CANN: Support MUL_MAT_ID in ACL graph#19228
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hipudding merged 1 commit intoggml-org:masterfrom Feb 10, 2026
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hipudding
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Feb 4, 2026
Implement ggml_cann_mul_mat_id_quant function to support quantized matrix multiplication for Mixture of Experts (MoE) architectures on CANN backend. Key features: - Support Q4_0 and Q8_0 quantized weight formats - Use IndexSelect to dynamically route expert-specific weights based on indices - Leverage WeightQuantBatchMatmulV2 for efficient quantized computation - Handle automatic F16 type conversion for hardware compatibility - Support both per-expert and broadcast input modes Implementation details: - Extract expert weights and scales using CANN IndexSelect operation - Process each batch and expert combination independently - Create proper tensor views with correct stride for matmul operations - Automatic input/output type casting to/from F16 as needed Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
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Morning @ggerganov, Could you please review this PR? Thanks. |
noemotiovon
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Feb 10, 2026
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LGTM. The current implementation no longer relies on device-to-host copies and can use the ACL graph.
ggerganov
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Feb 10, 2026
liparetejas
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Feb 23, 2026
Implement ggml_cann_mul_mat_id_quant function to support quantized matrix multiplication for Mixture of Experts (MoE) architectures on CANN backend. Key features: - Support Q4_0 and Q8_0 quantized weight formats - Use IndexSelect to dynamically route expert-specific weights based on indices - Leverage WeightQuantBatchMatmulV2 for efficient quantized computation - Handle automatic F16 type conversion for hardware compatibility - Support both per-expert and broadcast input modes Implementation details: - Extract expert weights and scales using CANN IndexSelect operation - Process each batch and expert combination independently - Create proper tensor views with correct stride for matmul operations - Automatic input/output type casting to/from F16 as needed Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
bartowski1182
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Mar 2, 2026
Implement ggml_cann_mul_mat_id_quant function to support quantized matrix multiplication for Mixture of Experts (MoE) architectures on CANN backend. Key features: - Support Q4_0 and Q8_0 quantized weight formats - Use IndexSelect to dynamically route expert-specific weights based on indices - Leverage WeightQuantBatchMatmulV2 for efficient quantized computation - Handle automatic F16 type conversion for hardware compatibility - Support both per-expert and broadcast input modes Implementation details: - Extract expert weights and scales using CANN IndexSelect operation - Process each batch and expert combination independently - Create proper tensor views with correct stride for matmul operations - Automatic input/output type casting to/from F16 as needed Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
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Implement ggml_cann_mul_mat_id_quant function to support quantized matrix
multiplication for Mixture of Experts (MoE) architectures on CANN backend.
Key features:
Implementation details:
Testing: All test cases passed for supported types (F32, F16, Q4_0, Q8_0).
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