02,
|
||||
/*.nb03 =*/ nb03,
|
||||
/*.nb10 =*/ nb10,
|
||||
/*.nb11 =*/ nb11,
|
||||
/*.nb12 =*/ nb12,
|
||||
/*.nb13 =*/ nb13,
|
||||
};
|
||||
|
||||
auto pipeline = ggml_metal_library_get_pipeline_count_equal(lib, op);
|
||||
|
||||
const size_t smem = pipeline.smem;
|
||||
|
||||
const int nth = 32*pipeline.nsg;
|
||||
|
||||
GGML_ASSERT(nth <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
|
||||
|
||||
ggml_metal_encoder_set_pipeline(enc, pipeline);
|
||||
ggml_metal_encoder_set_bytes(enc, &args, sizeof(args), 0);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[0]), 1);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op->src[1]), 2);
|
||||
ggml_metal_encoder_set_buffer(enc, ggml_metal_get_buffer_id(op), 3);
|
||||
|
||||
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
|
||||
ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
|
||||
}
|
||||
|
||||
return 1;
|
||||
}
|
||||
|
|
|
|||
|
|
@ -87,7 +87,6 @@ int ggml_metal_op_leaky_relu (ggml_metal_op_t ctx, int idx);
|
|||
int ggml_metal_op_tri (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_opt_step_adamw (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_opt_step_sgd (ggml_metal_op_t ctx, int idx);
|
||||
int ggml_metal_op_count_equal (ggml_metal_op_t ctx, int idx);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1790,7 +1790,6 @@ kernel void kernel_op_sum_f32(
|
|||
return;
|
||||
}
|
||||
|
||||
// TODO: become function constant
|
||||
const uint nsg = (ntg.x + 31) / 32;
|
||||
|
||||
float sumf = 0;
|
||||
|
|
@ -9915,75 +9914,3 @@ kernel void kernel_opt_step_sgd_f32(
|
|||
|
||||
x[gid] = x[gid] * (1.0f - pars[0] * pars[1]) - pars[0] * g[gid];
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_memset(
|
||||
constant ggml_metal_kargs_fill & args,
|
||||
device T * dst,
|
||||
uint tpig[[thread_position_in_grid]]) {
|
||||
dst[tpig] = args.val;
|
||||
}
|
||||
|
||||
typedef decltype(kernel_memset<int64_t>) kernel_memset_t;
|
||||
|
||||
template [[host_name("kernel_memset_i64")]] kernel kernel_memset_t kernel_memset<int64_t>;
|
||||
|
||||
constant short FC_count_equal_nsg [[function_constant(FC_COUNT_EQUAL + 0)]];
|
||||
|
||||
template<typename T>
|
||||
kernel void kernel_count_equal(
|
||||
constant ggml_metal_kargs_count_equal & args,
|
||||
device const char * src0,
|
||||
device const char * src1,
|
||||
device atomic_int * dst,
|
||||
threadgroup int32_t * shmem_i32 [[threadgroup(0)]],
|
||||
uint3 tgpig[[threadgroup_position_in_grid]],
|
||||
ushort3 tpitg[[thread_position_in_threadgroup]],
|
||||
ushort sgitg[[simdgroup_index_in_threadgroup]],
|
||||
ushort tiisg[[thread_index_in_simdgroup]],
|
||||
ushort3 ntg[[threads_per_threadgroup]]) {
|
||||
const short NSG = FC_count_equal_nsg;
|
||||
|
||||
const int i3 = tgpig.z;
|
||||
const int i2 = tgpig.y;
|
||||
const int i1 = tgpig.x;
|
||||
|
||||
if (i3 >= args.ne03 || i2 >= args.ne02 || i1 >= args.ne01) {
|
||||
return;
|
||||
}
|
||||
|
||||
int sum = 0;
|
||||
|
||||
device const char * base0 = src0 + i1*args.nb01 + i2*args.nb02 + i3*args.nb03;
|
||||
device const char * base1 = src1 + i1*args.nb11 + i2*args.nb12 + i3*args.nb13;
|
||||
|
||||
for (int64_t i0 = tpitg.x; i0 < args.ne00; i0 += ntg.x) {
|
||||
const T v0 = *(device const T *)(base0 + i0*args.nb00);
|
||||
const T v1 = *(device const T *)(base1 + i0*args.nb10);
|
||||
sum += (v0 == v1);
|
||||
}
|
||||
|
||||
sum = simd_sum(sum);
|
||||
|
||||
if (tiisg == 0) {
|
||||
shmem_i32[sgitg] = sum;
|
||||
}
|
||||
|
||||
threadgroup_barrier(mem_flags::mem_threadgroup);
|
||||
|
||||
if (sgitg == 0) {
|
||||
float v = 0.0f;
|
||||
if (tpitg.x < NSG) {
|
||||
v = shmem_i32[tpitg.x];
|
||||
}
|
||||
|
||||
float total = simd_sum(v);
|
||||
if (tpitg.x == 0) {
|
||||
atomic_fetch_add_explicit(dst, (int32_t) total, memory_order_relaxed);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
typedef decltype(kernel_count_equal<int32_t>) kernel_count_equal_t;
|
||||
|
||||
template [[host_name("kernel_count_equal_i32")]] kernel kernel_count_equal_t kernel_count_equal<int32_t>;
|
||||
|
|
|
|||
|
|
@ -1517,12 +1517,10 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input) {
|
|||
struct ggml_cgraph * graph = ggml_new_graph_custom(ctx, n_nodes, false);
|
||||
graph->n_nodes = n_nodes;
|
||||
std::unordered_map<uint64_t, const rpc_tensor*> tensor_ptrs;
|
||||
tensor_ptrs.reserve(n_tensors);
|
||||
for (uint32_t i = 0; i < n_tensors; i++) {
|
||||
tensor_ptrs.emplace(tensors[i].id, &tensors[i]);
|
||||
tensor_ptrs[tensors[i].id] = &tensors[i];
|
||||
}
|
||||
std::unordered_map<uint64_t, ggml_tensor*> tensor_map;
|
||||
tensor_map.reserve(n_nodes);
|
||||
for (uint32_t i = 0; i < n_nodes; i++) {
|
||||
int64_t id;
|
||||
memcpy(&id, &nodes[i], sizeof(id));
|
||||
|
|
|
|||
|
|
@ -434,15 +434,8 @@ static constexpr std::initializer_list<ggml_op> topk_moe_early_softmax_norm{ GGM
|
|||
GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
|
||||
GGML_OP_SUM_ROWS, GGML_OP_CLAMP, GGML_OP_DIV,
|
||||
GGML_OP_RESHAPE };
|
||||
|
||||
static constexpr std::initializer_list<ggml_op> topk_moe_sigmoid_norm_bias{ GGML_OP_UNARY, GGML_OP_RESHAPE, GGML_OP_ADD,
|
||||
GGML_OP_ARGSORT, GGML_OP_VIEW, GGML_OP_GET_ROWS,
|
||||
GGML_OP_RESHAPE, GGML_OP_SUM_ROWS, GGML_OP_CLAMP,
|
||||
GGML_OP_DIV, GGML_OP_RESHAPE };
|
||||
|
||||
static constexpr std::initializer_list<ggml_op> topk_moe_early_softmax { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT,
|
||||
GGML_OP_VIEW, GGML_OP_GET_ROWS };
|
||||
|
||||
static constexpr std::initializer_list<ggml_op> topk_moe_late_softmax { GGML_OP_ARGSORT, GGML_OP_VIEW,
|
||||
GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
|
||||
GGML_OP_SOFT_MAX, GGML_OP_RESHAPE };
|
||||
|
|
@ -471,32 +464,6 @@ static constexpr std::initializer_list<std::array<int, 3>> topk_moe_early_softma
|
|||
{ 9, 0, 8 }, // reshape->src[0] == div
|
||||
};
|
||||
|
||||
//node #436 ( UNARY): ffn_moe_probs-10 ( 256K) [Vulka ] use=2: ffn_moe_logits-10 ( 256K) [Vulka ]
|
||||
//node #437 ( RESHAPE): ffn_moe_probs-10 (re ( 256K) [Vulka ] use=1: ffn_moe_probs-10 ( 256K) [Vulka ]
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||||
//node #438 ( ADD): ffn_moe_probs_biased ( 256K) [Vulka ] use=1: ffn_moe_probs-10 ( 256K) [Vulka ] blk.10.exp_probs_b.b ( 0K) [Vulka ]
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||||
//node #439 ( ARGSORT): ffn_moe_argsort-10 ( 256K) [Vulka ] use=1: ffn_moe_probs_biased ( 256K) [Vulka ]
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||||
//node #440 ( VIEW): ffn_moe_topk-10 ( 255K) [Vulka ] use=3: ffn_moe_argsort-10 ( 256K) [Vulka ]
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||||
//node #441 ( GET_ROWS): ffn_moe_weights-10 ( 12K) [Vulka ] use=1: ffn_moe_probs-10 (re ( 256K) [Vulka ] ffn_moe_topk-10 ( 255K) [Vulka ]
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||||
//node #442 ( RESHAPE): ffn_moe_weights-10 ( ( 12K) [Vulka ] use=2: ffn_moe_weights-10 ( 12K) [Vulka ]
|
||||
//node #443 ( SUM_ROWS): ffn_moe_weights_sum- ( 2K) [Vulka ] use=1: ffn_moe_weights-10 ( ( 12K) [Vulka ]
|
||||
//node #444 ( CLAMP): ffn_moe_weights_sum_ ( 2K) [Vulka ] use=1: ffn_moe_weights_sum- ( 2K) [Vulka ]
|
||||
//node #445 ( DIV): ffn_moe_weights_norm ( 12K) [Vulka ] use=1: ffn_moe_weights-10 ( ( 12K) [Vulka ] ffn_moe_weights_sum_ ( 2K) [Vulka ]
|
||||
//node #446 ( RESHAPE): ffn_moe_weights_norm ( 12K) [Vulka ] use=1: ffn_moe_weights_norm ( 12K) [Vulka ]
|
||||
static constexpr std::initializer_list<std::array<int, 3>> topk_moe_sigmoid_norm_bias_edges {
|
||||
{ 1, 0, 0 }, // reshape->src[0] == sigmoid
|
||||
{ 2, 0, 0 }, // add->src[0] == sigmoid
|
||||
{ 3, 0, 2 }, // argsort->src[0] == add
|
||||
{ 4, 0, 3 }, // view->src[0] == argsort
|
||||
{ 5, 0, 1 }, // get_rows->src[0] == reshape
|
||||
{ 5, 1, 4 }, // get_rows->src[1] == view
|
||||
{ 6, 0, 5 }, // reshape->src[0] == get_rows
|
||||
{ 7, 0, 6 }, // sum_rows->src[0] == reshape
|
||||
{ 8, 0, 7 }, // clamp->src[0] == sum_rows
|
||||
{ 9, 0, 6 }, // div->src[0] == reshape
|
||||
{ 9, 1, 8 }, // div->src[1] == clamp
|
||||
{10, 0, 9 }, // reshape->src[0] == div
|
||||
};
|
||||
|
||||
// same as early_softmax_norm but ending after the get_rows
|
||||
static constexpr std::initializer_list<std::array<int, 3>> topk_moe_early_softmax_edges {
|
||||
{ 1, 0, 0 }, // reshape->src[0] == softmax
|
||||
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|
@ -524,10 +491,16 @@ enum topk_moe_mode {
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TOPK_MOE_EARLY_SOFTMAX,
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TOPK_MOE_EARLY_SOFTMAX_NORM,
|
||||
TOPK_MOE_LATE_SOFTMAX,
|
||||
TOPK_MOE_SIGMOID_NORM_BIAS,
|
||||
TOPK_MOE_COUNT,
|
||||
};
|
||||
|
||||
static topk_moe_mode ggml_vk_num_additional_ops_to_topk_moe_mode(uint32_t num) {
|
||||
topk_moe_mode mode = num == topk_moe_early_softmax_norm.size() - 1 ? TOPK_MOE_EARLY_SOFTMAX_NORM :
|
||||
num == topk_moe_early_softmax.size() - 1 ? TOPK_MOE_EARLY_SOFTMAX :
|
||||
TOPK_MOE_LATE_SOFTMAX;
|
||||
return mode;
|
||||
}
|
||||
|
||||
static constexpr std::initializer_list<std::array<int, 3>> rope_view_set_rows_edges {
|
||||
{ 1, 0, 0 }, // view->src[0] == rope
|
||||
{ 2, 0, 1 }, // set_rows->src[0] == view
|
||||
|
|
@ -765,9 +738,6 @@ struct vk_device_struct {
|
|||
vk_pipeline pipeline_topk_f32[num_topk_pipelines];
|
||||
vk_pipeline pipeline_sum_rows_f32;
|
||||
vk_pipeline pipeline_cumsum_f32;
|
||||
vk_pipeline pipeline_cumsum_small_f32;
|
||||
vk_pipeline pipeline_cumsum_multipass1_f32;
|
||||
vk_pipeline pipeline_cumsum_multipass2_f32;
|
||||
vk_pipeline pipeline_argmax_f32;
|
||||
vk_pipeline pipeline_count_equal_i32;
|
||||
std::map<vk_solve_tri_pipeline_state, vk_pipeline> pipeline_solve_tri_f32;
|
||||
|
|
@ -796,7 +766,7 @@ struct vk_device_struct {
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|||
vk_pipeline pipeline_count_experts;
|
||||
|
||||
// [2] is for whether to take n_experts from spec constant (0) or push constant (1)
|
||||
vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][2];
|
||||
vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT][2];
|
||||
|
||||
std::vector<vk_pipeline_ref> all_pipelines;
|
||||
|
||||
|
|
@ -1211,11 +1181,6 @@ struct vk_op_topk_moe_push_constants {
|
|||
uint32_t n_expert_used;
|
||||
float clamp_min;
|
||||
float clamp_max;
|
||||
uint32_t gating_func;
|
||||
uint32_t has_bias;
|
||||
uint32_t with_norm;
|
||||
float output_scale;
|
||||
float output_bias;
|
||||
};
|
||||
|
||||
struct vk_op_add_id_push_constants {
|
||||
|
|
@ -1806,8 +1771,6 @@ struct ggml_backend_vk_context {
|
|||
// Bit 'i' means nodes[start_of_fusion + i] writes to memory.
|
||||
// If there's no fusion, bit 0 is still set.
|
||||
int fused_ops_write_mask {};
|
||||
topk_moe_mode fused_topk_moe_mode {};
|
||||
bool fused_topk_moe_scale {};
|
||||
|
||||
// for GGML_VK_PERF_LOGGER
|
||||
std::unique_ptr<vk_perf_logger> perf_logger;
|
||||
|
|
@ -2705,7 +2668,7 @@ static bool ggml_vk_matmul_shmem_support(const vk_device& device, const std::vec
|
|||
switch (src0_type) {
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
lut_size = 2*2048 + 4*2048;
|
||||
lut_size = 2*2048;
|
||||
break;
|
||||
case GGML_TYPE_IQ2_XXS:
|
||||
lut_size = 8*256;
|
||||
|
|
@ -3630,7 +3593,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
uint32_t rm_kq = 2;
|
||||
uint32_t rm_stdq_int = 1;
|
||||
uint32_t rm_kq_int = 1;
|
||||
auto const &rm_iq_int = [](uint32_t i) { return i == 0 ? 8u : 4u; };
|
||||
if (device->vendor_id == VK_VENDOR_ID_AMD) {
|
||||
if (device->architecture == AMD_GCN) {
|
||||
rm_stdq = 2;
|
||||
|
|
@ -3734,10 +3696,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q4_K][i], "mul_mat_vec_q4_k_q8_1_f32", arr_dmmv_q4_k_q8_1_f32_len[reduc], arr_dmmv_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q5_K][i], "mul_mat_vec_q5_k_q8_1_f32", arr_dmmv_q5_k_q8_1_f32_len[reduc], arr_dmmv_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_Q6_K][i], "mul_mat_vec_q6_k_q8_1_f32", arr_dmmv_q6_k_q8_1_f32_len[reduc], arr_dmmv_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int, i+1}, 1, true, use_subgroups, subgroup_size_int);
|
||||
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_IQ1_S][i], "mul_mat_vec_iq1_s_q8_1_f32", arr_dmmv_iq1_s_q8_1_f32_len[reduc], arr_dmmv_iq1_s_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(i), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(i), i+1}, 1, true, use_subgroups, subgroup_size_int);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_q8_1_f32[w][GGML_TYPE_IQ1_M][i], "mul_mat_vec_iq1_m_q8_1_f32", arr_dmmv_iq1_m_q8_1_f32_len[reduc], arr_dmmv_iq1_m_q8_1_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(i), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(i), i+1}, 1, true, use_subgroups, subgroup_size_int);
|
||||
|
||||
}
|
||||
#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
|
||||
}
|
||||
|
|
@ -3784,9 +3742,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_k_q8_1_f32", arr_dmmv_id_q4_k_q8_1_f32_len[reduc], arr_dmmv_id_q4_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_k_q8_1_f32", arr_dmmv_id_q5_k_q8_1_f32_len[reduc], arr_dmmv_id_q5_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_k_q8_1_f32", arr_dmmv_id_q6_k_q8_1_f32_len[reduc], arr_dmmv_id_q6_k_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_kq_int, 1, 1}, {wg_size_subgroup_int, 1*rm_kq_int}, 1, true, use_subgroups, subgroup_size_int);
|
||||
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_IQ1_S], "mul_mat_vec_id_iq1_s_q8_1_f32", arr_dmmv_id_iq1_s_q8_1_f32_len[reduc], arr_dmmv_id_iq1_s_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(0), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(0)}, 1, true, use_subgroups, subgroup_size_int);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_q8_1_f32[w][GGML_TYPE_IQ1_M], "mul_mat_vec_id_iq1_m_q8_1_f32", arr_dmmv_id_iq1_m_q8_1_f32_len[reduc], arr_dmmv_id_iq1_m_q8_1_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_push_constants), {1*rm_iq_int(0), 1, 1}, {wg_size_subgroup_int, 1*rm_iq_int(0)}, 1, true, use_subgroups, subgroup_size_int);
|
||||
}
|
||||
#endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT
|
||||
}
|
||||
|
|
@ -3794,7 +3749,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
#if !defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
|
||||
GGML_UNUSED(rm_stdq_int);
|
||||
GGML_UNUSED(rm_kq_int);
|
||||
GGML_UNUSED(rm_iq_int);
|
||||
#endif
|
||||
|
||||
// dequant shaders
|
||||
|
|
@ -4181,11 +4135,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
|
||||
ggml_vk_create_pipeline(device, device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);
|
||||
|
||||
const uint32_t cumsum_elem_per_thread = (device->vendor_id == VK_VENDOR_ID_AMD || device->vendor_id == VK_VENDOR_ID_INTEL) ? 2 : 4;
|
||||
ggml_vk_create_pipeline(device, device->pipeline_cumsum_f32, "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 256, device->subgroup_size, cumsum_elem_per_thread }, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_cumsum_small_f32, "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 128, device->subgroup_size, 1 }, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_cumsum_multipass1_f32, "cumsum_multipass1_f32", cumsum_multipass1_f32_len, cumsum_multipass1_f32_data, "main", 3, sizeof(vk_op_sum_rows_push_constants), {256, 1, 1}, { 256, device->subgroup_size }, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_cumsum_multipass2_f32, "cumsum_multipass2_f32", cumsum_multipass2_f32_len, cumsum_multipass2_f32_data, "main", 3, sizeof(vk_op_sum_rows_push_constants), {256, 1, 1}, { 256, device->subgroup_size }, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline(device, device->pipeline_cumsum_f32, "cumsum_f32", cumsum_f32_len, cumsum_f32_data, "main", 2, sizeof(vk_op_sum_rows_push_constants), {1, 1, 1}, { 128, device->subgroup_size }, 1, true, true, device->subgroup_size);
|
||||
|
||||
ggml_vk_create_pipeline(device, device->pipeline_count_equal_i32, "count_equal_i32", count_equal_i32_len, count_equal_i32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, { device->subgroup_size }, 1);
|
||||
|
||||
|
|
@ -4341,7 +4291,9 @@ static void ggml_vk_load_shaders(vk_device& device) {
|
|||
|
||||
for (uint32_t use_push = 0; use_push < 2; ++use_push) {
|
||||
for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) {
|
||||
ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][use_push], "topk_moe_f32_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 4, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, use_push}, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX][use_push], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 0, use_push}, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM][use_push], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 1, 0, use_push}, 1, true, true, device->subgroup_size);
|
||||
ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX][use_push], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<<i, 0, 1, use_push}, 1, true, true, device->subgroup_size);
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -5632,8 +5584,6 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context *
|
|||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
break;
|
||||
default:
|
||||
return nullptr;
|
||||
|
|
@ -5790,8 +5740,6 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(ggml_backend_vk_context
|
|||
case GGML_TYPE_Q4_K:
|
||||
case GGML_TYPE_Q5_K:
|
||||
case GGML_TYPE_Q6_K:
|
||||
case GGML_TYPE_IQ1_S:
|
||||
case GGML_TYPE_IQ1_M:
|
||||
break;
|
||||
default:
|
||||
return nullptr;
|
||||
|
|
@ -7057,7 +7005,7 @@ static bool ggml_vk_should_use_mmvq(const vk_device& device, uint32_t m, uint32_
|
|||
// Quantization overhead is not worth it for small k
|
||||
switch (device->vendor_id) {
|
||||
case VK_VENDOR_ID_NVIDIA:
|
||||
if (src0_type == GGML_TYPE_Q2_K || src0_type == GGML_TYPE_IQ1_S || src0_type == GGML_TYPE_IQ1_M) {
|
||||
if (src0_type == GGML_TYPE_Q2_K) {
|
||||
return true;
|
||||
}
|
||||
|
||||
|
|
@ -8736,9 +8684,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
|
|||
if (ctx->num_additional_fused_ops) {
|
||||
uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0])));
|
||||
GGML_ASSERT(idx < num_topk_moe_pipelines);
|
||||
topk_moe_mode mode = ggml_vk_num_additional_ops_to_topk_moe_mode(ctx->num_additional_fused_ops);
|
||||
// use n_experts from push constant if it's not equal to the power of two spec constant
|
||||
bool use_push = dst->ne[0] != (1u << idx);
|
||||
return ctx->device->pipeline_topk_moe[idx][use_push];
|
||||
return ctx->device->pipeline_topk_moe[idx][mode][use_push];
|
||||
}
|
||||
|
||||
if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) {
|
||||
|
|
@ -8811,11 +8760,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
|
|||
return nullptr;
|
||||
case GGML_OP_CUMSUM:
|
||||
if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
|
||||
if (src0->ne[0] <= 512) {
|
||||
return ctx->device->pipeline_cumsum_small_f32;
|
||||
} else {
|
||||
return ctx->device->pipeline_cumsum_f32;
|
||||
}
|
||||
return ctx->device->pipeline_cumsum_f32;
|
||||
}
|
||||
return nullptr;
|
||||
case GGML_OP_SOLVE_TRI:
|
||||
|
|
@ -10401,16 +10346,14 @@ static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& sub
|
|||
}
|
||||
|
||||
static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, ggml_cgraph * cgraph, int node_idx) {
|
||||
topk_moe_mode mode = ctx->fused_topk_moe_mode;
|
||||
topk_moe_mode mode = ggml_vk_num_additional_ops_to_topk_moe_mode(ctx->num_additional_fused_ops);
|
||||
ggml_tensor * logits = cgraph->nodes[node_idx + 0]->src[0];
|
||||
ggml_tensor * bias = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 2]->src[1] : logits;
|
||||
ggml_tensor * weights = cgraph->nodes[node_idx + ctx->num_additional_fused_ops];
|
||||
ggml_tensor * ids = (mode == TOPK_MOE_SIGMOID_NORM_BIAS) ? cgraph->nodes[node_idx + 4] :
|
||||
(mode == TOPK_MOE_LATE_SOFTMAX) ? cgraph->nodes[node_idx + 1] :
|
||||
cgraph->nodes[node_idx + 3];
|
||||
ggml_tensor * weights = (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) ? cgraph->nodes[node_idx + 9] :
|
||||
(mode == TOPK_MOE_EARLY_SOFTMAX) ? cgraph->nodes[node_idx + 4] :
|
||||
cgraph->nodes[node_idx + 5];
|
||||
ggml_tensor * ids = (mode == TOPK_MOE_LATE_SOFTMAX) ? cgraph->nodes[node_idx + 1] : cgraph->nodes[node_idx + 3];
|
||||
|
||||
GGML_ASSERT(logits->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(bias->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(weights->type == GGML_TYPE_F32);
|
||||
GGML_ASSERT(ids->type == GGML_TYPE_I32);
|
||||
|
||||
|
|
@ -10425,7 +10368,6 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx,
|
|||
ggml_pipeline_request_descriptor_sets(ctx, pipeline, 1);
|
||||
|
||||
vk_subbuffer logits_buf = ggml_vk_tensor_subbuffer(ctx, logits);
|
||||
vk_subbuffer bias_buf = ggml_vk_tensor_subbuffer(ctx, bias);
|
||||
vk_subbuffer weights_buf = ggml_vk_tensor_subbuffer(ctx, weights);
|
||||
vk_subbuffer ids_buf = ggml_vk_tensor_subbuffer(ctx, ids);
|
||||
|
||||
|
|
@ -10433,45 +10375,18 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx,
|
|||
pc.n_rows = n_rows;
|
||||
pc.n_experts_push = n_experts;
|
||||
pc.n_expert_used = n_expert_used;
|
||||
pc.clamp_min = -std::numeric_limits<float>::infinity();
|
||||
pc.clamp_max = std::numeric_limits<float>::infinity();
|
||||
if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) {
|
||||
ggml_tensor * clamp = cgraph->nodes[node_idx + 7];
|
||||
GGML_ASSERT(clamp->op == GGML_OP_CLAMP);
|
||||
pc.clamp_min = ggml_get_op_params_f32(clamp, 0);
|
||||
pc.clamp_max = ggml_get_op_params_f32(clamp, 1);
|
||||
}
|
||||
if (mode == TOPK_MOE_SIGMOID_NORM_BIAS) {
|
||||
ggml_tensor * clamp = cgraph->nodes[node_idx + 8];
|
||||
GGML_ASSERT(clamp->op == GGML_OP_CLAMP);
|
||||
pc.clamp_min = ggml_get_op_params_f32(clamp, 0);
|
||||
pc.clamp_max = ggml_get_op_params_f32(clamp, 1);
|
||||
}
|
||||
|
||||
#define GATING_FUNC_SOFTMAX 0
|
||||
#define GATING_FUNC_SIGMOID 1
|
||||
#define GATING_FUNC_SOFTMAX_WEIGHT 2
|
||||
|
||||
pc.gating_func = mode == TOPK_MOE_SIGMOID_NORM_BIAS ? GATING_FUNC_SIGMOID :
|
||||
mode == TOPK_MOE_LATE_SOFTMAX ? GATING_FUNC_SOFTMAX_WEIGHT :
|
||||
GATING_FUNC_SOFTMAX;
|
||||
pc.has_bias = mode == TOPK_MOE_SIGMOID_NORM_BIAS;
|
||||
pc.with_norm = mode == TOPK_MOE_EARLY_SOFTMAX_NORM || mode == TOPK_MOE_SIGMOID_NORM_BIAS;
|
||||
if (ctx->fused_topk_moe_scale) {
|
||||
GGML_ASSERT(weights->op == GGML_OP_SCALE);
|
||||
pc.output_scale = ggml_get_op_params_f32(weights, 0);
|
||||
pc.output_bias = ggml_get_op_params_f32(weights, 1);
|
||||
} else {
|
||||
pc.output_scale = 1.0f;
|
||||
pc.output_bias = 0.0f;
|
||||
}
|
||||
|
||||
GGML_ASSERT(n_expert_used <= n_experts);
|
||||
|
||||
const uint32_t rows_per_block = 4;
|
||||
std::array<uint32_t, 3> elements = { CEIL_DIV(n_rows, rows_per_block), 1, 1 };
|
||||
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {logits_buf, bias_buf, weights_buf, ids_buf}, pc, elements);
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, {logits_buf, weights_buf, ids_buf}, pc, elements);
|
||||
}
|
||||
|
||||
static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_cgraph * cgraph, int node_idx, bool backprop) {
|
||||
|
|
@ -10719,50 +10634,8 @@ static void ggml_vk_mean(ggml_backend_vk_context * ctx, vk_context& subctx, cons
|
|||
}
|
||||
|
||||
static void ggml_vk_cumsum(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
||||
vk_op_sum_rows_push_constants pc = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
|
||||
// Use the single pass shader when the rows are small or there are enough rows to fill the GPU.
|
||||
// For fewer, larger rows, use the multipass shader to spread each row across SMs.
|
||||
if (dst->ne[0] <= 4096 || ggml_nrows(dst) >= ctx->device->shader_core_count) {
|
||||
ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CUMSUM, pc);
|
||||
return;
|
||||
}
|
||||
|
||||
// First pass computes partial sums within a block, and stores the last partial
|
||||
// to the temp buffer. Second pass sums the block partials from the temp buffer
|
||||
// and adds that to the result of the first pass.
|
||||
vk_pipeline pipeline1 = ctx->device->pipeline_cumsum_multipass1_f32;
|
||||
vk_pipeline pipeline2 = ctx->device->pipeline_cumsum_multipass2_f32;
|
||||
GGML_ASSERT(pipeline1 != nullptr && pipeline2 != nullptr);
|
||||
|
||||
ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1);
|
||||
ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1);
|
||||
|
||||
std::array<uint32_t, 3> elements;
|
||||
|
||||
elements[0] = dst->ne[0];
|
||||
elements[1] = (uint32_t)ggml_nrows(dst);
|
||||
elements[2] = 1;
|
||||
|
||||
size_t temp_size = sizeof(float) * elements[0] * ggml_nrows(dst);
|
||||
|
||||
if (ctx->prealloc_size_split_k < temp_size) {
|
||||
ctx->prealloc_size_split_k = temp_size;
|
||||
ggml_vk_preallocate_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
vk_subbuffer src_buf = ggml_vk_tensor_subbuffer(ctx, src0);
|
||||
vk_subbuffer dst_buf = ggml_vk_tensor_subbuffer(ctx, dst);
|
||||
vk_subbuffer temp_buf = ggml_vk_subbuffer(ctx, ctx->prealloc_split_k, 0);
|
||||
|
||||
if (ctx->prealloc_split_k_need_sync) {
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
}
|
||||
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, {src_buf, dst_buf, temp_buf}, pc, elements);
|
||||
ggml_vk_sync_buffers(ctx, subctx);
|
||||
ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, {src_buf, dst_buf, temp_buf}, pc, elements);
|
||||
|
||||
ctx->prealloc_split_k_need_sync = true;
|
||||
vk_op_sum_rows_push_constants p = vk_op_sum_rows_push_constants_init(src0, dst, src0->ne[0]);
|
||||
ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_CUMSUM, p);
|
||||
}
|
||||
|
||||
static void ggml_vk_argmax(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
|
||||
|
|
@ -12255,11 +12128,6 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
|
|||
|
||||
break;
|
||||
case GGML_OP_UNARY:
|
||||
if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
|
||||
ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx);
|
||||
break;
|
||||
}
|
||||
|
||||
switch (ggml_get_unary_op(node)) {
|
||||
case GGML_UNARY_OP_EXP:
|
||||
case GGML_UNARY_OP_SILU:
|
||||
|
|
@ -12307,7 +12175,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
|
|||
|
||||
break;
|
||||
case GGML_OP_SOFT_MAX:
|
||||
if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
|
||||
if (ctx->num_additional_fused_ops) {
|
||||
ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx);
|
||||
} else {
|
||||
ggml_vk_soft_max(ctx, compute_ctx, src0, src1, src2, node);
|
||||
|
|
@ -12327,7 +12195,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
|
|||
|
||||
break;
|
||||
case GGML_OP_ARGSORT:
|
||||
if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
|
||||
if (ctx->num_additional_fused_ops) {
|
||||
ggml_vk_topk_moe(ctx, compute_ctx, cgraph, node_idx);
|
||||
} else {
|
||||
ggml_vk_argsort(ctx, compute_ctx, src0, node);
|
||||
|
|
@ -13180,24 +13048,6 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
|
|||
get_rows = cgraph->nodes[node_idx + 4];
|
||||
argsort = cgraph->nodes[node_idx + 2];
|
||||
break;
|
||||
case TOPK_MOE_SIGMOID_NORM_BIAS:
|
||||
softmax = cgraph->nodes[node_idx + 0]; // really sigmoid
|
||||
weights = cgraph->nodes[node_idx + 10];
|
||||
get_rows = cgraph->nodes[node_idx + 5];
|
||||
argsort = cgraph->nodes[node_idx + 3];
|
||||
if (ggml_get_unary_op(softmax) != GGML_UNARY_OP_SIGMOID) {
|
||||
return false;
|
||||
}
|
||||
// bias is expected to be 1D
|
||||
if (ggml_nrows(cgraph->nodes[node_idx + 2]->src[1]) != 1 ||
|
||||
!ggml_is_contiguous(cgraph->nodes[node_idx + 2]->src[1])) {
|
||||
return false;
|
||||
}
|
||||
// sigmoid fusion seems to generate infinities on moltenvk
|
||||
if (ctx->device->driver_id == vk::DriverId::eMoltenvk) {
|
||||
return false;
|
||||
}
|
||||
break;
|
||||
case TOPK_MOE_EARLY_SOFTMAX:
|
||||
softmax = cgraph->nodes[node_idx + 0];
|
||||
weights = cgraph->nodes[node_idx + 4];
|
||||
|
|
@ -13221,28 +13071,26 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc
|
|||
probs = probs->src[0];
|
||||
ggml_tensor * selection_probs = argsort->src[0];
|
||||
|
||||
if (probs != selection_probs && mode != TOPK_MOE_SIGMOID_NORM_BIAS) {
|
||||
if (probs != selection_probs) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const float * op_params = (const float *)softmax->op_params;
|
||||
|
||||
float scale = op_params[0];
|
||||
float max_bias = op_params[1];
|
||||
|
||||
if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
if (softmax->op == GGML_OP_SOFT_MAX) {
|
||||
const float * op_params = (const float *)softmax->op_params;
|
||||
if (scale != 1.0f || max_bias != 0.0f) {
|
||||
return false;
|
||||
}
|
||||
|
||||
float scale = op_params[0];
|
||||
float max_bias = op_params[1];
|
||||
|
||||
if (scale != 1.0f || max_bias != 0.0f) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// don't fuse when masks or sinks are present
|
||||
if (softmax->src[1] || softmax->src[2]) {
|
||||
return false;
|
||||
}
|
||||
// don't fuse when masks or sinks are present
|
||||
if (softmax->src[1] || softmax->src[2]) {
|
||||
return false;
|
||||
}
|
||||
|
||||
const int n_expert = softmax->ne[0];
|
||||
|
|
@ -13515,8 +13363,6 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
|
|||
total_mul_mat_bytes += bytes;
|
||||
}
|
||||
|
||||
ctx->fused_topk_moe_mode = TOPK_MOE_COUNT;
|
||||
ctx->fused_topk_moe_scale = false;
|
||||
const char *fusion_string {};
|
||||
if (!ctx->device->disable_fusion) {
|
||||
uint32_t num_adds = ggml_vk_fuse_multi_add(ctx, cgraph, i);
|
||||
|
|
@ -13562,23 +13408,13 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
|
|||
ctx->num_additional_fused_ops = topk_moe_early_softmax_norm.size() - 1;
|
||||
// view of argsort writes to memory
|
||||
ctx->fused_ops_write_mask |= 1 << 3;
|
||||
ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX_NORM;
|
||||
fusion_string = "TOPK_MOE_EARLY_SOFTMAX_NORM";
|
||||
} else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_sigmoid_norm_bias, { i + 4, i + 10 }) &&
|
||||
ggml_check_edges(cgraph, i, topk_moe_sigmoid_norm_bias_edges) &&
|
||||
ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_SIGMOID_NORM_BIAS)) {
|
||||
ctx->num_additional_fused_ops = topk_moe_sigmoid_norm_bias.size() - 1;
|
||||
// view of argsort writes to memory
|
||||
ctx->fused_ops_write_mask |= 1 << 4;
|
||||
ctx->fused_topk_moe_mode = TOPK_MOE_SIGMOID_NORM_BIAS;
|
||||
fusion_string = "TOPK_MOE_SIGMOID_NORM_BIAS";
|
||||
} else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_early_softmax, { i + 3, i + 4 }) &&
|
||||
ggml_check_edges(cgraph, i, topk_moe_early_softmax_edges) &&
|
||||
ggml_vk_can_fuse_topk_moe(ctx, cgraph, i, TOPK_MOE_EARLY_SOFTMAX)) {
|
||||
ctx->num_additional_fused_ops = topk_moe_early_softmax.size() - 1;
|
||||
// view of argsort writes to memory
|
||||
ctx->fused_ops_write_mask |= 1 << 3;
|
||||
ctx->fused_topk_moe_mode = TOPK_MOE_EARLY_SOFTMAX;
|
||||
fusion_string = "TOPK_MOE_EARLY_SOFTMAX";
|
||||
} else if (ggml_can_fuse_subgraph(cgraph, i, topk_moe_late_softmax, { i + 1, i + 5 }) &&
|
||||
ggml_check_edges(cgraph, i, topk_moe_late_softmax_edges) &&
|
||||
|
|
@ -13586,17 +13422,8 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
|
|||
ctx->num_additional_fused_ops = topk_moe_late_softmax.size() - 1;
|
||||
// view of argsort writes to memory
|
||||
ctx->fused_ops_write_mask |= 1 << 1;
|
||||
ctx->fused_topk_moe_mode = TOPK_MOE_LATE_SOFTMAX;
|
||||
fusion_string = "TOPK_MOE_LATE_SOFTMAX";
|
||||
}
|
||||
if (ctx->fused_topk_moe_mode != TOPK_MOE_COUNT) {
|
||||
// Look for an additional scale op to fuse - occurs in deepseek2 and nemotron3 nano.
|
||||
if (ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops - 1, { GGML_OP_DIV, GGML_OP_RESHAPE, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 }) ||
|
||||
ggml_can_fuse_subgraph(cgraph, i + ctx->num_additional_fused_ops, { GGML_OP_GET_ROWS, GGML_OP_SCALE }, { i + ctx->num_additional_fused_ops + 1 })) {
|
||||
ctx->fused_topk_moe_scale = true;
|
||||
ctx->num_additional_fused_ops++;
|
||||
}
|
||||
}
|
||||
}
|
||||
ctx->fused_ops_write_mask |= 1 << ctx->num_additional_fused_ops;
|
||||
|
||||
|
|
@ -13775,9 +13602,6 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph *
|
|||
if (keep_pattern(topk_moe_early_softmax_norm)) {
|
||||
continue;
|
||||
}
|
||||
if (keep_pattern(topk_moe_sigmoid_norm_bias)) {
|
||||
continue;
|
||||
}
|
||||
if (keep_pattern(topk_moe_early_softmax)) {
|
||||
continue;
|
||||
}
|
||||
|
|
@ -13804,7 +13628,6 @@ static void ggml_vk_graph_optimize(ggml_backend_t backend, struct ggml_cgraph *
|
|||
}
|
||||
// Don't pull forward nodes from fusion patterns
|
||||
if (match_pattern(topk_moe_early_softmax_norm, j) ||
|
||||
match_pattern(topk_moe_sigmoid_norm_bias, j) ||
|
||||
match_pattern(topk_moe_early_softmax, j) ||
|
||||
match_pattern(topk_moe_late_softmax, j)) {
|
||||
continue;
|
||||
|
|
|
|||
|
|
@ -14,7 +14,6 @@ layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
|
|||
|
||||
layout (constant_id = 0) const uint BLOCK_SIZE = 128;
|
||||
layout (constant_id = 1) const uint SUBGROUP_SIZE = 32;
|
||||
layout (constant_id = 2) const uint ELEM_PER_THREAD = 4;
|
||||
|
||||
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
|
||||
|
||||
|
|
@ -39,45 +38,32 @@ void main() {
|
|||
last_sum = 0;
|
||||
}
|
||||
|
||||
uint col = tid * ELEM_PER_THREAD;
|
||||
uint num_iter = CEIL_DIV(p.n_cols, BLOCK_SIZE * ELEM_PER_THREAD);
|
||||
uint col = tid;
|
||||
uint num_iter = CEIL_DIV(p.n_cols, BLOCK_SIZE);
|
||||
for (int i = 0; i < num_iter; ++i) {
|
||||
FLOAT_TYPE v[ELEM_PER_THREAD];
|
||||
FLOAT_TYPE thread_sum = 0;
|
||||
[[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) {
|
||||
if (col + j < p.n_cols) {
|
||||
thread_sum += FLOAT_TYPE(data_a[src_idx + col + j]);
|
||||
}
|
||||
v[j] = thread_sum;
|
||||
FLOAT_TYPE v = 0;
|
||||
if (col < p.n_cols) {
|
||||
v = FLOAT_TYPE(data_a[src_idx + col]);
|
||||
}
|
||||
v = subgroupInclusiveAdd(v);
|
||||
|
||||
thread_sum = subgroupExclusiveAdd(thread_sum);
|
||||
[[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) {
|
||||
v[j] += thread_sum;
|
||||
}
|
||||
// Store the largest partial sum for each subgroup, then add the partials for all
|
||||
// lower subgroups and the final partial sum from the previous iteration.
|
||||
if (gl_SubgroupInvocationID == SUBGROUP_SIZE - 1) {
|
||||
partial[subgroup_id] = v[ELEM_PER_THREAD - 1];
|
||||
partial[subgroup_id] = v;
|
||||
}
|
||||
barrier();
|
||||
for (int s = 0; s < subgroup_id; ++s) {
|
||||
[[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) {
|
||||
v[j] += partial[s];
|
||||
}
|
||||
}
|
||||
[[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) {
|
||||
v[j] += last_sum;
|
||||
for (int j = 0; j < subgroup_id; ++j) {
|
||||
v += partial[j];
|
||||
}
|
||||
v += last_sum;
|
||||
barrier();
|
||||
if (tid == BLOCK_SIZE - 1) {
|
||||
last_sum = v[ELEM_PER_THREAD - 1];
|
||||
last_sum = v;
|
||||
}
|
||||
[[unroll]] for (uint j = 0; j < ELEM_PER_THREAD; ++j) {
|
||||
if (col + j < p.n_cols) {
|
||||
data_d[dst_idx + col + j] = D_TYPE(v[j]);
|
||||
}
|
||||
if (col < p.n_cols) {
|
||||
data_d[dst_idx + col] = D_TYPE(v);
|
||||
}
|
||||
col += BLOCK_SIZE * ELEM_PER_THREAD;
|
||||
col += BLOCK_SIZE;
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,60 +0,0 @@
|
|||
#version 450
|
||||
|
||||
#include "types.glsl"
|
||||
#include "sum_rows.glsl"
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : enable
|
||||
#extension GL_KHR_shader_subgroup_arithmetic : enable
|
||||
#extension GL_KHR_shader_subgroup_basic : enable
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
|
||||
layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};
|
||||
layout (binding = 2) writeonly buffer T {D_TYPE data_t[];};
|
||||
|
||||
layout (constant_id = 0) const uint BLOCK_SIZE = 128;
|
||||
layout (constant_id = 1) const uint SUBGROUP_SIZE = 32;
|
||||
|
||||
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
|
||||
|
||||
shared FLOAT_TYPE partial[BLOCK_SIZE / SUBGROUP_SIZE];
|
||||
|
||||
void main() {
|
||||
const uint row = gl_WorkGroupID.y;
|
||||
const uint tid = gl_LocalInvocationID.x;
|
||||
const uint col = gl_GlobalInvocationID.x;
|
||||
|
||||
const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L);
|
||||
const uint i03_offset = i03 * p.ne01*p.ne02;
|
||||
const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L);
|
||||
const uint i01 = row - i03_offset - i02*p.ne01;
|
||||
|
||||
const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03;
|
||||
const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13;
|
||||
|
||||
uint subgroup_id = tid / SUBGROUP_SIZE;
|
||||
|
||||
FLOAT_TYPE v = 0;
|
||||
if (col < p.n_cols) {
|
||||
v = FLOAT_TYPE(data_a[src_idx + col]);
|
||||
}
|
||||
v = subgroupInclusiveAdd(v);
|
||||
|
||||
// Store the largest partial sum for each subgroup, then add the partials for all
|
||||
// lower subgroups and the final partial sum from the previous iteration.
|
||||
if (gl_SubgroupInvocationID == SUBGROUP_SIZE - 1) {
|
||||
partial[subgroup_id] = v;
|
||||
}
|
||||
barrier();
|
||||
for (int j = 0; j < subgroup_id; ++j) {
|
||||
v += partial[j];
|
||||
}
|
||||
barrier();
|
||||
if (tid == BLOCK_SIZE - 1) {
|
||||
data_t[gl_WorkGroupID.x + gl_NumWorkGroups.x * row] = v;
|
||||
}
|
||||
if (col < p.n_cols) {
|
||||
data_d[dst_idx + col] = D_TYPE(v);
|
||||
}
|
||||
}
|
||||
|
|
@ -1,66 +0,0 @@
|
|||
#version 450
|
||||
|
||||
#include "types.glsl"
|
||||
#include "sum_rows.glsl"
|
||||
|
||||
#extension GL_EXT_control_flow_attributes : enable
|
||||
#extension GL_KHR_shader_subgroup_arithmetic : enable
|
||||
#extension GL_KHR_shader_subgroup_basic : enable
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
||||
|
||||
layout (binding = 0) readonly buffer A {A_TYPE data_a[];};
|
||||
layout (binding = 1) buffer D {D_TYPE data_d[];};
|
||||
layout (binding = 2) readonly buffer T {D_TYPE data_t[];};
|
||||
|
||||
layout (constant_id = 0) const uint BLOCK_SIZE = 128;
|
||||
layout (constant_id = 1) const uint SUBGROUP_SIZE = 32;
|
||||
|
||||
#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b))
|
||||
|
||||
shared FLOAT_TYPE temp[BLOCK_SIZE / SUBGROUP_SIZE];
|
||||
|
||||
void main() {
|
||||
const uint row = gl_WorkGroupID.y;
|
||||
const uint tid = gl_LocalInvocationID.x;
|
||||
|
||||
const uint i03 = fastdiv(row, p.ne0_12mp, p.ne0_12L);
|
||||
const uint i03_offset = i03 * p.ne01*p.ne02;
|
||||
const uint i02 = fastdiv(row - i03_offset, p.ne0_1mp, p.ne0_1L);
|
||||
const uint i01 = row - i03_offset - i02*p.ne01;
|
||||
|
||||
const uint src_idx = get_aoffset() + i01 * p.nb01 + i02 * p.nb02 + i03 * p.nb03;
|
||||
const uint dst_idx = get_doffset() + i01 * p.nb11 + i02 * p.nb12 + i03 * p.nb13;
|
||||
|
||||
const uint col = gl_GlobalInvocationID.x;
|
||||
|
||||
float v = 0;
|
||||
// prefetch value we're adding to
|
||||
if (col < p.n_cols) {
|
||||
v = data_d[dst_idx + col];
|
||||
}
|
||||
|
||||
// compute the sum of all previous blocks
|
||||
uint c = tid;
|
||||
float sum = 0;
|
||||
while (c < gl_WorkGroupID.x) {
|
||||
sum += data_t[c + gl_NumWorkGroups.x * row];
|
||||
c += BLOCK_SIZE;
|
||||
}
|
||||
|
||||
sum = subgroupAdd(sum);
|
||||
if (gl_SubgroupInvocationID == 0) {
|
||||
temp[gl_SubgroupID] = sum;
|
||||
}
|
||||
barrier();
|
||||
sum = 0;
|
||||
[[unroll]] for (uint s = 0; s < BLOCK_SIZE / SUBGROUP_SIZE; ++s) {
|
||||
sum += temp[s];
|
||||
}
|
||||
|
||||
// Add the sum to what the first pass computed
|
||||
if (col < p.n_cols) {
|
||||
data_d[dst_idx + col] = v + sum;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -14,8 +14,6 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
|
|||
#define K_PER_ITER 8
|
||||
#elif defined(DATA_A_QUANT_K)
|
||||
#define K_PER_ITER 16
|
||||
#elif defined(DATA_A_IQ1_S) || defined(DATA_A_IQ1_M)
|
||||
#define K_PER_ITER 32
|
||||
#else
|
||||
#error unimplemented
|
||||
#endif
|
||||
|
|
@ -51,15 +49,6 @@ void iter(inout FLOAT_TYPE temp[NUM_COLS][NUM_ROWS], const uint first_row, const
|
|||
cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 1];
|
||||
cache_b_qs[2] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 2];
|
||||
cache_b_qs[3] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + b_qs_idx * 4 + 3];
|
||||
#elif K_PER_ITER == 32
|
||||
cache_b_qs[0] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 ];
|
||||
cache_b_qs[1] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 1];
|
||||
cache_b_qs[2] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 2];
|
||||
cache_b_qs[3] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 3];
|
||||
cache_b_qs[4] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 4];
|
||||
cache_b_qs[5] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 5];
|
||||
cache_b_qs[6] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 6];
|
||||
cache_b_qs[7] = data_b[b_block_idx_outer].qs[b_block_idx_inner * 8 + 7];
|
||||
#else
|
||||
#error unimplemented
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -377,118 +377,3 @@ FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
|
|||
return FLOAT_TYPE(float(cache_b_ds.x) * float(d_scale) * float(q_sum));
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(DATA_A_IQ1_S)
|
||||
void repack8(uint ib, uint iqs, out i32vec4 out0, out i32vec4 out1) {
|
||||
const uint ib32 = iqs / 32;
|
||||
|
||||
const uint qh = data_a[ib].qh[ib32];
|
||||
|
||||
const uint qs16_0 = data_a_packed16[ib].qs[(4 * ib32 + 0) / 2];
|
||||
const uint qs16_1 = data_a_packed16[ib].qs[(4 * ib32 + 2) / 2];
|
||||
|
||||
const uint qs0 = qs16_0 & 0xFF;
|
||||
const uint qs1 = qs16_0 >> 8;
|
||||
const uint qs2 = qs16_1 & 0xFF;
|
||||
const uint qs3 = qs16_1 >> 8;
|
||||
|
||||
const uint hi0 = bitfieldExtract(qh, 3 * int(0), 3);
|
||||
const uint hi1 = bitfieldExtract(qh, 3 * int(1), 3);
|
||||
const uint hi2 = bitfieldExtract(qh, 3 * int(2), 3);
|
||||
const uint hi3 = bitfieldExtract(qh, 3 * int(3), 3);
|
||||
|
||||
const int32_t grid0 = int32_t(iq1s_grid_gpu[qs0 | (hi0 << 8)]);
|
||||
const int32_t grid1 = int32_t(iq1s_grid_gpu[qs1 | (hi1 << 8)]);
|
||||
const int32_t grid2 = int32_t(iq1s_grid_gpu[qs2 | (hi2 << 8)]);
|
||||
const int32_t grid3 = int32_t(iq1s_grid_gpu[qs3 | (hi3 << 8)]);
|
||||
|
||||
out0 = i32vec4((grid0 >> 0) & 0x0F0F0F0F,
|
||||
(grid0 >> 4) & 0x0F0F0F0F,
|
||||
(grid1 >> 0) & 0x0F0F0F0F,
|
||||
(grid1 >> 4) & 0x0F0F0F0F);
|
||||
out1 = i32vec4((grid2 >> 0) & 0x0F0F0F0F,
|
||||
(grid2 >> 4) & 0x0F0F0F0F,
|
||||
(grid3 >> 0) & 0x0F0F0F0F,
|
||||
(grid3 >> 4) & 0x0F0F0F0F);
|
||||
}
|
||||
|
||||
vec2 get_dm(uint ib, uint iqs) {
|
||||
const uint ib32 = iqs / 32;
|
||||
|
||||
const uint qh = data_a[ib].qh[ib32];
|
||||
const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA;
|
||||
|
||||
const float d = float(data_a[ib].d);
|
||||
const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1);
|
||||
|
||||
// the -1 cancels out the bias in iq1s_grid_gpu
|
||||
return FLOAT_TYPE_VEC2(dl, dl * (delta - 1));
|
||||
}
|
||||
|
||||
FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
|
||||
int32_t q_sum = 0;
|
||||
|
||||
const uint ib_k = ib_a / 8;
|
||||
const uint iqs_k = (ib_a % 8) * 32 + iqs * 32;
|
||||
|
||||
i32vec4 qs_a0;
|
||||
i32vec4 qs_a1;
|
||||
repack8(ib_k, iqs_k, qs_a0, qs_a1);
|
||||
|
||||
const vec2 dm = get_dm(ib_k, iqs_k);
|
||||
|
||||
q_sum += dotPacked4x8EXT(qs_a0.x, cache_b_qs[0]);
|
||||
q_sum += dotPacked4x8EXT(qs_a0.y, cache_b_qs[1]);
|
||||
q_sum += dotPacked4x8EXT(qs_a0.z, cache_b_qs[2]);
|
||||
q_sum += dotPacked4x8EXT(qs_a0.w, cache_b_qs[3]);
|
||||
q_sum += dotPacked4x8EXT(qs_a1.x, cache_b_qs[4]);
|
||||
q_sum += dotPacked4x8EXT(qs_a1.y, cache_b_qs[5]);
|
||||
q_sum += dotPacked4x8EXT(qs_a1.z, cache_b_qs[6]);
|
||||
q_sum += dotPacked4x8EXT(qs_a1.w, cache_b_qs[7]);
|
||||
|
||||
return FLOAT_TYPE(float(cache_b_ds.x) * float(dm.x) * float(q_sum) + float(dm.y) * float(cache_b_ds.y));
|
||||
}
|
||||
#endif
|
||||
|
||||
#if defined(DATA_A_IQ1_M)
|
||||
FLOAT_TYPE mmvq_dot_product(const uint ib_a, const uint iqs) {
|
||||
const uint ib_k = ib_a / 8;
|
||||
const uint iqs_k = (ib_a % 8) * 32 + iqs * 32;
|
||||
|
||||
const uint ib32 = iqs_k / 32;
|
||||
const uint ib64 = ib32 / 2;
|
||||
|
||||
const uint16_t[4] scales = data_a[ib_k].scales;
|
||||
const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12;
|
||||
const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x);
|
||||
|
||||
const uint qs32 = data_a_packed32[ib_k].qs[ib32];
|
||||
const uint qh16 = data_a_packed16[ib_k].qh[ib32];
|
||||
|
||||
float sum = 0;
|
||||
const uint sc = data_a[ib_k].scales[ib64];
|
||||
[[unroll]] for (int l = 0; l < 4; ++l) {
|
||||
const uint ib16 = 2 * ib32 + l / 2;
|
||||
const float dl = d * (2 * bitfieldExtract(sc, 3 * int(ib16 & 3), 3) + 1);
|
||||
const uint qh = qh16 >> (4 * l);
|
||||
const uint qs = (qs32 >> (8 * l)) & 0xFF;
|
||||
const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
|
||||
|
||||
const int32_t grid = int32_t(iq1s_grid_gpu[qs | ((qh & 7) << 8)]);
|
||||
|
||||
int32_t q_sum = 0;
|
||||
q_sum += dotPacked4x8EXT((grid >> 0) & 0x0F0F0F0F, cache_b_qs[2 * l + 0]);
|
||||
q_sum += dotPacked4x8EXT((grid >> 4) & 0x0F0F0F0F, cache_b_qs[2 * l + 1]);
|
||||
|
||||
int32_t y_sum = 0;
|
||||
y_sum += dotPacked4x8EXT(int(0x01010101), cache_b_qs[2 * l + 0]);
|
||||
y_sum += dotPacked4x8EXT(int(0x01010101), cache_b_qs[2 * l + 1]);
|
||||
|
||||
// the -1 cancels out the bias in iq1s_grid_gpu
|
||||
sum += dl * (q_sum + y_sum * (delta - 1));
|
||||
}
|
||||
sum *= float(cache_b_ds.x);
|
||||
|
||||
return sum;
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -7,10 +7,6 @@
|
|||
|
||||
#include "types.glsl"
|
||||
|
||||
#define GATING_FUNC_SOFTMAX 0
|
||||
#define GATING_FUNC_SIGMOID 1
|
||||
#define GATING_FUNC_SOFTMAX_WEIGHT 2
|
||||
|
||||
layout (push_constant) uniform parameter
|
||||
{
|
||||
uint n_rows;
|
||||
|
|
@ -18,18 +14,15 @@ layout (push_constant) uniform parameter
|
|||
uint n_expert_used;
|
||||
float clamp_min;
|
||||
float clamp_max;
|
||||
uint gating_func;
|
||||
uint has_bias;
|
||||
uint with_norm;
|
||||
float output_scale;
|
||||
float output_bias;
|
||||
};
|
||||
|
||||
layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in;
|
||||
|
||||
layout(constant_id = 0) const uint WARP_SIZE = 32;
|
||||
layout(constant_id = 1) const uint n_experts_spec = 512;
|
||||
layout(constant_id = 2) const bool nexperts_use_push = false;
|
||||
layout(constant_id = 2) const bool with_norm = true;
|
||||
layout(constant_id = 3) const bool late_softmax = false;
|
||||
layout(constant_id = 4) const bool nexperts_use_push = false;
|
||||
|
||||
uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec;
|
||||
|
||||
|
|
@ -38,9 +31,8 @@ uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec;
|
|||
const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE);
|
||||
|
||||
layout (binding = 0, std430) readonly buffer Logits {float logits[];};
|
||||
layout (binding = 1, std430) readonly buffer BiasProbs {float bias[];};
|
||||
layout (binding = 2, std430) writeonly buffer Weights {float weights[];};
|
||||
layout (binding = 3, std430) writeonly buffer Ids {uint ids[];};
|
||||
layout (binding = 1, std430) writeonly buffer Weights {float weights[];};
|
||||
layout (binding = 2, std430) writeonly buffer Ids {uint ids[];};
|
||||
|
||||
const float INFINITY = 1.0 / 0.0;
|
||||
|
||||
|
|
@ -95,40 +87,20 @@ void main() {
|
|||
}
|
||||
|
||||
const uint logits_offset = n_experts * row;
|
||||
const uint bias_offset = 0; // 1D
|
||||
const uint weights_offset = n_expert_used * row;
|
||||
const uint ids_offset = n_experts * row;
|
||||
const uint lane = gl_SubgroupInvocationID;
|
||||
|
||||
float probs[experts_per_thread];
|
||||
float wt[experts_per_thread];
|
||||
|
||||
[[unroll]]
|
||||
for (uint i = 0; i < n_experts; i += WARP_SIZE) {
|
||||
const uint expert = i + lane;
|
||||
probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? logits[logits_offset + expert] : -INFINITY;
|
||||
wt[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? logits[logits_offset + expert] : -INFINITY;
|
||||
}
|
||||
|
||||
if (gating_func == GATING_FUNC_SOFTMAX) {
|
||||
softmax_warp_inplace(probs, n_experts, lane, nexperts_use_push);
|
||||
} else if (gating_func == GATING_FUNC_SIGMOID) {
|
||||
[[unroll]]
|
||||
for (int i = 0; i < experts_per_thread; i++) {
|
||||
probs[i] = 1.f / (1.f + exp(-probs[i]));
|
||||
}
|
||||
}
|
||||
|
||||
float selection_probs[experts_per_thread];
|
||||
if (has_bias != 0) {
|
||||
[[unroll]]
|
||||
for (uint i = 0; i < n_experts; i += WARP_SIZE) {
|
||||
const uint expert = i + lane;
|
||||
selection_probs[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? probs[i / WARP_SIZE] + bias[bias_offset + expert] : -INFINITY;
|
||||
}
|
||||
} else {
|
||||
[[unroll]]
|
||||
for (int i = 0; i < experts_per_thread; i++) {
|
||||
selection_probs[i] = probs[i];
|
||||
}
|
||||
if (!late_softmax) {
|
||||
softmax_warp_inplace(wt, n_experts, lane, nexperts_use_push);
|
||||
}
|
||||
|
||||
// at this point, each thread holds a portion of softmax,
|
||||
|
|
@ -145,16 +117,14 @@ void main() {
|
|||
}
|
||||
|
||||
for (int k = 0; k < n_expert_used; k++) {
|
||||
float max_val = probs[0];
|
||||
float max_val_s = selection_probs[0];
|
||||
float max_val = wt[0];
|
||||
uint max_expert = lane;
|
||||
|
||||
[[unroll]]
|
||||
for (int i = 1; i < experts_per_thread; i++) {
|
||||
const uint expert = lane + i * WARP_SIZE;
|
||||
if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && selection_probs[i] > max_val_s) {
|
||||
max_val = probs[i];
|
||||
max_val_s = selection_probs[i];
|
||||
if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) {
|
||||
max_val = wt[i];
|
||||
max_expert = expert;
|
||||
}
|
||||
}
|
||||
|
|
@ -162,11 +132,9 @@ void main() {
|
|||
[[unroll]]
|
||||
for (uint mask = WARP_SIZE / 2; mask > 0; mask /= 2) {
|
||||
const float val = subgroupShuffleXor(max_val, mask);
|
||||
const float val_s = subgroupShuffleXor(max_val_s, mask);
|
||||
const uint expert = subgroupShuffleXor(max_expert, mask);
|
||||
if (val_s > max_val_s || (val_s == max_val_s && expert < max_expert)) {
|
||||
if (val > max_val || (val == max_val && expert < max_expert)) {
|
||||
max_val = val;
|
||||
max_val_s = val_s;
|
||||
max_expert = expert;
|
||||
}
|
||||
}
|
||||
|
|
@ -176,14 +144,16 @@ void main() {
|
|||
}
|
||||
|
||||
if ((max_expert & (WARP_SIZE - 1)) == lane) {
|
||||
selection_probs[max_expert / WARP_SIZE] = -INFINITY;
|
||||
wt[max_expert / WARP_SIZE] = -INFINITY;
|
||||
|
||||
ids[ids_offset + k] = max_expert;
|
||||
wt_sum += max_val;
|
||||
if (with_norm) {
|
||||
wt_sum += max_val;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (with_norm != 0) {
|
||||
if (with_norm) {
|
||||
wt_sum = subgroupAdd(wt_sum);
|
||||
wt_sum = clamp(wt_sum, clamp_min, clamp_max);
|
||||
const float inv_sum = 1.0f / wt_sum;
|
||||
|
|
@ -194,7 +164,7 @@ void main() {
|
|||
}
|
||||
}
|
||||
|
||||
if (gating_func == GATING_FUNC_SOFTMAX_WEIGHT) {
|
||||
if (late_softmax) {
|
||||
softmax_warp_inplace(output_weights, n_expert_used, lane, true);
|
||||
}
|
||||
|
||||
|
|
@ -202,7 +172,7 @@ void main() {
|
|||
for (uint i = 0; i < experts_per_thread; ++i) {
|
||||
uint idx = i * WARP_SIZE + lane;
|
||||
if (idx < n_expert_used) {
|
||||
weights[weights_offset + idx] = output_scale * output_weights[i] + output_bias;
|
||||
weights[weights_offset + idx] = output_weights[i];
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -396,12 +396,6 @@ struct block_iq1_s {
|
|||
uint16_t qh[QUANT_K_IQ1_S/32];
|
||||
};
|
||||
|
||||
struct block_iq1_s_packed16 {
|
||||
float16_t d;
|
||||
uint16_t qs[QUANT_K_IQ1_S/8/2];
|
||||
uint16_t qh[QUANT_K_IQ1_S/32];
|
||||
};
|
||||
|
||||
#define QUANT_K_IQ1_M 256
|
||||
#define QUANT_R_IQ1_M 1
|
||||
|
||||
|
|
@ -411,18 +405,6 @@ struct block_iq1_m {
|
|||
uint16_t scales[QUANT_K_IQ1_M/64];
|
||||
};
|
||||
|
||||
struct block_iq1_m_packed16 {
|
||||
uint16_t qs[QUANT_K_IQ1_M/8/2];
|
||||
uint16_t qh[QUANT_K_IQ1_M/16/2];
|
||||
uint16_t scales[QUANT_K_IQ1_M/64];
|
||||
};
|
||||
|
||||
struct block_iq1_m_packed32 {
|
||||
uint32_t qs[QUANT_K_IQ1_M/8/4];
|
||||
uint32_t qh[QUANT_K_IQ1_M/16/4];
|
||||
uint32_t scales[QUANT_K_IQ1_M/64/2];
|
||||
};
|
||||
|
||||
struct block_iq1_m_packed64 {
|
||||
uint64_t qs[QUANT_K_IQ1_M/8/8];
|
||||
uint64_t qh[QUANT_K_IQ1_M/16/8];
|
||||
|
|
@ -433,15 +415,12 @@ struct block_iq1_m_packed64 {
|
|||
#define QUANT_K QUANT_K_IQ1_S
|
||||
#define QUANT_R QUANT_R_IQ1_S
|
||||
#define A_TYPE block_iq1_s
|
||||
#define A_TYPE_PACKED16 block_iq1_s_packed16
|
||||
#endif
|
||||
|
||||
#if defined(DATA_A_IQ1_M)
|
||||
#define QUANT_K QUANT_K_IQ1_M
|
||||
#define QUANT_R QUANT_R_IQ1_M
|
||||
#define A_TYPE block_iq1_m
|
||||
#define A_TYPE_PACKED16 block_iq1_m_packed16
|
||||
#define A_TYPE_PACKED32 block_iq1_m_packed32
|
||||
#endif
|
||||
|
||||
#if defined(DATA_A_IQ1_S) || defined(DATA_A_IQ1_M)
|
||||
|
|
@ -580,270 +559,7 @@ const uint[1024] iq1s_grid_const = {
|
|||
0x55dd55df, 0x55d555d7, 0x5503550c, 0x557f5501, 0x5577557d, 0x55405575, 0x555d555f, 0x55555557
|
||||
};
|
||||
|
||||
// Same content as iq1s_grid_const except each 2-bit value is expanded to 4-bit
|
||||
// and has 1 added to it (allows packed values to be extracted with & 0x0F0F0F0F
|
||||
// and 0xF0F0F0F0).
|
||||
const uint32_t[2048] iq1s_grid_gpu_const = {
|
||||
0x00000000, 0x00000002, 0x00000101, 0x00000200, 0x00000202, 0x00010001, 0x00010101, 0x00020000,
|
||||
0x00020002, 0x00020200, 0x00020202, 0x01000101, 0x01010001, 0x01010100, 0x01010102, 0x01020101,
|
||||
0x02000000, 0x02000002, 0x02000200, 0x02000202, 0x02010101, 0x02020000, 0x02020002, 0x02020200,
|
||||
0x02020202, 0x00000110, 0x00000111, 0x00010011, 0x00010110, 0x00010112, 0x00010211, 0x00010212,
|
||||
0x00020111, 0x01000011, 0x01000112, 0x01000211, 0x01010012, 0x01010111, 0x01010212, 0x01020011,
|
||||
0x01020110, 0x01020112, 0x01020210, 0x02000111, 0x02010011, 0x02010110, 0x02010112, 0x02020111,
|
||||
0x00000020, 0x00000022, 0x00000220, 0x00000222, 0x00010121, 0x00020020, 0x00020022, 0x00020220,
|
||||
0x00020222, 0x01000121, 0x01010021, 0x01010221, 0x01020120, 0x01020221, 0x02000020, 0x02000022,
|
||||
0x02000220, 0x02000222, 0x02010021, 0x02010121, 0x02010221, 0x02020020, 0x02020022, 0x02020220,
|
||||
0x02020222, 0x00011001, 0x00011100, 0x00011102, 0x00021101, 0x01001001, 0x01001201, 0x01011101,
|
||||
0x01011202, 0x01021100, 0x01021101, 0x02011001, 0x02011201, 0x02021101, 0x00001011, 0x00001110,
|
||||
0x00001111, 0x00001112, 0x00011111, 0x00011210, 0x00011212, 0x00021211, 0x01001010, 0x01001111,
|
||||
0x01001212, 0x01011010, 0x01011011, 0x01011110, 0x01011111, 0x01011112, 0x01011211, 0x01021010,
|
||||
0x01021012, 0x01021111, 0x01021210, 0x01021212, 0x02001011, 0x02011011, 0x02011111, 0x02011210,
|
||||
0x02011212, 0x02021011, 0x02021110, 0x02021111, 0x02021112, 0x02021211, 0x00011120, 0x00011221,
|
||||
0x01001021, 0x01001120, 0x01011020, 0x01011022, 0x01011121, 0x01011220, 0x01021020, 0x01021021,
|
||||
0x01021122, 0x01021221, 0x02001121, 0x02011021, 0x02011120, 0x02011221, 0x00002000, 0x00002002,
|
||||
0x00002200, 0x00002202, 0x00012101, 0x00022000, 0x00022002, 0x00022200, 0x00022202, 0x01002101,
|
||||
0x01012001, 0x01012102, 0x01022101, 0x02002000, 0x02002002, 0x02002200, 0x02002202, 0x02012101,
|
||||
0x02022000, 0x02022002, 0x02022200, 0x02022202, 0x00002111, 0x00012011, 0x00012110, 0x00012211,
|
||||
0x00022110, 0x00022111, 0x01002011, 0x01012010, 0x01012011, 0x01012111, 0x01022011, 0x01022110,
|
||||
0x01022211, 0x02012011, 0x02012110, 0x02012112, 0x02012211, 0x02022111, 0x00002020, 0x00002022,
|
||||
0x00002220, 0x00002222, 0x00012121, 0x00022020, 0x00022022, 0x00022220, 0x00022222, 0x01002121,
|
||||
0x01012021, 0x01012221, 0x01022021, 0x01022121, 0x02002020, 0x02002022, 0x02002121, 0x02002220,
|
||||
0x02002222, 0x02012121, 0x02022020, 0x02022022, 0x02022220, 0x02022222, 0x00110000, 0x00110001,
|
||||
0x00110100, 0x00110201, 0x00120100, 0x00120101, 0x01100001, 0x01100100, 0x01110000, 0x01110101,
|
||||
0x01110200, 0x01120001, 0x01120100, 0x01120101, 0x01120201, 0x02110001, 0x02110100, 0x02110102,
|
||||
0x02120001, 0x02120101, 0x00100011, 0x00100110, 0x00100112, 0x00100211, 0x00110010, 0x00110012,
|
||||
0x00110111, 0x00110210, 0x00120011, 0x00120110, 0x00120211, 0x01100111, 0x01100212, 0x01110010,
|
||||
0x01110011, 0x01110012, 0x01110110, 0x01110111, 0x01110112, 0x01110211, 0x01120010, 0x01120111,
|
||||
0x02100110, 0x02110012, 0x02110111, 0x02120011, 0x02120110, 0x00110021, 0x00110120, 0x00110122,
|
||||
0x00120121, 0x01100020, 0x01100122, 0x01100221, 0x01110022, 0x01110121, 0x01110220, 0x01110222,
|
||||
0x01120120, 0x01120122, 0x02100121, 0x02110021, 0x02110120, 0x02110122, 0x02120121, 0x00101001,
|
||||
0x00101102, 0x00101201, 0x00111100, 0x00111101, 0x00111200, 0x00111201, 0x00121001, 0x00121102,
|
||||
0x01101001, 0x01101101, 0x01101102, 0x01101200, 0x01101202, 0x01111001, 0x01111100, 0x01111101,
|
||||
0x01111102, 0x01111201, 0x01121002, 0x01121101, 0x01121200, 0x02101100, 0x02101201, 0x02111000,
|
||||
0x02111100, 0x02111101, 0x02111200, 0x02111201, 0x02111202, 0x02121001, 0x02121100, 0x02121101,
|
||||
0x02121201, 0x00101012, 0x00101111, 0x00101212, 0x00111011, 0x00111110, 0x00111111, 0x00111112,
|
||||
0x00111211, 0x00121010, 0x00121012, 0x00121111, 0x00121210, 0x00121212, 0x01101011, 0x01101110,
|
||||
0x01101111, 0x01101112, 0x01111011, 0x01111012, 0x01111110, 0x01111111, 0x01111112, 0x01111211,
|
||||
0x01111212, 0x01121011, 0x01121110, 0x01121111, 0x01121112, 0x01121211, 0x02101010, 0x02101012,
|
||||
0x02101110, 0x02101111, 0x02101210, 0x02101212, 0x02111010, 0x02111011, 0x02111110, 0x02111111,
|
||||
0x02111112, 0x02111211, 0x02111212, 0x02121010, 0x02121012, 0x02121111, 0x00101021, 0x00101120,
|
||||
0x00101121, 0x00101122, 0x00111121, 0x00111122, 0x00111220, 0x00111222, 0x00121021, 0x00121122,
|
||||
0x01101020, 0x01101022, 0x01101120, 0x01101121, 0x01101220, 0x01101222, 0x01111021, 0x01111121,
|
||||
0x01111122, 0x01111220, 0x01111221, 0x01121021, 0x01121120, 0x01121121, 0x01121220, 0x01121221,
|
||||
0x01121222, 0x02101122, 0x02101222, 0x02111022, 0x02111121, 0x02121120, 0x02121221, 0x00112001,
|
||||
0x00112102, 0x00122101, 0x01102001, 0x01102100, 0x01102102, 0x01102201, 0x01112000, 0x01112101,
|
||||
0x01112200, 0x01112202, 0x01122000, 0x01122001, 0x01122100, 0x01122102, 0x01122201, 0x02102101,
|
||||
0x02112001, 0x02112100, 0x02122101, 0x00112010, 0x00112012, 0x00112111, 0x00112212, 0x00122011,
|
||||
0x00122111, 0x01102012, 0x01102110, 0x01102111, 0x01102210, 0x01112011, 0x01112110, 0x01112111,
|
||||
0x01112112, 0x01112211, 0x01112212, 0x01122010, 0x01122111, 0x01122212, 0x02102211, 0x02112011,
|
||||
0x02112012, 0x02112111, 0x02112210, 0x02122011, 0x02122112, 0x02122211, 0x00102221, 0x00112122,
|
||||
0x00122120, 0x00122122, 0x01102120, 0x01102122, 0x01102221, 0x01112020, 0x01112022, 0x01112121,
|
||||
0x01112220, 0x01122021, 0x01122122, 0x01122221, 0x02102121, 0x02112021, 0x02112122, 0x02112222,
|
||||
0x00200000, 0x00200002, 0x00200200, 0x00200202, 0x00210101, 0x00220000, 0x00220002, 0x00220101,
|
||||
0x00220200, 0x00220202, 0x01200101, 0x01210001, 0x01210201, 0x01220001, 0x01220101, 0x02200000,
|
||||
0x02200002, 0x02200200, 0x02200202, 0x02210101, 0x02220000, 0x02220002, 0x02220101, 0x02220200,
|
||||
0x02220202, 0x00200111, 0x00210011, 0x00210110, 0x00210211, 0x00220111, 0x01200012, 0x01200110,
|
||||
0x01200211, 0x01210111, 0x01210210, 0x01210212, 0x01220011, 0x01220110, 0x01220111, 0x01220112,
|
||||
0x02200111, 0x02210010, 0x02210112, 0x02210211, 0x02220111, 0x00200021, 0x00200220, 0x00200222,
|
||||
0x00210021, 0x00210121, 0x00220020, 0x00220022, 0x00220220, 0x00220222, 0x01200121, 0x01210021,
|
||||
0x01210122, 0x01210221, 0x01220121, 0x02200021, 0x02200220, 0x02200222, 0x02210021, 0x02210121,
|
||||
0x02220020, 0x02220022, 0x02220220, 0x02220222, 0x00201101, 0x00211100, 0x00211102, 0x00211201,
|
||||
0x00221101, 0x01201100, 0x01201101, 0x01201102, 0x01201201, 0x01211002, 0x01211101, 0x01211200,
|
||||
0x01211202, 0x01221102, 0x02201101, 0x02211001, 0x02211100, 0x02211201, 0x02221001, 0x02221101,
|
||||
0x00201211, 0x00211111, 0x00221011, 0x00221211, 0x01201010, 0x01201111, 0x01201210, 0x01211011,
|
||||
0x01211110, 0x01211111, 0x01211211, 0x01221012, 0x01221111, 0x01221210, 0x02201211, 0x02211010,
|
||||
0x02211110, 0x02211111, 0x02211210, 0x02211212, 0x02221011, 0x02221110, 0x02221112, 0x02221211,
|
||||
0x00201121, 0x00211020, 0x00211022, 0x00211221, 0x00221121, 0x01201021, 0x01201221, 0x01211121,
|
||||
0x01221020, 0x01221021, 0x01221221, 0x02201120, 0x02201122, 0x02211020, 0x02211222, 0x00202000,
|
||||
0x00202002, 0x00202200, 0x00202202, 0x00212101, 0x00222000, 0x00222002, 0x00222200, 0x00222202,
|
||||
0x01202101, 0x01212001, 0x01212100, 0x01222101, 0x02202000, 0x02202002, 0x02202200, 0x02202202,
|
||||
0x02222000, 0x02222002, 0x02222200, 0x02222202, 0x00202211, 0x00212011, 0x00212110, 0x00212211,
|
||||
0x00222111, 0x01202112, 0x01202211, 0x01212012, 0x01212111, 0x01222011, 0x01222110, 0x01222112,
|
||||
0x01222211, 0x02202111, 0x02212010, 0x02212112, 0x02212211, 0x02222110, 0x02222111, 0x00202020,
|
||||
0x00202022, 0x00202220, 0x00202222, 0x00222020, 0x00222022, 0x00222220, 0x00222222, 0x01202121,
|
||||
0x01212021, 0x01212122, 0x01212221, 0x01222121, 0x02202020, 0x02202022, 0x02202220, 0x02202222,
|
||||
0x02212121, 0x02222020, 0x02222022, 0x02222220, 0x02222222, 0x10000101, 0x10010001, 0x10010102,
|
||||
0x10020101, 0x11000201, 0x11010002, 0x11010101, 0x11010200, 0x11010202, 0x11020001, 0x11020100,
|
||||
0x11020102, 0x12010100, 0x12010201, 0x12020001, 0x12020102, 0x10000010, 0x10000011, 0x10000110,
|
||||
0x10000112, 0x10000211, 0x10010012, 0x10010111, 0x10010112, 0x10010210, 0x10010212, 0x10020011,
|
||||
0x10020112, 0x10020211, 0x11000111, 0x11000210, 0x11000212, 0x11010011, 0x11010110, 0x11010111,
|
||||
0x11010112, 0x11010211, 0x11010212, 0x11020111, 0x11020210, 0x11020212, 0x12000011, 0x12000110,
|
||||
0x12000112, 0x12010010, 0x12010012, 0x12010111, 0x12020010, 0x12020011, 0x12020012, 0x10000121,
|
||||
0x10010021, 0x10010120, 0x10010122, 0x10020121, 0x11000021, 0x11010022, 0x11010121, 0x11010222,
|
||||
0x11020120, 0x11020221, 0x12000221, 0x12010120, 0x12020121, 0x10001001, 0x10011101, 0x10011201,
|
||||
0x10021201, 0x11001101, 0x11001200, 0x11001202, 0x11011001, 0x11011100, 0x11011101, 0x11011102,
|
||||
0x11021001, 0x11021002, 0x11021101, 0x11021200, 0x11021202, 0x12001001, 0x12001102, 0x12001201,
|
||||
0x12011000, 0x12011002, 0x12011101, 0x12021000, 0x12021001, 0x12021201, 0x10001011, 0x10001012,
|
||||
0x10001111, 0x10001212, 0x10011011, 0x10011110, 0x10011111, 0x10011112, 0x10011211, 0x10021010,
|
||||
0x10021111, 0x10021212, 0x11001011, 0x11001110, 0x11001111, 0x11001112, 0x11001211, 0x11011010,
|
||||
0x11011011, 0x11011110, 0x11011111, 0x11011112, 0x11011210, 0x11011211, 0x11021011, 0x11021110,
|
||||
0x11021111, 0x11021112, 0x11021211, 0x12001012, 0x12001110, 0x12001111, 0x12001210, 0x12011011,
|
||||
0x12011110, 0x12011111, 0x12011112, 0x12011211, 0x12011212, 0x12021111, 0x12021210, 0x12021212,
|
||||
0x10001021, 0x10001121, 0x10001221, 0x10011120, 0x10011121, 0x10011220, 0x10011222, 0x10021021,
|
||||
0x10021120, 0x10021221, 0x11001020, 0x11001022, 0x11001121, 0x11001220, 0x11011020, 0x11011021,
|
||||
0x11011022, 0x11011121, 0x11011122, 0x11011221, 0x11021022, 0x11021121, 0x11021220, 0x12001021,
|
||||
0x12001121, 0x12001222, 0x12011120, 0x12011121, 0x12021021, 0x12021120, 0x12021122, 0x10002101,
|
||||
0x10012001, 0x10012101, 0x10012202, 0x10022101, 0x11002002, 0x11002201, 0x11012000, 0x11012101,
|
||||
0x11012200, 0x11022001, 0x11022100, 0x11022102, 0x11022201, 0x12002101, 0x12012001, 0x12012100,
|
||||
0x12012102, 0x12012201, 0x12022101, 0x10002011, 0x10002111, 0x10002112, 0x10002212, 0x10012010,
|
||||
0x10012110, 0x10012111, 0x10012210, 0x10022011, 0x10022110, 0x10022112, 0x11002010, 0x11002111,
|
||||
0x11002212, 0x11012011, 0x11012012, 0x11012110, 0x11012111, 0x11012112, 0x11012211, 0x11022010,
|
||||
0x11022012, 0x11022111, 0x11022112, 0x11022212, 0x12002112, 0x12002211, 0x12012012, 0x12012111,
|
||||
0x12012112, 0x12012210, 0x12022011, 0x12022110, 0x12022112, 0x12022211, 0x10012122, 0x11002120,
|
||||
0x11002122, 0x11002221, 0x11012121, 0x11012220, 0x11012222, 0x11022120, 0x11022221, 0x12012120,
|
||||
0x12022121, 0x10100001, 0x10100100, 0x10100101, 0x10100102, 0x10100201, 0x10110002, 0x10110101,
|
||||
0x10110202, 0x10120001, 0x10120100, 0x10120201, 0x11100000, 0x11100101, 0x11100200, 0x11110001,
|
||||
0x11110100, 0x11110101, 0x11110102, 0x11110201, 0x11120101, 0x11120200, 0x12100102, 0x12100201,
|
||||
0x12110101, 0x12110200, 0x12120000, 0x12120001, 0x12120102, 0x12120201, 0x10100111, 0x10100210,
|
||||
0x10100211, 0x10100212, 0x10110011, 0x10110110, 0x10110111, 0x10110112, 0x10110210, 0x10110211,
|
||||
0x10120010, 0x10120111, 0x10120112, 0x10120210, 0x10120212, 0x11100011, 0x11100110, 0x11100111,
|
||||
0x11100112, 0x11100211, 0x11110010, 0x11110011, 0x11110012, 0x11110110, 0x11110111, 0x11110112,
|
||||
0x11110210, 0x11110211, 0x11110212, 0x11120011, 0x11120110, 0x11120111, 0x11120112, 0x11120211,
|
||||
0x12100012, 0x12100111, 0x12110011, 0x12110110, 0x12110111, 0x12110112, 0x12110211, 0x12120010,
|
||||
0x12120111, 0x12120212, 0x10100021, 0x10100122, 0x10110022, 0x10110121, 0x10110222, 0x10120021,
|
||||
0x10120120, 0x11100022, 0x11100121, 0x11100222, 0x11110021, 0x11110120, 0x11110121, 0x11110122,
|
||||
0x11110221, 0x11120022, 0x11120121, 0x12100121, 0x12110020, 0x12110022, 0x12110121, 0x12110221,
|
||||
0x12110222, 0x12120120, 0x10101100, 0x10101101, 0x10111001, 0x10111100, 0x10111101, 0x10111102,
|
||||
0x10111200, 0x10111201, 0x10121001, 0x10121101, 0x10121200, 0x10121202, 0x11101001, 0x11101100,
|
||||
0x11101101, 0x11101102, 0x11101201, 0x11101202, 0x11111000, 0x11111001, 0x11111100, 0x11111101,
|
||||
0x11111102, 0x11111200, 0x11111201, 0x11111202, 0x11121001, 0x11121002, 0x11121100, 0x11121101,
|
||||
0x11121102, 0x11121201, 0x12101000, 0x12101200, 0x12101202, 0x12111001, 0x12111100, 0x12111101,
|
||||
0x12111102, 0x12111201, 0x12121001, 0x12121100, 0x12121101, 0x12121202, 0x10101011, 0x10101012,
|
||||
0x10101110, 0x10101111, 0x10101112, 0x10101211, 0x10111010, 0x10111011, 0x10111012, 0x10111110,
|
||||
0x10111111, 0x10111112, 0x10111211, 0x10111212, 0x10121011, 0x10121110, 0x10121111, 0x10121112,
|
||||
0x10121211, 0x11101010, 0x11101011, 0x11101012, 0x11101110, 0x11101111, 0x11101112, 0x11101210,
|
||||
0x11101211, 0x11111010, 0x11111011, 0x11111012, 0x11111110, 0x11111111, 0x11111112, 0x11111210,
|
||||
0x11111211, 0x11111212, 0x11121010, 0x11121011, 0x11121110, 0x11121111, 0x11121112, 0x11121210,
|
||||
0x11121211, 0x11121212, 0x12101011, 0x12101110, 0x12101111, 0x12101211, 0x12101212, 0x12111010,
|
||||
0x12111011, 0x12111110, 0x12111111, 0x12111112, 0x12111210, 0x12111211, 0x12121011, 0x12121110,
|
||||
0x12121111, 0x12121112, 0x12121211, 0x10101020, 0x10101021, 0x10101022, 0x10101120, 0x10101122,
|
||||
0x10101220, 0x10101221, 0x10111021, 0x10111120, 0x10111121, 0x10111220, 0x10111221, 0x10121020,
|
||||
0x10121021, 0x10121022, 0x10121120, 0x10121121, 0x10121122, 0x10121220, 0x10121221, 0x11101021,
|
||||
0x11101121, 0x11101122, 0x11101220, 0x11101221, 0x11101222, 0x11111020, 0x11111021, 0x11111022,
|
||||
0x11111120, 0x11111121, 0x11111122, 0x11111220, 0x11111221, 0x11111222, 0x11121021, 0x11121120,
|
||||
0x11121121, 0x11121221, 0x12101022, 0x12101121, 0x12101122, 0x12101220, 0x12101221, 0x12101222,
|
||||
0x12111021, 0x12111121, 0x12111222, 0x12121022, 0x12121121, 0x12121122, 0x12121220, 0x12121221,
|
||||
0x10102100, 0x10102101, 0x10102102, 0x10102201, 0x10112000, 0x10112101, 0x10112200, 0x10122001,
|
||||
0x10122202, 0x11102101, 0x11102200, 0x11102202, 0x11112001, 0x11112100, 0x11112101, 0x11112102,
|
||||
0x11112200, 0x11112201, 0x11122000, 0x11122002, 0x11122100, 0x11122101, 0x12102002, 0x12102201,
|
||||
0x12112000, 0x12112002, 0x12112101, 0x12112200, 0x12122001, 0x12122201, 0x10102011, 0x10102012,
|
||||
0x10102111, 0x10102212, 0x10112011, 0x10112110, 0x10112111, 0x10112112, 0x10112211, 0x10122111,
|
||||
0x11102011, 0x11102110, 0x11102111, 0x11102112, 0x11102211, 0x11112010, 0x11112011, 0x11112012,
|
||||
0x11112110, 0x11112111, 0x11112112, 0x11112210, 0x11112211, 0x11112212, 0x11122011, 0x11122110,
|
||||
0x11122111, 0x11122112, 0x11122211, 0x12102011, 0x12102111, 0x12102211, 0x12112011, 0x12112110,
|
||||
0x12112111, 0x12112112, 0x12112210, 0x12112211, 0x12122111, 0x10102120, 0x10102220, 0x10112121,
|
||||
0x10112222, 0x10122020, 0x10122121, 0x10122122, 0x10122221, 0x11102121, 0x11102220, 0x11102221,
|
||||
0x11112021, 0x11112121, 0x11112122, 0x11112220, 0x11112221, 0x11122022, 0x11122121, 0x11122220,
|
||||
0x11122222, 0x12102021, 0x12102222, 0x12112022, 0x12112121, 0x12112122, 0x12112220, 0x12112222,
|
||||
0x12122021, 0x10200101, 0x10210100, 0x10210102, 0x10210201, 0x10220101, 0x11200100, 0x11210000,
|
||||
0x11210101, 0x11210102, 0x11210200, 0x11210202, 0x11220001, 0x11220100, 0x11220102, 0x11220201,
|
||||
0x12200001, 0x12210102, 0x12220101, 0x10200011, 0x10200110, 0x10200112, 0x10200211, 0x10210012,
|
||||
0x10210111, 0x10220011, 0x10220012, 0x10220112, 0x10220211, 0x11200111, 0x11200211, 0x11210011,
|
||||
0x11210111, 0x11210112, 0x11210211, 0x11220111, 0x11220112, 0x11220212, 0x12200110, 0x12200212,
|
||||
0x12210012, 0x12210111, 0x12220011, 0x12220112, 0x12220211, 0x10210021, 0x10210122, 0x10210221,
|
||||
0x11200020, 0x11200021, 0x11200122, 0x11210121, 0x11210122, 0x11210220, 0x11220020, 0x12200121,
|
||||
0x12210021, 0x12210122, 0x12220121, 0x10211001, 0x10211002, 0x10211101, 0x10211102, 0x10211202,
|
||||
0x10221001, 0x10221102, 0x10221201, 0x11201000, 0x11201002, 0x11201101, 0x11201200, 0x11201202,
|
||||
0x11211001, 0x11211100, 0x11211101, 0x11211102, 0x11211201, 0x11211202, 0x11221000, 0x11221002,
|
||||
0x11221101, 0x12201100, 0x12201101, 0x12201201, 0x12211000, 0x12211002, 0x12211100, 0x12211101,
|
||||
0x12211102, 0x12211200, 0x12211202, 0x12221001, 0x12221100, 0x12221201, 0x10201111, 0x10201210,
|
||||
0x10201212, 0x10211011, 0x10211111, 0x10211112, 0x10211211, 0x11201110, 0x11201111, 0x11201112,
|
||||
0x11201211, 0x11211010, 0x11211011, 0x11211110, 0x11211111, 0x11211112, 0x11211211, 0x11221011,
|
||||
0x11221110, 0x11221111, 0x11221112, 0x11221211, 0x12201112, 0x12201211, 0x12201212, 0x12211011,
|
||||
0x12211111, 0x12211112, 0x12211211, 0x12211212, 0x12221012, 0x12221111, 0x12221112, 0x12221210,
|
||||
0x10201022, 0x10201221, 0x10211121, 0x10221020, 0x10221122, 0x10221220, 0x10221221, 0x11201020,
|
||||
0x11201121, 0x11201220, 0x11201222, 0x11211021, 0x11211120, 0x11211121, 0x11211122, 0x11211220,
|
||||
0x11211222, 0x11221020, 0x11221121, 0x11221220, 0x12201020, 0x12201022, 0x12201121, 0x12201222,
|
||||
0x12211120, 0x12211122, 0x12211220, 0x12211221, 0x12221020, 0x12221120, 0x12221122, 0x12221222,
|
||||
0x10212102, 0x10212201, 0x10222101, 0x11202001, 0x11212002, 0x11212101, 0x11212202, 0x11222001,
|
||||
0x11222201, 0x12202101, 0x12212001, 0x12212200, 0x12222102, 0x10202011, 0x10202110, 0x10212010,
|
||||
0x10212111, 0x10222011, 0x10222110, 0x10222112, 0x10222211, 0x11202010, 0x11202011, 0x11202111,
|
||||
0x11202112, 0x11202210, 0x11212011, 0x11212110, 0x11212111, 0x11212112, 0x11212211, 0x11222010,
|
||||
0x11222111, 0x11222212, 0x12202012, 0x12202110, 0x12202212, 0x12212111, 0x12222011, 0x12222110,
|
||||
0x12222111, 0x12222211, 0x10212021, 0x10212122, 0x10212220, 0x11202021, 0x11202120, 0x11202221,
|
||||
0x11212020, 0x11212121, 0x11212220, 0x11212222, 0x11222120, 0x11222121, 0x11222221, 0x12202122,
|
||||
0x12212120, 0x12212220, 0x12212222, 0x12222122, 0x20000000, 0x20000002, 0x20000200, 0x20000202,
|
||||
0x20020000, 0x20020002, 0x20020200, 0x20020202, 0x21000101, 0x21010000, 0x21010001, 0x21010100,
|
||||
0x21010102, 0x21010201, 0x21020101, 0x22000000, 0x22000002, 0x22000200, 0x22000202, 0x22010101,
|
||||
0x22020000, 0x22020002, 0x22020200, 0x22020202, 0x20000111, 0x20010011, 0x20010110, 0x20010112,
|
||||
0x20010211, 0x20020111, 0x21000011, 0x21000110, 0x21000211, 0x21010010, 0x21010012, 0x21010111,
|
||||
0x21010112, 0x21010210, 0x21010211, 0x21020110, 0x21020112, 0x21020211, 0x22000111, 0x22000211,
|
||||
0x22010110, 0x22010112, 0x22010211, 0x22020111, 0x20000020, 0x20000022, 0x20000220, 0x20000222,
|
||||
0x20010121, 0x20020020, 0x20020022, 0x20020220, 0x20020222, 0x21010021, 0x21010120, 0x21010221,
|
||||
0x21020121, 0x22000020, 0x22000022, 0x22000220, 0x22000222, 0x22010121, 0x22020020, 0x22020022,
|
||||
0x22020220, 0x22020222, 0x20011100, 0x20011201, 0x21001001, 0x21001100, 0x21011001, 0x21011101,
|
||||
0x21011202, 0x21021001, 0x21021100, 0x21021201, 0x22011100, 0x22011201, 0x20001011, 0x20001211,
|
||||
0x20011012, 0x20011111, 0x20011212, 0x20021112, 0x20021211, 0x21001010, 0x21001011, 0x21001111,
|
||||
0x21001210, 0x21011011, 0x21011110, 0x21011111, 0x21011112, 0x21011211, 0x21011212, 0x21021111,
|
||||
0x21021112, 0x21021210, 0x21021212, 0x22001011, 0x22001110, 0x22001112, 0x22001211, 0x22011010,
|
||||
0x22011012, 0x22011111, 0x22011210, 0x22021112, 0x20011021, 0x20011122, 0x20011221, 0x20021121,
|
||||
0x21001021, 0x21001120, 0x21001221, 0x21001222, 0x21011020, 0x21011121, 0x21011221, 0x21011222,
|
||||
0x21021021, 0x21021122, 0x21021222, 0x22001121, 0x22011021, 0x22011222, 0x22021120, 0x20002000,
|
||||
0x20002002, 0x20002200, 0x20002202, 0x20012101, 0x20022000, 0x20022002, 0x20022200, 0x20022202,
|
||||
0x21002001, 0x21002101, 0x21012001, 0x21012100, 0x21012201, 0x21022101, 0x21022201, 0x22002000,
|
||||
0x22002002, 0x22002200, 0x22002202, 0x22012101, 0x22022000, 0x22022002, 0x22022200, 0x22022202,
|
||||
0x20002111, 0x20002112, 0x20012011, 0x20012110, 0x20012112, 0x20022111, 0x21002011, 0x21002110,
|
||||
0x21002112, 0x21002211, 0x21012010, 0x21012012, 0x21012111, 0x21012212, 0x21022011, 0x21022110,
|
||||
0x22002111, 0x22012112, 0x22012211, 0x22022111, 0x20002020, 0x20002022, 0x20002220, 0x20002222,
|
||||
0x20012121, 0x20022020, 0x20022022, 0x20022220, 0x20022222, 0x21002121, 0x21012021, 0x21012120,
|
||||
0x21012122, 0x22002020, 0x22002022, 0x22002220, 0x22002222, 0x22012121, 0x22022020, 0x22022022,
|
||||
0x22022220, 0x22022222, 0x20100101, 0x20110001, 0x20110102, 0x20110200, 0x20110201, 0x20120101,
|
||||
0x21100001, 0x21100102, 0x21100201, 0x21110101, 0x21110200, 0x21110202, 0x21120201, 0x21120202,
|
||||
0x22100101, 0x22110001, 0x22110100, 0x22110102, 0x22110201, 0x22120101, 0x20100011, 0x20100110,
|
||||
0x20100112, 0x20100211, 0x20110010, 0x20110111, 0x20110210, 0x20110212, 0x20120011, 0x20120110,
|
||||
0x20120112, 0x20120211, 0x21100010, 0x21100111, 0x21110010, 0x21110011, 0x21110110, 0x21110111,
|
||||
0x21110112, 0x21110211, 0x21120012, 0x21120111, 0x22100110, 0x22100112, 0x22110012, 0x22110111,
|
||||
0x22110210, 0x22120011, 0x22120110, 0x22120112, 0x22120211, 0x20100121, 0x20110021, 0x20110120,
|
||||
0x20110221, 0x20120121, 0x21100120, 0x21100122, 0x21100221, 0x21110020, 0x21110022, 0x21110121,
|
||||
0x21110220, 0x21120122, 0x21120221, 0x22100121, 0x22110120, 0x22110122, 0x22120221, 0x20101001,
|
||||
0x20101100, 0x20101102, 0x20111000, 0x20111101, 0x20111200, 0x20121102, 0x21101000, 0x21101202,
|
||||
0x21111001, 0x21111100, 0x21111101, 0x21111102, 0x21111200, 0x21111201, 0x21121000, 0x21121001,
|
||||
0x21121002, 0x21121101, 0x22101100, 0x22101102, 0x22111002, 0x22111100, 0x22111101, 0x22111200,
|
||||
0x22121001, 0x22121201, 0x20101010, 0x20101111, 0x20101210, 0x20101212, 0x20111010, 0x20111011,
|
||||
0x20111110, 0x20111111, 0x20111112, 0x20111211, 0x20121011, 0x20121111, 0x20121211, 0x20121212,
|
||||
0x21101011, 0x21101110, 0x21101111, 0x21101112, 0x21101211, 0x21111010, 0x21111011, 0x21111012,
|
||||
0x21111110, 0x21111111, 0x21111112, 0x21111210, 0x21111211, 0x21111212, 0x21121011, 0x21121110,
|
||||
0x21121111, 0x21121112, 0x21121211, 0x22101011, 0x22101111, 0x22101210, 0x22111011, 0x22111012,
|
||||
0x22111110, 0x22111111, 0x22111112, 0x22111211, 0x22111212, 0x22121010, 0x22121012, 0x22121111,
|
||||
0x22121210, 0x22121212, 0x20101021, 0x20101120, 0x20111020, 0x20111121, 0x20111221, 0x20121020,
|
||||
0x20121122, 0x20121221, 0x21101121, 0x21101220, 0x21101221, 0x21111021, 0x21111022, 0x21111121,
|
||||
0x21111122, 0x21111221, 0x21121121, 0x21121220, 0x22101022, 0x22101120, 0x22101221, 0x22101222,
|
||||
0x22111022, 0x22111120, 0x22111121, 0x22121120, 0x22121122, 0x22121221, 0x20102101, 0x20112102,
|
||||
0x20112201, 0x20122101, 0x21102001, 0x21102102, 0x21112000, 0x21112002, 0x21112101, 0x21112102,
|
||||
0x21112202, 0x21122100, 0x21122101, 0x22102101, 0x22112001, 0x22112102, 0x22112201, 0x22122101,
|
||||
0x20102110, 0x20102112, 0x20102211, 0x20112010, 0x20112012, 0x20112111, 0x20112210, 0x20112212,
|
||||
0x20122010, 0x20122011, 0x20122110, 0x20122112, 0x21102010, 0x21102012, 0x21102111, 0x21102210,
|
||||
0x21102212, 0x21112011, 0x21112110, 0x21112111, 0x21112112, 0x21112211, 0x21122012, 0x21122111,
|
||||
0x21122112, 0x21122212, 0x22102011, 0x22102110, 0x22112010, 0x22112012, 0x22112111, 0x22112212,
|
||||
0x22122011, 0x22122112, 0x20102121, 0x20112121, 0x20122121, 0x21102120, 0x21102122, 0x21102221,
|
||||
0x21112020, 0x21112121, 0x21112220, 0x21122021, 0x22102121, 0x22112021, 0x22112120, 0x22112121,
|
||||
0x22112122, 0x20200000, 0x20200002, 0x20200200, 0x20200202, 0x20210101, 0x20220000, 0x20220002,
|
||||
0x20220200, 0x20220202, 0x21200101, 0x21210001, 0x21210100, 0x21210102, 0x21210201, 0x22200000,
|
||||
0x22200002, 0x22200200, 0x22200202, 0x22210101, 0x22220000, 0x22220002, 0x22220200, 0x22220202,
|
||||
0x20200111, 0x20200211, 0x20210011, 0x20210110, 0x20210112, 0x20210211, 0x20210212, 0x21200112,
|
||||
0x21200211, 0x21210011, 0x21210111, 0x21210210, 0x21210212, 0x21220011, 0x21220110, 0x22200111,
|
||||
0x22210010, 0x22210012, 0x22210112, 0x22210211, 0x20200022, 0x20200220, 0x20200222, 0x20210020,
|
||||
0x20210221, 0x20220022, 0x20220220, 0x20220222, 0x21200121, 0x21210021, 0x21210122, 0x21210221,
|
||||
0x21220121, 0x22200020, 0x22200022, 0x22200220, 0x22200222, 0x22210121, 0x22220020, 0x22220022,
|
||||
0x22220220, 0x22220222, 0x20211201, 0x20221101, 0x21201001, 0x21201100, 0x21211000, 0x21211100,
|
||||
0x21211101, 0x21211200, 0x21211202, 0x21221001, 0x21221101, 0x21221102, 0x21221200, 0x21221201,
|
||||
0x22201101, 0x20201112, 0x20201211, 0x20211010, 0x20211012, 0x20211111, 0x20211210, 0x20221112,
|
||||
0x20221211, 0x21201012, 0x21201111, 0x21211011, 0x21211110, 0x21211111, 0x21211112, 0x21211211,
|
||||
0x21221111, 0x21221212, 0x22201011, 0x22201110, 0x22201111, 0x22201112, 0x22201211, 0x22211012,
|
||||
0x22211111, 0x22211210, 0x20201121, 0x20211021, 0x20211122, 0x20211222, 0x20221021, 0x20221121,
|
||||
0x21201120, 0x21201122, 0x21201222, 0x21211022, 0x21211121, 0x21211122, 0x21211220, 0x21221020,
|
||||
0x21221022, 0x22201122, 0x22211020, 0x22211121, 0x22211122, 0x22211221, 0x22221021, 0x22221120,
|
||||
0x22221122, 0x20202000, 0x20202002, 0x20202200, 0x20202202, 0x20222000, 0x20222002, 0x20222200,
|
||||
0x20222202, 0x21212001, 0x21212100, 0x21212102, 0x21212201, 0x22202000, 0x22202002, 0x22202200,
|
||||
0x22202202, 0x22212101, 0x22222000, 0x22222002, 0x22222200, 0x22222202, 0x20202111, 0x20212110,
|
||||
0x20212211, 0x20222011, 0x20222111, 0x21202011, 0x21212010, 0x21212111, 0x21212212, 0x21222011,
|
||||
0x21222112, 0x21222211, 0x22212010, 0x22212112, 0x20202020, 0x20202022, 0x20202220, 0x20202222,
|
||||
0x20222020, 0x20222022, 0x20222220, 0x20222222, 0x21212021, 0x21212120, 0x21212122, 0x22202020,
|
||||
0x22202022, 0x22202220, 0x22202222, 0x22212121, 0x22222020, 0x22222022, 0x22222220, 0x22222222,
|
||||
};
|
||||
|
||||
shared uint16_t iq1s_grid[2048];
|
||||
shared uint32_t iq1s_grid_gpu[2048];
|
||||
|
||||
#define NEEDS_INIT_IQ_SHMEM
|
||||
void init_iq_shmem(uvec3 wgsize)
|
||||
|
|
@ -857,12 +573,6 @@ void init_iq_shmem(uvec3 wgsize)
|
|||
iq1s_grid[2*idx+1] = g.y;
|
||||
}
|
||||
}
|
||||
[[unroll]] for (uint i = 0; i < iq1s_grid_gpu_const.length(); i += wgsize.x) {
|
||||
uint idx = i + gl_LocalInvocationIndex.x;
|
||||
if (iq1s_grid_gpu_const.length() % wgsize.x == 0 || idx < iq1s_grid_gpu_const.length()) {
|
||||
iq1s_grid_gpu[idx] = iq1s_grid_gpu_const[idx];
|
||||
}
|
||||
}
|
||||
barrier();
|
||||
}
|
||||
#endif
|
||||
|
|
|
|||
|
|
@ -685,7 +685,7 @@ void process_shaders() {
|
|||
|
||||
// mul mat vec with integer dot product
|
||||
#if defined(GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT)
|
||||
if (is_legacy_quant(tname) || tname == "mxfp4" || is_k_quant(tname) || tname == "iq1_s" || tname == "iq1_m") {
|
||||
if (is_legacy_quant(tname) || tname == "mxfp4" || is_k_quant(tname)) {
|
||||
string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}}));
|
||||
string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}));
|
||||
string_to_spv("mul_mat_vec_" + tname + "_q8_1_f32_subgroup_no_shmem", "mul_mat_vecq.comp", merge_maps(base_dict, {{data_a_key, "1"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"FLOAT_TYPE_VEC2", "vec2"}, {"ACC_TYPE", "float"}, {"USE_SUBGROUP_ADD_NO_SHMEM", "1"}}));
|
||||
|
|
@ -944,8 +944,6 @@ void process_shaders() {
|
|||
string_to_spv("sum_rows_f32", "sum_rows.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
|
||||
string_to_spv("count_equal_i32", "count_equal.comp", merge_maps(base_dict, {{"A_TYPE", "int"}, {"B_TYPE", "int"}, {"D_TYPE", "int"}}));
|
||||
string_to_spv("cumsum_f32", "cumsum.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
|
||||
string_to_spv("cumsum_multipass1_f32", "cumsum_multipass1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
|
||||
string_to_spv("cumsum_multipass2_f32", "cumsum_multipass2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"D_TYPE", "float"}}));
|
||||
|
||||
string_to_spv("count_experts", "count_experts.comp", merge_maps(base_dict, {{"A_TYPE", "uint"}, {"D_TYPE", "uint"}}));
|
||||
|
||||
|
|
@ -1125,7 +1123,7 @@ void write_output_files() {
|
|||
|
||||
for (const std::string& btype : btypes) {
|
||||
for (const auto& tname : type_names) {
|
||||
if (btype == "q8_1" && !is_legacy_quant(tname) && tname != "mxfp4" && !is_k_quant(tname) && tname != "iq1_s" && tname != "iq1_m") {
|
||||
if (btype == "q8_1" && !is_legacy_quant(tname) && tname != "mxfp4" && !is_k_quant(tname)) {
|
||||
continue;
|
||||
}
|
||||
hdr << "extern const void * arr_dmmv_" << tname << "_" << btype << "_f32_data[3];\n";
|
||||
|
|
|
|||
|
|
@ -294,9 +294,7 @@ class Keys:
|
|||
USE_GELU = "clip.use_gelu"
|
||||
USE_SILU = "clip.use_silu"
|
||||
N_WA_PATTERN = "clip.vision.n_wa_pattern" # used by qwen2.5vl
|
||||
WA_LAYER_INDEXES = "clip.vision.wa_layer_indexes" # used by youtuvl
|
||||
IS_DEEPSTACK_LAYERS = "clip.vision.is_deepstack_layers"
|
||||
WINDOW_SIZE = "clip.vision.window_size"
|
||||
|
||||
class Attention:
|
||||
HEAD_COUNT = "clip.vision.attention.head_count"
|
||||
|
|
@ -454,7 +452,6 @@ class MODEL_ARCH(IntEnum):
|
|||
MISTRAL3 = auto()
|
||||
MIMO2 = auto()
|
||||
LLAMA_EMBED = auto()
|
||||
MAINCODER = auto()
|
||||
|
||||
|
||||
class VISION_PROJECTOR_TYPE(IntEnum):
|
||||
|
|
@ -853,7 +850,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
|
|||
MODEL_ARCH.MISTRAL3: "mistral3",
|
||||
MODEL_ARCH.MIMO2: "mimo2",
|
||||
MODEL_ARCH.LLAMA_EMBED: "llama-embed",
|
||||
MODEL_ARCH.MAINCODER: "maincoder",
|
||||
}
|
||||
|
||||
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
|
||||
|
|
@ -3261,22 +3257,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
|
|||
MODEL_TENSOR.FFN_DOWN_EXP,
|
||||
MODEL_TENSOR.FFN_UP_EXP,
|
||||
],
|
||||
MODEL_ARCH.MAINCODER: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_Q,
|
||||
MODEL_TENSOR.ATTN_Q_NORM,
|
||||
MODEL_TENSOR.ATTN_K,
|
||||
MODEL_TENSOR.ATTN_K_NORM,
|
||||
MODEL_TENSOR.ATTN_V,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_NORM,
|
||||
MODEL_TENSOR.FFN_GATE,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
],
|
||||
# TODO
|
||||
}
|
||||
|
||||
|
|
@ -3512,9 +3492,7 @@ class VisionProjectorType:
|
|||
COGVLM = "cogvlm"
|
||||
JANUS_PRO = "janus_pro"
|
||||
LFM2A = "lfm2a" # audio
|
||||
MUSIC_FLAMINGO = "musicflamingo" # audio
|
||||
GLM4V = "glm4v"
|
||||
YOUTUVL = "youtuvl"
|
||||
|
||||
|
||||
# Items here are (block size, type size)
|
||||
|
|
|
|||
|
|
@ -1129,40 +1129,11 @@ class GGUFWriter:
|
|||
self.add_uint32(Keys.ClipVision.Projector.SCALE_FACTOR, value)
|
||||
|
||||
def add_vision_n_wa_pattern(self, value: int) -> None:
|
||||
"""Add window attention pattern interval for vision models.
|
||||
|
||||
This defines the pattern interval for window attention vs full attention layers.
|
||||
For example, if n_wa_pattern=4, then layers 3, 7, 11, ... use full attention,
|
||||
while other layers use window attention.
|
||||
|
||||
Used by models like Qwen2.5-VL where full attention layers follow a regular pattern.
|
||||
"""
|
||||
self.add_uint32(Keys.ClipVision.N_WA_PATTERN, value)
|
||||
|
||||
def add_vision_wa_layer_indexes(self, layers: Sequence[int]) -> None:
|
||||
"""Add explicit layer indexes that use full attention in vision models.
|
||||
|
||||
This specifies the exact layer indices (0-based) that should use full attention
|
||||
instead of window attention. All other layers will use window attention.
|
||||
|
||||
Args:
|
||||
layers: List of layer indices that use full attention (e.g., [3, 7, 11, 15])
|
||||
|
||||
Used by models like YoutuVL where full attention layers are explicitly specified
|
||||
rather than following a regular pattern.
|
||||
|
||||
Difference from add_vision_n_wa_pattern:
|
||||
- n_wa_pattern: Defines a regular interval pattern (every Nth layer uses full attention)
|
||||
- wa_layer_indexes: Explicitly lists which layers use full attention (irregular pattern)
|
||||
"""
|
||||
self.add_array(Keys.ClipVision.WA_LAYER_INDEXES, layers)
|
||||
|
||||
def add_vision_is_deepstack_layers(self, layers: Sequence[bool]) -> None:
|
||||
self.add_array(Keys.ClipVision.IS_DEEPSTACK_LAYERS, layers)
|
||||
|
||||
def add_vision_window_size(self, value: int) -> None:
|
||||
self.add_uint32(Keys.ClipVision.WINDOW_SIZE, value)
|
||||
|
||||
# audio models
|
||||
|
||||
def add_audio_projection_dim(self, value: int) -> None:
|
||||
|
|
|
|||
|
|
@ -1221,7 +1221,6 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.V_MMPROJ: (
|
||||
"multi_modal_projector.linear_{bid}",
|
||||
"visual.merger.mlp.{bid}", # qwen2vl
|
||||
"merger.mlp.{bid}",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_MMPROJ_FC: (
|
||||
|
|
@ -1259,7 +1258,6 @@ class TensorNameMap:
|
|||
"visual.patch_embed.proj", # qwen2vl
|
||||
"vision_tower.patch_embed.proj", # kimi-vl
|
||||
"model.vision.patch_embedding.proj", # cogvlm
|
||||
"siglip2.vision_model.embeddings.patch_embedding",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_EMBD_NORM: (
|
||||
|
|
@ -1293,7 +1291,6 @@ class TensorNameMap:
|
|||
"vision_encoder.transformer.layers.{bid}.attention.wq", # pixtral
|
||||
"visual.blocks.{bid}.attn.q", # qwen2vl, generated
|
||||
"vision_tower.encoder.blocks.{bid}.wq", # kimi-vl, generated
|
||||
"siglip2.vision_model.encoder.layers.{bid}.self_attn.q_proj", # youtuvl
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_ATTN_Q_NORM: (
|
||||
|
|
@ -1311,7 +1308,6 @@ class TensorNameMap:
|
|||
"vision_encoder.transformer.layers.{bid}.attention.wk", # pixtral
|
||||
"visual.blocks.{bid}.attn.k", # qwen2vl, generated
|
||||
"vision_tower.encoder.blocks.{bid}.wk", # kimi-vl, generated
|
||||
"siglip2.vision_model.encoder.layers.{bid}.self_attn.k_proj",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_ATTN_K_NORM: (
|
||||
|
|
@ -1329,7 +1325,6 @@ class TensorNameMap:
|
|||
"vision_encoder.transformer.layers.{bid}.attention.wv", # pixtral
|
||||
"visual.blocks.{bid}.attn.v", # qwen2vl, generated
|
||||
"vision_tower.encoder.blocks.{bid}.wv", # kimi-vl, generated
|
||||
"siglip2.vision_model.encoder.layers.{bid}.self_attn.v_proj",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_INPUT_NORM: (
|
||||
|
|
@ -1344,7 +1339,6 @@ class TensorNameMap:
|
|||
"visual.blocks.{bid}.norm1", # qwen2vl
|
||||
"vision_tower.encoder.blocks.{bid}.norm0", # kimi-vl (norm0/norm1)
|
||||
"model.vision.transformer.layers.{bid}.input_layernorm", # cogvlm
|
||||
"siglip2.vision_model.encoder.layers.{bid}.layer_norm1",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_ATTN_O: (
|
||||
|
|
@ -1360,7 +1354,6 @@ class TensorNameMap:
|
|||
"visual.blocks.{bid}.attn.proj", # qwen2vl
|
||||
"vision_tower.encoder.blocks.{bid}.wo", # kimi-vl
|
||||
"model.vision.transformer.layers.{bid}.attention.dense", # cogvlm
|
||||
"siglip2.vision_model.encoder.layers.{bid}.self_attn.out_proj", # youtuvl
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_POST_ATTN_NORM: (
|
||||
|
|
@ -1375,7 +1368,6 @@ class TensorNameMap:
|
|||
"visual.blocks.{bid}.norm2", # qwen2vl
|
||||
"vision_tower.encoder.blocks.{bid}.norm1", # kimi-vl (norm0/norm1)
|
||||
"model.vision.transformer.layers.{bid}.post_attention_layernorm", # cogvlm
|
||||
"siglip2.vision_model.encoder.layers.{bid}.layer_norm2",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_FFN_UP: (
|
||||
|
|
@ -1391,7 +1383,6 @@ class TensorNameMap:
|
|||
"visual.blocks.{bid}.mlp.linear_fc1", # qwen3vl
|
||||
"vision_tower.encoder.blocks.{bid}.mlp.fc0", # kimi-vl (fc0/fc1)
|
||||
"model.vision.transformer.layers.{bid}.mlp.fc1", # cogvlm
|
||||
"siglip2.vision_model.encoder.layers.{bid}.mlp.fc1",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_ENC_FFN_GATE: (
|
||||
|
|
@ -1413,7 +1404,6 @@ class TensorNameMap:
|
|||
"visual.blocks.{bid}.mlp.linear_fc2", # qwen3vl
|
||||
"vision_tower.encoder.blocks.{bid}.mlp.fc1", # kimi-vl (fc0/fc1)
|
||||
"model.vision.transformer.layers.{bid}.mlp.fc2", # cogvlm
|
||||
"siglip2.vision_model.encoder.layers.{bid}.mlp.fc2",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_LAYER_SCALE_1: (
|
||||
|
|
@ -1440,7 +1430,6 @@ class TensorNameMap:
|
|||
"visual.merger.ln_q", # qwen2vl
|
||||
"vision_tower.encoder.final_layernorm", # kimi-vl
|
||||
"visual.post_layernorm", # glm4v
|
||||
"siglip2.vision_model.post_layernorm",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_MM_POST_NORM: (
|
||||
|
|
@ -1457,7 +1446,6 @@ class TensorNameMap:
|
|||
"multi_modal_projector.pre_norm",
|
||||
"pre_mm_projector_norm",
|
||||
"model.vision.linear_proj.norm1", # cogvlm
|
||||
"merger.ln_q",
|
||||
),
|
||||
|
||||
MODEL_TENSOR.V_MM_SOFT_EMB_NORM: (
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
ebc3a0f4a56be1c9424a89fbec09962ac34fde85
|
||||
130bc125a88bb57664b88932c48c38a1cb316fac
|
||||
|
|
|
|||
|
|
@ -87,7 +87,6 @@ add_library(llama
|
|||
models/llada.cpp
|
||||
models/llama-iswa.cpp
|
||||
models/llama.cpp
|
||||
models/maincoder.cpp
|
||||
models/mamba.cpp
|
||||
models/mimo2-iswa.cpp
|
||||
models/minicpm3.cpp
|
||||
|
|
|
|||
|
|
@ -118,7 +118,6 @@ static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
|
|||
{ LLM_ARCH_MISTRAL3, "mistral3" },
|
||||
{ LLM_ARCH_MIMO2, "mimo2" },
|
||||
{ LLM_ARCH_LLAMA_EMBED, "llama-embed" },
|
||||
{ LLM_ARCH_MAINCODER, "maincoder" },
|
||||
{ LLM_ARCH_UNKNOWN, "(unknown)" },
|
||||
};
|
||||
|
||||
|
|
@ -2235,23 +2234,6 @@ static std::set<llm_tensor> llm_get_tensor_names(llm_arch arch) {
|
|||
return {
|
||||
LLM_TENSOR_TOKEN_EMBD,
|
||||
};
|
||||
case LLM_ARCH_MAINCODER:
|
||||
return {
|
||||
LLM_TENSOR_TOKEN_EMBD,
|
||||
LLM_TENSOR_OUTPUT_NORM,
|
||||
LLM_TENSOR_OUTPUT,
|
||||
LLM_TENSOR_ATTN_NORM,
|
||||
LLM_TENSOR_ATTN_Q,
|
||||
LLM_TENSOR_ATTN_Q_NORM,
|
||||
LLM_TENSOR_ATTN_K,
|
||||
LLM_TENSOR_ATTN_K_NORM,
|
||||
LLM_TENSOR_ATTN_V,
|
||||
LLM_TENSOR_ATTN_OUT,
|
||||
LLM_TENSOR_FFN_NORM,
|
||||
LLM_TENSOR_FFN_GATE,
|
||||
LLM_TENSOR_FFN_DOWN,
|
||||
LLM_TENSOR_FFN_UP,
|
||||
};
|
||||
default:
|
||||
GGML_ABORT("unknown architecture for tensor mapping");
|
||||
}
|
||||
|
|
|
|||
|
|
@ -122,7 +122,6 @@ enum llm_arch {
|
|||
LLM_ARCH_MISTRAL3,
|
||||
LLM_ARCH_MIMO2,
|
||||
LLM_ARCH_LLAMA_EMBED,
|
||||
LLM_ARCH_MAINCODER,
|
||||
LLM_ARCH_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -74,7 +74,6 @@ static const std::map<std::string, llm_chat_template> LLM_CHAT_TEMPLATES = {
|
|||
{ "seed_oss", LLM_CHAT_TEMPLATE_SEED_OSS },
|
||||
{ "grok-2", LLM_CHAT_TEMPLATE_GROK_2 },
|
||||
{ "pangu-embedded", LLM_CHAT_TEMPLATE_PANGU_EMBED },
|
||||
{ "solar-open", LLM_CHAT_TEMPLATE_SOLAR_OPEN },
|
||||
};
|
||||
|
||||
llm_chat_template llm_chat_template_from_str(const std::string & name) {
|
||||
|
|
@ -217,8 +216,6 @@ llm_chat_template llm_chat_detect_template(const std::string & tmpl) {
|
|||
return LLM_CHAT_TEMPLATE_GROK_2;
|
||||
} else if (tmpl_contains(LU8("[unused9]系统：[unused10]"))) {
|
||||
return LLM_CHAT_TEMPLATE_PANGU_EMBED;
|
||||
} else if (tmpl_contains("<|begin|>") && tmpl_contains("<|end|>") && tmpl_contains("<|content|>")) {
|
||||
return LLM_CHAT_TEMPLATE_SOLAR_OPEN;
|
||||
}
|
||||
return LLM_CHAT_TEMPLATE_UNKNOWN;
|
||||
}
|
||||
|
|
@ -848,14 +845,6 @@ int32_t llm_chat_apply_template(
|
|||
if (add_ass) {
|
||||
ss << "[unused9]助手：";
|
||||
}
|
||||
} else if (tmpl == LLM_CHAT_TEMPLATE_SOLAR_OPEN) {
|
||||
for (auto message : chat) {
|
||||
std::string role(message->role);
|
||||
ss << "<|begin|>" << role << "<|content|>" << message->content << "<|end|>";
|
||||
}
|
||||
if (add_ass) {
|
||||
ss << "<|begin|>assistant";
|
||||
}
|
||||
} else {
|
||||
// template not supported
|
||||
return -1;
|
||||
|
|
|
|||
|
|
@ -54,7 +54,6 @@ enum llm_chat_template {
|
|||
LLM_CHAT_TEMPLATE_SEED_OSS,
|
||||
LLM_CHAT_TEMPLATE_GROK_2,
|
||||
LLM_CHAT_TEMPLATE_PANGU_EMBED,
|
||||
LLM_CHAT_TEMPLATE_SOLAR_OPEN,
|
||||
LLM_CHAT_TEMPLATE_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
|
|||
|
|
@ -1458,7 +1458,7 @@ ggml_cgraph * llama_context::graph_reserve(
|
|||
|
||||
if (n_tokens % n_seqs != 0) {
|
||||
n_tokens = ((n_tokens + (n_seqs - 1)) / n_seqs) * n_seqs; // round to next multiple of n_seqs
|
||||
n_outputs = std::max(n_outputs, n_tokens);
|
||||
n_outputs = std::min(n_outputs, n_tokens);
|
||||
|
||||
LLAMA_LOG_DEBUG("%s: making n_tokens a multiple of n_seqs - n_tokens = %u, n_seqs = %u, n_outputs = %u\n", __func__, n_tokens, n_seqs, n_outputs);
|
||||
}
|
||||
|
|
|
|||
|
|
@ -240,10 +240,9 @@ struct llama_file::impl {
|
|||
throw std::runtime_error("unexpectedly reached end of file");
|
||||
}
|
||||
} else {
|
||||
size_t bytes_read = 0;
|
||||
while (bytes_read < len) {
|
||||
const size_t to_read = len - bytes_read;
|
||||
ssize_t ret = ::read(fd, reinterpret_cast<char *>(ptr) + bytes_read, to_read);
|
||||
bool successful = false;
|
||||
while (!successful) {
|
||||
off_t ret = read(fd, ptr, len);
|
||||
|
||||
if (ret == -1) {
|
||||
if (errno == EINTR) {
|
||||
|
|
@ -252,16 +251,10 @@ struct llama_file::impl {
|
|||
throw std::runtime_error(format("read error: %s", strerror(errno)));
|
||||
}
|
||||
if (ret == 0) {
|
||||
// EOF: allow if this read was only pulling alignment padding past file end
|
||||
off_t pos = lseek(fd, 0, SEEK_CUR);
|
||||
if (pos != -1 && (size_t) pos == size) {
|
||||
std::memset(reinterpret_cast<char *>(ptr) + bytes_read, 0, len - bytes_read);
|
||||
return;
|
||||
}
|
||||
throw std::runtime_error("unexpectedly reached end of file");
|
||||
}
|
||||
|
||||
bytes_read += (size_t) ret;
|
||||
successful = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -126,7 +126,6 @@ const char * llm_type_name(llm_type type) {
|
|||
case LLM_TYPE_31B_A3_5B: return "31B.A3.5B";
|
||||
case LLM_TYPE_80B_A3B: return "80B.A3B";
|
||||
case LLM_TYPE_100B_A6B: return "100B.A6B";
|
||||
case LLM_TYPE_102B_A12B: return "102B.A12B";
|
||||
case LLM_TYPE_106B_A12B: return "106B.A12B";
|
||||
case LLM_TYPE_230B_A10B: return "230B.A10B";
|
||||
case LLM_TYPE_235B_A22B: return "235B.A22B";
|
||||
|
|
@ -1110,14 +1109,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
|||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_MAINCODER:
|
||||
{
|
||||
ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
|
||||
switch (hparams.n_layer) {
|
||||
case 32: type = LLM_TYPE_1B; break;
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_QWEN3VL:
|
||||
{
|
||||
ml.get_key(LLM_KV_NUM_DEEPSTACK_LAYERS, hparams.n_deepstack_layers, false);
|
||||
|
|
@ -1691,7 +1682,7 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
|||
ml.get_key(LLM_KV_ATTENTION_VALUE_LENGTH_MLA, hparams.n_embd_head_v_mla, false);
|
||||
ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp);
|
||||
ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale, false);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale);
|
||||
ml.get_key(LLM_KV_EXPERT_WEIGHTS_NORM, hparams.expert_weights_norm, false);
|
||||
ml.get_key(LLM_KV_EXPERT_GATING_FUNC, hparams.expert_gating_func, false);
|
||||
if (hparams.expert_gating_func == LLAMA_EXPERT_GATING_FUNC_TYPE_NONE) {
|
||||
|
|
@ -1787,7 +1778,6 @@ void llama_model::load_hparams(llama_model_loader & ml) {
|
|||
|
||||
switch (hparams.n_layer) {
|
||||
case 47: type = LLM_TYPE_106B_A12B; break; // GLM-4.5-Air (46 layers + 1 NextN layer)
|
||||
case 48: type = LLM_TYPE_102B_A12B; break; // Solar Open
|
||||
case 93: type = LLM_TYPE_355B_A32B; break; // GLM-4.5 (92 layers + 1 NextN layer)
|
||||
default: type = LLM_TYPE_UNKNOWN;
|
||||
}
|
||||
|
|
@ -3330,14 +3320,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
layer.attn_norm_2_b = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, TENSOR_NOT_REQUIRED);
|
||||
|
||||
const auto tn_ffn_up_weight = tn(LLM_TENSOR_FFN_UP, "weight", i);
|
||||
ggml_tensor * t_ffn_up = ml.get_tensor_meta(tn_ffn_up_weight.str().c_str());
|
||||
const int64_t n_ffn_up = t_ffn_up ? t_ffn_up->ne[1] : n_ff;
|
||||
|
||||
GGML_ASSERT(n_ffn_up == n_ff || n_ffn_up == n_ff * 2);
|
||||
layer.ffn_up = create_tensor(tn_ffn_up_weight, {n_embd, n_ffn_up}, 0);
|
||||
layer.ffn_up_b = create_tensor(tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ffn_up}, TENSOR_NOT_REQUIRED);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, layer.ffn_gate ? n_ff : n_ff * 2}, 0);
|
||||
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}, 0);
|
||||
layer.ffn_down_b = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, 0);
|
||||
|
|
@ -4793,11 +4776,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
// try to load output.weight, if not found, use token_embd (tied embeddings)
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
if (!output) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
|
@ -4860,11 +4839,7 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
// try to load output.weight, if not found, use token_embd (tied embeddings)
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
if (!output) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
|
@ -5231,9 +5206,9 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), { n_embd, n_embd_head_k * n_head }, flags);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), { n_embd, n_embd_k_gqa }, flags);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), { n_embd, n_embd_v_gqa }, flags);
|
||||
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd_head_k * n_head }, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_k_gqa }, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_v_gqa }, TENSOR_NOT_REQUIRED | flags);
|
||||
layer.bq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "bias", i), { n_embd_head_k * n_head }, flags);
|
||||
layer.bk = create_tensor(tn(LLM_TENSOR_ATTN_K, "bias", i), { n_embd_k_gqa }, flags);
|
||||
layer.bv = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", i), { n_embd_v_gqa }, flags);
|
||||
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd_head_k * n_head, n_embd }, flags);
|
||||
|
||||
|
|
@ -6786,37 +6761,6 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
layer.ffn_exp_probs_b = create_tensor(tn(LLM_TENSOR_FFN_EXP_PROBS_B, "bias", i), {n_expert}, TENSOR_NOT_REQUIRED);
|
||||
}
|
||||
} break;
|
||||
case LLM_ARCH_MAINCODER:
|
||||
{
|
||||
tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, 0);
|
||||
|
||||
// output
|
||||
output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
|
||||
output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, TENSOR_NOT_REQUIRED);
|
||||
// if output is NULL, init from the input tok embed
|
||||
if (output == NULL) {
|
||||
output = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, TENSOR_DUPLICATED);
|
||||
}
|
||||
|
||||
for (int i = 0; i < n_layer; ++i) {
|
||||
auto & layer = layers[i];
|
||||
|
||||
layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
|
||||
|
||||
layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_head_k * n_head}, 0);
|
||||
layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}, 0);
|
||||
layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}, 0);
|
||||
layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
|
||||
|
||||
layer.attn_k_norm = create_tensor(tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
layer.attn_q_norm = create_tensor(tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd_head_k}, 0);
|
||||
|
||||
layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
|
||||
layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
|
||||
layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
|
||||
layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
|
||||
}
|
||||
} break;
|
||||
default:
|
||||
throw std::runtime_error("unknown architecture");
|
||||
}
|
||||
|
|
@ -7462,10 +7406,6 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
{
|
||||
llm = std::make_unique<llm_build_llama<true>>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_MAINCODER:
|
||||
{
|
||||
llm = std::make_unique<llm_build_maincoder>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_DECI:
|
||||
{
|
||||
llm = std::make_unique<llm_build_deci>(*this, params);
|
||||
|
|
@ -7500,7 +7440,7 @@ ggml_cgraph * llama_model::build_graph(const llm_graph_params & params) const {
|
|||
} break;
|
||||
case LLM_ARCH_MODERN_BERT:
|
||||
{
|
||||
llm = std::make_unique<llm_build_modern_bert>(*this, params);
|
||||
llm = std::make_unique<llm_build_modern_bert<true>>(*this, params);
|
||||
} break;
|
||||
case LLM_ARCH_NEO_BERT:
|
||||
{
|
||||
|
|
@ -8074,7 +8014,6 @@ llama_rope_type llama_model_rope_type(const llama_model * model) {
|
|||
case LLM_ARCH_ERNIE4_5_MOE:
|
||||
case LLM_ARCH_MISTRAL3:
|
||||
case LLM_ARCH_LLAMA_EMBED:
|
||||
case LLM_ARCH_MAINCODER:
|
||||
return LLAMA_ROPE_TYPE_NORM;
|
||||
|
||||
// the pairs of head values are offset by n_rot/2
|
||||
|
|
|
|||
|
|
@ -119,7 +119,6 @@ enum llm_type {
|
|||
LLM_TYPE_31B_A3_5B,
|
||||
LLM_TYPE_80B_A3B, // Qwen3 Next
|
||||
LLM_TYPE_100B_A6B,
|
||||
LLM_TYPE_102B_A12B, // Solar-Open
|
||||
LLM_TYPE_106B_A12B, // GLM-4.5-Air
|
||||
LLM_TYPE_230B_A10B, // Minimax M2
|
||||
LLM_TYPE_235B_A22B,
|
||||
|
|
|
|||
|
|
@ -314,12 +314,6 @@ struct llm_tokenizer_bpe : llm_tokenizer {
|
|||
"[!\"#$%&'()*+,\\-./:;<=>?@\\[\\\\\\]^_`{|}~][A-Za-z]+|[^\r\n\\p{L}\\p{P}\\p{S}]?[\\p{L}\\p{M}]+| ?[\\p{P}\\p{S}]+[\r\n]*|\\s*[\r\n]+|\\s+(?!\\S)|\\s+",
|
||||
};
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_YOUTU:
|
||||
regex_exprs = {
|
||||
"[가-힣ㄱ-ㆎ]+|[！…“”‘’—：；，、-〿︰-﹏]+|[ㄅ-ㄯ]+|[一-龥-ゟ゠-ヿ]+",
|
||||
"[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]*[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]+(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|[^\\r\\n\\p{L}\\p{N}]?[\\p{Lu}\\p{Lt}\\p{Lm}\\p{Lo}\\p{M}]+[\\p{Ll}\\p{Lm}\\p{Lo}\\p{M}]*(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])?|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n/]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+",
|
||||
};
|
||||
break;
|
||||
case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_CODER:
|
||||
regex_exprs = {
|
||||
"[\r\n]",
|
||||
|
|
@ -361,7 +355,6 @@ struct llm_tokenizer_bpe : llm_tokenizer {
|
|||
case LLAMA_VOCAB_PRE_TYPE_STABLELM2:
|
||||
case LLAMA_VOCAB_PRE_TYPE_QWEN2:
|
||||
case LLAMA_VOCAB_PRE_TYPE_HUNYUAN:
|
||||
case LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN:
|
||||
regex_exprs = {
|
||||
// original regex from tokenizer.json
|
||||
// "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+"
|
||||
|
|
@ -1867,11 +1860,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
tokenizer_pre == "deepseek-v3") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_DEEPSEEK3_LLM;
|
||||
clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "youtu") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_YOUTU;
|
||||
clean_spaces = false;
|
||||
ignore_merges = true;
|
||||
} else if (
|
||||
tokenizer_pre == "falcon") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_FALCON;
|
||||
|
|
@ -2027,10 +2015,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
tokenizer_pre == "minimax-m2") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2;
|
||||
clean_spaces = false;
|
||||
} else if (
|
||||
tokenizer_pre == "solar-open") {
|
||||
pre_type = LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN;
|
||||
clean_spaces = false;
|
||||
} else {
|
||||
throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str()));
|
||||
}
|
||||
|
|
@ -2203,8 +2187,6 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
// for now, we apply this workaround to find the tokens based on their text
|
||||
|
||||
for (const auto & t : token_to_id) {
|
||||
auto & attr = id_to_token[t.second].attr;
|
||||
|
||||
// find EOT token: "<|eot_id|>", "<|im_end|>", "<end_of_turn>", etc.
|
||||
if (special_eot_id == LLAMA_TOKEN_NULL) {
|
||||
if (false
|
||||
|
|
@ -2220,10 +2202,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<end_of_utterance>" // smoldocling
|
||||
) {
|
||||
special_eot_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2234,10 +2216,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<|eom_id|>"
|
||||
) {
|
||||
special_eom_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2254,10 +2236,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<|code_prefix|>" // GLM-4.5
|
||||
) {
|
||||
special_fim_pre_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2274,10 +2256,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<|code_suffix|>" // GLM-4.5
|
||||
) {
|
||||
special_fim_suf_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2294,10 +2276,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<|code_middle|>" // GLM-4.5
|
||||
) {
|
||||
special_fim_mid_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2311,10 +2293,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<PAD>"
|
||||
) {
|
||||
special_fim_pad_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2329,10 +2311,10 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<reponame>" // Granite
|
||||
) {
|
||||
special_fim_rep_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
@ -2343,41 +2325,15 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<|file_sep|>" // Qwen
|
||||
) {
|
||||
special_fim_sep_id = t.second;
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// auto-detect unused tokens: e.g. control tokens with the word "unused"
|
||||
// ideally, these tokens should be marked as unused during conversion
|
||||
{
|
||||
uint32_t n_unused = 0;
|
||||
|
||||
for (const auto & t : token_to_id) {
|
||||
auto & attr = id_to_token[t.second].attr;
|
||||
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
continue;
|
||||
}
|
||||
|
||||
if ((attr & LLAMA_TOKEN_ATTR_UNUSED) == 0) {
|
||||
if (strstr(t.first.c_str(), "unused") != NULL) {
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_UNUSED);
|
||||
}
|
||||
}
|
||||
|
||||
if (attr & LLAMA_TOKEN_ATTR_UNUSED) {
|
||||
n_unused++;
|
||||
}
|
||||
}
|
||||
|
||||
LLAMA_LOG_INFO("%s: %u unused tokens\n", __func__, n_unused);
|
||||
}
|
||||
|
||||
// maintain a list of tokens that cause end-of-generation
|
||||
// this is currently determined based on the token text, which is obviously not ideal
|
||||
// ref: https://github.com/ggerganov/llama.cpp/issues/9606
|
||||
|
|
@ -2396,16 +2352,12 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
}
|
||||
|
||||
for (const auto & t : token_to_id) {
|
||||
auto & attr = id_to_token[t.second].attr;
|
||||
|
||||
if (false
|
||||
|| t.first == "<|eot_id|>"
|
||||
|| t.first == "<|im_end|>"
|
||||
|| t.first == "<|end|>"
|
||||
|| t.first == "<|return|>" // o200k_harmony
|
||||
|| t.first == "<|call|>" // o200k_harmony
|
||||
|| t.first == "<|flush|>" // solar-open
|
||||
|| t.first == "<|calls|>" // solar-open
|
||||
|| t.first == "<end_of_turn>"
|
||||
|| t.first == "<|endoftext|>"
|
||||
|| t.first == "<|eom_id|>"
|
||||
|
|
@ -2415,28 +2367,24 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
|| t.first == "<end_of_utterance>" // smoldocling
|
||||
) {
|
||||
special_eog_ids.insert(t.second);
|
||||
if ((attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
if ((id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL) == 0) {
|
||||
LLAMA_LOG_WARN("%s: control-looking token: %6d '%s' was not control-type; this is probably a bug in the model. its type will be overridden\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_CONTROL);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_CONTROL;
|
||||
}
|
||||
} else {
|
||||
if (attr & LLAMA_TOKEN_ATTR_CONTROL && !(attr & LLAMA_TOKEN_ATTR_UNUSED)) {
|
||||
// token is control, but not marked as EOG -> print a debug log
|
||||
if (special_eog_ids.count(t.second) == 0) {
|
||||
LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
}
|
||||
// token is control, but not marked as EOG -> print a debug log
|
||||
if (id_to_token[t.second].attr & LLAMA_TOKEN_ATTR_CONTROL && special_eog_ids.count(t.second) == 0) {
|
||||
LLAMA_LOG_DEBUG("%s: control token: %6d '%s' is not marked as EOG\n",
|
||||
__func__, t.second, t.first.c_str());
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// @ngxson : quick hack for gpt-oss, always render these tokens
|
||||
for (const auto & t : token_to_id) {
|
||||
auto & attr = id_to_token[t.second].attr;
|
||||
|
||||
if (t.first == "<|channel|>" || t.first == "<|message|>" || t.first == "<|start|>" || t.first == "<|constrain|>") {
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_USER_DEFINED);
|
||||
id_to_token[t.second].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -2456,42 +2404,34 @@ void llama_vocab::impl::load(llama_model_loader & ml, const LLM_KV & kv) {
|
|||
LLAMA_LOG_WARN("%s: special_eom_id is not in special_eog_ids - the tokenizer config may be incorrect\n", __func__);
|
||||
}
|
||||
|
||||
// TODO: workaround for o200k_harmony and solar-open tokenizer: the "<|end|>" token should not be EOG
|
||||
// we don't have a good way to detect this, so for now, if we have "<|return|>" and "<|call|>" tokens ("<|calls|>" and "<|flush|>" for solar-open),
|
||||
// TODO: workaround for o200k_harmony tokenizer: the "<|end|>" token should not be EOG
|
||||
// we don't have a good way to detect this, so for now, if we have "<|return|>" and "<|call|>" tokens,
|
||||
// we remove the "<|end|>" token from the EOG list
|
||||
{
|
||||
bool has_return = false;
|
||||
bool has_call = false;
|
||||
bool has_end = false;
|
||||
bool has_flush = false;
|
||||
|
||||
llama_token end_id = LLAMA_TOKEN_NULL;
|
||||
|
||||
LLAMA_LOG_INFO("%s: printing all EOG tokens:\n", __func__);
|
||||
for (auto tid : special_eog_ids) {
|
||||
auto & text = id_to_token[tid].text;
|
||||
LLAMA_LOG_INFO("%s: - %d ('%s')\n", __func__, tid, id_to_token[tid].text.c_str());
|
||||
|
||||
LLAMA_LOG_INFO("%s: - %d ('%s')\n", __func__, tid, text.c_str());
|
||||
|
||||
if (text == "<|return|>") {
|
||||
if (id_to_token[tid].text == "<|return|>") {
|
||||
has_return = true;
|
||||
} else if (text == "<|call|>" || text == "<|calls|>") {
|
||||
} else if (id_to_token[tid].text == "<|call|>") {
|
||||
has_call = true;
|
||||
} else if (text == "<|flush|>") {
|
||||
has_flush = true;
|
||||
} else if (text == "<|end|>") {
|
||||
} else if (id_to_token[tid].text == "<|end|>") {
|
||||
has_end = true;
|
||||
end_id = tid;
|
||||
}
|
||||
}
|
||||
|
||||
if ((has_return && has_call && has_end) || (has_call && has_flush && has_end)) {
|
||||
if (has_return && has_call && has_end) {
|
||||
special_eog_ids.erase(end_id);
|
||||
|
||||
auto & attr = id_to_token[end_id].attr;
|
||||
attr = (llama_token_attr) (attr | LLAMA_TOKEN_ATTR_USER_DEFINED);
|
||||
|
||||
LLAMA_LOG_WARN("%s: special_eog_ids contains both '<|return|>' and '<|call|>', or '<|calls|>' and '<|flush|>' tokens, removing '<|end|>' token from EOG list\n", __func__);
|
||||
id_to_token[end_id].attr = LLAMA_TOKEN_ATTR_USER_DEFINED;
|
||||
LLAMA_LOG_WARN("%s: special_eog_ids contains both '<|return|>' and '<|call|>' tokens, removing '<|end|>' token from EOG list\n", __func__);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
|
|
|||
|
|
@ -51,8 +51,6 @@ enum llama_vocab_pre_type {
|
|||
LLAMA_VOCAB_PRE_TYPE_GRANITE_DOCLING = 40,
|
||||
LLAMA_VOCAB_PRE_TYPE_MINIMAX_M2 = 41,
|
||||
LLAMA_VOCAB_PRE_TYPE_AFMOE = 42,
|
||||
LLAMA_VOCAB_PRE_TYPE_SOLAR_OPEN = 43,
|
||||
LLAMA_VOCAB_PRE_TYPE_YOUTU = 44,
|
||||
};
|
||||
|
||||
struct LLM_KV;
|
||||
|
|
|
|||
|
|
@ -142,13 +142,11 @@ llm_build_bert::llm_build_bert(const llama_model & model, const llm_graph_params
|
|||
LLM_FFN_GELU, LLM_FFN_SEQ, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else if (model.arch == LLM_ARCH_JINA_BERT_V2) {
|
||||
const bool up_contains_gate = !model.layers[il].ffn_gate && model.layers[il].ffn_up->ne[1] != hparams.n_ff();
|
||||
auto type_op = up_contains_gate ? LLM_FFN_GEGLU : LLM_FFN_GELU;
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, model.layers[il].ffn_up_b, NULL,
|
||||
model.layers[il].ffn_up, NULL, NULL,
|
||||
model.layers[il].ffn_gate, NULL, NULL,
|
||||
model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, NULL,
|
||||
type_op, LLM_FFN_PAR, il);
|
||||
model.layers[il].ffn_gate ? LLM_FFN_GELU : LLM_FFN_GEGLU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
} else {
|
||||
cur = build_ffn(cur,
|
||||
|
|
|
|||
|
|
@ -3,14 +3,12 @@
|
|||
llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const float kq_scale = 1.0f / sqrtf(float(n_embd_head));
|
||||
float kq_scale = 1.0f / sqrtf(float(n_embd_head));
|
||||
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
ggml_tensor * inpL;
|
||||
ggml_tensor * cur;
|
||||
|
||||
ggml_tensor *inpL, *cur;
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
|
@ -46,7 +44,7 @@ llm_build_cogvlm::llm_build_cogvlm(const llama_model & model, const llm_graph_pa
|
|||
}
|
||||
|
||||
ggml_tensor * inpSA = inpL;
|
||||
cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
cur = build_norm(inpSA, model.layers[il].attn_norm, NULL, LLM_NORM_RMS, il);
|
||||
|
||||
// build self attention
|
||||
{
|
||||
|
|
|
|||
|
|
@ -215,7 +215,7 @@ llm_build_deepseek2::llm_build_deepseek2(const llama_model & model, const llm_gr
|
|||
model.layers[il].ffn_exp_probs_b,
|
||||
n_expert, n_expert_used,
|
||||
LLM_FFN_SILU, hparams.expert_weights_norm,
|
||||
hparams.expert_weights_scale, hparams.expert_weights_scale,
|
||||
true, hparams.expert_weights_scale,
|
||||
(llama_expert_gating_func_type) hparams.expert_gating_func,
|
||||
il);
|
||||
cb(moe_out, "ffn_moe_out", il);
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
#include "models.h"
|
||||
|
||||
|
||||
|
||||
llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_k;
|
||||
|
|
@ -10,8 +12,10 @@ llm_build_gemma_embedding::llm_build_gemma_embedding(const llama_model & model,
|
|||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
|
||||
inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
if (ubatch.token) {
|
||||
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
}
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
|
|
|||
|
|
@ -10,9 +10,10 @@ llm_build_gemma3<iswa>::llm_build_gemma3(const llama_model & model, const llm_gr
|
|||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
|
||||
inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
|
||||
if (ubatch.token) {
|
||||
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
}
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
|
|
|
|||
|
|
@ -1,5 +1,7 @@
|
|||
#include "models.h"
|
||||
|
||||
|
||||
|
||||
llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const llm_graph_params & params) :
|
||||
llm_graph_context(params),
|
||||
model(model),
|
||||
|
|
@ -13,9 +15,10 @@ llm_build_gemma3n_iswa::llm_build_gemma3n_iswa(const llama_model & model, const
|
|||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// important: do not normalize weights for raw embeddings input (i.e. encoded image emdeddings)
|
||||
inpL = ggml_scale(ctx0, inpL, ubatch.token ? sqrtf(n_embd) : 1.0f);
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
|
||||
if (ubatch.token) {
|
||||
inpL = ggml_scale(ctx0, inpL, sqrtf(n_embd));
|
||||
cb(inpL, "inp_scaled", -1);
|
||||
}
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
|
|
@ -245,7 +248,7 @@ ggml_tensor * llm_build_gemma3n_iswa::view_2d_slice(ggml_tensor * x, int idx) {
|
|||
// equivalent to get_per_layer_inputs() in python code
|
||||
// output shape: [n_embd_altup, n_layer, n_tokens]
|
||||
ggml_tensor * llm_build_gemma3n_iswa::get_per_layer_inputs() {
|
||||
auto inp = std::make_unique<llm_graph_input_embd>();
|
||||
auto inp = std::make_unique<llm_graph_input_embd>();
|
||||
ggml_tensor * inp_per_layer;
|
||||
if (ubatch.token) {
|
||||
inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, ubatch.n_tokens);
|
||||
|
|
|
|||
|
|
@ -1,117 +0,0 @@
|
|||
#include "models.h"
|
||||
|
||||
llm_build_maincoder::llm_build_maincoder(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
|
||||
GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
|
||||
GGML_ASSERT(n_embd_head == hparams.n_rot);
|
||||
|
||||
ggml_tensor * cur;
|
||||
ggml_tensor * inpL;
|
||||
|
||||
inpL = build_inp_embd(model.tok_embd);
|
||||
|
||||
// inp_pos - contains the positions
|
||||
ggml_tensor * inp_pos = build_inp_pos();
|
||||
|
||||
auto * inp_attn = build_attn_inp_kv();
|
||||
|
||||
ggml_tensor * inp_out_ids = build_inp_out_ids();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
ggml_tensor * inpSA = inpL;
|
||||
|
||||
// norm
|
||||
cur = build_norm(inpL,
|
||||
model.layers[il].attn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "attn_norm", il);
|
||||
|
||||
// self-attention
|
||||
{
|
||||
// compute Q and K and RoPE them
|
||||
ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
|
||||
cb(Qcur, "Qcur", il);
|
||||
|
||||
ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
|
||||
cb(Kcur, "Kcur", il);
|
||||
|
||||
ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
|
||||
|
||||
Qcur = ggml_rope_ext(
|
||||
ctx0, Qcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
Kcur = ggml_rope_ext(
|
||||
ctx0, Kcur, inp_pos, nullptr,
|
||||
n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
|
||||
ext_factor, attn_factor, beta_fast, beta_slow
|
||||
);
|
||||
|
||||
Qcur = build_norm(Qcur, model.layers[il].attn_q_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(Qcur, "Qcur_normed", il);
|
||||
|
||||
Kcur = build_norm(Kcur, model.layers[il].attn_k_norm, NULL, LLM_NORM_RMS, il);
|
||||
cb(Kcur, "Kcur_normed", il);
|
||||
|
||||
cb(Qcur, "Qcur", il);
|
||||
cb(Kcur, "Kcur", il);
|
||||
cb(Vcur, "Vcur", il);
|
||||
|
||||
cur = build_attn(inp_attn,
|
||||
model.layers[il].wo, model.layers[il].bo,
|
||||
Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, 1.0f/sqrtf(float(n_embd_head)), il);
|
||||
}
|
||||
if (il == n_layer - 1 && inp_out_ids) {
|
||||
cur = ggml_get_rows(ctx0, cur, inp_out_ids);
|
||||
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
|
||||
}
|
||||
ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
|
||||
cb(ffn_inp, "ffn_inp", il);
|
||||
|
||||
// feed-forward network
|
||||
cur = build_norm(ffn_inp,
|
||||
model.layers[il].ffn_norm, NULL,
|
||||
LLM_NORM_RMS, il);
|
||||
cb(cur, "ffn_norm", il);
|
||||
|
||||
cur = build_ffn(cur,
|
||||
model.layers[il].ffn_up, NULL, NULL,
|
||||
model.layers[il].ffn_gate, NULL, NULL,
|
||||
model.layers[il].ffn_down, NULL, NULL,
|
||||
NULL,
|
||||
LLM_FFN_SILU, LLM_FFN_PAR, il);
|
||||
cb(cur, "ffn_out", il);
|
||||
|
||||
cur = ggml_add(ctx0, cur, ffn_inp);
|
||||
|
||||
cur = build_cvec(cur, il);
|
||||
cb(cur, "l_out", il);
|
||||
|
||||
// input for next layer
|
||||
inpL = cur;
|
||||
}
|
||||
cur = inpL;
|
||||
|
||||
cur = build_norm(cur,
|
||||
model.output_norm, NULL,
|
||||
LLM_NORM_RMS, -1);
|
||||
|
||||
cb(cur, "result_norm", -1);
|
||||
res->t_embd = cur;
|
||||
|
||||
// lm_head
|
||||
cur = build_lora_mm(model.output, cur);
|
||||
|
||||
cb(cur, "result_output", -1);
|
||||
res->t_logits = cur;
|
||||
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
|
@ -312,10 +312,6 @@ struct llm_build_llama_iswa : public llm_graph_context {
|
|||
llm_build_llama_iswa(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_maincoder : public llm_graph_context {
|
||||
llm_build_maincoder(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
struct llm_build_mamba : public llm_graph_context_mamba {
|
||||
llm_build_mamba(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
|
@ -336,6 +332,7 @@ struct llm_build_mistral3 : public llm_graph_context {
|
|||
llm_build_mistral3(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
||||
template <bool iswa>
|
||||
struct llm_build_modern_bert : public llm_graph_context {
|
||||
llm_build_modern_bert(const llama_model & model, const llm_graph_params & params);
|
||||
};
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
#include "models.h"
|
||||
|
||||
llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
template <bool iswa>
|
||||
llm_build_modern_bert<iswa>::llm_build_modern_bert(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
|
||||
const int64_t n_embd_head = hparams.n_embd_head_v;
|
||||
const int64_t n_embd_gqa = hparams.n_embd_v_gqa();
|
||||
|
||||
|
|
@ -23,7 +24,13 @@ llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const ll
|
|||
auto * inp_attn = build_attn_inp_no_cache();
|
||||
|
||||
for (int il = 0; il < n_layer; ++il) {
|
||||
float freq_base_l = model.get_rope_freq_base(cparams, il);
|
||||
float freq_base_l = 0.0f;
|
||||
|
||||
if constexpr (iswa) {
|
||||
freq_base_l = model.get_rope_freq_base(cparams, il);
|
||||
} else {
|
||||
freq_base_l = freq_base;
|
||||
}
|
||||
|
||||
cur = inpL;
|
||||
|
||||
|
|
@ -113,3 +120,7 @@ llm_build_modern_bert::llm_build_modern_bert(const llama_model & model, const ll
|
|||
res->t_embd = cur;
|
||||
ggml_build_forward_expand(gf, cur);
|
||||
}
|
||||
|
||||
// Explicit template instantiations
|
||||
template struct llm_build_modern_bert<false>;
|
||||
template struct llm_build_modern_bert<true>;
|
||||
|
|
|
|||
|
|
@ -964,11 +964,6 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
|
|||
{ "\\p{P}", unicode_cpt_flags::PUNCTUATION },
|
||||
{ "\\p{M}", unicode_cpt_flags::ACCENT_MARK },
|
||||
{ "\\p{S}", unicode_cpt_flags::SYMBOL },
|
||||
{ "\\p{Lu}", unicode_cpt_flags::LETTER }, // Uppercase letter
|
||||
{ "\\p{Ll}", unicode_cpt_flags::LETTER }, // Lowercase letter
|
||||
{ "\\p{Lt}", unicode_cpt_flags::LETTER }, // Titlecase letter
|
||||
{ "\\p{Lm}", unicode_cpt_flags::LETTER }, // Modifier letter
|
||||
{ "\\p{Lo}", unicode_cpt_flags::LETTER }, // Other letter
|
||||
};
|
||||
|
||||
static const std::map<int, int> k_ucat_cpt = {
|
||||
|
|
@ -1079,26 +1074,22 @@ std::vector<std::string> unicode_regex_split(const std::string & text, const std
|
|||
continue;
|
||||
}
|
||||
|
||||
// Match \p{...} Unicode properties of varying lengths
|
||||
if (regex_expr[i + 0] == '\\' && i + 3 < regex_expr.size() &&
|
||||
if (regex_expr[i + 0] == '\\' && i + 4 < regex_expr.size() &&
|
||||
regex_expr[i + 1] == 'p' &&
|
||||
regex_expr[i + 2] == '{') {
|
||||
// Find the closing brace
|
||||
size_t closing_brace = regex_expr.find('}', i + 3);
|
||||
if (closing_brace != std::string::npos && closing_brace <= i + 10) { // reasonable limit
|
||||
const std::string pat = regex_expr.substr(i, closing_brace - i + 1);
|
||||
if (k_ucat_enum.find(pat) != k_ucat_enum.end()) {
|
||||
if (!inside) {
|
||||
regex_expr_collapsed += '[';
|
||||
}
|
||||
regex_expr_collapsed += k_ucat_cpt.at(k_ucat_enum.at(pat));
|
||||
regex_expr_collapsed += k_ucat_map.at(k_ucat_enum.at(pat));
|
||||
if (!inside) {
|
||||
regex_expr_collapsed += ']';
|
||||
}
|
||||
i = closing_brace;
|
||||
continue;
|
||||
regex_expr[i + 2] == '{' &&
|
||||
regex_expr[i + 4] == '}') {
|
||||
const std::string pat = regex_expr.substr(i, 5);
|
||||
if (k_ucat_enum.find(pat) != k_ucat_enum.end()) {
|
||||
if (!inside) {
|
||||
regex_expr_collapsed += '[';
|
||||
}
|
||||
regex_expr_collapsed += k_ucat_cpt.at(k_ucat_enum.at(pat));
|
||||
regex_expr_collapsed += k_ucat_map.at(k_ucat_enum.at(pat));
|
||||
if (!inside) {
|
||||
regex_expr_collapsed += ']';
|
||||
}
|
||||
i += 4;
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -1158,7 +1158,6 @@ struct test_case {
|
|||
}
|
||||
|
||||
virtual bool run_whole_graph() { return false; }
|
||||
virtual std::vector<ggml_tensor *> fusion_test_nodes() { return {}; }
|
||||
|
||||
ggml_cgraph * gf = nullptr;
|
||||
ggml_cgraph * gb = nullptr;
|
||||
|
|
@ -1392,13 +1391,7 @@ struct test_case {
|
|||
GGML_UNUSED(index);
|
||||
};
|
||||
|
||||
std::vector<ggml_tensor *> fused_nodes_to_verify = fusion_test_nodes();
|
||||
if (fused_nodes_to_verify.size() == 0 && run_whole_graph()) {
|
||||
fused_nodes_to_verify.push_back(out);
|
||||
}
|
||||
const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud,
|
||||
run_whole_graph() ? fused_nodes_to_verify.data() : nullptr,
|
||||
fused_nodes_to_verify.size());
|
||||
const bool cmp_ok = ggml_backend_compare_graph_backend(backend1, backend2, gf, callback, &ud, run_whole_graph() ? out : nullptr);
|
||||
|
||||
ggml_backend_buffer_free(buf);
|
||||
|
||||
|
|
@ -5187,8 +5180,6 @@ struct test_topk_moe : public test_case {
|
|||
const bool bias_probs;
|
||||
const MoeGatingFunc gating_func;
|
||||
const float scale_w;
|
||||
ggml_tensor * weights {};
|
||||
ggml_tensor * selected_experts {};
|
||||
|
||||
test_topk_moe(std::array<int64_t, 4> ne = { 10, 5, 1, 1 },
|
||||
int n_expert_used = 1,
|
||||
|
|
@ -5226,16 +5217,16 @@ struct test_topk_moe : public test_case {
|
|||
|
||||
ggml_tensor * selection_probs = probs;
|
||||
if (bias_probs) {
|
||||
ggml_tensor * exp_probs_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ne[0]);
|
||||
ggml_tensor * exp_probs_b = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne.data());
|
||||
ggml_set_name(exp_probs_b, "exp_probs_b");
|
||||
selection_probs = ggml_add(ctx, probs, exp_probs_b);
|
||||
ggml_set_name(selection_probs, "selection_probs");
|
||||
}
|
||||
|
||||
selected_experts = ggml_argsort_top_k(ctx, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
|
||||
ggml_tensor * selected_experts = ggml_argsort_top_k(ctx, selection_probs, n_expert_used); // [n_expert_used, n_tokens]
|
||||
ggml_set_name(selected_experts, "selected_experts");
|
||||
|
||||
weights = ggml_get_rows(ctx, ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
|
||||
ggml_tensor * weights = ggml_get_rows(ctx, ggml_reshape_3d(ctx, probs, 1, n_expert, n_tokens), selected_experts); // [1, n_expert_used, n_tokens]
|
||||
ggml_set_name(weights, "weights");
|
||||
|
||||
if (gating_func == GATING_FUNC_SOFTMAX_WEIGHT) {
|
||||
|
|
@ -5261,21 +5252,6 @@ struct test_topk_moe : public test_case {
|
|||
ggml_set_name(weights, "weights");
|
||||
return weights;
|
||||
}
|
||||
// Verify two outputs
|
||||
std::vector<ggml_tensor *> fusion_test_nodes() override { return { selected_experts, weights }; }
|
||||
|
||||
// allow output in arbitrary order
|
||||
double err(const float * a, const float * b, size_t n) override {
|
||||
std::vector<float> a2(n);
|
||||
std::vector<float> b2(n);
|
||||
for (size_t i = 0; i < n; ++i) {
|
||||
a2[i] = a[i];
|
||||
b2[i] = b[i];
|
||||
}
|
||||
std::sort(a2.begin(), a2.end());
|
||||
std::sort(b2.begin(), b2.end());
|
||||
return nmse(a2.data(), b2.data(), n);
|
||||
}
|
||||
};
|
||||
|
||||
struct test_mul_mat_vec_fusion : public test_case {
|
||||
|
|
|
|||
|
|
@ -724,30 +724,6 @@ static void test_tools_oaicompat_json_conversion() {
|
|||
"]"
|
||||
),
|
||||
common_chat_tools_to_json_oaicompat<json>({special_function_tool}).dump(2));
|
||||
|
||||
{
|
||||
auto tools_no_params = common_chat_tools_parse_oaicompat(json::parse(
|
||||
R"([{"type": "function", "function": {"name": "test_func", "description": "A test"}}])"));
|
||||
assert_equals((size_t) 1, tools_no_params.size());
|
||||
assert_equals(std::string("test_func"), tools_no_params[0].name);
|
||||
assert_equals(std::string("A test"), tools_no_params[0].description);
|
||||
assert_equals(std::string("{}"), tools_no_params[0].parameters);
|
||||
}
|
||||
{
|
||||
auto tools_no_desc = common_chat_tools_parse_oaicompat(json::parse(
|
||||
R"([{"type": "function", "function": {"name": "test_func", "parameters": {"type": "object"}}}])"));
|
||||
assert_equals((size_t) 1, tools_no_desc.size());
|
||||
assert_equals(std::string("test_func"), tools_no_desc[0].name);
|
||||
assert_equals(std::string(""), tools_no_desc[0].description);
|
||||
}
|
||||
{
|
||||
auto tools_minimal = common_chat_tools_parse_oaicompat(json::parse(
|
||||
R"([{"type": "function", "function": {"name": "test_func"}}])"));
|
||||
assert_equals((size_t) 1, tools_minimal.size());
|
||||
assert_equals(std::string("test_func"), tools_minimal[0].name);
|
||||
assert_equals(std::string(""), tools_minimal[0].description);
|
||||
assert_equals(std::string("{}"), tools_minimal[0].parameters);
|
||||
}
|
||||
}
|
||||
|
||||
static void test_template_output_parsers() {
|
||||
|
|
|
|||
|
|
@ -27,7 +27,6 @@ add_library(mtmd
|
|||
models/qwen3vl.cpp
|
||||
models/siglip.cpp
|
||||
models/whisper-enc.cpp
|
||||
models/youtuvl.cpp
|
||||
)
|
||||
|
||||
set_target_properties(mtmd PROPERTIES
|
||||
|
|
|
|||
|
|
@ -45,14 +45,13 @@
|
|||
#define KEY_SPATIAL_MERGE_SIZE "clip.vision.spatial_merge_size"
|
||||
#define KEY_IS_DEEPSTACK_LAYERS "clip.vision.is_deepstack_layers"
|
||||
|
||||
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
|
||||
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
|
||||
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
|
||||
#define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern"
|
||||
#define KEY_WIN_ATTN_LAYER_INDEXES "clip.vision.wa_layer_indexes"
|
||||
#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size"
|
||||
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
|
||||
#define KEY_MINICPMV_QUERY_NUM "clip.minicpmv_query_num"
|
||||
#define KEY_MM_PATCH_MERGE_TYPE "clip.vision.mm_patch_merge_type"
|
||||
#define KEY_IMAGE_GRID_PINPOINTS "clip.vision.image_grid_pinpoints"
|
||||
#define KEY_IMAGE_CROP_RESOLUTION "clip.vision.image_crop_resolution"
|
||||
#define KEY_WIN_ATTN_PATTERN "clip.vision.n_wa_pattern"
|
||||
#define KEY_ATTN_WINDOW_SIZE "clip.vision.window_size"
|
||||
#define KEY_MINICPMV_VERSION "clip.minicpmv_version"
|
||||
#define KEY_MINICPMV_QUERY_NUM "clip.minicpmv_query_num"
|
||||
|
||||
// audio-specific
|
||||
#define KEY_AUDIO_PROJ_TYPE "clip.audio.projector_type" // for models with mixed modalities
|
||||
|
|
@ -181,7 +180,6 @@ enum projector_type {
|
|||
PROJECTOR_TYPE_GLMA,
|
||||
PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx
|
||||
PROJECTOR_TYPE_VOXTRAL,
|
||||
PROJECTOR_TYPE_MUSIC_FLAMINGO,
|
||||
PROJECTOR_TYPE_LFM2,
|
||||
PROJECTOR_TYPE_KIMIVL,
|
||||
PROJECTOR_TYPE_LIGHTONOCR,
|
||||
|
|
@ -189,7 +187,6 @@ enum projector_type {
|
|||
PROJECTOR_TYPE_JANUS_PRO,
|
||||
PROJECTOR_TYPE_LFM2A,
|
||||
PROJECTOR_TYPE_GLM4V,
|
||||
PROJECTOR_TYPE_YOUTUVL,
|
||||
PROJECTOR_TYPE_UNKNOWN,
|
||||
};
|
||||
|
||||
|
|
@ -212,7 +209,6 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
|
|||
{ PROJECTOR_TYPE_GLMA, "glma"},
|
||||
{ PROJECTOR_TYPE_QWEN25O, "qwen2.5o"},
|
||||
{ PROJECTOR_TYPE_VOXTRAL, "voxtral"},
|
||||
{ PROJECTOR_TYPE_MUSIC_FLAMINGO, "musicflamingo"},
|
||||
{ PROJECTOR_TYPE_LFM2, "lfm2"},
|
||||
{ PROJECTOR_TYPE_KIMIVL, "kimivl"},
|
||||
{ PROJECTOR_TYPE_LIGHTONOCR,"lightonocr"},
|
||||
|
|
@ -220,7 +216,6 @@ static std::map<projector_type, std::string> PROJECTOR_TYPE_NAMES = {
|
|||
{ PROJECTOR_TYPE_JANUS_PRO, "janus_pro"},
|
||||
{ PROJECTOR_TYPE_LFM2A, "lfm2a"},
|
||||
{ PROJECTOR_TYPE_GLM4V, "glm4v"},
|
||||
{ PROJECTOR_TYPE_YOUTUVL, "youtuvl"},
|
||||
};
|
||||
|
||||
static projector_type clip_projector_type_from_string(const std::string & str) {
|
||||
|
|
|
|||
|
|
@ -61,7 +61,6 @@ struct clip_hparams {
|
|||
std::unordered_set<int32_t> vision_feature_layer;
|
||||
int32_t attn_window_size = 0;
|
||||
int32_t n_wa_pattern = 0;
|
||||
std::unordered_set<int32_t> wa_layer_indexes; // explicit layer indexes that use full attention (for irregular patterns like YoutuVL)
|
||||
|
||||
// audio
|
||||
int32_t n_mel_bins = 0; // whisper preprocessor
|
||||
|
|
@ -320,8 +319,7 @@ struct clip_model {
|
|||
|
||||
bool audio_has_avgpool() const {
|
||||
return proj_type == PROJECTOR_TYPE_QWEN2A
|
||||
|| proj_type == PROJECTOR_TYPE_VOXTRAL
|
||||
|| proj_type == PROJECTOR_TYPE_MUSIC_FLAMINGO;
|
||||
|| proj_type == PROJECTOR_TYPE_VOXTRAL;
|
||||
}
|
||||
|
||||
bool audio_has_stack_frames() const {
|
||||
|
|
|
|||
|
|
@ -818,7 +818,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
|
|||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
case PROJECTOR_TYPE_QWEN2A:
|
||||
case PROJECTOR_TYPE_GLMA:
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
{
|
||||
builder = std::make_unique<clip_graph_whisper_enc>(ctx, img);
|
||||
} break;
|
||||
|
|
@ -846,10 +845,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32
|
|||
{
|
||||
builder = std::make_unique<clip_graph_glm4v>(ctx, img);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
builder = std::make_unique<clip_graph_youtuvl>(ctx, img);
|
||||
} break;
|
||||
default:
|
||||
GGML_ABORT("missing cgraph builder");
|
||||
}
|
||||
|
|
@ -1163,20 +1158,6 @@ struct clip_model_loader {
|
|||
LOG_WRN("%s: more info: https://github.com/ggml-org/llama.cpp/issues/16842\n\n", __func__);
|
||||
}
|
||||
} break;
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
hparams.n_merge = 2;
|
||||
get_u32(KEY_SPATIAL_MERGE_SIZE, hparams.n_merge, false);
|
||||
get_u32(KEY_ATTN_WINDOW_SIZE, hparams.attn_window_size, true);
|
||||
std::vector<int> wa_layer_indexes_vec;
|
||||
get_arr_int(KEY_WIN_ATTN_LAYER_INDEXES, wa_layer_indexes_vec, true);
|
||||
for (auto & layer : wa_layer_indexes_vec) {
|
||||
hparams.wa_layer_indexes.insert(layer);
|
||||
}
|
||||
// support max_height * max_width = 8000 * 8000. 8000/16/2 = 250 image tokens
|
||||
hparams.set_limit_image_tokens(1, 62500);
|
||||
hparams.set_warmup_n_tokens(16*16); // avoid OOM on warmup
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GLM4V:
|
||||
{
|
||||
hparams.rope_theta = 10000.0f;
|
||||
|
|
@ -1195,7 +1176,6 @@ struct clip_model_loader {
|
|||
case PROJECTOR_TYPE_QWEN2A:
|
||||
case PROJECTOR_TYPE_GLMA:
|
||||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
{
|
||||
bool require_stack = model.proj_type == PROJECTOR_TYPE_ULTRAVOX ||
|
||||
model.proj_type == PROJECTOR_TYPE_VOXTRAL ||
|
||||
|
|
@ -1245,14 +1225,7 @@ struct clip_model_loader {
|
|||
LOG_INF("%s: has_llava_proj: %d\n", __func__, hparams.has_llava_projector);
|
||||
LOG_INF("%s: minicpmv_version: %d\n", __func__, hparams.minicpmv_version);
|
||||
LOG_INF("%s: n_merge: %d\n", __func__, hparams.n_merge);
|
||||
LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern);
|
||||
if (!hparams.wa_layer_indexes.empty()) {
|
||||
LOG_INF("%s: wa_layer_indexes: ", __func__);
|
||||
for (auto & layer : hparams.wa_layer_indexes) {
|
||||
LOG_INF("%d ", layer);
|
||||
}
|
||||
LOG_INF("\n");
|
||||
}
|
||||
LOG_INF("%s: n_wa_pattern: %d\n", __func__, hparams.n_wa_pattern);
|
||||
if (hparams.image_min_pixels > 0) {
|
||||
LOG_INF("%s: image_min_pixels: %d%s\n", __func__, hparams.image_min_pixels, hparams.custom_image_min_tokens > 0 ? " (custom value)" : "");
|
||||
}
|
||||
|
|
@ -1520,14 +1493,6 @@ struct clip_model_loader {
|
|||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight"));
|
||||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
model.mm_input_norm_w = get_tensor(TN_MM_INP_NORM); // merger.ln_q (RMS norm)
|
||||
model.mm_0_w = get_tensor(string_format(TN_LLAVA_PROJ, 0, "weight")); // merger.mlp.0
|
||||
model.mm_0_b = get_tensor(string_format(TN_LLAVA_PROJ, 0, "bias"));
|
||||
model.mm_1_w = get_tensor(string_format(TN_LLAVA_PROJ, 2, "weight")); // merger.mlp.2
|
||||
model.mm_1_b = get_tensor(string_format(TN_LLAVA_PROJ, 2, "bias"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_GLM4V:
|
||||
{
|
||||
model.projection = get_tensor(TN_MM_PROJECTOR);
|
||||
|
|
@ -1611,17 +1576,6 @@ struct clip_model_loader {
|
|||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
|
||||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
{
|
||||
model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight"));
|
||||
model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias"));
|
||||
model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight"));
|
||||
model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias"));
|
||||
model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight"));
|
||||
model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias"));
|
||||
model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight"));
|
||||
model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias"));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
{
|
||||
model.mm_0_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 0, "weight"));
|
||||
|
|
@ -2730,57 +2684,6 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, str
|
|||
// res_imgs->data[0] = *res;
|
||||
res_imgs->entries.push_back(std::move(img_f32));
|
||||
} break;
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
const int patch_size = params.patch_size; // typically 16
|
||||
const int merge_size = params.n_merge; // typically 2
|
||||
const int align_size = patch_size * merge_size; // 32
|
||||
|
||||
const int max_num_patches = params.image_max_pixels > 0 ?
|
||||
params.image_max_pixels / (patch_size * patch_size) : 256;
|
||||
|
||||
// Linear search for optimal scale to fit within max_num_patches
|
||||
float scale = 1.0f;
|
||||
int target_height = original_size.height;
|
||||
int target_width = original_size.width;
|
||||
|
||||
auto get_scaled_image_size = [align_size](float scale, int size) -> int {
|
||||
float scaled_size = size * scale;
|
||||
// Round up to nearest multiple of align_size
|
||||
int aligned = static_cast<int>(std::ceil(scaled_size / align_size)) * align_size;
|
||||
// Ensure at least one patch
|
||||
return std::max(align_size, aligned);
|
||||
};
|
||||
|
||||
// Linear search with 0.02 step size
|
||||
while (scale > 0.0f) {
|
||||
target_height = get_scaled_image_size(scale, original_size.height);
|
||||
target_width = get_scaled_image_size(scale, original_size.width);
|
||||
|
||||
int num_patches_h = target_height / patch_size;
|
||||
int num_patches_w = target_width / patch_size;
|
||||
int num_patches = num_patches_h * num_patches_w;
|
||||
|
||||
if (num_patches > max_num_patches) {
|
||||
scale -= 0.02f;
|
||||
} else {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
clip_image_size new_size = {target_width, target_height};
|
||||
|
||||
// Resize the image
|
||||
clip_image_u8 resized;
|
||||
img_tool::resize(*img, resized, new_size, img_tool::RESIZE_ALGO_BILINEAR, false);
|
||||
|
||||
// Normalize to float32
|
||||
clip_image_f32_ptr img_f32(clip_image_f32_init());
|
||||
normalize_image_u8_to_f32(resized, *img_f32, params.image_mean, params.image_std);
|
||||
|
||||
// Add to results
|
||||
res_imgs->entries.push_back(std::move(img_f32));
|
||||
} break;
|
||||
|
||||
case PROJECTOR_TYPE_IDEFICS3:
|
||||
{
|
||||
|
|
@ -3013,7 +2916,6 @@ int clip_n_output_tokens_x(const struct clip_ctx * ctx, struct clip_image_f32 *
|
|||
case PROJECTOR_TYPE_QWEN25VL:
|
||||
case PROJECTOR_TYPE_QWEN3VL:
|
||||
case PROJECTOR_TYPE_GLM4V:
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
return (img->nx / params.patch_size) / 2;
|
||||
default:
|
||||
break;
|
||||
|
|
@ -3029,7 +2931,6 @@ int clip_n_output_tokens_y(const struct clip_ctx * ctx, struct clip_image_f32 *
|
|||
case PROJECTOR_TYPE_QWEN25VL:
|
||||
case PROJECTOR_TYPE_QWEN3VL:
|
||||
case PROJECTOR_TYPE_GLM4V:
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
return (img->ny / params.patch_size) / 2;
|
||||
default:
|
||||
break;
|
||||
|
|
@ -3090,7 +2991,6 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
|
|||
case PROJECTOR_TYPE_QWEN25VL:
|
||||
case PROJECTOR_TYPE_QWEN3VL:
|
||||
case PROJECTOR_TYPE_GLM4V:
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
// dynamic size (2 conv, so double patch size)
|
||||
int x_patch = img->nx / (params.patch_size * 2);
|
||||
|
|
@ -3131,7 +3031,6 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im
|
|||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
case PROJECTOR_TYPE_ULTRAVOX:
|
||||
case PROJECTOR_TYPE_QWEN2A:
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
{
|
||||
n_patches = img->nx;
|
||||
|
||||
|
|
@ -3218,6 +3117,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
const int pos_w = image_size_width / patch_size;
|
||||
const int pos_h = image_size_height / patch_size;
|
||||
|
||||
const bool use_window_attn = hparams.n_wa_pattern > 0; // for qwen2.5vl
|
||||
|
||||
auto get_inp_tensor = [&gf](const char * name) {
|
||||
ggml_tensor * inp = ggml_graph_get_tensor(gf, name);
|
||||
|
|
@ -3366,11 +3266,9 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
set_input_i32("positions", positions);
|
||||
} break;
|
||||
case PROJECTOR_TYPE_QWEN25VL:
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
{
|
||||
// pw * ph = number of tokens output by ViT after apply patch merger
|
||||
// ipw * ipw = number of vision token been processed inside ViT
|
||||
const bool use_window_attn = ctx->model.proj_type == PROJECTOR_TYPE_QWEN25VL ? hparams.n_wa_pattern > 0 : !hparams.wa_layer_indexes.empty();
|
||||
const int merge_ratio = 2;
|
||||
const int pw = image_size_width / patch_size / merge_ratio;
|
||||
const int ph = image_size_height / patch_size / merge_ratio;
|
||||
|
|
@ -3381,7 +3279,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
std::vector<int> inv_idx(ph * pw);
|
||||
|
||||
if (use_window_attn) {
|
||||
const int attn_window_size = hparams.attn_window_size > 0 ? hparams.attn_window_size : 112;
|
||||
const int attn_window_size = 112;
|
||||
const int grid_window = attn_window_size / patch_size / merge_ratio;
|
||||
int dst = 0;
|
||||
// [num_vision_tokens, num_vision_tokens] attention mask tensor
|
||||
|
|
@ -3505,7 +3403,6 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima
|
|||
case PROJECTOR_TYPE_ULTRAVOX:
|
||||
case PROJECTOR_TYPE_LFM2:
|
||||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
case PROJECTOR_TYPE_JANUS_PRO:
|
||||
case PROJECTOR_TYPE_COGVLM:
|
||||
{
|
||||
|
|
@ -3619,7 +3516,6 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|||
case PROJECTOR_TYPE_QWEN2VL:
|
||||
case PROJECTOR_TYPE_QWEN25VL:
|
||||
case PROJECTOR_TYPE_JANUS_PRO:
|
||||
case PROJECTOR_TYPE_YOUTUVL:
|
||||
return ctx->model.mm_1_b->ne[0];
|
||||
case PROJECTOR_TYPE_QWEN3VL:
|
||||
// main path + deepstack paths
|
||||
|
|
@ -3630,7 +3526,6 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) {
|
|||
return ctx->model.projection->ne[1];
|
||||
case PROJECTOR_TYPE_ULTRAVOX:
|
||||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
return ctx->model.mm_2_w->ne[1];
|
||||
case PROJECTOR_TYPE_INTERNVL:
|
||||
return ctx->model.mm_3_w->ne[1];
|
||||
|
|
@ -3692,8 +3587,7 @@ bool clip_has_whisper_encoder(const struct clip_ctx * ctx) {
|
|||
return ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX
|
||||
|| ctx->proj_type() == PROJECTOR_TYPE_QWEN2A
|
||||
|| ctx->proj_type() == PROJECTOR_TYPE_GLMA
|
||||
|| ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL
|
||||
|| ctx->proj_type() == PROJECTOR_TYPE_MUSIC_FLAMINGO;
|
||||
|| ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL;
|
||||
}
|
||||
|
||||
bool clip_encode_float_image (struct clip_ctx * ctx, int n_threads, float * img, int h, int w, float * vec) {
|
||||
|
|
|
|||
|
|
@ -27,11 +27,6 @@ struct clip_graph_qwen3vl : clip_graph {
|
|||
ggml_cgraph * build() override;
|
||||
};
|
||||
|
||||
struct clip_graph_youtuvl : clip_graph {
|
||||
clip_graph_youtuvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
|
||||
ggml_cgraph * build() override;
|
||||
};
|
||||
|
||||
struct clip_graph_minicpmv : clip_graph {
|
||||
clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {}
|
||||
ggml_cgraph * build() override;
|
||||
|
|
|
|||
|
|
@ -86,15 +86,6 @@ ggml_cgraph * clip_graph_whisper_enc::build() {
|
|||
FFN_GELU_ERF,
|
||||
-1);
|
||||
|
||||
} else if (proj_type == PROJECTOR_TYPE_MUSIC_FLAMINGO) {
|
||||
// projector
|
||||
cur = build_ffn(cur,
|
||||
model.mm_1_w, model.mm_1_b,
|
||||
nullptr, nullptr,
|
||||
model.mm_2_w, model.mm_2_b,
|
||||
FFN_GELU_ERF,
|
||||
-1);
|
||||
|
||||
} else if (proj_type == PROJECTOR_TYPE_GLMA) {
|
||||
cur = ggml_norm(ctx0, cur, hparams.eps);
|
||||
cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w);
|
||||
|
|
|
|||
|
|
@ -1,179 +0,0 @@
|
|||
#include "models.h"
|
||||
|
||||
ggml_cgraph * clip_graph_youtuvl::build() {
|
||||
GGML_ASSERT(model.class_embedding == nullptr);
|
||||
const int batch_size = 1;
|
||||
const bool use_window_attn = !hparams.wa_layer_indexes.empty();
|
||||
const int n_pos = n_patches;
|
||||
const int num_position_ids = n_pos * 4;
|
||||
const int m = 2;
|
||||
const int Wp = n_patches_x;
|
||||
const int Hp = n_patches_y;
|
||||
const int Hm = Hp / m;
|
||||
const int Wm = Wp / m;
|
||||
norm_type norm_t = NORM_TYPE_NORMAL;
|
||||
|
||||
int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4};
|
||||
|
||||
ggml_tensor * inp = build_inp_raw();
|
||||
|
||||
// change conv3d to linear
|
||||
// reshape and permute to get patches, permute from (patch_size, m, Wm, patch_size, m, Hm, C) to (C, patch_size, patch_size, m, m, Wm, Hm)
|
||||
{
|
||||
inp = ggml_reshape_4d(
|
||||
ctx0, inp,
|
||||
Wm * m * patch_size, m * patch_size, Hm, 3);
|
||||
inp = ggml_permute(ctx0, inp, 1, 2, 3, 0);
|
||||
inp = ggml_cont_4d(
|
||||
ctx0, inp,
|
||||
m * patch_size * 3, Wm, m * patch_size, Hm);
|
||||
|
||||
inp = ggml_permute(ctx0, inp, 0, 2, 1, 3);
|
||||
inp = ggml_cont_4d(
|
||||
ctx0, inp,
|
||||
m * patch_size * 3, patch_size, m, Hm * Wm);
|
||||
|
||||
inp = ggml_permute(ctx0, inp, 1, 0, 2, 3);
|
||||
inp = ggml_cont_4d(
|
||||
ctx0, inp,
|
||||
patch_size, 3, patch_size, Hm * Wm * m * m);
|
||||
|
||||
inp = ggml_permute(ctx0, inp, 2, 0, 1, 3);
|
||||
inp = ggml_cont_3d(
|
||||
ctx0, inp,
|
||||
3*patch_size* patch_size, Hm * Wm * m * m, 1);
|
||||
}
|
||||
inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp);
|
||||
|
||||
if (model.patch_bias) {
|
||||
inp = ggml_add(ctx0, inp, model.patch_bias);
|
||||
}
|
||||
|
||||
inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches);
|
||||
|
||||
ggml_tensor * inpL = inp;
|
||||
ggml_tensor * window_mask = nullptr;
|
||||
ggml_tensor * window_idx = nullptr;
|
||||
ggml_tensor * inv_window_idx = nullptr;
|
||||
|
||||
ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids);
|
||||
ggml_set_name(positions, "positions");
|
||||
ggml_set_input(positions);
|
||||
|
||||
// pre-layernorm
|
||||
if (model.pre_ln_w) {
|
||||
inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1);
|
||||
}
|
||||
if (use_window_attn) {
|
||||
inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4);
|
||||
ggml_set_name(inv_window_idx, "inv_window_idx");
|
||||
ggml_set_input(inv_window_idx);
|
||||
// mask for window attention
|
||||
window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos);
|
||||
ggml_set_name(window_mask, "window_mask");
|
||||
ggml_set_input(window_mask);
|
||||
|
||||
// if flash attn is used, we need to pad the mask and cast to f16
|
||||
if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) {
|
||||
window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16);
|
||||
}
|
||||
|
||||
// inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size]
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4);
|
||||
inpL = ggml_get_rows(ctx0, inpL, inv_window_idx);
|
||||
inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size);
|
||||
}
|
||||
|
||||
// loop over layers
|
||||
for (int il = 0; il < n_layer; il++) {
|
||||
const auto & layer = model.layers[il];
|
||||
const bool full_attn = use_window_attn ? hparams.wa_layer_indexes.count(il) > 0 : true;
|
||||
|
||||
ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states
|
||||
|
||||
// layernorm1
|
||||
cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il);
|
||||
// self-attention
|
||||
{
|
||||
ggml_tensor * Qcur = ggml_add(ctx0,
|
||||
ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b);
|
||||
ggml_tensor * Kcur = ggml_add(ctx0,
|
||||
ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b);
|
||||
ggml_tensor * Vcur = ggml_add(ctx0,
|
||||
ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b);
|
||||
|
||||
Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches);
|
||||
Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches);
|
||||
Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches);
|
||||
|
||||
Qcur = ggml_rope_multi(
|
||||
ctx0, Qcur, positions, nullptr,
|
||||
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
|
||||
Kcur = ggml_rope_multi(
|
||||
ctx0, Kcur, positions, nullptr,
|
||||
d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1);
|
||||
|
||||
ggml_tensor * attn_mask = full_attn ? nullptr : window_mask;
|
||||
|
||||
cur = build_attn(layer.o_w, layer.o_b,
|
||||
Qcur, Kcur, Vcur, attn_mask, kq_scale, il);
|
||||
}
|
||||
// re-add the layer input, e.g., residual
|
||||
cur = ggml_add(ctx0, cur, inpL);
|
||||
|
||||
inpL = cur; // inpL = residual, cur = hidden_states
|
||||
|
||||
// layernorm2
|
||||
cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il);
|
||||
|
||||
// ffn
|
||||
cur = build_ffn(cur,
|
||||
layer.ff_up_w, layer.ff_up_b,
|
||||
nullptr, nullptr,
|
||||
layer.ff_down_w, layer.ff_down_b,
|
||||
hparams.ffn_op, il);
|
||||
|
||||
// residual 2
|
||||
cur = ggml_add(ctx0, inpL, cur);
|
||||
|
||||
inpL = cur;
|
||||
}
|
||||
|
||||
ggml_tensor * embeddings = inpL;
|
||||
if (use_window_attn) {
|
||||
const int spatial_merge_unit = 4;
|
||||
window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / spatial_merge_unit);
|
||||
ggml_set_name(window_idx, "window_idx");
|
||||
ggml_set_input(window_idx);
|
||||
GGML_ASSERT(batch_size == 1);
|
||||
embeddings = ggml_reshape_2d(ctx0, embeddings, n_embd * spatial_merge_unit, n_patches / spatial_merge_unit);
|
||||
embeddings = ggml_get_rows(ctx0, embeddings, window_idx);
|
||||
embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd, n_patches, batch_size);
|
||||
cb(embeddings, "window_order_restored", -1);
|
||||
}
|
||||
|
||||
// post-layernorm (part of Siglip2VisionTransformer, applied after encoder)
|
||||
if (model.post_ln_w) {
|
||||
embeddings = build_norm(embeddings, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer);
|
||||
}
|
||||
|
||||
// Now apply merger (VLPatchMerger):
|
||||
// 1. Apply RMS norm (ln_q in VLPatchMerger)
|
||||
embeddings = build_norm(embeddings, model.mm_input_norm_w, nullptr, NORM_TYPE_RMS, 1e-6, -1);
|
||||
cb(embeddings, "merger_normed", -1);
|
||||
|
||||
// 2. First reshape for spatial merge (merge 2x2 patches)
|
||||
embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size);
|
||||
cb(embeddings, "merger_reshaped", -1);
|
||||
|
||||
embeddings = build_ffn(embeddings,
|
||||
model.mm_0_w, model.mm_0_b,
|
||||
nullptr, nullptr,
|
||||
model.mm_1_w, model.mm_1_b,
|
||||
FFN_GELU,
|
||||
-1);
|
||||
ggml_build_forward_expand(gf, embeddings);
|
||||
|
||||
return gf;
|
||||
}
|
||||
|
|
@ -283,7 +283,7 @@ struct mtmd_context {
|
|||
// https://github.com/huggingface/transformers/blob/1cd110c6cb6a6237614130c470e9a902dbc1a4bd/docs/source/en/model_doc/pixtral.md
|
||||
img_end = "[IMG_END]";
|
||||
|
||||
} else if (proj == PROJECTOR_TYPE_QWEN2VL || proj == PROJECTOR_TYPE_QWEN25VL || proj == PROJECTOR_TYPE_QWEN3VL || proj == PROJECTOR_TYPE_YOUTUVL) {
|
||||
} else if (proj == PROJECTOR_TYPE_QWEN2VL || proj == PROJECTOR_TYPE_QWEN25VL || proj == PROJECTOR_TYPE_QWEN3VL) {
|
||||
// <|vision_start|> ... (image embeddings) ... <|vision_end|>
|
||||
img_beg = "<|vision_start|>";
|
||||
img_end = "<|vision_end|>";
|
||||
|
|
@ -330,7 +330,6 @@ struct mtmd_context {
|
|||
case PROJECTOR_TYPE_ULTRAVOX:
|
||||
case PROJECTOR_TYPE_VOXTRAL:
|
||||
case PROJECTOR_TYPE_GLMA:
|
||||
case PROJECTOR_TYPE_MUSIC_FLAMINGO:
|
||||
audio_preproc = std::make_unique<mtmd_audio_preprocessor_whisper>(ctx_a);
|
||||
break;
|
||||
case PROJECTOR_TYPE_LFM2A:
|
||||
|
|
@ -353,9 +352,6 @@ struct mtmd_context {
|
|||
// [BEGIN_AUDIO] ... (embeddings) ...
|
||||
aud_beg = "[BEGIN_AUDIO]";
|
||||
|
||||
} else if (proj == PROJECTOR_TYPE_MUSIC_FLAMINGO) {
|
||||
// <sound> ... (embeddings) ...
|
||||
aud_beg = "<sound>";
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
|
|
@ -12,7 +12,6 @@
|
|||
#include <cmath>
|
||||
#include <cctype>
|
||||
#include <algorithm>
|
||||
#include <filesystem>
|
||||
|
||||
struct quant_option {
|
||||
std::string name;
|
||||
|
|
@ -644,11 +643,6 @@ int main(int argc, char ** argv) {
|
|||
return 1;
|
||||
}
|
||||
|
||||
if (std::error_code ec; std::filesystem::equivalent(fname_inp, fname_out, ec)) {
|
||||
fprintf(stderr, "%s: error: input and output files are the same: '%s'\n", __func__, fname_inp.c_str());
|
||||
return 1;
|
||||
}
|
||||
|
||||
print_build_info();
|
||||
|
||||
fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
|
||||
|
|
|
|||
Binary file not shown.
|
|
@ -65,7 +65,10 @@ export async function copyCodeToClipboard(
|
|||
successMessage = 'Code copied to clipboard',
|
||||
errorMessage = 'Failed to copy code'
|
||||
): Promise<boolean> {
|
||||
return copyToClipboard(rawCode, successMessage, errorMessage);
|
||||
const doc = new DOMParser().parseFromString(rawCode, 'text/html');
|
||||
const decodedCode = doc.body.textContent ?? rawCode;
|
||||
|
||||
return copyToClipboard(decodedCode, successMessage, errorMessage);
|
||||
}
|
||||
|
||||
/**
|
||||
|
|
|
|||
Loading…
Reference in New Issue