| lm_head: | |
| accum_format: SAME | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Linear | |
| output_format: SAME | |
| weight_format: SAME | |
| weight_sparseness: DENSE | |
| transformer.drop: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.0.attn.attn_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| transformer.h.0.attn.c_attn: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.0.attn.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.0.attn.resid_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.0.attn.softmax: | |
| approximation_function: SOFTMAX(base2,float16) | |
| input_format: SAME | |
| instance: Softmax | |
| output_format: SAME | |
| transformer.h.0.ln_1: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.0.ln_2: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.0.mlp.act: | |
| approximation_function: GELU(poly2,float16) | |
| input_format: SAME | |
| instance: GELU | |
| output_format: SAME | |
| transformer.h.0.mlp.c_fc: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.0.mlp.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.0.mlp.dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.1.attn.attn_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| transformer.h.1.attn.c_attn: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.1.attn.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.1.attn.resid_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.1.attn.softmax: | |
| approximation_function: SOFTMAX(base2,float16) | |
| input_format: SAME | |
| instance: Softmax | |
| output_format: SAME | |
| transformer.h.1.ln_1: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.1.ln_2: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.1.mlp.act: | |
| approximation_function: GELU(poly2,float16) | |
| input_format: SAME | |
| instance: GELU | |
| output_format: SAME | |
| transformer.h.1.mlp.c_fc: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.1.mlp.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.1.mlp.dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.2.attn.attn_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| transformer.h.2.attn.c_attn: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.2.attn.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.2.attn.resid_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.2.attn.softmax: | |
| approximation_function: SOFTMAX(base2,float16) | |
| input_format: SAME | |
| instance: Softmax | |
| output_format: SAME | |
| transformer.h.2.ln_1: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.2.ln_2: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.2.mlp.act: | |
| approximation_function: GELU(poly2,float16) | |
| input_format: SAME | |
| instance: GELU | |
| output_format: SAME | |
| transformer.h.2.mlp.c_fc: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.2.mlp.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.2.mlp.dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.3.attn.attn_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| transformer.h.3.attn.c_attn: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.3.attn.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.3.attn.resid_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.3.attn.softmax: | |
| approximation_function: SOFTMAX(base2,float16) | |
| input_format: SAME | |
| instance: Softmax | |
| output_format: SAME | |
| transformer.h.3.ln_1: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.3.ln_2: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.3.mlp.act: | |
| approximation_function: GELU(poly2,float16) | |
| input_format: SAME | |
| instance: GELU | |
| output_format: SAME | |
| transformer.h.3.mlp.c_fc: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.3.mlp.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.3.mlp.dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.4.attn.attn_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| transformer.h.4.attn.c_attn: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.4.attn.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.4.attn.resid_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.4.attn.softmax: | |
| approximation_function: SOFTMAX(base2,float16) | |
| input_format: SAME | |
| instance: Softmax | |
| output_format: SAME | |
| transformer.h.4.ln_1: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.4.ln_2: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.4.mlp.act: | |
| approximation_function: GELU(poly2,float16) | |
| input_format: SAME | |
| instance: GELU | |
| output_format: SAME | |
| transformer.h.4.mlp.c_fc: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.4.mlp.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.4.mlp.dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.5.attn.attn_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| transformer.h.5.attn.c_attn: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: BFP[8|8]{64,-1}(SN) | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.5.attn.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.5.attn.resid_dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.h.5.attn.softmax: | |
| approximation_function: SOFTMAX(base2,float16) | |
| input_format: SAME | |
| instance: Softmax | |
| output_format: SAME | |
| transformer.h.5.ln_1: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.5.ln_2: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |
| transformer.h.5.mlp.act: | |
| approximation_function: GELU(poly2,float16) | |
| input_format: SAME | |
| instance: GELU | |
| output_format: SAME | |
| transformer.h.5.mlp.c_fc: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.5.mlp.c_proj: | |
| approximation_function: NONE | |
| bias_format: SAME | |
| input_format: BFP[8|8]{64,-1}(SN) | |
| instance: HFTransformersConv1D | |
| output_format: SAME | |
| weight_format: BFP[8|8]{64,0}(SN) | |
| weight_sparseness: DENSE | |
| transformer.h.5.mlp.dropout: | |
| approximation_function: NONE | |
| input_format: SAME | |
| instance: Dropout | |
| output_format: SAME | |
| transformer.ln_f: | |
| approximation_function: LAYERNORM(fallback,4,float16) | |
| bias_format: SAME | |
| input_format: SAME | |
| instance: LayerNorm | |
| output_format: SAME | |
| weight_format: SAME | |