[quant] deepgemm-fp8w8a8-b128 quantize #952
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Summary of Changes
Hello @sufubao, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request introduces comprehensive support for FP8 quantization within the DeepGEMM framework, specifically targeting Mixture of Experts (MoE) models. It integrates a new Triton-based kernel for efficient block-wise FP8 weight quantization, refines MoE configuration handling, and sets up distributed groups for Deep Expert Parallelism, enhancing both performance and flexibility.
Highlights
- FP8 Quantization Implementation: Implemented block-wise FP8 (e4m3fn) weight quantization using a new Triton kernel (
fp8w8a8_block_quant_kernel.py). This kernel calculates per-block scales and quantizes weights, supporting both 2D and 3D tensors. - DeepGEMM Integration: Enabled the
deepgemm_quant.pymodule to utilize the new FP8 quantization kernel, moving beyond a "Not implemented" state. Theapplymethod was also made more flexible to handle optional input scales. - MoE Configuration & Inference Adjustments: Updated MoE layer weight initialization to provide default values for
scoring_func,n_group,topk_group, androuted_scaling_factor. The MoE inference path inqwen3_moe/layer_infer/transformer_layer_infer.pywas simplified by removing shared expert handling and explicitly disabling grouped top-k routing. - Distributed Environment Setup: Integrated Deep Expert Parallelism (DeepEP) group initialization into the Qwen3 MoE model's custom initialization. A new
run_oncedecorator was added and applied toinit_distributed_envto ensure distributed environment setup occurs only once.
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Code Review
This pull request introduces support for deepgemm-fp8w8a8-b128 quantization. A critical bug in the quantization kernel that could lead to NaN values was identified and a fix was provided. Several medium-severity suggestions were also included to improve maintainability and robustness.
lightllm/common/quantization/triton_quant/fp8/fp8w8a8_block_quant_kernel.py
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| network_config["n_group"] = network_config.get("n_group", 0) | ||
| self.num_experts_per_tok = network_config["num_experts_per_tok"] | ||
| self.use_grouped_topk = network_config["n_group"] > 0 | ||
| self.norm_topk_prob = network_config["norm_topk_prob"] | ||
| self.n_group = network_config["n_group"] | ||
| network_config["topk_group"] = network_config.get("topk_group", 0) | ||
| self.topk_group = network_config["topk_group"] | ||
| network_config["routed_scaling_factor"] = network_config.get("routed_scaling_factor", 0) | ||
| self.routed_scaling_factor = network_config["routed_scaling_factor"] |
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Modifying network_config directly can cause unexpected side effects. Instead, retrieve the values and assign them to instance attributes. This prevents unintended modifications to the original dictionary.
| network_config["n_group"] = network_config.get("n_group", 0) | |
| self.num_experts_per_tok = network_config["num_experts_per_tok"] | |
| self.use_grouped_topk = network_config["n_group"] > 0 | |
| self.norm_topk_prob = network_config["norm_topk_prob"] | |
| self.n_group = network_config["n_group"] | |
| network_config["topk_group"] = network_config.get("topk_group", 0) | |
| self.topk_group = network_config["topk_group"] | |
| network_config["routed_scaling_factor"] = network_config.get("routed_scaling_factor", 0) | |
| self.routed_scaling_factor = network_config["routed_scaling_factor"] | |
| self.n_group = network_config.get("n_group", 0) | |
| self.num_experts_per_tok = network_config["num_experts_per_tok"] | |
| self.use_grouped_topk = self.n_group > 0 | |
| self.norm_topk_prob = network_config["norm_topk_prob"] | |
| self.topk_group = network_config.get("topk_group", 0) | |
| self.routed_scaling_factor = network_config.get("routed_scaling_factor", 0) |
| def quantize(self, weight: torch.Tensor): | ||
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| raise Exception("Not implemented") | ||
| from lightllm.common.quantization.triton_quant.fp8.fp8w8a8_block_quant_kernel import weight_quant |
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Consider moving this import to the top of the file for better code organization and to avoid potential performance implications of local imports.
| return y_quant, s_scales | ||
| else: | ||
| y_quant, s_scales = mm_weight_quant(x, block_size) | ||
| return y_quant.t(), s_scales.t() |
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The function returns transposed tensors for 2D inputs but not for 3D inputs. Add a comment explaining why the transpose is necessary for 2D tensors but not for 3D tensors.
lightllm/models/qwen3_moe/model.py
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| # self.config["num_hidden_layers"] = 2 | ||
| # self.config["n_layer"] = 2 |
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Remove the commented-out code to keep the codebase clean.
lightllm/utils/dist_utils.py
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| def run_once(func): | ||
| has_run = False | ||
| def wrapper(*args, **kwargs): | ||
| nonlocal has_run | ||
| if not has_run: | ||
| has_run = True | ||
| return func(*args, **kwargs) | ||
| else: | ||
| return None | ||
| return wrapper |
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The run_once decorator is not thread-safe. Use a threading.Lock to avoid race conditions.
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