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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python3 | ||
| # Copyright 2023 ETH Zurich and University of Bologna. | ||
| # Licensed under the Apache License, Version 2.0, see LICENSE for details. | ||
| # SPDX-License-Identifier: Apache-2.0 | ||
| # | ||
| # Viviane Potocnik <[email protected]> | ||
| # Luca Colagrande <[email protected]> | ||
|
|
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| import argparse | ||
| import numpy as np | ||
| import pathlib | ||
| import hjson | ||
| import sys | ||
| import os | ||
| import torch | ||
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| sys.path.append(os.path.join(os.path.dirname(__file__), "../../../../util/sim/")) | ||
| import data_utils # noqa: E402 | ||
| from data_utils import emit_license, \ | ||
| format_struct_definition, format_array_definition, \ | ||
| format_array_declaration, format_ifdef_wrapper # noqa: E402 | ||
|
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| torch.manual_seed(42) | ||
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| # AXI splits bursts crossing 4KB address boundaries. To minimize | ||
| # the occurrence of these splits the data should be aligned to 4KB | ||
| BURST_ALIGNMENT = 4096 | ||
|
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| PRECISION = { | ||
| 'FP64': '64', | ||
| 'FP32': '32', | ||
| 'FP16': '16', | ||
| 'FP8': '8' | ||
| } | ||
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|
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| def torch_golden_model(Q, K, V): | ||
| return torch.nn.functional.scaled_dot_product_attention(Q, K, V) | ||
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| def exact_golden_model(Q, K, V, B_r, B_c): | ||
| # Convert torch tensors to numpy arrays | ||
| Q = Q.numpy() | ||
| K = K.numpy() | ||
| V = V.numpy() | ||
| # Get layer dimensions | ||
| N = Q.shape[0] | ||
| # Calculate tiling parameters | ||
| T_r = N // B_r | ||
| T_c = N // B_c | ||
| # Transpose K | ||
| K_t = np.transpose(K) | ||
| # Iterate tiles | ||
| O_tiles = [] | ||
| for i in range(T_r): | ||
| # Tile Q | ||
| start_row = i * B_r | ||
| end_row = start_row + B_r | ||
| Q_i = Q[start_row:end_row, :] | ||
| # Initialize l_i, m_i, O_i | ||
| m_i = np.full((B_r, 1), -np.inf) | ||
| for j in range(T_c): | ||
| # Tile K_t and V | ||
| start_col = j * B_c | ||
| end_col = start_col + B_c | ||
| K_t_j = K_t[:,start_col:end_col] | ||
| V_j = V[start_col:end_col, ] | ||
| # Compute O tile update | ||
| S_ij = np.matmul(Q_i, K_t_j) | ||
| m_i_prev = m_i | ||
| m_i = np.maximum(m_i_prev, np.max(S_ij, 1, keepdims=True)) | ||
| shifted_exp = np.exp(m_i_prev - m_i) | ||
| P_ij = np.exp(S_ij - m_i) | ||
| PxV = np.matmul(P_ij, V_j) | ||
| if j == 0: | ||
| l_i = np.sum(P_ij, 1, keepdims=True) | ||
| O_i = PxV | ||
| else: | ||
| l_i = (shifted_exp * l_i) + np.sum(P_ij, 1, keepdims=True) | ||
| diag = np.diag(shifted_exp[:, 0]) | ||
| diag_inv = np.linalg.inv(diag) | ||
| O_i = np.matmul(diag_inv, O_i) | ||
| O_i += PxV | ||
| # Finalize O tile | ||
| diag_l_i = np.diag(l_i[:, 0]) | ||
| diag_l_inv_i = np.linalg.inv(diag_l_i) | ||
| O_i = np.matmul(diag_l_inv_i, O_i) | ||
| O_tiles.append(O_i) | ||
| return np.concatenate(O_tiles, 0) | ||
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| def emit_header(section, params): | ||
| N = params['N'] | ||
| d = params['d'] | ||
| B_r = params['B_r'] | ||
| B_c = params['B_c'] | ||
| prec = PRECISION[params['dtype']] | ||
|
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| # Verify layer parameters are valid | ||
| assert (N % B_r) == 0, 'N is not an integer multiple of B_r' | ||
| assert (N % B_c) == 0, 'N is not an integer multiple of B_c' | ||
| assert (B_r % 8) == 0, 'B_r must be an integer multiple of the number of cores in a cluster' | ||
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| torch_type = data_utils.floating_point_torch_type(prec) | ||
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| Q = torch.rand(N, d, requires_grad=False, dtype=torch_type) | ||
| K = torch.rand(N, d, requires_grad=False, dtype=torch_type) | ||
| V = torch.rand(N, d, requires_grad=False, dtype=torch_type) | ||
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| output = exact_golden_model(Q, K, V, B_r, B_c) | ||
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| # Layer implementation assumes K is in (d, N) layout | ||
| K = torch.transpose(K, 0, 1) | ||
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| ctype = data_utils.floating_point_ctype(prec) | ||
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| q_uid = 'Q' | ||
| k_uid = 'K' | ||
| v_uid = 'V' | ||
| o_uid = 'O' | ||
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| layer_cfg = { | ||
| **params, | ||
| 'Q': q_uid, | ||
| 'K': k_uid, | ||
| 'V': v_uid, | ||
| 'O': o_uid, | ||
| } | ||
|
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| data_str = [emit_license()] | ||
| data_str += [format_array_declaration(ctype, q_uid, Q.shape)] | ||
| data_str += [format_array_declaration(ctype, k_uid, K.shape)] | ||
| data_str += [format_array_declaration(ctype, v_uid, V.shape)] | ||
| data_str += [format_array_declaration(ctype, o_uid, output.shape)] | ||
| data_str += [format_struct_definition('flashattention_2_layer_t', 'layer', layer_cfg)] | ||
| data_str += [format_array_definition(ctype, q_uid, Q)] | ||
| data_str += [format_array_definition(ctype, k_uid, K)] | ||
| data_str += [format_array_definition(ctype, v_uid, V)] | ||
| result_def = format_array_definition(ctype, 'golden', output) | ||
| data_str += [format_ifdef_wrapper('BIST', result_def)] | ||
| data_str = '\n\n'.join(data_str) | ||
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| return data_str | ||
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| def main(): | ||
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| parser = argparse.ArgumentParser(description='Generate data for layernorm kernel') | ||
| parser.add_argument( | ||
| "-c", "--cfg", | ||
| type=pathlib.Path, | ||
| required=True, | ||
| help='Select param config file kernel' | ||
| ) | ||
| parser.add_argument( | ||
| '--section', | ||
| type=str, | ||
| help='Section to store matrices in') | ||
| parser.add_argument( | ||
| 'output', | ||
| type=pathlib.Path, | ||
| help='Path of the output header file') | ||
| args = parser.parse_args() | ||
|
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| # Load param config file | ||
| with args.cfg.open() as f: | ||
| param = hjson.loads(f.read()) | ||
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| # Emit header file | ||
| with open(args.output, 'w') as f: | ||
| f.write(emit_header(args.section, param)) | ||
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| if __name__ == '__main__': | ||
| main() |
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,11 @@ | ||
| // Copyright 2020 ETH Zurich and University of Bologna. | ||
| // Solderpad Hardware License, Version 0.51, see LICENSE for details. | ||
| // SPDX-License-Identifier: SHL-0.51 | ||
|
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| { | ||
| N: 128 | ||
| d: 128 | ||
| B_r: 32 | ||
| B_c: 16 | ||
| dtype: FP64 | ||
| } |
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