diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 875eb766f3..e4f05258db 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -16,7 +16,7 @@ The project differentiates between 3 levels of contributors: - If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops` - Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR - Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly -- If your PR becomes stale, don't hesitate to ping the maintainers in the comments +- If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention - Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR - Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs - Using AI to generate PRs is permitted. However, you must (1) explicitly disclose how AI was used and (2) conduct a thorough manual review before publishing the PR. Note that trivial tab autocompletions do not require disclosure. diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 4590b23921..c641989c19 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -1524,6 +1524,79 @@ class TextModel(ModelBase): special_vocab._set_special_token("bos", 151643) special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_mistral(self): + if not _mistral_common_installed: + raise ImportError(_mistral_import_error_msg) + + vocab = MistralVocab(self.dir_model) + logger.info( + f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}." + ) + + self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model) + + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size, ( + f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})" + ) + + if vocab.tokenizer_type == MistralTokenizerType.tekken: + self.gguf_writer.add_tokenizer_pre("tekken") + self.gguf_writer.add_token_merges( + vocab.extract_vocab_merges_from_model() + ) + + logger.info( + f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}." + ) + + self.gguf_writer.add_bos_token_id(vocab.bos_id) + self.gguf_writer.add_eos_token_id(vocab.eos_id) + self.gguf_writer.add_unk_token_id(vocab.unk_id) + self.gguf_writer.add_pad_token_id(vocab.pad_id) + + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_vocab_size(vocab.vocab_size) + + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(False) + + local_template_file_path = self.dir_model / "chat_template.jinja" + + if self.is_mistral_format and local_template_file_path.is_file(): + # Ministral-3 and other new Mistral models come with chat templates. + # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main + logger.info("Using an existing Mistral local chat template.") + + with open(local_template_file_path, "r", encoding="utf-8") as f: + template = f.read() + elif not self.is_mistral_format or not self.disable_mistral_community_chat_template: + template_dir = Path(__file__).parent / "models/templates/" + + # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`. + if self.is_mistral_format: + logger.info( + "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. " + "Mistral recommends to use `mistral-common` to perform tokenization and detokenization." + ) + template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format) + else: + logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.") + template = None + + if template is not None: + self.gguf_writer.add_chat_template(template) + class MmprojModel(ModelBase): model_type = ModelType.MMPROJ @@ -2294,79 +2367,6 @@ class LlamaModel(TextModel): if self.hf_arch == "VLlama3ForCausalLM": self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) - def _set_vocab_mistral(self): - if not _mistral_common_installed: - raise ImportError(_mistral_import_error_msg) - - vocab = MistralVocab(self.dir_model) - logger.info( - f"Converting tokenizer {vocab.tokenizer_type} of size {vocab.vocab_size}." - ) - - self.gguf_writer.add_tokenizer_model(vocab.gguf_tokenizer_model) - - tokens = [] - scores = [] - toktypes = [] - - for text, score, toktype in vocab.all_tokens(): - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - - assert len(tokens) == vocab.vocab_size, ( - f"token count ({len(tokens)}) != vocab size ({vocab.vocab_size})" - ) - - if vocab.tokenizer_type == MistralTokenizerType.tekken: - self.gguf_writer.add_tokenizer_pre("tekken") - self.gguf_writer.add_token_merges( - vocab.extract_vocab_merges_from_model() - ) - - logger.info( - f"Setting bos, eos, unk and pad token IDs to {vocab.bos_id}, {vocab.eos_id}, {vocab.unk_id}, {vocab.pad_id}." - ) - - self.gguf_writer.add_bos_token_id(vocab.bos_id) - self.gguf_writer.add_eos_token_id(vocab.eos_id) - self.gguf_writer.add_unk_token_id(vocab.unk_id) - self.gguf_writer.add_pad_token_id(vocab.pad_id) - - self.gguf_writer.add_token_list(tokens) - self.gguf_writer.add_token_scores(scores) - self.gguf_writer.add_token_types(toktypes) - self.gguf_writer.add_vocab_size(vocab.vocab_size) - - self.gguf_writer.add_add_bos_token(True) - self.gguf_writer.add_add_eos_token(False) - - local_template_file_path = self.dir_model / "chat_template.jinja" - - if self.is_mistral_format and local_template_file_path.is_file(): - # Ministral-3 and other new Mistral models come with chat templates. - # ref: https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/tree/main - logger.info("Using an existing Mistral local chat template.") - - with open(local_template_file_path, "r", encoding="utf-8") as f: - template = f.read() - elif not self.is_mistral_format or not self.disable_mistral_community_chat_template: - template_dir = Path(__file__).parent / "models/templates/" - - # Log only for Mistral format that the official tokenization and detokenization is via `mistral-common`. - if self.is_mistral_format: - logger.info( - "Using a Mistral community chat template. These templates can be subject to errors in early days or weeks after a release. " - "Mistral recommends to use `mistral-common` to perform tokenization and detokenization." - ) - template = MistralModel.get_community_chat_template(vocab, template_dir, self.is_mistral_format) - else: - logger.info("Not using a Mistral local or community chat template. Ensure to perform the tokenization and detokenization via `mistral-common`.") - template = None - - if template is not None: - self.gguf_writer.add_chat_template(template) - def set_vocab(self): if self.is_mistral_format: return self._set_vocab_mistral() @@ -9924,17 +9924,109 @@ class MistralModel(LlamaModel): def set_gguf_parameters(self): super().set_gguf_parameters() - if "yarn" in self.hparams: - yarn_params = self.hparams["yarn"] - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(yarn_params["factor"]) - self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"]) - self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"]) - self.gguf_writer.add_rope_scaling_yarn_log_mul(1.0) # mscale_all_dim - self.gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"]) + MistralModel.set_mistral_config(self.gguf_writer, self.hparams) - if "llama_4_scaling" in self.hparams: - self.gguf_writer.add_attn_temperature_scale(self.hparams["llama_4_scaling"]["beta"]) + @staticmethod + def set_mistral_config(gguf_writer: gguf.GGUFWriter, hparams: dict): + if "yarn" in hparams: + yarn_params = hparams["yarn"] + gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + gguf_writer.add_rope_scaling_factor(yarn_params["factor"]) + gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_params["beta"]) + gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_params["alpha"]) + gguf_writer.add_rope_scaling_yarn_log_mul(1.0) # mscale_all_dim + gguf_writer.add_rope_scaling_orig_ctx_len(yarn_params["original_max_position_embeddings"]) + + if "llama_4_scaling" in hparams: + gguf_writer.add_attn_temperature_scale(hparams["llama_4_scaling"]["beta"]) + + +class MistralMoeModel(DeepseekV2Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + model_name = "Mistral" + hf_arch = "" + is_mistral_format = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + logger.info("Using MistralMoeModel") + # remap hparams from Mistral MoE format to DeepseekV2 format + # we do this way to be able to reuse DeepseekV2Model set_gguf_parameters logic + # ref: https://github.com/vllm-project/vllm/blob/b294e28db2c5dee61bc25157664edcada8b90b31/vllm/transformers_utils/configs/mistral.py + config = self.hparams + # Mistral key -> HF key + config_mapping = { + "dim": "hidden_size", + "norm_eps": "rms_norm_eps", + "n_kv_heads": "num_key_value_heads", + "n_layers": "num_hidden_layers", + "n_heads": "num_attention_heads", + "hidden_dim": "intermediate_size", + } + # HF key -> (Mistral key, default value) + top_level_mapping_with_default = { + "model_type": ("model_type", "transformer"), + "hidden_act": ("activation", "silu"), + "tie_word_embeddings": ("tied_embeddings", False), + "max_seq_len": ("max_seq_len", config.get("max_position_embeddings", 128_000)), + "max_position_embeddings": ("max_position_embeddings", 128_000), + } + # mapping top-level keys + for key, new_key in config_mapping.items(): + if key in config: + config[new_key] = config[key] + for new_key, (key, default_value) in top_level_mapping_with_default.items(): + config[new_key] = config.get(key, default_value) + # mapping MoE-specific keys + moe_config_map = { + "route_every_n": "moe_layer_freq", + "first_k_dense_replace": "first_k_dense_replace", + "num_experts_per_tok": "num_experts_per_tok", + "num_experts": "n_routed_experts", + "expert_hidden_dim": "moe_intermediate_size", + "routed_scale": "routed_scaling_factor", + "num_shared_experts": "n_shared_experts", + "num_expert_groups": "n_group", + "num_expert_groups_per_tok": "topk_group", + } + moe = config["moe"] + for key, new_key in moe_config_map.items(): + if key in moe: + config[new_key] = moe[key] + # provide missing values + config["topk_method"] = None + config["norm_topk_prob"] = True + config["scoring_func"] = "softmax" + + def set_vocab(self): + self._set_vocab_mistral() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + MistralModel.set_mistral_config(self.gguf_writer, self.hparams) + yarn_params = self.hparams["yarn"] + self.gguf_writer.add_attn_temperature_length(yarn_params["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1) # mscale_all_dim * 0.1 + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): + if name.startswith("vision_") or name.startswith("patch_merger.") or "mm_projector" in name: + return [] + + # rename certain tensors so that we can reuse DeepseekV2Model modify_tensors logic + if name.endswith(".qscale_act"): + name = name.replace(".qscale_act", ".input_scale") + if name.endswith(".qscale_weight"): + name = name.replace(".qscale_weight", ".weight_scale") + if ".wkv_b." in name: + name = name.replace(".wkv_b.", ".kv_b_proj.") + if ".experts." in name: + name = name.replace(".experts.", ".mlp.experts.") + name = name.replace(".w1.", ".gate_proj.") + name = name.replace(".w2.", ".down_proj.") + name = name.replace(".w3.", ".up_proj.") + name = "model." + name + + return super().modify_tensors(data_torch, name, bid) class PixtralModel(LlavaVisionModel): @@ -10490,6 +10582,8 @@ def main() -> None: elif args.mmproj: assert hparams.get("vision_encoder") is not None, "This model does not support multimodal" model_class = PixtralModel + elif "moe" in hparams: + model_class = MistralMoeModel else: model_class = MistralModel diff --git a/docs/ops.md b/docs/ops.md index 4136e8ab3b..65e33b5120 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -12,111 +12,111 @@ Legend: - 🟡 Partially supported by this backend - ❌ Not supported by this backend -| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | zDNN | -|-----------|------|------|------|------|------|------|------|------|------| -| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | -| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | -| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | -| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | -| CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | -| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | -| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | -| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | -| CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | -| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | -| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ❌ | -| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | -| FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | -| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | -| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | -| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | ❌ | -| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | -| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | -| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | -| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | -| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | -| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | -| OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | -| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | -| PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | -| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | -| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | -| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | -| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | -| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | -| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ❌ | -| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ❌ | -| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | -| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | -| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | -| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | -| SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | -| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | -| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | -| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ❌ | -| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ❌ | -| SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | -| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | -| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | -| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ❌ | -| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | -| TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | -| TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | -| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | -| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | -| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| Operation | BLAS | CANN | CPU | CUDA | Metal | OpenCL | SYCL | Vulkan | WebGPU | zDNN | +|-----------|------|------|------|------|------|------|------|------|------|------| +| ABS | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | +| CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | +| CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | +| CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | +| CONV_2D_DW | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | +| CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CROSS_ENTROPY_LOSS_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| CUMSUM | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| DIAG_MASK_INF | ❌ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ✅ | ❌ | ❌ | +| DIV | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| DUP | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | ✅ | ❌ | ❌ | +| ELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | +| EXP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| EXPM1 | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | +| FILL | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| FLASH_ATTN_EXT | ❌ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | +| FLOOR | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| GATED_LINEAR_ATTN | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | +| GEGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| GEGLU_ERF | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| GEGLU_QUICK | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| GELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| GELU_ERF | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| GELU_QUICK | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| GET_ROWS | ❌ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | +| GET_ROWS_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| GROUP_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| GROUP_NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| HARDSIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| HARDSWISH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| IM2COL | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | +| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | +| MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | +| MUL_MAT_ID | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | +| NEG | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ❌ | ❌ | +| NORM_MUL_ADD | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| OPT_STEP_ADAMW | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| OPT_STEP_SGD | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| OUT_PROD | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| PAD | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | +| PAD_REFLECT_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | +| POOL_2D | ❌ | 🟡 | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| REGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| RELU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| REPEAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | 🟡 | ❌ | ❌ | +| REPEAT_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| RMS_NORM | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| RMS_NORM_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| RMS_NORM_MUL_ADD | ❌ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | +| ROLL | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | +| ROPE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| ROPE_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| ROUND | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| RWKV_WKV6 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| RWKV_WKV7 | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| SCALE | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SET | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | +| SET_ROWS | ❌ | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | +| SGN | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | ❌ | ✅ | ❌ | +| SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | +| SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | +| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | +| SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | +| SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | +| SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| SOLVE_TRI | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | +| SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | +| SUB | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | ✅ | ❌ | +| SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | +| SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ✅ | ❌ | +| TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | +| TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | +| TOP_K | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | +| TRI | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | +| TRUNC | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | +| UPSCALE | ❌ | 🟡 | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | +| XIELU | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ✅ | ❌ | diff --git a/docs/ops/WebGPU.csv b/docs/ops/WebGPU.csv new file mode 100644 index 0000000000..bfff75e66f --- /dev/null +++ b/docs/ops/WebGPU.csv @@ -0,0 +1,18741 @@ +"backend_name","op_name","op_params","test_mode","supported","error_message","backend_reg_name" +"WebGPU: WebGPU","ABS","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","ABS","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SGN","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","SGN","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","NEG","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","NEG","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","STEP","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","STEP","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","WebGPU" +"WebGPU: WebGPU","TANH","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","WebGPU" +"WebGPU: 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WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=1,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=3,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=32,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f32,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=f16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=bf16,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q8_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","FLASH_ATTN_EXT","hsk=576,hsv=512,nh=4,nr23=[4,1],kv=512,nb=35,mask=0,sinks=0,max_bias=0.000000,logit_softcap=0.000000,prec=f32,type_KV=q4_0,permute=[0,1,2,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","CROSS_ENTROPY_LOSS_BACK","type=f32,ne=[30000,1,1,1]","support","0","no","WebGPU" +"WebGPU: WebGPU","OPT_STEP_ADAMW","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" +"WebGPU: WebGPU","OPT_STEP_SGD","type=f32,ne=[10,5,4,3]","support","0","no","WebGPU" diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index dab795fb90..91797cf78a 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -20,6 +20,7 @@ else() add_subdirectory(gguf-hash) add_subdirectory(gguf) + add_subdirectory(idle) add_subdirectory(lookahead) add_subdirectory(lookup) add_subdirectory(parallel) diff --git a/examples/idle/CMakeLists.txt b/examples/idle/CMakeLists.txt new file mode 100644 index 0000000000..d5018fec4b --- /dev/null +++ b/examples/idle/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET llama-idle) +add_executable(${TARGET} idle.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama common ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/idle/README.md b/examples/idle/README.md new file mode 100644 index 0000000000..d38e607994 --- /dev/null +++ b/examples/idle/README.md @@ -0,0 +1,3 @@ +# llama.cpp/example/idle + +https://github.com/ggml-org/llama.cpp/pull/17766 diff --git a/examples/idle/idle.cpp b/examples/idle/idle.cpp new file mode 100644 index 0000000000..000427143d --- /dev/null +++ b/examples/idle/idle.cpp @@ -0,0 +1,110 @@ +#include "arg.h" +#include "common.h" +#include "log.h" +#include "llama.h" + +#include +#include +#include +#include +#include +#include + +static void print_usage(int /*argc*/, char ** argv) { + printf("\nexample usage:\n"); + printf("\n %s -m model.gguf [-ngl n_gpu_layers]\n", argv[0]); + printf("\n"); +} + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON, print_usage)) { + return 1; + } + + common_init(); + + // init LLM + + llama_backend_init(); + llama_numa_init(params.numa); + + // initialize the model + + llama_model_params model_params = common_model_params_to_llama(params); + + llama_model * model = llama_model_load_from_file(params.model.path.c_str(), model_params); + + if (model == NULL) { + LOG_ERR("%s: error: unable to load model\n" , __func__); + return 1; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + + // we need just a dummy token to evaluate + std::vector prompt_tokens(1, llama_vocab_bos(vocab)); + + llama_context_params ctx_params = llama_context_default_params(); + ctx_params.n_ctx = 512; + ctx_params.n_batch = 512; + ctx_params.no_perf = false; + + llama_context * ctx = llama_init_from_model(model, ctx_params); + if (ctx == NULL) { + fprintf(stderr , "%s: error: failed to create the llama_context\n" , __func__); + return 1; + } + + llama_batch batch = llama_batch_get_one(prompt_tokens.data(), prompt_tokens.size()); + + const int n_iters = 3; + + // warm-up + llama_decode(ctx, batch); + llama_memory_clear(llama_get_memory(ctx), true); + llama_synchronize(ctx); + + for (int64_t t_pause_ms = 0; t_pause_ms <= 4000; t_pause_ms += 800) { + double t_sum_us = 0.0; + double t_sum2_us = 0.0; + + for (int i = 0; i < n_iters; i++) { + // this pause is important - it simulates "idle GPU" + std::this_thread::sleep_for(std::chrono::milliseconds(t_pause_ms)); + + const int64_t t_start_us = llama_time_us(); + + // this should take constant time + llama_decode(ctx, batch); + llama_synchronize(ctx); + + const int64_t t_end_us = llama_time_us(); + + const double t_cur_us = t_end_us - t_start_us; + +#if 1 + // print individual decode times + printf(" - decode time: %8.2f ms\n", t_cur_us / 1000); +#endif + + t_sum_us += t_cur_us; + t_sum2_us += t_cur_us * t_cur_us; + + llama_memory_clear(llama_get_memory(ctx), true); + llama_synchronize(ctx); // just in case + } + + const double t_avg_us = t_sum_us / n_iters; + const double t_dev_us = sqrt((t_sum2_us / (n_iters - 1)) - (t_avg_us * t_avg_us * n_iters) / (n_iters - 1)); + + printf("iters: %4d, pause: %5d ms, avg decode time: %8.2f +/- %4.2f ms\n", n_iters, (int) t_pause_ms, t_avg_us / 1000, t_dev_us / 1000); + fflush(stdout); + } + + llama_free(ctx); + llama_model_free(model); + + return 0; +} diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 615929c1e0..39d4464663 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -241,6 +241,12 @@ int main(int argc, char ** argv) { llama_batch_free(batch); + // this one is managed by common_init_result + //llama_free(ctx); + + llama_free(ctx2); + llama_free(ctx3); + if (result0 != result2) { fprintf(stderr, "\n%s : error : the seq restore generation is different\n", __func__); return 1; diff --git a/ggml/include/ggml-rpc.h b/ggml/include/ggml-rpc.h index 832c26c61d..df1ad2a516 100644 --- a/ggml/include/ggml-rpc.h +++ b/ggml/include/ggml-rpc.h @@ -1,6 +1,5 @@ #pragma once -#include "ggml.h" #include "ggml-backend.h" #ifdef __cplusplus @@ -8,7 +7,7 @@ extern "C" { #endif #define RPC_PROTO_MAJOR_VERSION 3 -#define RPC_PROTO_MINOR_VERSION 5 +#define RPC_PROTO_MINOR_VERSION 6 #define RPC_PROTO_PATCH_VERSION 0 #define GGML_RPC_MAX_SERVERS 16 diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index b0e10f5768..6bc762c069 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -2196,6 +2196,15 @@ extern "C" { int p2, int p3); + // pad each dimension with values on the other side of the torus (looping around) + GGML_API struct ggml_tensor * ggml_pad_circular( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1, + int p2, + int p3); + GGML_API struct ggml_tensor * ggml_pad_ext( struct ggml_context * ctx, struct ggml_tensor * a, @@ -2209,6 +2218,19 @@ extern "C" { int rp3 ); + // pad each dimension with values on the other side of the torus (looping around) + GGML_API struct ggml_tensor * ggml_pad_ext_circular( + struct ggml_context * ctx, + struct ggml_tensor * a, + int lp0, + int rp0, + int lp1, + int rp1, + int lp2, + int rp2, + int lp3, + int rp3); + // pad each dimension with reflection: [a, b, c, d] -> [b, a, b, c, d, c] GGML_API struct ggml_tensor * ggml_pad_reflect_1d( struct ggml_context * ctx, diff --git a/ggml/src/ggml-backend-reg.cpp b/ggml/src/ggml-backend-reg.cpp index e96b5c403d..88e6dc45c7 100644 --- a/ggml/src/ggml-backend-reg.cpp +++ b/ggml/src/ggml-backend-reg.cpp @@ -534,8 +534,12 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, fs::path best_path; for (const auto & search_path : search_paths) { - if (!fs::exists(search_path)) { - GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str()); + if (std::error_code ec; !fs::exists(search_path, ec)) { + if (ec) { + GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(search_path).c_str(), ec.message().c_str()); + } else { + GGML_LOG_DEBUG("%s: search path %s does not exist\n", __func__, path_str(search_path).c_str()); + } continue; } fs::directory_iterator dir_it(search_path, fs::directory_options::skip_permission_denied); @@ -575,8 +579,12 @@ static ggml_backend_reg_t ggml_backend_load_best(const char * name, bool silent, for (const auto & search_path : search_paths) { fs::path filename = backend_filename_prefix().native() + name_path.native() + backend_filename_extension().native(); fs::path path = search_path / filename; - if (fs::exists(path)) { + if (std::error_code ec; fs::exists(path, ec)) { return get_reg().load_backend(path, silent); + } else { + if (ec) { + GGML_LOG_DEBUG("%s: posix_stat(%s) failure, error-message: %s\n", __func__, path_str(path).c_str(), ec.message().c_str()); + } } } return nullptr; diff --git a/ggml/src/ggml-cann/ggml-cann.cpp b/ggml/src/ggml-cann/ggml-cann.cpp index 544c1e2a50..23dc0433c1 100644 --- a/ggml/src/ggml-cann/ggml-cann.cpp +++ b/ggml/src/ggml-cann/ggml-cann.cpp @@ -2551,6 +2551,8 @@ static bool ggml_backend_cann_supports_op(ggml_backend_dev_t dev, const ggml_ten case GGML_OP_ACC: case GGML_OP_GROUP_NORM: case GGML_OP_PAD: + // TODO: add circular padding support for cann, see https://github.com/ggml-org/llama.cpp/pull/16985 + return ggml_get_op_params_i32(op, 8) == 0; case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: diff --git a/ggml/src/ggml-cpu/ops.cpp b/ggml/src/ggml-cpu/ops.cpp index ac16b3681b..3032783971 100644 --- a/ggml/src/ggml-cpu/ops.cpp +++ b/ggml/src/ggml-cpu/ops.cpp @@ -6554,8 +6554,13 @@ static void ggml_call_mul_mat(ggml_type type, const ggml_compute_params * params ggml_compute_forward_mul_mat(params, &dst); } +static inline int64_t ggml_wrap_around(int64_t coord, int64_t size) { + return (coord + size) % size; // adding size avoids negative number weirdness +} + // ggml_compute_forward_conv_2d + static void ggml_compute_forward_conv_2d_impl(const ggml_compute_params * params, const ggml_tensor * kernel, // [KW, KH, IC, OC] const ggml_tensor * src, // [W, H, C, N] @@ -7591,6 +7596,7 @@ void ggml_compute_forward_upscale( // ggml_compute_forward_pad +template static void ggml_compute_forward_pad_f32( const ggml_compute_params * params, ggml_tensor * dst) { @@ -7615,23 +7621,40 @@ static void ggml_compute_forward_pad_f32( const int32_t lp3 = ggml_get_op_params_i32(dst, 6); const int32_t rp3 = ggml_get_op_params_i32(dst, 7); - // TODO: optimize for (int64_t i2 = 0; i2 < ne2; ++i2) { for (int64_t i1 = ith; i1 < ne1; i1 += nth) { for (int64_t i0 = 0; i0 < ne0; ++i0) { for (int64_t i3 = 0; i3 < ne3; ++i3) { - const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; - if ((i0 >= lp0 && i0 < ne0 - rp0) \ - && (i1 >= lp1 && i1 < ne1 - rp1) \ - && (i2 >= lp2 && i2 < ne2 - rp2) \ - && (i3 >= lp3 && i3 < ne3 - rp3)) { - const int64_t src_idx = (i3 - lp3)*nb03 + (i2 - lp2)*nb02 + (i1 - lp1)*nb01 + (i0 - lp0)*nb00; + // circular means wrap around on a torus, so x and y loop around + if constexpr (circular_t) { + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + const int64_t src_i0 = ggml_wrap_around(i0 - lp0, ne00); + const int64_t src_i1 = ggml_wrap_around(i1 - lp1, ne01); + const int64_t src_i2 = ggml_wrap_around(i2 - lp2, ne02); + const int64_t src_i3 = ggml_wrap_around(i3 - lp3, ne03); + + const int64_t src_idx = + src_i3*nb03 + + src_i2*nb02 + + src_i1*nb01 + + src_i0*nb00; + const float * src_ptr = (const float *)((char *) src0->data + src_idx); dst_ptr[dst_idx] = *src_ptr; } else { - dst_ptr[dst_idx] = 0; + const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; + if ((i0 >= lp0 && i0 < ne0 - rp0) \ + && (i1 >= lp1 && i1 < ne1 - rp1) \ + && (i2 >= lp2 && i2 < ne2 - rp2) \ + && (i3 >= lp3 && i3 < ne3 - rp3)) { + const int64_t src_idx = (i3 - lp3)*nb03 + (i2 - lp2)*nb02 + (i1 - lp1)*nb01 + (i0 - lp0)*nb00; + const float * src_ptr = (const float *)((char *) src0->data + src_idx); + dst_ptr[dst_idx] = *src_ptr; + } else { + dst_ptr[dst_idx] = 0; + } } } } @@ -7639,16 +7662,20 @@ static void ggml_compute_forward_pad_f32( } } + void ggml_compute_forward_pad( const ggml_compute_params * params, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - + const bool circular = (bool) ggml_get_op_params_i32(dst, 8); switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_pad_f32(params, dst); + if (circular) { + ggml_compute_forward_pad_f32(params, dst); + } else { + ggml_compute_forward_pad_f32(params, dst); + } } break; default: { diff --git a/ggml/src/ggml-cuda/mma.cuh b/ggml/src/ggml-cuda/mma.cuh index 625a367a5b..0b13293da9 100644 --- a/ggml/src/ggml-cuda/mma.cuh +++ b/ggml/src/ggml-cuda/mma.cuh @@ -560,7 +560,7 @@ namespace ggml_cuda_mma { xi[0] = xs[0]; xi[1] = xs[1]; #endif // defined(RDNA4) - }else if constexpr (I == 16 && J == 8) { + } else if constexpr (I == 16 && J == 8) { int64_t * xi = (int64_t *) t.x; #if defined(RDNA4) const int64_t * xs = (int64_t *) ((const int *) xs0 + (threadIdx.x % t.I) * stride + 4 * (threadIdx.x / t.I)); @@ -577,14 +577,13 @@ namespace ggml_cuda_mma { const int64_t * xs1 = xs + 2; xi[2] = xs1[0]; xi[3] = xs1[1]; - - }else{ +#endif // defined(RDNA4) + } else { NO_DEVICE_CODE; } } else { NO_DEVICE_CODE; } -#endif // defined(RDNA4) #else #pragma unroll for (int l = 0; l < t.ne; ++l) { diff --git a/ggml/src/ggml-cuda/mmf.cu b/ggml/src/ggml-cuda/mmf.cu index be2ad1c6b6..7cf33f0ddf 100644 --- a/ggml/src/ggml-cuda/mmf.cu +++ b/ggml/src/ggml-cuda/mmf.cu @@ -160,9 +160,9 @@ bool ggml_cuda_should_use_mmf(enum ggml_type type, int cc, int warp_size, const case GGML_TYPE_F32: return ampere_mma_available(cc); case GGML_TYPE_F16: - return volta_mma_available(cc) || turing_mma_available(cc) || amd_wmma_available(cc); + return volta_mma_available(cc) || turing_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc)); case GGML_TYPE_BF16: - return ampere_mma_available(cc) || amd_wmma_available(cc); + return ampere_mma_available(cc) || (amd_wmma_available(cc) && GGML_CUDA_CC_IS_RDNA4(cc)); default: return false; } diff --git a/ggml/src/ggml-cuda/pad.cu b/ggml/src/ggml-cuda/pad.cu index 29aef33c1a..660c192e48 100644 --- a/ggml/src/ggml-cuda/pad.cu +++ b/ggml/src/ggml-cuda/pad.cu @@ -1,9 +1,17 @@ #include "pad.cuh" +#include + +__device__ __forceinline__ int64_t wrap_around(int64_t coord, int64_t size) { + // + size ensures negatives are handled properly + return (coord + size) % size; +} + static __global__ void pad_f32(const float * src, float * dst, const int lp0, const int rp0, const int lp1, const int rp1, const int lp2, const int rp2, const int lp3, const int rp3, - const int ne0, const int ne1, const int ne2, const int ne3) { + const int ne0, const int ne1, const int ne2, const int ne3, + const bool circular) { // blockIdx.z: i3*ne2+i2 // blockIdx.y: i1 // blockIDx.x: i0 / CUDA_PAD_BLOCK_SIZE @@ -12,61 +20,84 @@ static __global__ void pad_f32(const float * src, float * dst, int i1 = blockIdx.y; int i2 = blockIdx.z % ne2; int i3 = blockIdx.z / ne2; + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { return; } - // operation - const int64_t dst_idx = i3*(ne0*ne1*ne2) + i2*(ne0*ne1) + i1*ne0 + i0; - if ((i0 >= lp0 && i0 < ne0 - rp0) && - (i1 >= lp1 && i1 < ne1 - rp1) && - (i2 >= lp2 && i2 < ne2 - rp2) && - (i3 >= lp3 && i3 < ne3 - rp3)) { - const int64_t i00 = i0 - lp0; - const int64_t i01 = i1 - lp1; - const int64_t i02 = i2 - lp2; - const int64_t i03 = i3 - lp3; - const int64_t ne02 = ne2 - lp2 - rp2; - const int64_t ne01 = ne1 - lp1 - rp1; - const int64_t ne00 = ne0 - lp0 - rp0; + const int64_t dst_idx = i3 * (ne0 * ne1 * ne2) + i2 * (ne0 * ne1) + i1 * ne0 + i0; - const int64_t src_idx = i03*(ne00*ne01*ne02) + i02*(ne00*ne01) + i01*ne00 + i00; + if (!circular) { + if ((i0 >= lp0 && i0 < ne0 - rp0) && (i1 >= lp1 && i1 < ne1 - rp1) && (i2 >= lp2 && i2 < ne2 - rp2) && + (i3 >= lp3 && i3 < ne3 - rp3)) { + const int64_t i00 = i0 - lp0; + const int64_t i01 = i1 - lp1; + const int64_t i02 = i2 - lp2; + const int64_t i03 = i3 - lp3; + const int64_t ne02 = ne2 - lp2 - rp2; + const int64_t ne01 = ne1 - lp1 - rp1; + const int64_t ne00 = ne0 - lp0 - rp0; + + const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00; + + dst[dst_idx] = src[src_idx]; + } else { + dst[dst_idx] = 0.0f; + } + } + // circular means on a torus, so x and y wrap around + else { + const int64_t ne00 = ne0 - lp0 - rp0; + const int64_t ne01 = ne1 - lp1 - rp1; + const int64_t ne02 = ne2 - lp2 - rp2; + const int64_t ne03 = ne3 - lp3 - rp3; + + const int64_t i00 = wrap_around(i0 - lp0, ne00); + const int64_t i01 = wrap_around(i1 - lp1, ne01); + const int64_t i02 = wrap_around(i2 - lp2, ne02); + const int64_t i03 = wrap_around(i3 - lp3, ne03); + + const int64_t src_idx = i03 * (ne00 * ne01 * ne02) + i02 * (ne00 * ne01) + i01 * ne00 + i00; dst[dst_idx] = src[src_idx]; - } else { - dst[dst_idx] = 0.0f; } } + static void pad_f32_cuda(const float * src, float * dst, const int lp0, const int rp0, const int lp1, const int rp1, const int lp2, const int rp2, const int lp3, const int rp3, - const int ne0, const int ne1, const int ne2, const int ne3, cudaStream_t stream) { - int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; - dim3 gridDim(num_blocks, ne1, ne2*ne3); - pad_f32<<>>(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, ne2, ne3); + const int ne0, const int ne1, const int ne2, const int ne3, + const bool circular, cudaStream_t stream) { + int num_blocks = (ne0 + CUDA_PAD_BLOCK_SIZE - 1) / CUDA_PAD_BLOCK_SIZE; + dim3 gridDim(num_blocks, ne1, ne2 * ne3); + pad_f32<<>>(src, dst, + lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, + ne0, ne1, ne2, ne3, circular); } void ggml_cuda_op_pad(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - const float * src0_d = (const float *)src0->data; - float * dst_d = (float *)dst->data; - cudaStream_t stream = ctx.stream(); + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *) src0->data; + float * dst_d = (float *) dst->data; + cudaStream_t stream = ctx.stream(); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_ASSERT(ggml_is_contiguous(src0)); - const int32_t lp0 = ((const int32_t*)(dst->op_params))[0]; - const int32_t rp0 = ((const int32_t*)(dst->op_params))[1]; - const int32_t lp1 = ((const int32_t*)(dst->op_params))[2]; - const int32_t rp1 = ((const int32_t*)(dst->op_params))[3]; - const int32_t lp2 = ((const int32_t*)(dst->op_params))[4]; - const int32_t rp2 = ((const int32_t*)(dst->op_params))[5]; - const int32_t lp3 = ((const int32_t*)(dst->op_params))[6]; - const int32_t rp3 = ((const int32_t*)(dst->op_params))[7]; + const int32_t lp0 = ((const int32_t *) (dst->op_params))[0]; + const int32_t rp0 = ((const int32_t *) (dst->op_params))[1]; + const int32_t lp1 = ((const int32_t *) (dst->op_params))[2]; + const int32_t rp1 = ((const int32_t *) (dst->op_params))[3]; + const int32_t lp2 = ((const int32_t *) (dst->op_params))[4]; + const int32_t rp2 = ((const int32_t *) (dst->op_params))[5]; + const int32_t lp3 = ((const int32_t *) (dst->op_params))[6]; + const int32_t rp3 = ((const int32_t *) (dst->op_params))[7]; + const int32_t circular = ((const int32_t *) (dst->op_params))[8]; pad_f32_cuda(src0_d, dst_d, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, - dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], stream); + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], + (bool) circular, stream); } diff --git a/ggml/src/ggml-metal/ggml-metal-context.m b/ggml/src/ggml-metal/ggml-metal-context.m index e66646284d..42a35736ee 100644 --- a/ggml/src/ggml-metal/ggml-metal-context.m +++ b/ggml/src/ggml-metal/ggml-metal-context.m @@ -24,9 +24,6 @@ struct ggml_metal_command_buffer { }; struct ggml_metal { - id device; - id queue; // currently a pointer to the device queue, but might become separate queue [TAG_QUEUE_PER_BACKEND] - ggml_metal_device_t dev; ggml_metal_library_t lib; @@ -91,15 +88,15 @@ ggml_metal_t ggml_metal_init(ggml_metal_device_t dev) { // init context ggml_metal_t res = calloc(1, sizeof(struct ggml_metal)); - res->device = ggml_metal_device_get_obj(dev); + id device = ggml_metal_device_get_obj(dev); - GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[res->device name] UTF8String]); + GGML_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] UTF8String]); // TODO: would it be better to have one queue for the backend and one queue for the device? // the graph encoders and async ops would use the backend queue while the sync ops would use the device queue? //res->queue = [device newCommandQueue]; [TAG_QUEUE_PER_BACKEND] - res->queue = ggml_metal_device_get_queue(dev); - if (res->queue == nil) { + id queue = ggml_metal_device_get_queue(dev); + if (queue == nil) { GGML_LOG_ERROR("%s: error: failed to create command queue\n", __func__); return NULL; } @@ -274,7 +271,8 @@ static struct ggml_metal_buffer_id ggml_metal_get_buffer_id(const struct ggml_te void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { @autoreleasepool { // wrap the source data into a Metal buffer - id buf_src = [ctx->device newBufferWithBytes:data + id device = ggml_metal_device_get_obj(ctx->dev); + id buf_src = [device newBufferWithBytes:data length:size options:MTLResourceStorageModeShared]; @@ -289,7 +287,8 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, // queue the copy operation into the queue of the Metal context // this will be queued at the end, after any currently ongoing GPU operations - id cmd_buf = [ctx->queue commandBuffer]; + id queue = ggml_metal_device_get_queue(ctx->dev); + id cmd_buf = [queue commandBuffer]; id encoder = [cmd_buf blitCommandEncoder]; [encoder copyFromBuffer:buf_src @@ -315,7 +314,8 @@ void ggml_metal_set_tensor_async(ggml_metal_t ctx, struct ggml_tensor * tensor, void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) { @autoreleasepool { - id buf_dst = [ctx->device newBufferWithBytesNoCopy:data + id device = ggml_metal_device_get_obj(ctx->dev); + id buf_dst = [device newBufferWithBytesNoCopy:data length:size options:MTLResourceStorageModeShared deallocator:nil]; @@ -331,7 +331,8 @@ void ggml_metal_get_tensor_async(ggml_metal_t ctx, const struct ggml_tensor * te // queue the copy operation into the queue of the Metal context // this will be queued at the end, after any currently ongoing GPU operations - id cmd_buf = [ctx->queue commandBuffer]; + id queue = ggml_metal_device_get_queue(ctx->dev); + id cmd_buf = [queue commandBuffer]; id encoder = [cmd_buf blitCommandEncoder]; [encoder copyFromBuffer:bid_src.metal @@ -362,6 +363,9 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * // number of threads in addition to the main thread const int n_cb = ctx->n_cb; + // keep the memory wired + ggml_metal_device_rsets_keep_alive(ctx->dev); + // submit the ggml compute graph to the GPU by creating command buffers and encoding the ops in them // the first n_nodes_0 are encoded and submitted for processing directly by the calling thread // while these nodes are processing, we start n_cb threads to enqueue the rest of the nodes @@ -389,7 +393,8 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * if (!ctx->capture_started) { // create capture scope - ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:ctx->device]; + id device = ggml_metal_device_get_obj(ctx->dev); + ctx->capture_scope = [[MTLCaptureManager sharedCaptureManager] newCaptureScopeWithDevice:device]; MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; descriptor.captureObject = ctx->capture_scope; @@ -406,10 +411,13 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * } } + // short-hand + id queue = ggml_metal_device_get_queue(ctx->dev); + // the main thread commits the first few commands immediately // cmd_buf[n_cb] { - id cmd_buf = [ctx->queue commandBufferWithUnretainedReferences]; + id cmd_buf = [queue commandBufferWithUnretainedReferences]; [cmd_buf retain]; if (ctx->cmd_bufs[n_cb].obj) { @@ -428,7 +436,7 @@ enum ggml_status ggml_metal_graph_compute(ggml_metal_t ctx, struct ggml_cgraph * // prepare the rest of the command buffers asynchronously (optional) // cmd_buf[0.. n_cb) for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { - id cmd_buf = [ctx->queue commandBufferWithUnretainedReferences]; + id cmd_buf = [queue commandBufferWithUnretainedReferences]; [cmd_buf retain]; if (ctx->cmd_bufs[cb_idx].obj) { @@ -589,9 +597,11 @@ void ggml_metal_set_abort_callback(ggml_metal_t ctx, ggml_abort_callback abort_c } bool ggml_metal_supports_family(ggml_metal_t ctx, int family) { - GGML_ASSERT(ctx->device != nil); + GGML_ASSERT(ctx->dev != nil); - return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; + id device = ggml_metal_device_get_obj(ctx->dev); + + return [device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } void ggml_metal_capture_next_compute(ggml_metal_t ctx) { diff --git a/ggml/src/ggml-metal/ggml-metal-device.h b/ggml/src/ggml-metal/ggml-metal-device.h index 4be0432ea7..77f2e98cfe 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.h +++ b/ggml/src/ggml-metal/ggml-metal-device.h @@ -186,6 +186,16 @@ struct ggml_metal_pipeline_with_params ggml_metal_library_get_pipeline_flash_att int32_t dv, int32_t nwg); +// MTLResidencySet wrapper + +typedef void * ggml_metal_rset_t; + +// a collection of residency sets (non-owning) +typedef struct ggml_metal_rsets * ggml_metal_rsets_t; + +ggml_metal_rsets_t ggml_metal_rsets_init(void); +void ggml_metal_rsets_free(ggml_metal_rsets_t rsets); + // // device // @@ -219,6 +229,11 @@ void * ggml_metal_device_get_queue(ggml_metal_device_t dev); // id @@ -519,11 +518,106 @@ struct ggml_metal_device { // ref: https://github.com/ggml-org/llama.cpp/pull/15906 id mtl_queue; + ggml_metal_rsets_t rsets; + ggml_metal_library_t library; struct ggml_metal_device_props props; }; +// +// MTLResidenceSet wrapper +// + +struct ggml_metal_rsets { + NSLock * lock; + + NSMutableArray * data; + + // number of seconds since the last graph computation + // keep the residency sets wired for that amount of time to avoid being collected by the OS + int keep_alive_s; + + // background heartbeat thread to keep the residency sets alive + atomic_bool d_stop; + atomic_int d_loop; + + dispatch_group_t d_group; +}; + +ggml_metal_rsets_t ggml_metal_rsets_init(void) { + ggml_metal_rsets_t res = calloc(1, sizeof(struct ggml_metal_rsets)); + + res->lock = [[NSLock alloc] init]; + res->data = [[NSMutableArray alloc] init]; + + // by default keep the memory wired for 3 minutes + res->keep_alive_s = 3*60; + + const char * GGML_METAL_RESIDENCY_KEEP_ALIVE_S = getenv("GGML_METAL_RESIDENCY_KEEP_ALIVE_S"); + if (GGML_METAL_RESIDENCY_KEEP_ALIVE_S) { + res->keep_alive_s = atoi(GGML_METAL_RESIDENCY_KEEP_ALIVE_S); + } + + if (res->keep_alive_s <= 0) { + res->keep_alive_s = 3*60; + } + + GGML_LOG_INFO("%s: creating a residency set collection (keep_alive = %d s)\n", __func__, res->keep_alive_s); + + atomic_store_explicit(&res->d_stop, false, memory_order_relaxed); + atomic_store_explicit(&res->d_loop, 2*res->keep_alive_s, memory_order_relaxed); + + res->d_group = dispatch_group_create(); + + // start a background thread that periodically requests residency for all the currently active sets in the collection + // the requests stop after a certain amount of time (keep_alive_s) of inactivity + dispatch_queue_t d_queue = dispatch_get_global_queue(QOS_CLASS_DEFAULT, 0); + dispatch_group_async(res->d_group, d_queue, ^{ +#if defined(GGML_METAL_HAS_RESIDENCY_SETS) + if (@available(macOS 15.0, iOS 18.0, tvOS 18.0, visionOS 2.0, *)) { + while (!atomic_load_explicit(&res->d_stop, memory_order_relaxed)) { + if (atomic_load_explicit(&res->d_loop, memory_order_relaxed) > 0) { + [res->lock lock]; + + for (int i = 0; i < (int) res->data.count; ++i) { + [res->data[i] requestResidency]; + } + + atomic_fetch_sub_explicit(&res->d_loop, 1, memory_order_relaxed); + + [res->lock unlock]; + } + + // half a second + usleep(500 * 1000); + } + } +#endif + }); + + return res; +} + +void ggml_metal_rsets_free(ggml_metal_rsets_t rsets) { + if (rsets == NULL) { + return; + } + + // note: if you hit this assert, most likely you haven't deallocated all Metal resources before exiting + GGML_ASSERT([rsets->data count] == 0); + + atomic_store_explicit(&rsets->d_stop, true, memory_order_relaxed); + + dispatch_group_wait(rsets->d_group, DISPATCH_TIME_FOREVER); + dispatch_release(rsets->d_group); + + [rsets->data release]; + [rsets->lock release]; + + free(rsets); +} + ggml_metal_device_t ggml_metal_device_init(void) { ggml_metal_device_t dev = calloc(1, sizeof(struct ggml_metal_device)); @@ -692,7 +786,11 @@ ggml_metal_device_t ggml_metal_device_init(void) { GGML_LOG_ERROR("%s: error: failed to create library\n", __func__); } - // -------------------------------------------------- + if (dev->props.use_residency_sets) { + dev->rsets = ggml_metal_rsets_init(); + } else { + dev->rsets = nil; + } // print MTL GPU family: GGML_LOG_INFO("%s: GPU name: %s\n", __func__, dev->props.name); @@ -745,6 +843,8 @@ ggml_metal_device_t ggml_metal_device_init(void) { void ggml_metal_device_free(ggml_metal_device_t dev) { assert(dev != NULL); + ggml_metal_rsets_free(dev->rsets); + ggml_metal_library_free(dev->library); dev->library = NULL; @@ -773,6 +873,42 @@ ggml_metal_library_t ggml_metal_device_get_library(ggml_metal_device_t dev) { return dev->library; } +void ggml_metal_device_rsets_add(ggml_metal_device_t dev, ggml_metal_rset_t rset) { + if (rset == nil) { + return; + } + + GGML_ASSERT(dev->rsets); + + [dev->rsets->lock lock]; + + [dev->rsets->data addObject:rset]; + + [dev->rsets->lock unlock]; +} + +void ggml_metal_device_rsets_rm(ggml_metal_device_t dev, ggml_metal_rset_t rset) { + if (rset == nil) { + return; + } + + GGML_ASSERT(dev->rsets); + + [dev->rsets->lock lock]; + + [dev->rsets->data removeObject:rset]; + + [dev->rsets->lock unlock]; +} + +void ggml_metal_device_rsets_keep_alive(ggml_metal_device_t dev) { + if (dev->rsets == NULL) { + return; + } + + atomic_store_explicit(&dev->rsets->d_loop, 2*dev->rsets->keep_alive_s, memory_order_relaxed); +} + void ggml_metal_device_get_memory(ggml_metal_device_t dev, size_t * free, size_t * total) { if (@available(macOS 10.12, iOS 16.0, *)) { *total = dev->mtl_device.recommendedMaxWorkingSetSize; @@ -901,6 +1037,11 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te case GGML_OP_POOL_2D: return op->src[0]->type == GGML_TYPE_F32; case GGML_OP_PAD: + // TODO: add circular padding support for metal, see https://github.com/ggml-org/llama.cpp/pull/16985 + if (ggml_get_op_params_i32(op, 8) != 0) { + return false; + } + return (ggml_get_op_params_i32(op, 0) == 0) && (ggml_get_op_params_i32(op, 2) == 0) && (ggml_get_op_params_i32(op, 4) == 0) && (ggml_get_op_params_i32(op, 6) == 0); case GGML_OP_PAD_REFLECT_1D: @@ -1066,9 +1207,8 @@ struct ggml_metal_buffer { // note: cannot use explicity "id" here because it is not available on certain OSes id rset; - // pointers to global device objects - id device; - id queue; + // pointers to global device + ggml_metal_device_t dev; }; static void ggml_metal_log_allocated_size(id device, size_t size_aligned) { @@ -1111,7 +1251,7 @@ static bool ggml_metal_buffer_rset_init(ggml_metal_buffer_t buf) { desc.initialCapacity = buf->n_buffers; NSError * error; - buf->rset = [buf->device newResidencySetWithDescriptor:desc error:&error]; + buf->rset = [buf->dev->mtl_device newResidencySetWithDescriptor:desc error:&error]; if (error) { GGML_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); [desc release]; @@ -1172,6 +1312,8 @@ static void * ggml_metal_host_malloc(size_t n) { ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, bool shared) { ggml_metal_buffer_t res = calloc(1, sizeof(struct ggml_metal_buffer)); + res->dev = dev; + const size_t size_page = sysconf(_SC_PAGESIZE); size_t size_aligned = size; @@ -1196,9 +1338,6 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, res->owned = true; - res->device = ggml_metal_device_get_obj(dev); - res->queue = ggml_metal_device_get_queue(dev); - res->n_buffers = 1; if (res->all_data != NULL) { @@ -1207,12 +1346,12 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, if (size_aligned > 0) { if (props_dev->use_shared_buffers && shared) { - res->buffers[0].metal = [res->device newBufferWithBytesNoCopy:res->all_data + res->buffers[0].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:res->all_data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; } else { - res->buffers[0].metal = [res->device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate]; + res->buffers[0].metal = [res->dev->mtl_device newBufferWithLength:size_aligned options:MTLResourceStorageModePrivate]; } } @@ -1233,6 +1372,8 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, return NULL; } + ggml_metal_device_rsets_add(dev, res->rset); + //ggml_metal_log_allocated_size(device, size_aligned); return res; @@ -1241,6 +1382,8 @@ ggml_metal_buffer_t ggml_metal_buffer_init(ggml_metal_device_t dev, size_t size, ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, size_t size, size_t max_tensor_size) { ggml_metal_buffer_t res = calloc(1, sizeof(struct ggml_metal_buffer)); + res->dev = dev; + res->all_data = ptr; res->all_size = size; @@ -1263,9 +1406,6 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s size_aligned += (size_page - (size_aligned % size_page)); } - res->device = ggml_metal_device_get_obj(dev); - res->queue = ggml_metal_device_get_queue(dev); - const struct ggml_metal_device_props * props_dev = ggml_metal_device_get_props(dev); // the buffer fits into the max buffer size allowed by the device @@ -1275,7 +1415,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s res->buffers[res->n_buffers].metal = nil; if (size_aligned > 0) { - res->buffers[res->n_buffers].metal = [res->device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; + res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:ptr length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (res->buffers[res->n_buffers].metal == nil) { GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); @@ -1284,7 +1424,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s } } - ggml_metal_log_allocated_size(res->device, size_aligned); + ggml_metal_log_allocated_size(res->dev->mtl_device, size_aligned); ++res->n_buffers; } else { @@ -1302,7 +1442,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s res->buffers[res->n_buffers].metal = nil; if (size_step_aligned > 0) { - res->buffers[res->n_buffers].metal = [res->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; + res->buffers[res->n_buffers].metal = [res->dev->mtl_device newBufferWithBytesNoCopy:(void *) ((uint8_t *) ptr + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (res->buffers[res->n_buffers].metal == nil) { GGML_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_step_aligned / 1024.0 / 1024.0); @@ -1311,7 +1451,7 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s } } - ggml_metal_log_allocated_size(res->device, size_step_aligned); + ggml_metal_log_allocated_size(res->dev->mtl_device, size_step_aligned); if (i + size_step < size) { GGML_LOG_INFO("\n"); @@ -1329,10 +1469,14 @@ ggml_metal_buffer_t ggml_metal_buffer_map(ggml_metal_device_t dev, void * ptr, s return NULL; } + ggml_metal_device_rsets_add(dev, res->rset); + return res; } void ggml_metal_buffer_free(ggml_metal_buffer_t buf) { + ggml_metal_device_rsets_rm(buf->dev, buf->rset); + for (int i = 0; i < buf->n_buffers; i++) { [buf->buffers[i].metal release]; } @@ -1369,8 +1513,7 @@ void ggml_metal_buffer_memset_tensor(ggml_metal_buffer_t buf, struct ggml_tensor struct ggml_metal_buffer_id bid_dst = ggml_metal_buffer_get_id(buf, tensor); bid_dst.offs += offset; - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; @@ -1396,7 +1539,7 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * @autoreleasepool { // src void * data_ptr = (void *)(uintptr_t) data; // "const cast" the src data - id buf_src = [buf->device newBufferWithBytesNoCopy:data_ptr + id buf_src = [buf->dev->mtl_device newBufferWithBytesNoCopy:data_ptr length:size options:MTLResourceStorageModeShared deallocator:nil]; @@ -1411,8 +1554,7 @@ void ggml_metal_buffer_set_tensor(ggml_metal_buffer_t buf, struct ggml_tensor * // this is alternative to waitUntilCompleted, which should be faster, but don't seem to make much difference dispatch_semaphore_t completion_semaphore = dispatch_semaphore_create(0); - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; @@ -1454,15 +1596,14 @@ void ggml_metal_buffer_get_tensor(ggml_metal_buffer_t buf, const struct ggml_ten bid_src.offs += offset; // dst - id buf_dst = [buf->device newBufferWithBytesNoCopy:data + id buf_dst = [buf->dev->mtl_device newBufferWithBytesNoCopy:data length:size options:MTLResourceStorageModeShared deallocator:nil]; GGML_ASSERT(buf_dst); - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; @@ -1488,8 +1629,7 @@ void ggml_metal_buffer_clear(ggml_metal_buffer_t buf, uint8_t value) { } @autoreleasepool { - id queue = buf->queue; - id cmd_buf = [queue commandBufferWithUnretainedReferences]; + id cmd_buf = [buf->dev->mtl_queue commandBufferWithUnretainedReferences]; { id encoder = [cmd_buf blitCommandEncoder]; diff --git a/ggml/src/ggml-opencl/ggml-opencl.cpp b/ggml/src/ggml-opencl/ggml-opencl.cpp index 277a30d30e..0d37587f60 100644 --- a/ggml/src/ggml-opencl/ggml-opencl.cpp +++ b/ggml/src/ggml-opencl/ggml-opencl.cpp @@ -3083,6 +3083,10 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te case GGML_OP_REPEAT: return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; // Assuming F32 for now, can be expanded case GGML_OP_PAD: + // TODO: add circular padding support for opencl, see https://github.com/ggml-org/llama.cpp/pull/16985 + if (ggml_get_op_params_i32(op, 8) != 0) { + return false; + } return op->src[0]->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32; case GGML_OP_UPSCALE: { ggml_scale_mode mode = (ggml_scale_mode)(ggml_get_op_params_i32(op, 0) & 0xFF); diff --git a/ggml/src/ggml-rpc/ggml-rpc.cpp b/ggml/src/ggml-rpc/ggml-rpc.cpp index 48fd99a762..18a45d2d96 100644 --- a/ggml/src/ggml-rpc/ggml-rpc.cpp +++ b/ggml/src/ggml-rpc/ggml-rpc.cpp @@ -128,6 +128,7 @@ struct rpc_msg_device_count_rsp { struct rpc_msg_get_alloc_size_req { uint32_t device; rpc_tensor tensor; + rpc_tensor srcs[GGML_MAX_SRC]; }; struct rpc_msg_get_alloc_size_rsp { @@ -572,6 +573,11 @@ static void * ggml_backend_rpc_buffer_get_base(ggml_backend_buffer_t buffer) { static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { rpc_tensor result; + if (!tensor) { + memset(&result, 0, sizeof(result)); + return result; + } + result.id = reinterpret_cast(tensor); result.type = tensor->type; if (tensor->buffer) { @@ -753,23 +759,41 @@ static size_t ggml_backend_rpc_get_max_size(ggml_backend_buffer_type_t buft) { } static size_t ggml_backend_rpc_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + // should we query the remote server for the actual size + bool rpc_get = false; + // See comments in init_tensor. - if (ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr)) { + rpc_get |= ggml_is_quantized(tensor->type) && (tensor->ne[0] % 512 != 0) && (tensor->view_src == nullptr); + + // ops that require additional memory for fleeting data on certain backends + // ref: https://github.com/ggml-org/llama.cpp/pull/15966 + rpc_get |= tensor->op == GGML_OP_FLASH_ATTN_EXT; + rpc_get |= tensor->op == GGML_OP_MUL_MAT_ID; + + if (rpc_get) { ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; auto sock = get_socket(buft_ctx->endpoint); - rpc_msg_get_alloc_size_req request; - request.device = buft_ctx->device; - request.tensor = serialize_tensor(tensor); + rpc_msg_get_alloc_size_req request = { + /*.device =*/ buft_ctx->device, + /*.tensor =*/ serialize_tensor(tensor), + /*.srcs =*/ {}, + }; + // .get_alloc_size could be a function of the tensor's srcs, so we must serialize them as well + for (int i = 0; i < GGML_MAX_SRC; i++) { + request.srcs[i] = serialize_tensor(tensor->src[i]); + } + + // TODO: cache the alloc responses to avoid extra RPC calls? rpc_msg_get_alloc_size_rsp response; bool status = send_rpc_cmd(sock, RPC_CMD_GET_ALLOC_SIZE, &request, sizeof(request), &response, sizeof(response)); RPC_STATUS_ASSERT(status); return response.alloc_size; - } else { - return ggml_nbytes(tensor); } + + return ggml_nbytes(tensor); } static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { @@ -1017,7 +1041,7 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_ } ggml_backend_buffer_type_t buft; struct ggml_init_params params { - /*.mem_size =*/ ggml_tensor_overhead(), + /*.mem_size =*/ ggml_tensor_overhead()*(1 + GGML_MAX_SRC), /*.mem_buffer =*/ NULL, /*.no_alloc =*/ true, }; @@ -1025,12 +1049,18 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_ ggml_context_ptr ctx_ptr { ggml_init(params) }; GGML_ASSERT(ctx_ptr != nullptr); ggml_context * ctx = ctx_ptr.get(); - ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); + ggml_tensor * tensor = deserialize_tensor(ctx, &request.tensor); if (tensor == nullptr) { GGML_LOG_ERROR("Null tensor pointer passed to server get_alloc_size function.\n"); return false; } + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (request.srcs[i].id != 0) { + tensor->src[i] = deserialize_tensor(ctx, &request.srcs[i]); + } + } + LOG_DBG("[%s] device: %d, buffer: %p, data: %p\n", __func__, dev_id, (void*)tensor->buffer, tensor->data); if (tensor->buffer == nullptr) { //No buffer allocated. @@ -1227,7 +1257,8 @@ bool rpc_server::get_cached_file(uint64_t hash, std::vector & data) { char hash_str[17]; snprintf(hash_str, sizeof(hash_str), "%016" PRIx64, hash); fs::path cache_file = fs::path(cache_dir) / hash_str; - if (!fs::exists(cache_file)) { + std::error_code ec; + if (!fs::exists(cache_file, ec)) { return false; } std::ifstream ifs(cache_file, std::ios::binary); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index a264ade0b7..7449a91609 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -4613,6 +4613,10 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_ACC: return true; case GGML_OP_PAD: + // TODO: add circular padding support for syscl, see https://github.com/ggml-org/llama.cpp/pull/16985 + if (ggml_get_op_params_i32(op, 8) != 0) { + return false; + } return ggml_is_contiguous(op->src[0]); case GGML_OP_LEAKY_RELU: case GGML_OP_TIMESTEP_EMBEDDING: diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index f917a745d5..2787f63d0e 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -353,10 +353,17 @@ enum vk_conv_shapes { CONV_SHAPE_COUNT, }; -uint32_t conv_shapes_wg_denoms[][3] = { - { 128, 128, 1 }, - { 64, 32, 1 }, - { 32, 256, 1 }, +struct vk_conv_block_size { + uint32_t K; + uint32_t NPQ; + uint32_t CRS; +}; + +vk_conv_block_size vk_conv_block_sizes[CONV_SHAPE_COUNT] = { + // K NPQ CRS + { 128, 128, 16 }, // CONV_SHAPE_128x128 + { 64, 32, 32 }, // CONV_SHAPE_64x32 + { 32, 256, 16 }, // CONV_SHAPE_32x256 }; enum dmmv_wg_sizes { @@ -519,6 +526,7 @@ struct vk_device_struct { bool fp16; bool bf16; bool pipeline_robustness; + bool memory_priority; vk::Device device; uint32_t vendor_id; vk::DriverId driver_id; @@ -1042,6 +1050,7 @@ struct vk_op_pad_push_constants { uint32_t ne00; uint32_t ne01; uint32_t ne02; uint32_t ne03; uint32_t nb00; uint32_t nb01; uint32_t nb02; uint32_t nb03; uint32_t ne10; uint32_t ne11; uint32_t ne12; uint32_t ne13; uint32_t nb10; uint32_t nb11; uint32_t nb12; uint32_t nb13; uint32_t misalign_offsets; + uint32_t circular; uint32_t lp0; uint32_t rp0; uint32_t lp1; uint32_t rp1; @@ -1084,6 +1093,7 @@ static vk_op_pad_push_constants vk_op_pad_push_constants_init(const ggml_tensor p.rp2 = dst->op_params[5]; p.lp3 = dst->op_params[6]; p.rp3 = dst->op_params[7]; + p.circular = dst->op_params[8]; return p; // fastdiv values and offsets are initialized later in ggml_vk_op } @@ -1343,20 +1353,11 @@ struct vk_op_conv2d_push_constants { uint32_t Cin; uint32_t N; - uint32_t KW; - uint32_t KH; uint32_t W; uint32_t H; uint32_t OW; uint32_t OH; - uint32_t s0; - uint32_t s1; - uint32_t p0; - uint32_t p1; - uint32_t d0; - uint32_t d1; - uint32_t nb01; uint32_t nb02; uint32_t nb03; @@ -1380,48 +1381,6 @@ template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) { init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL); } -struct vk_op_conv_transpose_2d_push_constants { - uint32_t Cout; - uint32_t Cin; - uint32_t N; - - uint32_t KW; - uint32_t KH; - uint32_t W; - uint32_t H; - uint32_t OW; - uint32_t OH; - - uint32_t s0; - uint32_t s1; - uint32_t p0; - uint32_t p1; - uint32_t d0; - uint32_t d1; - - uint32_t nb01; - uint32_t nb02; - uint32_t nb03; - - uint32_t nb11; - uint32_t nb12; - uint32_t nb13; - - uint32_t nb1; - uint32_t nb2; - uint32_t nb3; - - // init_fastdiv_values constants for dividing by OW, OW*OH - uint32_t OWmp; uint32_t OWL; - uint32_t OWOHmp; uint32_t OWOHL; -}; - -template <> void init_pushconst_fastdiv(vk_op_conv_transpose_2d_push_constants &p) { - // Compute magic values to divide by OW, OW*OH - init_fastdiv_values(p.OW, p.OWmp, p.OWL); - init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL); -} - struct vk_op_conv2d_dw_push_constants { uint32_t ne; uint32_t batches; @@ -2369,7 +2328,13 @@ static vk_buffer ggml_vk_create_buffer(vk_device& device, size_t size, const std vk::PhysicalDeviceMemoryProperties mem_props = device->physical_device.getMemoryProperties(); - const vk::MemoryAllocateFlagsInfo mem_flags_info { mem_flags }; + const vk::MemoryPriorityAllocateInfoEXT mem_priority_info { 1.0f }; + + vk::MemoryAllocateFlagsInfo mem_flags_info { mem_flags }; + + if (device->memory_priority) { + mem_flags_info.setPNext(&mem_priority_info); + } for (auto it = req_flags_list.begin(); it != req_flags_list.end(); it++) { const auto & req_flags = *it; @@ -3574,7 +3539,7 @@ static void ggml_vk_load_shaders(vk_device& device) { SHADER_REDUCTION_MODE_SHMEM; for (uint32_t i = 0; i < mul_mat_vec_max_cols; ++i) { - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32", arr_dmmv_f32_f32_f32_len[reduc], arr_dmmv_f32_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f32_f32", arr_dmmv_f32_f32_f32_len[reduc], arr_dmmv_f32_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f32_f32", arr_dmmv_f16_f32_f32_len[reduc], arr_dmmv_f16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f32_f32", arr_dmmv_bf16_f32_f32_len[reduc], arr_dmmv_bf16_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f32_f32", arr_dmmv_q4_0_f32_f32_len[reduc], arr_dmmv_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -3598,7 +3563,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_IQ4_NL][i], "mul_mat_vec_iq4_nl_f32_f32", arr_dmmv_iq4_nl_f32_f32_len[reduc16], arr_dmmv_iq4_nl_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f32_f32[w][GGML_TYPE_MXFP4][i], "mul_mat_vec_mxfp4_f32_f32", arr_dmmv_mxfp4_f32_f32_len[reduc16], arr_dmmv_mxfp4_f32_f32_data[reduc16], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {rm_iq, 1, 1}, {wg_size_subgroup16, rm_iq, i+1}, 1, true, use_subgroups16, force_subgroup_size16); - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32", arr_dmmv_f32_f16_f32_len[reduc], arr_dmmv_f32_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F32 ][i], "mul_mat_vec_f32_f16_f32", arr_dmmv_f32_f16_f32_len[reduc], arr_dmmv_f32_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, {wg_size_subgroup, 1, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_F16 ][i], "mul_mat_vec_f16_f16_f32", arr_dmmv_f16_f16_f32_len[reduc], arr_dmmv_f16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_BF16][i], "mul_mat_vec_bf16_f16_f32", arr_dmmv_bf16_f16_f32_len[reduc], arr_dmmv_bf16_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2, 1, 1}, {wg_size_subgroup, 2, i+1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_f16_f32[w][GGML_TYPE_Q4_0][i], "mul_mat_vec_q4_0_f16_f32", arr_dmmv_q4_0_f16_f32_len[reduc], arr_dmmv_q4_0_f16_f32_data[reduc], "main", mul_mat_vec_num_bindings, sizeof(vk_mat_vec_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq, i+1}, 1, true, use_subgroups, force_subgroup_size); @@ -3644,7 +3609,7 @@ static void ggml_vk_load_shaders(vk_device& device) { #endif // GGML_VULKAN_INTEGER_DOT_GLSLC_SUPPORT } - ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", arr_dmmv_id_f32_f32_f32_len[reduc], arr_dmmv_id_f32_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); + ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", arr_dmmv_id_f32_f32_f32_len[reduc], arr_dmmv_id_f32_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {1, 1, 1}, {wg_size_subgroup, 1}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", arr_dmmv_id_f16_f32_f32_len[reduc], arr_dmmv_id_f16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_BF16], "mul_mat_vec_id_bf16_f32", arr_dmmv_id_bf16_f32_f32_len[reduc], arr_dmmv_id_bf16_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2, 1, 1}, {wg_size_subgroup, 2}, 1, false, use_subgroups, force_subgroup_size); ggml_vk_create_pipeline(device, device->pipeline_dequant_mul_mat_vec_id_f32[w][GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", arr_dmmv_id_q4_0_f32_f32_len[reduc], arr_dmmv_id_q4_0_f32_f32_data[reduc], "main", mul_mat_vec_id_num_bindings, sizeof(vk_mat_vec_id_push_constants), {2*rm_stdq, 1, 1}, {wg_size_subgroup, 2*rm_stdq}, 1, true, use_subgroups, force_subgroup_size); @@ -4050,7 +4015,7 @@ static void ggml_vk_load_shaders(vk_device& device) { uint32_t nary_shmem = 2 * sizeof(int) * BLOCK_SIZE + sizeof(int) * device->subgroup_size + 2 * sizeof(int) + - (BLOCK_SIZE / device->subgroup_size) * sizeof(int); + 2 * (BLOCK_SIZE / device->subgroup_size) * sizeof(int); if (device->subgroup_arithmetic && device->subgroup_require_full_support && device->subgroup_shuffle && device->subgroup_ballot && nary_shmem <= device->properties.limits.maxComputeSharedMemorySize) { ggml_vk_create_pipeline2(device, device->pipeline_topk_f32[i], "topk_f32_"+std::to_string(i), topk_nary_search_f32_len, topk_nary_search_f32_data, "main", 2, sizeof(vk_op_topk_push_constants), {BLOCK_SIZE, 1, 1}, {BLOCK_SIZE, device->subgroup_size, device->subgroup_size_log2}, 1, true, true, device->subgroup_size); @@ -4070,10 +4035,16 @@ static void ggml_vk_load_shaders(vk_device& device) { for (auto &s : device->pipeline_solve_tri_f32) { const vk_solve_tri_pipeline_state &state = s.first; + + // Max number of rows to load at a time, limited by shared memory + const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((state.N + state.K) * sizeof(float)); + // Need at least K invocations, and prefer a minimum of 128 to spread out loading shared memory + const uint32_t block_size = std::max(128u, 1u << (uint32_t)ceilf(log2f(float(state.K)))); + ggml_vk_create_pipeline( device, s.second, "solve_tri_f32", solve_tri_f32_len, solve_tri_f32_data, "main", 3, - sizeof(vk_op_binary_push_constants), {1, 1, 1}, { 0, state.N, state.K }, 1, true); + sizeof(vk_op_binary_push_constants), {1, 1, 1}, { 0, state.N, state.K, batch_N, block_size }, 1, true); } #define IM2COL(bda) \ @@ -4119,12 +4090,10 @@ static void ggml_vk_load_shaders(vk_device& device) { // conv2d, conv_transpose_2d for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) { uint32_t conv2d_WG_SIZE = 256; - uint32_t conv2d_BS_K = 128; - uint32_t conv2d_BS_CRS = 16; uint32_t use_collectives = 0; // Enables subgroup ops for preventing the re-calculation of indices. - uint32_t conv2d_BS_NPQ = 128; - uint32_t conv2d_TS_K = 8; + uint32_t conv2d_TS_K = (s == CONV_SHAPE_64x32) ? 4 : 8; uint32_t conv2d_SHMEM_PAD = 4; + vk_conv_block_size conv2d_BS = vk_conv_block_sizes[s]; bool conv2d_UNROLL = true; #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) @@ -4138,29 +4107,9 @@ static void ggml_vk_load_shaders(vk_device& device) { conv2d_UNROLL = false; } else if (device->vendor_id == VK_VENDOR_ID_AMD) { conv2d_SHMEM_PAD = device->architecture == vk_device_architecture::AMD_GCN ? 1 : 4; - } - - switch (s) { - default: - case CONV_SHAPE_128x128: - conv2d_BS_K = conv_shapes_wg_denoms[CONV_SHAPE_128x128][0]; - conv2d_BS_NPQ = conv_shapes_wg_denoms[CONV_SHAPE_128x128][1]; - conv2d_BS_CRS = 16; - if (device->vendor_id == VK_VENDOR_ID_AMD && device->architecture != vk_device_architecture::AMD_GCN) { + if (s == CONV_SHAPE_128x128 && device->architecture != vk_device_architecture::AMD_GCN) { conv2d_UNROLL = false; } - break; - case CONV_SHAPE_64x32: - conv2d_BS_K = conv_shapes_wg_denoms[CONV_SHAPE_64x32][0]; - conv2d_BS_NPQ = conv_shapes_wg_denoms[CONV_SHAPE_64x32][1]; - conv2d_BS_CRS = 32; - conv2d_TS_K = 4; - break; - case CONV_SHAPE_32x256: - conv2d_BS_K = conv_shapes_wg_denoms[CONV_SHAPE_32x256][0]; - conv2d_BS_NPQ = conv_shapes_wg_denoms[CONV_SHAPE_32x256][1]; - conv2d_BS_CRS = 16; - break; } // Use collectives on pre-Turing NVIDIA GPUs and GCN AMD cards, which had slower integer math. @@ -4174,22 +4123,22 @@ static void ggml_vk_load_shaders(vk_device& device) { allow_collectives_nv && allow_collectives_amd) { use_collectives = 1; - conv2d_BS_CRS = std::min( + conv2d_BS.CRS = std::min( device->subgroup_size, - conv2d_BS_CRS); // CRS block size should be capped at subgroup size for correctness when shuffle is used. + conv2d_BS.CRS); // CRS block size should be capped at subgroup size for correctness when shuffle is used. } uint32_t conv2d_shmem_req = - (conv2d_BS_K * (conv2d_BS_CRS + conv2d_SHMEM_PAD) + conv2d_BS_CRS * (conv2d_BS_NPQ + conv2d_SHMEM_PAD)) * sizeof(float); + (conv2d_BS.K * (conv2d_BS.CRS + conv2d_SHMEM_PAD) + conv2d_BS.CRS * (conv2d_BS.NPQ + conv2d_SHMEM_PAD)) * sizeof(float); if (device->properties.limits.maxComputeSharedMemorySize < conv2d_shmem_req) { - conv2d_BS_CRS = 8; + conv2d_BS.CRS = 8; if (use_collectives) { - conv2d_BS_CRS = std::min(device->subgroup_size, conv2d_BS_CRS); + conv2d_BS.CRS = std::min(device->subgroup_size, conv2d_BS.CRS); } } - std::array wg_denoms = { conv2d_BS_K, conv2d_BS_NPQ, 1 }; - std::vector spec_constants = { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD }; + std::array wg_denoms = { conv2d_BS.K, 1, 1 }; + std::vector spec_constants = { conv2d_WG_SIZE, conv2d_BS.K, conv2d_BS.CRS, conv2d_BS.NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD }; #define CREATE_CONV(name, type_suffix, spv_suffix) \ for (auto &c : device->pipeline_##name##type_suffix[s]) { \ @@ -4206,15 +4155,13 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline( \ device, c.second, #name #type_suffix, \ name##type_suffix##spv_suffix##_len, name##type_suffix##spv_suffix##_data, "main", 3, \ - sizeof(vk_op_##name##_push_constants), wg_denoms, spec_constants_cpy, 1, true, use_collectives); \ + sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants_cpy, 1, true, use_collectives); \ } #define CREATE_CONVS(spv_suffix) \ CREATE_CONV(conv2d, _f32, spv_suffix) \ CREATE_CONV(conv2d, _f16_f32, spv_suffix) \ - if (device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_conv_transpose_2d_push_constants)) { \ - CREATE_CONV(conv_transpose_2d, _f32, spv_suffix) \ - CREATE_CONV(conv_transpose_2d, _f16_f32, spv_suffix) \ - } + CREATE_CONV(conv_transpose_2d, _f32, spv_suffix) \ + CREATE_CONV(conv_transpose_2d, _f16_f32, spv_suffix) #if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT) if (device->coopmat2) { CREATE_CONVS(_cm2) @@ -4235,9 +4182,9 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX], "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<pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "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<pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX], "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<pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX], "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<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "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<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX], "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<subgroup_size); } for (auto &c : compiles) { @@ -4340,6 +4287,9 @@ static vk_device ggml_vk_get_device(size_t idx) { #endif } else if (strcmp("VK_KHR_pipeline_executable_properties", properties.extensionName) == 0) { pipeline_executable_properties_support = true; + } else if (strcmp("VK_EXT_memory_priority", properties.extensionName) == 0 && + getenv("GGML_VK_ENABLE_MEMORY_PRIORITY")) { + device->memory_priority = true; } } @@ -4531,6 +4481,16 @@ static vk_device ggml_vk_get_device(size_t idx) { device_extensions.push_back("VK_EXT_pipeline_robustness"); } + VkPhysicalDeviceMemoryPriorityFeaturesEXT memory_priority_features; + memory_priority_features.pNext = nullptr; + memory_priority_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_MEMORY_PRIORITY_FEATURES_EXT; + memory_priority_features.memoryPriority = VK_FALSE; + if (device->memory_priority) { + last_struct->pNext = (VkBaseOutStructure *)&memory_priority_features; + last_struct = (VkBaseOutStructure *)&memory_priority_features; + device_extensions.push_back("VK_EXT_memory_priority"); + } + VkPhysicalDeviceSubgroupSizeControlFeaturesEXT subgroup_size_control_features; subgroup_size_control_features.pNext = nullptr; subgroup_size_control_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_SUBGROUP_SIZE_CONTROL_FEATURES_EXT; @@ -5110,7 +5070,7 @@ static void ggml_vk_print_gpu_info(size_t idx) { } } -static bool ggml_vk_instance_validation_ext_available(); +static bool ggml_vk_instance_layer_settings_available(); static bool ggml_vk_instance_portability_enumeration_ext_available(const std::vector& instance_extensions); static bool ggml_vk_instance_debug_utils_ext_available(const std::vector & instance_extensions); static bool ggml_vk_device_is_supported(const vk::PhysicalDevice & vkdev); @@ -5139,19 +5099,19 @@ static void ggml_vk_instance_init() { vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, api_version }; const std::vector instance_extensions = vk::enumerateInstanceExtensionProperties(); - const bool validation_ext = ggml_vk_instance_validation_ext_available(); + const bool layer_settings = ggml_vk_instance_layer_settings_available(); #ifdef __APPLE__ const bool portability_enumeration_ext = ggml_vk_instance_portability_enumeration_ext_available(instance_extensions); #endif const bool debug_utils_ext = ggml_vk_instance_debug_utils_ext_available(instance_extensions) && getenv("GGML_VK_DEBUG_MARKERS") != nullptr; std::vector layers; - if (validation_ext) { + if (layer_settings) { layers.push_back("VK_LAYER_KHRONOS_validation"); } std::vector extensions; - if (validation_ext) { - extensions.push_back("VK_EXT_validation_features"); + if (layer_settings) { + extensions.push_back("VK_EXT_layer_settings"); } #ifdef __APPLE__ if (portability_enumeration_ext) { @@ -5161,26 +5121,24 @@ static void ggml_vk_instance_init() { if (debug_utils_ext) { extensions.push_back("VK_EXT_debug_utils"); } - vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions); + VkBool32 enable_best_practice = layer_settings; + std::vector settings = { + { + "VK_LAYER_KHRONOS_validation", + "validate_best_practices", + vk::LayerSettingTypeEXT::eBool32, + 1, + &enable_best_practice + }, + }; + vk::LayerSettingsCreateInfoEXT layer_setting_info(settings); + vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags{}, &app_info, layers, extensions, &layer_setting_info); #ifdef __APPLE__ if (portability_enumeration_ext) { instance_create_info.flags |= vk::InstanceCreateFlagBits::eEnumeratePortabilityKHR; } #endif - std::vector features_enable; - vk::ValidationFeaturesEXT validation_features; - - if (validation_ext) { - features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; - validation_features = { - features_enable, - {}, - }; - validation_features.setPNext(nullptr); - instance_create_info.setPNext(&validation_features); - GGML_LOG_DEBUG("ggml_vulkan: Validation layers enabled\n"); - } vk_instance.instance = vk::createInstance(instance_create_info); vk_instance_initialized = true; @@ -6928,6 +6886,10 @@ 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) { + return true; + } + if (k <= 4096) { return false; } @@ -8260,59 +8222,23 @@ static void ggml_vk_flash_attn(ggml_backend_vk_context * ctx, vk_context& subctx } } -static std::array ggml_vk_get_conv_elements(const ggml_tensor *dst) { - const ggml_tensor *src0 = dst->src[0]; - const ggml_tensor *src1 = dst->src[1]; - - // src0 - kernel: [KW, KH, Cin, Cout] - // src1 - input: [W, H, Cin, N] - // dst - result: [OW, OH, Cout, N] - - // Copied from ggml.c: int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) - auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t { - return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; +static vk_conv_shapes ggml_vk_conv_select_shape(ggml_backend_vk_context * ctx, uint32_t K, uint32_t NPQ) { + auto n_tiles = [&](vk_conv_shapes s) { + return CEIL_DIV(K, vk_conv_block_sizes[s].K) + * CEIL_DIV(NPQ, vk_conv_block_sizes[s].NPQ); }; - // parallelize in {OW/BS_K, OH/BS_NPQ, 1} - int64_t W = src1->ne[0]; - int64_t H = src1->ne[1]; - int64_t KW = src0->ne[0]; - int64_t KH = src0->ne[1]; - int64_t Cout = src0->ne[3]; - int64_t N = src1->ne[3]; - int64_t OH = calc_conv_output_size(H, KH, dst->op_params[1], dst->op_params[3], dst->op_params[5]); - int64_t OW = calc_conv_output_size(W, KW, dst->op_params[0], dst->op_params[2], dst->op_params[4]); - int64_t NPQ = N * OW * OH; - // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups - std::array elements = { static_cast(Cout), static_cast(NPQ), 1 }; - return elements; -} + // We can't query number of shader cores on Intel, use 32 as a placeholder + // so small convolutions will still choose a smaller tile. + const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32; -static std::array ggml_vk_get_conv_transpose_2d_elements(const ggml_tensor *dst) { - const ggml_tensor *src0 = dst->src[0]; - const ggml_tensor *src1 = dst->src[1]; - - // src0 - kernel: [KW, KH, Cout, Cin] - // src1 - input: [W, H, Cin, N] - // dst - result: [OW, OH, Cout, N] - - auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t { - return (ins - 1) * s - 2 * p + (ks - 1) * d + 1; - }; - // parallelize in {OW/BS_K, OH/BS_NPQ, 1} - int64_t W = src1->ne[0]; - int64_t H = src1->ne[1]; - int64_t KW = src0->ne[0]; - int64_t KH = src0->ne[1]; - int64_t Cout = src0->ne[2]; - int64_t N = src1->ne[3]; - int64_t OH = calc_conv_output_size(H, KH, dst->op_params[0], 0, 1); - int64_t OW = calc_conv_output_size(W, KW, dst->op_params[0], 0, 1); - int64_t NPQ = N * OW * OH; - - // Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups - std::array elements = { static_cast(Cout), static_cast(NPQ), 1 }; - return elements; + if (K > 64 && n_tiles(CONV_SHAPE_128x128) >= shader_core_count * 2) { + return CONV_SHAPE_128x128; + } else if (K <= 32 && n_tiles(CONV_SHAPE_32x256) >= shader_core_count * 2) { + return CONV_SHAPE_32x256; + } else { + return CONV_SHAPE_64x32; + } } static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * dst, ggml_op op) { @@ -8775,39 +8701,20 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_CONV_2D: case GGML_OP_CONV_TRANSPOSE_2D: - if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 && - ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) { - std::array elements{}; - if (op == GGML_OP_CONV_2D) elements = ggml_vk_get_conv_elements(dst); - else if (op == GGML_OP_CONV_TRANSPOSE_2D) elements = ggml_vk_get_conv_transpose_2d_elements(dst); - vk_conv_shapes shape; - - uint32_t tiles[CONV_SHAPE_COUNT]; - for (uint32_t i = 0; i < CONV_SHAPE_COUNT; ++i) { - tiles[i] = CEIL_DIV(elements[0], conv_shapes_wg_denoms[i][0]) * CEIL_DIV(elements[1], conv_shapes_wg_denoms[i][1]); - } - - // We can't query number of shader cores on Intel, use 32 as a placeholder - // so small convolutions will still choose a smaller tile. - const uint32_t shader_core_count = ctx->device->shader_core_count > 0 ? ctx->device->shader_core_count : 32; - - if (elements[0] > 64 && tiles[CONV_SHAPE_128x128] >= shader_core_count * 2) { - shape = CONV_SHAPE_128x128; - } else if (elements[0] <= 32 && tiles[CONV_SHAPE_32x256] >= shader_core_count * 2) { - shape = CONV_SHAPE_32x256; - } else { - shape = CONV_SHAPE_64x32; - } - - uint32_t KW = static_cast(src0->ne[0]); - uint32_t KH = static_cast(src0->ne[1]); - uint32_t s0 = static_cast(dst->op_params[0]); - uint32_t s1 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[1]) : static_cast(dst->op_params[0]); - uint32_t p0 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[2]) : 0; - uint32_t p1 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[3]) : 0; - uint32_t d0 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[4]) : 1; - uint32_t d1 = op == GGML_OP_CONV_2D ? static_cast(dst->op_params[5]) : 1; + if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + uint32_t K = dst->ne[2]; // Cout + uint32_t NPQ = dst->ne[3] * dst->ne[1] * dst->ne[0]; // N * OH * OW + vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, K, NPQ); + bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D; + uint32_t KW = (uint32_t)src0->ne[0]; + uint32_t KH = (uint32_t)src0->ne[1]; + uint32_t s0 = (uint32_t)(ggml_get_op_params_i32(dst, 0)); + uint32_t s1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 1) : s0; + uint32_t p0 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 2) : 0; + uint32_t p1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 3) : 0; + uint32_t d0 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 4) : 1; + uint32_t d1 = !transpose ? (uint32_t)ggml_get_op_params_i32(dst, 5) : 1; vk_conv2d_pipeline_state conv2d_pipeline_state(s0, s1, p0, p1, d0, d1, KW, KH); std::map *pipelines = nullptr; @@ -9126,13 +9033,21 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co elements = { N * OC * OH * OW, 1, 1}; } break; case GGML_OP_CONV_2D: - { - elements = ggml_vk_get_conv_elements(dst); - } break; case GGML_OP_CONV_TRANSPOSE_2D: - { - elements = ggml_vk_get_conv_transpose_2d_elements(dst); - } break; + if constexpr (std::is_same_v) { + const uint32_t NPQ = pc.N * pc.OH * pc.OW; + const vk_conv_shapes shape = ggml_vk_conv_select_shape(ctx, pc.Cout, NPQ); + const uint32_t NPQ_blocks = CEIL_DIV(NPQ, vk_conv_block_sizes[shape].NPQ); + + elements = { pc.Cout, NPQ_blocks, 1 }; + if (elements[1] > 512) { + elements[2] = CEIL_DIV(elements[1], 512); + elements[1] = 512; + } + } else { + GGML_ABORT("invalid push constant type for CONV_2D"); + } + break; case GGML_OP_ADD: case GGML_OP_SUB: case GGML_OP_DIV: @@ -10683,30 +10598,24 @@ static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx, GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_TENSOR_BINARY_OP_LOCALS - GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); + bool transpose = dst->op == GGML_OP_CONV_TRANSPOSE_2D; + vk_op_conv2d_push_constants p{}; - p.Cout = static_cast(ne03); - p.Cin = static_cast(ne02); + p.Cout = static_cast(!transpose ? ne03 : ne02); + p.Cin = static_cast(!transpose ? ne02 : ne03); p.N = static_cast(ne13); + GGML_ASSERT(p.Cout == ne2); + GGML_ASSERT(p.Cin == ne12); - p.KW = static_cast(ne00); - p.KH = static_cast(ne01); p.W = static_cast(ne10); p.H = static_cast(ne11); p.OW = static_cast(ne0); p.OH = static_cast(ne1); - p.s0 = static_cast(dst->op_params[0]); - p.s1 = static_cast(dst->op_params[1]); - p.p0 = static_cast(dst->op_params[2]); - p.p1 = static_cast(dst->op_params[3]); - p.d0 = static_cast(dst->op_params[4]); - p.d1 = static_cast(dst->op_params[5]); - p.nb01 = static_cast(nb01 / nb00); p.nb02 = static_cast(nb02 / nb00); p.nb03 = static_cast(nb03 / nb00); @@ -10719,59 +10628,7 @@ static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx, p.nb2 = static_cast(nb2 / nb0); p.nb3 = static_cast(nb3 / nb0); - GGML_ASSERT(ne03 == ne2); - GGML_ASSERT(ne02 == ne12); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_2D, std::move(p)); -} - -static void ggml_vk_conv_transpose_2d(ggml_backend_vk_context * ctx, vk_context & subctx, const ggml_tensor * src0, - const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); - GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); - - GGML_TENSOR_BINARY_OP_LOCALS - - GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nb10 == sizeof(float)); - GGML_ASSERT(nb0 == sizeof(float)); - - vk_op_conv_transpose_2d_push_constants p{}; - p.Cout = static_cast(ne02); - p.Cin = static_cast(ne03); - p.N = static_cast(ne13); - - p.KW = static_cast(ne00); - p.KH = static_cast(ne01); - p.W = static_cast(ne10); - p.H = static_cast(ne11); - p.OW = static_cast(ne0); - p.OH = static_cast(ne1); - - p.s0 = static_cast(dst->op_params[0]); - p.s1 = static_cast(dst->op_params[0]); - p.p0 = 0; - p.p1 = 0; - p.d0 = 1; - p.d1 = 1; - - p.nb01 = static_cast(nb01 / nb00); - p.nb02 = static_cast(nb02 / nb00); - p.nb03 = static_cast(nb03 / nb00); - - p.nb11 = static_cast(nb11 / nb10); - p.nb12 = static_cast(nb12 / nb10); - p.nb13 = static_cast(nb13 / nb10); - - p.nb1 = static_cast(nb1 / nb0); - p.nb2 = static_cast(nb2 / nb0); - p.nb3 = static_cast(nb3 / nb0); - - GGML_ASSERT(ne02 == ne2); - GGML_ASSERT(ne03 == ne12); - - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONV_TRANSPOSE_2D, std::move(p)); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, nullptr, dst, dst->op, std::move(p)); } static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -12142,11 +11999,8 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr break; case GGML_OP_CONV_2D: - ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node); - - break; case GGML_OP_CONV_TRANSPOSE_2D: - ggml_vk_conv_transpose_2d(ctx, compute_ctx, src0, src1, node); + ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node); break; case GGML_OP_CONV_2D_DW: @@ -14179,10 +14033,12 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm const uint32_t N = op->src[0]->ne[0]; const uint32_t K = op->src[1]->ne[0]; // K dimension limited to workgroup size - if (K > 128) { + if (K > 1u << device->max_workgroup_size_log2) { return false; } - if (N * N * sizeof(float) + N * K * sizeof(float) > device->properties.limits.maxComputeSharedMemorySize) { + const uint32_t batch_N = device->properties.limits.maxComputeSharedMemorySize / ((N + K) * sizeof(float)); + + if (batch_N == 0) { return false; } return true; @@ -14255,13 +14111,6 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_OP_CONV_2D: case GGML_OP_CONV_TRANSPOSE_2D: { - // Op is disabled for Apple because it segfaults at pipeline create time on MoltenVK - ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context; - const vk_device& device = ggml_vk_get_device(ctx->device); - if (op->op == GGML_OP_CONV_TRANSPOSE_2D && - device->properties.limits.maxPushConstantsSize < sizeof(vk_op_conv_transpose_2d_push_constants)) { - return false; - } // Channel-contiguous format is not supported yet. return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && op->src[1]->type == GGML_TYPE_F32 && @@ -14386,21 +14235,21 @@ ggml_backend_reg_t ggml_backend_vk_reg() { } // Extension availability -static bool ggml_vk_instance_validation_ext_available() { +static bool ggml_vk_instance_layer_settings_available() { #ifdef GGML_VULKAN_VALIDATE // Check if validation layer provides the extension const std::string layer_name = "VK_LAYER_KHRONOS_validation"; for (const auto& layer : vk::enumerateInstanceLayerProperties()) { if (layer_name == layer.layerName.data()) { for (const auto& ext : vk::enumerateInstanceExtensionProperties(layer_name)) { - if (strcmp("VK_EXT_validation_features", ext.extensionName.data()) == 0) { + if (strcmp("VK_EXT_layer_settings", ext.extensionName.data()) == 0) { return true; } } } } - std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_validation_features not found." << std::endl; + std::cerr << "ggml_vulkan: WARNING: Validation layer or layer extension VK_EXT_layer_settings not found." << std::endl; #endif return false; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp index e9bdbf7db5..875c012cd3 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp @@ -32,22 +32,12 @@ layout(push_constant) uniform parameter { uint32_t Cin; uint32_t N; - // Tensor spatial sizes: kernel, input, output - uint32_t KW; - uint32_t KH; + // Tensor spatial sizes: input, output uint32_t W; uint32_t H; uint32_t OW; uint32_t OH; - // Parameters: stride, padding, dilation - 0=y, 1=x - uint32_t s0; - uint32_t s1; - uint32_t p0; - uint32_t p1; - uint32_t d0; - uint32_t d1; - // Strides in elements uint32_t nb01; uint32_t nb02; @@ -77,13 +67,14 @@ layout(constant_id = 3) const uint BS_NPQ = 128; layout(constant_id = 4) const uint TS_K = 8; layout(constant_id = 5) const uint use_collectives = 1; layout(constant_id = 6) const uint SHMEM_PAD = 4; - +// Stride, padding, dilation layout(constant_id = 7) const uint s0 = 1; layout(constant_id = 8) const uint s1 = 1; layout(constant_id = 9) const uint p0 = 0; layout(constant_id = 10) const uint p1 = 0; layout(constant_id = 11) const uint d0 = 1; layout(constant_id = 12) const uint d1 = 1; +// Kernel spatial sizes layout(constant_id = 13) const uint KW = 1; layout(constant_id = 14) const uint KH = 1; @@ -138,7 +129,7 @@ P,Q=OH,OW */ uint32_t B_idx_K = gl_WorkGroupID.x; -uint32_t B_idx_NPQ = gl_WorkGroupID.y; +uint32_t B_idx_NPQ = gl_WorkGroupID.y + gl_WorkGroupID.z * 512; uint32_t T_y = tid / NT_NPQ; uint32_t T_x = tid % NT_NPQ; @@ -178,6 +169,10 @@ ACC_TYPE perElemOpStore(const in uint32_t r, const in uint32_t c, const in ACC_T #endif void main() { + if (B_idx_NPQ * BS_NPQ >= NPQ) { + return; + } + #ifdef COOPMAT2 coopmat matC; matC = coopmat(0.0); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp index f3c8176872..5abd2f6fc6 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/pad.comp @@ -8,6 +8,7 @@ layout (push_constant) uniform parameter uint ne00; uint ne01; uint ne02; uint ne03; uint nb00; uint nb01; uint nb02; uint nb03; uint ne10; uint ne11; uint ne12; uint ne13; uint nb10; uint nb11; uint nb12; uint nb13; uint misalign_offsets; + uint circular; uint lp0; uint rp0; uint lp1; uint rp1; @@ -18,6 +19,10 @@ layout (push_constant) uniform parameter uint get_aoffset() { return p.misalign_offsets >> 16; } uint get_doffset() { return p.misalign_offsets & 0xFFFF; } +uint wrap_around(int coord, uint size) { + return (uint(coord + int(size))) % size; // add size to avoid issues with negative +} + layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; @@ -40,10 +45,20 @@ void main() { const uint src0_idx = (i3 - p.lp3)*p.nb03 + (i2 - p.lp2)*p.nb02 + (i1 - p.lp1)*p.nb01 + (i0 - p.lp0)*p.nb00; const uint dst_idx = i3*p.nb13 + i2*p.nb12 + i1*p.nb11 + i0*p.nb10; - const bool is_src0 = i0 >= p.lp0 && i0 < p.ne10 - p.rp0 && - i1 >= p.lp1 && i1 < p.ne11 - p.rp1 && - i2 >= p.lp2 && i2 < p.ne12 - p.rp2 && - i3 >= p.lp3 && i3 < p.ne13 - p.rp3; + if (p.circular != 0u) { + const uint ci0 = wrap_around(int(i0) - int(p.lp0), p.ne00); + const uint ci1 = wrap_around(int(i1) - int(p.lp1), p.ne01); + const uint ci2 = wrap_around(int(i2) - int(p.lp2), p.ne02); + const uint ci3 = wrap_around(int(i3) - int(p.lp3), p.ne03); + const uint circular_src_idx = ci3*p.nb03 + ci2*p.nb02 + ci1*p.nb01 + ci0*p.nb00; + data_d[get_doffset() + dst_idx] = D_TYPE(data_a[get_aoffset() + circular_src_idx]); + } else { + const bool is_src0 = i0 >= p.lp0 && i0 < p.ne10 - p.rp0 && + i1 >= p.lp1 && i1 < p.ne11 - p.rp1 && + i2 >= p.lp2 && i2 < p.ne12 - p.rp2 && + i3 >= p.lp3 && i3 < p.ne13 - p.rp3; + data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f); + } + - data_d[get_doffset() + dst_idx] = D_TYPE(is_src0 ? data_a[get_aoffset() + src0_idx] : 0.0f); } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp index 3a47949d5a..9d6d366542 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/rms_norm.comp @@ -131,8 +131,12 @@ void main() { rms_norm(num_blocks); } else if (num_blocks > 16) { rms_norm(32); - } else if (num_blocks > 8) { + } else if (num_blocks > 12) { rms_norm(16); + } else if (num_blocks > 10) { + rms_norm(12); + } else if (num_blocks > 8) { + rms_norm(10); } else if (num_blocks > 4) { rms_norm(8); } else if (num_blocks == 4) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp b/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp index 253a9e7efe..3b65145032 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/solve_tri.comp @@ -5,8 +5,9 @@ layout (constant_id = 1) const uint N = 64; layout (constant_id = 2) const uint K = 32; +layout (constant_id = 3) const uint BATCH_N = 32; -layout(local_size_x = 128, local_size_y = 1, local_size_z = 1) in; +layout(local_size_x_id = 4, local_size_y = 1, local_size_z = 1) in; uint a_base, b_base, x_base; @@ -22,8 +23,8 @@ void store_x(uint r, uint c, FLOAT_TYPE v) { data_d[x_base + r * p.nb21 + c * p.nb20] = D_TYPE(v); } -shared FLOAT_TYPE shA[N * N]; -shared FLOAT_TYPE shB[N * K]; +shared FLOAT_TYPE shA[BATCH_N * N]; +shared FLOAT_TYPE shB[BATCH_N * K]; void main() { const uint batch = gl_WorkGroupID.z * 262144 + gl_WorkGroupID.y * 512 + gl_WorkGroupID.x; @@ -39,34 +40,42 @@ void main() { b_base = get_boffset() + i2 * p.nb12 + i3 * p.nb13; x_base = get_doffset() + i2 * p.nb22 + i3 * p.nb23; - // Load the A matrix into shA - [[unroll]] for (uint i = 0; i < N * N; i += gl_WorkGroupSize.x) { - uint idx = i + tid; - if (((N * N) % gl_WorkGroupSize.x == 0) || idx < N * N) { - shA[idx] = get_a(idx / N, idx % N); - } - } - // Load the B matrix into shB - [[unroll]] for (uint i = 0; i < N * K; i += gl_WorkGroupSize.x) { - uint idx = i + tid; - if (((N * K) % gl_WorkGroupSize.x == 0) || idx < N * K) { - shB[idx] = get_b(idx / K, idx % K); - } - } - barrier(); - FLOAT_TYPE X[N]; - // Each thread solves one column - if (tid < K) { - [[unroll]] for (int r = 0; r < N; ++r) { - FLOAT_TYPE b = shB[r * K + tid]; - // Compute x[r,c] = (b[r,c] - sum(a[r,c]*x[c])) / a[r,r] - [[unroll]] for (int c = 0; c < r; ++c) { - b -= shA[r * N + c] * X[c]; + + // Loop over batches of rows + [[unroll]] for (uint row_base = 0; row_base < N; row_base += BATCH_N) { + const uint cur_N = min(BATCH_N, N - row_base); + + // Load the A matrix batch into shA + [[unroll]] for (uint i = 0; i < cur_N * N; i += gl_WorkGroupSize.x) { + uint idx = i + tid; + if (((cur_N * N) % gl_WorkGroupSize.x == 0) || idx < cur_N * N) { + shA[idx] = get_a(row_base + idx / N, idx % N); } - FLOAT_TYPE x = b / shA[r * N + r]; - X[r] = x; - store_x(r, tid, x); } + // Load the B matrix batch into shB + [[unroll]] for (uint i = 0; i < cur_N * K; i += gl_WorkGroupSize.x) { + uint idx = i + tid; + if (((cur_N * K) % gl_WorkGroupSize.x == 0) || idx < cur_N * K) { + shB[idx] = get_b(row_base + idx / K, idx % K); + } + } + barrier(); + + // Each thread solves one column + if (tid < K) { + [[unroll]] for (uint row_offset = 0; row_offset < cur_N; ++row_offset) { + uint r = row_base + row_offset; + FLOAT_TYPE b = shB[row_offset * K + tid]; + // Compute x[r,c] = (b[r,c] - sum(a[r,c]*x[c])) / a[r,r] + [[unroll]] for (int c = 0; c < r; ++c) { + b -= shA[row_offset * N + c] * X[c]; + } + FLOAT_TYPE x = b / shA[row_offset * N + r]; + X[r] = x; + store_x(r, tid, x); + } + } + barrier(); } } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp index bc1c278bf4..5cd0785d20 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -75,7 +75,7 @@ void softmax_warp_inplace(inout float vals[experts_per_thread], const uint limit } void main() { - const uint row = gl_WorkGroupID.x * gl_WorkGroupSize.y + gl_LocalInvocationID.y; + const uint row = gl_WorkGroupID.x * gl_WorkGroupSize.y + gl_SubgroupID; if (row >= n_rows) { return; } @@ -83,17 +83,18 @@ void main() { const uint logits_offset = n_experts * row; const uint weights_offset = n_expert_used * row; const uint ids_offset = n_experts * row; + const uint lane = gl_SubgroupInvocationID; float wt[experts_per_thread]; [[unroll]] for (uint i = 0; i < n_experts; i += WARP_SIZE) { - const uint expert = i + gl_LocalInvocationID.x; + const uint expert = i + lane; wt[i / WARP_SIZE] = (n_experts % WARP_SIZE == 0 || expert < n_experts) ? logits[logits_offset + expert] : -INFINITY; } if (!late_softmax) { - softmax_warp_inplace(wt, n_experts, gl_LocalInvocationID.x, false); + softmax_warp_inplace(wt, n_experts, lane, false); } // at this point, each thread holds a portion of softmax, @@ -111,11 +112,11 @@ void main() { for (int k = 0; k < n_expert_used; k++) { float max_val = wt[0]; - uint max_expert = gl_LocalInvocationID.x; + uint max_expert = lane; [[unroll]] for (int i = 1; i < experts_per_thread; i++) { - const uint expert = gl_LocalInvocationID.x + i * WARP_SIZE; + const uint expert = lane + i * WARP_SIZE; if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) { max_val = wt[i]; max_expert = expert; @@ -132,11 +133,11 @@ void main() { } } - if ((k & (WARP_SIZE - 1)) == gl_LocalInvocationID.x) { + if ((k & (WARP_SIZE - 1)) == lane) { output_weights[k / WARP_SIZE] = max_val; } - if ((max_expert & (WARP_SIZE - 1)) == gl_LocalInvocationID.x) { + if ((max_expert & (WARP_SIZE - 1)) == lane) { wt[max_expert / WARP_SIZE] = -INFINITY; ids[ids_offset + k] = max_expert; @@ -158,12 +159,12 @@ void main() { } if (late_softmax) { - softmax_warp_inplace(output_weights, n_expert_used, gl_LocalInvocationID.x, true); + softmax_warp_inplace(output_weights, n_expert_used, lane, true); } [[unroll]] for (uint i = 0; i < experts_per_thread; ++i) { - uint idx = i * WARP_SIZE + gl_LocalInvocationID.x; + uint idx = i * WARP_SIZE + lane; if (idx < n_expert_used) { weights[weights_offset + idx] = output_weights[i]; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp index f794285ee1..0b757f38e1 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_nary_search.comp @@ -38,6 +38,7 @@ shared int counts[SUBGROUP_SIZE]; shared int sh_min_idx; shared uint sh_total; shared uint offset_partials[BLOCK_SIZE / SUBGROUP_SIZE]; +shared uint eq_min_partials[BLOCK_SIZE / SUBGROUP_SIZE]; // Map float values to uint such that comparisons still work. // Positive values set the high bit, negative values are inverted. @@ -156,25 +157,66 @@ void topk(const uint row) { // We need to compact these values to the start of the dst_row array. // Have each subgroup count how many items it'll store, so other // subgroups can compute their base offset. - bool top = f2ui(intBitsToFloat(v.y)) >= range_min; - uvec4 b = subgroupBallot(top); - uint bit_count = subgroupBallotBitCount(b); - if ((tid % SUBGROUP_SIZE) == 0) { - offset_partials[tid / SUBGROUP_SIZE] = bit_count; - } - barrier(); - - uint out_idx = 0; - [[unroll]] for (int i = 0; i < BLOCK_SIZE / SUBGROUP_SIZE; ++i) { - if (i < tid / SUBGROUP_SIZE) { - out_idx += offset_partials[i]; + // Values strictly greater than range_min must be stored. For values equal + // to range_min, there can be ties and it's possible we'll need to store + // an arbitrary subset of them. + // If total == p.k, have a fast path where we don't need to handle ties. + if (total == p.k) { + bool top = f2ui(intBitsToFloat(v.y)) >= range_min; + uvec4 b = subgroupBallot(top); + uint bit_count = subgroupBallotBitCount(b); + if ((tid % SUBGROUP_SIZE) == 0) { + offset_partials[tid / SUBGROUP_SIZE] = bit_count; } - } + barrier(); - uint bit_count_ex = subgroupBallotExclusiveBitCount(b); - if (top) { - // TODO: Copy directly to the output? - dst_row[out_idx + bit_count_ex] = v; + uint out_idx = 0; + [[unroll]] for (int i = 0; i < BLOCK_SIZE / SUBGROUP_SIZE; ++i) { + if (i < tid / SUBGROUP_SIZE) { + out_idx += offset_partials[i]; + } + } + + uint bit_count_ex = subgroupBallotExclusiveBitCount(b); + if (top) { + // TODO: Copy directly to the output? + dst_row[out_idx + bit_count_ex] = v; + } + } else { + bool top = f2ui(intBitsToFloat(v.y)) > range_min; + bool eq_min = f2ui(intBitsToFloat(v.y)) == range_min; + uvec4 b_top = subgroupBallot(top); + uvec4 b_eq_min = subgroupBallot(eq_min); + uint bit_count_top = subgroupBallotBitCount(b_top); + uint bit_count_eq_min = subgroupBallotBitCount(b_eq_min); + if ((tid % SUBGROUP_SIZE) == 0) { + offset_partials[tid / SUBGROUP_SIZE] = bit_count_top; + eq_min_partials[tid / SUBGROUP_SIZE] = bit_count_eq_min; + } + barrier(); + + uint out_idx = 0; + uint eq_min_base = 0; + uint eq_min_idx = 0; + [[unroll]] for (int i = 0; i < BLOCK_SIZE / SUBGROUP_SIZE; ++i) { + if (i < tid / SUBGROUP_SIZE) { + out_idx += offset_partials[i]; + eq_min_idx += eq_min_partials[i]; + } + eq_min_base += offset_partials[i]; + } + // range_min values are stored at the end + eq_min_idx += eq_min_base; + + uint bit_count_ex_top = subgroupBallotExclusiveBitCount(b_top); + uint bit_count_ex_eq_min = subgroupBallotExclusiveBitCount(b_eq_min); + if (top) { + // TODO: Copy directly to the output? + dst_row[out_idx + bit_count_ex_top] = v; + } + if (eq_min && eq_min_idx + bit_count_ex_eq_min < p.k) { + dst_row[eq_min_idx + bit_count_ex_eq_min] = v; + } } barrier(); diff --git a/ggml/src/ggml-webgpu/ggml-webgpu.cpp b/ggml/src/ggml-webgpu/ggml-webgpu.cpp index a7476b109d..d0e99b6fe2 100644 --- a/ggml/src/ggml-webgpu/ggml-webgpu.cpp +++ b/ggml/src/ggml-webgpu/ggml-webgpu.cpp @@ -25,6 +25,9 @@ #include #include +#define ROUNDUP_POW2(x, pow2) (((x) + ((pow2) - 1)) & ~((pow2) - 1)) +#define CEIL_DIV(M, N) (((M) + (N) - 1) / (N)) + #ifdef GGML_WEBGPU_DEBUG # define WEBGPU_LOG_DEBUG(msg) std::cout << msg << std::endl # define WEBGPU_DEBUG_BUF_ELEMS 32 @@ -64,6 +67,9 @@ /* Constants */ +// Track https://github.com/gpuweb/gpuweb/issues/5315 for fixes to implementations so this can be removed. +#define WEBGPU_MAX_WG_SIZE 288 + #define WEBGPU_MUL_MAT_WG_SIZE 256 #define WEBGPU_NUM_PARAM_BUFS 32u #define WEBGPU_COMMAND_SUBMIT_BATCH_SIZE 8u @@ -272,36 +278,33 @@ struct webgpu_context_struct { wgpu::SubgroupMatrixConfig subgroup_matrix_config; #endif - // Separate this out from limits since on some Metal systems, the limit returned by - // querying the limits is higher than the actual allowed maximum. - uint32_t max_wg_size_x; - std::recursive_mutex mutex; std::atomic_uint inflight_threads = 0; webgpu_buf_pool param_buf_pool; webgpu_buf_pool set_rows_error_buf_pool; - webgpu_pipeline memset_pipeline; + std::map memset_pipelines; // variant or type index std::map>> mul_mat_pipelines; // src0_type, src1_type, vectorized std::map>> mul_mat_vec_pipelines; // src0_type, src1_type, vectorized - webgpu_pipeline mul_mat_pipeline[30][2]; - webgpu_pipeline set_rows_pipeline[1][2]; // dst->type, vectorized - webgpu_pipeline get_rows_pipeline[30]; - webgpu_pipeline get_rows_f32_no_vec_pipeline; - webgpu_pipeline cpy_pipeline[2][2]; // src type, dst type - webgpu_pipeline add_pipeline[2][2]; // type, inplace - webgpu_pipeline sub_pipeline[2][2]; // type, inplace - webgpu_pipeline mul_pipeline[2][2]; // type, inplace - webgpu_pipeline div_pipeline[2][2]; // type, inplace - webgpu_pipeline rms_norm_pipeline[2]; // inplace - webgpu_pipeline rope_pipeline[2][2][2]; // type, ff, inplace - webgpu_pipeline glu_pipeline[7][2][2]; // glu-op, type, split - webgpu_pipeline scale_pipeline[2]; // inplace - webgpu_pipeline soft_max_pipeline[3][2][2]; // (no_mask, f32_mask, f16_mask), has_sink, inplace + std::map> set_rows_pipelines; // dst_type, vectorized + std::map> get_rows_pipelines; // src_type, vectorized + + std::map> cpy_pipelines; // src_type, dst_type + std::map> add_pipelines; // type, inplace + std::map> sub_pipelines; // type, inplace + std::map> mul_pipelines; // type, inplace + std::map> div_pipelines; // type, inplace + + std::map rms_norm_pipelines; // inplace + std::map>> rope_pipelines; // type, ff, inplace + std::map>> glu_pipelines; // glu_op, type, split + std::map scale_pipelines; // inplace + std::map>> soft_max_pipelines; // mask_type, has_sink, inplace + std::map>> unary_pipelines; // unary_op, type, inplace size_t memset_bytes_per_thread; @@ -381,35 +384,10 @@ static std::string ggml_webgpu_process_shader_repls(const char * return s; } -static void ggml_webgpu_create_pipeline(wgpu::Device & device, - webgpu_pipeline & pipeline, - const char * shader_code, - const char * label, - const std::vector & constants = {}) { - wgpu::ShaderSourceWGSL shader_source; - shader_source.code = shader_code; - - wgpu::ShaderModuleDescriptor shader_desc; - shader_desc.nextInChain = &shader_source; - - wgpu::ShaderModule shader_module = device.CreateShaderModule(&shader_desc); - - wgpu::ComputePipelineDescriptor pipeline_desc; - pipeline_desc.label = label; - pipeline_desc.compute.module = shader_module; - pipeline_desc.compute.entryPoint = "main"; // Entry point in the WGSL code - pipeline_desc.layout = nullptr; // nullptr means auto layout - if (constants.size() > 0) { - pipeline_desc.compute.constants = constants.data(); - pipeline_desc.compute.constantCount = constants.size(); - } - pipeline = { device.CreateComputePipeline(&pipeline_desc), label }; -} - -static webgpu_pipeline ggml_webgpu_create_pipeline2(wgpu::Device & device, - const char * shader_code, - const char * label, - const std::vector & constants = {}) { +static webgpu_pipeline ggml_webgpu_create_pipeline(wgpu::Device & device, + const char * shader_code, + const char * label, + const std::vector & constants = {}) { wgpu::ShaderSourceWGSL shader_source; shader_source.code = shader_code; @@ -678,10 +656,10 @@ static void ggml_backend_webgpu_buffer_memset(webgpu_context & ctx, std::vector entries = { { .binding = 0, .buffer = buf, .offset = 0, .size = buf.GetSize() } }; - size_t bytes_per_wg = ctx->max_wg_size_x * ctx->memset_bytes_per_thread; - uint32_t wg_x = ((size + 3) + bytes_per_wg - 1) / bytes_per_wg; + size_t bytes_per_wg = WEBGPU_MAX_WG_SIZE * ctx->memset_bytes_per_thread; + uint32_t wg_x = CEIL_DIV(size + 3, bytes_per_wg); - webgpu_command command = ggml_backend_webgpu_build(ctx, ctx->memset_pipeline, params, entries, wg_x); + webgpu_command command = ggml_backend_webgpu_build(ctx, ctx->memset_pipelines[0], params, entries, wg_x); std::vector futures = { ggml_backend_webgpu_submit(ctx, { command }) }; ggml_backend_webgpu_wait(ctx, futures); } @@ -763,8 +741,7 @@ static size_t ggml_webgpu_tensor_align_offset(webgpu_context & ctx, ggml_tensor } static size_t ggml_webgpu_tensor_binding_size(webgpu_context & ctx, ggml_tensor * t) { - return (ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t) + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & - ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1); + return ROUNDUP_POW2(ggml_nbytes(t) + ggml_webgpu_tensor_misalignment(ctx, t), WEBGPU_STORAGE_BUF_BINDING_MULT); } // Used to determine if two tensors are the same for in-place operations @@ -800,9 +777,8 @@ static webgpu_command ggml_webgpu_cpy(webgpu_context & ctx, ggml_tensor * src, g .size = ggml_webgpu_tensor_binding_size(ctx, dst) } }; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ne + max_wg_size - 1) / max_wg_size; - return ggml_backend_webgpu_build(ctx, ctx->cpy_pipeline[src->type][dst->type], params, entries, wg_x); + uint32_t wg_x = CEIL_DIV(ne, WEBGPU_MAX_WG_SIZE); + return ggml_backend_webgpu_build(ctx, ctx->cpy_pipelines[src->type][dst->type], params, entries, wg_x); } static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, @@ -851,10 +827,8 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, { .binding = 3, .buffer = error_bufs.dev_buf, .offset = 0, .size = error_bufs.dev_buf.GetSize() } }; - size_t max_wg_size = ctx->max_wg_size_x; - int vectorized = src->ne[0] % 4 == 0; - webgpu_pipeline pipeline = ctx->set_rows_pipeline[0][vectorized]; + webgpu_pipeline pipeline = ctx->set_rows_pipelines[0][vectorized]; uint32_t threads; if (vectorized) { threads = (src->ne[1] * src->ne[2] * src->ne[3]) * (src->ne[0] / 4); @@ -862,7 +836,7 @@ static std::optional ggml_webgpu_set_rows(webgpu_context & ctx, threads = src->ne[0] * src->ne[1] * src->ne[2] * src->ne[3]; } - uint32_t wg_x = (threads + max_wg_size - 1) / max_wg_size; + uint32_t wg_x = CEIL_DIV(threads, WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, 1, error_bufs); } @@ -902,13 +876,10 @@ static webgpu_command ggml_webgpu_get_rows(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) } }; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (dst->ne[1] * dst->ne[2] * dst->ne[3] + max_wg_size - 1) / max_wg_size; + uint32_t wg_x = CEIL_DIV(dst->ne[1] * dst->ne[2] * dst->ne[3], WEBGPU_MAX_WG_SIZE); - webgpu_pipeline pipeline = ctx->get_rows_pipeline[src->type]; - if (src->type == GGML_TYPE_F32 && dst->ne[0] % 4 != 0) { - pipeline = ctx->get_rows_f32_no_vec_pipeline; - } + uint32_t vectorized = src->type == GGML_TYPE_F32 && dst->ne[0] % 4 == 0; + webgpu_pipeline pipeline = ctx->get_rows_pipelines[src->type][vectorized]; return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -950,10 +921,9 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }, }; - webgpu_pipeline pipeline = ctx->mul_mat_pipeline[src0->type][src1->type]; + webgpu_pipeline pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][0]; - uint32_t wg_x = - (dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3] + WEBGPU_MUL_MAT_WG_SIZE - 1) / WEBGPU_MUL_MAT_WG_SIZE; + uint32_t wg_x = CEIL_DIV(dst->ne[0] * dst->ne[1] * dst->ne[2] * dst->ne[3], WEBGPU_MUL_MAT_WG_SIZE); uint32_t wg_y = 1; bool use_fast = false; @@ -980,15 +950,13 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, int vectorized = src0->ne[0] % 4 == 0 && dst->ne[0] % 4 == 0 && dst->ne[1] % 4 == 0; if (dst->ne[1] == 1) { // We don't support vectorized mul_mat_vec for quantized types - vectorized = vectorized && (src0->type < 2); - pipeline = ctx->mul_mat_vec_pipelines[src0->type][src1->type][vectorized]; - uint32_t batches = dst->ne[2] * dst->ne[3]; - uint32_t output_groups = - (dst->ne[0] + WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG - 1) / WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; - uint32_t total_wg = output_groups * batches; - wg_x = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension; - wg_y = (total_wg + ctx->limits.maxComputeWorkgroupsPerDimension - 1) / - ctx->limits.maxComputeWorkgroupsPerDimension; + vectorized = vectorized && (src0->type < 2); + pipeline = ctx->mul_mat_vec_pipelines[src0->type][src1->type][vectorized]; + uint32_t batches = dst->ne[2] * dst->ne[3]; + uint32_t output_groups = CEIL_DIV(dst->ne[0], WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG); + uint32_t total_wg = output_groups * batches; + wg_x = total_wg % ctx->limits.maxComputeWorkgroupsPerDimension; + wg_y = CEIL_DIV(total_wg, ctx->limits.maxComputeWorkgroupsPerDimension); } else { pipeline = ctx->mul_mat_pipelines[src0->type][src1->type][vectorized]; uint32_t wg_m; @@ -998,16 +966,16 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, // The total number of subgroups/workgroups needed per matrix. uint32_t wg_m_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_M * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_M * ctx->subgroup_matrix_config.M; - wg_m = (dst->ne[0] + wg_m_sg_tile - 1) / wg_m_sg_tile; + wg_m = CEIL_DIV(dst->ne[0], wg_m_sg_tile); uint32_t wg_n_sg_tile = WEBGPU_MUL_MAT_SUBGROUP_N * WEBGPU_MUL_MAT_SUBGROUP_MATRIX_N * ctx->subgroup_matrix_config.N; - wg_n = (dst->ne[1] + wg_n_sg_tile - 1) / wg_n_sg_tile; + wg_n = CEIL_DIV(dst->ne[1], wg_n_sg_tile); } else { #endif uint32_t tile_m_s = WEBGPU_MUL_MAT_TILE_M * WEBGPU_MUL_MAT_WG_SIZE_M; uint32_t tile_n_s = WEBGPU_MUL_MAT_TILE_N * WEBGPU_MUL_MAT_WG_SIZE_N; - wg_m = (dst->ne[0] + tile_m_s - 1) / tile_m_s; - wg_n = (dst->ne[1] + tile_n_s - 1) / tile_n_s; + wg_m = CEIL_DIV(dst->ne[0], tile_m_s); + wg_n = CEIL_DIV(dst->ne[1], tile_n_s); #ifndef __EMSCRIPTEN__ } #endif @@ -1018,6 +986,60 @@ static webgpu_command ggml_webgpu_mul_mat(webgpu_context & ctx, return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x, wg_y); } +static webgpu_command ggml_webgpu_unary_op(webgpu_context & ctx, ggml_tensor * src, ggml_tensor * dst) { + uint32_t ne = (uint32_t) ggml_nelements(dst); + ggml_unary_op unary_op = ggml_get_unary_op(dst); + uint32_t inplace = ggml_webgpu_tensor_equal(src, dst); + + std::vector params = { + ne, (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, src) / ggml_type_size(src->type)), + (uint32_t) (ggml_webgpu_tensor_misalignment(ctx, dst) / ggml_type_size(dst->type)), + // Convert byte-strides to element-strides + (uint32_t) (src->nb[0] / ggml_type_size(src->type)), (uint32_t) (src->nb[1] / ggml_type_size(src->type)), + (uint32_t) (src->nb[2] / ggml_type_size(src->type)), (uint32_t) (src->nb[3] / ggml_type_size(src->type)), + (uint32_t) (dst->nb[0] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[1] / ggml_type_size(dst->type)), + (uint32_t) (dst->nb[2] / ggml_type_size(dst->type)), (uint32_t) (dst->nb[3] / ggml_type_size(dst->type)), + // Logical shapes + (uint32_t) src->ne[0], (uint32_t) src->ne[1], (uint32_t) src->ne[2], (uint32_t) dst->ne[0], + (uint32_t) dst->ne[1], (uint32_t) dst->ne[2] + }; + + switch (unary_op) { + case GGML_UNARY_OP_XIELU: + { + // Get float parameters and reinterpret their bit patterns as uint32_t + // for passing through the params buffer + float alpha_n = ggml_get_op_params_f32(dst, 1); + float alpha_p = ggml_get_op_params_f32(dst, 2); + float beta = ggml_get_op_params_f32(dst, 3); + float eps = ggml_get_op_params_f32(dst, 4); + params.push_back(*reinterpret_cast(&alpha_n)); + params.push_back(*reinterpret_cast(&alpha_p)); + params.push_back(*reinterpret_cast(&beta)); + params.push_back(*reinterpret_cast(&eps)); + break; + } + default: + break; + } + + std::vector entries = { + { .binding = 0, + .buffer = ggml_webgpu_tensor_buf(src), + .offset = ggml_webgpu_tensor_align_offset(ctx, src), + .size = ggml_webgpu_tensor_binding_size(ctx, src) }, + }; + if (!inplace) { + entries.push_back({ .binding = 1, + .buffer = ggml_webgpu_tensor_buf(dst), + .offset = ggml_webgpu_tensor_align_offset(ctx, dst), + .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); + } + + uint32_t wg_x = CEIL_DIV(ne, WEBGPU_MAX_WG_SIZE); + return ggml_backend_webgpu_build(ctx, ctx->unary_pipelines[unary_op][dst->type][inplace], params, entries, wg_x); +} + static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, ggml_tensor * src0, ggml_tensor * src1, @@ -1059,8 +1081,7 @@ static webgpu_command ggml_webgpu_binary_op(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(dst) + max_wg_size - 1) / max_wg_size; + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -1096,7 +1117,7 @@ static webgpu_command ggml_webgpu_rms_norm(webgpu_context & ctx, ggml_tensor * s .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - return ggml_backend_webgpu_build(ctx, ctx->rms_norm_pipeline[inplace], params, entries, ggml_nrows(src)); + return ggml_backend_webgpu_build(ctx, ctx->rms_norm_pipelines[inplace], params, entries, ggml_nrows(src)); } static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, @@ -1181,9 +1202,8 @@ static webgpu_command ggml_webgpu_rope(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - webgpu_pipeline pipeline = ctx->rope_pipeline[dst->type][has_freq_factor][inplace]; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(src0) / 2 + max_wg_size - 1) / max_wg_size; + webgpu_pipeline pipeline = ctx->rope_pipelines[dst->type][has_freq_factor][inplace]; + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -1234,9 +1254,8 @@ static webgpu_command ggml_webgpu_glu(webgpu_context & ctx, ggml_tensor * src0, .offset = ggml_webgpu_tensor_align_offset(ctx, dst), .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); - webgpu_pipeline pipeline = ctx->glu_pipeline[ggml_get_glu_op(dst)][dst->type][split]; - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(dst) + max_wg_size - 1) / max_wg_size; + webgpu_pipeline pipeline = ctx->glu_pipelines[ggml_get_glu_op(dst)][dst->type][split]; + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); return ggml_backend_webgpu_build(ctx, pipeline, params, entries, wg_x); } @@ -1273,9 +1292,8 @@ static webgpu_command ggml_webgpu_scale(webgpu_context & ctx, ggml_tensor * src, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - size_t max_wg_size = ctx->max_wg_size_x; - uint32_t wg_x = (ggml_nelements(dst) + max_wg_size - 1) / max_wg_size; - return ggml_backend_webgpu_build(ctx, ctx->scale_pipeline[inplace], params, entries, wg_x); + uint32_t wg_x = CEIL_DIV(ggml_nelements(dst), WEBGPU_MAX_WG_SIZE); + return ggml_backend_webgpu_build(ctx, ctx->scale_pipelines[inplace], params, entries, wg_x); } static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx, @@ -1347,7 +1365,7 @@ static webgpu_command ggml_webgpu_soft_max(webgpu_context & ctx, .size = ggml_webgpu_tensor_binding_size(ctx, dst) }); } - return ggml_backend_webgpu_build(ctx, ctx->soft_max_pipeline[mask_type][has_sink][inplace], params, entries, + return ggml_backend_webgpu_build(ctx, ctx->soft_max_pipelines[mask_type][has_sink][inplace], params, entries, ggml_nrows(dst)); } @@ -1382,22 +1400,22 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, case GGML_OP_ADD: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->add_pipelines[node->type][inplace], inplace); } case GGML_OP_SUB: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->sub_pipelines[node->type][inplace], inplace); } case GGML_OP_MUL: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->mul_pipelines[node->type][inplace], inplace); } case GGML_OP_DIV: { int inplace = ggml_webgpu_tensor_equal(src0, node); - return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipeline[node->type][inplace], inplace); + return ggml_webgpu_binary_op(ctx, src0, src1, node, ctx->div_pipelines[node->type][inplace], inplace); } case GGML_OP_RMS_NORM: return ggml_webgpu_rms_norm(ctx, src0, node); @@ -1409,6 +1427,8 @@ static std::optional ggml_webgpu_encode_node(webgpu_context ctx, return ggml_webgpu_scale(ctx, src0, node); case GGML_OP_SOFT_MAX: return ggml_webgpu_soft_max(ctx, src0, src1, src2, node); + case GGML_OP_UNARY: + return ggml_webgpu_unary_op(ctx, src0, node); default: return std::nullopt; } @@ -1641,8 +1661,7 @@ static ggml_backend_buffer_t ggml_backend_webgpu_buffer_type_alloc_buffer(ggml_b ggml_backend_webgpu_device_context * ctx = static_cast(buft->device->context); wgpu::Buffer buf; - ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, - (size + WEBGPU_STORAGE_BUF_BINDING_MULT - 1) & ~(WEBGPU_STORAGE_BUF_BINDING_MULT - 1), + ggml_webgpu_create_buffer(ctx->webgpu_ctx->device, buf, ROUNDUP_POW2(size, WEBGPU_STORAGE_BUF_BINDING_MULT), wgpu::BufferUsage::Storage | wgpu::BufferUsage::CopySrc | wgpu::BufferUsage::CopyDst, buf_name.c_str()); @@ -1717,58 +1736,64 @@ static std::vector ggml_webgpu_wg_size_entry(uint32_t wg_si static void ggml_webgpu_init_memset_pipeline(webgpu_context & webgpu_ctx) { // we use the maximum workgroup size for the memset pipeline - size_t max_wg_size = webgpu_ctx->max_wg_size_x; - size_t max_threads = max_wg_size * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension; + size_t max_threads = WEBGPU_MAX_WG_SIZE * webgpu_ctx->limits.maxComputeWorkgroupsPerDimension; // Size the bytes_per_thread so that the largest buffer size can be handled - webgpu_ctx->memset_bytes_per_thread = - (webgpu_ctx->limits.maxStorageBufferBindingSize + max_threads - 1) / max_threads; + webgpu_ctx->memset_bytes_per_thread = CEIL_DIV(webgpu_ctx->limits.maxStorageBufferBindingSize, max_threads); std::vector constants(2); - constants[0].key = "wg_size"; - constants[0].value = max_wg_size; - constants[1].key = "bytes_per_thread"; - constants[1].value = webgpu_ctx->memset_bytes_per_thread; - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->memset_pipeline, wgsl_memset, "memset", constants); + constants[0].key = "wg_size"; + constants[0].value = WEBGPU_MAX_WG_SIZE; + constants[1].key = "bytes_per_thread"; + constants[1].value = webgpu_ctx->memset_bytes_per_thread; + webgpu_ctx->memset_pipelines[0] = ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_memset, "memset", constants); } static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_0][GGML_TYPE_F32], - wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_1][GGML_TYPE_F32], - wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_0][GGML_TYPE_F32], - wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_1][GGML_TYPE_F32], - wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q8_0][GGML_TYPE_F32], - wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q2_K][GGML_TYPE_F32], - wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q3_K][GGML_TYPE_F32], - wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q4_K][GGML_TYPE_F32], - wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q5_K][GGML_TYPE_F32], - wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_Q6_K][GGML_TYPE_F32], - wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32], - wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_XS][GGML_TYPE_F32], - wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ2_S][GGML_TYPE_F32], - wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32], - wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ3_S][GGML_TYPE_F32], - wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ1_S][GGML_TYPE_F32], - wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ1_M][GGML_TYPE_F32], - wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_NL][GGML_TYPE_F32], - wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_mat_pipeline[GGML_TYPE_IQ4_XS][GGML_TYPE_F32], - wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); + // Q4/Q5/Q8 classic quantizations + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_0_f32, "mul_mat_q4_0_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_1][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_1_f32, "mul_mat_q4_1_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_0][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_0_f32, "mul_mat_q5_0_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_1][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_1_f32, "mul_mat_q5_1_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q8_0][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q8_0_f32, "mul_mat_q8_0_f32"); + + // K-quantizations + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q2_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q2_k_f32, "mul_mat_q2_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q3_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q3_k_f32, "mul_mat_q3_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q4_k_f32, "mul_mat_q4_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q5_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q5_k_f32, "mul_mat_q5_k_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q6_K][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_q6_k_f32, "mul_mat_q6_k_f32"); + + // IQ quantizations (2-, 3-, 4-bit variants) + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XXS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xxs_f32, "mul_mat_iq2_xxs_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_XS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_xs_f32, "mul_mat_iq2_xs_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ2_S][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq2_s_f32, "mul_mat_iq2_s_f32"); + + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_XXS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_xxs_f32, "mul_mat_iq3_xxs_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ3_S][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq3_s_f32, "mul_mat_iq3_s_f32"); + + // 1-bit and 4-bit IQ variants + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_S][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_s_f32, "mul_mat_iq1_s_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ1_M][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq1_m_f32, "mul_mat_iq1_m_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_NL][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_nl_f32, "mul_mat_iq4_nl_f32"); + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_IQ4_XS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_mat_iq4_xs_f32, "mul_mat_iq4_xs_f32"); std::string proc_mul_mat_f32_f32; std::string proc_mul_mat_f32_f32_vec; @@ -1828,21 +1853,21 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { } #endif - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f32_f32.c_str(), "mul_mat_f32_f32", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f32_f32_vec.c_str(), "mul_mat_f32_f32_vec", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f32.c_str(), "mul_mat_f16_f32", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f32_vec.c_str(), "mul_mat_f16_f32_vec", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f16.c_str(), "mul_mat_f16_f16", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_f16_f16_vec.c_str(), "mul_mat_f16_f16_vec", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_q4_0_f32.c_str(), "mul_mat_q4_0_f32", mul_mat_constants); - webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, proc_mul_mat_q4_0_f32_vec.c_str(), "mul_mat_q4_0_f32_vec", mul_mat_constants); std::vector mul_mat_vec_constants(3); @@ -1853,257 +1878,444 @@ static void ggml_webgpu_init_mul_mat_pipeline(webgpu_context & webgpu_ctx) { mul_mat_vec_constants[2].key = "OUTPUTS_PER_WG"; mul_mat_vec_constants[2].value = WEBGPU_MUL_MAT_VEC_OUTPUTS_PER_WG; - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32, "mul_mat_vec_f32_f32", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F32][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f32_f32_vec, "mul_mat_vec_f32_f32_vec", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32, "mul_mat_vec_f16_f32", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f32_vec, "mul_mat_vec_f16_f32_vec", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16, "mul_mat_vec_f16_f16", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_F16][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_f16_f16_vec, "mul_mat_vec_f16_f16_vec", mul_mat_vec_constants); - webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline2( + webgpu_ctx->mul_mat_vec_pipelines[GGML_TYPE_Q4_0][GGML_TYPE_F32][0] = ggml_webgpu_create_pipeline( webgpu_ctx->device, wgsl_mul_mat_vec_q4_0_f32, "mul_mat_vec_q4_0_f32", mul_mat_vec_constants); } static void ggml_webgpu_init_set_rows_pipeline(webgpu_context & webgpu_ctx) { - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline[0][0], wgsl_set_rows_f16, - "set_rows_f16", ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x)); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->set_rows_pipeline[0][1], wgsl_set_rows_f16_vec, - "set_rows_f16_vec", ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x)); + webgpu_ctx->set_rows_pipelines[0][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_set_rows_f16, "set_rows_f16", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); + webgpu_ctx->set_rows_pipelines[0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_set_rows_f16_vec, "set_rows_f16_vec", ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE)); } static void ggml_webgpu_init_get_rows_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_F32], wgsl_get_rows_f32_vec, - "get_rows_f32_vec", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_f32_no_vec_pipeline, wgsl_get_rows_f32, - "get_rows_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_F16], wgsl_get_rows_f16, - "get_rows_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_I32], wgsl_get_rows_i32, - "get_rows_i32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q4_0], wgsl_get_rows_q4_0, - "get_rows_q4_0", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q4_1], wgsl_get_rows_q4_1, - "get_rows_q4_1", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q5_0], wgsl_get_rows_q5_0, - "get_rows_q5_0", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q5_1], wgsl_get_rows_q5_1, - "get_rows_q5_1", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q8_0], wgsl_get_rows_q8_0, - "get_rows_q8_0", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q2_K], wgsl_get_rows_q2_k, - "get_rows_q2_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q3_K], wgsl_get_rows_q3_k, - "get_rows_q3_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q4_K], wgsl_get_rows_q4_k, - "get_rows_q4_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q5_K], wgsl_get_rows_q5_k, - "get_rows_q5_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_Q6_K], wgsl_get_rows_q6_k, - "get_rows_q6_k", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ2_XXS], - wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ2_XS], - wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ2_S], wgsl_get_rows_iq2_s, - "get_rows_iq2_s", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ3_XXS], - wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ3_S], wgsl_get_rows_iq3_s, - "get_rows_iq3_s", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ1_S], wgsl_get_rows_iq1_s, - "get_rows_iq1_s", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ1_M], wgsl_get_rows_iq1_m, - "get_rows_iq1_m", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ4_NL], - wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->get_rows_pipeline[GGML_TYPE_IQ4_XS], - wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32, "get_rows_f32", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f32_vec, "get_rows_f32_vec", constants); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_f16, "get_rows_f16", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_I32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_i32, "get_rows_i32", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_0, "get_rows_q4_0", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_1, "get_rows_q4_1", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_0, "get_rows_q5_0", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_1, "get_rows_q5_1", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q8_0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q8_0, "get_rows_q8_0", constants); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q2_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q2_k, "get_rows_q2_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q3_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q3_k, "get_rows_q3_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q4_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q4_k, "get_rows_q4_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q5_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q5_k, "get_rows_q5_k", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_Q6_K][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_q6_k, "get_rows_q6_k", constants); + + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XXS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xxs, "get_rows_iq2_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_XS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_xs, "get_rows_iq2_xs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ2_S][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq2_s, "get_rows_iq2_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_XXS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_xxs, "get_rows_iq3_xxs", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ3_S][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq3_s, "get_rows_iq3_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_S][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_s, "get_rows_iq1_s", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ1_M][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq1_m, "get_rows_iq1_m", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_NL][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_nl, "get_rows_iq4_nl", constants); + webgpu_ctx->get_rows_pipelines[GGML_TYPE_IQ4_XS][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_get_rows_iq4_xs, "get_rows_iq4_xs", constants); } static void ggml_webgpu_init_cpy_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F32][GGML_TYPE_F32], - wgsl_cpy_f32_f32, "cpy_f32_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F32][GGML_TYPE_F16], - wgsl_cpy_f32_f16, "cpy_f32_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F16][GGML_TYPE_F32], - wgsl_cpy_f16_f32, "cpy_f16_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->cpy_pipeline[GGML_TYPE_F16][GGML_TYPE_F16], - wgsl_cpy_f16_f16, "cpy_f16_f16", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F32] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f32, "cpy_f32_f32", constants); + webgpu_ctx->cpy_pipelines[GGML_TYPE_F32][GGML_TYPE_F16] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f32_f16, "cpy_f32_f16", constants); + webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F32] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f32, "cpy_f16_f32", constants); + webgpu_ctx->cpy_pipelines[GGML_TYPE_F16][GGML_TYPE_F16] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_cpy_f16_f16, "cpy_f16_f16", constants); } static void ggml_webgpu_init_add_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F32][0], wgsl_add_f32, "add_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F16][0], wgsl_add_f16, "add_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F32][1], wgsl_add_f32_inplace, - "add_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->add_pipeline[GGML_TYPE_F16][1], wgsl_add_f16_inplace, - "add_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->add_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32, "add_f32", constants); + webgpu_ctx->add_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16, "add_f16", constants); + webgpu_ctx->add_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f32_inplace, "add_f32_inplace", constants); + webgpu_ctx->add_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_add_f16_inplace, "add_f16_inplace", constants); } static void ggml_webgpu_init_sub_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F32][0], wgsl_sub_f32, "sub_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F16][0], wgsl_sub_f16, "sub_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F32][1], wgsl_sub_f32_inplace, - "sub_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->sub_pipeline[GGML_TYPE_F16][1], wgsl_sub_f16_inplace, - "sub_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->sub_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32, "sub_f32", constants); + webgpu_ctx->sub_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16, "sub_f16", constants); + webgpu_ctx->sub_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f32_inplace, "sub_f32_inplace", constants); + webgpu_ctx->sub_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sub_f16_inplace, "sub_f16_inplace", constants); } static void ggml_webgpu_init_mul_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F32][0], wgsl_mul_f32, "mul_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F16][0], wgsl_mul_f16, "mul_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F32][1], wgsl_mul_f32_inplace, - "mul_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->mul_pipeline[GGML_TYPE_F16][1], wgsl_mul_f16_inplace, - "mul_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->mul_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32, "mul_f32", constants); + webgpu_ctx->mul_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16, "mul_f16", constants); + webgpu_ctx->mul_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f32_inplace, "mul_f32_inplace", constants); + webgpu_ctx->mul_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_mul_f16_inplace, "mul_f16_inplace", constants); } static void ggml_webgpu_init_div_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F32][0], wgsl_div_f32, "div_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F16][0], wgsl_div_f16, "div_f16", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F32][1], wgsl_div_f32_inplace, - "div_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->div_pipeline[GGML_TYPE_F16][1], wgsl_div_f16_inplace, - "div_f16_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->div_pipelines[GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32, "div_f32", constants); + webgpu_ctx->div_pipelines[GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16, "div_f16", constants); + webgpu_ctx->div_pipelines[GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f32_inplace, "div_f32_inplace", constants); + webgpu_ctx->div_pipelines[GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_div_f16_inplace, "div_f16_inplace", constants); } static void ggml_webgpu_init_rms_norm_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rms_norm_pipeline[0], wgsl_rms_norm, "rms_norm", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rms_norm_pipeline[1], wgsl_rms_norm_inplace, - "rms_norm_inplace", constants); + + webgpu_ctx->rms_norm_pipelines[0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm, "rms_norm", constants); + webgpu_ctx->rms_norm_pipelines[1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rms_norm_inplace, "rms_norm_inplace", constants); } static void ggml_webgpu_init_rope_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][0][0], wgsl_rope_f32, - "rope_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][0][1], - wgsl_rope_f32_inplace, "rope_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][1][0], wgsl_rope_f32_ff, - "rope_f32_ff", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F32][1][1], - wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][0][0], wgsl_rope_f16, - "rope_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][0][1], - wgsl_rope_f16_inplace, "rope_f16_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][1][0], wgsl_rope_f16_ff, - "rope_f16_ff", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->rope_pipeline[GGML_TYPE_F16][1][1], - wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32, "rope_f32", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][0][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_inplace, "rope_f32_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff, "rope_f32_ff", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F32][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f32_ff_inplace, "rope_f32_ff_inplace", constants); + + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16, "rope_f16", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][0][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_inplace, "rope_f16_inplace", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff, "rope_f16_ff", constants); + webgpu_ctx->rope_pipelines[GGML_TYPE_F16][1][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_rope_f16_ff_inplace, "rope_f16_ff_inplace", constants); } static void ggml_webgpu_init_glu_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - // reglu - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0], - wgsl_reglu_f32, "reglu_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0], - wgsl_reglu_f16, "reglu_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1], - wgsl_reglu_f32_split, "reglu_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1], - wgsl_reglu_f16_split, "reglu_f16_split", constants); - // geglu - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0], - wgsl_geglu_f32, "geglu_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0], - wgsl_geglu_f16, "geglu_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1], - wgsl_geglu_f32_split, "geglu_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1], - wgsl_geglu_f16_split, "geglu_f16_split", constants); - // swiglu - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0], - wgsl_swiglu_f32, "swiglu_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0], - wgsl_swiglu_f16, "swiglu_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1], - wgsl_swiglu_f32_split, "swiglu_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1], - wgsl_swiglu_f16_split, "swiglu_f16_split", constants); - // swiglu_oai - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0], - wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1], - wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); - // geglu_erf - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0], - wgsl_geglu_erf_f32, "geglu_erf_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0], - wgsl_geglu_erf_f16, "geglu_erf_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1], - wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1], - wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); - // geglu_quick - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0], - wgsl_geglu_quick_f32, "geglu_quick_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0], - wgsl_geglu_quick_f16, "geglu_quick_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1], - wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->glu_pipeline[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1], - wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + // REGLU + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32, "reglu_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16, "reglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f32_split, "reglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_REGLU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_reglu_f16_split, "reglu_f16_split", constants); + + // GEGLU + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32, "geglu_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16, "geglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f32_split, "geglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_f16_split, "geglu_f16_split", constants); + + // SWIGLU + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32, "swiglu_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16, "swiglu_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f32_split, "swiglu_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_f16_split, "swiglu_f16_split", constants); + + // SWIGLU_OAI + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32, "swiglu_oai_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_SWIGLU_OAI][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_swiglu_oai_f32_split, "swiglu_oai_f32_split", constants); + + // GEGLU_ERF + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32, "geglu_erf_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16, "geglu_erf_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f32_split, "geglu_erf_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_ERF][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_erf_f16_split, "geglu_erf_f16_split", constants); + + // GEGLU_QUICK + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32, "geglu_quick_f32", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16, "geglu_quick_f16", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f32_split, "geglu_quick_f32_split", constants); + webgpu_ctx->glu_pipelines[GGML_GLU_OP_GEGLU_QUICK][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_geglu_quick_f16_split, "geglu_quick_f16_split", constants); +} + +static void ggml_webgpu_init_unary_pipeline(webgpu_context & webgpu_ctx) { + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + // ABS + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_f32, "abs_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_f16, "abs_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_inplace_f32, "abs_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ABS][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_abs_inplace_f16, "abs_inplace_f16", constants); + + // SGN + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_f32, "sgn_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_f16, "sgn_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_inplace_f32, "sgn_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SGN][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sgn_inplace_f16, "sgn_inplace_f16", constants); + + // NEG + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_f32, "neg_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_f16, "neg_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_inplace_f32, "neg_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_NEG][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_neg_inplace_f16, "neg_inplace_f16", constants); + + // STEP + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_f32, "step_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_f16, "step_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_inplace_f32, "step_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_STEP][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_step_inplace_f16, "step_inplace_f16", constants); + + // TANH + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_f32, "tanh_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_f16, "tanh_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_inplace_f32, "tanh_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_TANH][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_tanh_inplace_f16, "tanh_inplace_f16", constants); + + // ELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_f32, "elu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_f16, "elu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_inplace_f32, "elu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_ELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_elu_inplace_f16, "elu_inplace_f16", constants); + + // RELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_f32, "relu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_f16, "relu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_inplace_f32, "relu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_RELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_relu_inplace_f16, "relu_inplace_f16", constants); + + // SIGMOID + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_f32, "sigmoid_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_f16, "sigmoid_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_inplace_f32, "sigmoid_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SIGMOID][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_sigmoid_inplace_f16, "sigmoid_inplace_f16", constants); + + // GELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_f32, "gelu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_f16, "gelu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_inplace_f32, "gelu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_inplace_f16, "gelu_inplace_f16", constants); + + // GELU_QUICK + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_quick_f32, "gelu_quick_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_quick_f16, "gelu_quick_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_gelu_quick_inplace_f32, "gelu_quick_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_QUICK][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_gelu_quick_inplace_f16, "gelu_quick_inplace_f16", constants); + + // SILU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_f32, "silu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_f16, "silu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_inplace_f32, "silu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_SILU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_silu_inplace_f16, "silu_inplace_f16", constants); + + // HARDSWISH + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_f32, "hardswish_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_f16, "hardswish_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_inplace_f32, "hardswish_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSWISH][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardswish_inplace_f16, "hardswish_inplace_f16", constants); + + // HARDSIGMOID + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardsigmoid_f32, "hardsigmoid_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_hardsigmoid_f16, "hardsigmoid_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F32][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_hardsigmoid_inplace_f32, "hardsigmoid_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_HARDSIGMOID][GGML_TYPE_F16][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_hardsigmoid_inplace_f16, "hardsigmoid_inplace_f16", constants); + + // EXP + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_f32, "exp_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_f16, "exp_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_inplace_f32, "exp_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_EXP][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_exp_inplace_f16, "exp_inplace_f16", constants); + + // GELU_ERF + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_f32, "gelu_erf_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_f16, "gelu_erf_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_inplace_f32, "gelu_erf_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_GELU_ERF][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_gelu_erf_inplace_f16, "gelu_erf_inplace_f16", constants); + + // XIELU + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F32][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_f32, "xielu_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_f16, "xielu_f16", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F32][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f32, "xielu_inplace_f32", constants); + webgpu_ctx->unary_pipelines[GGML_UNARY_OP_XIELU][GGML_TYPE_F16][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_xielu_inplace_f16, "xielu_inplace_f16", constants); } static void ggml_webgpu_init_scale_pipeline(webgpu_context & webgpu_ctx) { - std::vector constants = ggml_webgpu_wg_size_entry(webgpu_ctx->max_wg_size_x); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->scale_pipeline[0], wgsl_scale_f32, "scale_f32", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->scale_pipeline[1], wgsl_scale_f32_inplace, - "scale_f32_inplace", constants); + std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_MAX_WG_SIZE); + + webgpu_ctx->scale_pipelines[0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32, "scale_f32", constants); + webgpu_ctx->scale_pipelines[1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_scale_f32_inplace, "scale_f32_inplace", constants); } static void ggml_webgpu_init_soft_max_pipeline(webgpu_context & webgpu_ctx) { std::vector constants = ggml_webgpu_wg_size_entry(WEBGPU_ROW_SPLIT_WG_SIZE); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][0][0], wgsl_soft_max_f32, - "soft_max_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][0][1], wgsl_soft_max_f32_inplace, - "soft_max_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][1][0], wgsl_soft_max_f32_sink, - "soft_max_f32_sink", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[2][1][1], - wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][0][0], wgsl_soft_max_f32_mask_f32, - "soft_max_f32_mask_f32", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][0][1], - wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][0][0], wgsl_soft_max_f32_mask_f16, - "soft_max_f32_mask_f16", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][0][1], - wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][1][0], - wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[0][1][1], - wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", - constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][1][0], - wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); - ggml_webgpu_create_pipeline(webgpu_ctx->device, webgpu_ctx->soft_max_pipeline[1][1][1], - wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", - constants); + + // f32 (no mask) + webgpu_ctx->soft_max_pipelines[2][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32, "soft_max_f32", constants); + webgpu_ctx->soft_max_pipelines[2][0][1] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_inplace, "soft_max_f32_inplace", constants); + webgpu_ctx->soft_max_pipelines[2][1][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_sink, "soft_max_f32_sink", constants); + webgpu_ctx->soft_max_pipelines[2][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_sink_inplace, "soft_max_f32_sink_inplace", constants); + + // f32 mask (mask_type = 0) + webgpu_ctx->soft_max_pipelines[0][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f32, "soft_max_f32_mask_f32", constants); + webgpu_ctx->soft_max_pipelines[0][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_inplace, "soft_max_f32_mask_f32_inplace", constants); + webgpu_ctx->soft_max_pipelines[0][1][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink, "soft_max_f32_mask_f32_sink", constants); + webgpu_ctx->soft_max_pipelines[0][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f32_sink_inplace, "soft_max_f32_mask_f32_sink_inplace", constants); + + // f16 mask (mask_type = 1) + webgpu_ctx->soft_max_pipelines[1][0][0] = + ggml_webgpu_create_pipeline(webgpu_ctx->device, wgsl_soft_max_f32_mask_f16, "soft_max_f32_mask_f16", constants); + webgpu_ctx->soft_max_pipelines[1][0][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_inplace, "soft_max_f32_mask_f16_inplace", constants); + webgpu_ctx->soft_max_pipelines[1][1][0] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink, "soft_max_f32_mask_f16_sink", constants); + webgpu_ctx->soft_max_pipelines[1][1][1] = ggml_webgpu_create_pipeline( + webgpu_ctx->device, wgsl_soft_max_f32_mask_f16_sink_inplace, "soft_max_f32_mask_f16_sink_inplace", constants); } static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, const char * params) { @@ -2125,12 +2337,12 @@ static ggml_backend_t ggml_backend_webgpu_device_init(ggml_backend_dev_t dev, co /* .device = */ dev, /* .context = */ &backend_ctx, }; - return &backend; } static ggml_backend_buffer_type_t ggml_backend_webgpu_device_get_buffer_type(ggml_backend_dev_t dev) { // See GGML Backend Buffer Type Interface section + static struct ggml_backend_buffer_type ggml_backend_webgpu_buffer_type = { /* .iface = */ { /* .get_name = */ ggml_backend_webgpu_buffer_type_get_name, @@ -2295,6 +2507,36 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const case GGML_OP_SOFT_MAX: supports_op = op->type == GGML_TYPE_F32; break; + case GGML_OP_UNARY: + { + const ggml_unary_op UNARY_OP = ggml_get_unary_op(op); + + switch (UNARY_OP) { + case GGML_UNARY_OP_ABS: + case GGML_UNARY_OP_SGN: + case GGML_UNARY_OP_NEG: + case GGML_UNARY_OP_STEP: + case GGML_UNARY_OP_TANH: + case GGML_UNARY_OP_ELU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_HARDSWISH: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_EXP: + case GGML_UNARY_OP_GELU_ERF: + case GGML_UNARY_OP_XIELU: + supports_op = supports_op = + (op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16) && (src0->type == op->type); + break; + default: + break; + } + } + break; + default: break; } @@ -2388,7 +2630,6 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t GGML_ASSERT(ctx->adapter != nullptr); ctx->adapter.GetLimits(&ctx->limits); - ctx->max_wg_size_x = 288; // default value wgpu::AdapterInfo info{}; #ifndef __EMSCRIPTEN__ @@ -2522,6 +2763,7 @@ static ggml_backend_dev_t ggml_backend_webgpu_reg_get_device(ggml_backend_reg_t ggml_webgpu_init_glu_pipeline(ctx); ggml_webgpu_init_scale_pipeline(ctx); ggml_webgpu_init_soft_max_pipeline(ctx); + ggml_webgpu_init_unary_pipeline(ctx); #ifdef GGML_WEBGPU_DEBUG // Initialize debug buffers diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py index ed8068d416..d61df5bb9e 100755 --- a/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py +++ b/ggml/src/ggml-webgpu/wgsl-shaders/embed_wgsl.py @@ -19,6 +19,15 @@ def parse_decls(decls_text): return decls +def replace_repl_placeholders(variant, template_map): + for repl, code in variant["REPLS"].items(): + for key, val in template_map.items(): + # Match "key" and avoid matching subsequences using by using \b + code = re.sub(rf'\b{re.escape(str(key))}\b', str(val), code) + variant["REPLS"][repl] = code + return variant + + def replace_placeholders(shader_text, replacements): for key, val in replacements.items(): # Match {{KEY}} literally, where KEY is escaped @@ -71,6 +80,10 @@ def generate_variants(fname, input_dir, output_dir, outfile): decls_map = parse_decls(extract_block(text, "DECLS")) except ValueError: decls_map = {} + try: + templates_map = ast.literal_eval(extract_block(text, "REPL_TEMPLATES")) + except ValueError: + templates_map = {} for fname in sorted(os.listdir(input_dir)): if fname.endswith(".tmpl"): @@ -90,9 +103,11 @@ def generate_variants(fname, input_dir, output_dir, outfile): if key not in decls_map: raise ValueError(f"DECLS key '{key}' not found.") decls_code += decls_map[key] + "\n\n" - final_shader = re.sub(r'\bDECLS\b', decls_code, shader_template) if "REPLS" in variant: + variant = replace_repl_placeholders(variant, templates_map) + final_shader = replace_placeholders(final_shader, variant["REPLS"]) + # second run to expand placeholders in repl_template final_shader = replace_placeholders(final_shader, variant["REPLS"]) final_shader = expand_includes(final_shader, input_dir) diff --git a/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl new file mode 100644 index 0000000000..d474ab107b --- /dev/null +++ b/ggml/src/ggml-webgpu/wgsl-shaders/unary_op.wgsl @@ -0,0 +1,461 @@ +#define(REPL_TEMPLATES) + +{ + "XIELU_FUNC": "{{MUTATE}}[dst_i] = select(((exp(min(src[src_i], {{TYPE}}(params.eps))) - 1.0) - src[src_i]) * {{TYPE}}(params.alpha_n) + {{TYPE}}(params.beta) * src[src_i], {{TYPE}}(params.alpha_p) * src[src_i] * src[src_i] + {{TYPE}}(params.beta) * src[src_i], src[src_i] > 0.0);", + "ABS_FUNC": "{{MUTATE}}[dst_i] = abs(src[src_i]);", + "SGN_FUNC": "{{MUTATE}}[dst_i] = select({{TYPE}}(select(0.0, -1.0, src[src_i] < 0.0)), {{TYPE}}(1.0), src[src_i] > 0.0);", + "NEG_FUNC": "{{MUTATE}}[dst_i] = -src[src_i];", + "STEP_FUNC": "{{MUTATE}}[dst_i] = {{TYPE}}(select(0.0, 1.0, src[src_i] > 0.0));", + "TANH_FUNC": "{{MUTATE}}[dst_i] = tanh(clamp(src[src_i], -9.010913, 9.010913)); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "RELU_FUNC": "{{MUTATE}}[dst_i] = select(0.0, src[src_i], src[src_i] > 0.0);", + "ELU_FUNC": "{{MUTATE}}[dst_i] = select(exp(src[src_i]) - 1.0, src[src_i], src[src_i] > 0.0);", + "HARDSIGMOID_FUNC": "{{MUTATE}}[dst_i] = min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", + "SIGMOID_FUNC": "{{MUTATE}}[dst_i] = 1.0 / (1.0 + exp(-src[src_i]));", + "SILU_FUNC": "{{MUTATE}}[dst_i] = src[src_i] / (1.0 + exp(-src[src_i]));", + "EXP_FUNC": "{{MUTATE}}[dst_i] = exp(src[src_i]);", + "HARDSWISH_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * min(1.0, max(0.0, (src[src_i] + 3.0) / 6.0));", + "GELU_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(sqrt(2.0 / 3.14159265) * (src[src_i] + 0.044715 * pow(src[src_i], 3.0)), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "GELU_QUICK_FUNC": "{{MUTATE}}[dst_i] = src[src_i] * 0.5 * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458", + "GELU_ERF_FUNC": "{{MUTATE}}[dst_i] = 0.5 * src[src_i] * (1.0 + tanh(clamp(0.79788456 * (src[src_i] + 0.044715 * src[src_i] * src[src_i] * src[src_i]), -9.010913, 9.010913))); // Regarding tanh() domain restrictions in wgsl https://github.com/gpuweb/gpuweb/issues/4458" +} + +#end(REPL_TEMPLATES) + +#define(VARIANTS) + +[ + { + "SHADER_NAME": "abs_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "abs_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "abs_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "abs_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ABS_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "sgn_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sgn_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sgn_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "sgn_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SGN_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "neg_f32", + "REPLS": { "TYPE": "f32", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "neg_f16", + "REPLS": { "TYPE": "f16", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "neg_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "neg_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "NEG_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "step_f32", + "REPLS": { "TYPE": "f32", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "step_f16", + "REPLS": { "TYPE": "f16", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "step_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "step_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "STEP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "tanh_f32", + "REPLS": { "TYPE": "f32", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "tanh_f16", + "REPLS": { "TYPE": "f16", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "tanh_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "tanh_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "TANH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "elu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "elu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "elu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "elu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "ELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "relu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "relu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "relu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "relu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "RELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "sigmoid_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sigmoid_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "sigmoid_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "sigmoid_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "silu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "silu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "silu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "silu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "SILU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "exp_f32", + "REPLS": { "TYPE": "f32", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "exp_f16", + "REPLS": { "TYPE": "f16", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "exp_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "exp_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "EXP_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "hardsigmoid_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardsigmoid_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardsigmoid_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "hardsigmoid_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSIGMOID_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "hardswish_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardswish_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "hardswish_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "hardswish_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "HARDSWISH_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "gelu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "gelu_quick_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_quick_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_quick_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_quick_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_QUICK_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + + { + "SHADER_NAME": "xielu_f32", + "REPLS": { "TYPE": "f32", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "xielu_f16", + "REPLS": { "TYPE": "f16", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "xielu_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "xielu_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "XIELU_FUNC", "EXT_PARAMS": "alpha_n: f32, alpha_p: f32, beta: f32, eps: f32", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "dst" }, + "DECLS": ["NOT_INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_inplace_f32", + "REPLS": { "TYPE": "f32", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + }, + { + "SHADER_NAME": "gelu_erf_inplace_f16", + "REPLS": { "TYPE": "f16", "FUNC": "GELU_ERF_FUNC", "EXT_PARAMS": "", "MUTATE": "src" }, + "DECLS": ["INPLACE"] + } +] + +#end(VARIANTS) + +#define(DECLS) + +#decl(INPLACE) + +@group(0) @binding(1) +var params: Params; + +#enddecl(INPLACE) + +#decl(NOT_INPLACE) + +@group(0) @binding(1) +var dst: array<{{TYPE}}>; + +@group(0) @binding(2) +var params: Params; + +#enddecl(NOT_INPLACE) + +#end(DECLS) + +#define(SHADER) + +enable f16; + +fn update(dst_i: u32, src_i: u32) { + {{FUNC}} +} + +@group(0) @binding(0) +var src: array<{{TYPE}}>; + +DECLS + +struct Params { + ne: u32, // total number of elements + offset_src: u32, // in elements + offset_dst: u32, // in elements + + // Strides (in elements) — may be permuted + stride_src0: u32, + stride_src1: u32, + stride_src2: u32, + stride_src3: u32, + + stride_dst0: u32, + stride_dst1: u32, + stride_dst2: u32, + stride_dst3: u32, + + // Logical shapes + src_ne0: u32, + src_ne1: u32, + src_ne2: u32, + + dst_ne0: u32, + dst_ne1: u32, + dst_ne2: u32, + + {{EXT_PARAMS}} +}; + +override wg_size: u32; +@compute @workgroup_size(wg_size) +fn main(@builtin(global_invocation_id) gid: vec3) { + if (gid.x >= params.ne) { + return; + } + + var i = gid.x; + let i3 = i / (params.src_ne2 * params.src_ne1 * params.src_ne0); + i = i % (params.src_ne2 * params.src_ne1 * params.src_ne0); + let i2 = i / (params.src_ne1 * params.src_ne0); + i = i % (params.src_ne1 * params.src_ne0); + let i1 = i / params.src_ne0; + let i0 = i % params.src_ne0; + + var j = gid.x; + let j3 = j / (params.dst_ne2 * params.dst_ne1 * params.dst_ne0); + j = j % (params.dst_ne2 * params.dst_ne1 * params.dst_ne0); + let j2 = j / (params.dst_ne1 * params.dst_ne0); + j = j % (params.dst_ne1 * params.dst_ne0); + let j1 = j / params.dst_ne0; + let j0 = j % params.dst_ne0; + + let src_idx = i0 * params.stride_src0 + i1 * params.stride_src1 + + i2 * params.stride_src2 + i3 * params.stride_src3; + + let dst_idx = j0 * params.stride_dst0 + j1 * params.stride_dst1 + + j2 * params.stride_dst2 + j3 * params.stride_dst3; + + + update(params.offset_dst + dst_idx, params.offset_src + src_idx); +} + +#end(SHADER) + diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index 17cf4d84bb..9f5cdc1398 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -4947,6 +4947,18 @@ struct ggml_tensor * ggml_pad( return ggml_pad_ext(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3); } +// ggml_pad_circular + +struct ggml_tensor * ggml_pad_circular( + struct ggml_context * ctx, + struct ggml_tensor * a, + int p0, + int p1, + int p2, + int p3) { + return ggml_pad_ext_circular(ctx, a, 0, p0, 0, p1, 0, p2, 0, p3); +} + struct ggml_tensor * ggml_pad_ext( struct ggml_context * ctx, struct ggml_tensor * a, @@ -4973,6 +4985,7 @@ struct ggml_tensor * ggml_pad_ext( ggml_set_op_params_i32(result, 5, rp2); ggml_set_op_params_i32(result, 6, lp3); ggml_set_op_params_i32(result, 7, rp3); + ggml_set_op_params_i32(result, 8, 0); // not circular by default result->op = GGML_OP_PAD; @@ -4981,6 +4994,25 @@ struct ggml_tensor * ggml_pad_ext( return result; } +// ggml_pad_ext_circular + +struct ggml_tensor * ggml_pad_ext_circular( + struct ggml_context * ctx, + struct ggml_tensor * a, + int lp0, + int rp0, + int lp1, + int rp1, + int lp2, + int rp2, + int lp3, + int rp3 + ) { + struct ggml_tensor * result = ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3); + ggml_set_op_params_i32(result, 8, 1); // circular + return result; +} + // ggml_pad_reflect_1d struct ggml_tensor * ggml_pad_reflect_1d( diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index a7b0973979..d9c87da194 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -376,6 +376,7 @@ class TensorNameMap: "model.layers.{bid}.block_sparse_moe.primary_router", # smallthinker "model.layers.{bid}.feed_forward.gate", # lfm2moe "model.layers.{bid}.mlp.router.gate", # afmoe + "layers.{bid}.gate", # mistral-large ), MODEL_TENSOR.FFN_GATE_INP_SHEXP: ( @@ -450,6 +451,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.shared_expert.up_proj", # llama4 "model.layers.{bid}.feed_forward.down_proj", "model.layers.{bid}.mlp.shared_mlp.up_proj", # hunyuan + "layers.{bid}.shared_experts.w3", # mistral-large ), MODEL_TENSOR.FFN_UP_CHEXP: ( @@ -496,6 +498,7 @@ class TensorNameMap: "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek deepseek2 "model.layers.{bid}.feed_forward.shared_expert.gate_proj", # llama4 "model.layers.{bid}.mlp.shared_mlp.gate_proj", # hunyuan + "layers.{bid}.shared_experts.w1", # mistral-large ), MODEL_TENSOR.FFN_GATE_CHEXP: ( @@ -557,6 +560,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.shared_expert.down_proj", # llama4 "model.layers.{bid}.shared_mlp.output_linear", # granitemoe "model.layers.{bid}.mlp.shared_mlp.down_proj", # hunyuan + "layers.{bid}.shared_experts.w2", # mistral-large ), MODEL_TENSOR.FFN_DOWN_CHEXP: ( @@ -924,14 +928,17 @@ class TensorNameMap: MODEL_TENSOR.ATTN_Q_A: ( "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 + "layers.{bid}.attention.wq_a", # mistral-large ), MODEL_TENSOR.ATTN_Q_B: ( "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 + "layers.{bid}.attention.wq_b", # mistral-large ), MODEL_TENSOR.ATTN_KV_A_MQA: ( "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 + "layers.{bid}.attention.wkv_a_with_mqa", # mistral-large ), MODEL_TENSOR.ATTN_KV_B: ( @@ -940,18 +947,22 @@ class TensorNameMap: MODEL_TENSOR.ATTN_K_B: ( "model.layers.{bid}.self_attn.k_b_proj", # deepseek2 + "layers.{bid}.attention.k_b_proj", # mistral-large ), MODEL_TENSOR.ATTN_V_B: ( "model.layers.{bid}.self_attn.v_b_proj", # deepseek2 + "layers.{bid}.attention.v_b_proj", # mistral-large ), MODEL_TENSOR.ATTN_Q_A_NORM: ( "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 + "layers.{bid}.attention.q_a_norm", # mistral-large ), MODEL_TENSOR.ATTN_KV_A_NORM: ( "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 + "layers.{bid}.attention.kv_a_norm", # mistral-large ), MODEL_TENSOR.ATTN_SUB_NORM: ( diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 764833749e..351dcb7baa 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -666,7 +666,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: std::map mapped; int blk_id = 0; - int pruned_attention_w = 0; // make a list of weights std::vector tensors; @@ -674,11 +673,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: for (const auto & it : ml.weights_map) { const std::string remapped_name(remap_layer(it.first, prune_list, mapped, blk_id)); if (remapped_name.empty()) { - if (it.first.find("attn_v.weight") != std::string::npos || - it.first.find("attn_qkv.weight") != std::string::npos || - it.first.find("attn_kv_b.weight") != std::string::npos) { - pruned_attention_w++; - } LLAMA_LOG_DEBUG("%s: pruning tensor %s\n", __func__, it.first.c_str()); continue; } @@ -703,7 +697,6 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: }); } - bool is_clip_model = false; for (const auto * it : tensors) { const struct ggml_tensor * tensor = it->tensor; @@ -717,30 +710,10 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } else if (name == LLM_TN(model.arch)(LLM_TENSOR_OUTPUT, "weight")) { qs.has_output = true; } - - is_clip_model |= name.rfind("mm.", 0) == 0; // check the "mm." prefix } qs.n_ffn_down = qs.n_ffn_gate = qs.n_ffn_up = (int)model.hparams.n_layer; - // sanity checks for models that have attention layers - if (qs.n_attention_wv != 0 && !is_clip_model) - { - int32_t n_layer_all = model.hparams.n_layer; - if (llama_model_has_encoder(&model)) { - // now n_layer_all is the number of attention layers in the encoder - // for each decoder block, there are 2 attention layers - n_layer_all += 2 * model.hparams.dec_n_layer; - } - - // note: for linear-attention models (such as Qwen3 Next) this is the number of linear layers - const int32_t n_layer_recr = std::count(model.hparams.recurrent_layer_arr.begin(), model.hparams.recurrent_layer_arr.end(), true); - - LLAMA_LOG_INFO("%s: n_layer_all = %d, n_layer_recr = %d, pruned_attention_w = %d\n", __func__, n_layer_all, n_layer_recr, pruned_attention_w); - - GGML_ASSERT((qs.n_attention_wv == n_layer_all - pruned_attention_w - n_layer_recr) && "n_attention_wv is unexpected"); - } - size_t total_size_org = 0; size_t total_size_new = 0; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index ec1fcd8549..f569f7a7f8 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -286,10 +286,11 @@ static double nmse(const float * a, const float * b, size_t n) { return mse_a_b / mse_a_0; } -// difference between 2 integer sets (Jaccard distance, 0 - no difference, 1 - no overlap) -static double jdst(const int32_t * a, const int32_t * b, size_t n) { - std::unordered_map set_a; - std::unordered_map set_b; +// difference between 2 sets (Jaccard distance, 0 - no difference, 1 - no overlap) +template +static double jdst(const T * a, const T * b, size_t n) { + std::unordered_map set_a; + std::unordered_map set_b; for (size_t i = 0; i < n; ++i) { set_a[a[i]]++; @@ -5001,42 +5002,94 @@ struct test_top_k : public test_case { const ggml_type type; const std::array ne; const int k; + const bool ties; + ggml_tensor * input {}; std::string vars() override { - return VARS_TO_STR3(type, ne, k); + return VARS_TO_STR4(type, ne, k, ties); } test_top_k(ggml_type type = GGML_TYPE_F32, std::array ne = {16, 10, 10, 10}, - int k = 4) - : type(type), ne(ne), k(k) {} + int k = 4, bool ties = false) + : type(type), ne(ne), k(k), ties(ties) {} double max_err() override { return 0.0; } + // When there are ties, only validate the final result. + // The logic in err can't handle the sentinel tensors. + bool run_whole_graph() override { return ties; } + double err(const float * a, const float * b, size_t n) override { - std::vector ia(n); - std::vector ib(n); + // When there are no ties, we expect the exact same set of indices, + // but possibly in a different order. When there are ties, the indices + // can be different but the input values they correspond to should be + // the same. The logic for ties could work for non-ties, but only for + // the output tensor, not for the sentinel tensors. + if (ties) { + std::vector src(ggml_nelements(input)); - double diff = 0.0f; + ggml_backend_tensor_get(input, src.data(), 0, ggml_nelements(input) * ggml_type_size(type)); - for (size_t i = 0; i < n; i++) { - ia[i] = (int32_t) a[i]; - ib[i] = (int32_t) b[i]; + double diff = 0.0f; - // penalize the result if the data is not integer valued - diff += std::fabs(a[i] - ia[i]); - diff += std::fabs(b[i] - ib[i]); + GGML_ASSERT(n == (size_t)(ggml_nrows(input) * k)); + int64_t cols = input->ne[0]; + std::vector ia(k); + std::vector ib(k); + std::vector asrc(k); + std::vector bsrc(k); + for (int64_t r = 0; r < ggml_nrows(input); r++) { + // Convert indices for the row back to integer + for (int64_t c = 0; c < k; c++) { + ia[c] = (int32_t)a[r * k + c]; + ib[c] = (int32_t)b[r * k + c]; + } + // The src values for each row should match. + for (int64_t c = 0; c < k; c++) { + asrc[c] = src[r * cols + ia[c]]; + bsrc[c] = src[r * cols + ib[c]]; + } + diff += jdst(asrc.data(), bsrc.data(), k); + // There should be no duplicate indices + std::sort(ia.begin(), ia.end()); + std::sort(ib.begin(), ib.end()); + if (std::adjacent_find(ia.begin(), ia.end()) != ia.end()) { + diff += 1; + } + if (std::adjacent_find(ib.begin(), ib.end()) != ib.end()) { + diff += 1; + } + } + return diff; + } else { + std::vector ia(n); + std::vector ib(n); + + double diff = 0.0f; + + for (size_t i = 0; i < n; i++) { + ia[i] = (int32_t) a[i]; + ib[i] = (int32_t) b[i]; + + // penalize the result if the data is not integer valued + diff += std::fabs(a[i] - ia[i]); + diff += std::fabs(b[i] - ib[i]); + } + + return diff + jdst(ia.data(), ib.data(), n); } - - return diff + jdst(ia.data(), ib.data(), n); } ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_set_name(a, "a"); + // Save 'a' for err() + input = a; + ggml_tensor * out = ggml_top_k(ctx, a, k); ggml_set_name(out, "out"); @@ -5047,11 +5100,16 @@ struct test_top_k : public test_case { std::random_device rd; std::default_random_engine rng(rd()); for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - // initialize with unique values to avoid ties + int tie_denom = std::max(1, std::min(10, k / 2)); for (int64_t r = 0; r < ggml_nrows(t); r++) { std::vector data(t->ne[0]); for (int i = 0; i < t->ne[0]; i++) { - data[i] = i; + if (ties) { + // integer division to introduce duplicates + data[i] = i / tie_denom; + } else { + data[i] = i; + } } std::shuffle(data.begin(), data.end(), rng); ggml_backend_tensor_set(t, data.data(), r * t->nb[1], t->ne[0] * sizeof(float)); @@ -5546,21 +5604,24 @@ struct test_pad : public test_case { const std::array ne_a; const int pad_0; const int pad_1; + const bool circular; std::string vars() override { - return VARS_TO_STR4(type, ne_a, pad_0, pad_1); + return VARS_TO_STR5(type, ne_a, pad_0, pad_1, circular); } test_pad(ggml_type type = GGML_TYPE_F32, std::array ne_a = {512, 512, 1, 1}, - int pad_0 = 1, int pad_1 = 1) - : type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1) {} + int pad_0 = 1, int pad_1 = 1, bool circular = false) + : type(type), ne_a(ne_a), pad_0(pad_0), pad_1(pad_1), circular(circular) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); ggml_set_name(a, "a"); - ggml_tensor * out = ggml_pad(ctx, a, pad_0, pad_1, 0, 0); + ggml_tensor * out = circular + ? ggml_pad_circular(ctx, a, pad_0, pad_1, 0, 0) + : ggml_pad(ctx, a, pad_0, pad_1, 0, 0); ggml_set_name(out, "out"); return out; @@ -5580,17 +5641,19 @@ struct test_pad_ext : public test_case { const int lp3; const int rp3; const bool v; + const bool circular; std::string vars() override { - return VARS_TO_STR11(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v); + return VARS_TO_STR12(type, ne_a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, v, circular); } test_pad_ext(ggml_type type = GGML_TYPE_F32, std::array ne_a = {512, 512, 3, 1}, int lp0 = 1, int rp0 = 1, int lp1 = 1, int rp1 = 1, int lp2 = 1, int rp2 = 1, int lp3 = 1, int rp3 = 1, - bool v = false) - : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3), v(v) {} + bool v = false, bool circular = false) + : type(type), ne_a(ne_a), lp0(lp0), rp0(rp0), lp1(lp1), rp1(rp1), lp2(lp2), rp2(rp2), lp3(lp3), rp3(rp3), + v(v), circular(circular) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); @@ -5601,7 +5664,9 @@ struct test_pad_ext : public test_case { ggml_set_name(a, "view of a"); } - ggml_tensor * out = ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3); + ggml_tensor * out = circular + ? ggml_pad_ext_circular(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3) + : ggml_pad_ext(ctx, a, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3); ggml_set_name(out, "out"); return out; @@ -6146,6 +6211,15 @@ struct test_solve_tri : public test_case { std::string vars() override { return VARS_TO_STR3(type, ne_lhs, ne_rhs); } + uint64_t op_flops(ggml_tensor * t) override { + GGML_UNUSED(t); + int64_t n = ne_lhs[0]; + int64_t k = ne_rhs[0]; + int64_t batch = ne_lhs[2] * ne_lhs[3]; + // n * (n + 1) / 2 non-zero elements of lhs, 2 flops each, for each col of rhs + return n * (n + 1) * k * batch; + } + test_solve_tri(ggml_type type = GGML_TYPE_F32, std::array ne_lhs = { 10, 10, 4, 3 }, std::array ne_rhs = { 3, 10, 4, 3 } @@ -6982,6 +7056,7 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_conv_transpose_2d({3, 2, 3, 1}, {2, 2, 1, 3}, 1)); test_cases.emplace_back(new test_conv_transpose_2d({10, 10, 9, 1}, {3, 3, 1, 9}, 2)); + test_cases.emplace_back(new test_conv_transpose_2d({129, 63, 35, 1}, {3, 3, 48, 35}, 1)); test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 500, 1, 1})); test_cases.emplace_back(new test_count_equal(GGML_TYPE_F32, {4, 5000, 1, 1})); @@ -7656,6 +7731,7 @@ static std::vector> make_test_cases_eval() { if (k <= 1<> make_test_cases_eval() { test_cases.emplace_back(new test_group_norm_mul_add(GGML_TYPE_F32, {9, 9, 1280, 1})); test_cases.emplace_back(new test_acc()); test_cases.emplace_back(new test_pad()); + test_cases.emplace_back(new test_pad(GGML_TYPE_F32, {33, 17, 2, 1}, 4, 3, true)); // circular test_cases.emplace_back(new test_pad_ext()); test_cases.emplace_back(new test_pad_reflect_1d()); test_cases.emplace_back(new test_pad_reflect_1d(GGML_TYPE_F32, {3000, 384, 4, 1})); @@ -7757,10 +7834,14 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 42, 42, 5, 2 }, { 10, 42, 5, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 2, 2 }, { 10, 64, 2, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 100, 100, 4, 4 }, { 41, 100, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 4 }, { 31, 128, 4, 4 })); + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 4 }, { 300, 64, 4, 4 })); for (bool v : {false, true}) { - test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v)); - test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v)); + for (bool circular : {false, true}) { + test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {512, 512, 1, 1}, 0, 1, 0, 1, 0, 0, 0, 0, v, circular)); + test_cases.emplace_back(new test_pad_ext(GGML_TYPE_F32, {11, 22, 33, 44}, 1, 2, 3, 4, 5, 6, 7, 8, v, circular)); + } } for (int hsk : { 40, 64, 72, 80, 96, 128, 192, 256, 576 }) { @@ -7898,6 +7979,8 @@ static std::vector> make_test_cases_perf() { { 58, 3, 64, 32, 8 }, // A deep layer of a ConvNet, several images in the batch { 16, 3, 512, 128, 8 }, + // High resolution output (large NPQ) + {1536, 3, 64, 32, 1 }, }; for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) { @@ -7955,6 +8038,10 @@ static std::vector> make_test_cases_perf() { test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 4, 2 }, { 6, 64, 4, 2 })); test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 1 }, { 8, 128, 4, 1 })); + // qwen3next with CHUNK_SIZE 64 + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 64, 64, 8, 32 }, { 64, 64, 8, 32 })); + // qwen3next with CHUNK_SIZE 128 + test_cases.emplace_back(new test_solve_tri(GGML_TYPE_F32, { 128, 128, 4, 32 }, { 128, 128, 4, 32 })); test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_LOWER, GGML_TYPE_F32, { 256, 256, 4, 4 })); test_cases.emplace_back(new test_tri(GGML_TRI_TYPE_UPPER_DIAG, GGML_TYPE_F32, { 1024, 1024, 8, 4 })); diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 85bdbca4ca..403131d93b 100644 Binary files a/tools/server/public/index.html.gz and b/tools/server/public/index.html.gz differ diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte index 2315611361..96ed56a775 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessage.svelte @@ -3,6 +3,7 @@ import { copyToClipboard, isIMEComposing } from '$lib/utils'; import ChatMessageAssistant from './ChatMessageAssistant.svelte'; import ChatMessageUser from './ChatMessageUser.svelte'; + import ChatMessageSystem from './ChatMessageSystem.svelte'; interface Props { class?: string; @@ -140,8 +141,7 @@ } function handleSaveEdit() { - if (message.role === 'user') { - // For user messages, trim to avoid accidental whitespace + if (message.role === 'user' || message.role === 'system') { onEditWithBranching?.(message, editedContent.trim()); } else { // For assistant messages, preserve exact content including trailing whitespace @@ -167,7 +167,28 @@ } -{#if message.role === 'user'} +{#if message.role === 'system'} + +{:else if message.role === 'user'} + import { Check, X } from '@lucide/svelte'; + import { Card } from '$lib/components/ui/card'; + import { Button } from '$lib/components/ui/button'; + import { MarkdownContent } from '$lib/components/app'; + import { INPUT_CLASSES } from '$lib/constants/input-classes'; + import { config } from '$lib/stores/settings.svelte'; + import ChatMessageActions from './ChatMessageActions.svelte'; + + interface Props { + class?: string; + message: DatabaseMessage; + isEditing: boolean; + editedContent: string; + siblingInfo?: ChatMessageSiblingInfo | null; + showDeleteDialog: boolean; + deletionInfo: { + totalCount: number; + userMessages: number; + assistantMessages: number; + messageTypes: string[]; + } | null; + onCancelEdit: () => void; + onSaveEdit: () => void; + onEditKeydown: (event: KeyboardEvent) => void; + onEditedContentChange: (content: string) => void; + onCopy: () => void; + onEdit: () => void; + onDelete: () => void; + onConfirmDelete: () => void; + onNavigateToSibling?: (siblingId: string) => void; + onShowDeleteDialogChange: (show: boolean) => void; + textareaElement?: HTMLTextAreaElement; + } + + let { + class: className = '', + message, + isEditing, + editedContent, + siblingInfo = null, + showDeleteDialog, + deletionInfo, + onCancelEdit, + onSaveEdit, + onEditKeydown, + onEditedContentChange, + onCopy, + onEdit, + onDelete, + onConfirmDelete, + onNavigateToSibling, + onShowDeleteDialogChange, + textareaElement = $bindable() + }: Props = $props(); + + let isMultiline = $state(false); + let messageElement: HTMLElement | undefined = $state(); + let isExpanded = $state(false); + let contentHeight = $state(0); + const MAX_HEIGHT = 200; // pixels + const currentConfig = config(); + + let showExpandButton = $derived(contentHeight > MAX_HEIGHT); + + $effect(() => { + if (!messageElement || !message.content.trim()) return; + + if (message.content.includes('\n')) { + isMultiline = true; + } + + const resizeObserver = new ResizeObserver((entries) => { + for (const entry of entries) { + const element = entry.target as HTMLElement; + const estimatedSingleLineHeight = 24; + + isMultiline = element.offsetHeight > estimatedSingleLineHeight * 1.5; + contentHeight = element.scrollHeight; + } + }); + + resizeObserver.observe(messageElement); + + return () => { + resizeObserver.disconnect(); + }; + }); + + function toggleExpand() { + isExpanded = !isExpanded; + } + + +
+ {#if isEditing} +
+ + +
+ + + +
+
+ {:else} + {#if message.content.trim()} +
+ +
+ {/if} +
+ + {#if isExpanded && showExpandButton} +
+ +
+ {/if} + + + + {/if} + + {#if message.timestamp} +
+ +
+ {/if} + {/if} + diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte index 8556cbef5b..3d2b8dd35b 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessageUser.svelte @@ -145,7 +145,7 @@ {#if message.content.trim()} {#if currentConfig.renderUserContentAsMarkdown} diff --git a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte index f307f829bc..2e5f57cb61 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatMessages/ChatMessages.svelte @@ -2,6 +2,7 @@ import { ChatMessage } from '$lib/components/app'; import { chatStore } from '$lib/stores/chat.svelte'; import { conversationsStore, activeConversation } from '$lib/stores/conversations.svelte'; + import { config } from '$lib/stores/settings.svelte'; import { getMessageSiblings } from '$lib/utils'; interface Props { @@ -13,6 +14,7 @@ let { class: className, messages = [], onUserAction }: Props = $props(); let allConversationMessages = $state([]); + const currentConfig = config(); function refreshAllMessages() { const conversation = activeConversation(); @@ -40,7 +42,12 @@ return []; } - return messages.map((message) => { + // Filter out system messages if showSystemMessage is false + const filteredMessages = currentConfig.showSystemMessage + ? messages + : messages.filter((msg) => msg.type !== 'system'); + + return filteredMessages.map((message) => { const siblingInfo = getMessageSiblings(allConversationMessages, message.id); return { diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte index 5097a80db4..4a4850c9bb 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettings.svelte @@ -36,12 +36,6 @@ title: 'General', icon: Settings, fields: [ - { key: 'apiKey', label: 'API Key', type: 'input' }, - { - key: 'systemMessage', - label: 'System Message (will be disabled if left empty)', - type: 'textarea' - }, { key: 'theme', label: 'Theme', @@ -52,6 +46,12 @@ { value: 'dark', label: 'Dark', icon: Moon } ] }, + { key: 'apiKey', label: 'API Key', type: 'input' }, + { + key: 'systemMessage', + label: 'System Message', + type: 'textarea' + }, { key: 'pasteLongTextToFileLen', label: 'Paste long text to file length', diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte index ef46e2d176..a6f51f47d6 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsFields.svelte @@ -95,7 +95,7 @@ {#if field.help || SETTING_CONFIG_INFO[field.key]}

- {field.help || SETTING_CONFIG_INFO[field.key]} + {@html field.help || SETTING_CONFIG_INFO[field.key]}

{/if} {:else if field.type === 'textarea'} @@ -112,13 +112,28 @@ value={String(localConfig[field.key] ?? '')} onchange={(e) => onConfigChange(field.key, e.currentTarget.value)} placeholder={`Default: ${SETTING_CONFIG_DEFAULT[field.key] ?? 'none'}`} - class="min-h-[100px] w-full md:max-w-2xl" + class="min-h-[10rem] w-full md:max-w-2xl" /> + {#if field.help || SETTING_CONFIG_INFO[field.key]}

{field.help || SETTING_CONFIG_INFO[field.key]}

{/if} + + {#if field.key === 'systemMessage'} +
+ onConfigChange('showSystemMessage', Boolean(checked))} + /> + + +
+ {/if} {:else if field.type === 'select'} {@const selectedOption = field.options?.find( (opt: { value: string; label: string; icon?: Component }) => diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte index a113acbcb7..1d313e284e 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebar.svelte @@ -8,6 +8,7 @@ import * as AlertDialog from '$lib/components/ui/alert-dialog'; import Input from '$lib/components/ui/input/input.svelte'; import { conversationsStore, conversations } from '$lib/stores/conversations.svelte'; + import { chatStore } from '$lib/stores/chat.svelte'; import ChatSidebarActions from './ChatSidebarActions.svelte'; const sidebar = Sidebar.useSidebar(); @@ -98,6 +99,10 @@ await goto(`#/chat/${id}`); } + + function handleStopGeneration(id: string) { + chatStore.stopGenerationForChat(id); + } @@ -132,6 +137,7 @@ onSelect={selectConversation} onEdit={handleEditConversation} onDelete={handleDeleteConversation} + onStop={handleStopGeneration} /> {/each} diff --git a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte index 82685f83a5..bf2fa4f9e9 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSidebar/ChatSidebarConversationItem.svelte @@ -1,6 +1,7 @@