Updated the fma and fp8 format

Author: faiziktk

models/H100TC.m

@@ -23,4 +23,83 @@
 %   D: Result of the operation D = A * B + C computed under the 
 %      specified tensor core configuration.
 
-D = B200TC(alpha, A, B, beta, C, informat, outformat);
+function D=H100TC(alpha, A, B, beta, C, informat, outformat)
+%
+% H100TC  Compute GEMM with a model of a tensor core of the H100 GPU.
+%
+% This function evaluates the expression D = A * B + C using the 
+% H200 TC numerical-feature-based model. The accumulation of block
+% products is performed using recursive summation.
+%
+% Inputs
+%   A: Left matrix operand for the matrix multiplication A * B.
+%   B: Right matrix operand for the matrix multiplication A * B.
+%   C: Matrix added to the product A * B.
+%   informat: a string specifying the format of A and B.
+%             Supported input formats:
+%                  fp8-(e5m2,e4m3), fp16, binary16, half,
+%                  bf16, bfloat16, tensorfloat32, tf32.
+%   outformat: a string specifying the numerical format for C and D.
+%              Supported output formats:
+%                  fp32, single, binary32,
+%                  fp16, binary16, half.
+%
+% Output
+%   D: Result of the operation D = A * B + C computed under the 
+%      specified tensor core configuration.
+
+% Allowed formats
+allowedOutFormats = {'fp32', 'single', 'binary32',...
+    'fp16', 'binary16', 'half'};
+allowedInFormats = {'fp8-e5m2','fp8-e4m3','e5m2','e4m3',...
+    'fp16','binary16', 'half','bf16','bfloat16','tensorfloat32','tf32'};
+
+if exist('informat', 'var')
+    if (~ismember(lower(informat), allowedInFormats))
+        error('The specified input format is not supported.');
+    end
+    informat=lower(informat);
+end
+if (exist('outformat', 'var'))
+    if (~ismember(lower(outformat), allowedOutFormats))
+        error('The specified output format is not supported.');
+    end
+    outformat=lower(outformat);
+end
+
+% Default structures assuming fp16 in and fp32 output. See
+% Generic_TC_Model.m for the information.
+def_params.fma = 16;          % Fused multiply-add (FMA) size
+def_params.neab = 2;          % TC extra alignment bits
+def_params.frmode = 'rz';     % TC final rounding mode
+def_params.stkbitenabled = 0;
+def_params.inter_pattern=0;
+
+% Set up the model according to the formats specified.
+if ismember(informat, {'fp16','half','binary16'})
+    if exist('outformat', 'var')
+        if ismember(outformat, {'fp16','binary16','half'})
+            def_params.frmode='rne'; % TC final rounding mode
+        end  
+    end
+elseif ismember(informat, {'tf32', 'tensorfloat32'})
+         def_params.fma=8;
+elseif ismember(informat, {'fp8-e5m2','fp8-e4m3','e5m2','e4m3'}) 
+        % FMA size is 16, but interleaved pattern is used to join two
+        % 16-element vectors.
+        def_params.fma = 32;
+        def_params.inter_pattern=0;
+        def_params.neab=-10;
+        if exist('outformat', 'var')
+            def_outopts.format = outformat;
+            if ismember(outformat, {'fp16','binary16','half'})
+                def_params.frmode='rne';
+            end
+        end
+end
+
+
+     D = GEMM(alpha, A, B, beta, C, informat, outformat, def_params);
+        
+end
+